diff --git a/.gitignore b/.gitignore index 9b5a202..3254386 100644 --- a/.gitignore +++ b/.gitignore @@ -1,12 +1,11 @@ test-results/ **.log **/report.html +.env docker-compose **/node_modules/ - -*.pyc - +**.venv/ +**pycache**/ .DS_Store .env.development -__pycache__/ -*.pyc +*.pyc \ No newline at end of file diff --git a/setup-scripts/build-image-to-registry/buildpush-genaistudio-images.yml b/setup-scripts/build-image-to-registry/buildpush-genaistudio-images.yml index 20839a2..c3ded6a 100755 --- a/setup-scripts/build-image-to-registry/buildpush-genaistudio-images.yml +++ b/setup-scripts/build-image-to-registry/buildpush-genaistudio-images.yml @@ -10,6 +10,7 @@ --build-arg http_proxy="{{ http_proxy }}" --build-arg https_proxy="{{ http_proxy }}" --build-arg no_proxy="{{ no_proxy }}" + --build-context sample_workflows="../sample-workflows" -t "{{ container_registry }}/{{ item.image_name }}:{{ container_tag }}" . args: chdir: "{{ item.directory }}" @@ -20,6 +21,7 @@ loop: - { directory: '../../studio-frontend/', image_name: 'studio-frontend' } - { directory: '../../studio-backend/', image_name: 'studio-backend' } + - { directory: '../../studio-eval/', image_name: 'studio-eval' } - { directory: '../../app-frontend/', image_name: 'app-frontend' } - { directory: '../../app-backend/', image_name: 'app-backend' } register: build_results @@ -33,6 +35,7 @@ loop: - { image_name: 'studio-frontend' } - { image_name: 'studio-backend' } + - { image_name: 'studio-eval' } - { image_name: 'app-frontend' } - { image_name: 'app-backend' } @@ -42,5 +45,6 @@ loop: - { image_name: 'studio-frontend' } - { image_name: 'studio-backend' } + - { image_name: 'studio-eval' } - { image_name: 'app-frontend' } - { image_name: 'app-backend' } \ No newline at end of file diff --git a/setup-scripts/setup-development/.env.example b/setup-scripts/setup-development/.env.example new file mode 100644 index 0000000..190e2e4 --- /dev/null +++ b/setup-scripts/setup-development/.env.example @@ -0,0 +1,59 @@ +# ============================================================================= +# GenAI Studio — Local Development Environment +# docker compose -f docker-compose.local.yml reads this file automatically +# Copy this file to .env and adjust values for your environment +# ============================================================================= + +# --- Proxy (leave blank if not behind a proxy) --- +http_proxy= +https_proxy= +no_proxy=localhost,127.0.0.1,mysql,studio-backend,studio-eval,ollama,finetuning-server + +# --- Container images for sandbox deployments --- +APP_FRONTEND_IMAGE=opea/app-frontend:latest +APP_BACKEND_IMAGE=opea/app-backend:latest + +# --- MySQL (shared by studio-frontend and studio-eval) --- +MYSQL_HOST=mysql +MYSQL_PORT=3306 +MYSQL_ROOT_PASSWORD=password +MYSQL_FLOWISE_DB=flowise +MYSQL_EVAL_DB=eval +MYSQL_USER=root +MYSQL_PASSWORD=password + +# --- Service endpoints --- +# studio-frontend server-side code runs inside Docker, so it should use Docker DNS. +# The browser-side UI should use the Vite /studio-backend proxy on port 8088 +STUDIO_SERVER_URL=http://studio-backend:5000 + +# studio-frontend UI dev flags +NODE_TLS_REJECT_UNAUTHORIZED=0 +NODE_ENV=development +VITE_DISABLE_KEYCLOAK=true +VITE_HOST=0.0.0.0 +VITE_PORT=8088 + +# studio-backend -> studio-eval (host:port, no scheme) +EVAL_SERVICE_DNS=studio-eval:8000 + +# Local compose: skip Grafana dashboard import/delete during sandbox lifecycle. +DISABLE_GRAFANA_DASHBOARD_SYNC=true + +# --- Sandbox routing from local-nginx to K8s namespaces --- +# CoreDNS ClusterIP of your cluster (kubeadm commonly 10.96.0.10, k3s 10.43.0.10) +K8S_DNS_RESOLVER=10.96.0.10 + +# studio-eval -> Ollama (host:port, no scheme) +OLLAMA_DNS=ollama:11434 + +# --- Finetuning service --- +# host:port, no scheme +FINETUNING_SERVICE_DNS=finetuning-server:8015 +FINETUNING_RAY_DNS=finetuning-server:8265 + +# studio-eval -> sandbox app-backend via kubectl port-forward on host. +# This mirrors app-backend behavior while bypassing local Docker->K8s DNS issues. +# Ensure you have `kubectl -n port-forward --address service/app-backend 18899:8899` running on your host. +# To get docker bridge IP: docker network inspect bridge --format '{{range .IPAM.Config}}{{.Gateway}}{{end}}' +APP_BACKEND_URL_TEMPLATE=http://host.docker.internal:18899/v1/app-backend diff --git a/setup-scripts/setup-development/docker-compose.local.yml b/setup-scripts/setup-development/docker-compose.local.yml new file mode 100644 index 0000000..217ec25 --- /dev/null +++ b/setup-scripts/setup-development/docker-compose.local.yml @@ -0,0 +1,248 @@ +# Local development compose — simulates K8s studio namespace +# Sandbox deploy can target a real cluster when studio-backend has a working +# kubeconfig and any cluster-side dependencies it calls are reachable. +# Run: docker compose -f docker-compose.local.yml up -d + +services: + + studio-frontend: + build: + context: ../../studio-frontend + additional_contexts: + sample_workflows: ../../sample-workflows + target: builder + args: + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + no_proxy: ${no_proxy} + container_name: studio-frontend + ports: + - "3000:3000" + - "8088:8088" + volumes: + - ../../studio-frontend:/usr/src + - ./.env:/usr/src/packages/server/.env:ro + - ./.env:/usr/src/packages/ui/.env:ro + - ../../sample-workflows:/usr/src/sample-workflows:ro + - node_modules:/usr/src/node_modules + - pnpm-store:/usr/src/.pnpm-store + - /usr/src/packages/ui/build + - finetuning-output:/tmp/finetuning/output + command: ["sh", "/usr/src/docker-entrypoint.dev.sh"] + environment: + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + - no_proxy=${no_proxy},studio-backend,studio-eval,mysql,ollama,finetuning-server,local-nginx,.svc.cluster.local,.cluster.local + - STUDIO_SERVER_URL=${STUDIO_SERVER_URL:-http://studio-backend:5000} + - PREPARE_DOC_REDIS_PREP_DNS=${PREPARE_DOC_REDIS_PREP_DNS:-local-nginx} + - CHOKIDAR_USEPOLLING=1 + - FINETUNING_SERVICE_DNS=${FINETUNING_SERVICE_DNS:-finetuning-server:8015} + - FINETUNING_RAY_DNS=${FINETUNING_RAY_DNS:-finetuning-server:8265} + - DATABASE_TYPE=mysql + - DATABASE_HOST=${MYSQL_HOST:-mysql} + - DATABASE_PORT=${MYSQL_PORT:-3306} + - DATABASE_NAME=${MYSQL_FLOWISE_DB:-flowise} + - DATABASE_USER=${MYSQL_USER:-root} + - DATABASE_PASSWORD=${MYSQL_PASSWORD:-root} + ulimits: + nofile: + soft: 65536 + hard: 65536 + stdin_open: true + tty: true + networks: + - studio-local + depends_on: + - studio-backend + restart: unless-stopped + + studio-backend: + build: + context: ../../studio-backend + args: + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + no_proxy: ${no_proxy} + container_name: studio-backend + ports: + - "5001:5000" + volumes: + - ../../studio-backend/app:/usr/src/app + - ${HOME}/.kube:/root/.kube:ro + command: ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "5000", "--reload"] + environment: + - STUDIO_EVAL_DNS=${STUDIO_EVAL_DNS:-studio-eval:8000} + - GRAFANA_DNS=${GRAFANA_DNS:-grafana:3000} + - DISABLE_GRAFANA_DASHBOARD_SYNC=${DISABLE_GRAFANA_DASHBOARD_SYNC:-true} + - CLICKHOUSE_DNS=${CLICKHOUSE_DNS:-clickhouse:9000} + - APP_FRONTEND_IMAGE=${APP_FRONTEND_IMAGE:-opea/app-frontend:latest} + - APP_BACKEND_IMAGE=${APP_BACKEND_IMAGE:-opea/app-backend:latest} + - SBX_HTTP_PROXY=${http_proxy} + - SBX_NO_PROXY=${no_proxy},studio-eval,mysql,ollama,studio-frontend,finetuning-server,grafana,clickhouse,.svc.cluster.local,.cluster.local + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + - no_proxy=${no_proxy},studio-eval,mysql,ollama,studio-frontend,finetuning-server,grafana,clickhouse,.svc.cluster.local,.cluster.local + networks: + - studio-local + depends_on: + mysql: + condition: service_healthy + restart: unless-stopped + + studio-eval: + build: + context: ../../studio-eval + args: + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + no_proxy: ${no_proxy} + container_name: studio-eval + ports: + - "8000:8000" + volumes: + - ../../studio-eval/app:/app/app + command: ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"] + environment: + - OLLAMA_DNS=${OLLAMA_DNS:-ollama:11434} + - MYSQL_HOST=${MYSQL_HOST:-mysql} + - MYSQL_USER=${MYSQL_USER:-root} + - MYSQL_PASSWORD=${MYSQL_PASSWORD:-password} + - MYSQL_DB=${MYSQL_EVAL_DB:-eval} + - APP_BACKEND_URL_TEMPLATE=${APP_BACKEND_URL_TEMPLATE:-http://local-nginx/v1/app-backend?ns={sandbox_id}} + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + - no_proxy=${no_proxy},host.docker.internal,localhost,127.0.0.1,ollama,mysql,studio-backend,studio-frontend,finetuning-server,.svc.cluster.local,.cluster.local + extra_hosts: + - "host.docker.internal:host-gateway" + networks: + - studio-local + depends_on: + mysql: + condition: service_healthy + ollama: + condition: service_started + restart: unless-stopped + + ollama: + image: ollama/ollama:latest + container_name: ollama + ports: + - "11434:11434" + volumes: + - ollama-models:/root/.ollama + environment: + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + - no_proxy=${no_proxy},studio-eval,studio-backend,studio-frontend,mysql,finetuning-server + - HTTP_PROXY=${http_proxy} + - HTTPS_PROXY=${https_proxy} + - NO_PROXY=${no_proxy},studio-eval,studio-backend,studio-frontend,mysql,finetuning-server + - OLLAMA_INSECURE=true + networks: + - studio-local + # Pull models after startup (OLLAMA_HOST must be overridden for docker exec to work): + # docker exec -e OLLAMA_HOST=http://127.0.0.1:11434 ollama ollama pull nomic-embed-text + # docker exec -e OLLAMA_HOST=http://127.0.0.1:11434 ollama ollama pull qwen2.5:14b + restart: unless-stopped + + mysql: + image: mysql:8.0 + container_name: mysql + ports: + - "3306:3306" + environment: + - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD:-password} + - MYSQL_DATABASE=${MYSQL_FLOWISE_DB:-flowise} + volumes: + - mysql-data:/var/lib/mysql + configs: + - source: mysql-init-sql + target: /docker-entrypoint-initdb.d/01-create-eval-db.sql + networks: + - studio-local + healthcheck: + test: ["CMD", "mysql", "-uroot", "-ppassword", "-e", "SELECT 1"] + interval: 10s + timeout: 5s + retries: 10 + start_period: 60s + restart: unless-stopped + + # Local equivalent of studio-nginx in studio-manifest.yaml. + # Routes /?ns= to app-frontend in the deployed K8s sandbox, + # and everything else to studio-frontend. + # Access the studio via http://localhost:30010 (not localhost:8088) so that + # the ?ns= routing works correctly when opening sandbox app-frontends. + local-nginx: + image: nginx:1.27.1 + container_name: local-nginx + ports: + - "30010:80" + volumes: + - ./local-nginx/nginx.conf.template:/tmp/nginx.conf.template:ro + # Mirror the studio-manifest init-container approach: envsubst only *_DNS and + # K8S_DNS_RESOLVER so that nginx's own $namespace, $host, etc. are preserved. + command: + - /bin/sh + - -c + - >- + envsubst '$$STUDIO_FRONTEND_DNS $$APP_FRONTEND_DNS $$APP_BACKEND_DNS + $$GRAFANA_DNS $$APP_CHATHISTORY_DNS $$PREPARE_DOC_REDIS_PREP_DNS + $$STUDIO_BACKEND_DNS $$K8S_DNS_RESOLVER' + < /tmp/nginx.conf.template > /etc/nginx/conf.d/default.conf && + nginx -g 'daemon off;' + environment: + # Local docker service names for studio-layer services + - STUDIO_FRONTEND_DNS=studio-frontend:8088 + - STUDIO_BACKEND_DNS=studio-backend:5000 + - GRAFANA_DNS=${GRAFANA_DNS:-grafana:3000} + # K8s cluster DNS names — $namespace is left as a literal nginx variable + # ($$namespace in compose becomes $namespace in the container, which + # envsubst leaves untouched so nginx resolves it per-request) + - APP_FRONTEND_DNS=app-frontend.$$namespace.svc.cluster.local:5275 + - APP_BACKEND_DNS=app-backend.$$namespace.svc.cluster.local:8899 + - APP_CHATHISTORY_DNS=chathistory-mongo.$$namespace.svc.cluster.local:6012 + - PREPARE_DOC_REDIS_PREP_DNS=opea-prepare-doc-redis-prep-0.$$namespace.svc.cluster.local:6007 + # CoreDNS ClusterIP of your K8s cluster — required for nginx to resolve + # *.svc.cluster.local names at request time. + # Common values: 10.96.0.10 (kubeadm), 10.43.0.10 (k3s) + - K8S_DNS_RESOLVER=${K8S_DNS_RESOLVER:?K8S_DNS_RESOLVER must be set to the CoreDNS ClusterIP (e.g. 10.96.0.10)} + networks: + - studio-local + depends_on: + - studio-frontend + restart: unless-stopped + + finetuning-server: + image: opea/finetuning:latest + container_name: finetuning-server + user: "0:0" + ipc: host + ports: + - "8015:8015" + - "8265:8265" + environment: + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + - NO_PROXY=${no_proxy} + volumes: + - finetuning-output:/home/user/comps/finetuning/src/output + networks: + - studio-local + restart: unless-stopped + +networks: + studio-local: + driver: bridge + +configs: + mysql-init-sql: + content: | + CREATE DATABASE IF NOT EXISTS ${MYSQL_EVAL_DB:-eval}; + +volumes: + ollama-models: + mysql-data: + node_modules: + pnpm-store: + finetuning-output: diff --git a/setup-scripts/setup-development/local-nginx/nginx.conf.template b/setup-scripts/setup-development/local-nginx/nginx.conf.template new file mode 100644 index 0000000..bb857c5 --- /dev/null +++ b/setup-scripts/setup-development/local-nginx/nginx.conf.template @@ -0,0 +1,210 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# +# Local-compose equivalent of the studio-nginx config in studio-manifest.yaml. +# Mirrors the same ?ns= routing logic so that sandbox app-frontend is reachable +# from the browser when running docker-compose.local.yml. +# +# Access the studio via http://localhost:30010 (not localhost:3000 directly) +# so that the nginx can route /?ns= to the K8s sandbox app-frontend. +# +# Required env var: +# K8S_DNS_RESOLVER — CoreDNS ClusterIP of your K8s cluster (e.g. 10.96.0.10 +# for kubeadm or 10.43.0.10 for k3s). Needed so nginx +# can resolve app-frontend..svc.cluster.local +# at request time. + +server { + listen 80; + listen [::]:80; + + proxy_connect_timeout 600; + proxy_send_timeout 600; + proxy_read_timeout 600; + send_timeout 600; + + client_max_body_size 10G; + + # K8s CoreDNS — lets nginx resolve *.svc.cluster.local names at request time. + resolver ${K8S_DNS_RESOLVER} valid=30s; + resolver_timeout 5s; + + # Location block for studio-frontend and app-frontend + location / { + # Initialize the default variable for namespace + set $namespace ""; + + # Check if the Referer contains '?ns=' and extract the ns argument + if ($http_referer ~* "([&?]ns=([^&]+))") { + set $namespace $2; # Capture the value of 'ns' + } + + # If 'ns' is not in the Referer, fallback to 'arg_ns' + if ($namespace = "") { + set $namespace $arg_ns; # Use the value from the 'arg_ns' + } + + # When no namespace — pass straight through to studio-frontend unchanged + if ($namespace = "") { + proxy_pass http://${STUDIO_FRONTEND_DNS}; + break; + } + + # Namespace present — route to sandbox app-frontend with no-cache headers + add_header Cache-Control "no-cache, no-store, must-revalidate"; + add_header Pragma "no-cache"; + add_header Expires "0"; + + set $app_frontend http://${APP_FRONTEND_DNS}; + rewrite ^/(.*)$ /$1?ns=$namespace break; + proxy_pass $app_frontend; + + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + } + + # Location block for app-backend + location /v1/app-backend { + # Initialize the variable for namespace + set $namespace ""; + if ($http_referer ~* "([&?]ns=([^&]+))") { + set $namespace $2; # Capture the value of 'ns' + } + + # If 'ns' is not in the Referer, fallback to 'arg_ns' + if ($namespace = "") { + set $namespace $arg_ns; + } + + # Rewrite the request to include the namespace + rewrite ^/(.*)$ /$1?ns=$namespace break; + + # Proxy to the desired service using the namespace + proxy_pass http://${APP_BACKEND_DNS}; + + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + + # Disable buffering for SSE + proxy_buffering off; + proxy_cache off; + proxy_http_version 1.1; + proxy_set_header Connection ''; + chunked_transfer_encoding off; + } + + # Location block for opea_service@prepare_doc_redis_prep + location /v1/dataprep { + # Initialize the variable for namespace + set $namespace ""; + if ($http_referer ~* "([&?]ns=([^&]+))") { + set $namespace $2; # Capture the value of 'ns' + } + + # If 'ns' is not in the Referer, fallback to 'arg_ns' + # (used by server-side requests from studio-frontend Express) + if ($namespace = "") { + set $namespace $arg_ns; + } + + # Rewrite the request to include the namespace + rewrite ^/(.*)$ /$1?ns=$namespace break; + + # Proxy to the desired service using the namespace + proxy_pass http://${PREPARE_DOC_REDIS_PREP_DNS}; + + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + } + + # Additional location block for Grafana static assets + location /grafana { + set $grafana_upstream http://${GRAFANA_DNS}; + proxy_pass $grafana_upstream; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + } + + # Proxy Grafana Live WebSocket connections + location /grafana/api/live/ { + set $grafana_upstream http://${GRAFANA_DNS}; + proxy_pass $grafana_upstream; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + + # Ensure WebSocket upgrade headers + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection "upgrade"; + } + + # WebSocket connection for fine-tuning job monitoring and downloads + # Routes directly to studio-frontend's socket.io handlers + location /socket.io { + proxy_pass http://${STUDIO_FRONTEND_DNS}; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + + # Ensure WebSocket upgrade headers + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection "upgrade"; + + # Disable buffering for WebSocket + proxy_buffering off; + proxy_cache off; + } + + location /studio-backend { + proxy_pass http://${STUDIO_BACKEND_DNS}; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + } + + location /studio-backend/ws { + proxy_pass http://${STUDIO_BACKEND_DNS}/studio-backend/ws; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + + # Ensure WebSocket upgrade headers + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection "upgrade"; + } + + # Location block for chathistory service + location /v1/chathistory { + # Initialize the variable for namespace + set $namespace ""; + if ($http_referer ~* "([&?]ns=([^&]+))") { + set $namespace $2; # Capture the value of 'ns' + } + + # Rewrite the request to include the namespace + rewrite ^/(.*)$ /$1?ns=$namespace break; + + # Proxy to the desired service using the namespace + proxy_pass http://${APP_CHATHISTORY_DNS}; + + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + } +} diff --git a/setup-scripts/setup-genai-studio/genai-studio.yml b/setup-scripts/setup-genai-studio/genai-studio.yml index 0c86d5b..ac9d484 100644 --- a/setup-scripts/setup-genai-studio/genai-studio.yml +++ b/setup-scripts/setup-genai-studio/genai-studio.yml @@ -16,5 +16,8 @@ - name: Deploy tracing import_playbook: playbooks/deploy-tracing.yml +- name: Deploy Ollama + import_playbook: playbooks/deploy-ollama.yml + - name: Deploy studio import_playbook: playbooks/deploy-studio.yml \ No newline at end of file diff --git a/setup-scripts/setup-genai-studio/helm-values/mysqldb.yaml b/setup-scripts/setup-genai-studio/helm-values/mysqldb.yaml index 9abe0c2..e3b1727 100644 --- a/setup-scripts/setup-genai-studio/helm-values/mysqldb.yaml +++ b/setup-scripts/setup-genai-studio/helm-values/mysqldb.yaml @@ -42,6 +42,7 @@ initdbScripts: -- Create databases for GenAIStudio CREATE DATABASE IF NOT EXISTS keycloak; CREATE DATABASE IF NOT EXISTS studio; + CREATE DATABASE IF NOT EXISTS eval; -- Create the Chinook database CREATE DATABASE IF NOT EXISTS Chinook; diff --git a/setup-scripts/setup-genai-studio/manifests/ollama-manifest.yaml b/setup-scripts/setup-genai-studio/manifests/ollama-manifest.yaml new file mode 100644 index 0000000..c88c405 --- /dev/null +++ b/setup-scripts/setup-genai-studio/manifests/ollama-manifest.yaml @@ -0,0 +1,101 @@ +--- +apiVersion: v1 +kind: Namespace +metadata: + name: ollama +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: ollama-models + namespace: ollama +spec: + accessModes: + - ReadWriteOnce + storageClassName: local-path + resources: + requests: + storage: 100Gi +--- +apiVersion: v1 +kind: Service +metadata: + name: ollama + namespace: ollama +spec: + type: ClusterIP + ports: + - protocol: TCP + port: 11434 + targetPort: 11434 + selector: + app: ollama +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: ollama + namespace: ollama + labels: + app: ollama +spec: + replicas: 1 + selector: + matchLabels: + app: ollama + template: + metadata: + labels: + app: ollama + spec: + containers: + - name: ollama + image: ollama/ollama:latest + imagePullPolicy: IfNotPresent + ports: + - containerPort: 11434 + env: + - name: OLLAMA_HOST + value: "0.0.0.0" + - name: OLLAMA_KEEP_ALIVE + value: "24h" + - name: OLLAMA_MAX_LOADED_MODELS + value: "2" + - name: OLLAMA_NUM_PARALLEL + value: "2" + - name: HTTP_PROXY + value: "${HTTP_PROXY}" + - name: HTTPS_PROXY + value: "${HTTP_PROXY}" + - name: NO_PROXY + value: "${NO_PROXY}" + resources: + requests: + memory: "12Gi" + cpu: "4" + limits: + memory: "32Gi" + cpu: "16" + volumeMounts: + - name: ollama-models + mountPath: /root/.ollama + livenessProbe: + httpGet: + path: / + port: 11434 + initialDelaySeconds: 30 + periodSeconds: 30 + timeoutSeconds: 10 + failureThreshold: 3 + readinessProbe: + httpGet: + path: / + port: 11434 + initialDelaySeconds: 15 + periodSeconds: 10 + timeoutSeconds: 5 + failureThreshold: 3 + volumes: + - name: ollama-models + persistentVolumeClaim: + claimName: ollama-models diff --git a/setup-scripts/setup-genai-studio/manifests/studio-manifest-aws-ecr.yaml b/setup-scripts/setup-genai-studio/manifests/studio-manifest-aws-ecr.yaml index 988d03b..1304f05 100644 --- a/setup-scripts/setup-genai-studio/manifests/studio-manifest-aws-ecr.yaml +++ b/setup-scripts/setup-genai-studio/manifests/studio-manifest-aws-ecr.yaml @@ -270,7 +270,7 @@ metadata: rules: - apiGroups: [""] resources: ["namespaces"] - verbs: ["get", "create", "delete"] + verbs: ["get", "list", "watch", "create", "delete"] - apiGroups: [""] resources: ["services"] verbs: ["get", "create", "list"] diff --git a/setup-scripts/setup-genai-studio/manifests/studio-manifest.yaml b/setup-scripts/setup-genai-studio/manifests/studio-manifest.yaml index d2e2f5b..b924952 100644 --- a/setup-scripts/setup-genai-studio/manifests/studio-manifest.yaml +++ b/setup-scripts/setup-genai-studio/manifests/studio-manifest.yaml @@ -305,7 +305,7 @@ metadata: rules: - apiGroups: [""] resources: ["namespaces"] - verbs: ["get", "create", "delete"] + verbs: ["get", "list", "watch", "create", "delete"] - apiGroups: [""] resources: ["services"] verbs: ["get", "create", "list"] @@ -712,4 +712,71 @@ spec: volumes: - name: shared-output-volume persistentVolumeClaim: - claimName: shared-finetuning-output \ No newline at end of file + claimName: shared-finetuning-output +--- +apiVersion: v1 +kind: Service +metadata: + name: studio-eval + namespace: studio +spec: + type: ClusterIP + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 + selector: + app: studio-eval +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: studio-eval + namespace: studio + labels: + app: studio-eval +spec: + replicas: 1 + selector: + matchLabels: + app: studio-eval + template: + metadata: + labels: + app: studio-eval + spec: + containers: + - name: studio-eval + image: "${REGISTRY}/studio-eval:${TAG}" + imagePullPolicy: Always + ports: + - containerPort: 8000 + envFrom: + - configMapRef: + name: studio-config + env: + - name: MYSQL_DB + value: "eval" + - name: MYSQL_HOST + value: ${MYSQL_HOST} + - name: MYSQL_PORT + value: "3306" + - name: MYSQL_USER + value: "studio" + - name: MYSQL_PASSWORD + value: "studio" + resources: + requests: + memory: "512Mi" + cpu: "500m" + limits: + memory: "2Gi" + cpu: "2" + livenessProbe: + httpGet: + path: /health + port: 8000 + initialDelaySeconds: 60 + periodSeconds: 30 + timeoutSeconds: 10 + failureThreshold: 3 \ No newline at end of file diff --git a/setup-scripts/setup-genai-studio/playbooks/deploy-ollama.yml b/setup-scripts/setup-genai-studio/playbooks/deploy-ollama.yml new file mode 100644 index 0000000..d1617bd --- /dev/null +++ b/setup-scripts/setup-genai-studio/playbooks/deploy-ollama.yml @@ -0,0 +1,29 @@ +--- +- name: Deploy Ollama in ollama namespace + hosts: localhost + vars_files: + - ../vars.yml + tasks: + - name: Check if ollama namespace exists + shell: kubectl get namespace ollama --ignore-not-found + register: namespace_check + changed_when: false + + - name: End playbook if ollama namespace exists + meta: end_play + when: namespace_check.stdout != "" + + - name: Apply ollama manifest + shell: "envsubst '${HTTP_PROXY} ${NO_PROXY}' < ../manifests/ollama-manifest.yaml | kubectl apply -f -" + environment: + HTTP_PROXY: "{{ http_proxy }}" + NO_PROXY: "{{ no_proxy }}" + + - name: Wait for ollama pod to be ready + command: kubectl wait --namespace ollama --for=condition=ready pod -l app=ollama --timeout=300s + + - name: Pre-pull nomic-embed-text model + shell: kubectl exec -n ollama $(kubectl get pod -n ollama -l app=ollama -o jsonpath="{.items[0].metadata.name}") -- env OLLAMA_HOST=http://127.0.0.1:11434 HTTP_PROXY={{ http_proxy }} HTTPS_PROXY={{ http_proxy }} NO_PROXY={{ no_proxy }} ollama pull nomic-embed-text + + - name: Pre-pull qwen2.5:14b model + shell: kubectl exec -n ollama $(kubectl get pod -n ollama -l app=ollama -o jsonpath="{.items[0].metadata.name}") -- env OLLAMA_HOST=http://127.0.0.1:11434 HTTP_PROXY={{ http_proxy }} HTTPS_PROXY={{ http_proxy }} NO_PROXY={{ no_proxy }} ollama pull qwen2.5:14b \ No newline at end of file diff --git a/setup-scripts/setup-genai-studio/studio-config.yaml b/setup-scripts/setup-genai-studio/studio-config.yaml index e876692..b87974c 100644 --- a/setup-scripts/setup-genai-studio/studio-config.yaml +++ b/setup-scripts/setup-genai-studio/studio-config.yaml @@ -7,6 +7,8 @@ data: KC_ASSETS_GIT_URL: "https://github.com/opea-project/GenAIStudio/tree/main/assets/keycloak" TELEMETRY_ENDPOINT: "http://tracing-opentelemetry-collector.tracing.svc.cluster.local:4318/v1/traces" CLICKHOUSE_DNS: "clickhouse.tracing.svc.cluster.local:9000" + OLLAMA_DNS: "ollama.ollama.svc.cluster.local:11434" + MYSQL_HOST: "mysql.mysql.svc.cluster.local" KEYCLOAK_DNS: "keycloak.studio.svc.cluster.local:8443" GRAFANA_DNS: "kube-prometheus-stack-grafana.monitoring.svc.cluster.local" STUDIO_FRONTEND_DNS: "studio-frontend.studio.svc.cluster.local:3000" @@ -15,4 +17,6 @@ data: APP_CHATHISTORY_DNS: "chathistory-mongo.$namespace.svc.cluster.local:6012" PREPARE_DOC_REDIS_PREP_DNS: "opea-prepare-doc-redis-prep-0.$namespace.svc.cluster.local:6007" STUDIO_BACKEND_DNS: "studio-backend.studio.svc.cluster.local:5000" - FINETUNING_HOST: "finetuning-server.studio.svc.cluster.local" \ No newline at end of file + FINETUNING_SERVICE_DNS: "finetuning-server.studio.svc.cluster.local:8015" + FINETUNING_RAY_DNS: "finetuning-server.studio.svc.cluster.local:8265" + STUDIO_EVAL_DNS: "studio-eval.studio.svc.cluster.local:8000" \ No newline at end of file diff --git a/studio-backend/app/main.py b/studio-backend/app/main.py index 85c291b..e129e37 100644 --- a/studio-backend/app/main.py +++ b/studio-backend/app/main.py @@ -32,11 +32,12 @@ def read_health(): allow_headers=["*"], ) -from .routers import user_router, download_router, sandbox_router, llmtraces_router, debuglog_router, clickdeploy_router +from .routers import user_router, download_router, sandbox_router, llmtraces_router, debuglog_router, clickdeploy_router, evaluation_router app.include_router(user_router.router, prefix="/studio-backend") app.include_router(download_router.router, prefix="/studio-backend") app.include_router(sandbox_router.router, prefix="/studio-backend") app.include_router(llmtraces_router.router, prefix="/studio-backend") app.include_router(debuglog_router.router, prefix="/studio-backend") -app.include_router(clickdeploy_router.router, prefix="/studio-backend") \ No newline at end of file +app.include_router(clickdeploy_router.router, prefix="/studio-backend") +app.include_router(evaluation_router.router, prefix="/studio-backend") \ No newline at end of file diff --git a/studio-backend/app/requirements.txt b/studio-backend/app/requirements.txt index fa5e16c..00f875a 100644 --- a/studio-backend/app/requirements.txt +++ b/studio-backend/app/requirements.txt @@ -7,4 +7,5 @@ pydantic==1.10.18 starlette==0.49.1 websockets==10.3 clickhouse-driver==0.2.9 -paramiko==3.5.1 \ No newline at end of file +paramiko==3.5.1 +httpx==0.27.2 \ No newline at end of file diff --git a/studio-backend/app/routers/evaluation_router.py b/studio-backend/app/routers/evaluation_router.py new file mode 100644 index 0000000..b549f1b --- /dev/null +++ b/studio-backend/app/routers/evaluation_router.py @@ -0,0 +1,54 @@ +import os + +import httpx +from fastapi import APIRouter, HTTPException, Request +from fastapi.responses import Response + +STUDIO_EVAL_DNS = os.getenv( + "STUDIO_EVAL_DNS", + "studio-eval-service.studio.svc.cluster.local:8000", +) + +router = APIRouter() + + +@router.api_route("/evaluation/{path:path}", methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS", "HEAD"]) +async def proxy_evaluation(path: str, request: Request): + target_url = f"http://{STUDIO_EVAL_DNS}/eval/{path}" + upstream_timeout = 60.0 + query = request.url.query + if query: + target_url = f"{target_url}?{query}" + + headers = dict(request.headers) + # Remove hop-by-hop headers that must not be forwarded + for hop in ("host", "transfer-encoding", "connection"): + headers.pop(hop, None) + + body = await request.body() + + try: + async with httpx.AsyncClient(timeout=upstream_timeout) as client: + upstream = await client.request( + method=request.method, + url=target_url, + headers=headers, + content=body, + ) + except httpx.ConnectError as exc: + raise HTTPException(status_code=502, detail=f"Cannot reach eval service: {exc}") + except httpx.TimeoutException as exc: + raise HTTPException(status_code=504, detail=f"Eval service timed out: {exc}") + + # Strip hop-by-hop headers from the upstream response before forwarding + excluded = {"transfer-encoding", "connection", "keep-alive", "te", "trailers", "upgrade"} + response_headers = { + k: v for k, v in upstream.headers.items() if k.lower() not in excluded + } + + return Response( + content=upstream.content, + status_code=upstream.status_code, + headers=response_headers, + media_type=upstream.headers.get("content-type"), + ) diff --git a/studio-backend/app/routers/sandbox_router.py b/studio-backend/app/routers/sandbox_router.py index 5bdf4d7..136a067 100644 --- a/studio-backend/app/routers/sandbox_router.py +++ b/studio-backend/app/routers/sandbox_router.py @@ -36,6 +36,22 @@ async def delete_sandbox(request: WorkflowId): return response +@router.get("/sandbox/list") +async def list_sandboxes(): + core_v1_api = client.CoreV1Api() + try: + namespaces = core_v1_api.list_namespace() + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + sandboxes = [ + {"id": ns.metadata.name, "status": ns.status.phase} + for ns in namespaces.items + if ns.metadata.name.startswith("sandbox-") + ] + return {"sandboxes": sandboxes} + + @router.websocket("/ws/sandbox-status") async def check_sandbox_status(websocket: WebSocket): print('checking sandbox status') diff --git a/studio-backend/app/services/dashboard_service.py b/studio-backend/app/services/dashboard_service.py index 8535429..2f4fb0c 100644 --- a/studio-backend/app/services/dashboard_service.py +++ b/studio-backend/app/services/dashboard_service.py @@ -8,8 +8,17 @@ # Read environment variables template_file_path = os.path.join(os.path.dirname(__file__), '..', 'templates', 'grafana-dashboards', 'sandbox-dashboard.json') + +def _is_grafana_disabled() -> bool: + return os.getenv("DISABLE_GRAFANA_DASHBOARD_SYNC", "false").lower() in {"1", "true", "yes", "on"} + def import_grafana_dashboards(namespace_name): + if _is_grafana_disabled(): + msg = "Grafana dashboard sync disabled by DISABLE_GRAFANA_DASHBOARD_SYNC" + print(msg) + return msg + print("Getting post_url") grafana_url = os.getenv("GRAFANA_DNS", "localhost:30007") post_url = f"http://{grafana_url}/grafana/api/dashboards/db" @@ -32,16 +41,21 @@ def import_grafana_dashboards(namespace_name): print(f"Importing dashboard {namespace_name}") print(f"post_url: {post_url}") + response = None try: response = requests.post(post_url, headers=headers, data=json.dumps(dashboard_json)) print(f"Response Status Code: {response.status_code}") print(f"Response Text: {response.text}") except Exception as e: print(f"Exception occurred: {e}") - - return response.text + + return response.text if response is not None else "Grafana dashboard import skipped due to request error" def delete_dashboard(namespace_name): + if _is_grafana_disabled(): + print("Grafana dashboard delete skipped by DISABLE_GRAFANA_DASHBOARD_SYNC") + return + grafana_url = os.getenv("GRAFANA_DNS", "localhost:30007") post_url = f"http://{grafana_url}/grafana/api/dashboards/uid/{namespace_name.replace('sandbox-','')}" auth_str = f"admin:prom-operator" diff --git a/studio-backend/app/templates/microsvc-composes/tgi.yaml b/studio-backend/app/templates/microsvc-composes/tgi.yaml index 25c2421..186d9e3 100644 --- a/studio-backend/app/templates/microsvc-composes/tgi.yaml +++ b/studio-backend/app/templates/microsvc-composes/tgi.yaml @@ -1,5 +1,5 @@ "{{endpoint}}": - image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu + image: ghcr.io/huggingface/text-generation-inference:2.4.1-intel-cpu container_name: "{{endpoint}}" ports: - "{{port_key}}:80" diff --git a/studio-backend/app/templates/microsvc-manifests/tgi.yaml b/studio-backend/app/templates/microsvc-manifests/tgi.yaml index e47d703..00db4ff 100644 --- a/studio-backend/app/templates/microsvc-manifests/tgi.yaml +++ b/studio-backend/app/templates/microsvc-manifests/tgi.yaml @@ -67,7 +67,7 @@ spec: name: config-{endpoint} securityContext: {} - image: "ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu" + image: "ghcr.io/huggingface/text-generation-inference:2.4.1-intel-cpu" imagePullPolicy: IfNotPresent volumeMounts: - mountPath: /data diff --git a/studio-backend/tests/exporter-groundtruth/gt_app-compose.yaml b/studio-backend/tests/exporter-groundtruth/gt_app-compose.yaml index 9c856d0..9fe5bbe 100644 --- a/studio-backend/tests/exporter-groundtruth/gt_app-compose.yaml +++ b/studio-backend/tests/exporter-groundtruth/gt_app-compose.yaml @@ -22,7 +22,7 @@ services: https_proxy: ${https_proxy} command: --model-id BAAI/bge-large-en-v1.5 --auto-truncate tgi-0: - image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu + image: ghcr.io/huggingface/text-generation-inference:2.4.1-intel-cpu container_name: tgi-0 ports: - 3081:80 diff --git a/studio-backend/tests/exporter-groundtruth/gt_app-manifest-with-nginx.yaml b/studio-backend/tests/exporter-groundtruth/gt_app-manifest-with-nginx.yaml index 342122e..e5a3b53 100644 --- a/studio-backend/tests/exporter-groundtruth/gt_app-manifest-with-nginx.yaml +++ b/studio-backend/tests/exporter-groundtruth/gt_app-manifest-with-nginx.yaml @@ -248,7 +248,7 @@ spec: - configMapRef: name: config-tgi-0 securityContext: {} - image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu + image: ghcr.io/huggingface/text-generation-inference:2.4.1-intel-cpu imagePullPolicy: IfNotPresent volumeMounts: - mountPath: /data diff --git a/studio-backend/tests/exporter-groundtruth/gt_app-manifest.yaml b/studio-backend/tests/exporter-groundtruth/gt_app-manifest.yaml index 317a09e..062d766 100644 --- a/studio-backend/tests/exporter-groundtruth/gt_app-manifest.yaml +++ b/studio-backend/tests/exporter-groundtruth/gt_app-manifest.yaml @@ -248,7 +248,7 @@ spec: - configMapRef: name: config-tgi-0 securityContext: {} - image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu + image: ghcr.io/huggingface/text-generation-inference:2.4.1-intel-cpu imagePullPolicy: IfNotPresent volumeMounts: - mountPath: /data diff --git a/studio-eval/Dockerfile b/studio-eval/Dockerfile new file mode 100644 index 0000000..76fad51 --- /dev/null +++ b/studio-eval/Dockerfile @@ -0,0 +1,22 @@ +FROM python:3.11-slim + +ARG http_proxy +ARG https_proxy +ARG no_proxy + +ENV http_proxy=${http_proxy} +ENV https_proxy=${https_proxy} +ENV no_proxy=${no_proxy} +ENV DEEPEVAL_TELEMETRY_OPT_OUT=YES +ENV DEEPEVAL_UPDATE_WARNING_OPT_OUT=YES + +WORKDIR /app + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY app/ ./app/ + +EXPOSE 8000 + +CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/studio-eval/app/__init__.py b/studio-eval/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/studio-eval/app/compat.py b/studio-eval/app/compat.py new file mode 100644 index 0000000..0803fa6 --- /dev/null +++ b/studio-eval/app/compat.py @@ -0,0 +1,85 @@ +"""Compatibility shims for third-party libraries.""" + +import os + +import pydantic.config + + +def ensure_pydantic_v2_apis() -> None: + """Set DeepEval opt-out env vars and apply third-party compatibility patches. + + DeepEval performs a PyPI version check and telemetry IP lookup at import + time. Default those opt-outs to YES so evaluation and synthesis do not + wait on external DeepEval calls unless a deployment explicitly opts back in. + """ + os.environ.setdefault("DEEPEVAL_TELEMETRY_OPT_OUT", "YES") + os.environ.setdefault("DEEPEVAL_UPDATE_WARNING_OPT_OUT", "YES") + + # DeepEval Synthesizer imports ExtraValues from pydantic.config. This + # exists in Pydantic v2 but guard defensively in case of version skew. + if not hasattr(pydantic.config, "ExtraValues"): + from typing import Literal + pydantic.config.ExtraValues = Literal["allow", "ignore", "forbid"] # type: ignore[attr-defined] + + # DeepEval imports InvalidCollectionException from chromadb.errors, but + # newer chromadb versions expose NotFoundError instead when a collection + # is missing. Alias the exact runtime exception so DeepEval's except block + # still catches a missing collection and creates it. + try: + import chromadb.errors as _ce + if not hasattr(_ce, "InvalidCollectionException"): + if hasattr(_ce, "NotFoundError"): + _ce.InvalidCollectionException = _ce.NotFoundError # type: ignore[attr-defined] + else: + class InvalidCollectionException(_ce.ChromaError): # type: ignore[misc] + pass + _ce.InvalidCollectionException = InvalidCollectionException # type: ignore[attr-defined] + except ImportError: + pass + + +def patch_deepeval_document_chunker() -> None: + """Patch DeepEval's DocumentChunker to collapse dot-leaders before chunking. + + PDF table-of-contents lines like "Chapter 1 ............... 5" produce + hundreds of single-dot tokens that overflow the embedding context window. + Collapsing runs of 4+ dots down to "..." prevents the overflow and avoids + the downstream ChromaDB "Collection does not exist" error that results from + a failed chunking call. + """ + import re + + try: + from deepeval.synthesizer.chunking import doc_chunker + except ImportError: + return + + Chunker = doc_chunker.DocumentChunker + if getattr(Chunker, "_dot_leader_patch_applied", False): + return + + dot_pattern = re.compile(r"\.{4,}") + + original_load = Chunker.load_doc + original_aload = Chunker.a_load_doc + + def _process(chunker_instance) -> None: + if not chunker_instance.sections: + return + for section in chunker_instance.sections: + section.page_content = dot_pattern.sub("...", section.page_content) + chunker_instance.text_token_count = chunker_instance.count_tokens( + chunker_instance.sections + ) + + def patched_load_doc(self, path: str): # type: ignore[override] + original_load(self, path) + _process(self) + + async def patched_a_load_doc(self, path: str): # type: ignore[override] + await original_aload(self, path) + _process(self) + + Chunker.load_doc = patched_load_doc # type: ignore[method-assign] + Chunker.a_load_doc = patched_a_load_doc # type: ignore[method-assign] + Chunker._dot_leader_patch_applied = True # type: ignore[attr-defined] diff --git a/studio-eval/app/db/__init__.py b/studio-eval/app/db/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/studio-eval/app/db/database.py b/studio-eval/app/db/database.py new file mode 100644 index 0000000..a8a8180 --- /dev/null +++ b/studio-eval/app/db/database.py @@ -0,0 +1,27 @@ +import os +from sqlalchemy import create_engine +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import sessionmaker + +MYSQL_HOST = os.getenv("MYSQL_HOST", "localhost") +MYSQL_PORT = os.getenv("MYSQL_PORT", "3306") +MYSQL_USER = os.getenv("MYSQL_USER", "root") +MYSQL_PASSWORD = os.getenv("MYSQL_PASSWORD", "") +MYSQL_DB = os.getenv("MYSQL_DB", "eval") + +DATABASE_URL = ( + f"mysql+pymysql://{MYSQL_USER}:{MYSQL_PASSWORD}" + f"@{MYSQL_HOST}:{MYSQL_PORT}/{MYSQL_DB}" +) + +engine = create_engine(DATABASE_URL, pool_pre_ping=True) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) +Base = declarative_base() + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() diff --git a/studio-eval/app/db/schema.py b/studio-eval/app/db/schema.py new file mode 100644 index 0000000..deb9146 --- /dev/null +++ b/studio-eval/app/db/schema.py @@ -0,0 +1,103 @@ +from datetime import datetime +import uuid + +from sqlalchemy import ( + Boolean, + Column, + DateTime, + Float, + ForeignKey, + Integer, + JSON, + String, + Text, +) +from sqlalchemy.orm import relationship + +from app.db.database import Base + + +class EvalDataset(Base): + __tablename__ = "eval_datasets" + + id = Column(String(36), primary_key=True, index=True, default=lambda: str(uuid.uuid4())) + name = Column(String(255), nullable=False) + description = Column(Text, nullable=True) + status = Column(String(50), nullable=False, default="completed") # pending/synthesizing/completed/failed/stopped + error = Column(Text, nullable=True) + completed_contexts = Column(Integer, nullable=True) + total_contexts = Column(Integer, nullable=True) + completed_goldens = Column(Integer, nullable=True) + target_goldens = Column(Integer, nullable=True) + created_at = Column(DateTime, default=datetime.utcnow) + updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow) + + entries = relationship( + "EvalDatasetEntry", + back_populates="dataset", + cascade="all, delete-orphan", + ) + runs = relationship("EvalRun", back_populates="dataset", passive_deletes=True) + + +class EvalDatasetEntry(Base): + __tablename__ = "eval_dataset_entries" + + id = Column(Integer, primary_key=True, index=True) + dataset_id = Column(String(36), ForeignKey("eval_datasets.id"), nullable=False) + input = Column(Text, nullable=False) + expected_output = Column(Text, nullable=True) + context = Column(JSON, nullable=True) # list[str] + created_at = Column(DateTime, default=datetime.utcnow) + + dataset = relationship("EvalDataset", back_populates="entries") + results = relationship("EvalRunResult", back_populates="entry") + + +class EvalRun(Base): + __tablename__ = "eval_runs" + + id = Column(String(36), primary_key=True, index=True, default=lambda: str(uuid.uuid4())) + name = Column(String(255), nullable=False) + dataset_id = Column(String(36), ForeignKey("eval_datasets.id", ondelete="SET NULL"), nullable=True) + sandbox_id = Column(String(255), nullable=False) + model_name = Column(String(255), nullable=False) + metrics = Column(JSON, nullable=False) # list[str] + status = Column(String(50), default="pending") # pending/running/completed/failed/stopped + completed_count = Column(Integer, nullable=True) + total_count = Column(Integer, nullable=True) + created_at = Column(DateTime, default=datetime.utcnow) + completed_at = Column(DateTime, nullable=True) + error = Column(Text, nullable=True) + # App-backend request parameters for RAG evaluation + system_prompt = Column(Text, nullable=True) + temperature = Column(Float, nullable=True) + max_tokens = Column(Integer, nullable=True) + request_model = Column(String(255), nullable=True) + configuration_snapshot = Column(JSON, nullable=True) + dataset_name_snapshot = Column(String(255), nullable=True) + dataset_entries_snapshot = Column(JSON, nullable=True) + # shape: [{"id": int, "input": str, "expected_output": str|null, "context": list[str]|null}, ...] + + dataset = relationship("EvalDataset", back_populates="runs") + results = relationship( + "EvalRunResult", + back_populates="run", + cascade="all, delete-orphan", + ) + + +class EvalRunResult(Base): + __tablename__ = "eval_run_results" + + id = Column(Integer, primary_key=True, index=True) + run_id = Column(String(36), ForeignKey("eval_runs.id"), nullable=False) + entry_id = Column(Integer, ForeignKey("eval_dataset_entries.id"), nullable=False) + actual_output = Column(Text, nullable=True) + metric_scores = Column(JSON, nullable=True) # {metric_name: {score, passed, reason}} + passed = Column(Boolean, nullable=True) + reason = Column(Text, nullable=True) + created_at = Column(DateTime, default=datetime.utcnow) + + run = relationship("EvalRun", back_populates="results") + entry = relationship("EvalDatasetEntry", back_populates="results") diff --git a/studio-eval/app/main.py b/studio-eval/app/main.py new file mode 100644 index 0000000..9d8dff6 --- /dev/null +++ b/studio-eval/app/main.py @@ -0,0 +1,136 @@ +import logging +import time + +from fastapi import FastAPI +from sqlalchemy import inspect, text +from sqlalchemy.exc import OperationalError + +from app.db.database import Base, engine +from app.routers.eval_router import router as eval_router + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s — %(message)s", +) +logger = logging.getLogger(__name__) + + +def _migrate_eval_datasets_table() -> None: + inspector = inspect(engine) + if "eval_datasets" not in inspector.get_table_names(): + return + + columns = inspector.get_columns("eval_datasets") + id_col = next((c for c in columns if c["name"] == "id"), None) + if id_col and "int" in str(id_col["type"]).lower(): + logger.info("Detected schema change: migrating eval_datasets table from Integer to UUID id — dropping all tables…") + Base.metadata.drop_all(bind=engine) + logger.info("Dropped old tables") + return + + existing = {c["name"] for c in columns} + with engine.begin() as connection: + if "status" not in existing: + logger.info("Adding missing status column to eval_datasets…") + connection.execute( + text("ALTER TABLE eval_datasets ADD COLUMN status VARCHAR(50) NOT NULL DEFAULT 'completed'") + ) + if "error" not in existing: + logger.info("Adding missing error column to eval_datasets…") + connection.execute( + text("ALTER TABLE eval_datasets ADD COLUMN error TEXT NULL") + ) + if "completed_contexts" not in existing: + logger.info("Adding missing completed_contexts column to eval_datasets…") + connection.execute( + text("ALTER TABLE eval_datasets ADD COLUMN completed_contexts INT NULL") + ) + if "total_contexts" not in existing: + logger.info("Adding missing total_contexts column to eval_datasets…") + connection.execute( + text("ALTER TABLE eval_datasets ADD COLUMN total_contexts INT NULL") + ) + if "completed_goldens" not in existing: + logger.info("Adding missing completed_goldens column to eval_datasets…") + connection.execute( + text("ALTER TABLE eval_datasets ADD COLUMN completed_goldens INT NULL") + ) + if "target_goldens" not in existing: + logger.info("Adding missing target_goldens column to eval_datasets…") + connection.execute( + text("ALTER TABLE eval_datasets ADD COLUMN target_goldens INT NULL") + ) + + +def _migrate_eval_runs_table() -> None: + inspector = inspect(engine) + if "eval_runs" not in inspector.get_table_names(): + return + + columns = inspector.get_columns("eval_runs") + id_col = next((c for c in columns if c["name"] == "id"), None) + if id_col and "int" in str(id_col["type"]).lower(): + logger.info("Detected schema change: migrating eval_runs table from Integer to UUID id…") + Base.metadata.drop_all(bind=engine) + logger.info("Dropped old tables") + return + + column_names = {column["name"] for column in columns} + if "configuration_snapshot" not in column_names: + logger.info("Adding missing configuration_snapshot column to eval_runs…") + with engine.begin() as connection: + connection.execute( + text("ALTER TABLE eval_runs ADD COLUMN configuration_snapshot JSON NULL") + ) + + with engine.begin() as connection: + if "completed_count" not in column_names: + logger.info("Adding missing completed_count column to eval_runs…") + connection.execute( + text("ALTER TABLE eval_runs ADD COLUMN completed_count INT NULL") + ) + if "total_count" not in column_names: + logger.info("Adding missing total_count column to eval_runs…") + connection.execute( + text("ALTER TABLE eval_runs ADD COLUMN total_count INT NULL") + ) + +app = FastAPI( + title="Studio Eval Service", + description=( + "Evaluation service for GenAI Studio: golden dataset management, " + "DeepEval-based metric evaluation, and Ollama model management." + ), + version="1.0.0", +) + + +@app.on_event("startup") +async def startup() -> None: + logger.info("Creating database tables (if not present)…") + # Import schema module so that all ORM models register with Base.metadata + from app.db import schema # noqa: F401 + + retries = 10 + delay = 3 + for attempt in range(1, retries + 1): + try: + _migrate_eval_datasets_table() + _migrate_eval_runs_table() + Base.metadata.create_all(bind=engine) + logger.info("Database tables ready") + return + except OperationalError as e: + if attempt == retries: + logger.error("Could not connect to MySQL after %d attempts: %s", retries, e) + raise + logger.warning("MySQL not ready (attempt %d/%d), retrying in %ds…", attempt, retries, delay) + time.sleep(delay) + + +app.include_router(eval_router) + + +@app.get("/health", tags=["health"]) +def health_check(): + return {"status": "ok"} diff --git a/studio-eval/app/models/__init__.py b/studio-eval/app/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/studio-eval/app/models/eval_models.py b/studio-eval/app/models/eval_models.py new file mode 100644 index 0000000..f1cd349 --- /dev/null +++ b/studio-eval/app/models/eval_models.py @@ -0,0 +1,356 @@ +from datetime import datetime +from typing import Any, Dict, List, Optional, Set + +from pydantic import BaseModel, ConfigDict, field_validator + +# --------------------------------------------------------------------------- +# Supported DeepEval metrics +# --------------------------------------------------------------------------- + +SUPPORTED_METRICS: Set[str] = { + "AnswerRelevancy", + "Faithfulness", +} + +# --------------------------------------------------------------------------- +# Dataset Entry +# --------------------------------------------------------------------------- + + +class EvalDatasetEntryCreate(BaseModel): + input: str + expected_output: Optional[str] = None + context: Optional[List[str]] = None + + +class EvalDatasetEntryResponse(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: int + dataset_id: str + input: str + expected_output: Optional[str] = None + context: Optional[List[str]] = None + created_at: datetime + + +# --------------------------------------------------------------------------- +# Dataset +# --------------------------------------------------------------------------- + + +class EvalDatasetCreate(BaseModel): + name: str + description: Optional[str] = None + entries: Optional[List[EvalDatasetEntryCreate]] = [] + + +class EvalDatasetUpdate(BaseModel): + name: Optional[str] = None + description: Optional[str] = None + + +class EvalDatasetEntryUpdate(BaseModel): + input: Optional[str] = None + expected_output: Optional[str] = None + context: Optional[List[str]] = None + + +class AddEntriesRequest(BaseModel): + entries: List[EvalDatasetEntryCreate] + + @field_validator("entries") + @classmethod + def entries_not_empty(cls, v): + if not v: + raise ValueError("entries must not be empty") + return v + + +class AddEntriesResponse(BaseModel): + added: int + entries: List[EvalDatasetEntryResponse] + + +class EvalDatasetResponse(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: str + name: str + description: Optional[str] = None + status: str = "completed" + error: Optional[str] = None + completed_contexts: Optional[int] = None + total_contexts: Optional[int] = None + completed_goldens: Optional[int] = None + target_goldens: Optional[int] = None + created_at: datetime + updated_at: datetime + entries: List[EvalDatasetEntryResponse] = [] + + +class EvalDatasetSummaryResponse(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: str + name: str + description: Optional[str] = None + status: str = "completed" + error: Optional[str] = None + completed_contexts: Optional[int] = None + total_contexts: Optional[int] = None + completed_goldens: Optional[int] = None + target_goldens: Optional[int] = None + created_at: datetime + updated_at: datetime + entry_count: int = 0 + + +# --------------------------------------------------------------------------- +# Synthesis request +# --------------------------------------------------------------------------- + + +class SynthesizeRequest(BaseModel): + name: str + description: Optional[str] = None + contexts: List[List[str]] + model_name: str + target_goldens: int = 10 + num_goldens_per_context: int = 2 + num_evolutions: int = 1 + input_quality_threshold: float = 0.4 + llm_timeout: float = 0 + async_mode: bool = False + max_concurrent: int = 1 + + @field_validator("contexts") + @classmethod + def contexts_not_empty(cls, v): + if not v: + raise ValueError("contexts must not be empty") + return v + + @field_validator("target_goldens") + @classmethod + def target_goldens_positive(cls, v): + if v < 1: + raise ValueError("target_goldens must be >= 1") + return v + + @field_validator("num_goldens_per_context") + @classmethod + def goldens_positive(cls, v): + if v < 1: + raise ValueError("num_goldens_per_context must be >= 1") + return v + + @field_validator("num_evolutions") + @classmethod + def evolutions_positive(cls, v): + if v < 1: + raise ValueError("num_evolutions must be >= 1") + return v + + @field_validator("input_quality_threshold") + @classmethod + def input_quality_range(cls, v): + if v < 0 or v > 1: + raise ValueError("input_quality_threshold must be between 0 and 1") + return v + + @field_validator("llm_timeout") + @classmethod + def llm_timeout_non_negative(cls, v): + if v < 0: + raise ValueError("llm_timeout must be >= 0") + return v + + @field_validator("max_concurrent") + @classmethod + def max_concurrent_positive(cls, v): + if v < 1: + raise ValueError("max_concurrent must be >= 1") + return v + + +# --------------------------------------------------------------------------- +# Synthesis request (document-based — file upload path) +# --------------------------------------------------------------------------- + + +class SynthesizeFromDocRequest(BaseModel): + """Synthesis request that accepts a raw document file. + + The file bytes + extension are passed separately to the background task; + this model carries only the scalar configuration fields. + """ + + name: str + description: Optional[str] = None + model_name: str + embed_model_name: str = "nomic-embed-text" + target_goldens: Optional[int] = None + max_goldens_per_context: int = 2 + max_contexts: int = 5 + max_context_length: int = 3 + chunk_size: int = 1024 + chunk_overlap: int = 0 + num_evolutions: int = 1 + input_quality_threshold: float = 0.4 + llm_timeout: float = 0 + async_mode: bool = False + max_concurrent: int = 1 + + @field_validator("target_goldens") + @classmethod + def target_goldens_positive(cls, v): + if v is not None and v < 1: + raise ValueError("target_goldens must be >= 1") + return v + + @field_validator("max_goldens_per_context") + @classmethod + def goldens_positive(cls, v): + if v < 1: + raise ValueError("max_goldens_per_context must be >= 1") + return v + + @field_validator("input_quality_threshold") + @classmethod + def input_quality_range(cls, v): + if v < 0 or v > 1: + raise ValueError("input_quality_threshold must be between 0 and 1") + return v + + @field_validator("max_concurrent") + @classmethod + def max_concurrent_positive(cls, v): + if v < 1: + raise ValueError("max_concurrent must be >= 1") + return v + + +# --------------------------------------------------------------------------- +# Eval Run +# --------------------------------------------------------------------------- + + +class EvalRunCreate(BaseModel): + name: str + dataset_id: str + sandbox_id: str + model_name: str + metrics: List[str] + configuration_snapshot: Optional[Dict[str, Any]] = None + # Optional app-backend request parameters. + # Context for Faithfulness / AnswerRelevancy is always taken from the + # dataset entries so that RAG evaluation works even when the backend + # does not return retrieval context in its response. + system_prompt: Optional[str] = "You are a helpful assistant" + temperature: Optional[float] = 0.4 + max_tokens: Optional[int] = 100 + request_model: Optional[str] = "NA" + + @field_validator("metrics") + @classmethod + def validate_metrics(cls, v): + if not v: + raise ValueError("At least one metric must be specified") + invalid = set(v) - SUPPORTED_METRICS + if invalid: + raise ValueError( + f"Unsupported metrics: {sorted(invalid)}. " + f"Supported: {sorted(SUPPORTED_METRICS)}" + ) + return v + + +class MetricScoreDetail(BaseModel): + score: Optional[float] = None + passed: Optional[bool] = None + reason: Optional[str] = None + + +class EvalRunResultResponse(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: int + run_id: str + entry_id: int + actual_output: Optional[str] = None + metric_scores: Optional[Dict[str, Any]] = None + passed: Optional[bool] = None + reason: Optional[str] = None + created_at: datetime + entry: Optional[EvalDatasetEntryResponse] = None + + +class EvalRunSummaryResponse(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: str + name: str + dataset_id: Optional[str] = None + sandbox_id: str + model_name: str + metrics: List[str] + status: str + completed_count: Optional[int] = None + total_count: Optional[int] = None + created_at: datetime + completed_at: Optional[datetime] = None + error: Optional[str] = None + system_prompt: Optional[str] = None + temperature: Optional[float] = None + max_tokens: Optional[int] = None + request_model: Optional[str] = None + configuration_snapshot: Optional[Dict[str, Any]] = None + dataset_name_snapshot: Optional[str] = None + + +class EvalRunResponse(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: str + name: str + dataset_id: Optional[str] = None + sandbox_id: str + model_name: str + metrics: List[str] + status: str + completed_count: Optional[int] = None + total_count: Optional[int] = None + created_at: datetime + completed_at: Optional[datetime] = None + error: Optional[str] = None + system_prompt: Optional[str] = None + temperature: Optional[float] = None + max_tokens: Optional[int] = None + request_model: Optional[str] = None + configuration_snapshot: Optional[Dict[str, Any]] = None + dataset_name_snapshot: Optional[str] = None + dataset_entries_snapshot: Optional[List[Dict[str, Any]]] = None + results: List[EvalRunResultResponse] = [] + + +# --------------------------------------------------------------------------- +# Ollama model management +# --------------------------------------------------------------------------- + + +class OllamaModelInfo(BaseModel): + name: str + size: Optional[int] = None + digest: Optional[str] = None + + +class ModelPullRequest(BaseModel): + model_name: str + + @field_validator("model_name") + @classmethod + def model_name_not_empty(cls, v): + if not v.strip(): + raise ValueError("model_name must not be empty") + return v.strip() diff --git a/studio-eval/app/routers/__init__.py b/studio-eval/app/routers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/studio-eval/app/routers/eval_router.py b/studio-eval/app/routers/eval_router.py new file mode 100644 index 0000000..30a0654 --- /dev/null +++ b/studio-eval/app/routers/eval_router.py @@ -0,0 +1,310 @@ +import asyncio +import logging +from pathlib import Path +from typing import List, Optional + +from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, HTTPException, UploadFile +from sqlalchemy.orm import Session + +from app.db.database import get_db +from app.models.eval_models import ( + AddEntriesRequest, + AddEntriesResponse, + EvalDatasetCreate, + EvalDatasetEntryResponse, + EvalDatasetEntryUpdate, + EvalDatasetResponse, + EvalDatasetSummaryResponse, + EvalDatasetUpdate, + EvalRunCreate, + EvalRunResponse, + EvalRunSummaryResponse, + ModelPullRequest, + OllamaModelInfo, + SynthesizeFromDocRequest, +) +from app.services import dataset_service, eval_service +from app.services.ollama_service import ensure_model, get_pull_status, list_models + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/eval", tags=["eval"]) + +ALLOWED_SYNTHESIS_FILE_EXTENSIONS = {".pdf", ".docx"} + +def _resolve_synthesis_file_ext(filename: Optional[str]) -> str: + file_ext = Path(filename).suffix.lower() if filename else ".pdf" + if file_ext not in ALLOWED_SYNTHESIS_FILE_EXTENSIONS: + allowed = ", ".join(sorted(ALLOWED_SYNTHESIS_FILE_EXTENSIONS)) + raise HTTPException( + status_code=400, + detail=f"Unsupported file type '{file_ext}'. Supported types: {allowed}", + ) + return file_ext + +# --------------------------------------------------------------------------- +# Eval runs +# --------------------------------------------------------------------------- + + +@router.post("/runs", response_model=EvalRunSummaryResponse, status_code=202) +async def create_eval_run( + payload: EvalRunCreate, + background_tasks: BackgroundTasks, + db: Session = Depends(get_db), +): + """Kick off an eval run in the background and return immediately.""" + run = eval_service.create_eval_run(db, payload) + background_tasks.add_task(eval_service._run_eval_job, run.id) + return run + + +@router.get("/runs", response_model=List[EvalRunSummaryResponse]) +def list_eval_runs(db: Session = Depends(get_db)): + return eval_service.list_runs(db) + + +@router.get("/runs/{run_id}", response_model=EvalRunResponse) +def get_eval_run(run_id: str, db: Session = Depends(get_db)): + run = eval_service.get_run(db, run_id) + if not run: + raise HTTPException(status_code=404, detail=f"Eval run {run_id} not found") + return run + + +@router.delete("/runs/{run_id}", status_code=204) +def delete_eval_run(run_id: str, db: Session = Depends(get_db)): + if not eval_service.delete_run(db, run_id): + raise HTTPException(status_code=404, detail=f"Eval run {run_id} not found") + + +@router.post("/runs/{run_id}/stop", status_code=200) +def stop_eval_run(run_id: str, db: Session = Depends(get_db)): + """Request cancellation of a pending or running eval job.""" + result = eval_service.stop_run(db, run_id) + if result is None: + raise HTTPException(status_code=404, detail=f"Eval run {run_id} not found") + return {"status": result} + + +# --------------------------------------------------------------------------- +# Datasets +# Note: /datasets/synthesize MUST be registered before /datasets/{dataset_id} +# so FastAPI does not treat "synthesize" as a dataset_id string that happens to match. +# --------------------------------------------------------------------------- + + +@router.post("/datasets", response_model=EvalDatasetResponse, status_code=201) +def create_dataset(payload: EvalDatasetCreate, db: Session = Depends(get_db)): + return dataset_service.create_dataset(db, payload) + + +@router.post("/datasets/synthesize", response_model=EvalDatasetSummaryResponse, status_code=202) +async def synthesize_dataset( + background_tasks: BackgroundTasks, + db: Session = Depends(get_db), + file: UploadFile = File(...), + name: str = Form(...), + model_name: str = Form(...), + embed_model_name: str = Form("nomic-embed-text"), + description: Optional[str] = Form(None), + num_goldens: Optional[int] = Form(None), + max_goldens_per_document: int = Form(2), + max_contexts: int = Form(5), + min_contexts: int = Form(1), + average_chunks_per_context: int = Form(3), + chunk_size: int = Form(1024), + chunk_overlap: int = Form(0), + num_evolutions: int = Form(1), + input_quality: float = Form(0.4), + llm_timeout: float = Form(0), + async_mode: str = Form("false"), + max_concurrent: int = Form(1), +): + """Receive an uploaded document, build contexts via OllamaEmbeddingModel, + and synthesize golden Q/A pairs with DeepEval in the background.""" + try: + file_bytes = await file.read() + file_ext = _resolve_synthesis_file_ext(file.filename) + + payload = SynthesizeFromDocRequest( + name=name, + description=description or None, + model_name=model_name, + embed_model_name=embed_model_name, + target_goldens=num_goldens, + max_goldens_per_context=max_goldens_per_document, + max_contexts=max_contexts, + max_context_length=average_chunks_per_context, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + num_evolutions=num_evolutions, + input_quality_threshold=input_quality, + llm_timeout=llm_timeout, + async_mode=str(async_mode).lower() in ("true", "1", "yes"), + max_concurrent=max_concurrent, + ) + + dataset = dataset_service.create_pending_dataset(db, payload) + task = asyncio.create_task( + dataset_service.run_synthesis_background(dataset.id, payload, file_bytes, file_ext) + ) + dataset_service.register_synthesis_task(dataset.id, task) + return EvalDatasetSummaryResponse( + id=dataset.id, + name=dataset.name, + description=dataset.description, + status="pending", + error=None, + completed_contexts=0, + total_contexts=0, + completed_goldens=0, + target_goldens=( + payload.target_goldens + if payload.target_goldens is not None + else payload.max_contexts * payload.max_goldens_per_context + ), + created_at=dataset.created_at, + updated_at=dataset.updated_at, + entry_count=0, + ) + except HTTPException: + raise + except Exception as exc: + logger.error("Failed to create synthesis job: %s", exc) + raise HTTPException(status_code=500, detail=str(exc)) + + +@router.get("/datasets", response_model=List[EvalDatasetSummaryResponse]) +def list_datasets(db: Session = Depends(get_db)): + datasets = dataset_service.list_datasets(db) + return [ + EvalDatasetSummaryResponse( + id=ds.id, + name=ds.name, + description=ds.description, + status=ds.status, + error=ds.error, + completed_contexts=ds.completed_contexts, + total_contexts=ds.total_contexts, + completed_goldens=ds.completed_goldens, + target_goldens=ds.target_goldens, + created_at=ds.created_at, + updated_at=ds.updated_at, + entry_count=len(ds.entries), + ) + for ds in datasets + ] + + +@router.get("/datasets/{dataset_id}", response_model=EvalDatasetResponse) +def get_dataset(dataset_id: str, db: Session = Depends(get_db)): + dataset = dataset_service.get_dataset(db, dataset_id) + if not dataset: + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + return dataset + + +@router.delete("/datasets/{dataset_id}", status_code=204) +def delete_dataset(dataset_id: str, db: Session = Depends(get_db)): + if not dataset_service.delete_dataset(db, dataset_id): + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + + +@router.post("/datasets/{dataset_id}/stop", status_code=200) +def stop_dataset_synthesis(dataset_id: str, db: Session = Depends(get_db)): + """Request cancellation of a pending or synthesizing dataset job.""" + result = dataset_service.stop_dataset(db, dataset_id) + if result is None: + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + return {"status": result} + + +@router.put("/datasets/{dataset_id}", response_model=EvalDatasetResponse) +def update_dataset(dataset_id: str, payload: EvalDatasetUpdate, db: Session = Depends(get_db)): + dataset = dataset_service.update_dataset(db, dataset_id, payload) + if not dataset: + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + return dataset + + +@router.post("/datasets/{dataset_id}/entries", response_model=AddEntriesResponse, status_code=201) +def add_entries( + dataset_id: str, + payload: AddEntriesRequest, + db: Session = Depends(get_db), +): + dataset = dataset_service.get_dataset(db, dataset_id) + if not dataset: + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + if dataset.status == "synthesizing": + raise HTTPException(status_code=409, detail="Cannot add entries while dataset is synthesizing") + new_entries = dataset_service.add_entries(db, dataset_id, payload) + return AddEntriesResponse(added=len(new_entries), entries=new_entries) + + +@router.put("/datasets/{dataset_id}/entries/{entry_id}", response_model=EvalDatasetEntryResponse) +def update_entry( + dataset_id: str, + entry_id: int, + payload: EvalDatasetEntryUpdate, + db: Session = Depends(get_db), +): + dataset = dataset_service.get_dataset(db, dataset_id) + if not dataset: + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + entry = dataset_service.update_entry(db, dataset_id, entry_id, payload) + if not entry: + raise HTTPException(status_code=404, detail=f"Entry {entry_id} not found in dataset {dataset_id}") + return entry + + +@router.delete("/datasets/{dataset_id}/entries/{entry_id}", status_code=204) +def delete_entry( + dataset_id: str, + entry_id: int, + db: Session = Depends(get_db), +): + dataset = dataset_service.get_dataset(db, dataset_id) + if not dataset: + raise HTTPException(status_code=404, detail=f"Dataset {dataset_id} not found") + if dataset.status == "synthesizing": + raise HTTPException(status_code=409, detail="Cannot delete entries while dataset is synthesizing") + if not dataset_service.delete_entry(db, dataset_id, entry_id): + raise HTTPException(status_code=404, detail=f"Entry {entry_id} not found in dataset {dataset_id}") + + +# --------------------------------------------------------------------------- +# Ollama model management +# --------------------------------------------------------------------------- + + +@router.get("/models", response_model=List[OllamaModelInfo]) +async def list_ollama_models(): + """List all models currently available in Ollama.""" + try: + models = await list_models() + return [ + OllamaModelInfo( + name=m.get("name", ""), + size=m.get("size"), + digest=m.get("digest"), + ) + for m in models + ] + except Exception as exc: + logger.error("Failed to reach Ollama: %s", exc) + raise HTTPException(status_code=502, detail=f"Failed to reach Ollama: {exc}") + + +@router.get("/models/pull/status") +async def get_model_pull_status(): + """Return current pull status for all models that have been requested.""" + return get_pull_status() + + +@router.post("/models/pull", status_code=202) +async def pull_model(payload: ModelPullRequest, background_tasks: BackgroundTasks): + """Trigger an async pull of an Ollama model.""" + background_tasks.add_task(ensure_model, payload.model_name) + return {"message": f"Pulling model '{payload.model_name}' in the background"} diff --git a/studio-eval/app/services/__init__.py b/studio-eval/app/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/studio-eval/app/services/dataset_service.py b/studio-eval/app/services/dataset_service.py new file mode 100644 index 0000000..40dc772 --- /dev/null +++ b/studio-eval/app/services/dataset_service.py @@ -0,0 +1,718 @@ +import asyncio +import json +import logging +import math +import multiprocessing +import os +import tempfile +from queue import Empty +from typing import Any, Dict, List, Optional + +import pandas as pd +from sqlalchemy.orm import Session + +from app.compat import ensure_pydantic_v2_apis +from app.db.database import SessionLocal +from app.db.schema import EvalDataset, EvalDatasetEntry +from app.models.eval_models import ( + AddEntriesRequest, + EvalDatasetCreate, + EvalDatasetEntryUpdate, + EvalDatasetUpdate, + SynthesizeFromDocRequest, + SynthesizeRequest, +) +from app.services.ollama_service import OllamaEmbeddingModel, OllamaJudge, ensure_model + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# In-process cancellation registry +# --------------------------------------------------------------------------- +_cancelled_datasets: set = set() +_synthesis_tasks: Dict[str, asyncio.Task] = {} +_synthesis_processes: Dict[str, multiprocessing.Process] = {} + +def request_dataset_cancellation(dataset_id: str) -> None: + """Signal the background synthesis job for *dataset_id* to exit at its next checkpoint.""" + _cancelled_datasets.add(dataset_id) + process = _synthesis_processes.get(dataset_id) + if process and process.is_alive(): + process.terminate() + task = _synthesis_tasks.get(dataset_id) + if task and not task.done(): + task.cancel() + + +def register_synthesis_task(dataset_id: str, task: asyncio.Task) -> None: + """Register the asyncio Task running synthesis so it can be cancelled.""" + _synthesis_tasks[dataset_id] = task + + +def stop_dataset(db: Session, dataset_id: str) -> Optional[str]: + """Mark a dataset synthesis as stopped and register its cancellation. + + Returns the resulting status string, or None if the dataset was not found. + """ + dataset = db.query(EvalDataset).filter(EvalDataset.id == dataset_id).first() + if not dataset: + return None + if dataset.status not in ("pending", "synthesizing"): + return dataset.status # already finished — nothing to do + request_dataset_cancellation(dataset_id) + dataset.status = "stopped" + db.commit() + return "stopped" + + +def _normalize_context(context) -> Optional[List[str]]: + if context is None: + return None + + if isinstance(context, str): + stripped = context.strip() + if not stripped: + return None + try: + loaded = json.loads(stripped) + except json.JSONDecodeError: + return [stripped] + return _normalize_context(loaded) + + if isinstance(context, (list, tuple, set)): + normalized = [] + for item in context: + if item is None: + continue + if isinstance(item, str): + stripped = item.strip() + if stripped: + normalized.append(stripped) + else: + normalized.append(str(item)) + return normalized or None + + return [str(context)] + + +def _goldens_to_records(goldens: list) -> List[Dict[str, Any]]: + return [ + { + "input": getattr(golden, "input", None), + "expected_output": getattr(golden, "expected_output", None), + "context": getattr(golden, "context", None), + } + for golden in goldens + ] + + +def _update_golden_progress(db: Session, dataset_id: str, completed_goldens: int) -> None: + """Persist the current golden count so the UI can poll progress.""" + try: + dataset = db.query(EvalDataset).filter(EvalDataset.id == dataset_id).first() + if dataset and dataset.status == "synthesizing": + dataset.completed_goldens = completed_goldens + db.commit() + except Exception: + logger.debug("Failed to update golden progress for %s", dataset_id, exc_info=True) + try: + db.rollback() + except Exception: + pass + + +def _start_synthesis_process( + dataset_id: str, + tmp_path: str, + payload: SynthesizeFromDocRequest, +): + ctx = multiprocessing.get_context("spawn") + result_queue = ctx.Queue() + process = ctx.Process( + target=_run_synthesizer_from_docs_worker, + args=(tmp_path, payload.dict(), result_queue), + daemon=True, + ) + process.start() + _synthesis_processes[dataset_id] = process + return process, result_queue + + +def _stop_synthesis_process(dataset_id: str, process: Optional[multiprocessing.Process]) -> None: + if process is None: + return + try: + if process.is_alive(): + process.terminate() + process.join(timeout=1) + elif process.exitcode is None: + process.join(timeout=1) + if process.is_alive(): + process.kill() + process.join(timeout=1) + except Exception: + logger.warning("Failed to stop synthesis process for dataset %s", dataset_id, exc_info=True) + finally: + _synthesis_processes.pop(dataset_id, None) + + +async def _wait_for_synthesis_process_result( + dataset_id: str, + process: multiprocessing.Process, + result_queue, + db: Optional[Session] = None, +) -> Dict[str, Any]: + """Consume messages from the synthesis child process. + + The child sends: + * ``{"type": "progress", "completed_goldens": N, "batch": [...]}`` + after each context's goldens are generated. + * ``{"type": "done"}`` when generation finishes successfully. + * ``{"type": "error", "message": "..."}`` on failure. + + Legacy callers that send a single ``{"records": [...]}`` or + ``{"error": "..."}`` dict are still supported. + """ + all_records: List[Dict[str, Any]] = [] + + while True: + if dataset_id in _cancelled_datasets: + raise asyncio.CancelledError() + + try: + msg = await asyncio.to_thread(result_queue.get, True, 0.25) + except Empty: + if process.is_alive(): + continue + # Process exited without a message — fall through to drain. + break + + msg_type = msg.get("type") + + if msg_type == "progress": + batch = msg.get("batch") or [] + all_records.extend(batch) + completed = msg.get("completed_goldens", len(all_records)) + if db is not None: + _update_golden_progress(db, dataset_id, completed) + continue + + if msg_type == "done": + await asyncio.to_thread(process.join, 1) + return {"records": all_records} + + if msg_type == "error": + await asyncio.to_thread(process.join, 1) + return {"error": msg.get("message", "Unknown error")} + + # Legacy single-shot protocol (no "type" key). + await asyncio.to_thread(process.join, 1) + return msg + + # Process died — try to drain one last message. + await asyncio.to_thread(process.join, 1) + + try: + msg = await asyncio.to_thread(result_queue.get, True, 1) + if msg.get("type") == "error": + return {"error": msg.get("message", "Unknown error")} + if msg.get("type") == "progress": + all_records.extend(msg.get("batch") or []) + elif "records" in msg or "error" in msg: + return msg + except Empty: + pass + + if all_records: + return {"records": all_records} + + if process.exitcode in (0, None): + raise RuntimeError("Synthesis process exited without returning results") + raise RuntimeError(f"Synthesis process exited with code {process.exitcode}") + + +# --------------------------------------------------------------------------- +# CRUD helpers +# --------------------------------------------------------------------------- + + +def create_dataset(db: Session, payload: EvalDatasetCreate) -> EvalDataset: + dataset = EvalDataset(name=payload.name, description=payload.description) + db.add(dataset) + db.flush() # obtain the generated id before inserting entries + + for entry_data in payload.entries or []: + db.add( + EvalDatasetEntry( + dataset_id=dataset.id, + input=entry_data.input, + expected_output=entry_data.expected_output, + context=_normalize_context(entry_data.context), + ) + ) + + db.commit() + db.refresh(dataset) + return dataset + + +def get_dataset(db: Session, dataset_id: str) -> Optional[EvalDataset]: + return db.query(EvalDataset).filter(EvalDataset.id == dataset_id).first() + + +def list_datasets(db: Session) -> List[EvalDataset]: + return db.query(EvalDataset).all() + + +def delete_dataset(db: Session, dataset_id: str) -> bool: + dataset = get_dataset(db, dataset_id) + if not dataset: + return False + # Signal background job to stop if active so it exits before the FK is gone. + if dataset.status in ("pending", "synthesizing"): + request_dataset_cancellation(dataset_id) + db.delete(dataset) + db.commit() + return True + + +def update_dataset(db: Session, dataset_id: str, payload: EvalDatasetUpdate) -> Optional[EvalDataset]: + dataset = get_dataset(db, dataset_id) + if not dataset: + return None + updates = payload.dict(exclude_none=True) + for field, value in updates.items(): + setattr(dataset, field, value) + db.commit() + db.refresh(dataset) + return dataset + + +def get_entry(db: Session, dataset_id: str, entry_id: int) -> Optional[EvalDatasetEntry]: + return ( + db.query(EvalDatasetEntry) + .filter(EvalDatasetEntry.id == entry_id, EvalDatasetEntry.dataset_id == dataset_id) + .first() + ) + + +def update_entry( + db: Session, dataset_id: str, entry_id: int, payload: EvalDatasetEntryUpdate +) -> Optional[EvalDatasetEntry]: + entry = get_entry(db, dataset_id, entry_id) + if not entry: + return None + if payload.input is not None: + entry.input = payload.input + if payload.expected_output is not None: + entry.expected_output = payload.expected_output + if payload.context is not None: + entry.context = _normalize_context(payload.context) + db.commit() + db.refresh(entry) + return entry + + +def add_entries(db: Session, dataset_id: str, payload: AddEntriesRequest) -> List[EvalDatasetEntry]: + dataset = get_dataset(db, dataset_id) + if not dataset: + return [] + new_entries = [] + for entry_data in payload.entries: + entry = EvalDatasetEntry( + dataset_id=dataset_id, + input=entry_data.input, + expected_output=entry_data.expected_output, + context=_normalize_context(entry_data.context), + ) + db.add(entry) + new_entries.append(entry) + db.commit() + for entry in new_entries: + db.refresh(entry) + return new_entries + + +def delete_entry(db: Session, dataset_id: str, entry_id: int) -> bool: + entry = get_entry(db, dataset_id, entry_id) + if not entry: + return False + db.delete(entry) + db.commit() + return True + + +# --------------------------------------------------------------------------- +# DeepEval Synthesizer → pandas → MySQL +# --------------------------------------------------------------------------- + + +def _run_synthesizer(payload: SynthesizeRequest, judge: OllamaJudge, contexts: List[List[str]], max_per_context: int): + """Blocking call to DeepEval Synthesizer — run via asyncio.to_thread.""" + ensure_pydantic_v2_apis() + from deepeval.synthesizer import Synthesizer # noqa: PLC0415 + from deepeval.synthesizer.config import EvolutionConfig, FiltrationConfig # noqa: PLC0415 + + synthesizer = Synthesizer( + model=judge, + async_mode=payload.async_mode, + max_concurrent=payload.max_concurrent, + filtration_config=FiltrationConfig( + synthetic_input_quality_threshold=payload.input_quality_threshold, + critic_model=judge, + ), + evolution_config=EvolutionConfig(num_evolutions=payload.num_evolutions), + ) + return synthesizer.generate_goldens_from_contexts( + contexts=contexts, + max_goldens_per_context=max_per_context, + ) + + +def _resolve_doc_generation_limits(payload: SynthesizeFromDocRequest) -> tuple[int, int]: + max_contexts = max(payload.max_contexts, 1) + max_goldens_per_context = max(payload.max_goldens_per_context, 1) + + if payload.target_goldens is None: + return max_contexts, max_goldens_per_context + + target_goldens = max(payload.target_goldens, 1) + best_plan: Optional[tuple[int, int]] = None + best_total: Optional[int] = None + + for contexts in range(1, max_contexts + 1): + goldens_per_context = math.ceil(target_goldens / contexts) + if goldens_per_context > max_goldens_per_context: + continue + total_goldens = contexts * goldens_per_context + if ( + best_plan is None + or total_goldens < best_total + or (total_goldens == best_total and contexts > best_plan[0]) + ): + best_plan = (contexts, goldens_per_context) + best_total = total_goldens + + if best_plan is not None: + return best_plan + + return max_contexts, math.ceil(target_goldens / max_contexts) + + +def _run_synthesizer_from_docs( + tmp_path: str, + payload: SynthesizeFromDocRequest, + judge: OllamaJudge, + embedder: OllamaEmbeddingModel, + progress_queue=None, +) -> list: + """Blocking call to DeepEval Synthesizer.generate_goldens_from_docs. + + When *progress_queue* is provided the synthesizer processes one context + at a time and pushes a progress message after each context so the parent + process can update the DB (and therefore the UI) incrementally. + """ + ensure_pydantic_v2_apis() + from app.compat import patch_deepeval_document_chunker # noqa: PLC0415 + patch_deepeval_document_chunker() + from deepeval.synthesizer import Synthesizer # noqa: PLC0415 + from deepeval.synthesizer.config import ( # noqa: PLC0415 + ContextConstructionConfig, + EvolutionConfig, + FiltrationConfig, + ) + + # When a progress queue is available, subclass the synthesizer so that + # ``generate_goldens_from_contexts`` runs one context at a time and + # sends each batch through the queue as a small message (avoiding the + # pipe-buffer deadlock that occurs when the full result set is sent in + # a single ``queue.put``). + if progress_queue is not None: + class _ProgressSynthesizer(Synthesizer): # noqa: N801 + def generate_goldens_from_contexts(self, contexts, *args, **kwargs): + all_goldens = [] + for context in contexts: + batch = super().generate_goldens_from_contexts( + [context], *args, **kwargs + ) + all_goldens.extend(batch) + try: + progress_queue.put({ + "type": "progress", + "completed_goldens": len(all_goldens), + "batch": _goldens_to_records(batch), + }) + except Exception: + pass + return all_goldens + + SynthesizerClass = _ProgressSynthesizer + else: + SynthesizerClass = Synthesizer + + synthesizer = SynthesizerClass( + model=judge, + async_mode=payload.async_mode, + max_concurrent=payload.max_concurrent, + filtration_config=FiltrationConfig( + synthetic_input_quality_threshold=payload.input_quality_threshold, + critic_model=judge, + ), + evolution_config=EvolutionConfig(num_evolutions=payload.num_evolutions), + ) + max_contexts_per_document, max_goldens_per_context = _resolve_doc_generation_limits(payload) + return synthesizer.generate_goldens_from_docs( + document_paths=[tmp_path], + include_expected_output=True, + max_goldens_per_context=max_goldens_per_context, + context_construction_config=ContextConstructionConfig( + embedder=embedder, + critic_model=judge, + chunk_size=payload.chunk_size, + chunk_overlap=payload.chunk_overlap, + max_context_length=payload.max_context_length, + max_contexts_per_document=max_contexts_per_document, + ), + ) + + +def _run_synthesizer_from_docs_worker( + tmp_path: str, + payload_data: Dict[str, Any], + result_queue, +) -> None: + payload = SynthesizeFromDocRequest(**payload_data) + judge = OllamaJudge(model_name=payload.model_name, timeout=payload.llm_timeout) + embedder = OllamaEmbeddingModel(model_name=payload.embed_model_name) + + try: + # progress_queue=result_queue makes the synthesizer send small + # per-context batches instead of one giant payload at the end, + # which avoids the pipe-buffer deadlock. + _run_synthesizer_from_docs( + tmp_path, payload, judge, embedder, progress_queue=result_queue, + ) + result_queue.put({"type": "done"}) + except Exception as exc: + message = str(exc).strip() or exc.__class__.__name__ + logger.exception("Document synthesis worker failed for %s", tmp_path) + result_queue.put({"type": "error", "message": message}) + + +async def synthesize_dataset(db: Session, payload: SynthesizeRequest) -> EvalDataset: + """Generate a golden dataset with DeepEval Synthesizer and persist it. + + Steps: + 1. ensure_model — pull the judge model if absent. + 2. Run Synthesizer in a thread (blocking LLM calls). + 3. Normalise goldens with pandas (drop rows with null input). + 4. Persist EvalDataset + EvalDatasetEntry rows. + """ + await ensure_model(payload.model_name) + judge = OllamaJudge(model_name=payload.model_name, timeout=payload.llm_timeout) + + logger.info( + "Synthesizing dataset '%s': %d contexts, target=%d, %d goldens each, async=%s, max_concurrent=%d", + payload.name, + len(payload.contexts), + payload.target_goldens, + payload.num_goldens_per_context, + payload.async_mode, + payload.max_concurrent, + ) + + goldens = await asyncio.to_thread( + _run_synthesizer, + payload, + judge, + payload.contexts, + payload.num_goldens_per_context, + ) + + # Normalise with pandas + records = _goldens_to_records(goldens) + + df = pd.DataFrame(records) + df = df.dropna(subset=["input"]).reset_index(drop=True) + + if payload.target_goldens and len(df) > payload.target_goldens: + df = df.head(payload.target_goldens).reset_index(drop=True) + + if df.empty: + raise ValueError("Synthesizer returned no valid goldens (all inputs were null)") + + dataset = EvalDataset(name=payload.name, description=payload.description) + db.add(dataset) + db.flush() + + for _, row in df.iterrows(): + db.add( + EvalDatasetEntry( + dataset_id=dataset.id, + input=row["input"], + expected_output=row.get("expected_output"), + context=_normalize_context(row.get("context")), + ) + ) + + db.commit() + db.refresh(dataset) + logger.info("Synthesized dataset '%s' with %d entries", dataset.name, len(df)) + return dataset + + +# --------------------------------------------------------------------------- +# Background synthesis helpers (non-blocking endpoint pattern) +# --------------------------------------------------------------------------- + + +def create_pending_dataset(db: Session, payload: SynthesizeFromDocRequest) -> EvalDataset: + """Create a dataset record with status='pending' and return immediately.""" + dataset = EvalDataset( + name=payload.name, + description=payload.description, + status="pending", + ) + db.add(dataset) + db.commit() + db.refresh(dataset) + return dataset + + +async def run_synthesis_background( + dataset_id: str, + payload: SynthesizeFromDocRequest, + file_bytes: bytes, + file_ext: str, +) -> None: + """Background task: synthesize a dataset from an uploaded document file. + + Writes the file to a temp path, calls DeepEval's generate_goldens_from_docs + with OllamaEmbeddingModel for chunking/context construction, then persists + the resulting goldens. Cleans up the temp file on exit. + """ + tmp_path: Optional[str] = None + synthesis_process: Optional[multiprocessing.Process] = None + result_queue = None + db: Session = SessionLocal() + try: + dataset = db.query(EvalDataset).filter(EvalDataset.id == dataset_id).first() + if not dataset: + logger.error("Background synthesis: dataset %s not found", dataset_id) + return + dataset.status = "synthesizing" + dataset.total_contexts = 0 + dataset.completed_contexts = 0 + # When target_goldens is not set by the user, use the computed maximum + # (max_contexts × max_goldens_per_context) so the UI progress bar + # always has a denominator. DeepEval may produce fewer goldens than + # this estimate (quality filtering), so it is only an upper bound. + dataset.target_goldens = ( + payload.target_goldens + if payload.target_goldens is not None + else payload.max_contexts * payload.max_goldens_per_context + ) + dataset.completed_goldens = 0 + db.commit() + + # Cancellation check before pulling models / writing file. + if dataset_id in _cancelled_datasets: + _cancelled_datasets.discard(dataset_id) + logger.info("Synthesis for dataset %s cancelled before start", dataset_id) + return + + # Ensure both models are available (pull if absent). + await ensure_model(payload.model_name) + await ensure_model(payload.embed_model_name) + + judge = OllamaJudge(model_name=payload.model_name, timeout=payload.llm_timeout) + embedder = OllamaEmbeddingModel(model_name=payload.embed_model_name) + + # Write document bytes to a temporary file that DeepEval can read. + suffix = file_ext if file_ext.startswith(".") else f".{file_ext}" + with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp: + tmp.write(file_bytes) + tmp_path = tmp.name + + logger.info( + "Background synthesis for dataset %s '%s': embed=%s, file=%s, target=%s", + dataset_id, payload.name, payload.embed_model_name, tmp_path, payload.target_goldens, + ) + + # Final cancellation check before the expensive synthesizer call. + if dataset_id in _cancelled_datasets: + _cancelled_datasets.discard(dataset_id) + logger.info("Synthesis for dataset %s cancelled before synthesizer", dataset_id) + return + + synthesis_process, result_queue = _start_synthesis_process(dataset_id, tmp_path, payload) + worker_result = await _wait_for_synthesis_process_result( + dataset_id, synthesis_process, result_queue, db=db, + ) + + if worker_result.get("error"): + raise RuntimeError(worker_result["error"]) + + records = worker_result.get("records") or [] + + # Cancellation check after synthesis, before DB write. + if dataset_id in _cancelled_datasets: + _cancelled_datasets.discard(dataset_id) + logger.info("Synthesis for dataset %s cancelled after synthesizer", dataset_id) + return + + df = pd.DataFrame(records) + df = df.dropna(subset=["input"]).reset_index(drop=True) + + if payload.target_goldens and len(df) > payload.target_goldens: + df = df.head(payload.target_goldens).reset_index(drop=True) + + if df.empty: + raise ValueError("Synthesizer returned no valid goldens (all inputs were null)") + + dataset = db.query(EvalDataset).filter(EvalDataset.id == dataset_id).first() + for _, row in df.iterrows(): + db.add( + EvalDatasetEntry( + dataset_id=dataset.id, + input=row["input"], + expected_output=row.get("expected_output"), + context=_normalize_context(row.get("context")), + ) + ) + dataset.status = "completed" + dataset.completed_goldens = len(df) + dataset.error = None + db.commit() + logger.info("Background synthesis completed for dataset %s with %d entries", dataset_id, len(df)) + + except asyncio.CancelledError: + _stop_synthesis_process(dataset_id, synthesis_process) + logger.info("Synthesis task for dataset %s was cancelled", dataset_id) + # Status was already set to 'stopped' by stop_dataset / request_dataset_cancellation. + except Exception as exc: + logger.error("Background synthesis failed for dataset %s: %s", dataset_id, exc) + try: + dataset = db.query(EvalDataset).filter(EvalDataset.id == dataset_id).first() + if dataset: + dataset.status = "failed" + dataset.error = str(exc) + db.commit() + except Exception: + pass + finally: + _stop_synthesis_process(dataset_id, synthesis_process) + if result_queue is not None: + try: + result_queue.close() + result_queue.join_thread() + except Exception: + logger.debug("Failed to close synthesis result queue for dataset %s", dataset_id, exc_info=True) + if tmp_path and os.path.exists(tmp_path): + try: + os.unlink(tmp_path) + except OSError: + pass + _synthesis_tasks.pop(dataset_id, None) + _cancelled_datasets.discard(dataset_id) + db.close() diff --git a/studio-eval/app/services/eval_service.py b/studio-eval/app/services/eval_service.py new file mode 100644 index 0000000..f015f68 --- /dev/null +++ b/studio-eval/app/services/eval_service.py @@ -0,0 +1,379 @@ +import asyncio +import json +import logging +from datetime import datetime +from typing import Dict, List, Optional + +from sqlalchemy.orm import Session + +from app.compat import ensure_pydantic_v2_apis +from app.db.database import SessionLocal +from app.db.schema import EvalDataset, EvalDatasetEntry, EvalRun, EvalRunResult +from app.models.eval_models import EvalRunCreate +from app.services.ollama_service import OllamaJudge, ensure_model +from app.services.response_collector import ( # noqa: PLC0415 + DEFAULT_SYSTEM_PROMPT, + collect_response, +) + +logger = logging.getLogger(__name__) + +# Lazy import cache — avoids importing deepeval at module load time. +_METRIC_CLASSES: Optional[dict] = None + +# --------------------------------------------------------------------------- +# In-process cancellation registry +# --------------------------------------------------------------------------- +_cancelled_runs: set = set() +_run_tasks: Dict[str, asyncio.Task] = {} + + +def request_run_cancellation(run_id: str) -> None: + """Signal the background job for *run_id* to exit at its next checkpoint.""" + _cancelled_runs.add(run_id) + task = _run_tasks.get(run_id) + if task and not task.done(): + task.cancel() + + +def register_run_task(run_id: str, task: asyncio.Task) -> None: + """Register the asyncio Task running an eval so it can be cancelled.""" + _run_tasks[run_id] = task + + +def stop_run(db: Session, run_id: str) -> Optional[str]: + """Mark a run as stopped and register its cancellation. + + Returns the resulting status string, or None if the run was not found. + """ + run = db.query(EvalRun).filter(EvalRun.id == run_id).first() + if not run: + return None + if run.status not in ("pending", "running"): + return run.status # already finished — nothing to do + request_run_cancellation(run_id) + run.status = "stopped" + run.completed_at = datetime.utcnow() + db.commit() + return "stopped" + + +def _normalize_context(context) -> List[str]: + if context is None: + return [] + + if isinstance(context, str): + stripped = context.strip() + if not stripped: + return [] + try: + loaded = json.loads(stripped) + except json.JSONDecodeError: + return [stripped] + return _normalize_context(loaded) + + if isinstance(context, (list, tuple, set)): + normalized: List[str] = [] + for item in context: + if item is None: + continue + if isinstance(item, str): + stripped = item.strip() + if stripped: + normalized.append(stripped) + else: + normalized.append(str(item)) + return normalized + + return [str(context)] + + +def _format_exception(exc: Exception) -> str: + message = str(exc).strip() + return message or exc.__class__.__name__ + + +def _get_metric_classes() -> dict: + global _METRIC_CLASSES + if _METRIC_CLASSES is None: + ensure_pydantic_v2_apis() + from deepeval.metrics import ( # noqa: PLC0415 + AnswerRelevancyMetric, + FaithfulnessMetric, + ) + + _METRIC_CLASSES = { + "AnswerRelevancy": AnswerRelevancyMetric, + "Faithfulness": FaithfulnessMetric, + } + return _METRIC_CLASSES + + +# --------------------------------------------------------------------------- +# CRUD helpers +# --------------------------------------------------------------------------- + + +def create_eval_run(db: Session, payload: EvalRunCreate) -> EvalRun: + dataset = db.query(EvalDataset).filter_by(id=payload.dataset_id).first() + dataset_name_snap = dataset.name if dataset else None + entries_snap = None + if dataset: + entries = db.query(EvalDatasetEntry).filter_by(dataset_id=dataset.id).all() + entries_snap = [ + { + "id": e.id, + "input": e.input, + "expected_output": e.expected_output, + "context": e.context, + } + for e in entries + ] + + run = EvalRun( + name=payload.name, + dataset_id=payload.dataset_id, + sandbox_id=payload.sandbox_id, + model_name=payload.model_name, + metrics=payload.metrics, + status="pending", + system_prompt=payload.system_prompt, + temperature=payload.temperature, + max_tokens=payload.max_tokens, + request_model=payload.request_model, + configuration_snapshot=payload.configuration_snapshot, + dataset_name_snapshot=dataset_name_snap, + dataset_entries_snapshot=entries_snap, + ) + db.add(run) + db.commit() + db.refresh(run) + return run + + +def get_run(db: Session, run_id: str) -> Optional[EvalRun]: + from sqlalchemy.orm import joinedload + return ( + db.query(EvalRun) + .filter(EvalRun.id == run_id) + .options(joinedload(EvalRun.results).joinedload(EvalRunResult.entry)) + .first() + ) + + +def list_runs(db: Session) -> List[EvalRun]: + return db.query(EvalRun).all() + + +def delete_run(db: Session, run_id: str) -> bool: + run = db.query(EvalRun).filter(EvalRun.id == run_id).first() + if not run: + return False + # Signal background job to stop if active so it exits before the FK is gone. + if run.status in ("pending", "running"): + request_run_cancellation(run_id) + db.delete(run) + db.commit() + return True + + +# --------------------------------------------------------------------------- +# Background job +# --------------------------------------------------------------------------- + + +def _raise_if_cancelled(run_id: str, checkpoint: str) -> None: + if run_id not in _cancelled_runs: + return + logger.info("Eval run %s stopped at %s", run_id, checkpoint) + raise asyncio.CancelledError() + + +async def _measure_metric(metric, test_case) -> None: + """Use async DeepEval execution when available so task cancellation propagates.""" + async_measure = getattr(metric, "a_measure", None) + if callable(async_measure): + await async_measure(test_case) + return + await asyncio.to_thread(metric.measure, test_case) + + +async def _run_eval_job(run_id: str) -> None: # noqa: C901 + """Background task that drives a full eval run. + + Lifecycle: + pending → running → completed | failed + """ + db: Session = SessionLocal() + task = asyncio.current_task() + if task is not None: + register_run_task(run_id, task) + + try: + run: Optional[EvalRun] = ( + db.query(EvalRun).filter(EvalRun.id == run_id).first() + ) + if not run: + logger.error("Eval run %s not found in DB", run_id) + return + + _raise_if_cancelled(run_id, "startup") + + run.status = "running" + db.commit() + + # --- ensure judge model ----------------------------------------------- + _raise_if_cancelled(run_id, "model preparation") + await ensure_model(run.model_name) + judge = OllamaJudge(model_name=run.model_name) + + metric_classes = _get_metric_classes() + + # --- load entries ------------------------------------------------------- + entries: List[EvalDatasetEntry] = ( + db.query(EvalDatasetEntry) + .filter(EvalDatasetEntry.dataset_id == run.dataset_id) + .all() + ) + + if not entries: + run.status = "failed" + run.error = "Dataset has no entries" + run.completed_at = datetime.utcnow() + db.commit() + return + + run.total_count = len(entries) + run.completed_count = 0 + db.commit() + + # --- process each entry ------------------------------------------------ + for entry in entries: + _raise_if_cancelled(run_id, f"entry {entry.id} start") + + actual_output: Optional[str] = None + metric_scores: Dict[str, dict] = {} + all_passed = True + reasons: List[str] = [] + normalized_context = _normalize_context(entry.context) + + try: + actual_output = await collect_response( + sandbox_id=run.sandbox_id, + user_input=entry.input, + system_prompt=run.system_prompt or DEFAULT_SYSTEM_PROMPT, + temperature=run.temperature if run.temperature is not None else 0.4, + max_tokens=run.max_tokens if run.max_tokens is not None else 100, + request_model=run.request_model or "NA", + ) + actual_output = actual_output or "" + _raise_if_cancelled(run_id, f"entry {entry.id} response collection") + + from deepeval.test_case import LLMTestCase # noqa: PLC0415 + + test_case = LLMTestCase( + input=entry.input, + actual_output=actual_output, + expected_output=entry.expected_output or "", + context=normalized_context, + retrieval_context=normalized_context, + ) + + for metric_name in run.metrics: + _raise_if_cancelled(run_id, f"entry {entry.id} metric {metric_name} start") + cls = metric_classes.get(metric_name) + if cls is None: + logger.warning("Unknown metric '%s' — skipping", metric_name) + continue + try: + metric = cls(model=judge, threshold=0.5) + await _measure_metric(metric, test_case) + _raise_if_cancelled(run_id, f"entry {entry.id} metric {metric_name} completion") + + score = getattr(metric, "score", None) + passed = getattr(metric, "success", None) + if passed is None and score is not None: + passed = score >= 0.5 + reason = getattr(metric, "reason", None) + + metric_scores[metric_name] = { + "score": score, + "passed": passed, + "reason": reason, + } + if not passed: + all_passed = False + if reason: + reasons.append(f"{metric_name}: {reason}") + + except Exception as exc: + logger.error( + "Metric '%s' failed for entry %d: %s", + metric_name, + entry.id, + _format_exception(exc), + ) + failure_reason = _format_exception(exc) + metric_scores[metric_name] = { + "score": None, + "passed": False, + "reason": failure_reason, + } + all_passed = False + reasons.append(f"{metric_name}: {failure_reason}") + + except Exception as exc: + logger.error( + "Error processing entry %d: %s", + entry.id, + _format_exception(exc), + ) + all_passed = False + reasons.append(_format_exception(exc)) + + _raise_if_cancelled(run_id, f"entry {entry.id} result persistence") + + db.add( + EvalRunResult( + run_id=run.id, + entry_id=entry.id, + actual_output=actual_output, + metric_scores=metric_scores or None, + passed=all_passed, + reason="; ".join(reasons) if reasons else None, + ) + ) + run.completed_count = (run.completed_count or 0) + 1 + db.commit() + + run.status = "completed" + run.completed_at = datetime.utcnow() + db.commit() + logger.info("Eval run %s completed successfully", run_id) + + except asyncio.CancelledError: + logger.info("Eval run %s cancelled", run_id) + try: + db.rollback() + run = db.query(EvalRun).filter(EvalRun.id == run_id).first() + if run and run.status in ("pending", "running"): + run.status = "stopped" + run.completed_at = datetime.utcnow() + db.commit() + except Exception as inner: + logger.error("Could not persist stopped status for run %s: %s", run_id, inner) + except Exception as exc: + logger.error("Eval run %s failed with unhandled error: %s", run_id, exc) + try: + run = db.query(EvalRun).filter(EvalRun.id == run_id).first() + if run: + run.status = "failed" + run.error = str(exc) + run.completed_at = datetime.utcnow() + db.commit() + except Exception as inner: + logger.error("Could not persist failed status for run %s: %s", run_id, inner) + finally: + _run_tasks.pop(run_id, None) + _cancelled_runs.discard(run_id) + db.close() diff --git a/studio-eval/app/services/ollama_service.py b/studio-eval/app/services/ollama_service.py new file mode 100644 index 0000000..f5a6325 --- /dev/null +++ b/studio-eval/app/services/ollama_service.py @@ -0,0 +1,390 @@ +import json +import logging +import os +from datetime import datetime, timezone +from typing import Any, List + +import httpx + +from app.compat import ensure_pydantic_v2_apis + +logger = logging.getLogger(__name__) + + +def _parse_timeout(env_name: str, default: str): + raw_value = os.getenv(env_name, default) + if raw_value is None: + return None + + normalized = raw_value.strip().lower() + if normalized in {"", "0", "none", "false", "off", "disable", "disabled"}: + return None + + parsed = float(raw_value) + return None if parsed <= 0 else parsed + +OLLAMA_DNS = os.getenv("OLLAMA_DNS", "ollama.ollama.svc.cluster.local:11434") +OLLAMA_BASE_URL = f"http://{OLLAMA_DNS}" +OLLAMA_PULL_TIMEOUT = _parse_timeout("OLLAMA_PULL_TIMEOUT", "600") +OLLAMA_GENERATE_TIMEOUT = _parse_timeout("OLLAMA_GENERATE_TIMEOUT", "0") + + +def _normalize_generate_timeout(value): + if value is None: + return OLLAMA_GENERATE_TIMEOUT + + try: + parsed = float(value) + except (TypeError, ValueError): + return OLLAMA_GENERATE_TIMEOUT + + return None if parsed <= 0 else parsed + + +def _schema_to_ollama_format(schema) -> Any: + return "json" + + +def _load_structured_response(raw_response: str) -> Any: + content = raw_response.strip() + + if content.startswith("```"): + lines = content.splitlines() + if lines and lines[0].startswith("```"): + lines = lines[1:] + if lines and lines[-1].strip().startswith("```"): + lines = lines[:-1] + content = "\n".join(lines).strip() + + try: + return json.loads(content) + except json.JSONDecodeError: + json_start_candidates = [ + index for index in (content.find("{"), content.find("[")) if index != -1 + ] + json_end_candidates = [ + index for index in (content.rfind("}"), content.rfind("]")) if index != -1 + ] + if not json_start_candidates or not json_end_candidates: + raise + + json_start = min(json_start_candidates) + json_end = max(json_end_candidates) + 1 + return json.loads(content[json_start:json_end]) + + +def _parse_structured_response(schema, raw_response: str) -> Any: + payload = _load_structured_response(raw_response) + + if hasattr(schema, "model_validate"): + return schema.model_validate(payload) + if hasattr(schema, "parse_obj"): + return schema.parse_obj(payload) + if callable(schema): + if isinstance(payload, dict): + return schema(**payload) + return schema(payload) + return payload + + +# --------------------------------------------------------------------------- +# Async helpers — used by router and background jobs +# --------------------------------------------------------------------------- + + +async def list_models() -> list: + """Return the list of locally available Ollama models (GET /api/tags).""" + async with httpx.AsyncClient(timeout=30.0, trust_env=False) as client: + resp = await client.get(f"{OLLAMA_BASE_URL}/api/tags") + resp.raise_for_status() + return resp.json().get("models", []) + + +async def is_model_available(model_name: str) -> bool: + """True if *model_name* is already present in Ollama.""" + try: + models = await list_models() + normalized_target = model_name.strip() + target_with_default_tag = ( + normalized_target + if ":" in normalized_target + else f"{normalized_target}:latest" + ) + for m in models: + name = (m.get("name", "") or "").strip() + if name == normalized_target or name == target_with_default_tag: + return True + return False + except Exception: + return False + + +# --------------------------------------------------------------------------- +# Pull status tracker (in-memory; reset on pod restart) +# --------------------------------------------------------------------------- + +# model_name → pull-status snapshot used by the UI poller +_pull_status: dict[str, dict] = {} + + +def _utc_timestamp() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _set_pull_status(model_name: str, **fields) -> None: + current = _pull_status.get(model_name, {}) + _pull_status[model_name] = { + **current, + **fields, + "updated_at": _utc_timestamp(), + } + + +def get_pull_status() -> dict[str, dict]: + """Return a shallow copy of the current pull status map.""" + return dict(_pull_status) + + +async def pull_model(model_name: str) -> None: + """Pull *model_name* from the Ollama registry; blocks until complete.""" + logger.info("Pulling Ollama model: %s", model_name) + _set_pull_status( + model_name, + status="pulling", + detail="Preparing download", + error=None, + completed=None, + total=None, + digest=None, + ) + + try: + async with httpx.AsyncClient(timeout=OLLAMA_PULL_TIMEOUT, trust_env=False) as client: + async with client.stream( + "POST", + f"{OLLAMA_BASE_URL}/api/pull", + json={"name": model_name, "stream": True}, + ) as resp: + resp.raise_for_status() + + async for line in resp.aiter_lines(): + if not line: + continue + + event = json.loads(line) + detail = event.get("status") or "Pulling model" + completed = event.get("completed") + total = event.get("total") + digest = event.get("digest") + + _set_pull_status( + model_name, + status="pulling", + detail=detail, + error=None, + completed=completed, + total=total, + digest=digest, + ) + + if event.get("error"): + raise RuntimeError(str(event["error"])) + + if event.get("status") == "success": + _set_pull_status( + model_name, + status="ready", + detail="Pull complete", + error=None, + completed=completed, + total=total, + digest=digest, + ) + + logger.info("Model pulled successfully: %s", model_name) + current = _pull_status.get(model_name, {}) + if current.get("status") != "ready": + _set_pull_status( + model_name, + status="ready", + detail=current.get("detail") or "Pull complete", + error=None, + ) + except Exception as exc: + logger.error("Failed to pull model '%s': %s", model_name, exc) + _set_pull_status( + model_name, + status="error", + detail="Pull failed", + error=str(exc), + ) + raise + + +async def ensure_model(model_name: str) -> None: + """Ensure *model_name* is available in Ollama, pulling it if absent.""" + if not await is_model_available(model_name): + logger.info("Model '%s' not found locally — pulling...", model_name) + await pull_model(model_name) + else: + logger.debug("Model '%s' already available", model_name) + _set_pull_status( + model_name, + status="ready", + detail="Model already available", + error=None, + completed=None, + total=None, + digest=None, + ) + + +# --------------------------------------------------------------------------- +# OllamaJudge — DeepEvalBaseLLM wrapper +# --------------------------------------------------------------------------- + +ensure_pydantic_v2_apis() + +try: + from deepeval.models import DeepEvalBaseLLM # deepeval >= 0.21 +except ImportError: + from deepeval.models.base_model import DeepEvalBaseLLM # older deepeval + +try: + from deepeval.models.base_model import DeepEvalBaseEmbeddingModel +except ImportError: + from deepeval.models import DeepEvalBaseEmbeddingModel + + +class OllamaJudge(DeepEvalBaseLLM): + """Custom DeepEval LLM that routes all generation requests to Ollama. + + Both synchronous (``generate``) and asynchronous (``a_generate``) paths + are implemented so that DeepEval can pick the appropriate one depending on + whether ``metric.measure()`` or ``metric.a_measure()`` is called. + + When *schema* is provided (structured-output request), the Ollama API is + asked to return JSON via ``"format": "json"``. + """ + + def __init__(self, model_name: str, timeout=None) -> None: + self._model_name = model_name + self._base_url = OLLAMA_BASE_URL + self._timeout = _normalize_generate_timeout(timeout) + + # -- DeepEvalBaseLLM contract ------------------------------------------- + + def get_model_name(self) -> str: + return self._model_name + + def load_model(self): + # Model lives in Ollama; nothing to load locally. + return self + + def generate(self, prompt: str, schema=None) -> Any: + """Synchronous generation — used by ``metric.measure()``.""" + payload = { + "model": self._model_name, + "prompt": prompt, + "stream": False, + } + if schema is not None: + payload["format"] = _schema_to_ollama_format(schema) + + with httpx.Client(timeout=self._timeout, trust_env=False) as client: + resp = client.post(f"{self._base_url}/api/generate", json=payload) + resp.raise_for_status() + response_text = resp.json()["response"] + if schema is None: + return response_text + try: + return _parse_structured_response(schema, response_text) + except Exception as exc: + raise TypeError("Structured response parsing failed") from exc + + async def a_generate(self, prompt: str, schema=None) -> Any: + """Async generation — used by ``metric.a_measure()``.""" + payload = { + "model": self._model_name, + "prompt": prompt, + "stream": False, + } + if schema is not None: + payload["format"] = _schema_to_ollama_format(schema) + + async with httpx.AsyncClient(timeout=self._timeout, trust_env=False) as client: + resp = await client.post( + f"{self._base_url}/api/generate", json=payload + ) + resp.raise_for_status() + response_text = resp.json()["response"] + if schema is None: + return response_text + try: + return _parse_structured_response(schema, response_text) + except Exception as exc: + raise TypeError("Structured response parsing failed") from exc + + +# --------------------------------------------------------------------------- +# OllamaEmbeddingModel — DeepEvalBaseEmbeddingModel wrapper +# --------------------------------------------------------------------------- + + +class OllamaEmbeddingModel(DeepEvalBaseEmbeddingModel): + """DeepEval embedding model backed by Ollama's /api/embed endpoint. + + Uses the same OLLAMA_BASE_URL as OllamaJudge so no additional config + is required. Default model is ``nomic-embed-text``. + """ + + _EMBED_TIMEOUT = 120.0 + + def __init__(self, model_name: str = "nomic-embed-text") -> None: + self._base_url = OLLAMA_BASE_URL + # super().__init__ sets self.model_name and calls self.load_model() + super().__init__(model_name) + + # -- DeepEvalBaseEmbeddingModel contract -------------------------------- + + def load_model(self): + # Ollama is a remote service; nothing to load locally. + return self + + def get_model_name(self) -> str: + return self.model_name + + # -- Internal HTTP helpers ---------------------------------------------- + + def _call_embed(self, texts: List[str]) -> List[List[float]]: + with httpx.Client(timeout=self._EMBED_TIMEOUT) as client: + resp = client.post( + f"{self._base_url}/api/embed", + json={"model": self.model_name, "input": texts}, + ) + resp.raise_for_status() + return resp.json()["embeddings"] + + async def _a_call_embed(self, texts: List[str]) -> List[List[float]]: + async with httpx.AsyncClient(timeout=self._EMBED_TIMEOUT) as client: + resp = await client.post( + f"{self._base_url}/api/embed", + json={"model": self.model_name, "input": texts}, + ) + resp.raise_for_status() + return resp.json()["embeddings"] + + # -- Public embedding interface ----------------------------------------- + + def embed_text(self, text: str) -> List[float]: + return self._call_embed([text])[0] + + def embed_texts(self, texts: List[str]) -> List[List[float]]: + return self._call_embed(texts) + + async def a_embed_text(self, text: str) -> List[float]: + result = await self._a_call_embed([text]) + return result[0] + + async def a_embed_texts(self, texts: List[str]) -> List[List[float]]: + return await self._a_call_embed(texts) diff --git a/studio-eval/app/services/response_collector.py b/studio-eval/app/services/response_collector.py new file mode 100644 index 0000000..a97f92e --- /dev/null +++ b/studio-eval/app/services/response_collector.py @@ -0,0 +1,162 @@ +import json +import logging +import os +from typing import Optional + +import httpx + +logger = logging.getLogger(__name__) + + +def _parse_timeout(env_name: str, default: str): + raw_value = os.getenv(env_name, default) + if raw_value is None: + return None + + normalized = raw_value.strip().lower() + if normalized in {"", "0", "none", "false", "off", "disable", "disabled"}: + return None + + parsed = float(raw_value) + return None if parsed <= 0 else parsed + +_APP_BACKEND_PORT = 8899 +_CHAT_PATH = "/v1/app-backend" +_TIMEOUT = _parse_timeout("APP_BACKEND_TIMEOUT", "0") +_APP_BACKEND_URL_TEMPLATE = os.getenv( + "APP_BACKEND_URL_TEMPLATE", + f"http://app-backend.{{sandbox_id}}.svc.cluster.local:{_APP_BACKEND_PORT}{_CHAT_PATH}", +) + +DEFAULT_SYSTEM_PROMPT = "You are a helpful assistant" + + +def _app_backend_url(sandbox_id: str) -> str: + return _APP_BACKEND_URL_TEMPLATE.format(sandbox_id=sandbox_id) + + +# --------------------------------------------------------------------------- +# SSE parsing helpers +# --------------------------------------------------------------------------- + +def _parse_sse_token(payload: str) -> Optional[str]: + """Extract the text token from a single SSE data payload.""" + if payload.strip() == "[DONE]": + return None + try: + chunk = json.loads(payload) + except json.JSONDecodeError: + return payload if payload else None + if not isinstance(chunk, dict): + # Raw token that happens to be a valid JSON literal (e.g. a number + # like 1 or 3.40). Return the original payload text to preserve + # exact formatting (e.g. trailing zeros). + return payload.strip() if payload.strip() else None + choices = chunk.get("choices", []) + if not choices: + return None + delta = choices[0].get("delta", {}) + return delta.get("content") or choices[0].get("text", "") or None + + +def _extract_sse_answer(body: str) -> Optional[str]: + """Parse a full SSE response body and concatenate all content tokens.""" + parts = [] + for raw_line in body.splitlines(): + line = raw_line.rstrip("\r") + if not line or not line.startswith("data:"): + continue + # Strip the "data:" prefix. Per SSE spec, a single space after the + # colon is optional and should be ignored if present. + payload = line[5:] # skip "data:" + if payload.startswith(" "): + payload = payload[1:] # strip the optional single leading space + if payload.strip() == "[DONE]": + break + token = _parse_sse_token(payload) + if token is not None: + parts.append(token) + return "".join(parts).strip() if parts else None + + +def _parse_non_streaming(data: dict) -> Optional[str]: + """Extract the assistant reply from a non-streaming JSON response.""" + choices = data.get("choices") or [] + if choices: + choice = choices[0] + content = choice.get("message", {}).get("content") or choice.get("text") + if content: + return content + for key in ("answer", "response", "content", "actual_output"): + if key in data and isinstance(data[key], str) and data[key].strip(): + return data[key] + return None + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +async def collect_response( + sandbox_id: str, + user_input: str, + system_prompt: str = DEFAULT_SYSTEM_PROMPT, + temperature: float = 0.4, + max_tokens: int = 100, + request_model: str = "NA", +) -> Optional[str]: + """POST a chat message to the app-backend in *sandbox_id* and return the + assistant reply, or ``None`` if the response contains no usable content. + + Supports both streaming SSE (``text/event-stream``) and plain JSON + non-streaming responses. Raises ``httpx.HTTPError`` on non-2xx status. + + Parameters + ---------- + sandbox_id: Target sandbox namespace. + user_input: The user message to send. + system_prompt: System message prepended to the chat messages list. + temperature: Sampling temperature forwarded to the model. + max_tokens: Maximum number of tokens to generate. + request_model: Model identifier forwarded in the request body. + """ + url = _app_backend_url(sandbox_id) + + messages = [] + if system_prompt.strip(): + messages.append({"role": "system", "content": system_prompt}) + messages.append({"role": "user", "content": user_input}) + + payload = { + "messages": messages, + "model": request_model, + "temperature": temperature, + "max_tokens": max_tokens, + "stream": True, + } + + logger.debug("Collecting response: sandbox=%s input=%.80s", sandbox_id, user_input) + + async with httpx.AsyncClient(timeout=_TIMEOUT) as client: + async with client.stream("POST", url, json=payload) as resp: + resp.raise_for_status() + body = (await resp.aread()).decode("utf-8") + content_type = resp.headers.get("content-type", "") + + if "text/event-stream" in content_type or body.lstrip().startswith("data:"): + answer = _extract_sse_answer(body) + if answer: + return answer + logger.warning("SSE parsing returned empty answer for sandbox=%s", sandbox_id) + return None + + try: + data = json.loads(body) + answer = _parse_non_streaming(data) + if answer: + return answer + logger.warning("app-backend returned no usable content for sandbox=%s", sandbox_id) + return None + except json.JSONDecodeError: + # Last resort: try SSE parse on a non-event-stream content-type body + return _extract_sse_answer(body) diff --git a/studio-eval/requirements.txt b/studio-eval/requirements.txt new file mode 100644 index 0000000..876d97d --- /dev/null +++ b/studio-eval/requirements.txt @@ -0,0 +1,15 @@ +fastapi>=0.100.0,<0.200.0 +uvicorn[standard]>=0.23.0 +deepeval==2.5.5 +pandas>=2.0.0 +sqlalchemy>=2.0.0 +pymysql>=1.1.0 +httpx>=0.24.0 +pydantic>=2.0,<3.0 +cryptography>=41.0.0 +python-multipart>=0.0.7 +chromadb==1.5.8 +langchain>=0.2.0,<1.0.0 +langchain-core>=0.2.0,<1.0.0 +langchain-community>=0.2.0,<1.0.0 +pypdf>=3.0.0 diff --git a/studio-eval/tests/test_eval_service.py b/studio-eval/tests/test_eval_service.py new file mode 100644 index 0000000..eabe352 --- /dev/null +++ b/studio-eval/tests/test_eval_service.py @@ -0,0 +1,422 @@ +import asyncio + +from app.services import eval_service +from app.services import dataset_service +from app.compat import ensure_pydantic_v2_apis +from app.models.eval_models import SynthesizeFromDocRequest + +def test_normalize_context_handles_json_string_list(): + context = '["alpha", "beta"]' + + assert eval_service._normalize_context(context) == ["alpha", "beta"] + + +def test_normalize_context_handles_plain_string(): + assert eval_service._normalize_context("single context") == ["single context"] + + +def test_format_exception_uses_class_name_when_message_empty(): + assert eval_service._format_exception(AssertionError()) == "AssertionError" + + +def test_run_synthesizer_applies_pydantic_compat(monkeypatch): + compat_calls = [] + + def fake_ensure_pydantic_v2_apis(): + compat_calls.append(True) + + class FakeSynthesizer: + def __init__(self, model): + self.model = model + + def generate_goldens_from_contexts(self, contexts, max_goldens_per_context): + return [ + { + "contexts": contexts, + "max_goldens_per_context": max_goldens_per_context, + "model": self.model, + } + ] + + import sys + from types import ModuleType + + deepeval_module = ModuleType("deepeval") + synthesizer_module = ModuleType("deepeval.synthesizer") + synthesizer_module.Synthesizer = FakeSynthesizer + + monkeypatch.setattr(dataset_service, "ensure_pydantic_v2_apis", fake_ensure_pydantic_v2_apis) + monkeypatch.setitem(sys.modules, "deepeval", deepeval_module) + monkeypatch.setitem(sys.modules, "deepeval.synthesizer", synthesizer_module) + monkeypatch.setitem(sys.modules, "deepeval.synthesizer.config", ModuleType("deepeval.synthesizer.config")) + + class Payload: + async_mode = False + max_concurrent = 1 + input_quality_threshold = 0.4 + num_evolutions = 1 + + result = dataset_service._run_synthesizer(Payload(), "judge", [["context"]], 2) + + assert compat_calls == [True] + assert result[0]["max_goldens_per_context"] == 2 + + +def test_request_dataset_cancellation_stops_registered_work(): + class FakeTask: + def __init__(self): + self.cancelled = False + + def done(self): + return False + + def cancel(self): + self.cancelled = True + + class FakeProcess: + def __init__(self): + self.terminated = False + + def is_alive(self): + return True + + def terminate(self): + self.terminated = True + + dataset_id = "dataset-123" + task = FakeTask() + process = FakeProcess() + + dataset_service._cancelled_datasets.discard(dataset_id) + dataset_service._synthesis_tasks[dataset_id] = task + dataset_service._synthesis_processes[dataset_id] = process + + try: + dataset_service.request_dataset_cancellation(dataset_id) + + assert dataset_id in dataset_service._cancelled_datasets + assert task.cancelled is True + assert process.terminated is True + finally: + dataset_service._cancelled_datasets.discard(dataset_id) + dataset_service._synthesis_tasks.pop(dataset_id, None) + dataset_service._synthesis_processes.pop(dataset_id, None) + + +def test_wait_for_synthesis_process_result_drains_queue_before_process_exit(): + class FakeProcess: + def __init__(self): + self.alive = True + self.exitcode = 0 + self.join_calls = [] + + def is_alive(self): + return self.alive + + def join(self, timeout=None): + self.join_calls.append(timeout) + self.alive = False + + class FakeQueue: + def __init__(self, process): + self.process = process + self.calls = [] + + def get(self, block=True, timeout=None): + self.calls.append((block, timeout)) + self.process.alive = False + return {"records": [{"input": "hello"}]} + + async def run_wait(process, result_queue): + return await asyncio.wait_for( + dataset_service._wait_for_synthesis_process_result("dataset-123", process, result_queue), + timeout=1, + ) + + process = FakeProcess() + result_queue = FakeQueue(process) + + result = asyncio.run(run_wait(process, result_queue)) + + assert result == {"records": [{"input": "hello"}]} + assert result_queue.calls == [(True, 0.25)] + assert process.join_calls == [1] + + +def test_ensure_pydantic_v2_apis_aliases_invalid_collection_exception(monkeypatch): + import chromadb.errors as chroma_errors + + monkeypatch.delattr(chroma_errors, "InvalidCollectionException", raising=False) + + ensure_pydantic_v2_apis() + + assert chroma_errors.InvalidCollectionException is chroma_errors.NotFoundError + + +def test_resolve_doc_generation_limits_matches_target_without_overshoot(): + payload = SynthesizeFromDocRequest( + name="dataset", + model_name="qwen2.5:14b", + target_goldens=10, + max_goldens_per_context=5, + max_contexts=5, + ) + + assert dataset_service._resolve_doc_generation_limits(payload) == (5, 2) + + +def test_resolve_doc_generation_limits_falls_back_when_target_exceeds_user_cap(): + payload = SynthesizeFromDocRequest( + name="dataset", + model_name="qwen2.5:14b", + target_goldens=17, + max_goldens_per_context=2, + max_contexts=5, + ) + + assert dataset_service._resolve_doc_generation_limits(payload) == (5, 4) + + +def test_run_synthesizer_from_docs_uses_resolved_limits(monkeypatch): + compat_calls = [] + chunker_patch_calls = [] + + def fake_ensure_pydantic_v2_apis(): + compat_calls.append(True) + + def fake_patch_deepeval_document_chunker(): + chunker_patch_calls.append(True) + + class FakeSynthesizer: + def __init__(self, **kwargs): + self.kwargs = kwargs + + def generate_goldens_from_docs(self, **kwargs): + return [kwargs] + + class FakeContextConstructionConfig: + def __init__(self, **kwargs): + self.kwargs = kwargs + + class FakeEvolutionConfig: + def __init__(self, **kwargs): + self.kwargs = kwargs + + class FakeFiltrationConfig: + def __init__(self, **kwargs): + self.kwargs = kwargs + + import sys + from types import ModuleType + + deepeval_module = ModuleType("deepeval") + synthesizer_module = ModuleType("deepeval.synthesizer") + config_module = ModuleType("deepeval.synthesizer.config") + compat_module = ModuleType("app.compat") + + synthesizer_module.Synthesizer = FakeSynthesizer + config_module.ContextConstructionConfig = FakeContextConstructionConfig + config_module.EvolutionConfig = FakeEvolutionConfig + config_module.FiltrationConfig = FakeFiltrationConfig + compat_module.patch_deepeval_document_chunker = fake_patch_deepeval_document_chunker + + monkeypatch.setattr(dataset_service, "ensure_pydantic_v2_apis", fake_ensure_pydantic_v2_apis) + monkeypatch.setitem(sys.modules, "deepeval", deepeval_module) + monkeypatch.setitem(sys.modules, "deepeval.synthesizer", synthesizer_module) + monkeypatch.setitem(sys.modules, "deepeval.synthesizer.config", config_module) + monkeypatch.setitem(sys.modules, "app.compat", compat_module) + + payload = SynthesizeFromDocRequest( + name="dataset", + model_name="qwen2.5:14b", + target_goldens=10, + max_goldens_per_context=5, + max_contexts=5, + chunk_size=1024, + chunk_overlap=64, + max_context_length=3, + ) + + result = dataset_service._run_synthesizer_from_docs( + "/tmp/source.pdf", + payload, + "judge", + "embedder", + ) + + assert compat_calls == [True] + assert chunker_patch_calls == [True] + assert result[0]["max_goldens_per_context"] == 2 + assert result[0]["context_construction_config"].kwargs["max_contexts_per_document"] == 5 + +# --------------------------------------------------------------------------- +# Progress-protocol tests +# --------------------------------------------------------------------------- + +def test_wait_for_synthesis_handles_progress_then_done(): + """Parent must collect batches from progress messages and return them when done.""" + + class FakeProcess: + def __init__(self): + self.alive = True + self.exitcode = 0 + + def is_alive(self): + return self.alive + + def join(self, timeout=None): + self.alive = False + + class FakeQueue: + def __init__(self, messages, process): + self._msgs = list(messages) + self._process = process + + def get(self, block=True, timeout=None): + if self._msgs: + msg = self._msgs.pop(0) + if not self._msgs: + self._process.alive = False + return msg + from queue import Empty + raise Empty + + messages = [ + {"type": "progress", "completed_goldens": 2, "batch": [{"input": "q1"}, {"input": "q2"}]}, + {"type": "progress", "completed_goldens": 4, "batch": [{"input": "q3"}, {"input": "q4"}]}, + {"type": "done"}, + ] + + process = FakeProcess() + queue = FakeQueue(messages, process) + + result = asyncio.run(asyncio.wait_for( + dataset_service._wait_for_synthesis_process_result("ds-1", process, queue), + timeout=2, + )) + + assert result == {"records": [{"input": "q1"}, {"input": "q2"}, {"input": "q3"}, {"input": "q4"}]} + + +def test_wait_for_synthesis_handles_error_message(): + """Parent must return the error from a typed error message.""" + + class FakeProcess: + def __init__(self): + self.alive = True + self.exitcode = 1 + + def is_alive(self): + return self.alive + + def join(self, timeout=None): + self.alive = False + + class FakeQueue: + def __init__(self, msg, process): + self._msg = msg + self._process = process + + def get(self, block=True, timeout=None): + if self._msg: + msg = self._msg + self._msg = None + self._process.alive = False + return msg + from queue import Empty + raise Empty + + process = FakeProcess() + queue = FakeQueue({"type": "error", "message": "boom"}, process) + + result = asyncio.run(asyncio.wait_for( + dataset_service._wait_for_synthesis_process_result("ds-2", process, queue), + timeout=2, + )) + + assert result == {"error": "boom"} + +def test_progress_synthesizer_sends_per_context_batches(monkeypatch): + """_run_synthesizer_from_docs with progress_queue must send per-context messages.""" + compat_calls = [] + chunker_patch_calls = [] + + def fake_ensure_pydantic_v2_apis(): + compat_calls.append(True) + + def fake_patch_deepeval_document_chunker(): + chunker_patch_calls.append(True) + + class FakeGolden: + def __init__(self, text): + self.input = text + self.expected_output = f"answer-{text}" + self.context = [f"ctx-{text}"] + + class FakeSynthesizer: + def __init__(self, **kwargs): + self.kwargs = kwargs + + def generate_goldens_from_docs(self, **kwargs): + # DeepEval internally calls self.generate_goldens_from_contexts + return self.generate_goldens_from_contexts( + [["ctx-a"], ["ctx-b"]], + max_goldens_per_context=kwargs.get("max_goldens_per_context", 2), + ) + + def generate_goldens_from_contexts(self, contexts, *args, **kwargs): + # Returns one golden per context. + goldens = [] + for ctx in contexts: + goldens.append(FakeGolden(ctx[0])) + return goldens + + class FakeConfig: + def __init__(self, **kwargs): + self.kwargs = kwargs + + import sys + from types import ModuleType + + synthesizer_module = ModuleType("deepeval.synthesizer") + config_module = ModuleType("deepeval.synthesizer.config") + compat_module = ModuleType("app.compat") + synthesizer_module.Synthesizer = FakeSynthesizer + config_module.ContextConstructionConfig = FakeConfig + config_module.EvolutionConfig = FakeConfig + config_module.FiltrationConfig = FakeConfig + compat_module.patch_deepeval_document_chunker = fake_patch_deepeval_document_chunker + + monkeypatch.setattr(dataset_service, "ensure_pydantic_v2_apis", fake_ensure_pydantic_v2_apis) + monkeypatch.setitem(sys.modules, "deepeval", ModuleType("deepeval")) + monkeypatch.setitem(sys.modules, "deepeval.synthesizer", synthesizer_module) + monkeypatch.setitem(sys.modules, "deepeval.synthesizer.config", config_module) + monkeypatch.setitem(sys.modules, "app.compat", compat_module) + + from queue import Queue + progress_queue = Queue() + + payload = SynthesizeFromDocRequest( + name="ds", model_name="m", target_goldens=4, + max_goldens_per_context=2, max_contexts=5, + ) + + dataset_service._run_synthesizer_from_docs( + "/tmp/fake.pdf", payload, "judge", "embedder", + progress_queue=progress_queue, + ) + + # The ProgressSynthesizer should have sent one message per context. + msgs = [] + while not progress_queue.empty(): + msgs.append(progress_queue.get_nowait()) + + assert len(msgs) == 2 # two contexts + assert msgs[0]["type"] == "progress" + assert msgs[0]["completed_goldens"] == 1 + assert msgs[1]["completed_goldens"] == 2 + # Each batch should have exactly one record (one golden per context). + assert len(msgs[0]["batch"]) == 1 + assert len(msgs[1]["batch"]) == 1 + assert msgs[0]["batch"][0]["input"] == "ctx-a" + assert msgs[1]["batch"][0]["input"] == "ctx-b" \ No newline at end of file diff --git a/studio-eval/tests/test_ollama_service.py b/studio-eval/tests/test_ollama_service.py new file mode 100644 index 0000000..2538f18 --- /dev/null +++ b/studio-eval/tests/test_ollama_service.py @@ -0,0 +1,118 @@ +import importlib +import sys +from types import ModuleType + +from pydantic import BaseModel + + +def _load_ollama_service(monkeypatch): + deepeval_module = ModuleType("deepeval") + deepeval_models_module = ModuleType("deepeval.models") + deepeval_base_model_module = ModuleType("deepeval.models.base_model") + + class DeepEvalBaseLLM: + pass + + deepeval_models_module.DeepEvalBaseLLM = DeepEvalBaseLLM + deepeval_base_model_module.DeepEvalBaseLLM = DeepEvalBaseLLM + + monkeypatch.setitem(sys.modules, "deepeval", deepeval_module) + monkeypatch.setitem(sys.modules, "deepeval.models", deepeval_models_module) + monkeypatch.setitem( + sys.modules, + "deepeval.models.base_model", + deepeval_base_model_module, + ) + sys.modules.pop("app.services.ollama_service", None) + return importlib.import_module("app.services.ollama_service") + + +class ClaimsSchema(BaseModel): + claims: list[str] + + +class StatementsSchema(BaseModel): + statements: list[str] + + +def test_generate_returns_schema_instance(monkeypatch): + ollama_service = _load_ollama_service(monkeypatch) + captured = {} + + class FakeResponse: + def raise_for_status(self): + return None + + def json(self): + return {"response": '{"claims": ["alpha", "beta"]}'} + + class FakeClient: + def __init__(self, *args, **kwargs): + pass + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, tb): + return False + + def post(self, url, json): + captured["url"] = url + captured["json"] = json + return FakeResponse() + + monkeypatch.setattr(ollama_service.httpx, "Client", FakeClient) + + judge = ollama_service.OllamaJudge(model_name="judge-model") + result = judge.generate("prompt", schema=ClaimsSchema) + + assert isinstance(result, ClaimsSchema) + assert result.claims == ["alpha", "beta"] + assert captured["json"]["format"] == "json" + + +def test_parse_structured_response_handles_fenced_json(monkeypatch): + ollama_service = _load_ollama_service(monkeypatch) + + result = ollama_service._parse_structured_response( + StatementsSchema, + '```json\n{"statements": ["first statement"]}\n```', + ) + + assert isinstance(result, StatementsSchema) + assert result.statements == ["first statement"] + + +def test_generate_raises_type_error_on_invalid_structured_payload(monkeypatch): + ollama_service = _load_ollama_service(monkeypatch) + + class FakeResponse: + def raise_for_status(self): + return None + + def json(self): + return {"response": 'not-json'} + + class FakeClient: + def __init__(self, *args, **kwargs): + pass + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, tb): + return False + + def post(self, url, json): + return FakeResponse() + + monkeypatch.setattr(ollama_service.httpx, "Client", FakeClient) + + judge = ollama_service.OllamaJudge(model_name="judge-model") + + try: + judge.generate("prompt", schema=ClaimsSchema) + except TypeError as exc: + assert str(exc) == "Structured response parsing failed" + else: + raise AssertionError("Expected TypeError for invalid structured payload") \ No newline at end of file diff --git a/studio-eval/tests/test_timeout_config.py b/studio-eval/tests/test_timeout_config.py new file mode 100644 index 0000000..1eed5bb --- /dev/null +++ b/studio-eval/tests/test_timeout_config.py @@ -0,0 +1,54 @@ +import importlib +import os +import sys + +from app import compat + + +def _reload_module(module_name: str): + sys.modules.pop(module_name, None) + return importlib.import_module(module_name) + + +def test_response_collector_timeout_disabled_by_default(monkeypatch): + monkeypatch.delenv("APP_BACKEND_TIMEOUT", raising=False) + + response_collector = _reload_module("app.services.response_collector") + + assert response_collector._TIMEOUT is None + + +def test_response_collector_timeout_can_be_enabled(monkeypatch): + monkeypatch.setenv("APP_BACKEND_TIMEOUT", "300") + + response_collector = _reload_module("app.services.response_collector") + + assert response_collector._TIMEOUT == 300.0 + + +def test_ollama_generate_timeout_disabled_by_default(monkeypatch): + monkeypatch.delenv("OLLAMA_GENERATE_TIMEOUT", raising=False) + monkeypatch.setenv("OLLAMA_PULL_TIMEOUT", "600") + + ollama_service = _reload_module("app.services.ollama_service") + + assert ollama_service.OLLAMA_GENERATE_TIMEOUT is None + + +def test_ollama_generate_timeout_can_be_enabled(monkeypatch): + monkeypatch.setenv("OLLAMA_GENERATE_TIMEOUT", "240") + monkeypatch.setenv("OLLAMA_PULL_TIMEOUT", "600") + + ollama_service = _reload_module("app.services.ollama_service") + + assert ollama_service.OLLAMA_GENERATE_TIMEOUT == 240.0 + + +def test_deepeval_network_timeouts_disabled_by_default(monkeypatch): + monkeypatch.delenv("DEEPEVAL_TELEMETRY_OPT_OUT", raising=False) + monkeypatch.delenv("DEEPEVAL_UPDATE_WARNING_OPT_OUT", raising=False) + + compat.ensure_pydantic_v2_apis() + + assert os.getenv("DEEPEVAL_TELEMETRY_OPT_OUT") == "YES" + assert os.getenv("DEEPEVAL_UPDATE_WARNING_OPT_OUT") == "YES" \ No newline at end of file diff --git a/studio-frontend/.env.development b/studio-frontend/.env.development deleted file mode 100644 index 0a6398e..0000000 --- a/studio-frontend/.env.development +++ /dev/null @@ -1,6 +0,0 @@ -NODE_TLS_REJECT_UNAUTHORIZED=0 -VITE_DISABLE_KEYCLOAK=true -NODE_ENV=development -VITE_HOST=0.0.0.0 -VITE_PORT=8088 -FINETUNING_HOST= # Command to get your host ip: ip route get 1.1.1.1 | awk '{print $7}' \ No newline at end of file diff --git a/studio-frontend/Dockerfile b/studio-frontend/Dockerfile index 828818e..c73a525 100644 --- a/studio-frontend/Dockerfile +++ b/studio-frontend/Dockerfile @@ -31,11 +31,15 @@ RUN pnpm install # Copy source code COPY . . +COPY --from=sample_workflows . ./sample-workflows -# Build the app and clean up -RUN pnpm build && \ - # Prune to production dependencies only - pnpm prune --prod && \ +# Build the app (separate stage target so dev compose stops here) +RUN pnpm build + +FROM builder AS pruned + +# Prune to production dependencies only +RUN pnpm prune --prod && \ rm -rf node_modules/.cache && \ rm -rf packages/*/node_modules/.cache @@ -54,7 +58,7 @@ ENV no_proxy=${no_proxy} # Install only runtime dependencies with patched npm RUN apk update && apk upgrade && \ apk add --no-cache gcompat chromium && \ - npm install -g npm@latest pnpm@latest && \ + npm install -g pnpm@9 && \ rm -rf /var/cache/apk/* ENV PUPPETEER_SKIP_DOWNLOAD=true @@ -63,10 +67,11 @@ ENV NODE_OPTIONS=--max-old-space-size=8192 WORKDIR /usr/src -# Copy only necessary files from builder -COPY --from=builder /usr/src/package.json /usr/src/pnpm-workspace.yaml ./ -COPY --from=builder /usr/src/packages ./packages -COPY --from=builder /usr/src/node_modules ./node_modules +# Copy only necessary files from pruned builder +COPY --from=pruned /usr/src/package.json /usr/src/pnpm-workspace.yaml ./ +COPY --from=pruned /usr/src/packages ./packages +COPY --from=pruned /usr/src/node_modules ./node_modules +COPY --from=pruned /usr/src/sample-workflows ./sample-workflows EXPOSE 3000 diff --git a/studio-frontend/docker-compose.dev.yml b/studio-frontend/docker-compose.dev.yml deleted file mode 100644 index 3624dcb..0000000 --- a/studio-frontend/docker-compose.dev.yml +++ /dev/null @@ -1,47 +0,0 @@ -version: '3.8' - -services: - finetuning-server: - image: opea/finetuning:latest - container_name: finetuning-server - user: "0:0" - ipc: host - ports: - - "8015:8015" - - "8265:8265" - environment: - - http_proxy=${http_proxy} - - https_proxy=${https_proxy} - - no_proxy=${no_proxy} - volumes: - - finetune-output:/home/user/comps/finetuning/src/output - restart: unless-stopped - - studio-frontend: - image: studio-frontend:latest - build: - context: . - dockerfile: Dockerfile - container_name: studio-frontend-dev - ports: - - "3000:3000" - - "8088:8088" - volumes: - - .:/usr/src - - node_modules:/usr/src/node_modules - - pnpm_store:/usr/src/.pnpm-store - - /usr/src/packages/ui/build - - finetune-output:/tmp/finetuning/output - command: ["sh", "-c", "pnpm install && pnpm dev"] - environment: - - http_proxy=${http_proxy} - - https_proxy=${https_proxy} - - no_proxy=${no_proxy} - stdin_open: true - tty: true - restart: unless-stopped - -volumes: - node_modules: - pnpm_store: - finetune-output: \ No newline at end of file diff --git a/studio-frontend/docker-entrypoint.dev.sh b/studio-frontend/docker-entrypoint.dev.sh new file mode 100755 index 0000000..329b5d2 --- /dev/null +++ b/studio-frontend/docker-entrypoint.dev.sh @@ -0,0 +1,17 @@ +#!/bin/sh +echo '[studio-frontend] Installing dependencies...' + +# Background watchdog prints progress every 15s +( while true; do + sleep 15 + count=$(ls /usr/src/node_modules 2>/dev/null | wc -l) + echo "[studio-frontend] pnpm install in progress... (node_modules: ${count} packages)" +done ) & +watchdog=$! + +pnpm install --frozen-lockfile --config.confirmModulesPurge=false || pnpm install --config.confirmModulesPurge=false +kill "$watchdog" 2>/dev/null +wait "$watchdog" 2>/dev/null + +echo '[studio-frontend] Install complete. Starting dev server...' +exec pnpm dev diff --git a/studio-frontend/package.json b/studio-frontend/package.json index e4bd9cc..a29024d 100644 --- a/studio-frontend/package.json +++ b/studio-frontend/package.json @@ -10,9 +10,9 @@ "build": "turbo run build", "build-force": "pnpm clean && turbo run build --force", "dev": "turbo run dev --parallel --no-cache", - "start": "run-script-os", - "start:windows": "cd packages/server/bin && run start", - "start:default": "cd packages/server/bin && ./run start", + "start": "cd packages/server && node bin/run", + "start:windows": "cd packages/server && node bin/run", + "start:default": "cd packages/server && node bin/run", "clean": "pnpm --filter \"./packages/**\" clean", "nuke": "pnpm --filter \"./packages/**\" nuke && rimraf node_modules .turbo", "format": "prettier --write \"**/*.{ts,tsx,md}\"", @@ -26,10 +26,10 @@ "*.{js,jsx,ts,tsx,json,md}": "eslint --fix" }, "dependencies": { - "i18next": "^25.3.2", - "i18next-browser-languagedetector": "^8.2.0", - "i18next-http-backend": "^3.0.2", - "react-i18next": "^15.6.0" + "@opentelemetry/exporter-trace-otlp-grpc": "^0.57.2", + "@opentelemetry/exporter-trace-otlp-proto": "^0.57.2", + "@opentelemetry/sdk-trace-node": "^1.30.1", + "react-toastify": "^11.0.5" }, "devDependencies": { "@babel/preset-env": "^7.19.4", @@ -134,11 +134,5 @@ } ] ] - }, - "dependencies": { - "@opentelemetry/exporter-trace-otlp-grpc": "^0.57.2", - "@opentelemetry/exporter-trace-otlp-proto": "^0.57.2", - "@opentelemetry/sdk-trace-node": "^1.30.1", - "react-toastify": "^11.0.5" } } \ No newline at end of file diff --git a/studio-frontend/packages/server/bin/run b/studio-frontend/packages/server/bin/run index a7635de..f937d55 100755 --- a/studio-frontend/packages/server/bin/run +++ b/studio-frontend/packages/server/bin/run @@ -1,5 +1,3 @@ #!/usr/bin/env node -const oclif = require('@oclif/core') - -oclif.run().then(require('@oclif/core/flush')).catch(require('@oclif/core/handle')) +require('../dist/index').start() diff --git a/studio-frontend/packages/server/nodemon.json b/studio-frontend/packages/server/nodemon.json index d4688a5..4d162dc 100644 --- a/studio-frontend/packages/server/nodemon.json +++ b/studio-frontend/packages/server/nodemon.json @@ -1,6 +1,7 @@ { "ignore": ["**/*.spec.ts", ".git", "node_modules"], - "watch": ["commands", "index.ts", "src", "../components/nodes", "../components/src"], - "exec": "pnpm start", - "ext": "ts" + "watch": ["src"], + "exec": "pnpm build && pnpm start", + "ext": "ts", + "legacyWatch": true } diff --git a/studio-frontend/packages/server/nodemon.json.bak b/studio-frontend/packages/server/nodemon.json.bak new file mode 100644 index 0000000..6897d08 --- /dev/null +++ b/studio-frontend/packages/server/nodemon.json.bak @@ -0,0 +1,6 @@ +{ + "ignore": ["**/*.spec.ts", ".git", "node_modules"], + "watch": ["src"], + "exec": "ts-node --transpile-only src/index.ts", + "ext": "ts" +} diff --git a/studio-frontend/packages/server/package.json b/studio-frontend/packages/server/package.json index 672fe82..358bd2c 100644 --- a/studio-frontend/packages/server/package.json +++ b/studio-frontend/packages/server/package.json @@ -8,40 +8,19 @@ "flowise": "./bin/run" }, "files": [ - "bin", - "marketplaces", - "dist", - "npm-shrinkwrap.json", - "oclif.manifest.json", - "oauth2.html" + "dist" ], - "oclif": { - "bin": "flowise", - "commands": "./dist/commands" - }, "scripts": { "build": "tsc", - "start": "run-script-os", + "start": "node ./bin/run", "clean": "rimraf dist", "nuke": "rimraf dist node_modules .turbo", - "start:windows": "cd bin && run start", - "start:default": "cd bin && ./run start", "dev": "nodemon", - "oclif-dev": "run-script-os", - "oclif-dev:windows": "cd bin && dev start", - "oclif-dev:default": "cd bin && ./dev start", - "postpack": "shx rm -f oclif.manifest.json", - "prepack": "pnpm build && oclif manifest && oclif readme", "typeorm": "typeorm-ts-node-commonjs", "typeorm:migration-generate": "pnpm typeorm migration:generate -d ./src/utils/typeormDataSource.ts", "typeorm:migration-run": "pnpm typeorm migration:run -d ./src/utils/typeormDataSource.ts", "typeorm:migration-revert": "pnpm typeorm migration:revert -d ./src/utils/typeormDataSource.ts", "watch": "tsc --watch", - "version": "oclif readme && git add README.md", - "cypress:open": "cypress open", - "cypress:run": "cypress run", - "e2e": "start-server-and-test dev http://localhost:3000 cypress:run", - "cypress:ci": "START_SERVER_AND_TEST_INSECURE=1 start-server-and-test start https-get://localhost:3000 cypress:run", "test": "jest --runInBand --detectOpenHandles --forceExit" }, "keywords": [], @@ -55,9 +34,9 @@ }, "license": "SEE LICENSE IN LICENSE.md", "dependencies": { - "@oclif/core": "4.0.7", "@types/lodash": "^4.17.20", "@types/uuid": "^9.0.7", + "archiver": "^6.0.1", "async-mutex": "^0.4.0", "axios": "1.12.0", "content-disposition": "0.5.4", @@ -67,22 +46,22 @@ "express": "^4.17.3", "express-basic-auth": "^1.2.1", "express-rate-limit": "^6.9.0", - "flowise-components": "^3.0.8", "flowise-ui": "workspace:^", + "form-data": "^4.0.0", "http-errors": "^2.0.0", "http-status-codes": "^2.3.0", + "https-proxy-agent": "^7.0.4", "@langchain/core": "^1.1.15", "@langchain/langgraph": "^0.0.15", "langchainhub": "^0.0.11", "lodash": "^4.17.21", + "mammoth": "^1.11.0", "moment": "^2.29.3", "moment-timezone": "^0.5.34", "multer": "^2.0.2", "mysql2": "^3.11.3", - "form-data": "^4.0.0", - "openai": "^4.96.0", "pg": "^8.11.1", - "posthog-node": "^3.5.0", + "pdf-parse": "^1.1.1", "reflect-metadata": "^0.1.13", "sanitize-html": "^2.11.0", "socket.io": "^4.6.1", @@ -90,28 +69,25 @@ "typeorm": "^0.3.6", "uuid": "^9.0.1", "winston": "^3.9.0", - "https-proxy-agent": "^7.0.4", - "archiver": "^6.0.1", - "turndown": "^7.2.0" + "winston-daily-rotate-file": "^5.0.0" }, "devDependencies": { + "@types/archiver": "^6.0.2", "@types/content-disposition": "0.5.8", "@types/cors": "^2.8.12", - "@types/express": "^4.17.17", "@types/crypto-js": "^4.1.1", + "@types/express": "^4.17.17", "@types/multer": "^1.4.7", "@types/sanitize-html": "^2.9.5", "concurrently": "^7.1.0", "cypress": "^13.13.0", "nodemon": "^2.0.22", - "oclif": "^4.20.5", "rimraf": "^5.0.5", "run-script-os": "^1.1.6", "shx": "^0.3.3", "start-server-and-test": "^2.0.3", "ts-node": "^10.7.0", "tsc-watch": "^6.0.4", - "typescript": "^5.4.5", - "@types/archiver": "^6.0.2" + "typescript": "^5.4.5" } } diff --git a/studio-frontend/packages/server/src/ChatflowPool.ts b/studio-frontend/packages/server/src/ChatflowPool.ts index 5f1e3a8..38fe29f 100644 --- a/studio-frontend/packages/server/src/ChatflowPool.ts +++ b/studio-frontend/packages/server/src/ChatflowPool.ts @@ -1,5 +1,4 @@ -import { ICommonObject } from 'flowise-components' -import { IActiveChatflows, INodeData, IReactFlowNode } from './Interface' +import { ICommonObject, IActiveChatflows, INodeData, IReactFlowNode } from './Interface' import logger from './utils/logger' /** diff --git a/studio-frontend/packages/server/src/Interface.ts b/studio-frontend/packages/server/src/Interface.ts index 228ddc3..cc552a4 100644 --- a/studio-frontend/packages/server/src/Interface.ts +++ b/studio-frontend/packages/server/src/Interface.ts @@ -1,4 +1,80 @@ -import { IAction, ICommonObject, IFileUpload, INode, INodeData as INodeDataFromComponent, INodeParams } from 'flowise-components' +// Local type definitions (previously from flowise-components) +export interface ICommonObject { + [key: string]: any +} + +export interface IFileUpload { + data?: string + type: string + name: string + mime: string +} + +export interface IAction { + id?: string + elements?: ICommonObject[] + mapping?: ICommonObject +} + +export interface INodeParams { + label?: string + name: string + type: string + default?: any + optional?: boolean + hint?: string + acceptVariable?: boolean + options?: ICommonObject[] + rows?: number + list?: boolean + placeholder?: string + additionalParams?: boolean + credentialNames?: string[] + fileType?: string + id?: string + inputs?: INodeParams[] + [key: string]: any +} + +export interface INode { + label?: string + name: string + type?: string + icon?: string + category?: string + version?: number + description?: string + baseClasses?: string[] + inputs?: INodeParams[] + outputs?: INodeParams[] + outputAnchors?: INodeParams[] + credential?: ICommonObject + filePath?: string + badge?: string + tags?: string[] + author?: string + loadMethods?: ICommonObject + vectorStoreMethods?: ICommonObject + [key: string]: any +} + +export interface INodeDataFromComponent { + id: string + label: string + version?: number + name: string + type: string + category: string + icon?: string + description?: string + baseClasses?: string[] + inputs?: ICommonObject + outputs?: ICommonObject + instance?: any + loadMethod?: string + credential?: ICommonObject + [key: string]: any +} export type MessageType = 'apiMessage' | 'userMessage' diff --git a/studio-frontend/packages/server/src/NodesPool.ts b/studio-frontend/packages/server/src/NodesPool.ts index 62c5595..0f2c51e 100644 --- a/studio-frontend/packages/server/src/NodesPool.ts +++ b/studio-frontend/packages/server/src/NodesPool.ts @@ -1,9 +1,6 @@ -import { IComponentNodes, IComponentCredentials } from './Interface' +import { IComponentNodes, IComponentCredentials, ICommonObject } from './Interface' import path from 'path' -import { Dirent } from 'fs' -import { getNodeModulesPackagePath } from './utils' import { promises } from 'fs' -import { ICommonObject } from 'flowise-components' import logger from './utils/logger' import { appConfig } from './AppConfig' @@ -24,9 +21,7 @@ export class NodesPool { * Initialize nodes */ private async initializeNodes() { - // const packagePath = getNodeModulesPackagePath('flowise-components') - // const nodesPath = path.join(packagePath, 'dist', 'nodes') - const nodesPath = '../src/nodes' + const nodesPath = path.join(__dirname, '..', 'src', 'nodes') const nodeFiles = await this.getFiles(nodesPath) // const nodeFiles: Array =[] return Promise.all( @@ -83,21 +78,8 @@ export class NodesPool { * Initialize credentials */ private async initializeCredentials() { - const packagePath = getNodeModulesPackagePath('flowise-components') - const nodesPath = path.join(packagePath, 'dist', 'credentials') - const nodeFiles = await this.getFiles(nodesPath) - return Promise.all( - nodeFiles.map(async (file) => { - if (file.endsWith('.credential.js')) { - const credentialModule = await require(file) - if (credentialModule.credClass) { - const newCredInstance = new credentialModule.credClass() - newCredInstance.icon = this.credentialIconPath[newCredInstance.name] ?? '' - this.componentCredentials[newCredInstance.name] = newCredInstance - } - } - }) - ) + // flowise-components removed — credentials loaded from node icons only + return } /** diff --git a/studio-frontend/packages/server/src/commands/start.ts b/studio-frontend/packages/server/src/commands/start.ts index 579053a..2b403ce 100644 --- a/studio-frontend/packages/server/src/commands/start.ts +++ b/studio-frontend/packages/server/src/commands/start.ts @@ -1,4 +1,3 @@ -import { Command, Flags, Args } from '@oclif/core' import path from 'path' import * as Server from '../index' import * as DataSource from '../DataSource' @@ -13,50 +12,9 @@ enum EXIT_CODE { } let processExitCode = EXIT_CODE.SUCCESS -export default class Start extends Command { +export default class Start { static args = {} - static flags = { - FLOWISE_USERNAME: Flags.string(), - FLOWISE_PASSWORD: Flags.string(), - FLOWISE_FILE_SIZE_LIMIT: Flags.string(), - PORT: Flags.string(), - CORS_ORIGINS: Flags.string(), - IFRAME_ORIGINS: Flags.string(), - DEBUG: Flags.string(), - BLOB_STORAGE_PATH: Flags.string(), - APIKEY_STORAGE_TYPE: Flags.string(), - APIKEY_PATH: Flags.string(), - SECRETKEY_PATH: Flags.string(), - FLOWISE_SECRETKEY_OVERWRITE: Flags.string(), - LOG_PATH: Flags.string(), - LOG_LEVEL: Flags.string(), - TOOL_FUNCTION_BUILTIN_DEP: Flags.string(), - TOOL_FUNCTION_EXTERNAL_DEP: Flags.string(), - NUMBER_OF_PROXIES: Flags.string(), - DISABLE_CHATFLOW_REUSE: Flags.string(), - DATABASE_TYPE: Flags.string(), - DATABASE_PATH: Flags.string(), - DATABASE_PORT: Flags.string(), - DATABASE_HOST: Flags.string(), - DATABASE_NAME: Flags.string(), - DATABASE_USER: Flags.string(), - DATABASE_PASSWORD: Flags.string(), - DATABASE_SSL: Flags.string(), - DATABASE_SSL_KEY_BASE64: Flags.string(), - LANGCHAIN_TRACING_V2: Flags.string(), - LANGCHAIN_ENDPOINT: Flags.string(), - LANGCHAIN_API_KEY: Flags.string(), - LANGCHAIN_PROJECT: Flags.string(), - DISABLE_FLOWISE_TELEMETRY: Flags.string(), - MODEL_LIST_CONFIG_JSON: Flags.string(), - STORAGE_TYPE: Flags.string(), - S3_STORAGE_BUCKET_NAME: Flags.string(), - S3_STORAGE_ACCESS_KEY_ID: Flags.string(), - S3_STORAGE_SECRET_ACCESS_KEY: Flags.string(), - S3_STORAGE_REGION: Flags.string(), - S3_ENDPOINT_URL: Flags.string(), - SHOW_COMMUNITY_NODES: Flags.string() - } + static flags: Record = {} async stopProcess() { logger.info('Shutting down Flowise...') @@ -90,7 +48,7 @@ export default class Start extends Command { logger.error('unhandledRejection: ', err) }) - const { flags } = await this.parse(Start) + const flags: Record = {} if (flags.PORT) process.env.PORT = flags.PORT if (flags.CORS_ORIGINS) process.env.CORS_ORIGINS = flags.CORS_ORIGINS diff --git a/studio-frontend/packages/server/src/controllers/assistants/index.ts b/studio-frontend/packages/server/src/controllers/assistants/index.ts deleted file mode 100644 index c252dc5..0000000 --- a/studio-frontend/packages/server/src/controllers/assistants/index.ts +++ /dev/null @@ -1,87 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import assistantsService from '../../services/assistants' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' - -const createAssistant = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: assistantsController.createAssistant - body not provided!` - ) - } - const apiResponse = await assistantsService.createAssistant(req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const deleteAssistant = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: assistantsController.deleteAssistant - id not provided!` - ) - } - const apiResponse = await assistantsService.deleteAssistant(req.params.id, req.query.isDeleteBoth) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getAllAssistants = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await assistantsService.getAllAssistants() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getAssistantById = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: assistantsController.getAssistantById - id not provided!` - ) - } - const apiResponse = await assistantsService.getAssistantById(req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const updateAssistant = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: assistantsController.updateAssistant - id not provided!` - ) - } - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: assistantsController.updateAssistant - body not provided!` - ) - } - const apiResponse = await assistantsService.updateAssistant(req.params.id, req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - createAssistant, - deleteAssistant, - getAllAssistants, - getAssistantById, - updateAssistant -} diff --git a/studio-frontend/packages/server/src/controllers/chatflows/index.ts b/studio-frontend/packages/server/src/controllers/chatflows/index.ts index fe13033..784e50d 100644 --- a/studio-frontend/packages/server/src/controllers/chatflows/index.ts +++ b/studio-frontend/packages/server/src/controllers/chatflows/index.ts @@ -7,36 +7,6 @@ import { InternalFlowiseError } from '../../errors/internalFlowiseError' import { ChatflowType } from '../../Interface' import chatflowsService from '../../services/chatflows' -const checkIfChatflowIsValidForStreaming = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: chatflowsRouter.checkIfChatflowIsValidForStreaming - id not provided!` - ) - } - const apiResponse = await chatflowsService.checkIfChatflowIsValidForStreaming(req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const checkIfChatflowIsValidForUploads = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: chatflowsRouter.checkIfChatflowIsValidForUploads - id not provided!` - ) - } - const apiResponse = await chatflowsService.checkIfChatflowIsValidForUploads(req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - const deleteChatflow = async (req: Request, res: Response, next: NextFunction) => { try { if (typeof req.params === 'undefined' || !req.params.id) { @@ -173,21 +143,6 @@ const getSinglePublicChatflow = async (req: Request, res: Response, next: NextFu } } -const getSinglePublicChatbotConfig = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: chatflowsRouter.getSinglePublicChatbotConfig - id not provided!` - ) - } - const apiResponse = await chatflowsService.getSinglePublicChatbotConfig(req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - const deployChatflowSandbox = async (req: Request, res: Response, next: NextFunction) => { try { console.log('Deploying chatflow sandbox') @@ -350,8 +305,6 @@ const oneClickDeployment = async (req: Request, res: Response, next: NextFunctio } export default { - checkIfChatflowIsValidForStreaming, - checkIfChatflowIsValidForUploads, deleteChatflow, getAllChatflows, getAllChatflowsbyUserId, @@ -362,7 +315,6 @@ export default { importChatflows, updateChatflow, getSinglePublicChatflow, - getSinglePublicChatbotConfig, deployChatflowSandbox, stopChatflowSandbox, buildDeploymentPackage, diff --git a/studio-frontend/packages/server/src/controllers/documentstore/index.ts b/studio-frontend/packages/server/src/controllers/documentstore/index.ts deleted file mode 100644 index 1004135..0000000 --- a/studio-frontend/packages/server/src/controllers/documentstore/index.ts +++ /dev/null @@ -1,366 +0,0 @@ -import { NextFunction, Request, Response } from 'express' -import { StatusCodes } from 'http-status-codes' -import documentStoreService from '../../services/documentstore' -import { DocumentStore } from '../../database/entities/DocumentStore' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { DocumentStoreDTO } from '../../Interface' - -const createDocumentStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.createDocumentStore - body not provided!` - ) - } - const body = req.body - const docStore = DocumentStoreDTO.toEntity(body) - const apiResponse = await documentStoreService.createDocumentStore(docStore) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getAllDocumentStores = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await documentStoreService.getAllDocumentStores() - return res.json(DocumentStoreDTO.fromEntities(apiResponse)) - } catch (error) { - next(error) - } -} - -const deleteLoaderFromDocumentStore = async (req: Request, res: Response, next: NextFunction) => { - try { - const storeId = req.params.id - const loaderId = req.params.loaderId - - if (!storeId || !loaderId) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.deleteLoaderFromDocumentStore - missing storeId or loaderId.` - ) - } - const apiResponse = await documentStoreService.deleteLoaderFromDocumentStore(storeId, loaderId) - return res.json(DocumentStoreDTO.fromEntity(apiResponse)) - } catch (error) { - next(error) - } -} - -const getDocumentStoreById = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.id === 'undefined' || req.params.id === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.getDocumentStoreById - id not provided!` - ) - } - const apiResponse = await documentStoreService.getDocumentStoreById(req.params.id) - if (apiResponse && apiResponse.whereUsed) { - apiResponse.whereUsed = JSON.stringify(await documentStoreService.getUsedChatflowNames(apiResponse)) - } - return res.json(DocumentStoreDTO.fromEntity(apiResponse)) - } catch (error) { - next(error) - } -} - -const getDocumentStoreFileChunks = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.storeId === 'undefined' || req.params.storeId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.getDocumentStoreFileChunks - storeId not provided!` - ) - } - if (typeof req.params.fileId === 'undefined' || req.params.fileId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.getDocumentStoreFileChunks - fileId not provided!` - ) - } - const page = req.params.pageNo ? parseInt(req.params.pageNo) : 1 - const apiResponse = await documentStoreService.getDocumentStoreFileChunks(req.params.storeId, req.params.fileId, page) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const deleteDocumentStoreFileChunk = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.storeId === 'undefined' || req.params.storeId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.deleteDocumentStoreFileChunk - storeId not provided!` - ) - } - if (typeof req.params.loaderId === 'undefined' || req.params.loaderId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.deleteDocumentStoreFileChunk - loaderId not provided!` - ) - } - if (typeof req.params.chunkId === 'undefined' || req.params.chunkId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.deleteDocumentStoreFileChunk - chunkId not provided!` - ) - } - const apiResponse = await documentStoreService.deleteDocumentStoreFileChunk( - req.params.storeId, - req.params.loaderId, - req.params.chunkId - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const editDocumentStoreFileChunk = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.storeId === 'undefined' || req.params.storeId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.editDocumentStoreFileChunk - storeId not provided!` - ) - } - if (typeof req.params.loaderId === 'undefined' || req.params.loaderId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.editDocumentStoreFileChunk - loaderId not provided!` - ) - } - if (typeof req.params.chunkId === 'undefined' || req.params.chunkId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.editDocumentStoreFileChunk - chunkId not provided!` - ) - } - const body = req.body - if (typeof body === 'undefined') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.editDocumentStoreFileChunk - body not provided!` - ) - } - const apiResponse = await documentStoreService.editDocumentStoreFileChunk( - req.params.storeId, - req.params.loaderId, - req.params.chunkId, - body.pageContent, - body.metadata - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const processFileChunks = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.processFileChunks - body not provided!` - ) - } - const body = req.body - const apiResponse = await documentStoreService.processAndSaveChunks(body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const updateDocumentStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.id === 'undefined' || req.params.id === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.updateDocumentStore - storeId not provided!` - ) - } - if (typeof req.body === 'undefined') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.updateDocumentStore - body not provided!` - ) - } - const store = await documentStoreService.getDocumentStoreById(req.params.id) - if (!store) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Error: documentStoreController.updateDocumentStore - DocumentStore ${req.params.id} not found in the database` - ) - } - const body = req.body - const updateDocStore = new DocumentStore() - Object.assign(updateDocStore, body) - const apiResponse = await documentStoreService.updateDocumentStore(store, updateDocStore) - return res.json(DocumentStoreDTO.fromEntity(apiResponse)) - } catch (error) { - next(error) - } -} - -const deleteDocumentStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.id === 'undefined' || req.params.id === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.deleteDocumentStore - storeId not provided!` - ) - } - const apiResponse = await documentStoreService.deleteDocumentStore(req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const previewFileChunks = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.previewFileChunks - body not provided!` - ) - } - const body = req.body - body.preview = true - const apiResponse = await documentStoreService.previewChunks(body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getDocumentLoaders = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await documentStoreService.getDocumentLoaders() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const insertIntoVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new Error('Error: documentStoreController.insertIntoVectorStore - body not provided!') - } - const body = req.body - const apiResponse = await documentStoreService.insertIntoVectorStore(body) - return res.json(DocumentStoreDTO.fromEntity(apiResponse)) - } catch (error) { - next(error) - } -} - -const queryVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new Error('Error: documentStoreController.queryVectorStore - body not provided!') - } - const body = req.body - const apiResponse = await documentStoreService.queryVectorStore(body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const deleteVectorStoreFromStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.storeId === 'undefined' || req.params.storeId === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: documentStoreController.deleteVectorStoreFromStore - storeId not provided!` - ) - } - const apiResponse = await documentStoreService.deleteVectorStoreFromStore(req.params.storeId) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const saveVectorStoreConfig = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new Error('Error: documentStoreController.saveVectorStoreConfig - body not provided!') - } - const body = req.body - const apiResponse = await documentStoreService.saveVectorStoreConfig(body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const updateVectorStoreConfigOnly = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined') { - throw new Error('Error: documentStoreController.updateVectorStoreConfigOnly - body not provided!') - } - const body = req.body - const apiResponse = await documentStoreService.updateVectorStoreConfigOnly(body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getEmbeddingProviders = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await documentStoreService.getEmbeddingProviders() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getVectorStoreProviders = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await documentStoreService.getVectorStoreProviders() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getRecordManagerProviders = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await documentStoreService.getRecordManagerProviders() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - deleteDocumentStore, - createDocumentStore, - getAllDocumentStores, - deleteLoaderFromDocumentStore, - getDocumentStoreById, - getDocumentStoreFileChunks, - updateDocumentStore, - processFileChunks, - previewFileChunks, - getDocumentLoaders, - deleteDocumentStoreFileChunk, - editDocumentStoreFileChunk, - insertIntoVectorStore, - getEmbeddingProviders, - getVectorStoreProviders, - getRecordManagerProviders, - saveVectorStoreConfig, - queryVectorStore, - deleteVectorStoreFromStore, - updateVectorStoreConfigOnly -} diff --git a/studio-frontend/packages/server/src/controllers/feedback/index.ts b/studio-frontend/packages/server/src/controllers/feedback/index.ts deleted file mode 100644 index 936a3b8..0000000 --- a/studio-frontend/packages/server/src/controllers/feedback/index.ts +++ /dev/null @@ -1,66 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import feedbackService from '../../services/feedback' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' - -const getAllChatMessageFeedback = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: feedbackController.getAllChatMessageFeedback - id not provided!` - ) - } - const chatflowid = req.params.id - const chatId = req.query?.chatId as string | undefined - const sortOrder = req.query?.order as string | undefined - const startDate = req.query?.startDate as string | undefined - const endDate = req.query?.endDate as string | undefined - const apiResponse = await feedbackService.getAllChatMessageFeedback(chatflowid, chatId, sortOrder, startDate, endDate) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const createChatMessageFeedbackForChatflow = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: feedbackController.createChatMessageFeedbackForChatflow - body not provided!` - ) - } - const apiResponse = await feedbackService.createChatMessageFeedbackForChatflow(req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const updateChatMessageFeedbackForChatflow = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: feedbackController.updateChatMessageFeedbackForChatflow - body not provided!` - ) - } - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: feedbackController.updateChatMessageFeedbackForChatflow - id not provided!` - ) - } - const apiResponse = await feedbackService.updateChatMessageFeedbackForChatflow(req.params.id, req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getAllChatMessageFeedback, - createChatMessageFeedbackForChatflow, - updateChatMessageFeedbackForChatflow -} diff --git a/studio-frontend/packages/server/src/controllers/fetch-links/index.ts b/studio-frontend/packages/server/src/controllers/fetch-links/index.ts deleted file mode 100644 index f3acce5..0000000 --- a/studio-frontend/packages/server/src/controllers/fetch-links/index.ts +++ /dev/null @@ -1,33 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import fetchLinksService from '../../services/fetch-links' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' - -const getAllLinks = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.query === 'undefined' || !req.query.url) { - throw new InternalFlowiseError(StatusCodes.PRECONDITION_FAILED, `Error: fetchLinksController.getAllLinks - url not provided!`) - } - if (typeof req.query === 'undefined' || !req.query.relativeLinksMethod) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: fetchLinksController.getAllLinks - relativeLinksMethod not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.limit) { - throw new InternalFlowiseError(StatusCodes.PRECONDITION_FAILED, `Error: fetchLinksController.getAllLinks - limit not provided!`) - } - const apiResponse = await fetchLinksService.getAllLinks( - req.query.url as string, - req.query.relativeLinksMethod as string, - req.query.limit as string - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getAllLinks -} diff --git a/studio-frontend/packages/server/src/controllers/get-upload-file/index.ts b/studio-frontend/packages/server/src/controllers/get-upload-file/index.ts index 09a8784..04018a5 100644 --- a/studio-frontend/packages/server/src/controllers/get-upload-file/index.ts +++ b/studio-frontend/packages/server/src/controllers/get-upload-file/index.ts @@ -1,10 +1,20 @@ import { Request, Response, NextFunction } from 'express' import fs from 'fs' +import path from 'path' import contentDisposition from 'content-disposition' -import { streamStorageFile } from 'flowise-components' import { StatusCodes } from 'http-status-codes' import { InternalFlowiseError } from '../../errors/internalFlowiseError' +const getStoragePath = (): string => { + return process.env.BLOB_STORAGE_PATH + ? path.join(process.env.BLOB_STORAGE_PATH) + : path.join(getUserHome(), '.flowise', 'storage') +} + +const getUserHome = (): string => { + return process.env.HOME || process.cwd() +} + interface AuthenticatedRequest extends Request { user?: { activeOrganizationId?: string @@ -19,17 +29,13 @@ const streamUploadedFile = async (req: Request, res: Response, next: NextFunctio const chatflowId = req.query.chatflowId as string const chatId = req.query.chatId as string const fileName = req.query.fileName as string - const orgId = (req as AuthenticatedRequest).user?.activeOrganizationId || '' - res.setHeader('Content-Disposition', contentDisposition(fileName)) - const fileStream = await streamStorageFile(chatflowId, chatId, fileName, orgId) - - if (!fileStream) throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error: streamStorageFile`) - - if (fileStream instanceof fs.ReadStream && fileStream?.pipe) { - fileStream.pipe(res) - } else { - res.send(fileStream) + const filePath = path.join(getStoragePath(), chatflowId, chatId, fileName) + if (!fs.existsSync(filePath)) { + throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `File not found`) } + res.setHeader('Content-Disposition', contentDisposition(fileName)) + const fileStream = fs.createReadStream(filePath) + fileStream.pipe(res) } catch (error) { next(error) } diff --git a/studio-frontend/packages/server/src/controllers/get-upload-path/index.ts b/studio-frontend/packages/server/src/controllers/get-upload-path/index.ts index 05e7659..44dfd25 100644 --- a/studio-frontend/packages/server/src/controllers/get-upload-path/index.ts +++ b/studio-frontend/packages/server/src/controllers/get-upload-path/index.ts @@ -1,5 +1,13 @@ import { Request, Response, NextFunction } from 'express' -import { getStoragePath } from 'flowise-components' +import path from 'path' + +const getUserHome = (): string => process.env.HOME || process.cwd() + +const getStoragePath = (): string => { + return process.env.BLOB_STORAGE_PATH + ? path.join(process.env.BLOB_STORAGE_PATH) + : path.join(getUserHome(), '.flowise', 'storage') +} const getPathForUploads = async (req: Request, res: Response, next: NextFunction) => { try { diff --git a/studio-frontend/packages/server/src/controllers/internal-predictions/index.ts b/studio-frontend/packages/server/src/controllers/internal-predictions/index.ts deleted file mode 100644 index b251eec..0000000 --- a/studio-frontend/packages/server/src/controllers/internal-predictions/index.ts +++ /dev/null @@ -1,46 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import { utilBuildChatflow } from '../../utils/buildChatflow' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { getErrorMessage } from '../../errors/utils' - -// Send input message and get prediction result (Internal) -const createInternalPrediction = async (req: Request, res: Response, next: NextFunction) => { - try { - if (req.body.streaming || req.body.streaming === 'true') { - createAndStreamInternalPrediction(req, res, next) - return - } else { - const apiResponse = await utilBuildChatflow(req, true) - return res.json(apiResponse) - } - } catch (error) { - next(error) - } -} - -// Send input message and stream prediction result using SSE (Internal) -const createAndStreamInternalPrediction = async (req: Request, res: Response, next: NextFunction) => { - const chatId = req.body.chatId - const sseStreamer = getRunningExpressApp().sseStreamer - try { - sseStreamer.addClient(chatId, res) - res.setHeader('Content-Type', 'text/event-stream') - res.setHeader('Cache-Control', 'no-cache') - res.setHeader('Connection', 'keep-alive') - res.setHeader('X-Accel-Buffering', 'no') //nginx config: https://serverfault.com/a/801629 - res.flushHeaders() - - const apiResponse = await utilBuildChatflow(req, true) - sseStreamer.streamMetadataEvent(apiResponse.chatId, apiResponse) - } catch (error) { - if (chatId) { - sseStreamer.streamErrorEvent(chatId, getErrorMessage(error)) - } - next(error) - } finally { - sseStreamer.removeClient(chatId) - } -} -export default { - createInternalPrediction -} diff --git a/studio-frontend/packages/server/src/controllers/leads/index.ts b/studio-frontend/packages/server/src/controllers/leads/index.ts deleted file mode 100644 index db403a0..0000000 --- a/studio-frontend/packages/server/src/controllers/leads/index.ts +++ /dev/null @@ -1,40 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import leadsService from '../../services/leads' -import { StatusCodes } from 'http-status-codes' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' - -const getAllLeadsForChatflow = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params.id === 'undefined' || req.params.id === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: leadsController.getAllLeadsForChatflow - id not provided!` - ) - } - const chatflowid = req.params.id - const apiResponse = await leadsService.getAllLeads(chatflowid) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const createLeadInChatflow = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined' || req.body === '') { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: leadsController.createLeadInChatflow - body not provided!` - ) - } - const apiResponse = await leadsService.createLead(req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - createLeadInChatflow, - getAllLeadsForChatflow -} diff --git a/studio-frontend/packages/server/src/controllers/load-prompts/index.ts b/studio-frontend/packages/server/src/controllers/load-prompts/index.ts deleted file mode 100644 index 6437e49..0000000 --- a/studio-frontend/packages/server/src/controllers/load-prompts/index.ts +++ /dev/null @@ -1,23 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import loadPromptsService from '../../services/load-prompts' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' - -const createPrompt = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.body === 'undefined' || !req.body.promptName) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: loadPromptsController.createPrompt - promptName not provided!` - ) - } - const apiResponse = await loadPromptsService.createPrompt(req.body.promptName as string) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - createPrompt -} diff --git a/studio-frontend/packages/server/src/controllers/marketplaces/index.ts b/studio-frontend/packages/server/src/controllers/marketplaces/index.ts deleted file mode 100644 index db94715..0000000 --- a/studio-frontend/packages/server/src/controllers/marketplaces/index.ts +++ /dev/null @@ -1,60 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import marketplacesService from '../../services/marketplaces' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' - -// Get all templates for marketplaces -const getAllTemplates = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await marketplacesService.getAllTemplates() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const deleteCustomTemplate = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: marketplacesService.deleteCustomTemplate - id not provided!` - ) - } - const apiResponse = await marketplacesService.deleteCustomTemplate(req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const getAllCustomTemplates = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await marketplacesService.getAllCustomTemplates() - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const saveCustomTemplate = async (req: Request, res: Response, next: NextFunction) => { - try { - if ((!req.body && !(req.body.chatflowId || req.body.tool)) || !req.body.name) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: marketplacesService.saveCustomTemplate - body not provided!` - ) - } - const apiResponse = await marketplacesService.saveCustomTemplate(req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getAllTemplates, - getAllCustomTemplates, - saveCustomTemplate, - deleteCustomTemplate -} diff --git a/studio-frontend/packages/server/src/controllers/openai-assistants-vector-store/index.ts b/studio-frontend/packages/server/src/controllers/openai-assistants-vector-store/index.ts deleted file mode 100644 index 55a6d15..0000000 --- a/studio-frontend/packages/server/src/controllers/openai-assistants-vector-store/index.ts +++ /dev/null @@ -1,201 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import { StatusCodes } from 'http-status-codes' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import openAIAssistantVectorStoreService from '../../services/openai-assistants-vector-store' - -const getAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.getAssistantVectorStore - id not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.getAssistantVectorStore - credential not provided!` - ) - } - const apiResponse = await openAIAssistantVectorStoreService.getAssistantVectorStore(req.query.credential as string, req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const listAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.listAssistantVectorStore - credential not provided!` - ) - } - const apiResponse = await openAIAssistantVectorStoreService.listAssistantVectorStore(req.query.credential as string) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const createAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.createAssistantVectorStore - body not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.createAssistantVectorStore - credential not provided!` - ) - } - const apiResponse = await openAIAssistantVectorStoreService.createAssistantVectorStore(req.query.credential as string, req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const updateAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.updateAssistantVectorStore - id not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.updateAssistantVectorStore - credential not provided!` - ) - } - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.updateAssistantVectorStore - body not provided!` - ) - } - const apiResponse = await openAIAssistantVectorStoreService.updateAssistantVectorStore( - req.query.credential as string, - req.params.id, - req.body - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const deleteAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.deleteAssistantVectorStore - id not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.updateAssistantVectorStore - credential not provided!` - ) - } - const apiResponse = await openAIAssistantVectorStoreService.deleteAssistantVectorStore( - req.query.credential as string, - req.params.id as string - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const uploadFilesToAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.uploadFilesToAssistantVectorStore - body not provided!` - ) - } - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.uploadFilesToAssistantVectorStore - id not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.uploadFilesToAssistantVectorStore - credential not provided!` - ) - } - const files = req.files ?? [] - const uploadFiles: { filePath: string; fileName: string }[] = [] - - if (Array.isArray(files)) { - for (const file of files) { - uploadFiles.push({ - filePath: file.path, - fileName: file.originalname - }) - } - } - - const apiResponse = await openAIAssistantVectorStoreService.uploadFilesToAssistantVectorStore( - req.query.credential as string, - req.params.id as string, - uploadFiles - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const deleteFilesFromAssistantVectorStore = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.deleteFilesFromAssistantVectorStore - body not provided!` - ) - } - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.deleteFilesFromAssistantVectorStore - id not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.deleteFilesFromAssistantVectorStore - credential not provided!` - ) - } - - const apiResponse = await openAIAssistantVectorStoreService.deleteFilesFromAssistantVectorStore( - req.query.credential as string, - req.params.id as string, - req.body.file_ids - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getAssistantVectorStore, - listAssistantVectorStore, - createAssistantVectorStore, - updateAssistantVectorStore, - deleteAssistantVectorStore, - uploadFilesToAssistantVectorStore, - deleteFilesFromAssistantVectorStore -} diff --git a/studio-frontend/packages/server/src/controllers/openai-assistants/index.ts b/studio-frontend/packages/server/src/controllers/openai-assistants/index.ts deleted file mode 100644 index 8c4175e..0000000 --- a/studio-frontend/packages/server/src/controllers/openai-assistants/index.ts +++ /dev/null @@ -1,104 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import * as fs from 'fs' -import openaiAssistantsService from '../../services/openai-assistants' -import contentDisposition from 'content-disposition' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' -import { streamStorageFile } from 'flowise-components' - -// List available assistants -const getAllOpenaiAssistants = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsController.getAllOpenaiAssistants - credential not provided!` - ) - } - const apiResponse = await openaiAssistantsService.getAllOpenaiAssistants(req.query.credential as string) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -// Get assistant object -const getSingleOpenaiAssistant = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsController.getSingleOpenaiAssistant - id not provided!` - ) - } - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsController.getSingleOpenaiAssistant - credential not provided!` - ) - } - const apiResponse = await openaiAssistantsService.getSingleOpenaiAssistant(req.query.credential as string, req.params.id) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -// Download file from assistant -const getFileFromAssistant = async (req: Request, res: Response, next: NextFunction) => { - try { - if (!req.body.chatflowId || !req.body.chatId || !req.body.fileName) { - return res.status(500).send(`Invalid file path`) - } - const chatflowId = req.body.chatflowId as string - const chatId = req.body.chatId as string - const fileName = req.body.fileName as string - const orgId = '' // Organization ID not required in this context - res.setHeader('Content-Disposition', contentDisposition(fileName)) - const fileStream = await streamStorageFile(chatflowId, chatId, fileName, orgId) - - if (!fileStream) throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error: getFileFromAssistant`) - - if (fileStream instanceof fs.ReadStream && fileStream?.pipe) { - fileStream.pipe(res) - } else { - res.send(fileStream) - } - } catch (error) { - next(error) - } -} - -const uploadAssistantFiles = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.query === 'undefined' || !req.query.credential) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: openaiAssistantsVectorStoreController.uploadFilesToAssistantVectorStore - credential not provided!` - ) - } - const files = req.files ?? [] - const uploadFiles: { filePath: string; fileName: string }[] = [] - - if (Array.isArray(files)) { - for (const file of files) { - uploadFiles.push({ - filePath: file.path, - fileName: file.originalname - }) - } - } - - const apiResponse = await openaiAssistantsService.uploadFilesToAssistant(req.query.credential as string, uploadFiles) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getAllOpenaiAssistants, - getSingleOpenaiAssistant, - getFileFromAssistant, - uploadAssistantFiles -} diff --git a/studio-frontend/packages/server/src/controllers/predictions/index.ts b/studio-frontend/packages/server/src/controllers/predictions/index.ts deleted file mode 100644 index 504d651..0000000 --- a/studio-frontend/packages/server/src/controllers/predictions/index.ts +++ /dev/null @@ -1,102 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import { getRateLimiter } from '../../utils/rateLimit' -import chatflowsService from '../../services/chatflows' -import logger from '../../utils/logger' -import predictionsServices from '../../services/predictions' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { v4 as uuidv4 } from 'uuid' -import { getErrorMessage } from '../../errors/utils' - -// Send input message and get prediction result (External) -const createPrediction = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: predictionsController.createPrediction - id not provided!` - ) - } - if (!req.body) { - throw new InternalFlowiseError( - StatusCodes.PRECONDITION_FAILED, - `Error: predictionsController.createPrediction - body not provided!` - ) - } - const chatflow = await chatflowsService.getChatflowById(req.params.id) - if (!chatflow) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Chatflow ${req.params.id} not found`) - } - let isDomainAllowed = true - logger.info(`[server]: Request originated from ${req.headers.origin || 'UNKNOWN ORIGIN'}`) - if (chatflow.chatbotConfig) { - const parsedConfig = JSON.parse(chatflow.chatbotConfig) - // check whether the first one is not empty. if it is empty that means the user set a value and then removed it. - const isValidAllowedOrigins = parsedConfig.allowedOrigins?.length && parsedConfig.allowedOrigins[0] !== '' - if (isValidAllowedOrigins && req.headers.origin) { - const originHeader = req.headers.origin - const origin = new URL(originHeader).host - isDomainAllowed = - parsedConfig.allowedOrigins.filter((domain: string) => { - try { - const allowedOrigin = new URL(domain).host - return origin === allowedOrigin - } catch (e) { - return false - } - }).length > 0 - } - } - if (isDomainAllowed) { - const streamable = await chatflowsService.checkIfChatflowIsValidForStreaming(req.params.id) - const isStreamingRequested = req.body.streaming === 'true' || req.body.streaming === true - if (streamable?.isStreaming && isStreamingRequested) { - const sseStreamer = getRunningExpressApp().sseStreamer - let chatId = req.body.chatId - if (!req.body.chatId) { - chatId = req.body.chatId ?? req.body.overrideConfig?.sessionId ?? uuidv4() - req.body.chatId = chatId - } - try { - sseStreamer.addExternalClient(chatId, res) - res.setHeader('Content-Type', 'text/event-stream') - res.setHeader('Cache-Control', 'no-cache') - res.setHeader('Connection', 'keep-alive') - res.setHeader('X-Accel-Buffering', 'no') //nginx config: https://serverfault.com/a/801629 - res.flushHeaders() - - const apiResponse = await predictionsServices.buildChatflow(req) - sseStreamer.streamMetadataEvent(apiResponse.chatId, apiResponse) - } catch (error) { - if (chatId) { - sseStreamer.streamErrorEvent(chatId, getErrorMessage(error)) - } - next(error) - } finally { - sseStreamer.removeClient(chatId) - } - } else { - const apiResponse = await predictionsServices.buildChatflow(req) - return res.json(apiResponse) - } - } else { - throw new InternalFlowiseError(StatusCodes.UNAUTHORIZED, `This site is not allowed to access this chatbot`) - } - } catch (error) { - next(error) - } -} - -const getRateLimiterMiddleware = async (req: Request, res: Response, next: NextFunction) => { - try { - return getRateLimiter(req, res, next) - } catch (error) { - next(error) - } -} - -export default { - createPrediction, - getRateLimiterMiddleware -} diff --git a/studio-frontend/packages/server/src/controllers/prompts-lists/index.ts b/studio-frontend/packages/server/src/controllers/prompts-lists/index.ts deleted file mode 100644 index 88a048c..0000000 --- a/studio-frontend/packages/server/src/controllers/prompts-lists/index.ts +++ /dev/null @@ -1,16 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import promptsListsService from '../../services/prompts-lists' - -// Prompt from Hub -const createPromptsList = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await promptsListsService.createPromptsList(req.body) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - createPromptsList -} diff --git a/studio-frontend/packages/server/src/controllers/stats/index.ts b/studio-frontend/packages/server/src/controllers/stats/index.ts deleted file mode 100644 index a964645..0000000 --- a/studio-frontend/packages/server/src/controllers/stats/index.ts +++ /dev/null @@ -1,71 +0,0 @@ -import { StatusCodes } from 'http-status-codes' -import { Request, Response, NextFunction } from 'express' -import statsService from '../../services/stats' -import { ChatMessageRatingType, chatType } from '../../Interface' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const getChatflowStats = async (req: Request, res: Response, next: NextFunction) => { - try { - if (typeof req.params === 'undefined' || !req.params.id) { - throw new InternalFlowiseError(StatusCodes.PRECONDITION_FAILED, `Error: statsController.getChatflowStats - id not provided!`) - } - const chatflowid = req.params.id - let chatTypeFilter = req.query?.chatType as chatType | undefined - const startDate = req.query?.startDate as string | undefined - const endDate = req.query?.endDate as string | undefined - let feedbackTypeFilters = req.query?.feedbackType as ChatMessageRatingType[] | undefined - if (chatTypeFilter) { - try { - const chatTypeFilterArray = JSON.parse(chatTypeFilter) - if (chatTypeFilterArray.includes(chatType.EXTERNAL) && chatTypeFilterArray.includes(chatType.INTERNAL)) { - chatTypeFilter = undefined - } else if (chatTypeFilterArray.includes(chatType.EXTERNAL)) { - chatTypeFilter = chatType.EXTERNAL - } else if (chatTypeFilterArray.includes(chatType.INTERNAL)) { - chatTypeFilter = chatType.INTERNAL - } - } catch (e) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: statsController.getChatflowStats - ${getErrorMessage(e)}` - ) - } - } - if (feedbackTypeFilters) { - try { - const feedbackTypeFilterArray = JSON.parse(JSON.stringify(feedbackTypeFilters)) - if ( - feedbackTypeFilterArray.includes(ChatMessageRatingType.THUMBS_UP) && - feedbackTypeFilterArray.includes(ChatMessageRatingType.THUMBS_DOWN) - ) { - feedbackTypeFilters = [ChatMessageRatingType.THUMBS_UP, ChatMessageRatingType.THUMBS_DOWN] - } else if (feedbackTypeFilterArray.includes(ChatMessageRatingType.THUMBS_UP)) { - feedbackTypeFilters = [ChatMessageRatingType.THUMBS_UP] - } else if (feedbackTypeFilterArray.includes(ChatMessageRatingType.THUMBS_DOWN)) { - feedbackTypeFilters = [ChatMessageRatingType.THUMBS_DOWN] - } else { - feedbackTypeFilters = undefined - } - } catch (e) { - return res.status(500).send(e) - } - } - const apiResponse = await statsService.getChatflowStats( - chatflowid, - chatTypeFilter, - startDate, - endDate, - '', - true, - feedbackTypeFilters - ) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getChatflowStats -} diff --git a/studio-frontend/packages/server/src/controllers/upsert-history/index.ts b/studio-frontend/packages/server/src/controllers/upsert-history/index.ts deleted file mode 100644 index 43f36d5..0000000 --- a/studio-frontend/packages/server/src/controllers/upsert-history/index.ts +++ /dev/null @@ -1,30 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import upsertHistoryService from '../../services/upsert-history' - -const getAllUpsertHistory = async (req: Request, res: Response, next: NextFunction) => { - try { - const sortOrder = req.query?.order as string | undefined - const chatflowid = req.params?.id as string | undefined - const startDate = req.query?.startDate as string | undefined - const endDate = req.query?.endDate as string | undefined - const apiResponse = await upsertHistoryService.getAllUpsertHistory(sortOrder, chatflowid, startDate, endDate) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const patchDeleteUpsertHistory = async (req: Request, res: Response, next: NextFunction) => { - try { - const ids = req.body.ids ?? [] - const apiResponse = await upsertHistoryService.patchDeleteUpsertHistory(ids) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - getAllUpsertHistory, - patchDeleteUpsertHistory -} diff --git a/studio-frontend/packages/server/src/controllers/vectors/index.ts b/studio-frontend/packages/server/src/controllers/vectors/index.ts deleted file mode 100644 index 5d10bb6..0000000 --- a/studio-frontend/packages/server/src/controllers/vectors/index.ts +++ /dev/null @@ -1,36 +0,0 @@ -import { Request, Response, NextFunction } from 'express' -import vectorsService from '../../services/vectors' -import { getRateLimiter } from '../../utils/rateLimit' - -const getRateLimiterMiddleware = async (req: Request, res: Response, next: NextFunction) => { - try { - return getRateLimiter(req, res, next) - } catch (error) { - next(error) - } -} - -const upsertVectorMiddleware = async (req: Request, res: Response, next: NextFunction) => { - try { - const apiResponse = await vectorsService.upsertVectorMiddleware(req) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -const createInternalUpsert = async (req: Request, res: Response, next: NextFunction) => { - try { - const isInternal = true - const apiResponse = await vectorsService.upsertVectorMiddleware(req, isInternal) - return res.json(apiResponse) - } catch (error) { - next(error) - } -} - -export default { - upsertVectorMiddleware, - createInternalUpsert, - getRateLimiterMiddleware -} diff --git a/studio-frontend/packages/server/src/index.ts b/studio-frontend/packages/server/src/index.ts index 06b236d..3939fcb 100644 --- a/studio-frontend/packages/server/src/index.ts +++ b/studio-frontend/packages/server/src/index.ts @@ -13,14 +13,11 @@ import { getDataSource } from './DataSource' import { NodesPool } from './NodesPool' import { ChatFlow } from './database/entities/ChatFlow' import { ChatflowPool } from './ChatflowPool' -import { CachePool } from './CachePool' import { initializeRateLimiter } from './utils/rateLimit' import { getAPIKeys } from './utils/apiKey' import { sanitizeMiddleware, getCorsOptions, getAllowedIframeOrigins } from './utils/XSS' -import { Telemetry } from './utils/telemetry' import flowiseApiV1Router from './routes' import errorHandlerMiddleware from './middlewares/errors' -import { SSEStreamer } from './utils/SSEStreamer' import { validateAPIKey } from './utils/validateKey' import { setupFineTuningDownloadHandlers } from './ws/finetuningDownload' import { setupFineTuningStatusHandlers } from './ws/finetuningStatus' @@ -37,10 +34,7 @@ export class App { app: express.Application nodesPool: NodesPool chatflowPool: ChatflowPool - cachePool: CachePool - telemetry: Telemetry AppDataSource: DataSource = getDataSource() - sseStreamer: SSEStreamer constructor() { this.app = express() @@ -78,11 +72,6 @@ export class App { const AllChatFlow: IChatFlow[] = await getAllChatFlow() await initializeRateLimiter(AllChatFlow) - // Initialize cache pool - this.cachePool = new CachePool() - - // Initialize telemetry - this.telemetry = new Telemetry() logger.info('📦 [server]: Data Source has been initialized!') } catch (error) { logger.error('❌ [server]: Error during Data Source initialization:', error) @@ -136,11 +125,6 @@ export class App { '/api/v1/vector/upsert/', '/api/v1/node-icon/', '/api/v1/components-credentials-icon/', - '/api/v1/chatflows-streaming', - '/api/v1/chatflows-uploads', - '/api/v1/openai-assistants-file/download', - '/api/v1/feedback', - '/api/v1/leads', '/api/v1/get-upload-file', '/api/v1/ip', '/api/v1/ping', @@ -211,7 +195,6 @@ export class App { } this.app.use('/api/v1', flowiseApiV1Router) - this.sseStreamer = new SSEStreamer(this.app) // ---------------------------------------- // Configure number of proxies in Host Environment @@ -267,9 +250,7 @@ export class App { async stopApp() { try { - const removePromises: any[] = [] - removePromises.push(this.telemetry.flush()) - await Promise.all(removePromises) + logger.info('[server]: Flowise Server shutting down...') } catch (e) { logger.error(`❌[server]: Flowise Server shut down error: ${e}`) } diff --git a/studio-frontend/packages/server/src/routes/assistants/index.ts b/studio-frontend/packages/server/src/routes/assistants/index.ts deleted file mode 100644 index 1499855..0000000 --- a/studio-frontend/packages/server/src/routes/assistants/index.ts +++ /dev/null @@ -1,19 +0,0 @@ -import express from 'express' -import assistantsController from '../../controllers/assistants' - -const router: express.Router = express.Router() - -// CREATE -router.post('/', assistantsController.createAssistant) - -// READ -router.get('/', assistantsController.getAllAssistants) -router.get(['/', '/:id'], assistantsController.getAssistantById) - -// UPDATE -router.put(['/', '/:id'], assistantsController.updateAssistant) - -// DELETE -router.delete(['/', '/:id'], assistantsController.deleteAssistant) - -export default router diff --git a/studio-frontend/packages/server/src/routes/chatflows-streaming/index.ts b/studio-frontend/packages/server/src/routes/chatflows-streaming/index.ts deleted file mode 100644 index 41ad33b..0000000 --- a/studio-frontend/packages/server/src/routes/chatflows-streaming/index.ts +++ /dev/null @@ -1,9 +0,0 @@ -import express from 'express' -import chatflowsController from '../../controllers/chatflows' - -const router: express.Router = express.Router() - -// READ -router.get(['/', '/:id'], chatflowsController.checkIfChatflowIsValidForStreaming) - -export default router diff --git a/studio-frontend/packages/server/src/routes/chatflows-uploads/index.ts b/studio-frontend/packages/server/src/routes/chatflows-uploads/index.ts deleted file mode 100644 index bcc55a8..0000000 --- a/studio-frontend/packages/server/src/routes/chatflows-uploads/index.ts +++ /dev/null @@ -1,9 +0,0 @@ -import express from 'express' -import chatflowsController from '../../controllers/chatflows' - -const router: express.Router = express.Router() - -// READ -router.get(['/', '/:id'], chatflowsController.checkIfChatflowIsValidForUploads) - -export default router diff --git a/studio-frontend/packages/server/src/routes/documentstore/index.ts b/studio-frontend/packages/server/src/routes/documentstore/index.ts deleted file mode 100644 index ddc774a..0000000 --- a/studio-frontend/packages/server/src/routes/documentstore/index.ts +++ /dev/null @@ -1,54 +0,0 @@ -import express from 'express' -import documentStoreController from '../../controllers/documentstore' -const router: express.Router = express.Router() - -/** Document Store Routes */ -// Create document store -router.post('/store', documentStoreController.createDocumentStore) -// List all stores -router.get('/store', documentStoreController.getAllDocumentStores) -// Get specific store -router.get('/store/:id', documentStoreController.getDocumentStoreById) -// Update documentStore -router.put('/store/:id', documentStoreController.updateDocumentStore) -// Delete documentStore -router.delete('/store/:id', documentStoreController.deleteDocumentStore) - -/** Component Nodes = Document Store - Loaders */ -// Get all loaders -router.get('/components/loaders', documentStoreController.getDocumentLoaders) - -// delete loader from document store -router.delete('/loader/:id/:loaderId', documentStoreController.deleteLoaderFromDocumentStore) -// chunking preview -router.post('/loader/preview', documentStoreController.previewFileChunks) -// chunking process -router.post('/loader/process', documentStoreController.processFileChunks) - -/** Document Store - Loaders - Chunks */ -// delete specific file chunk from the store -router.delete('/chunks/:storeId/:loaderId/:chunkId', documentStoreController.deleteDocumentStoreFileChunk) -// edit specific file chunk from the store -router.put('/chunks/:storeId/:loaderId/:chunkId', documentStoreController.editDocumentStoreFileChunk) -// Get all file chunks from the store -router.get('/chunks/:storeId/:fileId/:pageNo', documentStoreController.getDocumentStoreFileChunks) - -// add chunks to the selected vector store -router.post('/vectorstore/insert', documentStoreController.insertIntoVectorStore) -// save the selected vector store -router.post('/vectorstore/save', documentStoreController.saveVectorStoreConfig) -// delete data from the selected vector store -router.delete('/vectorstore/:storeId', documentStoreController.deleteVectorStoreFromStore) -// query the vector store -router.post('/vectorstore/query', documentStoreController.queryVectorStore) -// Get all embedding providers -router.get('/components/embeddings', documentStoreController.getEmbeddingProviders) -// Get all vector store providers -router.get('/components/vectorstore', documentStoreController.getVectorStoreProviders) -// Get all Record Manager providers -router.get('/components/recordmanager', documentStoreController.getRecordManagerProviders) - -// update the selected vector store from the playground -router.post('/vectorstore/update', documentStoreController.updateVectorStoreConfigOnly) - -export default router diff --git a/studio-frontend/packages/server/src/routes/evaluation/index.ts b/studio-frontend/packages/server/src/routes/evaluation/index.ts new file mode 100644 index 0000000..2983f4c --- /dev/null +++ b/studio-frontend/packages/server/src/routes/evaluation/index.ts @@ -0,0 +1,226 @@ +import express from 'express' +import axios from 'axios' +import multer from 'multer' +import { Request, Response, NextFunction } from 'express' +import { parseDatasetUpload } from '../../services/evaluationUploads' + +const router: express.Router = express.Router() +const upload = multer({ storage: multer.memoryStorage() }) + +const getStudioServerUrl = () => process.env.STUDIO_SERVER_URL || 'http://studio-backend.studio.svc.cluster.local:5000' + +// URL template for fetching dataprep file list from a sandbox. +// Builds the dataprep GET URL for a given sandboxId. +// Uses PREPARE_DOC_REDIS_PREP_DNS env var (also used by studio-nginx for K8s proxying). +const getDataprepUrl = (sandboxId: string): string => { + const dns = + process.env.PREPARE_DOC_REDIS_PREP_DNS || + 'opea-prepare-doc-redis-prep-0.$namespace.svc.cluster.local:6007' + if (dns.includes('$namespace')) { + // K8s direct: replace $namespace placeholder, prepend scheme, append path + return `http://${dns.replace('$namespace', sandboxId)}/v1/dataprep/get` + } + // Proxy mode (local-nginx): DNS is a host only — pass sandbox via ?ns= query param + return `http://${dns}/v1/dataprep/get?ns=${sandboxId}` +} + +/** + * Generic proxy helper – forwards a request to studio-backend and pipes the response back. + */ +const proxy = async ( + method: 'get' | 'post' | 'put' | 'delete' | 'patch', + targetPath: string, + req: Request, + res: Response, + next: NextFunction, + options?: { + data?: unknown + headers?: Record + timeout?: number + } +) => { + try { + const url = `${getStudioServerUrl()}/${targetPath}` + const hasOverrideData = Boolean(options && Object.prototype.hasOwnProperty.call(options, 'data')) + const response = await axios({ + method, + url, + data: hasOverrideData ? options?.data : ['post', 'put', 'patch'].includes(method) ? req.body : undefined, + params: req.query, + headers: options?.headers || { 'Content-Type': 'application/json' }, + timeout: options?.timeout ?? 60_000 + }) + return res.status(response.status).json(response.data) + } catch (error: unknown) { + const err = error as any + if (err?.response?.status) { + return res.status(err.response.status).json(err.response.data) + } + next(error) + } +} + +// ── Sandboxes ───────────────────────────────────────────────────────────────── +// GET /api/v1/evaluation/sandboxes → studio-backend/sandbox/list +router.get('/sandboxes', (req: Request, res: Response, next: NextFunction) => + proxy('get', 'studio-backend/sandbox/list', req, res, next) +) + +// ── Models ──────────────────────────────────────────────────────────────────── +// GET /api/v1/evaluation/models → studio-backend/evaluation/models +router.get('/models', (req: Request, res: Response, next: NextFunction) => + proxy('get', 'studio-backend/evaluation/models', req, res, next) +) + +// GET /api/v1/evaluation/models/pull/status → studio-backend/evaluation/models/pull/status +router.get('/models/pull/status', (req: Request, res: Response, next: NextFunction) => + proxy('get', 'studio-backend/evaluation/models/pull/status', req, res, next) +) + +// POST /api/v1/evaluation/models/pull → studio-backend/evaluation/models/pull +router.post('/models/pull', (req: Request, res: Response, next: NextFunction) => + proxy('post', 'studio-backend/evaluation/models/pull', req, res, next) +) + +// POST /api/v1/evaluation/sandbox-files +// Body: { sandbox_id: string } +// Uses the same path as sandbox app-frontend: app-nginx -> /v1/dataprep/get +router.post('/sandbox-files', async (req: Request, res: Response, next: NextFunction) => { + try { + const sandboxId = String(req.body?.sandbox_id || '') + + if (!sandboxId) { + return res.status(400).json({ message: 'sandbox_id is required' }) + } + + const isValidNamespace = /^[a-z0-9-]+$/.test(sandboxId) + if (!isValidNamespace) { + return res.status(400).json({ message: 'Invalid sandbox_id' }) + } + + const url = getDataprepUrl(sandboxId) + console.log(`[sandbox-files] Calling: POST ${url}`) + let response: any + try { + response = await axios.post( + url, + {}, + { + headers: { 'Content-Type': 'application/json' }, + timeout: 15_000, + proxy: false // bypass corporate proxy; Docker bridge DNS resolves service names + } + ) + } catch (axiosErr: any) { + const status = axiosErr?.response?.status + const msg = axiosErr?.message + const body = axiosErr?.response?.data + console.error(`[sandbox-files] Request failed: status=${status} msg=${msg}`, body ?? '') + if (status) { + return res.status(status).json({ message: `Dataprep service returned ${status}`, detail: body }) + } + return res.status(502).json({ message: `Cannot reach dataprep service: ${msg}`, url }) + } + + const data = response.data + console.log(`[sandbox-files] Response:`, JSON.stringify(data).slice(0, 200)) + const files = Array.isArray(data) ? data : Array.isArray(data?.files) ? data.files : [] + + return res.status(200).json({ files }) + } catch (error: unknown) { + next(error) + } +}) + +// ── Datasets ────────────────────────────────────────────────────────────────── +router.get('/datasets', (req: Request, res: Response, next: NextFunction) => + proxy('get', 'studio-backend/evaluation/datasets', req, res, next) +) + +router.get('/datasets/:id', (req: Request, res: Response, next: NextFunction) => + proxy('get', `studio-backend/evaluation/datasets/${req.params.id}`, req, res, next) +) + +router.post('/datasets', upload.single('file'), async (req: Request, res: Response, next: NextFunction) => { + try { + const payload = req.file ? parseDatasetUpload(req.file, req.body as Record) : req.body + return proxy('post', 'studio-backend/evaluation/datasets', req, res, next, { data: payload }) + } catch (error) { + const message = error instanceof Error ? error.message : 'Failed to process dataset upload.' + return res.status(400).json({ message }) + } +}) + +router.post('/datasets/synthesize', upload.single('file'), async (req: Request, res: Response, next: NextFunction) => { + try { + if (!req.file) { + return res.status(400).json({ message: 'A source file is required for synthesis.' }) + } + const FormDataLib = require('form-data') + const fd = new FormDataLib() + fd.append('file', req.file.buffer, { + filename: req.file.originalname, + contentType: req.file.mimetype || 'application/octet-stream', + }) + // Forward all text fields unchanged + for (const [key, value] of Object.entries(req.body as Record)) { + if (value !== undefined && value !== null) { + fd.append(key, String(value)) + } + } + return proxy('post', 'studio-backend/evaluation/datasets/synthesize', req, res, next, { + data: fd, + headers: fd.getHeaders(), + }) + } catch (error) { + const message = error instanceof Error ? error.message : 'Failed to process synthesis upload.' + return res.status(400).json({ message }) + } +}) + +router.put('/datasets/:id', (req: Request, res: Response, next: NextFunction) => + proxy('put', `studio-backend/evaluation/datasets/${req.params.id}`, req, res, next) +) + +router.delete('/datasets/:id', (req: Request, res: Response, next: NextFunction) => + proxy('delete', `studio-backend/evaluation/datasets/${req.params.id}`, req, res, next) +) + +router.post('/datasets/:id/stop', (req: Request, res: Response, next: NextFunction) => + proxy('post', `studio-backend/evaluation/datasets/${req.params.id}/stop`, req, res, next) +) + +router.post('/datasets/:id/entries', (req: Request, res: Response, next: NextFunction) => + proxy('post', `studio-backend/evaluation/datasets/${req.params.id}/entries`, req, res, next) +) + +router.put('/datasets/:id/entries/:entryId', (req: Request, res: Response, next: NextFunction) => + proxy('put', `studio-backend/evaluation/datasets/${req.params.id}/entries/${req.params.entryId}`, req, res, next) +) + +router.delete('/datasets/:id/entries/:entryId', (req: Request, res: Response, next: NextFunction) => + proxy('delete', `studio-backend/evaluation/datasets/${req.params.id}/entries/${req.params.entryId}`, req, res, next) +) + +// ── Runs ────────────────────────────────────────────────────────────────────── +router.get('/runs', (req: Request, res: Response, next: NextFunction) => + proxy('get', 'studio-backend/evaluation/runs', req, res, next) +) + +router.get('/runs/:id', (req: Request, res: Response, next: NextFunction) => + proxy('get', `studio-backend/evaluation/runs/${req.params.id}`, req, res, next) +) + +router.post('/runs', (req: Request, res: Response, next: NextFunction) => + proxy('post', 'studio-backend/evaluation/runs', req, res, next) +) + +router.post('/runs/:id/stop', (req: Request, res: Response, next: NextFunction) => + proxy('post', `studio-backend/evaluation/runs/${req.params.id}/stop`, req, res, next) +) + +router.delete('/runs/:id', (req: Request, res: Response, next: NextFunction) => + proxy('delete', `studio-backend/evaluation/runs/${req.params.id}`, req, res, next) +) + +export default router diff --git a/studio-frontend/packages/server/src/routes/feedback/index.ts b/studio-frontend/packages/server/src/routes/feedback/index.ts deleted file mode 100644 index b642535..0000000 --- a/studio-frontend/packages/server/src/routes/feedback/index.ts +++ /dev/null @@ -1,14 +0,0 @@ -import express from 'express' -import feedbackController from '../../controllers/feedback' -const router: express.Router = express.Router() - -// CREATE -router.post(['/', '/:id'], feedbackController.createChatMessageFeedbackForChatflow) - -// READ -router.get(['/', '/:id'], feedbackController.getAllChatMessageFeedback) - -// UPDATE -router.put(['/', '/:id'], feedbackController.updateChatMessageFeedbackForChatflow) - -export default router diff --git a/studio-frontend/packages/server/src/routes/fetch-links/index.ts b/studio-frontend/packages/server/src/routes/fetch-links/index.ts deleted file mode 100644 index 462296a..0000000 --- a/studio-frontend/packages/server/src/routes/fetch-links/index.ts +++ /dev/null @@ -1,8 +0,0 @@ -import express from 'express' -import fetchLinksController from '../../controllers/fetch-links' -const router: express.Router = express.Router() - -// READ -router.get('/', fetchLinksController.getAllLinks) - -export default router diff --git a/studio-frontend/packages/server/src/routes/index.ts b/studio-frontend/packages/server/src/routes/index.ts index bd57a14..ee0c879 100644 --- a/studio-frontend/packages/server/src/routes/index.ts +++ b/studio-frontend/packages/server/src/routes/index.ts @@ -1,45 +1,25 @@ import express from 'express' import apikeyRouter from './apikey' -import assistantsRouter from './assistants' import chatMessageRouter from './chat-messages' import chatflowsRouter from './chatflows' import chatflowsSandboxRouter from './chatflows-sandbox' -import chatflowsStreamingRouter from './chatflows-streaming' -import chatflowsUploadsRouter from './chatflows-uploads' import componentsCredentialsRouter from './components-credentials' import componentsCredentialsIconRouter from './components-credentials-icon' import credentialsRouter from './credentials' -import documentStoreRouter from './documentstore' import exportImportRouter from './export-import' -import feedbackRouter from './feedback' -import fetchLinksRouter from './fetch-links' import finetuningRouter from './finetuning' +import evaluationRouter from './evaluation' import flowConfigRouter from './flow-config' import getUploadFileRouter from './get-upload-file' import getUploadPathRouter from './get-upload-path' -import internalChatmessagesRouter from './internal-chat-messages' -import internalPredictionRouter from './internal-predictions' -import leadsRouter from './leads' -import loadPromptRouter from './load-prompts' -import marketplacesRouter from './marketplaces' import nodeConfigRouter from './node-configs' import nodeCustomFunctionRouter from './node-custom-functions' import nodeIconRouter from './node-icons' import nodeLoadMethodRouter from './node-load-methods' import nodesRouter from './nodes' -import openaiAssistantsRouter from './openai-assistants' -import openaiAssistantsFileRouter from './openai-assistants-files' -import openaiAssistantsVectorStoreRouter from './openai-assistants-vector-store' import pingRouter from './ping' -import predictionRouter from './predictions' -import promptListsRouter from './prompts-lists' -import publicChatbotRouter from './public-chatbots' -import publicChatflowsRouter from './public-chatflows' -import statsRouter from './stats' import toolsRouter from './tools' -import upsertHistoryRouter from './upsert-history' import variablesRouter from './variables' -import vectorRouter from './vectors' import verifyRouter from './verify' import versionRouter from './versions' @@ -47,46 +27,27 @@ const router: express.Router = express.Router() router.use('/ping', pingRouter) router.use('/apikey', apikeyRouter) -router.use('/assistants', assistantsRouter) router.use('/chatflows', chatflowsRouter) router.use('/chatflows-sandbox', chatflowsSandboxRouter) -router.use('/chatflows-streaming', chatflowsStreamingRouter) router.use('/chatmessage', chatMessageRouter) router.use('/components-credentials', componentsCredentialsRouter) router.use('/components-credentials-icon', componentsCredentialsIconRouter) -router.use('/chatflows-uploads', chatflowsUploadsRouter) router.use('/credentials', credentialsRouter) -router.use('/document-store', documentStoreRouter) router.use('/export-import', exportImportRouter) -router.use('/feedback', feedbackRouter) -router.use('/fetch-links', fetchLinksRouter) router.use('/finetuning', finetuningRouter) +router.use('/evaluation', evaluationRouter) router.use('/flow-config', flowConfigRouter) -router.use('/internal-chatmessage', internalChatmessagesRouter) -router.use('/internal-prediction', internalPredictionRouter) router.use('/get-upload-file', getUploadFileRouter) router.use('/get-upload-path', getUploadPathRouter) -router.use('/leads', leadsRouter) -router.use('/load-prompt', loadPromptRouter) -router.use('/marketplaces', marketplacesRouter) router.use('/node-config', nodeConfigRouter) router.use('/node-custom-function', nodeCustomFunctionRouter) router.use('/node-icon', nodeIconRouter) router.use('/node-load-method', nodeLoadMethodRouter) router.use('/nodes', nodesRouter) -router.use('/openai-assistants', openaiAssistantsRouter) -router.use('/openai-assistants-file', openaiAssistantsFileRouter) -router.use('/openai-assistants-vector-store', openaiAssistantsVectorStoreRouter) -router.use('/prediction', predictionRouter) -router.use('/prompts-list', promptListsRouter) -router.use('/public-chatbotConfig', publicChatbotRouter) -router.use('/public-chatflows', publicChatflowsRouter) -router.use('/stats', statsRouter) router.use('/tools', toolsRouter) router.use('/variables', variablesRouter) -router.use('/vector', vectorRouter) router.use('/verify', verifyRouter) router.use('/version', versionRouter) -router.use('/upsert-history', upsertHistoryRouter) export default router + diff --git a/studio-frontend/packages/server/src/routes/internal-chat-messages/index.ts b/studio-frontend/packages/server/src/routes/internal-chat-messages/index.ts deleted file mode 100644 index b5814c8..0000000 --- a/studio-frontend/packages/server/src/routes/internal-chat-messages/index.ts +++ /dev/null @@ -1,14 +0,0 @@ -import express from 'express' -import chatMessagesController from '../../controllers/chat-messages' -const router: express.Router = express.Router() - -// CREATE - -// READ -router.get(['/', '/:id'], chatMessagesController.getAllInternalChatMessages) - -// UPDATE - -// DELETE - -export default router diff --git a/studio-frontend/packages/server/src/routes/internal-predictions/index.ts b/studio-frontend/packages/server/src/routes/internal-predictions/index.ts deleted file mode 100644 index 80fb8e1..0000000 --- a/studio-frontend/packages/server/src/routes/internal-predictions/index.ts +++ /dev/null @@ -1,8 +0,0 @@ -import express from 'express' -import internalPredictionsController from '../../controllers/internal-predictions' -const router: express.Router = express.Router() - -// CREATE -router.post(['/', '/:id'], internalPredictionsController.createInternalPrediction) - -export default router diff --git a/studio-frontend/packages/server/src/routes/leads/index.ts b/studio-frontend/packages/server/src/routes/leads/index.ts deleted file mode 100644 index 8acdfc4..0000000 --- a/studio-frontend/packages/server/src/routes/leads/index.ts +++ /dev/null @@ -1,11 +0,0 @@ -import express from 'express' -import leadsController from '../../controllers/leads' -const router: express.Router = express.Router() - -// CREATE -router.post('/', leadsController.createLeadInChatflow) - -// READ -router.get(['/', '/:id'], leadsController.getAllLeadsForChatflow) - -export default router diff --git a/studio-frontend/packages/server/src/routes/load-prompts/index.ts b/studio-frontend/packages/server/src/routes/load-prompts/index.ts deleted file mode 100644 index b11e7c1..0000000 --- a/studio-frontend/packages/server/src/routes/load-prompts/index.ts +++ /dev/null @@ -1,8 +0,0 @@ -import express from 'express' -import loadPromptsController from '../../controllers/load-prompts' -const router: express.Router = express.Router() - -// CREATE -router.post('/', loadPromptsController.createPrompt) - -export default router diff --git a/studio-frontend/packages/server/src/routes/marketplaces/index.ts b/studio-frontend/packages/server/src/routes/marketplaces/index.ts deleted file mode 100644 index 3242789..0000000 --- a/studio-frontend/packages/server/src/routes/marketplaces/index.ts +++ /dev/null @@ -1,16 +0,0 @@ -import express from 'express' -import marketplacesController from '../../controllers/marketplaces' -const router: express.Router = express.Router() - -// READ -router.get('/templates', marketplacesController.getAllTemplates) - -router.post('/custom', marketplacesController.saveCustomTemplate) - -// READ -router.get('/custom', marketplacesController.getAllCustomTemplates) - -// DELETE -router.delete(['/', '/custom/:id'], marketplacesController.deleteCustomTemplate) - -export default router diff --git a/studio-frontend/packages/server/src/routes/openai-assistants-files/index.ts b/studio-frontend/packages/server/src/routes/openai-assistants-files/index.ts deleted file mode 100644 index 419fcef..0000000 --- a/studio-frontend/packages/server/src/routes/openai-assistants-files/index.ts +++ /dev/null @@ -1,12 +0,0 @@ -import express from 'express' -import multer from 'multer' -import path from 'path' -import openaiAssistantsController from '../../controllers/openai-assistants' - -const router: express.Router = express.Router() -const upload = multer({ dest: `${path.join(__dirname, '..', '..', '..', 'uploads')}/` }) - -router.post('/download/', openaiAssistantsController.getFileFromAssistant) -router.post('/upload/', upload.array('files'), openaiAssistantsController.uploadAssistantFiles) - -export default router diff --git a/studio-frontend/packages/server/src/routes/openai-assistants-vector-store/index.ts b/studio-frontend/packages/server/src/routes/openai-assistants-vector-store/index.ts deleted file mode 100644 index efd0f8e..0000000 --- a/studio-frontend/packages/server/src/routes/openai-assistants-vector-store/index.ts +++ /dev/null @@ -1,30 +0,0 @@ -import express from 'express' -import multer from 'multer' -import path from 'path' -import openaiAssistantsVectorStoreController from '../../controllers/openai-assistants-vector-store' - -const router: express.Router = express.Router() -const upload = multer({ dest: `${path.join(__dirname, '..', '..', '..', 'uploads')}/` }) - -// CREATE -router.post('/', openaiAssistantsVectorStoreController.createAssistantVectorStore) - -// READ -router.get('/:id', openaiAssistantsVectorStoreController.getAssistantVectorStore) - -// LIST -router.get('/', openaiAssistantsVectorStoreController.listAssistantVectorStore) - -// UPDATE -router.put(['/', '/:id'], openaiAssistantsVectorStoreController.updateAssistantVectorStore) - -// DELETE -router.delete(['/', '/:id'], openaiAssistantsVectorStoreController.deleteAssistantVectorStore) - -// POST -router.post('/:id', upload.array('files'), openaiAssistantsVectorStoreController.uploadFilesToAssistantVectorStore) - -// DELETE -router.patch(['/', '/:id'], openaiAssistantsVectorStoreController.deleteFilesFromAssistantVectorStore) - -export default router diff --git a/studio-frontend/packages/server/src/routes/openai-assistants/index.ts b/studio-frontend/packages/server/src/routes/openai-assistants/index.ts deleted file mode 100644 index cfac2ea..0000000 --- a/studio-frontend/packages/server/src/routes/openai-assistants/index.ts +++ /dev/null @@ -1,15 +0,0 @@ -import express from 'express' -import openaiAssistantsController from '../../controllers/openai-assistants' -const router: express.Router = express.Router() - -// CREATE - -// READ -router.get('/', openaiAssistantsController.getAllOpenaiAssistants) -router.get(['/', '/:id'], openaiAssistantsController.getSingleOpenaiAssistant) - -// UPDATE - -// DELETE - -export default router diff --git a/studio-frontend/packages/server/src/routes/predictions/index.ts b/studio-frontend/packages/server/src/routes/predictions/index.ts deleted file mode 100644 index a8cf640..0000000 --- a/studio-frontend/packages/server/src/routes/predictions/index.ts +++ /dev/null @@ -1,13 +0,0 @@ -import express from 'express' -import multer from 'multer' -import path from 'path' -import predictionsController from '../../controllers/predictions' - -const router: express.Router = express.Router() - -const upload = multer({ dest: `${path.join(__dirname, '..', '..', '..', 'uploads')}/` }) - -// CREATE -router.post(['/', '/:id'], upload.array('files'), predictionsController.getRateLimiterMiddleware, predictionsController.createPrediction) - -export default router diff --git a/studio-frontend/packages/server/src/routes/prompts-lists/index.ts b/studio-frontend/packages/server/src/routes/prompts-lists/index.ts deleted file mode 100644 index 51800a4..0000000 --- a/studio-frontend/packages/server/src/routes/prompts-lists/index.ts +++ /dev/null @@ -1,8 +0,0 @@ -import express from 'express' -import promptsListController from '../../controllers/prompts-lists' -const router: express.Router = express.Router() - -// CREATE -router.post('/', promptsListController.createPromptsList) - -export default router diff --git a/studio-frontend/packages/server/src/routes/public-chatbots/index.ts b/studio-frontend/packages/server/src/routes/public-chatbots/index.ts deleted file mode 100644 index 7750fe7..0000000 --- a/studio-frontend/packages/server/src/routes/public-chatbots/index.ts +++ /dev/null @@ -1,14 +0,0 @@ -import express from 'express' -import chatflowsController from '../../controllers/chatflows' -const router: express.Router = express.Router() - -// CREATE - -// READ -router.get(['/', '/:id'], chatflowsController.getSinglePublicChatbotConfig) - -// UPDATE - -// DELETE - -export default router diff --git a/studio-frontend/packages/server/src/routes/public-chatflows/index.ts b/studio-frontend/packages/server/src/routes/public-chatflows/index.ts deleted file mode 100644 index 31c930f..0000000 --- a/studio-frontend/packages/server/src/routes/public-chatflows/index.ts +++ /dev/null @@ -1,14 +0,0 @@ -import express from 'express' -import chatflowsController from '../../controllers/chatflows' -const router: express.Router = express.Router() - -// CREATE - -// READ -router.get(['/', '/:id'], chatflowsController.getSinglePublicChatflow) - -// UPDATE - -// DELETE - -export default router diff --git a/studio-frontend/packages/server/src/routes/stats/index.ts b/studio-frontend/packages/server/src/routes/stats/index.ts deleted file mode 100644 index 7708b3a..0000000 --- a/studio-frontend/packages/server/src/routes/stats/index.ts +++ /dev/null @@ -1,9 +0,0 @@ -import express from 'express' -import statsController from '../../controllers/stats' - -const router: express.Router = express.Router() - -// READ -router.get(['/', '/:id'], statsController.getChatflowStats) - -export default router diff --git a/studio-frontend/packages/server/src/routes/upsert-history/index.ts b/studio-frontend/packages/server/src/routes/upsert-history/index.ts deleted file mode 100644 index cdf5512..0000000 --- a/studio-frontend/packages/server/src/routes/upsert-history/index.ts +++ /dev/null @@ -1,15 +0,0 @@ -import express from 'express' -import upsertHistoryController from '../../controllers/upsert-history' -const router: express.Router = express.Router() - -// CREATE - -// READ -router.get(['/', '/:id'], upsertHistoryController.getAllUpsertHistory) - -// PATCH -router.patch('/', upsertHistoryController.patchDeleteUpsertHistory) - -// DELETE - -export default router diff --git a/studio-frontend/packages/server/src/routes/vectors/index.ts b/studio-frontend/packages/server/src/routes/vectors/index.ts deleted file mode 100644 index 843ea63..0000000 --- a/studio-frontend/packages/server/src/routes/vectors/index.ts +++ /dev/null @@ -1,19 +0,0 @@ -import express from 'express' -import multer from 'multer' -import path from 'path' -import vectorsController from '../../controllers/vectors' - -const router: express.Router = express.Router() - -const upload = multer({ dest: `${path.join(__dirname, '..', '..', '..', 'uploads')}/` }) - -// CREATE -router.post( - ['/upsert/', '/upsert/:id'], - upload.array('files'), - vectorsController.getRateLimiterMiddleware, - vectorsController.upsertVectorMiddleware -) -router.post(['/internal-upsert/', '/internal-upsert/:id'], upload.array('files'), vectorsController.createInternalUpsert) - -export default router diff --git a/studio-frontend/packages/server/src/services/assistants/index.ts b/studio-frontend/packages/server/src/services/assistants/index.ts deleted file mode 100644 index 10b176a..0000000 --- a/studio-frontend/packages/server/src/services/assistants/index.ts +++ /dev/null @@ -1,348 +0,0 @@ -import OpenAI from 'openai' -import { StatusCodes } from 'http-status-codes' -import { uniqWith, isEqual, cloneDeep } from 'lodash' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { Assistant } from '../../database/entities/Assistant' -import { Credential } from '../../database/entities/Credential' -import { decryptCredentialData, getAppVersion } from '../../utils' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' -import { DeleteResult } from 'typeorm' - -const createAssistant = async (requestBody: any): Promise => { - try { - const appServer = getRunningExpressApp() - if (!requestBody.details) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Invalid request body`) - } - const assistantDetails = JSON.parse(requestBody.details) - try { - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: requestBody.credential - }) - - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${requestBody.credential} not found`) - } - - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - const openai = new OpenAI({ apiKey: openAIApiKey }) - - // Prepare tools - let tools = [] - if (assistantDetails.tools) { - for (const tool of assistantDetails.tools ?? []) { - tools.push({ - type: tool - }) - } - } - - // Save tool_resources to be stored later into database - const savedToolResources = cloneDeep(assistantDetails.tool_resources) - - // Cleanup tool_resources for creating assistant - if (assistantDetails.tool_resources) { - for (const toolResource in assistantDetails.tool_resources) { - if (toolResource === 'file_search') { - assistantDetails.tool_resources['file_search'] = { - vector_store_ids: assistantDetails.tool_resources['file_search'].vector_store_ids - } - } else if (toolResource === 'code_interpreter') { - assistantDetails.tool_resources['code_interpreter'] = { - file_ids: assistantDetails.tool_resources['code_interpreter'].file_ids - } - } - } - } - - // If the assistant doesn't exist, create a new one - if (!assistantDetails.id) { - const newAssistant = await openai.beta.assistants.create({ - name: assistantDetails.name, - description: assistantDetails.description, - instructions: assistantDetails.instructions, - model: assistantDetails.model, - tools, - tool_resources: assistantDetails.tool_resources, - temperature: assistantDetails.temperature, - top_p: assistantDetails.top_p - }) - assistantDetails.id = newAssistant.id - } else { - const retrievedAssistant = await openai.beta.assistants.retrieve(assistantDetails.id) - let filteredTools = uniqWith([...retrievedAssistant.tools.filter((tool) => tool.type === 'function'), ...tools], isEqual) - // Remove empty functions - filteredTools = filteredTools.filter((tool) => !(tool.type === 'function' && !(tool as any).function)) - - await openai.beta.assistants.update(assistantDetails.id, { - name: assistantDetails.name, - description: assistantDetails.description ?? '', - instructions: assistantDetails.instructions ?? '', - model: assistantDetails.model, - tools: filteredTools, - tool_resources: assistantDetails.tool_resources, - temperature: assistantDetails.temperature, - top_p: assistantDetails.top_p - }) - } - - const newAssistantDetails = { - ...assistantDetails - } - if (savedToolResources) newAssistantDetails.tool_resources = savedToolResources - - requestBody.details = JSON.stringify(newAssistantDetails) - } catch (error) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error creating new assistant - ${getErrorMessage(error)}`) - } - const newAssistant = new Assistant() - Object.assign(newAssistant, requestBody) - - const assistant = appServer.AppDataSource.getRepository(Assistant).create(newAssistant) - const dbResponse = await appServer.AppDataSource.getRepository(Assistant).save(assistant) - - await appServer.telemetry.sendTelemetry('assistant_created', { - version: await getAppVersion(), - assistantId: dbResponse.id - }) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: assistantsService.createAssistant - ${getErrorMessage(error)}` - ) - } -} - -const deleteAssistant = async (assistantId: string, isDeleteBoth: any): Promise => { - try { - const appServer = getRunningExpressApp() - const assistant = await appServer.AppDataSource.getRepository(Assistant).findOneBy({ - id: assistantId - }) - if (!assistant) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Assistant ${assistantId} not found`) - } - try { - const assistantDetails = JSON.parse(assistant.details) - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: assistant.credential - }) - - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${assistant.credential} not found`) - } - - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await appServer.AppDataSource.getRepository(Assistant).delete({ id: assistantId }) - if (isDeleteBoth) await openai.beta.assistants.del(assistantDetails.id) - return dbResponse - } catch (error: any) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error deleting assistant - ${getErrorMessage(error)}`) - } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: assistantsService.deleteAssistant - ${getErrorMessage(error)}` - ) - } -} - -const getAllAssistants = async (): Promise => { - try { - const appServer = getRunningExpressApp() - const dbResponse = await appServer.AppDataSource.getRepository(Assistant).find() - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: assistantsService.getAllAssistants - ${getErrorMessage(error)}` - ) - } -} - -const getAssistantById = async (assistantId: string): Promise => { - try { - const appServer = getRunningExpressApp() - const dbResponse = await appServer.AppDataSource.getRepository(Assistant).findOneBy({ - id: assistantId - }) - if (!dbResponse) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Assistant ${assistantId} not found`) - } - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: assistantsService.getAssistantById - ${getErrorMessage(error)}` - ) - } -} - -const updateAssistant = async (assistantId: string, requestBody: any): Promise => { - try { - const appServer = getRunningExpressApp() - const assistant = await appServer.AppDataSource.getRepository(Assistant).findOneBy({ - id: assistantId - }) - - if (!assistant) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Assistant ${assistantId} not found`) - } - try { - const openAIAssistantId = JSON.parse(assistant.details)?.id - const body = requestBody - const assistantDetails = JSON.parse(body.details) - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: body.credential - }) - - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${body.credential} not found`) - } - - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - - let tools = [] - if (assistantDetails.tools) { - for (const tool of assistantDetails.tools ?? []) { - tools.push({ - type: tool - }) - } - } - - // Save tool_resources to be stored later into database - const savedToolResources = cloneDeep(assistantDetails.tool_resources) - - // Cleanup tool_resources before updating - if (assistantDetails.tool_resources) { - for (const toolResource in assistantDetails.tool_resources) { - if (toolResource === 'file_search') { - assistantDetails.tool_resources['file_search'] = { - vector_store_ids: assistantDetails.tool_resources['file_search'].vector_store_ids - } - } else if (toolResource === 'code_interpreter') { - assistantDetails.tool_resources['code_interpreter'] = { - file_ids: assistantDetails.tool_resources['code_interpreter'].file_ids - } - } - } - } - - const retrievedAssistant = await openai.beta.assistants.retrieve(openAIAssistantId) - let filteredTools = uniqWith([...retrievedAssistant.tools.filter((tool) => tool.type === 'function'), ...tools], isEqual) - filteredTools = filteredTools.filter((tool) => !(tool.type === 'function' && !(tool as any).function)) - - await openai.beta.assistants.update(openAIAssistantId, { - name: assistantDetails.name, - description: assistantDetails.description, - instructions: assistantDetails.instructions, - model: assistantDetails.model, - tools: filteredTools, - tool_resources: assistantDetails.tool_resources, - temperature: assistantDetails.temperature, - top_p: assistantDetails.top_p - }) - - const newAssistantDetails = { - ...assistantDetails, - id: openAIAssistantId - } - if (savedToolResources) newAssistantDetails.tool_resources = savedToolResources - - const updateAssistant = new Assistant() - body.details = JSON.stringify(newAssistantDetails) - Object.assign(updateAssistant, body) - - appServer.AppDataSource.getRepository(Assistant).merge(assistant, updateAssistant) - const dbResponse = await appServer.AppDataSource.getRepository(Assistant).save(assistant) - return dbResponse - } catch (error) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error updating assistant - ${getErrorMessage(error)}`) - } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: assistantsService.updateAssistant - ${getErrorMessage(error)}` - ) - } -} - -const importAssistants = async (newAssistants: Partial[]): Promise => { - try { - const appServer = getRunningExpressApp() - - // step 1 - check whether array is zero - if (newAssistants.length == 0) return - - // step 2 - check whether ids are duplicate in database - let ids = '(' - let count: number = 0 - const lastCount = newAssistants.length - 1 - newAssistants.forEach((newAssistant) => { - ids += `'${newAssistant.id}'` - if (lastCount != count) ids += ',' - if (lastCount == count) ids += ')' - count += 1 - }) - - const selectResponse = await appServer.AppDataSource.getRepository(Assistant) - .createQueryBuilder('assistant') - .select('assistant.id') - .where(`assistant.id IN ${ids}`) - .getMany() - const foundIds = selectResponse.map((response) => { - return response.id - }) - - // step 3 - remove ids that are only duplicate - const prepVariables: Partial[] = newAssistants.map((newAssistant) => { - let id: string = '' - if (newAssistant.id) id = newAssistant.id - if (foundIds.includes(id)) { - newAssistant.id = undefined - } - return newAssistant - }) - - // step 4 - transactional insert array of entities - const insertResponse = await appServer.AppDataSource.getRepository(Assistant).insert(prepVariables) - - return insertResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: variableService.importVariables - ${getErrorMessage(error)}` - ) - } -} - -export default { - createAssistant, - deleteAssistant, - getAllAssistants, - getAssistantById, - updateAssistant, - importAssistants -} diff --git a/studio-frontend/packages/server/src/services/chat-messages/index.ts b/studio-frontend/packages/server/src/services/chat-messages/index.ts index 9f2d53f..3f44418 100644 --- a/studio-frontend/packages/server/src/services/chat-messages/index.ts +++ b/studio-frontend/packages/server/src/services/chat-messages/index.ts @@ -5,7 +5,6 @@ import { utilGetChatMessage } from '../../utils/getChatMessage' import { utilAddChatMessage } from '../../utils/addChatMesage' import { getRunningExpressApp } from '../../utils/getRunningExpressApp' import { ChatMessageFeedback } from '../../database/entities/ChatMessageFeedback' -import { removeFilesFromStorage } from 'flowise-components' import logger from '../../utils/logger' import { ChatMessage } from '../../database/entities/ChatMessage' import { InternalFlowiseError } from '../../errors/internalFlowiseError' @@ -110,14 +109,6 @@ const removeAllChatMessages = async ( const feedbackDeleteOptions: FindOptionsWhere = { chatId } await appServer.AppDataSource.getRepository(ChatMessageFeedback).delete(feedbackDeleteOptions) - // Delete all uploads corresponding to this chatflow/chatId - if (chatId) { - try { - await removeFilesFromStorage(chatflowid, chatId) - } catch (e) { - logger.error(`[server]: Error deleting file storage for chatflow ${chatflowid}, chatId ${chatId}: ${e}`) - } - } const dbResponse = await appServer.AppDataSource.getRepository(ChatMessage).delete(deleteOptions) return dbResponse } catch (error) { diff --git a/studio-frontend/packages/server/src/services/chatflows/index.ts b/studio-frontend/packages/server/src/services/chatflows/index.ts index d5abbcd..e67134f 100644 --- a/studio-frontend/packages/server/src/services/chatflows/index.ts +++ b/studio-frontend/packages/server/src/services/chatflows/index.ts @@ -1,5 +1,6 @@ -import { removeFolderFromStorage } from 'flowise-components' import { StatusCodes } from 'http-status-codes' +import fs from 'fs/promises' +import path from 'path' import { ChatflowType, IReactFlowObject } from '../../Interface' import { ChatFlow } from '../../database/entities/ChatFlow' import { ChatMessage } from '../../database/entities/ChatMessage' @@ -7,92 +8,36 @@ import { ChatMessageFeedback } from '../../database/entities/ChatMessageFeedback import { UpsertHistory } from '../../database/entities/UpsertHistory' import { InternalFlowiseError } from '../../errors/internalFlowiseError' import { getErrorMessage } from '../../errors/utils' -import documentStoreService from '../../services/documentstore' -import { constructGraphs, getAppVersion, getEndingNodes, getTelemetryFlowObj, isFlowValidForStream } from '../../utils' -import { containsBase64File, updateFlowDataWithFilePaths } from '../../utils/fileRepository' +import { getAppVersion } from '../../utils' import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { utilGetUploadsConfig } from '../../utils/getUploadsConfig' import logger from '../../utils/logger' -import axios, { AxiosRequestConfig } from 'axios' -import { HttpsProxyAgent } from 'https-proxy-agent' - -// Configure github axios to support HTTP_PROXY/HTTPS_PROXY environment variables -const getGithubAxiosConfig = (): AxiosRequestConfig => { - const http_proxy = process.env.http_proxy || process.env.HTTP_PROXY - const agent = (http_proxy && http_proxy.trim() !== "") ? new HttpsProxyAgent(http_proxy) : undefined - - return { - headers: { - Accept: 'application/vnd.github.v3+json', - }, - proxy: false, - ...(agent && { - httpAgent: agent, - httpsAgent: agent, - }), - } -} +import axios from 'axios' -const STUDIO_SERVER_URL = process.env.STUDIO_SERVER_URL || 'http://studio-backend.studio.svc.cluster.local:5000' +const SAMPLE_WORKFLOWS_DIR = process.env.SAMPLE_WORKFLOWS_DIR || path.resolve(process.cwd(), '..', '..', 'sample-workflows') -// Check if chatflow valid for streaming -const checkIfChatflowIsValidForStreaming = async (chatflowId: string): Promise => { - try { - const appServer = getRunningExpressApp() - //** - const chatflow = await appServer.AppDataSource.getRepository(ChatFlow).findOneBy({ - id: chatflowId - }) - if (!chatflow) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Chatflow ${chatflowId} not found`) - } +const getStudioServerUrl = () => process.env.STUDIO_SERVER_URL || 'http://studio-backend.studio.svc.cluster.local:5000' - /*** Get Ending Node with Directed Graph ***/ - const flowData = chatflow.flowData - const parsedFlowData: IReactFlowObject = JSON.parse(flowData) - const nodes = parsedFlowData.nodes - const edges = parsedFlowData.edges - const { graph, nodeDependencies } = constructGraphs(nodes, edges) - - const endingNodes = getEndingNodes(nodeDependencies, graph, nodes) - - let isStreaming = false - for (const endingNode of endingNodes) { - const endingNodeData = endingNode.data - const isEndingNode = endingNodeData?.outputs?.output === 'EndingNode' - // Once custom function ending node exists, flow is always unavailable to stream - if (isEndingNode) { - return { isStreaming: false } - } - isStreaming = isFlowValidForStream(nodes, endingNodeData) - } +const loadLocalSampleChatflows = async (userid: string, type?: ChatflowType): Promise[]> => { + const files = (await fs.readdir(SAMPLE_WORKFLOWS_DIR)) + .filter((fileName) => fileName.endsWith('.json')) + .sort() - // If it is a Multi/Sequential Agents, always enable streaming - if (endingNodes.filter((node) => node.data.category === 'Multi Agents' || node.data.category === 'Sequential Agents').length > 0) { - return { isStreaming: true } - } + const chatflows: Partial[] = [] - const dbResponse = { isStreaming: isStreaming } - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: chatflowsService.checkIfChatflowIsValidForStreaming - ${getErrorMessage(error)}` - ) + for (const fileName of files) { + const filePath = path.join(SAMPLE_WORKFLOWS_DIR, fileName) + const parsedFlowData = JSON.parse(await fs.readFile(filePath, 'utf8')) + chatflows.push({ + userid, + name: fileName.replace('.json', ''), + flowData: JSON.stringify(parsedFlowData), + type: type || 'OPEA', + deployed: false, + isPublic: false + }) } -} -// Check if chatflow valid for uploads -const checkIfChatflowIsValidForUploads = async (chatflowId: string): Promise => { - try { - const dbResponse = await utilGetUploadsConfig(chatflowId) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: chatflowsService.checkIfChatflowIsValidForUploads - ${getErrorMessage(error)}` - ) - } + return chatflows } const deleteChatflow = async (chatflowId: string): Promise => { @@ -105,10 +50,6 @@ const deleteChatflow = async (chatflowId: string): Promise => { } const dbResponse = await appServer.AppDataSource.getRepository(ChatFlow).delete({ id: chatflowId }) try { - // Delete all uploads corresponding to this chatflow - await removeFolderFromStorage(chatflowId) - await documentStoreService.updateDocumentStoreUsage(chatflowId, undefined) - // Delete all chat messages await appServer.AppDataSource.getRepository(ChatMessage).delete({ chatflowid: chatflowId }) @@ -178,40 +119,17 @@ const getAllChatflowsbyUserId = async (userid: string, type?: ChatflowType): Pro const importSampleChatflowsbyUserId = async (userid: string, type?: ChatflowType): Promise => { try { - const axiosConfig = getGithubAxiosConfig() - - console.log('Importing sample chatflows for user:', userid); - - const response = await axios.get( - 'https://api.github.com/repos/opea-project/GenAIStudio/contents/sample-workflows', - axiosConfig - ); - - console.log('Response from GitHub:', response.data); - - const files = response.data.filter((item: any) => item.type === 'file'); - - const chatflows: Partial[] = []; - for (const file of files) { - const fileResponse = await axios.get(file.download_url, axiosConfig); - const parsedFlowData = fileResponse.data; - const newChatflow: Partial = { - userid: userid, - name: file.name.replace('.json', ''), - flowData: JSON.stringify(parsedFlowData), - type: 'OPEA', - deployed: false, - isPublic: false - }; - chatflows.push(newChatflow); - } - const insertResponse = await importChatflows(chatflows); - return insertResponse; + console.log('Importing sample chatflows for user:', userid) + const chatflows = await loadLocalSampleChatflows(userid, type) + logger.info(`[server]: Loaded ${chatflows.length} sample chatflows from local directory ${SAMPLE_WORKFLOWS_DIR}`) + + const insertResponse = await importChatflows(chatflows) + return insertResponse } catch (error) { throw new InternalFlowiseError( StatusCodes.INTERNAL_SERVER_ERROR, `Error: chatflowsService.importSampleChatflowsbyUserId - ${getErrorMessage(error)}` - ); + ) } } @@ -260,30 +178,8 @@ const getChatflowById = async (chatflowId: string): Promise => { const saveChatflow = async (newChatFlow: ChatFlow): Promise => { try { const appServer = getRunningExpressApp() - let dbResponse: ChatFlow - if (containsBase64File(newChatFlow)) { - // we need a 2-step process, as we need to save the chatflow first and then update the file paths - // this is because we need the chatflow id to create the file paths - - // step 1 - save with empty flowData - const incomingFlowData = newChatFlow.flowData - newChatFlow.flowData = JSON.stringify({}) - const chatflow = appServer.AppDataSource.getRepository(ChatFlow).create(newChatFlow) - const step1Results = await appServer.AppDataSource.getRepository(ChatFlow).save(chatflow) - - // step 2 - convert base64 to file paths and update the chatflow - step1Results.flowData = await updateFlowDataWithFilePaths(step1Results.id, incomingFlowData) - await _checkAndUpdateDocumentStoreUsage(step1Results) - dbResponse = await appServer.AppDataSource.getRepository(ChatFlow).save(step1Results) - } else { - const chatflow = appServer.AppDataSource.getRepository(ChatFlow).create(newChatFlow) - dbResponse = await appServer.AppDataSource.getRepository(ChatFlow).save(chatflow) - } - await appServer.telemetry.sendTelemetry('chatflow_created', { - version: await getAppVersion(), - chatflowId: dbResponse.id, - flowGraph: getTelemetryFlowObj(JSON.parse(dbResponse.flowData)?.nodes, JSON.parse(dbResponse.flowData)?.edges) - }) + const chatflow = appServer.AppDataSource.getRepository(ChatFlow).create(newChatFlow) + const dbResponse = await appServer.AppDataSource.getRepository(ChatFlow).save(chatflow) return dbResponse } catch (error) { throw new InternalFlowiseError( @@ -349,17 +245,9 @@ const importChatflows = async (newChatflows: Partial[]): Promise const updateChatflow = async (chatflow: ChatFlow, updateChatFlow: ChatFlow): Promise => { try { const appServer = getRunningExpressApp() - if (updateChatFlow.flowData && containsBase64File(updateChatFlow)) { - updateChatFlow.flowData = await updateFlowDataWithFilePaths(chatflow.id, updateChatFlow.flowData) - } const newDbChatflow = appServer.AppDataSource.getRepository(ChatFlow).merge(chatflow, updateChatFlow) - await _checkAndUpdateDocumentStoreUsage(newDbChatflow) const dbResponse = await appServer.AppDataSource.getRepository(ChatFlow).save(newDbChatflow) - - // chatFlowPool is initialized only when a flow is opened - // if the user attempts to rename/update category without opening any flow, chatFlowPool will be undefined if (appServer.chatflowPool) { - // Update chatflowpool inSync to false, to build flow from scratch again because data has been changed appServer.chatflowPool.updateInSync(chatflow.id, false) } return dbResponse @@ -407,13 +295,9 @@ const getSinglePublicChatbotConfig = async (chatflowId: string): Promise => if (!dbResponse) { throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Chatflow ${chatflowId} not found`) } - const uploadsConfig = await utilGetUploadsConfig(chatflowId) - // even if chatbotConfig is not set but uploads are enabled - // send uploadsConfig to the chatbot - if (dbResponse.chatbotConfig || uploadsConfig) { + if (dbResponse.chatbotConfig) { try { - const parsedConfig = dbResponse.chatbotConfig ? JSON.parse(dbResponse.chatbotConfig) : {} - return { ...parsedConfig, uploads: uploadsConfig } + return JSON.parse(dbResponse.chatbotConfig) } catch (e) { throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error parsing Chatbot Config for Chatflow ${chatflowId}`) } @@ -451,7 +335,7 @@ const deployChatflowSandboxService = async (chatflowId: string) => { console.log('deployChatflowSandboxService', chatflowId) try { const chatflow = await generatePipelineJson(chatflowId) - const studioServerUrl = STUDIO_SERVER_URL + const studioServerUrl = getStudioServerUrl() const deploySandboxEndpoint = 'studio-backend/deploy-sandbox' console.log('chatflow', JSON.stringify(chatflow)) console.log('studioServerUrl', studioServerUrl) @@ -475,7 +359,7 @@ const deployChatflowSandboxService = async (chatflowId: string) => { const stopChatflowSandboxService = async (chatflowId: string) => { console.log('stopChatflowSandboxService', chatflowId) try { - const studioServerUrl = STUDIO_SERVER_URL + const studioServerUrl = getStudioServerUrl() const deleteSandboxEndpoint = 'studio-backend/delete-sandbox' console.log('studioServerUrl', studioServerUrl) console.log('deleteSandboxEndpoint', deleteSandboxEndpoint) @@ -499,7 +383,7 @@ const buildDeploymentPackageService = async (chatflowId: string, deploymentConfi console.log('buildDeploymentPackageService', chatflowId, deploymentConfig) try { const chatflow = await generatePipelineJson(chatflowId) - const studioServerUrl = STUDIO_SERVER_URL + const studioServerUrl = getStudioServerUrl() const buildDeploymentPackageEndpoint = 'studio-backend/download-zip' console.log('chatflow', JSON.stringify(chatflow)) console.log('studioServerUrl', studioServerUrl) @@ -525,7 +409,7 @@ const oneClickDeploymentService = async (chatflowId: string, deploymentConfig: R console.log('oneClickDeploymentService', chatflowId, deploymentConfig) try { const chatflow = await generatePipelineJson(chatflowId) - const studioServerUrl = STUDIO_SERVER_URL + const studioServerUrl = getStudioServerUrl() const endpoint = 'studio-backend/click-deployment' // console.log('chatflow', JSON.stringify(chatflow)) // console.log('studioServerUrl', studioServerUrl) @@ -608,21 +492,7 @@ const updateDeploymentStatus = async (chatflowId: string, status: string, messag } } -const _checkAndUpdateDocumentStoreUsage = async (chatflow: ChatFlow) => { - const parsedFlowData: IReactFlowObject = JSON.parse(chatflow.flowData) - const nodes = parsedFlowData.nodes - // from the nodes array find if there is a node with name == documentStore) - const node = nodes.length > 0 && nodes.find((node) => node.data.name === 'documentStore') - if (!node || !node.data || !node.data.inputs || node.data.inputs['selectedStore'] === undefined) { - await documentStoreService.updateDocumentStoreUsage(chatflow.id, undefined) - } else { - await documentStoreService.updateDocumentStoreUsage(chatflow.id, node.data.inputs['selectedStore']) - } -} - export default { - checkIfChatflowIsValidForStreaming, - checkIfChatflowIsValidForUploads, deleteChatflow, getAllChatflows, getAllChatflowsbyUserId, @@ -636,7 +506,6 @@ export default { deployChatflowSandboxService, stopChatflowSandboxService, buildDeploymentPackageService, - getSinglePublicChatbotConfig, oneClickDeploymentService, updateDeploymentStatus } \ No newline at end of file diff --git a/studio-frontend/packages/server/src/services/documentstore/index.ts b/studio-frontend/packages/server/src/services/documentstore/index.ts deleted file mode 100644 index 53b3cef..0000000 --- a/studio-frontend/packages/server/src/services/documentstore/index.ts +++ /dev/null @@ -1,1275 +0,0 @@ -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { DocumentStore } from '../../database/entities/DocumentStore' -import { - addSingleFileToStorage, - getFileFromStorage, - ICommonObject, - IDocument, - removeFilesFromStorage, - removeSpecificFileFromStorage -} from 'flowise-components' -import { - chatType, - DocumentStoreStatus, - IDocumentStoreFileChunkPagedResponse, - IDocumentStoreLoader, - IDocumentStoreLoaderFile, - IDocumentStoreLoaderForPreview, - IDocumentStoreWhereUsed, - INodeData -} from '../../Interface' -import { DocumentStoreFileChunk } from '../../database/entities/DocumentStoreFileChunk' -import { v4 as uuidv4 } from 'uuid' -import { databaseEntities, getAppVersion, saveUpsertFlowData } from '../../utils' -import logger from '../../utils/logger' -import nodesService from '../nodes' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' -import { getErrorMessage } from '../../errors/utils' -import { ChatFlow } from '../../database/entities/ChatFlow' -import { Document } from '@langchain/core/documents' -import { App } from '../../index' -import { UpsertHistory } from '../../database/entities/UpsertHistory' -import { cloneDeep, omit } from 'lodash' -import telemetryService from '../telemetry' - -const DOCUMENT_STORE_BASE_FOLDER = 'docustore' - -const createDocumentStore = async (newDocumentStore: DocumentStore) => { - try { - const appServer = getRunningExpressApp() - const documentStore = appServer.AppDataSource.getRepository(DocumentStore).create(newDocumentStore) - const dbResponse = await appServer.AppDataSource.getRepository(DocumentStore).save(documentStore) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.createDocumentStore - ${getErrorMessage(error)}` - ) - } -} - -const getAllDocumentStores = async () => { - try { - const appServer = getRunningExpressApp() - const entities = await appServer.AppDataSource.getRepository(DocumentStore).find() - return entities - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getAllDocumentStores - ${getErrorMessage(error)}` - ) - } -} - -const getAllDocumentFileChunks = async () => { - try { - const appServer = getRunningExpressApp() - const entities = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).find() - return entities - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getAllDocumentFileChunks - ${getErrorMessage(error)}` - ) - } -} - -const deleteLoaderFromDocumentStore = async (storeId: string, loaderId: string) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Error: documentStoreServices.deleteLoaderFromDocumentStore - Document store ${storeId} not found` - ) - } - const existingLoaders = JSON.parse(entity.loaders) - const found = existingLoaders.find((uFile: IDocumentStoreLoader) => uFile.id === loaderId) - if (found) { - if (found.files?.length) { - for (const file of found.files) { - if (file.name) { - await removeSpecificFileFromStorage(DOCUMENT_STORE_BASE_FOLDER, storeId, file.name) - } - } - } - const index = existingLoaders.indexOf(found) - if (index > -1) { - existingLoaders.splice(index, 1) - } - // remove the chunks - await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).delete({ docId: found.id }) - - entity.loaders = JSON.stringify(existingLoaders) - const results = await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - return results - } else { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Unable to locate loader in Document Store ${entity.name}`) - } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.deleteLoaderFromDocumentStore - ${getErrorMessage(error)}` - ) - } -} - -const getDocumentStoreById = async (storeId: string) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Error: documentStoreServices.getDocumentStoreById - Document store ${storeId} not found` - ) - } - return entity - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getDocumentStoreById - ${getErrorMessage(error)}` - ) - } -} - -const getUsedChatflowNames = async (entity: DocumentStore) => { - try { - const appServer = getRunningExpressApp() - if (entity.whereUsed) { - const whereUsed = JSON.parse(entity.whereUsed) - const updatedWhereUsed: IDocumentStoreWhereUsed[] = [] - for (let i = 0; i < whereUsed.length; i++) { - const associatedChatflow = await appServer.AppDataSource.getRepository(ChatFlow).findOne({ - where: { id: whereUsed[i] }, - select: ['id', 'name'] - }) - if (associatedChatflow) { - updatedWhereUsed.push({ - id: whereUsed[i], - name: associatedChatflow.name - }) - } - } - return updatedWhereUsed - } - return [] - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getUsedChatflowNames - ${getErrorMessage(error)}` - ) - } -} - -// Get chunks for a specific loader or store -const getDocumentStoreFileChunks = async (storeId: string, fileId: string, pageNo: number = 1) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Error: documentStoreServices.getDocumentStoreById - Document store ${storeId} not found` - ) - } - const loaders = JSON.parse(entity.loaders) - - let found: IDocumentStoreLoader | undefined - if (fileId !== 'all') { - found = loaders.find((loader: IDocumentStoreLoader) => loader.id === fileId) - if (!found) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Error: documentStoreServices.getDocumentStoreById - Document file ${fileId} not found` - ) - } - } - let totalChars = 0 - loaders.forEach((loader: IDocumentStoreLoader) => { - totalChars += loader.totalChars - }) - if (found) { - found.totalChars = totalChars - found.id = fileId - found.status = entity.status - } - const PAGE_SIZE = 50 - const skip = (pageNo - 1) * PAGE_SIZE - const take = PAGE_SIZE - let whereCondition: any = { docId: fileId } - if (fileId === 'all') { - whereCondition = { storeId: storeId } - } - const count = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).count({ - where: whereCondition - }) - const chunksWithCount = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).find({ - skip, - take, - where: whereCondition, - order: { - chunkNo: 'ASC' - } - }) - - if (!chunksWithCount) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `File ${fileId} not found`) - } - - const response: IDocumentStoreFileChunkPagedResponse = { - chunks: chunksWithCount, - count: count, - file: found, - currentPage: pageNo, - storeName: entity.name, - description: entity.description - } - return response - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getDocumentStoreFileChunks - ${getErrorMessage(error)}` - ) - } -} - -const deleteDocumentStore = async (storeId: string) => { - try { - const appServer = getRunningExpressApp() - // delete all the chunks associated with the store - await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).delete({ - storeId: storeId - }) - // now delete the files associated with the store - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${storeId} not found`) - } - await removeFilesFromStorage(DOCUMENT_STORE_BASE_FOLDER, entity.id) - - // delete upsert history - await appServer.AppDataSource.getRepository(UpsertHistory).delete({ - chatflowid: storeId - }) - - // now delete the store - const tbd = await appServer.AppDataSource.getRepository(DocumentStore).delete({ - id: storeId - }) - - return { deleted: tbd.affected } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.deleteDocumentStore - ${getErrorMessage(error)}` - ) - } -} - -const deleteDocumentStoreFileChunk = async (storeId: string, docId: string, chunkId: string) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${storeId} not found`) - } - const loaders = JSON.parse(entity.loaders) - const found = loaders.find((ldr: IDocumentStoreLoader) => ldr.id === docId) - if (!found) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store loader ${docId} not found`) - } - - const tbdChunk = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).findOneBy({ - id: chunkId - }) - if (!tbdChunk) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document Chunk ${chunkId} not found`) - } - await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).delete(chunkId) - found.totalChunks-- - found.totalChars -= tbdChunk.pageContent.length - entity.loaders = JSON.stringify(loaders) - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - return getDocumentStoreFileChunks(storeId, docId) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.deleteDocumentStoreFileChunk - ${getErrorMessage(error)}` - ) - } -} - -const deleteVectorStoreFromStore = async (storeId: string) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${storeId} not found`) - } - - if (!entity.embeddingConfig) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Embedding for Document store ${storeId} not found`) - } - - if (!entity.vectorStoreConfig) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Vector Store for Document store ${storeId} not found`) - } - - if (!entity.recordManagerConfig) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Record Manager for Document Store ${storeId} is needed to delete data from Vector Store` - ) - } - - const options: ICommonObject = { - chatflowid: storeId, - appDataSource: appServer.AppDataSource, - databaseEntities, - logger - } - - // Get Record Manager Instance - const recordManagerConfig = JSON.parse(entity.recordManagerConfig) - const recordManagerObj = await _createRecordManagerObject( - appServer, - { recordManagerName: recordManagerConfig.name, recordManagerConfig: recordManagerConfig.config }, - options - ) - - // Get Embeddings Instance - const embeddingConfig = JSON.parse(entity.embeddingConfig) - const embeddingObj = await _createEmbeddingsObject( - appServer, - { embeddingName: embeddingConfig.name, embeddingConfig: embeddingConfig.config }, - options - ) - - // Get Vector Store Node Data - const vectorStoreConfig = JSON.parse(entity.vectorStoreConfig) - const vStoreNodeData = _createVectorStoreNodeData( - appServer, - { vectorStoreName: vectorStoreConfig.name, vectorStoreConfig: vectorStoreConfig.config }, - embeddingObj, - recordManagerObj - ) - - // Get Vector Store Instance - const vectorStoreObj = await _createVectorStoreObject( - appServer, - { vectorStoreName: vectorStoreConfig.name, vectorStoreConfig: vectorStoreConfig.config }, - vStoreNodeData - ) - const idsToDelete: string[] = [] // empty ids because we get it dynamically from the record manager - - // Call the delete method of the vector store - if (vectorStoreObj.vectorStoreMethods.delete) { - await vectorStoreObj.vectorStoreMethods.delete(vStoreNodeData, idsToDelete, options) - } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.deleteVectorStoreFromStore - ${getErrorMessage(error)}` - ) - } -} - -const editDocumentStoreFileChunk = async (storeId: string, docId: string, chunkId: string, content: string, metadata: ICommonObject) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${storeId} not found`) - } - const loaders = JSON.parse(entity.loaders) - const found = loaders.find((ldr: IDocumentStoreLoader) => ldr.id === docId) - if (!found) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store loader ${docId} not found`) - } - - const editChunk = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).findOneBy({ - id: chunkId - }) - if (!editChunk) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document Chunk ${chunkId} not found`) - } - found.totalChars -= editChunk.pageContent.length - editChunk.pageContent = content - editChunk.metadata = JSON.stringify(metadata) - found.totalChars += content.length - await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).save(editChunk) - entity.loaders = JSON.stringify(loaders) - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - return getDocumentStoreFileChunks(storeId, docId) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.editDocumentStoreFileChunk - ${getErrorMessage(error)}` - ) - } -} - -// Update documentStore -const updateDocumentStore = async (documentStore: DocumentStore, updatedDocumentStore: DocumentStore) => { - try { - const appServer = getRunningExpressApp() - const tmpUpdatedDocumentStore = appServer.AppDataSource.getRepository(DocumentStore).merge(documentStore, updatedDocumentStore) - const dbResponse = await appServer.AppDataSource.getRepository(DocumentStore).save(tmpUpdatedDocumentStore) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.updateDocumentStore - ${getErrorMessage(error)}` - ) - } -} - -const _saveFileToStorage = async (fileBase64: string, entity: DocumentStore) => { - const splitDataURI = fileBase64.split(',') - const filename = splitDataURI.pop()?.split(':')[1] ?? '' - const bf = Buffer.from(splitDataURI.pop() || '', 'base64') - const mimePrefix = splitDataURI.pop() - let mime = '' - if (mimePrefix) { - mime = mimePrefix.split(';')[0].split(':')[1] - } - await addSingleFileToStorage(mime, bf, filename, DOCUMENT_STORE_BASE_FOLDER, entity.id) - return { - id: uuidv4(), - name: filename, - mimePrefix: mime, - size: bf.length, - status: DocumentStoreStatus.NEW, - uploaded: new Date() - } -} - -const _splitIntoChunks = async (data: IDocumentStoreLoaderForPreview) => { - try { - const appServer = getRunningExpressApp() - let splitterInstance = null - if (data.splitterConfig && Object.keys(data.splitterConfig).length > 0) { - const nodeInstanceFilePath = appServer.nodesPool.componentNodes[data.splitterId].filePath as string - const nodeModule = await import(nodeInstanceFilePath) - const newNodeInstance = new nodeModule.nodeClass() - let nodeData = { - inputs: { ...data.splitterConfig }, - id: 'splitter_0' - } - splitterInstance = await newNodeInstance.init(nodeData) - } - const nodeInstanceFilePath = appServer.nodesPool.componentNodes[data.loaderId].filePath as string - const nodeModule = await import(nodeInstanceFilePath) - // doc loader configs - const nodeData = { - credential: data.credential || undefined, - inputs: { ...data.loaderConfig, textSplitter: splitterInstance }, - outputs: { output: 'document' } - } - const options: ICommonObject = { - chatflowid: uuidv4(), - appDataSource: appServer.AppDataSource, - databaseEntities, - logger - } - const docNodeInstance = new nodeModule.nodeClass() - let docs: IDocument[] = await docNodeInstance.init(nodeData, '', options) - return docs - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.splitIntoChunks - ${getErrorMessage(error)}` - ) - } -} - -const _normalizeFilePaths = async (data: IDocumentStoreLoaderForPreview, entity: DocumentStore | null) => { - const keys = Object.getOwnPropertyNames(data.loaderConfig) - let rehydrated = false - for (let i = 0; i < keys.length; i++) { - const input = data.loaderConfig[keys[i]] - if (!input) { - continue - } - if (typeof input !== 'string') { - continue - } - let documentStoreEntity: DocumentStore | null = entity - if (input.startsWith('FILE-STORAGE::')) { - if (!documentStoreEntity) { - const appServer = getRunningExpressApp() - documentStoreEntity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: data.storeId - }) - if (!documentStoreEntity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${data.storeId} not found`) - } - } - const fileName = input.replace('FILE-STORAGE::', '') - let files: string[] = [] - if (fileName.startsWith('[') && fileName.endsWith(']')) { - files = JSON.parse(fileName) - } else { - files = [fileName] - } - const loaders = JSON.parse(documentStoreEntity.loaders) - const currentLoader = loaders.find((ldr: IDocumentStoreLoader) => ldr.id === data.id) - if (currentLoader) { - const base64Files: string[] = [] - for (const file of files) { - const bf = await getFileFromStorage(file, DOCUMENT_STORE_BASE_FOLDER, documentStoreEntity.id) - // find the file entry that has the same name as the file - const uploadedFile = currentLoader.files.find((uFile: IDocumentStoreLoaderFile) => uFile.name === file) - const mimePrefix = 'data:' + uploadedFile.mimePrefix + ';base64' - const base64String = mimePrefix + ',' + bf.toString('base64') + `,filename:${file}` - base64Files.push(base64String) - } - data.loaderConfig[keys[i]] = JSON.stringify(base64Files) - rehydrated = true - } - } - } - data.rehydrated = rehydrated -} - -const previewChunks = async (data: IDocumentStoreLoaderForPreview) => { - try { - if (data.preview) { - if ( - data.loaderId === 'cheerioWebScraper' || - data.loaderId === 'puppeteerWebScraper' || - data.loaderId === 'playwrightWebScraper' - ) { - data.loaderConfig['limit'] = 3 - } - } - if (!data.rehydrated) { - await _normalizeFilePaths(data, null) - } - let docs = await _splitIntoChunks(data) - const totalChunks = docs.length - // if -1, return all chunks - if (data.previewChunkCount === -1) data.previewChunkCount = totalChunks - // return all docs if the user ask for more than we have - if (totalChunks <= data.previewChunkCount) data.previewChunkCount = totalChunks - // return only the first n chunks - if (totalChunks > data.previewChunkCount) docs = docs.slice(0, data.previewChunkCount) - - return { chunks: docs, totalChunks: totalChunks, previewChunkCount: data.previewChunkCount } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.previewChunks - ${getErrorMessage(error)}` - ) - } -} - -const processAndSaveChunks = async (data: IDocumentStoreLoaderForPreview) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: data.storeId - }) - if (!entity) { - throw new InternalFlowiseError( - StatusCodes.NOT_FOUND, - `Error: documentStoreServices.processAndSaveChunks - Document store ${data.storeId} not found` - ) - } - const existingLoaders = JSON.parse(entity.loaders) - const newLoaderId = data.id ?? uuidv4() - const found = existingLoaders.find((ldr: IDocumentStoreLoader) => ldr.id === newLoaderId) - if (found) { - const foundIndex = existingLoaders.findIndex((ldr: IDocumentStoreLoader) => ldr.id === newLoaderId) - - if (!data.loaderId) data.loaderId = found.loaderId - if (!data.loaderName) data.loaderName = found.loaderName - if (!data.loaderConfig) data.loaderConfig = found.loaderConfig - if (!data.splitterId) data.splitterId = found.splitterId - if (!data.splitterName) data.splitterName = found.splitterName - if (!data.splitterConfig) data.splitterConfig = found.splitterConfig - if (found.credential) { - data.credential = found.credential - } - - let loader: IDocumentStoreLoader = { - ...found, - loaderId: data.loaderId, - loaderName: data.loaderName, - loaderConfig: data.loaderConfig, - splitterId: data.splitterId, - splitterName: data.splitterName, - splitterConfig: data.splitterConfig, - totalChunks: 0, - totalChars: 0, - status: DocumentStoreStatus.SYNCING - } - if (data.credential) { - loader.credential = data.credential - } - - existingLoaders[foundIndex] = loader - entity.loaders = JSON.stringify(existingLoaders) - } else { - let loader: IDocumentStoreLoader = { - id: newLoaderId, - loaderId: data.loaderId, - loaderName: data.loaderName, - loaderConfig: data.loaderConfig, - splitterId: data.splitterId, - splitterName: data.splitterName, - splitterConfig: data.splitterConfig, - totalChunks: 0, - totalChars: 0, - status: DocumentStoreStatus.SYNCING - } - if (data.credential) { - loader.credential = data.credential - } - existingLoaders.push(loader) - entity.loaders = JSON.stringify(existingLoaders) - } - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - // this method will run async, will have to be moved to a worker thread - _saveChunksToStorage(data, entity, newLoaderId).then(() => {}) - return getDocumentStoreFileChunks(data.storeId as string, newLoaderId) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.processAndSaveChunks - ${getErrorMessage(error)}` - ) - } -} - -const _saveChunksToStorage = async (data: IDocumentStoreLoaderForPreview, entity: DocumentStore, newLoaderId: string) => { - const re = new RegExp('^data.*;base64', 'i') - - try { - const appServer = getRunningExpressApp() - //step 1: restore the full paths, if any - await _normalizeFilePaths(data, entity) - //step 2: split the file into chunks - previewChunks(data).then(async (response) => { - //step 3: remove all files associated with the loader - const existingLoaders = JSON.parse(entity.loaders) - const loader = existingLoaders.find((ldr: IDocumentStoreLoader) => ldr.id === newLoaderId) - if (data.id) { - const index = existingLoaders.indexOf(loader) - if (index > -1) { - existingLoaders.splice(index, 1) - if (!data.rehydrated) { - if (loader.files) { - loader.files.map(async (file: IDocumentStoreLoaderFile) => { - await removeSpecificFileFromStorage(DOCUMENT_STORE_BASE_FOLDER, entity.id, file.name) - }) - } - } - } - } - //step 4: save new file to storage - let filesWithMetadata = [] - const keys = Object.getOwnPropertyNames(data.loaderConfig) - for (let i = 0; i < keys.length; i++) { - const input = data.loaderConfig[keys[i]] - if (!input) { - continue - } - if (typeof input !== 'string') { - continue - } - if (input.startsWith('[') && input.endsWith(']')) { - const files = JSON.parse(input) - const fileNames: string[] = [] - for (let j = 0; j < files.length; j++) { - const file = files[j] - if (re.test(file)) { - const fileMetadata = await _saveFileToStorage(file, entity) - fileNames.push(fileMetadata.name) - filesWithMetadata.push(fileMetadata) - } - } - data.loaderConfig[keys[i]] = 'FILE-STORAGE::' + JSON.stringify(fileNames) - } else if (re.test(input)) { - const fileNames: string[] = [] - const fileMetadata = await _saveFileToStorage(input, entity) - fileNames.push(fileMetadata.name) - filesWithMetadata.push(fileMetadata) - data.loaderConfig[keys[i]] = 'FILE-STORAGE::' + JSON.stringify(fileNames) - break - } - } - //step 5: update with the new files and loaderConfig - if (filesWithMetadata.length > 0) { - loader.loaderConfig = data.loaderConfig - loader.files = filesWithMetadata - } - //step 6: update the loaders with the new loaderConfig - if (data.id) { - existingLoaders.push(loader) - } - //step 7: remove all previous chunks - await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).delete({ docId: newLoaderId }) - if (response.chunks) { - //step 8: now save the new chunks - const totalChars = response.chunks.reduce((acc, chunk) => { - if (chunk.pageContent) { - return acc + chunk.pageContent.length - } - return acc - }, 0) - response.chunks.map(async (chunk: IDocument, index: number) => { - const docChunk: DocumentStoreFileChunk = { - docId: newLoaderId, - storeId: data.storeId || '', - id: uuidv4(), - chunkNo: index + 1, - pageContent: chunk.pageContent, - metadata: JSON.stringify(chunk.metadata) - } - const dChunk = appServer.AppDataSource.getRepository(DocumentStoreFileChunk).create(docChunk) - await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).save(dChunk) - }) - // update the loader with the new metrics - loader.totalChunks = response.totalChunks - loader.totalChars = totalChars - } - loader.status = 'SYNC' - // have a flag and iterate over the loaders and update the entity status to SYNC - const allSynced = existingLoaders.every((ldr: IDocumentStoreLoader) => ldr.status === 'SYNC') - entity.status = allSynced ? DocumentStoreStatus.SYNC : DocumentStoreStatus.STALE - entity.loaders = JSON.stringify(existingLoaders) - //step 9: update the entity in the database - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - return - }) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices._saveChunksToStorage - ${getErrorMessage(error)}` - ) - } -} - -// Get all component nodes -const getDocumentLoaders = async () => { - const removeDocumentLoadersWithName = ['documentStore', 'vectorStoreToDocument', 'unstructuredFolderLoader', 'folderFiles'] - - try { - const dbResponse = await nodesService.getAllNodesForCategory('Document Loaders') - return dbResponse.filter((node) => !removeDocumentLoadersWithName.includes(node.name)) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getDocumentLoaders - ${getErrorMessage(error)}` - ) - } -} - -const updateDocumentStoreUsage = async (chatId: string, storeId: string | undefined) => { - try { - // find the document store - const appServer = getRunningExpressApp() - // find all entities that have the chatId in their whereUsed - const entities = await appServer.AppDataSource.getRepository(DocumentStore).find() - entities.map(async (entity: DocumentStore) => { - const whereUsed = JSON.parse(entity.whereUsed) - const found = whereUsed.find((w: string) => w === chatId) - if (found) { - if (!storeId) { - // remove the chatId from the whereUsed, as the store is being deleted - const index = whereUsed.indexOf(chatId) - if (index > -1) { - whereUsed.splice(index, 1) - entity.whereUsed = JSON.stringify(whereUsed) - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - } - } else if (entity.id === storeId) { - // do nothing, already found and updated - } else if (entity.id !== storeId) { - // remove the chatId from the whereUsed, as a new store is being used - const index = whereUsed.indexOf(chatId) - if (index > -1) { - whereUsed.splice(index, 1) - entity.whereUsed = JSON.stringify(whereUsed) - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - } - } - } else { - if (entity.id === storeId) { - // add the chatId to the whereUsed - whereUsed.push(chatId) - entity.whereUsed = JSON.stringify(whereUsed) - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - } - } - }) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.updateDocumentStoreUsage - ${getErrorMessage(error)}` - ) - } -} - -const updateVectorStoreConfigOnly = async (data: ICommonObject) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: data.storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${data.storeId} not found`) - } - - if (data.vectorStoreName) { - entity.vectorStoreConfig = JSON.stringify({ - config: data.vectorStoreConfig, - name: data.vectorStoreName - }) - - const updatedEntity = await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - return updatedEntity - } - return {} - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.updateVectorStoreConfig - ${getErrorMessage(error)}` - ) - } -} -const saveVectorStoreConfig = async (data: ICommonObject) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: data.storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Document store ${data.storeId} not found`) - } - - if (data.embeddingName) { - entity.embeddingConfig = JSON.stringify({ - config: data.embeddingConfig, - name: data.embeddingName - }) - } else if (entity.embeddingConfig && !data.embeddingName && !data.embeddingConfig) { - data.embeddingConfig = JSON.parse(entity.embeddingConfig)?.config - data.embeddingName = JSON.parse(entity.embeddingConfig)?.name - } else if (!data.embeddingName && !data.embeddingConfig) { - entity.embeddingConfig = null - } - - if (data.vectorStoreName) { - entity.vectorStoreConfig = JSON.stringify({ - config: data.vectorStoreConfig, - name: data.vectorStoreName - }) - } else if (entity.vectorStoreConfig && !data.vectorStoreName && !data.vectorStoreConfig) { - data.vectorStoreConfig = JSON.parse(entity.vectorStoreConfig)?.config - data.vectorStoreName = JSON.parse(entity.vectorStoreConfig)?.name - } else if (!data.vectorStoreName && !data.vectorStoreConfig) { - entity.vectorStoreConfig = null - } - - if (data.recordManagerName) { - entity.recordManagerConfig = JSON.stringify({ - config: data.recordManagerConfig, - name: data.recordManagerName - }) - } else if (entity.recordManagerConfig && !data.recordManagerName && !data.recordManagerConfig) { - data.recordManagerConfig = JSON.parse(entity.recordManagerConfig)?.config - data.recordManagerName = JSON.parse(entity.recordManagerConfig)?.name - } else if (!data.recordManagerName && !data.recordManagerConfig) { - entity.recordManagerConfig = null - } - - if (entity.status !== DocumentStoreStatus.UPSERTED && (data.vectorStoreName || data.recordManagerName || data.embeddingName)) { - // if the store is not already in sync, mark it as sync - // this also means that the store is not yet sync'ed to vector store - entity.status = DocumentStoreStatus.SYNC - } - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - return entity - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.saveVectorStoreConfig - ${getErrorMessage(error)}` - ) - } -} - -const insertIntoVectorStore = async (data: ICommonObject) => { - try { - const appServer = getRunningExpressApp() - const entity = await saveVectorStoreConfig(data) - entity.status = DocumentStoreStatus.UPSERTING - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - - // TODO: to be moved into a worker thread... - const indexResult = await _insertIntoVectorStoreWorkerThread(data) - return indexResult - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.insertIntoVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const _insertIntoVectorStoreWorkerThread = async (data: ICommonObject) => { - try { - const appServer = getRunningExpressApp() - const entity = await saveVectorStoreConfig(data) - let upsertHistory: Record = {} - const chatflowid = data.storeId // fake chatflowid because this is not tied to any chatflow - - const options: ICommonObject = { - chatflowid, - appDataSource: appServer.AppDataSource, - databaseEntities, - logger - } - - let recordManagerObj = undefined - - // Get Record Manager Instance - if (data.recordManagerName && data.recordManagerConfig) { - recordManagerObj = await _createRecordManagerObject(appServer, data, options, upsertHistory) - } - - // Get Embeddings Instance - const embeddingObj = await _createEmbeddingsObject(appServer, data, options, upsertHistory) - - // Get Vector Store Node Data - const vStoreNodeData = _createVectorStoreNodeData(appServer, data, embeddingObj, recordManagerObj) - // Prepare docs for upserting - const chunks = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).find({ - where: { - storeId: data.storeId - } - }) - const docs: Document[] = chunks.map((chunk: DocumentStoreFileChunk) => { - return new Document({ - pageContent: chunk.pageContent, - metadata: JSON.parse(chunk.metadata) - }) - }) - vStoreNodeData.inputs.document = docs - - // Get Vector Store Instance - const vectorStoreObj = await _createVectorStoreObject(appServer, data, vStoreNodeData, upsertHistory) - const indexResult = await vectorStoreObj.vectorStoreMethods.upsert(vStoreNodeData, options) - - // Save to DB - if (indexResult) { - const result = cloneDeep(upsertHistory) - result['flowData'] = JSON.stringify(result['flowData']) - result['result'] = JSON.stringify(omit(indexResult, ['totalKeys', 'addedDocs'])) - result.chatflowid = chatflowid - const newUpsertHistory = new UpsertHistory() - Object.assign(newUpsertHistory, result) - const upsertHistoryItem = appServer.AppDataSource.getRepository(UpsertHistory).create(newUpsertHistory) - await appServer.AppDataSource.getRepository(UpsertHistory).save(upsertHistoryItem) - } - - await telemetryService.createEvent({ - name: `vector_upserted`, - data: { - version: await getAppVersion(), - chatlowId: chatflowid, - type: chatType.INTERNAL, - flowGraph: omit(indexResult['result'], ['totalKeys', 'addedDocs']) - } - }) - - entity.status = DocumentStoreStatus.UPSERTED - await appServer.AppDataSource.getRepository(DocumentStore).save(entity) - - return indexResult ?? { result: 'Successfully Upserted' } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices._insertIntoVectorStoreWorkerThread - ${getErrorMessage(error)}` - ) - } -} - -// Get all component nodes - Embeddings -const getEmbeddingProviders = async () => { - try { - const dbResponse = await nodesService.getAllNodesForCategory('Embeddings') - return dbResponse.filter((node) => !node.tags?.includes('LlamaIndex')) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getEmbeddingProviders - ${getErrorMessage(error)}` - ) - } -} - -// Get all component nodes - Vector Stores -const getVectorStoreProviders = async () => { - try { - const dbResponse = await nodesService.getAllNodesForCategory('Vector Stores') - return dbResponse.filter((node) => !node.tags?.includes('LlamaIndex') && node.name !== 'documentStoreVS') - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getVectorStoreProviders - ${getErrorMessage(error)}` - ) - } -} -// Get all component nodes - Vector Stores -const getRecordManagerProviders = async () => { - try { - const dbResponse = await nodesService.getAllNodesForCategory('Record Manager') - return dbResponse.filter((node) => !node.tags?.includes('LlamaIndex')) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.getRecordManagerProviders - ${getErrorMessage(error)}` - ) - } -} - -const queryVectorStore = async (data: ICommonObject) => { - try { - const appServer = getRunningExpressApp() - const entity = await appServer.AppDataSource.getRepository(DocumentStore).findOneBy({ - id: data.storeId - }) - if (!entity) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Document store ${data.storeId} not found`) - } - const options: ICommonObject = { - chatflowid: uuidv4(), - appDataSource: appServer.AppDataSource, - databaseEntities, - logger - } - - if (!entity.embeddingConfig) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Embedding for ${data.storeId} is not configured`) - } - - if (!entity.vectorStoreConfig) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Vector Store for ${data.storeId} is not configured`) - } - - const embeddingConfig = JSON.parse(entity.embeddingConfig) - data.embeddingName = embeddingConfig.name - data.embeddingConfig = embeddingConfig.config - let embeddingObj = await _createEmbeddingsObject(appServer, data, options) - - const vsConfig = JSON.parse(entity.vectorStoreConfig) - data.vectorStoreName = vsConfig.name - data.vectorStoreConfig = vsConfig.config - if (data.inputs) { - data.vectorStoreConfig = { ...vsConfig.config, ...data.inputs } - } - - const vStoreNodeData = _createVectorStoreNodeData(appServer, data, embeddingObj, undefined) - - // Get Vector Store Instance - const vectorStoreObj = await _createVectorStoreObject(appServer, data, vStoreNodeData) - const retriever = await vectorStoreObj.init(vStoreNodeData, '', options) - if (!retriever) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Failed to create retriever`) - } - const startMillis = Date.now() - const results = await retriever.invoke(data.query, undefined) - if (!results) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Failed to retrieve results`) - } - const endMillis = Date.now() - const timeTaken = endMillis - startMillis - const docs: any = results.map((result: IDocument) => { - return { - pageContent: result.pageContent, - metadata: result.metadata, - id: uuidv4() - } - }) - // query our document store chunk with the storeId and pageContent - for (const doc of docs) { - const documentStoreChunk = await appServer.AppDataSource.getRepository(DocumentStoreFileChunk).findOneBy({ - storeId: data.storeId, - pageContent: doc.pageContent - }) - if (documentStoreChunk) { - doc.id = documentStoreChunk.id - doc.chunkNo = documentStoreChunk.chunkNo - } else { - // this should not happen, only possible if the vector store has more content - // than our document store - doc.id = uuidv4() - doc.chunkNo = -1 - } - } - - return { - timeTaken: timeTaken, - docs: docs - } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: documentStoreServices.queryVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const _createEmbeddingsObject = async ( - appServer: App, - data: ICommonObject, - options: ICommonObject, - upsertHistory?: Record -): Promise => { - // prepare embedding node data - const embeddingComponent = appServer.nodesPool.componentNodes[data.embeddingName] - const embeddingNodeData: any = { - inputs: { ...data.embeddingConfig }, - outputs: { output: 'document' }, - id: `${embeddingComponent.name}_0`, - label: embeddingComponent.label, - name: embeddingComponent.name, - category: embeddingComponent.category, - inputParams: embeddingComponent.inputs || [] - } - if (data.embeddingConfig.credential) { - embeddingNodeData.credential = data.embeddingConfig.credential - } - - // save to upsert history - if (upsertHistory) upsertHistory['flowData'] = saveUpsertFlowData(embeddingNodeData, upsertHistory) - - // init embedding object - const embeddingNodeInstanceFilePath = embeddingComponent.filePath as string - const embeddingNodeModule = await import(embeddingNodeInstanceFilePath) - const embeddingNodeInstance = new embeddingNodeModule.nodeClass() - const embeddingObj = await embeddingNodeInstance.init(embeddingNodeData, '', options) - if (!embeddingObj) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Failed to create EmbeddingObj`) - } - return embeddingObj -} - -const _createRecordManagerObject = async ( - appServer: App, - data: ICommonObject, - options: ICommonObject, - upsertHistory?: Record -) => { - // prepare record manager node data - const recordManagerComponent = appServer.nodesPool.componentNodes[data.recordManagerName] - const rmNodeData: any = { - inputs: { ...data.recordManagerConfig }, - id: `${recordManagerComponent.name}_0`, - inputParams: recordManagerComponent.inputs, - label: recordManagerComponent.label, - name: recordManagerComponent.name, - category: recordManagerComponent.category - } - if (data.recordManagerConfig.credential) { - rmNodeData.credential = data.recordManagerConfig.credential - } - - // save to upsert history - if (upsertHistory) upsertHistory['flowData'] = saveUpsertFlowData(rmNodeData, upsertHistory) - - // init record manager object - const rmNodeInstanceFilePath = recordManagerComponent.filePath as string - const rmNodeModule = await import(rmNodeInstanceFilePath) - const rmNodeInstance = new rmNodeModule.nodeClass() - const recordManagerObj = await rmNodeInstance.init(rmNodeData, '', options) - if (!recordManagerObj) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Failed to create RecordManager obj`) - } - return recordManagerObj -} - -const _createVectorStoreNodeData = (appServer: App, data: ICommonObject, embeddingObj: any, recordManagerObj?: any) => { - const vectorStoreComponent = appServer.nodesPool.componentNodes[data.vectorStoreName] - const vStoreNodeData: any = { - id: `${vectorStoreComponent.name}_0`, - inputs: { ...data.vectorStoreConfig }, - outputs: { output: 'retriever' }, - label: vectorStoreComponent.label, - name: vectorStoreComponent.name, - category: vectorStoreComponent.category - } - if (data.vectorStoreConfig.credential) { - vStoreNodeData.credential = data.vectorStoreConfig.credential - } - - if (embeddingObj) { - vStoreNodeData.inputs.embeddings = embeddingObj - } - - if (recordManagerObj) { - vStoreNodeData.inputs.recordManager = recordManagerObj - } - - // Get all input params except the ones that are anchor points to avoid JSON stringify circular error - const filterInputParams = ['document', 'embeddings', 'recordManager'] - const inputParams = vectorStoreComponent.inputs?.filter((input) => !filterInputParams.includes(input.name)) - vStoreNodeData.inputParams = inputParams - return vStoreNodeData -} - -const _createVectorStoreObject = async ( - appServer: App, - data: ICommonObject, - vStoreNodeData: INodeData, - upsertHistory?: Record -) => { - const vStoreNodeInstanceFilePath = appServer.nodesPool.componentNodes[data.vectorStoreName].filePath as string - const vStoreNodeModule = await import(vStoreNodeInstanceFilePath) - const vStoreNodeInstance = new vStoreNodeModule.nodeClass() - if (upsertHistory) upsertHistory['flowData'] = saveUpsertFlowData(vStoreNodeData, upsertHistory) - return vStoreNodeInstance -} - -export default { - updateDocumentStoreUsage, - deleteDocumentStore, - createDocumentStore, - deleteLoaderFromDocumentStore, - getAllDocumentStores, - getAllDocumentFileChunks, - getDocumentStoreById, - getUsedChatflowNames, - getDocumentStoreFileChunks, - updateDocumentStore, - previewChunks, - processAndSaveChunks, - deleteDocumentStoreFileChunk, - editDocumentStoreFileChunk, - getDocumentLoaders, - insertIntoVectorStore, - getEmbeddingProviders, - getVectorStoreProviders, - getRecordManagerProviders, - saveVectorStoreConfig, - queryVectorStore, - deleteVectorStoreFromStore, - updateVectorStoreConfigOnly -} diff --git a/studio-frontend/packages/server/src/services/evaluationUploads/index.ts b/studio-frontend/packages/server/src/services/evaluationUploads/index.ts new file mode 100644 index 0000000..a06f8f0 --- /dev/null +++ b/studio-frontend/packages/server/src/services/evaluationUploads/index.ts @@ -0,0 +1,173 @@ +import path from 'path' + +type DatasetEntryPayload = { + input: string + expected_output?: string + context?: string[] +} + +type DatasetCreatePayload = { + name: string + description?: string + entries: DatasetEntryPayload[] +} + +const DATASET_FILE_TYPES = new Set(['.json', '.jsonl']) +const DATASET_INPUT_KEYS = ['input', 'question', 'prompt', 'query'] +const DATASET_EXPECTED_KEYS = ['expected_output', 'expectedOutput', 'expected', 'answer', 'ground_truth', 'groundTruth'] +const DATASET_CONTEXT_KEYS = ['context', 'contexts', 'retrieval_context', 'retrievalContexts', 'reference_context'] + +const getStringField = (value: unknown): string => { + if (typeof value !== 'string') return '' + return value.trim() +} + +const deriveNameFromFilename = (filename: string): string => path.basename(filename, path.extname(filename)).trim() + +const getExtension = (filename: string): string => path.extname(filename || '').toLowerCase() + +const normalizeWhitespace = (value: string): string => value.replace(/\r\n/g, '\n').replace(/\uFEFF/g, '').trim() + +const getFirstNonEmptyValue = (record: Record, keys: string[]): string => { + for (const key of keys) { + const value = record[key] + if (typeof value === 'string' && value.trim()) return value.trim() + } + return '' +} + +const normalizeContext = (value: unknown): string[] | undefined => { + if (value == null) return undefined + + if (Array.isArray(value)) { + const normalized = value + .map((item) => (typeof item === 'string' ? item.trim() : String(item || '').trim())) + .filter(Boolean) + return normalized.length ? normalized : undefined + } + + if (typeof value === 'string') { + const trimmed = value.trim() + if (!trimmed) return undefined + + try { + const parsed = JSON.parse(trimmed) + if (Array.isArray(parsed)) return normalizeContext(parsed) + } catch { + return [trimmed] + } + + return [trimmed] + } + + return [String(value).trim()].filter(Boolean) +} + +const ensureDatasetExtension = (filename: string) => { + const extension = getExtension(filename) + if (!DATASET_FILE_TYPES.has(extension)) { + throw new Error('Dataset upload must be a .json or .jsonl file.') + } + return extension +} + +const normalizeDatasetEntry = (item: unknown, index: number): DatasetEntryPayload => { + if (typeof item === 'string') { + const input = item.trim() + if (!input) throw new Error(`Dataset entry ${index + 1} is empty.`) + return { input } + } + + if (!item || typeof item !== 'object' || Array.isArray(item)) { + throw new Error(`Dataset entry ${index + 1} must be a JSON object.`) + } + + const record = item as Record + const input = getFirstNonEmptyValue(record, DATASET_INPUT_KEYS) + if (!input) { + throw new Error(`Dataset entry ${index + 1} is missing an input field.`) + } + + const expectedOutput = getFirstNonEmptyValue(record, DATASET_EXPECTED_KEYS) + let context: string[] | undefined + for (const key of DATASET_CONTEXT_KEYS) { + context = normalizeContext(record[key]) + if (context?.length) break + } + + return { + input, + ...(expectedOutput ? { expected_output: expectedOutput } : {}), + ...(context?.length ? { context } : {}) + } +} + +const parseJsonDatasetEntries = (rawText: string): { name?: string; description?: string; entries: DatasetEntryPayload[] } => { + const parsed = JSON.parse(rawText) + const topLevel = parsed && typeof parsed === 'object' && !Array.isArray(parsed) ? (parsed as Record) : null + + let rawEntries: unknown[] = [] + if (Array.isArray(parsed)) { + rawEntries = parsed + } else if (topLevel && Array.isArray(topLevel.entries)) { + rawEntries = topLevel.entries + } else if (topLevel && Array.isArray(topLevel.items)) { + rawEntries = topLevel.items + } else if (topLevel && Array.isArray(topLevel.data)) { + rawEntries = topLevel.data + } else if (topLevel) { + rawEntries = [topLevel] + } + + if (!rawEntries.length) { + throw new Error('Dataset file does not contain any entries.') + } + + return { + name: topLevel ? getStringField(topLevel.name) || undefined : undefined, + description: topLevel ? getStringField(topLevel.description) || undefined : undefined, + entries: rawEntries.map((entry, index) => normalizeDatasetEntry(entry, index)) + } +} + +const parseJsonlDatasetEntries = (rawText: string): DatasetEntryPayload[] => { + const lines = rawText + .split(/\r?\n/) + .map((line) => line.trim()) + .filter(Boolean) + + if (!lines.length) { + throw new Error('Dataset JSONL file is empty.') + } + + return lines.map((line, index) => { + try { + return normalizeDatasetEntry(JSON.parse(line), index) + } catch (error) { + const message = error instanceof Error ? error.message : 'Invalid JSONL entry.' + throw new Error(`Invalid JSONL on line ${index + 1}: ${message}`) + } + }) +} + +export const parseDatasetUpload = (file: Express.Multer.File, fields: Record): DatasetCreatePayload => { + const extension = ensureDatasetExtension(file.originalname) + const rawText = normalizeWhitespace(file.buffer.toString('utf8')) + if (!rawText) { + throw new Error('Uploaded dataset file is empty.') + } + + const parsed = extension === '.jsonl' ? { entries: parseJsonlDatasetEntries(rawText) } : parseJsonDatasetEntries(rawText) + const name = getStringField(fields.name) || parsed.name || deriveNameFromFilename(file.originalname) + if (!name) { + throw new Error('Dataset name is required.') + } + + const description = getStringField(fields.description) || parsed.description + + return { + name, + ...(description ? { description } : {}), + entries: parsed.entries + } +} \ No newline at end of file diff --git a/studio-frontend/packages/server/src/services/export-import/index.ts b/studio-frontend/packages/server/src/services/export-import/index.ts index 9899d24..568dde0 100644 --- a/studio-frontend/packages/server/src/services/export-import/index.ts +++ b/studio-frontend/packages/server/src/services/export-import/index.ts @@ -2,21 +2,18 @@ import { StatusCodes } from 'http-status-codes' import { ChatFlow } from '../../database/entities/ChatFlow' import { Tool } from '../../database/entities/Tool' import { Variable } from '../../database/entities/Variable' -import { Assistant } from '../../database/entities/Assistant' import { InternalFlowiseError } from '../../errors/internalFlowiseError' import { getErrorMessage } from '../../errors/utils' import { getRunningExpressApp } from '../../utils/getRunningExpressApp' import chatflowService from '../chatflows' import toolsService from '../tools' import variableService from '../variables' -import assistantService from '../assistants' type ExportInput = { tool: boolean chatflow: boolean agentflow: boolean variable: boolean - assistant: boolean } type ExportData = { @@ -24,7 +21,6 @@ type ExportData = { ChatFlow: ChatFlow[] AgentFlow: ChatFlow[] Variable: Variable[] - Assistant: Assistant[] } const convertExportInput = (body: any): ExportInput => { @@ -62,16 +58,12 @@ const exportData = async (exportInput: ExportInput): Promise<{ FileDefaultName: let allVars: Variable[] = [] if (exportInput.variable === true) allVars = await variableService.getAllVariables() - let allAssistants: Assistant[] = [] - if (exportInput.assistant === true) allAssistants = await assistantService.getAllAssistants() - return { FileDefaultName, Tool: allTool, ChatFlow: allChatflow, AgentFlow: allMultiAgent, - Variable: allVars, - Assistant: allAssistants + Variable: allVars } } catch (error) { throw new InternalFlowiseError( @@ -96,7 +88,6 @@ const importData = async (importData: ExportData) => { // step 3 - importAgentlows if (importData.AgentFlow.length > 0) await chatflowService.importChatflows(importData.AgentFlow) if (importData.Variable.length > 0) await variableService.importVariables(importData.Variable) - if (importData.Assistant.length > 0) await assistantService.importAssistants(importData.Assistant) queryRunner.commitTransaction() } catch (error) { queryRunner.rollbackTransaction() diff --git a/studio-frontend/packages/server/src/services/fetch-links/index.ts b/studio-frontend/packages/server/src/services/fetch-links/index.ts deleted file mode 100644 index 53c6e94..0000000 --- a/studio-frontend/packages/server/src/services/fetch-links/index.ts +++ /dev/null @@ -1,34 +0,0 @@ -import { webCrawl, xmlScrape } from 'flowise-components' -import { StatusCodes } from 'http-status-codes' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const getAllLinks = async (requestUrl: string, relativeLinksMethod: string, queryLimit: string): Promise => { - try { - const url = decodeURIComponent(requestUrl) - if (!relativeLinksMethod) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Please choose a Relative Links Method in Additional Parameters!` - ) - } - const limit = parseInt(queryLimit) - if (process.env.DEBUG === 'true') console.info(`Start ${relativeLinksMethod}`) - const links: string[] = relativeLinksMethod === 'webCrawl' ? await webCrawl(url, limit) : await xmlScrape(url, limit) - if (process.env.DEBUG === 'true') console.info(`Finish ${relativeLinksMethod}`) - const dbResponse = { - status: 'OK', - links - } - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: fetchLinksService.getAllLinks - ${getErrorMessage(error)}` - ) - } -} - -export default { - getAllLinks -} diff --git a/studio-frontend/packages/server/src/services/finetuning/index.ts b/studio-frontend/packages/server/src/services/finetuning/index.ts index da74ff9..c5445a7 100644 --- a/studio-frontend/packages/server/src/services/finetuning/index.ts +++ b/studio-frontend/packages/server/src/services/finetuning/index.ts @@ -19,8 +19,26 @@ declare function clearTimeout(id: any): void const execAsync = promisify(exec) -const FINETUNING_SERVICE_URL = process.env.FINETUNING_HOST ? `http://${process.env.FINETUNING_HOST}:8015` : 'undefined' +const normalizeFinetuningUrl = (rawValue: string | undefined) => { + if (!rawValue || rawValue.trim() === '') return undefined + + const withScheme = /^https?:\/\//i.test(rawValue) ? rawValue : `http://${rawValue}` + const url = new URL(withScheme) + + url.pathname = '' + url.search = '' + url.hash = '' + + return url +} + +const finetuningServiceUrl = normalizeFinetuningUrl(process.env.FINETUNING_SERVICE_DNS) +const finetuningRayUrl = normalizeFinetuningUrl(process.env.FINETUNING_RAY_DNS) + +const FINETUNING_SERVICE_URL = finetuningServiceUrl?.toString().replace(/\/$/, '') || 'undefined' +const FINETUNING_RAY_URL = finetuningRayUrl?.toString().replace(/\/$/, '') || 'undefined' console.debug('finetuningService - FINETUNING_SERVICE_URL', FINETUNING_SERVICE_URL) +console.debug('finetuningService - FINETUNING_RAY_URL', FINETUNING_RAY_URL) // Create an axios client with keep-alive to reduce connection churn const agentOptions = { keepAlive: true, maxSockets: 20 } @@ -865,20 +883,19 @@ const downloadFineTuningOutput = async (jobId: string): Promise = /** * Get logs for a fine-tuning job by querying the Ray head node HTTP API. * It will call: http:///api/jobs//logs - * Environment: set RAY_HEAD_NODE to the host:port of the Ray head (e.g. "ray-head.example.com:8265"). + * Environment: FINETUNING_SERVICE_DNS and FINETUNING_RAY_DNS determine the + * finetuning service and Ray API endpoints. */ const getFineTuningJobLogs = async ( fineTuningJobId: string, options: { ray_job_id?: string } = {} ) => { try { - const rayHost = process.env.FINETUNING_HOST ? `${process.env.FINETUNING_HOST}:8265` : 'undefined' - // If caller provided an explicit ray_job_id, use it. Otherwise attempt to discover the Ray submission id let submissionId: string | undefined = options.ray_job_id // Query Ray /api/jobs/ and select entries where entrypoint contains the FT id (jq-like) - const listUrl = `http://${rayHost}/api/jobs/` + const listUrl = `${FINETUNING_RAY_URL}/api/jobs/` console.debug('finetuningService.getFineTuningJobLogs - listUrl:', listUrl) try { const listResp = await axios.get(listUrl, { timeout: 20000 }) @@ -916,7 +933,7 @@ const getFineTuningJobLogs = async ( } // Construct logs URL with optional tail and fetch logs - const url = `http://${rayHost}/api/jobs/${encodeURIComponent(String(submissionId))}/logs` + const url = `${FINETUNING_RAY_URL}/api/jobs/${encodeURIComponent(String(submissionId))}/logs` const resp = await axios.get(url, { timeout: 30000 }) // Normalize logs response so newlines are preserved and objects/arrays are readable try { diff --git a/studio-frontend/packages/server/src/services/leads/index.ts b/studio-frontend/packages/server/src/services/leads/index.ts deleted file mode 100644 index 85e38d3..0000000 --- a/studio-frontend/packages/server/src/services/leads/index.ts +++ /dev/null @@ -1,43 +0,0 @@ -import { v4 as uuidv4 } from 'uuid' -import { StatusCodes } from 'http-status-codes' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { Lead } from '../../database/entities/Lead' -import { ILead } from '../../Interface' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const getAllLeads = async (chatflowid: string) => { - try { - const appServer = getRunningExpressApp() - const dbResponse = await appServer.AppDataSource.getRepository(Lead).find({ - where: { - chatflowid - } - }) - return dbResponse - } catch (error) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error: leadsService.getAllLeads - ${getErrorMessage(error)}`) - } -} - -const createLead = async (body: Partial) => { - try { - const chatId = body.chatId ?? uuidv4() - - const newLead = new Lead() - Object.assign(newLead, body) - Object.assign(newLead, { chatId }) - - const appServer = getRunningExpressApp() - const lead = appServer.AppDataSource.getRepository(Lead).create(newLead) - const dbResponse = await appServer.AppDataSource.getRepository(Lead).save(lead) - return dbResponse - } catch (error) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error: leadsService.createLead - ${getErrorMessage(error)}`) - } -} - -export default { - createLead, - getAllLeads -} diff --git a/studio-frontend/packages/server/src/services/load-prompts/index.ts b/studio-frontend/packages/server/src/services/load-prompts/index.ts deleted file mode 100644 index 5f041a8..0000000 --- a/studio-frontend/packages/server/src/services/load-prompts/index.ts +++ /dev/null @@ -1,28 +0,0 @@ -import { Client } from 'langchainhub' -import { StatusCodes } from 'http-status-codes' -import { parsePrompt } from '../../utils/hub' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const createPrompt = async (promptName: string): Promise => { - try { - let hub = new Client() - const prompt = await hub.pull(promptName) - const templates = parsePrompt(prompt) - const dbResponse = { - status: 'OK', - prompt: promptName, - templates: templates - } - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: loadPromptsService.createPrompt - ${getErrorMessage(error)}` - ) - } -} - -export default { - createPrompt -} diff --git a/studio-frontend/packages/server/src/services/marketplaces/index.ts b/studio-frontend/packages/server/src/services/marketplaces/index.ts deleted file mode 100644 index ee5d131..0000000 --- a/studio-frontend/packages/server/src/services/marketplaces/index.ts +++ /dev/null @@ -1,248 +0,0 @@ -import path from 'path' -import * as fs from 'fs' -import { StatusCodes } from 'http-status-codes' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' -import { IReactFlowEdge, IReactFlowNode } from '../../Interface' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { DeleteResult } from 'typeorm' -import { CustomTemplate } from '../../database/entities/CustomTemplate' - -import chatflowsService from '../chatflows' - -type ITemplate = { - badge: string - description: string - framework: string[] - usecases: string[] - nodes: IReactFlowNode[] - edges: IReactFlowEdge[] -} - -const getCategories = (fileDataObj: ITemplate) => { - return Array.from(new Set(fileDataObj?.nodes?.map((node) => node.data.category).filter((category) => category))) -} - -// Get all templates for marketplaces -const getAllTemplates = async () => { - try { - let marketplaceDir = path.join(__dirname, '..', '..', '..', 'marketplaces', 'chatflows') - let jsonsInDir = fs.readdirSync(marketplaceDir).filter((file) => path.extname(file) === '.json') - let templates: any[] = [] - jsonsInDir.forEach((file, index) => { - const filePath = path.join(__dirname, '..', '..', '..', 'marketplaces', 'chatflows', file) - const fileData = fs.readFileSync(filePath) - const fileDataObj = JSON.parse(fileData.toString()) as ITemplate - - const template = { - id: index, - templateName: file.split('.json')[0], - flowData: fileData.toString(), - badge: fileDataObj?.badge, - framework: fileDataObj?.framework, - usecases: fileDataObj?.usecases, - categories: getCategories(fileDataObj), - type: 'Chatflow', - description: fileDataObj?.description || '' - } - templates.push(template) - }) - - marketplaceDir = path.join(__dirname, '..', '..', '..', 'marketplaces', 'tools') - jsonsInDir = fs.readdirSync(marketplaceDir).filter((file) => path.extname(file) === '.json') - jsonsInDir.forEach((file, index) => { - const filePath = path.join(__dirname, '..', '..', '..', 'marketplaces', 'tools', file) - const fileData = fs.readFileSync(filePath) - const fileDataObj = JSON.parse(fileData.toString()) - const template = { - ...fileDataObj, - id: index, - type: 'Tool', - framework: fileDataObj?.framework, - badge: fileDataObj?.badge, - usecases: fileDataObj?.usecases, - categories: [], - templateName: file.split('.json')[0] - } - templates.push(template) - }) - - marketplaceDir = path.join(__dirname, '..', '..', '..', 'marketplaces', 'agentflows') - jsonsInDir = fs.readdirSync(marketplaceDir).filter((file) => path.extname(file) === '.json') - jsonsInDir.forEach((file, index) => { - const filePath = path.join(__dirname, '..', '..', '..', 'marketplaces', 'agentflows', file) - const fileData = fs.readFileSync(filePath) - const fileDataObj = JSON.parse(fileData.toString()) - const template = { - id: index, - templateName: file.split('.json')[0], - flowData: fileData.toString(), - badge: fileDataObj?.badge, - framework: fileDataObj?.framework, - usecases: fileDataObj?.usecases, - categories: getCategories(fileDataObj), - type: 'Agentflow', - description: fileDataObj?.description || '' - } - templates.push(template) - }) - const sortedTemplates = templates.sort((a, b) => a.templateName.localeCompare(b.templateName)) - const FlowiseDocsQnAIndex = sortedTemplates.findIndex((tmp) => tmp.templateName === 'Flowise Docs QnA') - if (FlowiseDocsQnAIndex > 0) { - sortedTemplates.unshift(sortedTemplates.splice(FlowiseDocsQnAIndex, 1)[0]) - } - const dbResponse = sortedTemplates - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: marketplacesService.getAllTemplates - ${getErrorMessage(error)}` - ) - } -} - -const deleteCustomTemplate = async (templateId: string): Promise => { - try { - const appServer = getRunningExpressApp() - return await appServer.AppDataSource.getRepository(CustomTemplate).delete({ id: templateId }) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: marketplacesService.deleteCustomTemplate - ${getErrorMessage(error)}` - ) - } -} - -const getAllCustomTemplates = async (): Promise => { - try { - const appServer = getRunningExpressApp() - const templates: any[] = await appServer.AppDataSource.getRepository(CustomTemplate).find() - templates.map((template) => { - template.usecases = template.usecases ? JSON.parse(template.usecases) : '' - if (template.type === 'Tool') { - template.flowData = JSON.parse(template.flowData) - template.iconSrc = template.flowData.iconSrc - template.schema = template.flowData.schema - template.func = template.flowData.func - template.categories = [] - template.flowData = undefined - } else { - template.categories = getCategories(JSON.parse(template.flowData)) - } - if (!template.badge) { - template.badge = '' - } - if (!template.framework) { - template.framework = '' - } - }) - return templates - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: marketplacesService.getAllCustomTemplates - ${getErrorMessage(error)}` - ) - } -} - -const saveCustomTemplate = async (body: any): Promise => { - try { - const appServer = getRunningExpressApp() - let flowDataStr = '' - let derivedFramework = '' - const customTemplate = new CustomTemplate() - Object.assign(customTemplate, body) - - if (body.chatflowId) { - const chatflow = await chatflowsService.getChatflowById(body.chatflowId) - const flowData = JSON.parse(chatflow.flowData) - const { framework, exportJson } = _generateExportFlowData(flowData) - flowDataStr = JSON.stringify(exportJson) - customTemplate.framework = framework - } else if (body.tool) { - const flowData = { - iconSrc: body.tool.iconSrc, - schema: body.tool.schema, - func: body.tool.func - } - customTemplate.framework = '' - customTemplate.type = 'Tool' - flowDataStr = JSON.stringify(flowData) - } - customTemplate.framework = derivedFramework - if (customTemplate.usecases) { - customTemplate.usecases = JSON.stringify(customTemplate.usecases) - } - const entity = appServer.AppDataSource.getRepository(CustomTemplate).create(customTemplate) - entity.flowData = flowDataStr - const flowTemplate = await appServer.AppDataSource.getRepository(CustomTemplate).save(entity) - return flowTemplate - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: marketplacesService.saveCustomTemplate - ${getErrorMessage(error)}` - ) - } -} - -const _generateExportFlowData = (flowData: any) => { - const nodes = flowData.nodes - const edges = flowData.edges - - let framework = 'Langchain' - for (let i = 0; i < nodes.length; i += 1) { - nodes[i].selected = false - const node = nodes[i] - - const newNodeData = { - id: node.data.id, - label: node.data.label, - version: node.data.version, - name: node.data.name, - type: node.data.type, - baseClasses: node.data.baseClasses, - tags: node.data.tags, - category: node.data.category, - description: node.data.description, - inputParams: node.data.inputParams, - inputAnchors: node.data.inputAnchors, - inputs: {}, - outputAnchors: node.data.outputAnchors, - outputs: node.data.outputs, - selected: false - } - - if (node.data.tags && node.data.tags.length) { - if (node.data.tags.includes('LlamaIndex')) { - framework = 'LlamaIndex' - } - } - - // Remove password, file & folder - if (node.data.inputs && Object.keys(node.data.inputs).length) { - const nodeDataInputs: any = {} - for (const input in node.data.inputs) { - const inputParam = node.data.inputParams.find((inp: any) => inp.name === input) - if (inputParam && inputParam.type === 'password') continue - if (inputParam && inputParam.type === 'file') continue - if (inputParam && inputParam.type === 'folder') continue - nodeDataInputs[input] = node.data.inputs[input] - } - newNodeData.inputs = nodeDataInputs - } - - nodes[i].data = newNodeData - } - const exportJson = { - nodes, - edges - } - return { exportJson, framework } -} - -export default { - getAllTemplates, - getAllCustomTemplates, - saveCustomTemplate, - deleteCustomTemplate -} diff --git a/studio-frontend/packages/server/src/services/nodes/index.ts b/studio-frontend/packages/server/src/services/nodes/index.ts index 7dd8b2f..7ab1488 100644 --- a/studio-frontend/packages/server/src/services/nodes/index.ts +++ b/studio-frontend/packages/server/src/services/nodes/index.ts @@ -1,13 +1,18 @@ import { cloneDeep } from 'lodash' import { StatusCodes } from 'http-status-codes' import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { INodeData } from '../../Interface' -import { INodeOptionsValue, ICommonObject, handleEscapeCharacters } from 'flowise-components' -import { databaseEntities } from '../../utils' +import { INodeData, ICommonObject } from '../../Interface' +import { databaseEntities, handleEscapeCharacters } from '../../utils' import logger from '../../utils/logger' import { InternalFlowiseError } from '../../errors/internalFlowiseError' import { getErrorMessage } from '../../errors/utils' +export interface INodeOptionsValue { + label: string + name: string + description?: string +} + // Get all component nodes const getAllNodes = async () => { try { diff --git a/studio-frontend/packages/server/src/services/openai-assistants-vector-store/index.ts b/studio-frontend/packages/server/src/services/openai-assistants-vector-store/index.ts deleted file mode 100644 index 7b598e1..0000000 --- a/studio-frontend/packages/server/src/services/openai-assistants-vector-store/index.ts +++ /dev/null @@ -1,258 +0,0 @@ -import OpenAI from 'openai' -import { StatusCodes } from 'http-status-codes' -import fs from 'fs' -import { Credential } from '../../database/entities/Credential' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { decryptCredentialData } from '../../utils' - -const getAssistantVectorStore = async (credentialId: string, vectorStoreId: string) => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await openai.vectorStores.retrieve(vectorStoreId) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.getAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const listAssistantVectorStore = async (credentialId: string) => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await openai.vectorStores.list() - return dbResponse.data - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.listAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const createAssistantVectorStore = async (credentialId: string, obj: OpenAI.VectorStores.VectorStoreCreateParams) => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await openai.vectorStores.create(obj) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.createAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const updateAssistantVectorStore = async ( - credentialId: string, - vectorStoreId: string, - obj: OpenAI.VectorStores.VectorStoreUpdateParams -) => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await openai.vectorStores.update(vectorStoreId, obj) - const vectorStoreFiles = await openai.vectorStores.files.list(vectorStoreId) - if (vectorStoreFiles.data?.length) { - const files = [] - for (const file of vectorStoreFiles.data) { - const fileData = await openai.files.retrieve(file.id) - files.push(fileData) - } - ;(dbResponse as any).files = files - } - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.updateAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const deleteAssistantVectorStore = async (credentialId: string, vectorStoreId: string) => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await openai.vectorStores.del(vectorStoreId) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.deleteAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const uploadFilesToAssistantVectorStore = async ( - credentialId: string, - vectorStoreId: string, - files: { filePath: string; fileName: string }[] -): Promise => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const uploadedFiles = [] - for (const file of files) { - const toFile = await OpenAI.toFile(fs.readFileSync(file.filePath), file.fileName) - const createdFile = await openai.files.create({ - file: toFile, - purpose: 'assistants' - }) - uploadedFiles.push(createdFile) - fs.unlinkSync(file.filePath) - } - - const file_ids = [...uploadedFiles.map((file) => file.id)] - - const res = await openai.vectorStores.fileBatches.createAndPoll(vectorStoreId, { - file_ids - }) - if (res.status === 'completed' && res.file_counts.completed === uploadedFiles.length) return uploadedFiles - else if (res.status === 'failed') - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - 'Error: openaiAssistantsVectorStoreService.uploadFilesToAssistantVectorStore - Upload failed!' - ) - else - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - 'Error: openaiAssistantsVectorStoreService.uploadFilesToAssistantVectorStore - Upload cancelled!' - ) - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.uploadFilesToAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -const deleteFilesFromAssistantVectorStore = async (credentialId: string, vectorStoreId: string, file_ids: string[]) => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const deletedFileIds = [] - let count = 0 - for (const file of file_ids) { - const res = await openai.vectorStores.files.del(vectorStoreId, file) - if (res.deleted) { - deletedFileIds.push(file) - count += 1 - } - } - - return { deletedFileIds, count } - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsVectorStoreService.uploadFilesToAssistantVectorStore - ${getErrorMessage(error)}` - ) - } -} - -export default { - getAssistantVectorStore, - listAssistantVectorStore, - createAssistantVectorStore, - updateAssistantVectorStore, - deleteAssistantVectorStore, - uploadFilesToAssistantVectorStore, - deleteFilesFromAssistantVectorStore -} diff --git a/studio-frontend/packages/server/src/services/openai-assistants/index.ts b/studio-frontend/packages/server/src/services/openai-assistants/index.ts deleted file mode 100644 index 7c3df3e..0000000 --- a/studio-frontend/packages/server/src/services/openai-assistants/index.ts +++ /dev/null @@ -1,120 +0,0 @@ -import OpenAI from 'openai' -import fs from 'fs' -import { StatusCodes } from 'http-status-codes' -import { decryptCredentialData } from '../../utils' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { Credential } from '../../database/entities/Credential' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -// ---------------------------------------- -// Assistants -// ---------------------------------------- - -// List available assistants -const getAllOpenaiAssistants = async (credentialId: string): Promise => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - const openai = new OpenAI({ apiKey: openAIApiKey }) - const retrievedAssistants = await openai.beta.assistants.list() - const dbResponse = retrievedAssistants.data - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsService.getAllOpenaiAssistants - ${getErrorMessage(error)}` - ) - } -} - -// Get assistant object -const getSingleOpenaiAssistant = async (credentialId: string, assistantId: string): Promise => { - try { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const dbResponse = await openai.beta.assistants.retrieve(assistantId) - const resp = await openai.files.list() - const existingFiles = resp.data ?? [] - if (dbResponse.tool_resources?.code_interpreter?.file_ids?.length) { - ;(dbResponse.tool_resources.code_interpreter as any).files = [ - ...existingFiles.filter((file) => dbResponse.tool_resources?.code_interpreter?.file_ids?.includes(file.id)) - ] - } - if (dbResponse.tool_resources?.file_search?.vector_store_ids?.length) { - // Since there can only be 1 vector store per assistant - const vectorStoreId = dbResponse.tool_resources.file_search.vector_store_ids[0] - const vectorStoreFiles = await openai.vectorStores.files.list(vectorStoreId) - const fileIds = vectorStoreFiles.data?.map((file) => file.id) ?? [] - ;(dbResponse.tool_resources.file_search as any).files = [...existingFiles.filter((file) => fileIds.includes(file.id))] - ;(dbResponse.tool_resources.file_search as any).vector_store_object = await openai.vectorStores.retrieve(vectorStoreId) - } - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: openaiAssistantsService.getSingleOpenaiAssistant - ${getErrorMessage(error)}` - ) - } -} - -const uploadFilesToAssistant = async (credentialId: string, files: { filePath: string; fileName: string }[]) => { - const appServer = getRunningExpressApp() - const credential = await appServer.AppDataSource.getRepository(Credential).findOneBy({ - id: credentialId - }) - if (!credential) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Credential ${credentialId} not found in the database!`) - } - // Decrpyt credentialData - const decryptedCredentialData = await decryptCredentialData(credential.encryptedData) - const openAIApiKey = decryptedCredentialData['openAIApiKey'] - if (!openAIApiKey) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `OpenAI ApiKey not found`) - } - - const openai = new OpenAI({ apiKey: openAIApiKey }) - const uploadedFiles = [] - - for (const file of files) { - const toFile = await OpenAI.toFile(fs.readFileSync(file.filePath), file.fileName) - const createdFile = await openai.files.create({ - file: toFile, - purpose: 'assistants' - }) - uploadedFiles.push(createdFile) - fs.unlinkSync(file.filePath) - } - - return uploadedFiles -} - -export default { - getAllOpenaiAssistants, - getSingleOpenaiAssistant, - uploadFilesToAssistant -} diff --git a/studio-frontend/packages/server/src/services/predictions/index.ts b/studio-frontend/packages/server/src/services/predictions/index.ts deleted file mode 100644 index 6f2dbe1..0000000 --- a/studio-frontend/packages/server/src/services/predictions/index.ts +++ /dev/null @@ -1,21 +0,0 @@ -import { Request } from 'express' -import { StatusCodes } from 'http-status-codes' -import { utilBuildChatflow } from '../../utils/buildChatflow' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const buildChatflow = async (fullRequest: Request) => { - try { - const dbResponse = await utilBuildChatflow(fullRequest) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: predictionsServices.buildChatflow - ${getErrorMessage(error)}` - ) - } -} - -export default { - buildChatflow -} diff --git a/studio-frontend/packages/server/src/services/prompts-lists/index.ts b/studio-frontend/packages/server/src/services/prompts-lists/index.ts deleted file mode 100644 index 62a97c8..0000000 --- a/studio-frontend/packages/server/src/services/prompts-lists/index.ts +++ /dev/null @@ -1,22 +0,0 @@ -import axios from 'axios' - -const createPromptsList = async (requestBody: any) => { - try { - const tags = requestBody.tags ? `tags=${requestBody.tags}` : '' - // Default to 100, TODO: add pagination and use offset & limit - const url = `https://api.hub.langchain.com/repos/?limit=100&${tags}has_commits=true&sort_field=num_likes&sort_direction=desc&is_archived=false` - const resp = await axios.get(url) - if (resp.data.repos) { - return { - status: 'OK', - repos: resp.data.repos - } - } - } catch (error) { - return { status: 'ERROR', repos: [] } - } -} - -export default { - createPromptsList -} diff --git a/studio-frontend/packages/server/src/services/stats/index.ts b/studio-frontend/packages/server/src/services/stats/index.ts deleted file mode 100644 index 8d9b99d..0000000 --- a/studio-frontend/packages/server/src/services/stats/index.ts +++ /dev/null @@ -1,53 +0,0 @@ -import { StatusCodes } from 'http-status-codes' -import { ChatMessageRatingType, chatType } from '../../Interface' -import { ChatMessage } from '../../database/entities/ChatMessage' -import { utilGetChatMessage } from '../../utils/getChatMessage' -import { ChatMessageFeedback } from '../../database/entities/ChatMessageFeedback' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -// get stats for showing in chatflow -const getChatflowStats = async ( - chatflowid: string, - chatTypeFilter: chatType | undefined, - startDate?: string, - endDate?: string, - messageId?: string, - feedback?: boolean, - feedbackTypes?: ChatMessageRatingType[] -): Promise => { - try { - const chatmessages = (await utilGetChatMessage( - chatflowid, - chatTypeFilter, - undefined, - undefined, - undefined, - undefined, - startDate, - endDate, - messageId, - feedback, - feedbackTypes - )) as Array - const totalMessages = chatmessages.length - const totalFeedback = chatmessages.filter((message) => message?.feedback).length - const positiveFeedback = chatmessages.filter((message) => message?.feedback?.rating === 'THUMBS_UP').length - const dbResponse = { - totalMessages, - totalFeedback, - positiveFeedback - } - - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: statsService.getChatflowStats - ${getErrorMessage(error)}` - ) - } -} - -export default { - getChatflowStats -} diff --git a/studio-frontend/packages/server/src/services/telemetry/index.ts b/studio-frontend/packages/server/src/services/telemetry/index.ts deleted file mode 100644 index 4c77efd..0000000 --- a/studio-frontend/packages/server/src/services/telemetry/index.ts +++ /dev/null @@ -1,10 +0,0 @@ -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' - -const createEvent = async (eventInfo: any) => { - const appServer = getRunningExpressApp() - await appServer.telemetry.sendTelemetry(eventInfo.name, eventInfo.data) -} - -export default { - createEvent -} diff --git a/studio-frontend/packages/server/src/services/tools/index.ts b/studio-frontend/packages/server/src/services/tools/index.ts index dcfdc94..e0393cf 100644 --- a/studio-frontend/packages/server/src/services/tools/index.ts +++ b/studio-frontend/packages/server/src/services/tools/index.ts @@ -2,7 +2,6 @@ import { StatusCodes } from 'http-status-codes' import { Tool } from '../../database/entities/Tool' import { InternalFlowiseError } from '../../errors/internalFlowiseError' import { getErrorMessage } from '../../errors/utils' -import { getAppVersion } from '../../utils' import { getRunningExpressApp } from '../../utils/getRunningExpressApp' const createTool = async (requestBody: any): Promise => { @@ -12,11 +11,6 @@ const createTool = async (requestBody: any): Promise => { Object.assign(newTool, requestBody) const tool = await appServer.AppDataSource.getRepository(Tool).create(newTool) const dbResponse = await appServer.AppDataSource.getRepository(Tool).save(tool) - await appServer.telemetry.sendTelemetry('tool_created', { - version: await getAppVersion(), - toolId: dbResponse.id, - toolName: dbResponse.name - }) return dbResponse } catch (error) { throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error: toolsService.createTool - ${getErrorMessage(error)}`) diff --git a/studio-frontend/packages/server/src/services/upsert-history/index.ts b/studio-frontend/packages/server/src/services/upsert-history/index.ts deleted file mode 100644 index 72e484b..0000000 --- a/studio-frontend/packages/server/src/services/upsert-history/index.ts +++ /dev/null @@ -1,75 +0,0 @@ -import { MoreThanOrEqual, LessThanOrEqual } from 'typeorm' -import { StatusCodes } from 'http-status-codes' -import { getRunningExpressApp } from '../../utils/getRunningExpressApp' -import { UpsertHistory } from '../../database/entities/UpsertHistory' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const getAllUpsertHistory = async ( - sortOrder: string | undefined, - chatflowid: string | undefined, - startDate: string | undefined, - endDate: string | undefined -) => { - try { - const appServer = getRunningExpressApp() - - const setDateToStartOrEndOfDay = (dateTimeStr: string, setHours: 'start' | 'end') => { - const date = new Date(dateTimeStr) - if (isNaN(date.getTime())) { - return undefined - } - setHours === 'start' ? date.setHours(0, 0, 0, 0) : date.setHours(23, 59, 59, 999) - return date - } - - let fromDate - if (startDate) fromDate = setDateToStartOrEndOfDay(startDate, 'start') - - let toDate - if (endDate) toDate = setDateToStartOrEndOfDay(endDate, 'end') - - let upsertHistory = await appServer.AppDataSource.getRepository(UpsertHistory).find({ - where: { - chatflowid, - ...(fromDate && { date: MoreThanOrEqual(fromDate) }), - ...(toDate && { date: LessThanOrEqual(toDate) }) - }, - order: { - date: sortOrder === 'DESC' ? 'DESC' : 'ASC' - } - }) - upsertHistory = upsertHistory.map((hist) => { - return { - ...hist, - result: hist.result ? JSON.parse(hist.result) : {}, - flowData: hist.flowData ? JSON.parse(hist.flowData) : {} - } - }) - - return upsertHistory - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: upsertHistoryServices.getAllUpsertHistory - ${getErrorMessage(error)}` - ) - } -} - -const patchDeleteUpsertHistory = async (ids: string[] = []): Promise => { - try { - const appServer = getRunningExpressApp() - const dbResponse = await appServer.AppDataSource.getRepository(UpsertHistory).delete(ids) - return dbResponse - } catch (error) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Error: upsertHistoryServices.patchDeleteUpsertHistory - ${getErrorMessage(error)}` - ) - } -} - -export default { - getAllUpsertHistory, - patchDeleteUpsertHistory -} diff --git a/studio-frontend/packages/server/src/services/vectors/index.ts b/studio-frontend/packages/server/src/services/vectors/index.ts deleted file mode 100644 index 15892f5..0000000 --- a/studio-frontend/packages/server/src/services/vectors/index.ts +++ /dev/null @@ -1,17 +0,0 @@ -import { Request } from 'express' -import { StatusCodes } from 'http-status-codes' -import { upsertVector } from '../../utils/upsertVector' -import { InternalFlowiseError } from '../../errors/internalFlowiseError' -import { getErrorMessage } from '../../errors/utils' - -const upsertVectorMiddleware = async (req: Request, isInternal: boolean = false) => { - try { - return await upsertVector(req, isInternal) - } catch (error) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error: vectorsService.upsertVector - ${getErrorMessage(error)}`) - } -} - -export default { - upsertVectorMiddleware -} diff --git a/studio-frontend/packages/server/src/utils/SSEStreamer.ts b/studio-frontend/packages/server/src/utils/SSEStreamer.ts deleted file mode 100644 index ef7139d..0000000 --- a/studio-frontend/packages/server/src/utils/SSEStreamer.ts +++ /dev/null @@ -1,325 +0,0 @@ -import express from 'express' -import { Response } from 'express' -import { IServerSideEventStreamer } from 'flowise-components' - -// define a new type that has a client type (INTERNAL or EXTERNAL) and Response type -type Client = { - // future use - clientType: 'INTERNAL' | 'EXTERNAL' - response: Response - // optional property with default value - started?: boolean -} - -export class SSEStreamer implements IServerSideEventStreamer { - clients: { [id: string]: Client } = {} - app: express.Application - - constructor(app: express.Application) { - this.app = app - } - - addExternalClient(chatId: string, res: Response) { - this.clients[chatId] = { clientType: 'EXTERNAL', response: res, started: false } - } - - addClient(chatId: string, res: Response) { - this.clients[chatId] = { clientType: 'INTERNAL', response: res, started: false } - } - - removeClient(chatId: string) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'end', - data: '[DONE]' - } - client.response.write('message\ndata:' + JSON.stringify(clientResponse) + '\n\n') - client.response.end() - delete this.clients[chatId] - } - } - - // Send SSE message to a specific client - streamEvent(chatId: string, data: string) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'start', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamCustomEvent(chatId: string, eventType: string, data: any) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: eventType, - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamStartEvent(chatId: string, data: string) { - const client = this.clients[chatId] - // prevent multiple start events being streamed to the client - if (client && !client.started) { - const clientResponse = { - event: 'start', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - client.started = true - } - } - - streamTokenEvent(chatId: string, data: string) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'token', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamSourceDocumentsEvent(chatId: string, data: any) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'sourceDocuments', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamArtifactsEvent(chatId: string, data: any) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'artifacts', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamUsedToolsEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'usedTools', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamFileAnnotationsEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'fileAnnotations', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamToolEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'tool', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamAgentReasoningEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'agentReasoning', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamNextAgentEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'nextAgent', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - streamActionEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'action', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamAbortEvent(chatId: string): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'abort', - data: '[DONE]' - } - client.response.write('message\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamEndEvent(_: string) { - // placeholder for future use - } - - streamErrorEvent(chatId: string, msg: string) { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'error', - data: msg - } - client.response.write('message\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamMetadataEvent(chatId: string, apiResponse: any) { - const metadataJson: any = {} - if (apiResponse.chatId) { - metadataJson['chatId'] = apiResponse.chatId - } - if (apiResponse.chatMessageId) { - metadataJson['chatMessageId'] = apiResponse.chatMessageId - } - if (apiResponse.question) { - metadataJson['question'] = apiResponse.question - } - if (apiResponse.sessionId) { - metadataJson['sessionId'] = apiResponse.sessionId - } - if (apiResponse.memoryType) { - metadataJson['memoryType'] = apiResponse.memoryType - } - if (apiResponse.followUpPrompts) { - metadataJson['followUpPrompts'] = - typeof apiResponse.followUpPrompts === 'string' ? JSON.parse(apiResponse.followUpPrompts) : apiResponse.followUpPrompts - } - if (apiResponse.flowVariables) { - metadataJson['flowVariables'] = - typeof apiResponse.flowVariables === 'string' ? JSON.parse(apiResponse.flowVariables) : apiResponse.flowVariables - } - if (Object.keys(metadataJson).length > 0) { - this.streamCustomEvent(chatId, 'metadata', metadataJson) - } - } - - streamCalledToolsEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'calledTools', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamAgentFlowExecutedDataEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'agentFlowExecutedData', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamAgentFlowEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'agentFlowEvent', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamNextAgentFlowEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'nextAgentFlow', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamUsageMetadataEvent(chatId: string, data: any): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'usageMetadata', - data: data - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamTTSStartEvent(chatId: string, chatMessageId: string, format: string): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'tts_start', - data: { chatMessageId, format } - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamTTSDataEvent(chatId: string, chatMessageId: string, audioChunk: string): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'tts_data', - data: { chatMessageId, audioChunk } - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamTTSEndEvent(chatId: string, chatMessageId: string): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'tts_end', - data: { chatMessageId } - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } - - streamTTSAbortEvent(chatId: string, chatMessageId: string): void { - const client = this.clients[chatId] - if (client) { - const clientResponse = { - event: 'tts_abort', - data: { chatMessageId } - } - client.response.write('message:\ndata:' + JSON.stringify(clientResponse) + '\n\n') - } - } -} diff --git a/studio-frontend/packages/server/src/utils/apiKey.ts b/studio-frontend/packages/server/src/utils/apiKey.ts index a50b2b5..43e2d95 100644 --- a/studio-frontend/packages/server/src/utils/apiKey.ts +++ b/studio-frontend/packages/server/src/utils/apiKey.ts @@ -1,5 +1,5 @@ import { randomBytes, scryptSync, timingSafeEqual } from 'crypto' -import { ICommonObject } from 'flowise-components' +import { ICommonObject } from '../Interface' import moment from 'moment' import fs from 'fs' import path from 'path' diff --git a/studio-frontend/packages/server/src/utils/buildAgentGraph.ts b/studio-frontend/packages/server/src/utils/buildAgentGraph.ts deleted file mode 100644 index fd9a198..0000000 --- a/studio-frontend/packages/server/src/utils/buildAgentGraph.ts +++ /dev/null @@ -1,1046 +0,0 @@ -import { - ICommonObject, - IMultiAgentNode, - IAgentReasoning, - IAction, - ITeamState, - ConsoleCallbackHandler, - additionalCallbacks, - ISeqAgentsState, - ISeqAgentNode, - IUsedTool, - IDocument, - IServerSideEventStreamer -} from 'flowise-components' -import { omit, cloneDeep, flatten, uniq } from 'lodash' -import { StateGraph, END, START } from '@langchain/langgraph' -import { Document } from '@langchain/core/documents' -import { StatusCodes } from 'http-status-codes' -import { v4 as uuidv4 } from 'uuid' -import { StructuredTool } from '@langchain/core/tools' -import { BaseMessage, HumanMessage, AIMessage, AIMessageChunk, ToolMessage } from '@langchain/core/messages' -import { - IChatFlow, - IComponentNodes, - IDepthQueue, - IReactFlowNode, - IReactFlowObject, - IReactFlowEdge, - IMessage, - IncomingInput -} from '../Interface' -import { - buildFlow, - getStartingNodes, - getEndingNodes, - constructGraphs, - databaseEntities, - getSessionChatHistory, - getMemorySessionId, - clearSessionMemory -} from '../utils' -import { getRunningExpressApp } from './getRunningExpressApp' -import { replaceInputsWithConfig, resolveVariables } from '.' -import { InternalFlowiseError } from '../errors/internalFlowiseError' -import { getErrorMessage } from '../errors/utils' -import logger from './logger' - -/** - * Build Agent Graph - * @param {IChatFlow} chatflow - * @param {string} chatId - * @param {string} sessionId - * @param {ICommonObject} incomingInput - * @param {boolean} isInternal - * @param {string} baseURL - */ -export const buildAgentGraph = async ( - chatflow: IChatFlow, - chatId: string, - sessionId: string, - incomingInput: IncomingInput, - isInternal: boolean, - baseURL?: string, - sseStreamer?: IServerSideEventStreamer, - shouldStreamResponse?: boolean -): Promise => { - try { - const appServer = getRunningExpressApp() - const chatflowid = chatflow.id - - /*** Get chatflows and prepare data ***/ - const flowData = chatflow.flowData - const parsedFlowData: IReactFlowObject = JSON.parse(flowData) - const nodes = parsedFlowData.nodes - const edges = parsedFlowData.edges - - /*** Get Ending Node with Directed Graph ***/ - const { graph, nodeDependencies } = constructGraphs(nodes, edges) - const directedGraph = graph - - const endingNodes = getEndingNodes(nodeDependencies, directedGraph, nodes) - - /*** Get Starting Nodes with Reversed Graph ***/ - const constructedObj = constructGraphs(nodes, edges, { isReversed: true }) - const nonDirectedGraph = constructedObj.graph - let startingNodeIds: string[] = [] - let depthQueue: IDepthQueue = {} - const endingNodeIds = endingNodes.map((n) => n.id) - for (const endingNodeId of endingNodeIds) { - const resx = getStartingNodes(nonDirectedGraph, endingNodeId) - startingNodeIds.push(...resx.startingNodeIds) - depthQueue = Object.assign(depthQueue, resx.depthQueue) - } - startingNodeIds = [...new Set(startingNodeIds)] - - /*** Get Memory Node for Chat History ***/ - let chatHistory: IMessage[] = [] - const memoryNode = nodes.find((node) => node.data.name === 'agentMemory') - if (memoryNode) { - chatHistory = await getSessionChatHistory( - chatflowid, - getMemorySessionId(memoryNode, incomingInput, chatId, isInternal), - memoryNode, - appServer.nodesPool.componentNodes, - appServer.AppDataSource, - databaseEntities, - logger, - incomingInput.history - ) - } - - // Initialize nodes like ChatModels, Tools, etc. - const reactFlowNodes: IReactFlowNode[] = await buildFlow({ - startingNodeIds, - reactFlowNodes: nodes, - reactFlowEdges: edges, - graph, - depthQueue, - componentNodes: appServer.nodesPool.componentNodes, - question: incomingInput.question, - chatHistory, - chatId, - sessionId, - chatflowid, - appDataSource: appServer.AppDataSource, - overrideConfig: incomingInput?.overrideConfig, - cachePool: appServer.cachePool, - isUpsert: false, - uploads: incomingInput.uploads, - baseURL - }) - - const options = { - chatId, - sessionId, - chatflowid, - logger, - analytic: chatflow.analytic, - appDataSource: appServer.AppDataSource, - databaseEntities: databaseEntities, - cachePool: appServer.cachePool, - uploads: incomingInput.uploads, - baseURL, - signal: new AbortController() - } - - let streamResults - let finalResult = '' - let finalSummarization = '' - let lastWorkerResult = '' - let agentReasoning: IAgentReasoning[] = [] - let isSequential = false - let lastMessageRaw = {} as AIMessageChunk - let finalAction: IAction = {} - let totalSourceDocuments: IDocument[] = [] - let totalUsedTools: IUsedTool[] = [] - let totalArtifacts: ICommonObject[] = [] - - const workerNodes = reactFlowNodes.filter((node) => node.data.name === 'worker') - const supervisorNodes = reactFlowNodes.filter((node) => node.data.name === 'supervisor') - const seqAgentNodes = reactFlowNodes.filter((node) => node.data.category === 'Sequential Agents') - - const mapNameToLabel: Record = {} - - for (const node of [...workerNodes, ...supervisorNodes, ...seqAgentNodes]) { - if (!Object.prototype.hasOwnProperty.call(mapNameToLabel, node.data.instance.name)) { - mapNameToLabel[node.data.instance.name] = { - label: node.data.instance.label, - nodeName: node.data.name - } - } - } - - try { - if (!seqAgentNodes.length) { - streamResults = await compileMultiAgentsGraph( - chatflow, - mapNameToLabel, - reactFlowNodes, - endingNodeIds, - appServer.nodesPool.componentNodes, - options, - startingNodeIds, - incomingInput.question, - incomingInput.history, - chatHistory, - incomingInput?.overrideConfig, - sessionId || chatId, - seqAgentNodes.some((node) => node.data.inputs?.summarization) - ) - } else { - isSequential = true - streamResults = await compileSeqAgentsGraph( - depthQueue, - chatflow, - reactFlowNodes, - edges, - appServer.nodesPool.componentNodes, - options, - incomingInput.question, - incomingInput.history, - chatHistory, - incomingInput?.overrideConfig, - sessionId || chatId, - incomingInput.action - ) - } - - if (streamResults) { - let isStreamingStarted = false - for await (const output of await streamResults) { - if (!output?.__end__) { - for (const agentName of Object.keys(output)) { - if (!mapNameToLabel[agentName]) continue - - const nodeId = output[agentName]?.messages - ? output[agentName].messages[output[agentName].messages.length - 1]?.additional_kwargs?.nodeId - : '' - const usedTools = output[agentName]?.messages - ? output[agentName].messages.map((msg: BaseMessage) => msg.additional_kwargs?.usedTools) - : [] - const sourceDocuments = output[agentName]?.messages - ? output[agentName].messages.map((msg: BaseMessage) => msg.additional_kwargs?.sourceDocuments) - : [] - const artifacts = output[agentName]?.messages - ? output[agentName].messages.map((msg: BaseMessage) => msg.additional_kwargs?.artifacts) - : [] - const messages = output[agentName]?.messages - ? output[agentName].messages.map((msg: BaseMessage) => (typeof msg === 'string' ? msg : msg.content)) - : [] - lastMessageRaw = output[agentName]?.messages - ? output[agentName].messages[output[agentName].messages.length - 1] - : {} - - const state = omit(output[agentName], ['messages']) - - if (usedTools && usedTools.length) { - const cleanedTools = usedTools.filter((tool: IUsedTool) => tool) - if (cleanedTools.length) totalUsedTools.push(...cleanedTools) - } - - if (sourceDocuments && sourceDocuments.length) { - const cleanedDocs = sourceDocuments.filter((documents: IDocument) => documents) - if (cleanedDocs.length) totalSourceDocuments.push(...cleanedDocs) - } - - if (artifacts && artifacts.length) { - const cleanedArtifacts = artifacts.filter((artifact: ICommonObject) => artifact) - if (cleanedArtifacts.length) totalArtifacts.push(...cleanedArtifacts) - } - - /* - * Check if the next node is a condition node, if yes, then add the agent reasoning of the condition node - */ - if (isSequential) { - const inputEdges = edges.filter( - (edg) => edg.target === nodeId && edg.targetHandle.includes(`${nodeId}-input-sequentialNode`) - ) - - inputEdges.forEach((edge) => { - const parentNode = reactFlowNodes.find((nd) => nd.id === edge.source) - if (parentNode) { - if (parentNode.data.name.includes('seqCondition')) { - const newMessages = messages.slice(0, -1) - newMessages.push(mapNameToLabel[agentName].label) - const reasoning = { - agentName: parentNode.data.instance?.label || parentNode.data.type, - messages: newMessages, - nodeName: parentNode.data.name, - nodeId: parentNode.data.id - } - agentReasoning.push(reasoning) - } - } - }) - } - - const reasoning = { - agentName: mapNameToLabel[agentName].label, - messages, - next: output[agentName]?.next, - instructions: output[agentName]?.instructions, - usedTools: flatten(usedTools) as IUsedTool[], - sourceDocuments: flatten(sourceDocuments) as Document[], - artifacts: flatten(artifacts) as ICommonObject[], - state, - nodeName: isSequential ? mapNameToLabel[agentName].nodeName : undefined, - nodeId - } - agentReasoning.push(reasoning) - - finalSummarization = output[agentName]?.summarization ?? '' - - lastWorkerResult = - output[agentName]?.messages?.length && - output[agentName].messages[output[agentName].messages.length - 1]?.additional_kwargs?.type === 'worker' - ? output[agentName].messages[output[agentName].messages.length - 1].content - : lastWorkerResult - - if (shouldStreamResponse) { - if (!isStreamingStarted) { - isStreamingStarted = true - if (sseStreamer) { - sseStreamer.streamStartEvent(chatId, agentReasoning) - } - } - - if (sseStreamer) { - sseStreamer.streamAgentReasoningEvent(chatId, agentReasoning) - } - - // Send loading next agent indicator - if (reasoning.next && reasoning.next !== 'FINISH' && reasoning.next !== 'END') { - if (sseStreamer) { - sseStreamer.streamNextAgentEvent(chatId, mapNameToLabel[reasoning.next].label || reasoning.next) - } - } - } - } - } else { - finalResult = output.__end__.messages.length ? output.__end__.messages.pop()?.content : '' - if (Array.isArray(finalResult)) finalResult = output.__end__.instructions - if (shouldStreamResponse && sseStreamer) { - sseStreamer.streamTokenEvent(chatId, finalResult) - } - } - } - - /* - * For multi agents mode, sometimes finalResult is empty - * 1.) Provide lastWorkerResult as final result if available - * 2.) Provide summary as final result if available - */ - if (!isSequential && !finalResult) { - if (lastWorkerResult) finalResult = lastWorkerResult - else if (finalSummarization) finalResult = finalSummarization - if (shouldStreamResponse && sseStreamer) { - sseStreamer.streamTokenEvent(chatId, finalResult) - } - } - - /* - * For sequential mode, sometimes finalResult is empty - * Use last agent message as final result - */ - if (isSequential && !finalResult && agentReasoning.length) { - const lastMessages = agentReasoning[agentReasoning.length - 1].messages - const lastAgentReasoningMessage = lastMessages[lastMessages.length - 1] - - // If last message is an AI Message with tool calls, that means the last node was interrupted - if (lastMessageRaw.tool_calls && lastMessageRaw.tool_calls.length > 0) { - // The last node that got interrupted - const node = reactFlowNodes.find((node) => node.id === lastMessageRaw.additional_kwargs.nodeId) - - // Find the next tool node that is connected to the interrupted node, to get the approve/reject button text - const tooNodeId = edges.find( - (edge) => - edge.target.includes('seqToolNode') && - edge.source === (lastMessageRaw.additional_kwargs && lastMessageRaw.additional_kwargs.nodeId) - )?.target - const connectedToolNode = reactFlowNodes.find((node) => node.id === tooNodeId) - - // Map raw tool calls to used tools, to be shown on interrupted message - const mappedToolCalls = lastMessageRaw.tool_calls.map((toolCall: any) => { - return { tool: toolCall.name, toolInput: toolCall.args, toolOutput: '' } - }) - - // Emit the interrupt message to the client - let approveButtonText = 'Yes' - let rejectButtonText = 'No' - - if (connectedToolNode || node) { - if (connectedToolNode) { - const result = await connectedToolNode.data.instance.node.seekPermissionMessage(mappedToolCalls) - finalResult = result || 'Do you want to proceed?' - approveButtonText = connectedToolNode.data.inputs?.approveButtonText || 'Yes' - rejectButtonText = connectedToolNode.data.inputs?.rejectButtonText || 'No' - } else if (node) { - const result = await node.data.instance.agentInterruptToolNode.seekPermissionMessage(mappedToolCalls) - finalResult = result || 'Do you want to proceed?' - approveButtonText = node.data.inputs?.approveButtonText || 'Yes' - rejectButtonText = node.data.inputs?.rejectButtonText || 'No' - } - finalAction = { - id: uuidv4(), - mapping: { approve: approveButtonText, reject: rejectButtonText, toolCalls: lastMessageRaw.tool_calls }, - elements: [ - { type: 'approve-button', label: approveButtonText }, - { type: 'reject-button', label: rejectButtonText } - ] - } - if (shouldStreamResponse && sseStreamer) { - sseStreamer.streamTokenEvent(chatId, finalResult) - sseStreamer.streamActionEvent(chatId, finalAction) - } - } - totalUsedTools.push(...mappedToolCalls) - } else if (lastAgentReasoningMessage) { - finalResult = lastAgentReasoningMessage - if (shouldStreamResponse && sseStreamer) { - sseStreamer.streamTokenEvent(chatId, finalResult) - } - } - } - - totalSourceDocuments = uniq(flatten(totalSourceDocuments)) - totalUsedTools = uniq(flatten(totalUsedTools)) - totalArtifacts = uniq(flatten(totalArtifacts)) - - if (shouldStreamResponse && sseStreamer) { - sseStreamer.streamUsedToolsEvent(chatId, totalUsedTools) - sseStreamer.streamSourceDocumentsEvent(chatId, totalSourceDocuments) - sseStreamer.streamArtifactsEvent(chatId, totalArtifacts) - sseStreamer.streamEndEvent(chatId) - } - - return { - finalResult, - finalAction, - sourceDocuments: totalSourceDocuments, - artifacts: totalArtifacts, - usedTools: totalUsedTools, - agentReasoning - } - } - } catch (e) { - // clear agent memory because checkpoints were saved during runtime - await clearSessionMemory(nodes, appServer.nodesPool.componentNodes, chatId, appServer.AppDataSource, sessionId) - if (getErrorMessage(e).includes('Aborted')) { - if (shouldStreamResponse && sseStreamer) { - sseStreamer.streamAbortEvent(chatId) - } - return { finalResult, agentReasoning } - } - throw new Error(getErrorMessage(e)) - } - return streamResults - } catch (e) { - logger.error('[server]: Error:', e) - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error buildAgentGraph - ${getErrorMessage(e)}`) - } -} - -/** - * Compile Multi Agents Graph - * @param {IChatFlow} chatflow - * @param {Record} mapNameToLabel - * @param {IReactFlowNode[]} reactflowNodes - * @param {string[]} workerNodeIds - * @param {IComponentNodes} componentNodes - * @param {ICommonObject} options - * @param {string[]} startingNodeIds - * @param {string} question - * @param {ICommonObject} overrideConfig - * @param {string} threadId - * @param {boolean} summarization - */ -const compileMultiAgentsGraph = async ( - chatflow: IChatFlow, - mapNameToLabel: Record, - reactflowNodes: IReactFlowNode[] = [], - workerNodeIds: string[], - componentNodes: IComponentNodes, - options: ICommonObject, - startingNodeIds: string[], - question: string, - prependHistoryMessages: IMessage[] = [], - chatHistory: IMessage[] = [], - overrideConfig?: ICommonObject, - threadId?: string, - summarization?: boolean -) => { - const appServer = getRunningExpressApp() - const channels: ITeamState = { - messages: { - value: (x: BaseMessage[], y: BaseMessage[]) => x.concat(y), - default: () => [] - }, - next: 'initialState', - instructions: "Solve the user's request.", - team_members: [] - } - - if (summarization) channels.summarization = 'summarize' - - const workflowGraph = new StateGraph({ - //@ts-ignore - channels - }) - - const workerNodes = reactflowNodes.filter((node) => workerNodeIds.includes(node.data.id)) - - let supervisorWorkers: { [key: string]: IMultiAgentNode[] } = {} - - // Init worker nodes - for (const workerNode of workerNodes) { - const nodeInstanceFilePath = componentNodes[workerNode.data.name].filePath as string - const nodeModule = await import(nodeInstanceFilePath) - const newNodeInstance = new nodeModule.nodeClass() - - let flowNodeData = cloneDeep(workerNode.data) - if (overrideConfig) flowNodeData = replaceInputsWithConfig(flowNodeData, overrideConfig) - flowNodeData = await resolveVariables(appServer.AppDataSource, flowNodeData, reactflowNodes, question, chatHistory, overrideConfig) - - try { - const workerResult: IMultiAgentNode = await newNodeInstance.init(flowNodeData, question, options) - const parentSupervisor = workerResult.parentSupervisorName - if (!parentSupervisor || workerResult.type !== 'worker') continue - if (Object.prototype.hasOwnProperty.call(supervisorWorkers, parentSupervisor)) { - supervisorWorkers[parentSupervisor].push(workerResult) - } else { - supervisorWorkers[parentSupervisor] = [workerResult] - } - - workflowGraph.addNode(workerResult.name, workerResult.node) - } catch (e) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error initialize worker nodes - ${getErrorMessage(e)}`) - } - } - - // Init supervisor nodes - for (const supervisor in supervisorWorkers) { - const supervisorInputLabel = mapNameToLabel[supervisor].label - const supervisorNode = reactflowNodes.find((node) => supervisorInputLabel === node.data.inputs?.supervisorName) - if (!supervisorNode) continue - - const nodeInstanceFilePath = componentNodes[supervisorNode.data.name].filePath as string - const nodeModule = await import(nodeInstanceFilePath) - const newNodeInstance = new nodeModule.nodeClass() - - let flowNodeData = cloneDeep(supervisorNode.data) - - if (overrideConfig) flowNodeData = replaceInputsWithConfig(flowNodeData, overrideConfig) - flowNodeData = await resolveVariables(appServer.AppDataSource, flowNodeData, reactflowNodes, question, chatHistory, overrideConfig) - - if (flowNodeData.inputs) flowNodeData.inputs.workerNodes = supervisorWorkers[supervisor] - - try { - const supervisorResult: IMultiAgentNode = await newNodeInstance.init(flowNodeData, question, options) - if (!supervisorResult.workers?.length) continue - - if (supervisorResult.moderations && supervisorResult.moderations.length > 0) { - try { - for (const moderation of supervisorResult.moderations) { - question = await moderation.checkForViolations(question) - } - } catch (e) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, getErrorMessage(e)) - } - } - - workflowGraph.addNode(supervisorResult.name, supervisorResult.node) - - for (const worker of supervisorResult.workers) { - //@ts-ignore - workflowGraph.addEdge(worker, supervisorResult.name) - } - - let conditionalEdges: { [key: string]: string } = {} - for (let i = 0; i < supervisorResult.workers.length; i++) { - conditionalEdges[supervisorResult.workers[i]] = supervisorResult.workers[i] - } - - //@ts-ignore - workflowGraph.addConditionalEdges(supervisorResult.name, (x: ITeamState) => x.next, { - ...conditionalEdges, - FINISH: END - }) - - //@ts-ignore - workflowGraph.addEdge(START, supervisorResult.name) - - // Add agentflow to pool - ;(workflowGraph as any).signal = options.signal - appServer.chatflowPool.add( - `${chatflow.id}_${options.chatId}`, - workflowGraph as any, - reactflowNodes.filter((node) => startingNodeIds.includes(node.id)), - overrideConfig - ) - - // Get memory - let memory = supervisorResult?.checkpointMemory - - const graph = workflowGraph.compile({ checkpointer: memory }) - - const loggerHandler = new ConsoleCallbackHandler(logger) - const callbacks = await additionalCallbacks(flowNodeData, options) - const config = { configurable: { thread_id: threadId } } - - let prependMessages = [] - // Only append in the first message - if (prependHistoryMessages.length === chatHistory.length) { - for (const message of prependHistoryMessages) { - if (message.role === 'apiMessage' || message.type === 'apiMessage') { - prependMessages.push(new AIMessage({ content: message.message || message.content || '' })) - } else if (message.role === 'userMessage' || message.type === 'userMessage') { - prependMessages.push(new HumanMessage({ content: message.message || message.content || '' })) - } - } - } - - // Return stream result as we should only have 1 supervisor - return await graph.stream( - { - messages: [...prependMessages, new HumanMessage({ content: question })] - }, - { recursionLimit: supervisorResult?.recursionLimit ?? 100, callbacks: [loggerHandler, ...callbacks], configurable: config } - ) - } catch (e) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error initialize supervisor nodes - ${getErrorMessage(e)}`) - } - } -} - -/** - * Compile Seq Agents Graph - * @param {IDepthQueue} depthQueue - * @param {IChatFlow} chatflow - * @param {IReactFlowNode[]} reactflowNodes - * @param {IReactFlowEdge[]} reactflowEdges - * @param {IComponentNodes} componentNodes - * @param {ICommonObject} options - * @param {string} question - * @param {IMessage[]} chatHistory - * @param {ICommonObject} overrideConfig - * @param {string} threadId - * @param {IAction} action - */ -const compileSeqAgentsGraph = async ( - depthQueue: IDepthQueue, - chatflow: IChatFlow, - reactflowNodes: IReactFlowNode[] = [], - reactflowEdges: IReactFlowEdge[] = [], - componentNodes: IComponentNodes, - options: ICommonObject, - question: string, - prependHistoryMessages: IMessage[] = [], - chatHistory: IMessage[] = [], - overrideConfig?: ICommonObject, - threadId?: string, - action?: IAction -) => { - const appServer = getRunningExpressApp() - - let channels: ISeqAgentsState = { - messages: { - value: (x: BaseMessage[], y: BaseMessage[]) => x.concat(y), - default: () => [] - } - } - - // Get state - const seqStateNode = reactflowNodes.find((node: IReactFlowNode) => node.data.name === 'seqState') - if (seqStateNode) { - channels = { - ...seqStateNode.data.instance.node, - ...channels - } - } - - let seqGraph = new StateGraph({ - //@ts-ignore - channels - }) - - /*** Validate Graph ***/ - const startAgentNodes: IReactFlowNode[] = reactflowNodes.filter((node: IReactFlowNode) => node.data.name === 'seqStart') - if (!startAgentNodes.length) throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, 'Start node not found') - if (startAgentNodes.length > 1) - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, 'Graph should have only one start node') - - const endAgentNodes: IReactFlowNode[] = reactflowNodes.filter((node: IReactFlowNode) => node.data.name === 'seqEnd') - const loopNodes: IReactFlowNode[] = reactflowNodes.filter((node: IReactFlowNode) => node.data.name === 'seqLoop') - if (!endAgentNodes.length && !loopNodes.length) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, 'Graph should have at least one End/Loop node') - } - /*** End of Validation ***/ - - let flowNodeData - let conditionalEdges: Record; func: any }> = {} - let interruptedRouteMapping: Record> = {} - let conditionalToolNodes: Record = {} - let bindModel: Record = {} - let interruptToolNodeNames = [] - - const initiateNode = async (node: IReactFlowNode) => { - const nodeInstanceFilePath = componentNodes[node.data.name].filePath as string - const nodeModule = await import(nodeInstanceFilePath) - const newNodeInstance = new nodeModule.nodeClass() - - flowNodeData = cloneDeep(node.data) - if (overrideConfig) flowNodeData = replaceInputsWithConfig(flowNodeData, overrideConfig) - flowNodeData = await resolveVariables(appServer.AppDataSource, flowNodeData, reactflowNodes, question, chatHistory, overrideConfig) - - const seqAgentNode: ISeqAgentNode = await newNodeInstance.init(flowNodeData, question, options) - return seqAgentNode - } - - /* - * Two objectives we want to achieve here: - * 1.) Prepare the mapping of conditional outputs to next nodes. This mapping will ONLY be used to add conditional edges to the Interrupted Agent connected next to Condition/ConditionAgent Node. - * For example, if the condition node has 2 outputs 'Yes' and 'No', and 'Yes' leads to 'agentName1' and 'No' leads to 'agentName2', then the mapping should be like: - * { - * : { 'Yes': 'agentName1', 'No': 'agentName2' } - * } - * 2.) With the interruptedRouteMapping object, avoid adding conditional edges to the Interrupted Agent for the nodes that are already interrupted by tools. It will be separately added from the function - agentInterruptToolFunc - */ - const processInterruptedRouteMapping = (conditionNodeId: string) => { - const conditionEdges = reactflowEdges.filter((edge) => edge.source === conditionNodeId) ?? [] - - for (const conditionEdge of conditionEdges) { - const nextNodeId = conditionEdge.target - const conditionNodeOutputAnchorId = conditionEdge.sourceHandle - - const nextNode = reactflowNodes.find((node) => node.id === nextNodeId) - if (!nextNode) continue - - const conditionNode = reactflowNodes.find((node) => node.id === conditionNodeId) - if (!conditionNode) continue - - const outputAnchors = conditionNode?.data.outputAnchors - if (!outputAnchors || !outputAnchors.length || !outputAnchors[0].options) continue - - const conditionOutputAnchorLabel = - outputAnchors[0].options.find((option: any) => option.id === conditionNodeOutputAnchorId)?.label ?? '' - if (!conditionOutputAnchorLabel) continue - - if (Object.prototype.hasOwnProperty.call(interruptedRouteMapping, conditionNodeId)) { - interruptedRouteMapping[conditionNodeId] = { - ...interruptedRouteMapping[conditionNodeId], - [conditionOutputAnchorLabel]: nextNode.data.instance.name - } - } else { - interruptedRouteMapping[conditionNodeId] = { - [conditionOutputAnchorLabel]: nextNode.data.instance.name - } - } - } - } - - /* - * Prepare Conditional Edges - * Example: { - * 'seqCondition_1': { nodes: { 'Yes': 'agentName1', 'No': 'agentName2' }, func: , disabled: true }, - * 'seqCondition_2': { nodes: { 'Yes': 'agentName3', 'No': 'agentName4' }, func: } - * } - */ - const prepareConditionalEdges = (nodeId: string, nodeInstance: ISeqAgentNode) => { - const conditionEdges = reactflowEdges.filter((edge) => edge.target === nodeId && edge.source.includes('seqCondition')) ?? [] - - for (const conditionEdge of conditionEdges) { - const conditionNodeId = conditionEdge.source - const conditionNodeOutputAnchorId = conditionEdge.sourceHandle - - const conditionNode = reactflowNodes.find((node) => node.id === conditionNodeId) - const outputAnchors = conditionNode?.data.outputAnchors - - if (!outputAnchors || !outputAnchors.length || !outputAnchors[0].options) continue - - const conditionOutputAnchorLabel = - outputAnchors[0].options.find((option: any) => option.id === conditionNodeOutputAnchorId)?.label ?? '' - - if (!conditionOutputAnchorLabel) continue - - if (Object.prototype.hasOwnProperty.call(conditionalEdges, conditionNodeId)) { - conditionalEdges[conditionNodeId] = { - ...conditionalEdges[conditionNodeId], - nodes: { ...conditionalEdges[conditionNodeId].nodes, [conditionOutputAnchorLabel]: nodeInstance.name } - } - } else { - conditionalEdges[conditionNodeId] = { - nodes: { [conditionOutputAnchorLabel]: nodeInstance.name }, - func: conditionNode.data.instance.node - } - } - } - } - - /* - * Prepare Conditional Tool Edges. This is just for LLMNode -> ToolNode - * Example: { - * 'agent_1': { source: agent, toolNodes: [node] } - * } - */ - const prepareLLMToToolEdges = (predecessorAgent: ISeqAgentNode, toolNodeInstance: ISeqAgentNode) => { - if (Object.prototype.hasOwnProperty.call(conditionalToolNodes, predecessorAgent.id)) { - const toolNodes = conditionalToolNodes[predecessorAgent.id].toolNodes - toolNodes.push(toolNodeInstance) - conditionalToolNodes[predecessorAgent.id] = { source: predecessorAgent, toolNodes } - } else { - conditionalToolNodes[predecessorAgent.id] = { - source: predecessorAgent, - toolNodes: [toolNodeInstance] - } - } - } - - /*** This is to bind the tools to the model of LLMNode, when the LLMNode is predecessor/successor of ToolNode ***/ - const createBindModel = (agent: ISeqAgentNode, toolNodeInstance: ISeqAgentNode) => { - const tools = flatten(toolNodeInstance.node?.tools) - bindModel[agent.id] = agent.llm.bindTools(tools) - } - - /*** Start processing every Agent nodes ***/ - for (const agentNodeId of getSortedDepthNodes(depthQueue)) { - const agentNode = reactflowNodes.find((node) => node.id === agentNodeId) - if (!agentNode) continue - - const eligibleSeqNodes = ['seqAgent', 'seqEnd', 'seqLoop', 'seqToolNode', 'seqLLMNode'] - const nodesToAdd = ['seqAgent', 'seqToolNode', 'seqLLMNode'] - - if (eligibleSeqNodes.includes(agentNode.data.name)) { - try { - const agentInstance: ISeqAgentNode = await initiateNode(agentNode) - - if (nodesToAdd.includes(agentNode.data.name)) { - // Add node to graph - seqGraph.addNode(agentInstance.name, agentInstance.node) - - /* - * If it is an Interrupted Agent, we want to: - * 1.) Add conditional edges to the Interrupted Agent via agentInterruptToolFunc - * 2.) Add agent to the interruptToolNodeNames list - */ - if (agentInstance.type === 'agent' && agentNode.data.inputs?.interrupt) { - interruptToolNodeNames.push(agentInstance.agentInterruptToolNode.name) - - const nextNodeId = reactflowEdges.find((edge) => edge.source === agentNode.id)?.target - const nextNode = reactflowNodes.find((node) => node.id === nextNodeId) - - let nextNodeSeqAgentName = '' - if (nextNodeId && nextNode) { - nextNodeSeqAgentName = nextNode.data.instance.name - - // If next node is Condition Node, process the interrupted route mapping, see more details from comments of processInterruptedRouteMapping - if (nextNode.data.name.includes('seqCondition')) { - const conditionNode = nextNodeId - processInterruptedRouteMapping(conditionNode) - seqGraph = await agentInstance.agentInterruptToolFunc( - seqGraph, - undefined, - nextNode.data.instance.node, - interruptedRouteMapping[conditionNode] - ) - } else { - seqGraph = await agentInstance.agentInterruptToolFunc(seqGraph, nextNodeSeqAgentName) - } - } else { - seqGraph = await agentInstance.agentInterruptToolFunc(seqGraph, nextNodeSeqAgentName) - } - } - } - - if (agentInstance.predecessorAgents) { - const predecessorAgents: ISeqAgentNode[] = agentInstance.predecessorAgents - - const edges = [] - for (const predecessorAgent of predecessorAgents) { - // Add start edge and set entry point - if (predecessorAgent.name === START) { - if (agentInstance.moderations && agentInstance.moderations.length > 0) { - try { - for (const moderation of agentInstance.moderations) { - question = await moderation.checkForViolations(question) - } - } catch (e) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, getErrorMessage(e)) - } - } - //@ts-ignore - seqGraph.addEdge(START, agentInstance.name) - } else if (predecessorAgent.type === 'condition') { - /* - * If current node is Condition Node, AND predecessor is an Interrupted Agent - * Don't add conditional edges to the Interrupted Agent, as it will be added separately from the function - agentInterruptToolFunc - */ - if (!Object.prototype.hasOwnProperty.call(interruptedRouteMapping, predecessorAgent.id)) { - prepareConditionalEdges(agentNode.data.id, agentInstance) - } - } else if (agentNode.data.name === 'seqToolNode') { - // Prepare the conditional edges for LLMNode -> ToolNode AND bind the tools to LLMNode - prepareLLMToToolEdges(predecessorAgent, agentInstance) - createBindModel(predecessorAgent, agentInstance) - - // If current ToolNode has interrupt turned on, add the ToolNode name to interruptToolNodeNames - if (agentInstance.node.interrupt) { - interruptToolNodeNames.push(agentInstance.name) - } - } else if (predecessorAgent.name) { - // In the scenario when ToolNode -> LLMNode, bind the tools to LLMNode - if (agentInstance.type === 'llm' && predecessorAgent.type === 'tool') { - createBindModel(agentInstance, predecessorAgent) - } - - // Add edge to graph ONLY when predecessor is not an Interrupted Agent - if (!predecessorAgent.agentInterruptToolNode) { - edges.push(predecessorAgent.name) - } - } - } - - // Edges can be multiple, in the case of parallel node executions - if (edges.length > 1) { - //@ts-ignore - seqGraph.addEdge(edges, agentInstance.name) - } else if (edges.length === 1) { - //@ts-ignore - seqGraph.addEdge(...edges, agentInstance.name) - } - } - } catch (e) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error initialize agent nodes - ${getErrorMessage(e)}`) - } - } - } - - /*** Add conditional edges to graph for condition nodes ***/ - for (const conditionNodeId in conditionalEdges) { - const startConditionEdges = reactflowEdges.filter((edge) => edge.target === conditionNodeId) - if (!startConditionEdges.length) continue - - for (const startConditionEdge of startConditionEdges) { - const startConditionNode = reactflowNodes.find((node) => node.id === startConditionEdge.source) - if (!startConditionNode) continue - seqGraph.addConditionalEdges( - startConditionNode.data.instance.name, - conditionalEdges[conditionNodeId].func, - //@ts-ignore - conditionalEdges[conditionNodeId].nodes - ) - } - } - - /*** Add conditional edges to graph for LLMNode -> ToolNode ***/ - for (const llmSourceNodeId in conditionalToolNodes) { - const connectedToolNodes = conditionalToolNodes[llmSourceNodeId].toolNodes - const sourceNode = conditionalToolNodes[llmSourceNodeId].source - - const routeMessage = (state: ISeqAgentsState) => { - const messages = state.messages as unknown as BaseMessage[] - const lastMessage = messages[messages.length - 1] as AIMessage - - if (!lastMessage.tool_calls?.length) { - return END - } - - for (const toolCall of lastMessage.tool_calls) { - for (const toolNode of connectedToolNodes) { - const tools = (toolNode.node?.tools as StructuredTool[]) || ((toolNode as any).tools as StructuredTool[]) - if (tools.some((tool) => tool.name === toolCall.name)) { - return toolNode.name - } - } - } - return END - } - - seqGraph.addConditionalEdges( - //@ts-ignore - sourceNode.name, - routeMessage - ) - } - - /*** Add agentflow to pool ***/ - ;(seqGraph as any).signal = options.signal - appServer.chatflowPool.add( - `${chatflow.id}_${options.chatId}`, - seqGraph as any, - reactflowNodes.filter((node) => startAgentNodes.map((nd) => nd.id).includes(node.id)), - overrideConfig - ) - - /*** Get memory ***/ - const startNode = reactflowNodes.find((node: IReactFlowNode) => node.data.name === 'seqStart') - let memory = startNode?.data.instance?.checkpointMemory - - try { - const graph = seqGraph.compile({ checkpointer: memory, interruptBefore: interruptToolNodeNames as any }) - - const loggerHandler = new ConsoleCallbackHandler(logger) - const callbacks = await additionalCallbacks(flowNodeData as any, options) - const config = { configurable: { thread_id: threadId }, bindModel } - - let prependMessages = [] - // Only append in the first message - if (prependHistoryMessages.length === chatHistory.length) { - for (const message of prependHistoryMessages) { - if (message.role === 'apiMessage' || message.type === 'apiMessage') { - prependMessages.push(new AIMessage({ content: message.message || message.content || '' })) - } else if (message.role === 'userMessage' || message.type === 'userMessage') { - prependMessages.push(new HumanMessage({ content: message.message || message.content || '' })) - } - } - } - - let humanMsg: { messages: HumanMessage[] | ToolMessage[] } | null = { - messages: [...prependMessages, new HumanMessage({ content: question })] - } - - if (action && action.mapping && question === action.mapping.approve) { - humanMsg = null - } else if (action && action.mapping && question === action.mapping.reject) { - humanMsg = { - messages: action.mapping.toolCalls.map((toolCall) => { - return new ToolMessage({ - name: toolCall.name, - content: `Tool ${toolCall.name} call denied by user. Acknowledge that, and DONT perform further actions. Only ask if user have other questions`, - tool_call_id: toolCall.id!, - additional_kwargs: { toolCallsDenied: true } - }) - }) - } - } - return await graph.stream(humanMsg, { callbacks: [loggerHandler, ...callbacks], configurable: config }) - } catch (e) { - logger.error('Error compile graph', e) - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, `Error compile graph - ${getErrorMessage(e)}`) - } -} - -const getSortedDepthNodes = (depthQueue: IDepthQueue) => { - // Step 1: Convert the object into an array of [key, value] pairs and sort them by the value - const sortedEntries = Object.entries(depthQueue).sort((a, b) => a[1] - b[1]) - - // Step 2: Group keys by their depth values - const groupedByDepth: Record = {} - sortedEntries.forEach(([key, value]) => { - if (!groupedByDepth[value]) { - groupedByDepth[value] = [] - } - groupedByDepth[value].push(key) - }) - - // Step 3: Create the final sorted array with grouped keys - const sortedArray: (string | string[])[] = [] - Object.keys(groupedByDepth) - .sort((a, b) => parseInt(a) - parseInt(b)) - .forEach((depth) => { - const items = groupedByDepth[parseInt(depth)] - sortedArray.push(...items) - }) - - return sortedArray.flat() -} diff --git a/studio-frontend/packages/server/src/utils/buildChatflow.ts b/studio-frontend/packages/server/src/utils/buildChatflow.ts deleted file mode 100644 index 429492f..0000000 --- a/studio-frontend/packages/server/src/utils/buildChatflow.ts +++ /dev/null @@ -1,571 +0,0 @@ -import { Request } from 'express' -import { - IFileUpload, - convertSpeechToText, - ICommonObject, - addSingleFileToStorage, - addArrayFilesToStorage, - mapMimeTypeToInputField, - IServerSideEventStreamer -} from 'flowise-components' -import { StatusCodes } from 'http-status-codes' -import { - IncomingInput, - IMessage, - INodeData, - IReactFlowObject, - IReactFlowNode, - IDepthQueue, - chatType, - IChatMessage, - IChatFlow, - IReactFlowEdge -} from '../Interface' -import { InternalFlowiseError } from '../errors/internalFlowiseError' -import { ChatFlow } from '../database/entities/ChatFlow' -import { getRunningExpressApp } from '../utils/getRunningExpressApp' -import { - isFlowValidForStream, - buildFlow, - getTelemetryFlowObj, - getAppVersion, - resolveVariables, - getSessionChatHistory, - findMemoryNode, - replaceInputsWithConfig, - getStartingNodes, - isStartNodeDependOnInput, - getMemorySessionId, - isSameOverrideConfig, - getEndingNodes, - constructGraphs, - isSameChatId -} from '../utils' -import { validateChatflowAPIKey } from './validateKey' -import { databaseEntities } from '.' -import { v4 as uuidv4 } from 'uuid' -import { omit } from 'lodash' -import * as fs from 'fs' -import logger from './logger' -import { utilAddChatMessage } from './addChatMesage' -import { buildAgentGraph } from './buildAgentGraph' -import { getErrorMessage } from '../errors/utils' -import { ChatMessage } from '../database/entities/ChatMessage' -import { IAction } from 'flowise-components' - -/** - * Build Chatflow - * @param {Request} req - * @param {boolean} isInternal - */ -export const utilBuildChatflow = async (req: Request, isInternal: boolean = false): Promise => { - try { - const appServer = getRunningExpressApp() - const chatflowid = req.params.id - - const httpProtocol = req.get('x-forwarded-proto') || req.protocol - const baseURL = `${httpProtocol}://${req.get('host')}` - - let incomingInput: IncomingInput = req.body - let nodeToExecuteData: INodeData - const chatflow = await appServer.AppDataSource.getRepository(ChatFlow).findOneBy({ - id: chatflowid - }) - if (!chatflow) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Chatflow ${chatflowid} not found`) - } - - const chatId = incomingInput.chatId ?? incomingInput.overrideConfig?.sessionId ?? uuidv4() - const userMessageDateTime = new Date() - if (!isInternal) { - const isKeyValidated = await validateChatflowAPIKey(req, chatflow) - if (!isKeyValidated) { - throw new InternalFlowiseError(StatusCodes.UNAUTHORIZED, `Unauthorized`) - } - } - - let fileUploads: IFileUpload[] = [] - if (incomingInput.uploads) { - fileUploads = incomingInput.uploads - for (let i = 0; i < fileUploads.length; i += 1) { - const upload = fileUploads[i] - - if ((upload.type === 'file' || upload.type === 'audio') && upload.data) { - const filename = upload.name - const splitDataURI = upload.data.split(',') - const bf = Buffer.from(splitDataURI.pop() || '', 'base64') - const mime = splitDataURI[0].split(':')[1].split(';')[0] - await addSingleFileToStorage(mime, bf, filename, chatflowid, chatId) - upload.type = 'stored-file' - // Omit upload.data since we don't store the content in database - fileUploads[i] = omit(upload, ['data']) - } - - if (upload.type === 'url' && upload.data) { - const filename = upload.name - const urlData = upload.data - fileUploads[i] = { data: urlData, name: filename, type: 'url', mime: upload.mime ?? 'image/png' } - } - - // Run Speech to Text conversion - if (upload.mime === 'audio/webm' || upload.mime === 'audio/mp4' || upload.mime === 'audio/ogg') { - logger.debug(`Attempting a speech to text conversion...`) - let speechToTextConfig: ICommonObject = {} - if (chatflow.speechToText) { - const speechToTextProviders = JSON.parse(chatflow.speechToText) - for (const provider in speechToTextProviders) { - const providerObj = speechToTextProviders[provider] - if (providerObj.status) { - speechToTextConfig = providerObj - speechToTextConfig['name'] = provider - break - } - } - } - if (speechToTextConfig) { - const options: ICommonObject = { - chatId, - chatflowid, - appDataSource: appServer.AppDataSource, - databaseEntities: databaseEntities - } - const speechToTextResult = await convertSpeechToText(upload, speechToTextConfig, options) - logger.debug(`Speech to text result: ${speechToTextResult}`) - if (speechToTextResult) { - incomingInput.question = speechToTextResult - } - } - } - } - } - - let isStreamValid = false - - const files = (req.files as Express.Multer.File[]) || [] - - if (files.length) { - const overrideConfig: ICommonObject = { ...req.body } - const fileNames: string[] = [] - for (const file of files) { - const fileBuffer = fs.readFileSync(file.path) - - const storagePath = await addArrayFilesToStorage(file.mimetype, fileBuffer, file.originalname, fileNames, chatflowid) - - const fileInputField = mapMimeTypeToInputField(file.mimetype) - - overrideConfig[fileInputField] = storagePath - - fs.unlinkSync(file.path) - } - incomingInput = { - question: req.body.question ?? 'hello', - overrideConfig - } - } - - /*** Get chatflows and prepare data ***/ - const flowData = chatflow.flowData - const parsedFlowData: IReactFlowObject = JSON.parse(flowData) - const nodes = parsedFlowData.nodes - const edges = parsedFlowData.edges - - /*** Get session ID ***/ - const memoryNode = findMemoryNode(nodes, edges) - const memoryType = memoryNode?.data.label - let sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal) - - /*** Get Ending Node with Directed Graph ***/ - const { graph, nodeDependencies } = constructGraphs(nodes, edges) - const directedGraph = graph - const endingNodes = getEndingNodes(nodeDependencies, directedGraph, nodes) - /*** If the graph is an agent graph, build the agent response ***/ - if (endingNodes.filter((node) => node.data.category === 'Multi Agents' || node.data.category === 'Sequential Agents').length) { - return await utilBuildAgentResponse( - chatflow, - isInternal, - chatId, - memoryType ?? '', - sessionId, - userMessageDateTime, - fileUploads, - incomingInput, - nodes, - edges, - baseURL, - appServer.sseStreamer, - true - ) - } - - // Get prepend messages - const prependMessages = incomingInput.history - - /* Reuse the flow without having to rebuild (to avoid duplicated upsert, recomputation, reinitialization of memory) when all these conditions met: - * - Reuse of flows is not disabled - * - Node Data already exists in pool - * - Still in sync (i.e the flow has not been modified since) - * - Existing overrideConfig and new overrideConfig are the same - * - Existing chatId and new chatId is the same - * - Flow doesn't start with/contain nodes that depend on incomingInput.question - ***/ - const isFlowReusable = () => { - return ( - process.env.DISABLE_CHATFLOW_REUSE !== 'true' && - Object.prototype.hasOwnProperty.call(appServer.chatflowPool.activeChatflows, chatflowid) && - appServer.chatflowPool.activeChatflows[chatflowid].inSync && - appServer.chatflowPool.activeChatflows[chatflowid].endingNodeData && - isSameChatId(appServer.chatflowPool.activeChatflows[chatflowid].chatId, chatId) && - isSameOverrideConfig( - isInternal, - appServer.chatflowPool.activeChatflows[chatflowid].overrideConfig, - incomingInput.overrideConfig - ) && - !isStartNodeDependOnInput(appServer.chatflowPool.activeChatflows[chatflowid].startingNodes, nodes) - ) - } - - if (isFlowReusable()) { - nodeToExecuteData = appServer.chatflowPool.activeChatflows[chatflowid].endingNodeData as INodeData - isStreamValid = isFlowValidForStream(nodes, nodeToExecuteData) - logger.debug( - `[server]: Reuse existing chatflow ${chatflowid} with ending node ${nodeToExecuteData.label} (${nodeToExecuteData.id})` - ) - } else { - const isCustomFunctionEndingNode = endingNodes.some((node) => node.data?.outputs?.output === 'EndingNode') - - for (const endingNode of endingNodes) { - const endingNodeData = endingNode.data - - const isEndingNode = endingNodeData?.outputs?.output === 'EndingNode' - - // Once custom function ending node exists, no need to do follow-up checks. - if (isEndingNode) continue - - if ( - endingNodeData.outputs && - Object.keys(endingNodeData.outputs).length && - !Object.values(endingNodeData.outputs ?? {}).includes(endingNodeData.name) - ) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - `Output of ${endingNodeData.label} (${endingNodeData.id}) must be ${endingNodeData.label}, can't be an Output Prediction` - ) - } - - isStreamValid = isFlowValidForStream(nodes, endingNodeData) - } - - // Once custom function ending node exists, flow is always unavailable to stream - isStreamValid = isCustomFunctionEndingNode ? false : isStreamValid - - let chatHistory: IMessage[] = [] - - // When {{chat_history}} is used in Format Prompt Value, fetch the chat conversations from memory node - for (const endingNode of endingNodes) { - const endingNodeData = endingNode.data - - if (!endingNodeData.inputs?.memory) continue - - const memoryNodeId = endingNodeData.inputs?.memory.split('.')[0].replace('{{', '') - const memoryNode = nodes.find((node) => node.data.id === memoryNodeId) - - if (!memoryNode) continue - - chatHistory = await getSessionChatHistory( - chatflowid, - getMemorySessionId(memoryNode, incomingInput, chatId, isInternal), - memoryNode, - appServer.nodesPool.componentNodes, - appServer.AppDataSource, - databaseEntities, - logger, - prependMessages - ) - } - - /*** Get Starting Nodes with Reversed Graph ***/ - const constructedObj = constructGraphs(nodes, edges, { isReversed: true }) - const nonDirectedGraph = constructedObj.graph - let startingNodeIds: string[] = [] - let depthQueue: IDepthQueue = {} - const endingNodeIds = endingNodes.map((n) => n.id) - for (const endingNodeId of endingNodeIds) { - const resx = getStartingNodes(nonDirectedGraph, endingNodeId) - startingNodeIds.push(...resx.startingNodeIds) - depthQueue = Object.assign(depthQueue, resx.depthQueue) - } - startingNodeIds = [...new Set(startingNodeIds)] - - const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.id)) - - logger.debug(`[server]: Start building chatflow ${chatflowid}`) - /*** BFS to traverse from Starting Nodes to Ending Node ***/ - const reactFlowNodes = await buildFlow({ - startingNodeIds, - reactFlowNodes: nodes, - reactFlowEdges: edges, - graph, - depthQueue, - componentNodes: appServer.nodesPool.componentNodes, - question: incomingInput.question, - chatHistory, - chatId, - sessionId: sessionId ?? '', - chatflowid, - appDataSource: appServer.AppDataSource, - overrideConfig: incomingInput?.overrideConfig, - cachePool: appServer.cachePool, - isUpsert: false, - uploads: incomingInput.uploads, - baseURL - }) - - const nodeToExecute = - endingNodeIds.length === 1 - ? reactFlowNodes.find((node: IReactFlowNode) => endingNodeIds[0] === node.id) - : reactFlowNodes[reactFlowNodes.length - 1] - if (!nodeToExecute) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Node not found`) - } - - if (incomingInput.overrideConfig) { - nodeToExecute.data = replaceInputsWithConfig(nodeToExecute.data, incomingInput.overrideConfig) - } - - const flowData: ICommonObject = { - chatflowid, - chatId, - sessionId, - chatHistory, - ...incomingInput.overrideConfig - } - - const reactFlowNodeData: INodeData = await resolveVariables( - appServer.AppDataSource, - nodeToExecute.data, - reactFlowNodes, - incomingInput.question, - chatHistory, - flowData - ) - nodeToExecuteData = reactFlowNodeData - - appServer.chatflowPool.add(chatflowid, nodeToExecuteData, startingNodes, incomingInput?.overrideConfig, chatId) - } - - logger.debug(`[server]: Running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`) - - const nodeInstanceFilePath = appServer.nodesPool.componentNodes[nodeToExecuteData.name].filePath as string - const nodeModule = await import(nodeInstanceFilePath) - const nodeInstance = new nodeModule.nodeClass({ sessionId }) - - let result = isStreamValid - ? await nodeInstance.run(nodeToExecuteData, incomingInput.question, { - chatId, - chatflowid, - logger, - appDataSource: appServer.AppDataSource, - databaseEntities, - analytic: chatflow.analytic, - uploads: incomingInput.uploads, - prependMessages, - sseStreamer: appServer.sseStreamer, - shouldStreamResponse: isStreamValid - }) - : await nodeInstance.run(nodeToExecuteData, incomingInput.question, { - chatId, - chatflowid, - logger, - appDataSource: appServer.AppDataSource, - databaseEntities, - analytic: chatflow.analytic, - uploads: incomingInput.uploads, - prependMessages - }) - result = typeof result === 'string' ? { text: result } : result - - // Retrieve threadId from assistant if exists - if (typeof result === 'object' && result.assistant) { - sessionId = result.assistant.threadId - } - - const userMessage: Omit = { - role: 'userMessage', - content: incomingInput.question, - chatflowid, - chatType: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - chatId, - memoryType, - sessionId, - createdDate: userMessageDateTime, - fileUploads: incomingInput.uploads ? JSON.stringify(fileUploads) : undefined, - leadEmail: incomingInput.leadEmail - } - await utilAddChatMessage(userMessage) - - let resultText = '' - if (result.text) resultText = result.text - else if (result.json) resultText = '```json\n' + JSON.stringify(result.json, null, 2) - else resultText = JSON.stringify(result, null, 2) - - const apiMessage: Omit = { - role: 'apiMessage', - content: resultText, - chatflowid, - chatType: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - chatId, - memoryType, - sessionId - } - if (result?.sourceDocuments) apiMessage.sourceDocuments = JSON.stringify(result.sourceDocuments) - if (result?.usedTools) apiMessage.usedTools = JSON.stringify(result.usedTools) - if (result?.fileAnnotations) apiMessage.fileAnnotations = JSON.stringify(result.fileAnnotations) - if (result?.artifacts) apiMessage.artifacts = JSON.stringify(result.artifacts) - - const chatMessage = await utilAddChatMessage(apiMessage) - - logger.debug(`[server]: Finished running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`) - await appServer.telemetry.sendTelemetry('prediction_sent', { - version: await getAppVersion(), - chatflowId: chatflowid, - chatId, - type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - flowGraph: getTelemetryFlowObj(nodes, edges) - }) - - // Prepare response - // return the question in the response - // this is used when input text is empty but question is in audio format - result.question = incomingInput.question - result.chatId = chatId - result.chatMessageId = chatMessage?.id - result.isStreamValid = isStreamValid - - if (sessionId) result.sessionId = sessionId - if (memoryType) result.memoryType = memoryType - - return result - } catch (e) { - logger.error('[server]: Error:', e) - if (e instanceof InternalFlowiseError && e.statusCode === StatusCodes.UNAUTHORIZED) { - throw e - } else { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, getErrorMessage(e)) - } - } -} - -const utilBuildAgentResponse = async ( - agentflow: IChatFlow, - isInternal: boolean, - chatId: string, - memoryType: string, - sessionId: string, - userMessageDateTime: Date, - fileUploads: IFileUpload[], - incomingInput: IncomingInput, - nodes: IReactFlowNode[], - edges: IReactFlowEdge[], - baseURL?: string, - sseStreamer?: IServerSideEventStreamer, - shouldStreamResponse?: boolean -) => { - try { - const appServer = getRunningExpressApp() - const streamResults = await buildAgentGraph( - agentflow, - chatId, - sessionId, - incomingInput, - isInternal, - baseURL, - sseStreamer, - shouldStreamResponse - ) - if (streamResults) { - const { finalResult, finalAction, sourceDocuments, artifacts, usedTools, agentReasoning } = streamResults - const userMessage: Omit = { - role: 'userMessage', - content: incomingInput.question, - chatflowid: agentflow.id, - chatType: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - chatId, - memoryType, - sessionId, - createdDate: userMessageDateTime, - fileUploads: incomingInput.uploads ? JSON.stringify(fileUploads) : undefined, - leadEmail: incomingInput.leadEmail - } - await utilAddChatMessage(userMessage) - - const apiMessage: Omit = { - role: 'apiMessage', - content: finalResult, - chatflowid: agentflow.id, - chatType: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - chatId, - memoryType, - sessionId - } - if (sourceDocuments?.length) apiMessage.sourceDocuments = JSON.stringify(sourceDocuments) - if (artifacts?.length) apiMessage.artifacts = JSON.stringify(artifacts) - if (usedTools?.length) apiMessage.usedTools = JSON.stringify(usedTools) - if (agentReasoning?.length) apiMessage.agentReasoning = JSON.stringify(agentReasoning) - if (finalAction && Object.keys(finalAction).length) apiMessage.action = JSON.stringify(finalAction) - const chatMessage = await utilAddChatMessage(apiMessage) - - await appServer.telemetry.sendTelemetry('agentflow_prediction_sent', { - version: await getAppVersion(), - agentflowId: agentflow.id, - chatId, - type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - flowGraph: getTelemetryFlowObj(nodes, edges) - }) - - // Find the previous chat message with the same action id and remove the action - if (incomingInput.action && Object.keys(incomingInput.action).length) { - let query = await appServer.AppDataSource.getRepository(ChatMessage) - .createQueryBuilder('chat_message') - .where('chat_message.chatId = :chatId', { chatId }) - .orWhere('chat_message.sessionId = :sessionId', { sessionId }) - .orderBy('chat_message.createdDate', 'DESC') - .getMany() - - for (const result of query) { - if (result.action) { - try { - const action: IAction = JSON.parse(result.action) - if (action.id === incomingInput.action.id) { - const newChatMessage = new ChatMessage() - Object.assign(newChatMessage, result) - newChatMessage.action = null - const cm = await appServer.AppDataSource.getRepository(ChatMessage).create(newChatMessage) - await appServer.AppDataSource.getRepository(ChatMessage).save(cm) - break - } - } catch (e) { - // error converting action to JSON - } - } - } - } - - // Prepare response - let result: ICommonObject = {} - result.text = finalResult - result.question = incomingInput.question - result.chatId = chatId - result.chatMessageId = chatMessage?.id - if (sessionId) result.sessionId = sessionId - if (memoryType) result.memoryType = memoryType - if (agentReasoning?.length) result.agentReasoning = agentReasoning - if (finalAction && Object.keys(finalAction).length) result.action = finalAction - - return result - } - return undefined - } catch (e) { - logger.error('[server]: Error:', e) - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, getErrorMessage(e)) - } -} diff --git a/studio-frontend/packages/server/src/utils/fileRepository.ts b/studio-frontend/packages/server/src/utils/fileRepository.ts deleted file mode 100644 index 245b9ec..0000000 --- a/studio-frontend/packages/server/src/utils/fileRepository.ts +++ /dev/null @@ -1,97 +0,0 @@ -import { ChatFlow } from '../database/entities/ChatFlow' -import { IReactFlowObject } from '../Interface' -import { addBase64FilesToStorage } from 'flowise-components' - -export const containsBase64File = (chatflow: ChatFlow) => { - const parsedFlowData: IReactFlowObject = JSON.parse(chatflow.flowData) - const re = new RegExp('^data.*;base64', 'i') - let found = false - const nodes = parsedFlowData.nodes - for (const node of nodes) { - if (node.data.category !== 'Document Loaders') { - continue - } - const inputs = node.data.inputs - if (inputs) { - const keys = Object.getOwnPropertyNames(inputs) - for (let i = 0; i < keys.length; i++) { - const input = inputs[keys[i]] - if (!input) { - continue - } - if (typeof input !== 'string') { - continue - } - if (input.startsWith('[')) { - try { - const files = JSON.parse(input) - for (let j = 0; j < files.length; j++) { - const file = files[j] - if (re.test(file)) { - found = true - break - } - } - } catch (e) { - continue - } - } - if (re.test(input)) { - found = true - break - } - } - } - } - return found -} - -export const updateFlowDataWithFilePaths = async (chatflowid: string, flowData: string, orgId: string = '') => { - try { - const parsedFlowData: IReactFlowObject = JSON.parse(flowData) - const re = new RegExp('^data.*;base64', 'i') - const nodes = parsedFlowData.nodes - for (let j = 0; j < nodes.length; j++) { - const node = nodes[j] - if (node.data.category !== 'Document Loaders') { - continue - } - if (node.data.inputs) { - const inputs = node.data.inputs - const keys = Object.getOwnPropertyNames(inputs) - for (let i = 0; i < keys.length; i++) { - const fileNames: string[] = [] - const key = keys[i] - const input = inputs?.[key] - if (!input) { - continue - } - if (typeof input !== 'string') { - continue - } - if (input.startsWith('[')) { - try { - const files = JSON.parse(input) - for (let j = 0; j < files.length; j++) { - const file = files[j] - if (re.test(file)) { - const result = await addBase64FilesToStorage(file, chatflowid, fileNames, orgId) - node.data.inputs[key] = result.path - } - } - } catch (e) { - continue - } - } else if (re.test(input)) { - const result = await addBase64FilesToStorage(input, chatflowid, fileNames, orgId) - node.data.inputs[key] = result.path - } - } - } - } - - return JSON.stringify(parsedFlowData) - } catch (e) { - return '' - } -} diff --git a/studio-frontend/packages/server/src/utils/getUploadsConfig.ts b/studio-frontend/packages/server/src/utils/getUploadsConfig.ts deleted file mode 100644 index 4688d68..0000000 --- a/studio-frontend/packages/server/src/utils/getUploadsConfig.ts +++ /dev/null @@ -1,121 +0,0 @@ -import { StatusCodes } from 'http-status-codes' -import { INodeParams } from 'flowise-components' -import { ChatFlow } from '../database/entities/ChatFlow' -import { getRunningExpressApp } from '../utils/getRunningExpressApp' -import { IUploadFileSizeAndTypes, IReactFlowNode, IReactFlowEdge } from '../Interface' -import { InternalFlowiseError } from '../errors/internalFlowiseError' - -type IUploadConfig = { - isSpeechToTextEnabled: boolean - isImageUploadAllowed: boolean - isFileUploadAllowed: boolean - imgUploadSizeAndTypes: IUploadFileSizeAndTypes[] - fileUploadSizeAndTypes: IUploadFileSizeAndTypes[] -} - -/** - * Method that checks if uploads are enabled in the chatflow - * @param {string} chatflowid - */ -export const utilGetUploadsConfig = async (chatflowid: string): Promise => { - const appServer = getRunningExpressApp() - const chatflow = await appServer.AppDataSource.getRepository(ChatFlow).findOneBy({ - id: chatflowid - }) - if (!chatflow) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Chatflow ${chatflowid} not found`) - } - - const flowObj = JSON.parse(chatflow.flowData) - const nodes: IReactFlowNode[] = flowObj.nodes - const edges: IReactFlowEdge[] = flowObj.edges - - let isSpeechToTextEnabled = false - let isImageUploadAllowed = false - let isFileUploadAllowed = false - - /* - * Check for STT - */ - if (chatflow.speechToText) { - const speechToTextProviders = JSON.parse(chatflow.speechToText) - for (const provider in speechToTextProviders) { - if (provider !== 'none') { - const providerObj = speechToTextProviders[provider] - if (providerObj.status) { - isSpeechToTextEnabled = true - break - } - } - } - } - - /* - * Condition for isFileUploadAllowed - * 1.) vector store with fileUpload = true && connected to a document loader with fileType - */ - const fileUploadSizeAndTypes: IUploadFileSizeAndTypes[] = [] - for (const node of nodes) { - if (node.data.category === 'Vector Stores' && node.data.inputs?.fileUpload) { - // Get the connected document loader node fileTypes - const sourceDocumentEdges = edges.filter( - (edge) => edge.target === node.id && edge.targetHandle === `${node.id}-input-document-Document` - ) - for (const edge of sourceDocumentEdges) { - const sourceNode = nodes.find((node) => node.id === edge.source) - if (!sourceNode) continue - const fileType = sourceNode.data.inputParams.find((param) => param.type === 'file' && param.fileType)?.fileType - if (fileType) { - fileUploadSizeAndTypes.push({ - fileTypes: fileType.split(', '), - maxUploadSize: 500 - }) - isFileUploadAllowed = true - } - } - break - } - } - - /* - * Condition for isImageUploadAllowed - * 1.) one of the imgUploadAllowedNodes exists - * 2.) one of the imgUploadLLMNodes exists + allowImageUploads is ON - */ - const imgUploadSizeAndTypes: IUploadFileSizeAndTypes[] = [] - const imgUploadAllowedNodes = [ - 'llmChain', - 'conversationChain', - 'reactAgentChat', - 'conversationalAgent', - 'toolAgent', - 'supervisor', - 'seqStart' - ] - const imgUploadLLMNodes = ['chatOpenAI', 'chatAnthropic', 'awsChatBedrock', 'azureChatOpenAI', 'chatGoogleGenerativeAI'] - - if (nodes.some((node) => imgUploadAllowedNodes.includes(node.data.name))) { - nodes.forEach((node: IReactFlowNode) => { - if (imgUploadLLMNodes.indexOf(node.data.name) > -1) { - // TODO: for now the maxUploadSize is hardcoded to 5MB, we need to add it to the node properties - node.data.inputParams.map((param: INodeParams) => { - if (param.name === 'allowImageUploads' && node.data.inputs?.['allowImageUploads']) { - imgUploadSizeAndTypes.push({ - fileTypes: 'image/gif;image/jpeg;image/png;image/webp;'.split(';'), - maxUploadSize: 5 - }) - isImageUploadAllowed = true - } - }) - } - }) - } - - return { - isSpeechToTextEnabled, - isImageUploadAllowed, - isFileUploadAllowed, - imgUploadSizeAndTypes, - fileUploadSizeAndTypes - } -} diff --git a/studio-frontend/packages/server/src/utils/index.ts b/studio-frontend/packages/server/src/utils/index.ts index 874d194..ea178c5 100644 --- a/studio-frontend/packages/server/src/utils/index.ts +++ b/studio-frontend/packages/server/src/utils/index.ts @@ -16,30 +16,55 @@ import { IReactFlowEdge, IReactFlowNode, IVariableDict, - IncomingInput -} from '../Interface' -import { cloneDeep, get, isEqual } from 'lodash' -import { - convertChatHistoryToText, - getInputVariables, - handleEscapeCharacters, - getEncryptionKeyPath, + IncomingInput, ICommonObject, - IDatabaseEntity, IMessage, - FlowiseMemory, IFileUpload -} from 'flowise-components' +} from '../Interface' +import { cloneDeep, get, isEqual } from 'lodash' import { randomBytes } from 'crypto' import { AES, enc } from 'crypto-js' +// Local stubs for flowise-components utilities +export type IDatabaseEntity = { [key: string]: any } + +export interface FlowiseMemory { + getChatMessages(sessionId: string, returnBaseMessages?: boolean, prependMessages?: IMessage[]): Promise + clearChatMessages(sessionId: string): Promise +} + +export const handleEscapeCharacters = (input: string, toEscape: boolean): string => { + if (toEscape) { + return input.replace(/\n/g, 'FLOWISE_NEWLINE').replace(/\t/g, 'FLOWISE_TAB') + } else { + return input.replace(/FLOWISE_NEWLINE/g, '\n').replace(/FLOWISE_TAB/g, '\t') + } +} + +export const convertChatHistoryToText = (chatHistory: IMessage[]): string => { + return chatHistory.map((msg) => `${msg.role ?? msg.type}: ${msg.content ?? msg.message}`).join('\n') +} + +export const getInputVariables = (paramValue: string): string[] => { + if (typeof paramValue !== 'string') return [] + const matches = paramValue.match(/\{\{(.*?)\}\}/g) || [] + return matches.map((m) => m.slice(2, -2).trim()) +} + +export const getEncryptionKeyPath = (): string => { + if (process.env.SECRETKEY_PATH) { + return require('path').join(process.env.SECRETKEY_PATH, 'encryption.key') + } + const home = process.env[process.platform === 'win32' ? 'USERPROFILE' : 'HOME'] ?? process.cwd() + return require('path').join(home, '.flowise', 'encryption.key') +} + import { ChatFlow } from '../database/entities/ChatFlow' import { ChatMessage } from '../database/entities/ChatMessage' import { Credential } from '../database/entities/Credential' import { Tool } from '../database/entities/Tool' import { Assistant } from '../database/entities/Assistant' import { DataSource } from 'typeorm' -import { CachePool } from '../CachePool' import { Variable } from '../database/entities/Variable' import { DocumentStore } from '../database/entities/DocumentStore' import { DocumentStoreFileChunk } from '../database/entities/DocumentStoreFileChunk' @@ -430,7 +455,6 @@ type BuildFlowParams = { chatflowid: string appDataSource: DataSource overrideConfig?: ICommonObject - cachePool?: CachePool isUpsert?: boolean stopNodeId?: string uploads?: IFileUpload[] @@ -455,7 +479,6 @@ export const buildFlow = async ({ chatflowid, appDataSource, overrideConfig, - cachePool, isUpsert, stopNodeId, uploads, @@ -525,7 +548,6 @@ export const buildFlow = async ({ logger, appDataSource, databaseEntities, - cachePool, dynamicVariables, uploads, baseURL @@ -549,7 +571,6 @@ export const buildFlow = async ({ logger, appDataSource, databaseEntities, - cachePool, isUpsert, dynamicVariables, uploads, @@ -1116,7 +1137,7 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component obj = { node: flowNode.data.label, nodeId: flowNode.data.id, - label: inputParam.label, + label: inputParam.label ?? '', name: 'files', type: inputParam.fileType ?? inputParam.type } @@ -1124,7 +1145,7 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component obj = { node: flowNode.data.label, nodeId: flowNode.data.id, - label: inputParam.label, + label: inputParam.label ?? '', name: inputParam.name, type: inputParam.options ? inputParam.options @@ -1143,9 +1164,9 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component obj = { node: flowNode.data.label, nodeId: flowNode.data.id, - label: input.label, + label: input.label ?? '', name: input.name, - type: input.type === 'password' ? 'string' : input.type + type: input.type === 'password' ? 'string' : input.type ?? '' } configs.push(obj) } @@ -1156,7 +1177,7 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component obj = { node: flowNode.data.label, nodeId: flowNode.data.id, - label: inputParam.label, + label: inputParam.label ?? '', name: inputParam.name, type: inputParam.type === 'password' ? 'string' : inputParam.type } diff --git a/studio-frontend/packages/server/src/utils/telemetry.ts b/studio-frontend/packages/server/src/utils/telemetry.ts deleted file mode 100644 index 4b033f2..0000000 --- a/studio-frontend/packages/server/src/utils/telemetry.ts +++ /dev/null @@ -1,52 +0,0 @@ -import { v4 as uuidv4 } from 'uuid' -import { PostHog } from 'posthog-node' -import path from 'path' -import fs from 'fs' -import { getUserHome, getUserSettingsFilePath } from '.' - -export class Telemetry { - postHog?: PostHog - - constructor() { - if (process.env.DISABLE_FLOWISE_TELEMETRY !== 'true') { - this.postHog = new PostHog('phc_jEDuFYnOnuXsws986TLWzuisbRjwFqTl9JL8tDMgqme') - } else { - this.postHog = undefined - } - } - - async id(): Promise { - try { - const settingsContent = await fs.promises.readFile(getUserSettingsFilePath(), 'utf8') - const settings = JSON.parse(settingsContent) - return settings.instanceId - } catch (error) { - const instanceId = uuidv4() - const settings = { - instanceId - } - const defaultLocation = process.env.SECRETKEY_PATH - ? path.join(process.env.SECRETKEY_PATH, 'settings.json') - : path.join(getUserHome(), '.flowise', 'settings.json') - await fs.promises.writeFile(defaultLocation, JSON.stringify(settings, null, 2)) - return instanceId - } - } - - async sendTelemetry(event: string, properties = {}): Promise { - if (this.postHog) { - const distinctId = await this.id() - this.postHog.capture({ - event, - distinctId, - properties - }) - } - } - - async flush(): Promise { - if (this.postHog) { - await this.postHog.shutdownAsync() - } - } -} diff --git a/studio-frontend/packages/server/src/utils/upsertVector.ts b/studio-frontend/packages/server/src/utils/upsertVector.ts deleted file mode 100644 index a6f67e9..0000000 --- a/studio-frontend/packages/server/src/utils/upsertVector.ts +++ /dev/null @@ -1,182 +0,0 @@ -import { Request } from 'express' -import * as fs from 'fs' -import { cloneDeep, omit } from 'lodash' -import { ICommonObject, IMessage, addArrayFilesToStorage, mapMimeTypeToInputField } from 'flowise-components' -import telemetryService from '../services/telemetry' -import logger from '../utils/logger' -import { - buildFlow, - constructGraphs, - getAllConnectedNodes, - findMemoryNode, - getMemorySessionId, - getAppVersion, - getTelemetryFlowObj, - getStartingNodes -} from '../utils' -import { validateChatflowAPIKey } from './validateKey' -import { IncomingInput, INodeDirectedGraph, IReactFlowObject, chatType } from '../Interface' -import { ChatFlow } from '../database/entities/ChatFlow' -import { getRunningExpressApp } from '../utils/getRunningExpressApp' -import { UpsertHistory } from '../database/entities/UpsertHistory' -import { InternalFlowiseError } from '../errors/internalFlowiseError' -import { StatusCodes } from 'http-status-codes' -import { getErrorMessage } from '../errors/utils' - -/** - * Upsert documents - * @param {Request} req - * @param {boolean} isInternal - */ -export const upsertVector = async (req: Request, isInternal: boolean = false) => { - try { - const appServer = getRunningExpressApp() - const chatflowid = req.params.id - let incomingInput: IncomingInput = req.body - - const chatflow = await appServer.AppDataSource.getRepository(ChatFlow).findOneBy({ - id: chatflowid - }) - if (!chatflow) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, `Chatflow ${chatflowid} not found`) - } - - if (!isInternal) { - const isKeyValidated = await validateChatflowAPIKey(req, chatflow) - if (!isKeyValidated) { - throw new InternalFlowiseError(StatusCodes.UNAUTHORIZED, `Unauthorized`) - } - } - - const files = (req.files as Express.Multer.File[]) || [] - - if (files.length) { - const overrideConfig: ICommonObject = { ...req.body } - const fileNames: string[] = [] - for (const file of files) { - const fileBuffer = fs.readFileSync(file.path) - - const storagePath = await addArrayFilesToStorage(file.mimetype, fileBuffer, file.originalname, fileNames, chatflowid) - - const fileInputField = mapMimeTypeToInputField(file.mimetype) - - overrideConfig[fileInputField] = storagePath - - fs.unlinkSync(file.path) - } - incomingInput = { - question: req.body.question ?? 'hello', - overrideConfig, - stopNodeId: req.body.stopNodeId - } - if (req.body.chatId) { - incomingInput.chatId = req.body.chatId - } - } - - /*** Get chatflows and prepare data ***/ - const flowData = chatflow.flowData - const parsedFlowData: IReactFlowObject = JSON.parse(flowData) - const nodes = parsedFlowData.nodes - const edges = parsedFlowData.edges - - let stopNodeId = incomingInput?.stopNodeId ?? '' - let chatHistory: IMessage[] = [] - let chatId = incomingInput.chatId ?? '' - let isUpsert = true - - // Get session ID - const memoryNode = findMemoryNode(nodes, edges) - let sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal) - - const vsNodes = nodes.filter((node) => node.data.category === 'Vector Stores') - - // Get StopNodeId for vector store which has fielUpload - const vsNodesWithFileUpload = vsNodes.filter((node) => node.data.inputs?.fileUpload) - if (vsNodesWithFileUpload.length > 1) { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, 'Multiple vector store nodes with fileUpload enabled') - } else if (vsNodesWithFileUpload.length === 1 && !stopNodeId) { - stopNodeId = vsNodesWithFileUpload[0].data.id - } - - // Check if multiple vector store nodes exist, and if stopNodeId is specified - if (vsNodes.length > 1 && !stopNodeId) { - throw new InternalFlowiseError( - StatusCodes.INTERNAL_SERVER_ERROR, - 'There are multiple vector nodes, please provide stopNodeId in body request' - ) - } else if (vsNodes.length === 1 && !stopNodeId) { - stopNodeId = vsNodes[0].data.id - } else if (!vsNodes.length && !stopNodeId) { - throw new InternalFlowiseError(StatusCodes.NOT_FOUND, 'No vector node found') - } - - const { graph } = constructGraphs(nodes, edges, { isReversed: true }) - - const nodeIds = getAllConnectedNodes(graph, stopNodeId) - - const filteredGraph: INodeDirectedGraph = {} - for (const key of nodeIds) { - if (Object.prototype.hasOwnProperty.call(graph, key)) { - filteredGraph[key] = graph[key] - } - } - - const { startingNodeIds, depthQueue } = getStartingNodes(filteredGraph, stopNodeId) - - const upsertedResult = await buildFlow({ - startingNodeIds, - reactFlowNodes: nodes, - reactFlowEdges: edges, - graph: filteredGraph, - depthQueue, - componentNodes: appServer.nodesPool.componentNodes, - question: incomingInput.question, - chatHistory, - chatId, - sessionId: sessionId ?? '', - chatflowid, - appDataSource: appServer.AppDataSource, - overrideConfig: incomingInput?.overrideConfig, - cachePool: appServer.cachePool, - isUpsert, - stopNodeId - }) - - const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.data.id)) - - await appServer.chatflowPool.add(chatflowid, undefined, startingNodes, incomingInput?.overrideConfig, chatId) - - // Save to DB - if (upsertedResult['flowData'] && upsertedResult['result']) { - const result = cloneDeep(upsertedResult) - result['flowData'] = JSON.stringify(result['flowData']) - result['result'] = JSON.stringify(omit(result['result'], ['totalKeys', 'addedDocs'])) - result.chatflowid = chatflowid - const newUpsertHistory = new UpsertHistory() - Object.assign(newUpsertHistory, result) - const upsertHistory = appServer.AppDataSource.getRepository(UpsertHistory).create(newUpsertHistory) - await appServer.AppDataSource.getRepository(UpsertHistory).save(upsertHistory) - } - - await telemetryService.createEvent({ - name: `vector_upserted`, - data: { - version: await getAppVersion(), - chatlowId: chatflowid, - type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL, - flowGraph: getTelemetryFlowObj(nodes, edges), - stopNodeId - } - }) - - return upsertedResult['result'] ?? { result: 'Successfully Upserted' } - } catch (e) { - logger.error('[server]: Error:', e) - if (e instanceof InternalFlowiseError && e.statusCode === StatusCodes.UNAUTHORIZED) { - throw e - } else { - throw new InternalFlowiseError(StatusCodes.INTERNAL_SERVER_ERROR, getErrorMessage(e)) - } - } -} diff --git a/studio-frontend/packages/ui/package.json b/studio-frontend/packages/ui/package.json index 9939ac0..f659737 100644 --- a/studio-frontend/packages/ui/package.json +++ b/studio-frontend/packages/ui/package.json @@ -42,8 +42,8 @@ "flowise-react-json-view": "*", "formik": "^2.2.6", "framer-motion": "^4.1.13", - "history": "^5.0.0", "html-react-parser": "^3.0.4", + "i18next": "^25.3.2", "keycloak-js": "^26.0.5", "lodash": "^4.17.21", "lowlight": "^3.3.0", @@ -56,6 +56,7 @@ "react-datepicker": "^4.21.0", "react-device-detect": "^1.17.0", "react-dom": "^18.2.0", + "react-i18next": "^15.6.0", "react-markdown": "^8.0.6", "react-perfect-scrollbar": "^1.5.8", "react-redux": "^8.0.5", @@ -69,9 +70,7 @@ "rehype-mathjax": "^4.0.2", "remark-gfm": "^3.0.1", "remark-math": "^5.1.1", - "showdown": "^2.1.0", "socket.io-client": "^4.6.1", - "tippy.js": "^6.3.7", "uuid": "^9.0.1", "yup": "^0.32.9" }, diff --git a/studio-frontend/packages/ui/src/api/evaluation.js b/studio-frontend/packages/ui/src/api/evaluation.js new file mode 100644 index 0000000..fd5ac8e --- /dev/null +++ b/studio-frontend/packages/ui/src/api/evaluation.js @@ -0,0 +1,73 @@ +import client from './client' + +const evaluationApi = { + // ── Runs ────────────────────────────────────────────────────────────────── + + // List all evaluation runs + getRuns: () => client.get('/evaluation/runs'), + + // Get a single evaluation run (used for polling) + getRun: (runId) => client.get(`/evaluation/runs/${runId}`), + + // Create a new evaluation run + createRun: (payload) => client.post('/evaluation/runs', payload), + + // ── Datasets ────────────────────────────────────────────────────────────── + + // List all golden datasets + getDatasets: () => client.get('/evaluation/datasets'), + + // Get a single dataset + getDataset: (datasetId) => client.get(`/evaluation/datasets/${datasetId}`), + + // Create a new dataset (manual entries) + createDataset: (payload) => client.post('/evaluation/datasets', payload), + + // Synthesize a dataset from a sandbox + synthesizeDataset: (payload) => client.post('/evaluation/datasets/synthesize', payload), + + // ── Lookup helpers (for modal dropdowns) ───────────────────────────────── + + // List sandboxes available for evaluation + getSandboxes: () => client.get('/evaluation/sandboxes'), + + // List judge models available + getModels: () => client.get('/evaluation/models'), + + // Pull/register a new judge model + pullModel: (payload) => client.post('/evaluation/models/pull', payload), + + // Get pull progress for all models currently being pulled + getPullStatus: () => client.get('/evaluation/models/pull/status'), + + // Check files in sandbox Data Management (dataprep) + getSandboxDataManagementFiles: (payload) => client.post('/evaluation/sandbox-files', payload), + + // Delete an evaluation run + deleteRun: (runId) => client.delete(`/evaluation/runs/${runId}`), + + // Stop a running/pending evaluation run + stopRun: (runId) => client.post(`/evaluation/runs/${runId}/stop`), + + // Delete a dataset + deleteDataset: (datasetId) => client.delete(`/evaluation/datasets/${datasetId}`), + + // Stop a pending/synthesizing dataset job + stopDataset: (datasetId) => client.post(`/evaluation/datasets/${datasetId}/stop`), + + // Update dataset metadata (name / description) + updateDataset: (datasetId, payload) => client.put(`/evaluation/datasets/${datasetId}`, payload), + + // Update a single entry inside a dataset + updateEntry: (datasetId, entryId, payload) => + client.put(`/evaluation/datasets/${datasetId}/entries/${entryId}`, payload), + + // Add one or more entries to an existing dataset + addEntries: (datasetId, payload) => client.post(`/evaluation/datasets/${datasetId}/entries`, payload), + + // Delete a single entry from a dataset + deleteEntry: (datasetId, entryId) => + client.delete(`/evaluation/datasets/${datasetId}/entries/${entryId}`), +} + +export default evaluationApi diff --git a/studio-frontend/packages/ui/src/i18n.js b/studio-frontend/packages/ui/src/i18n.js index 83aed59..460f898 100644 --- a/studio-frontend/packages/ui/src/i18n.js +++ b/studio-frontend/packages/ui/src/i18n.js @@ -8,11 +8,22 @@ const resources = { zh: { translation: zh } }; +const getInitialLanguage = () => { + if (typeof window === 'undefined') return 'en'; + + try { + const preferredLanguage = window.localStorage.getItem('preferredLanguage') || ''; + return preferredLanguage.toLowerCase().startsWith('zh') ? 'zh' : 'en'; + } catch (error) { + return 'en'; + } +}; + i18n .use(initReactI18next) .init({ resources, - lng: 'zh', // 默认中文 + lng: getInitialLanguage(), fallbackLng: 'en', interpolation: { escapeValue: false diff --git a/studio-frontend/packages/ui/src/layout/MainLayout/Header/index.jsx b/studio-frontend/packages/ui/src/layout/MainLayout/Header/index.jsx index 69bffcc..3ccc8c6 100644 --- a/studio-frontend/packages/ui/src/layout/MainLayout/Header/index.jsx +++ b/studio-frontend/packages/ui/src/layout/MainLayout/Header/index.jsx @@ -1,8 +1,10 @@ import PropTypes from 'prop-types' +import { useState } from 'react' import { useTheme, styled } from '@mui/material/styles' -import { Avatar, Box, ButtonBase, Switch, Button, IconButton, useMediaQuery } from '@mui/material' +import { Avatar, Box, ButtonBase, Switch, Button, IconButton, useMediaQuery, Menu, MenuItem } from '@mui/material' import MenuIcon from '@mui/icons-material/Menu' import LogoutIcon from '@mui/icons-material/Logout' +import LanguageIcon from '@mui/icons-material/Language' import { useKeycloak } from '../../../KeycloakContext' import { useTranslation } from 'react-i18next' import LogoSection from '../LogoSection' @@ -61,9 +63,25 @@ const Header = ({ userId, handleLeftDrawerToggle }) => { const matchDownMd = useMediaQuery(theme.breakpoints.down('md')) const { i18n } = useTranslation() const keycloak = useKeycloak() + const isChinese = i18n.language?.toLowerCase().startsWith('zh') + const selectedLanguage = isChinese ? 'zh' : 'en' + + const [anchorEl, setAnchorEl] = useState(null) + const handleLanguageMenuOpen = (event) => { + setAnchorEl(event.currentTarget) + } + const handleLanguageMenuClose = () => { + setAnchorEl(null) + } const handleLanguageChange = (lng) => { + try { + window.localStorage.setItem('preferredLanguage', lng) + } catch (error) { + // Ignore storage errors and still change language in-memory. + } i18n.changeLanguage(lng) + handleLanguageMenuClose() } const getAvatarLetters = (uid = '') => { @@ -138,38 +156,34 @@ const Header = ({ userId, handleLeftDrawerToggle }) => { - - - + handleLanguageChange('en')} selected={selectedLanguage === 'en'}> + English + + handleLanguageChange('zh')} selected={selectedLanguage === 'zh'}> + 中文 + + keycloak.logout({ redirectUri: window.location.origin })} diff --git a/studio-frontend/packages/ui/src/menu-items/dashboard.js b/studio-frontend/packages/ui/src/menu-items/dashboard.js index ba88227..0d196bc 100644 --- a/studio-frontend/packages/ui/src/menu-items/dashboard.js +++ b/studio-frontend/packages/ui/src/menu-items/dashboard.js @@ -10,11 +10,12 @@ import { IconVariable, IconFiles, IconApps, - IconBrain + IconBrain, + IconListCheck } from '@tabler/icons-react' // constant -const icons = { IconUsersGroup, IconHierarchy, IconBuildingStore, IconKey, IconTool, IconLock, IconRobot, IconVariable, IconFiles, IconApps, IconBrain } +const icons = { IconUsersGroup, IconHierarchy, IconBuildingStore, IconKey, IconTool, IconLock, IconRobot, IconVariable, IconFiles, IconApps, IconBrain, IconListCheck } // ==============================|| DASHBOARD MENU ITEMS ||============================== // @@ -38,6 +39,14 @@ const dashboard = { url: '/finetuning', icon: icons.IconBrain, breadcrumbs: true + }, + { + id: 'evaluation', + title: 'Evaluation', + type: 'item', + url: '/evaluation', + icon: icons.IconListCheck, + breadcrumbs: true } // { // id: 'chatflows', diff --git a/studio-frontend/packages/ui/src/routes/MainRoutes.jsx b/studio-frontend/packages/ui/src/routes/MainRoutes.jsx index 6b7a388..fd9bacc 100644 --- a/studio-frontend/packages/ui/src/routes/MainRoutes.jsx +++ b/studio-frontend/packages/ui/src/routes/MainRoutes.jsx @@ -10,6 +10,9 @@ const Opeaflows = Loadable(lazy(() => import('@/views/opeaflows'))) // finetuning routing const Finetuning = Loadable(lazy(() => import('@/views/finetuning'))) +// evaluation routing +const Evaluation = Loadable(lazy(() => import('@/views/evaluation'))) + // tracer routing const Tracer = Loadable(lazy(() => import('@/views/tracer'))) @@ -66,6 +69,10 @@ const MainRoutes = { path: '/finetuning', element: }, + { + path: '/evaluation', + element: + }, { path:'/tracer/:ns', element: diff --git a/studio-frontend/packages/ui/src/ui-component/file/FileUploadArea.jsx b/studio-frontend/packages/ui/src/ui-component/file/FileUploadArea.jsx new file mode 100644 index 0000000..24546e8 --- /dev/null +++ b/studio-frontend/packages/ui/src/ui-component/file/FileUploadArea.jsx @@ -0,0 +1,327 @@ +import { useState, useRef } from 'react' +import PropTypes from 'prop-types' + +// material-ui +import { + Box, + Button, + LinearProgress, + Paper, + Stack, + Typography, + IconButton, + Chip, + Alert +} from '@mui/material' +import { useTheme, alpha } from '@mui/material/styles' + +// icons +import { + IconUpload, + IconFile, + IconX, + IconCheck, + IconAlertTriangle +} from '@tabler/icons-react' + +const FileUploadArea = ({ + onFileUpload, + acceptedTypes = ['.json', '.jsonl', '.csv'], + maxSizeMB = 10, + error = null, + invalidTypeMessage, + title = 'Drop your training dataset file here or click to browse', + subtitle, + buttonLabel = 'Choose File' +}) => { + const theme = useTheme() + const fileInputRef = useRef(null) + + const [dragActive, setDragActive] = useState(false) + const [uploadedFile, setUploadedFile] = useState(null) + const [uploadProgress, setUploadProgress] = useState(0) + const [uploadError, setUploadError] = useState(null) + const [preview, setPreview] = useState(null) + + const maxSizeBytes = maxSizeMB * 1024 * 1024 + + const validateFile = (file) => { + const errors = [] + + // Check file type + const fileExtension = '.' + file.name.split('.').pop().toLowerCase() + if (!acceptedTypes.includes(fileExtension)) { + errors.push(invalidTypeMessage || `File type ${fileExtension} not supported. Accepted types: ${acceptedTypes.join(', ')}`) + } + + // Check file size + if (file.size > maxSizeBytes) { + errors.push(`File size (${(file.size / 1024 / 1024).toFixed(2)}MB) exceeds limit of ${maxSizeMB}MB`) + } + + return errors + } + + const previewFile = async (file) => { + try { + const text = await file.text() + const lines = text.split('\n').slice(0, 5) // First 5 lines + setPreview({ + lines, + totalSize: file.size, + totalLines: text.split('\n').length + }) + } catch (error) { + console.error('Error previewing file:', error) + setPreview(null) + } + } + + const handleFileUpload = async (file) => { + const validationErrors = validateFile(file) + if (validationErrors.length > 0) { + setUploadError(validationErrors[0]) + return + } + + setUploadError(null) + setUploadProgress(0) + + // Simulate upload progress + const uploadInterval = setInterval(() => { + setUploadProgress(prev => { + if (prev >= 90) { + clearInterval(uploadInterval) + return prev + } + return prev + 10 + }) + }, 100) + + try { + // Simulate upload delay + await new Promise(resolve => setTimeout(resolve, 1000)) + + setUploadProgress(100) + + // Store the actual File object so parent can upload the real Blob/File + setUploadedFile(file) + + // Generate preview + await previewFile(file) + + // Notify parent with the real File object (not a plain JS object) + // Parent will perform the FormData upload and receive server response + onFileUpload(file) + + setTimeout(() => setUploadProgress(0), 500) + } catch (error) { + console.error('Upload error:', error) + setUploadError('Failed to upload file. Please try again.') + setUploadProgress(0) + } + } + + const handleDrag = (e) => { + e.preventDefault() + e.stopPropagation() + if (e.type === 'dragenter' || e.type === 'dragover') { + setDragActive(true) + } else if (e.type === 'dragleave') { + setDragActive(false) + } + } + + const handleDrop = (e) => { + e.preventDefault() + e.stopPropagation() + setDragActive(false) + + if (e.dataTransfer.files && e.dataTransfer.files[0]) { + handleFileUpload(e.dataTransfer.files[0]) + } + } + + const handleFileSelect = (e) => { + if (e.target.files && e.target.files[0]) { + handleFileUpload(e.target.files[0]) + } + } + + const removeFile = () => { + setUploadedFile(null) + setPreview(null) + setUploadError(null) + setUploadProgress(0) + onFileUpload(null) + + if (fileInputRef.current) { + fileInputRef.current.value = '' + } + } + + const openFileDialog = () => { + fileInputRef.current?.click() + } + + const formatFileSize = (bytes) => { + if (bytes === 0) return '0 Bytes' + const k = 1024 + const sizes = ['Bytes', 'KB', 'MB', 'GB'] + const i = Math.floor(Math.log(bytes) / Math.log(k)) + return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i] + } + + const helperText = subtitle || `Supported formats: ${acceptedTypes.join(', ')} (Max filesize: ${maxSizeMB}MB)` + + return ( + + + + {!uploadedFile ? ( + + + + + + + {title} + + + {helperText} + + + + + + + ) : ( + + + {/* File Info */} + + + + + {uploadedFile.name} + + } + label="Selected" + color="primary" + size="small" + variant="outlined" + /> + + + + + + + + {formatFileSize(uploadedFile.size)} + + + {/* Upload Progress */} + {uploadProgress > 0 && uploadProgress < 100 && ( + + )} + + {/* File Preview */} + {preview && ( + + + Preview ({preview.totalLines} total lines): + + + + {preview.lines.join('\n')} + {preview.lines.length < preview.totalLines && '\n...'} + + + + )} + + + )} + + {/* Error Display */} + {(uploadError || error) && ( + } + > + {uploadError || error} + + )} + + ) +} + +FileUploadArea.propTypes = { + onFileUpload: PropTypes.func.isRequired, + acceptedTypes: PropTypes.arrayOf(PropTypes.string), + maxSizeMB: PropTypes.number, + error: PropTypes.string, + invalidTypeMessage: PropTypes.string, + title: PropTypes.string, + subtitle: PropTypes.string, + buttonLabel: PropTypes.string +} + +export default FileUploadArea \ No newline at end of file diff --git a/studio-frontend/packages/ui/src/views/evaluation/CreateRunModal.jsx b/studio-frontend/packages/ui/src/views/evaluation/CreateRunModal.jsx new file mode 100644 index 0000000..95658c8 --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/CreateRunModal.jsx @@ -0,0 +1,415 @@ +import { useState, useEffect } from 'react' +import PropTypes from 'prop-types' + +// material-ui +import { + Box, + Button, + Checkbox, + CircularProgress, + Collapse, + Dialog, + DialogActions, + DialogContent, + DialogTitle, + Divider, + FormControl, + FormControlLabel, + FormGroup, + FormLabel, + IconButton, + InputAdornment, + InputLabel, + MenuItem, + Select, + Stack, + TextField, + Typography +} from '@mui/material' + +// icons +import { IconX, IconChevronDown, IconChevronUp } from '@tabler/icons-react' + +// API +import evaluationApi from '@/api/evaluation' +import chatflowsApi from '@/api/chatflows' + +// Components +import ModelSelect from './ModelSelect' + +const METRICS = [ + 'AnswerRelevancy', + 'Faithfulness' +] + +const sanitizeWorkflowNodes = (nodes = []) => + nodes + .map((node) => { + const inputs = Object.fromEntries( + Object.entries(node?.data?.inputs || {}).filter( + ([, value]) => + value !== '' && + value !== null && + value !== undefined && + !String(value).startsWith('{{') + ) + ) + + return { + id: node?.id, + data: { + label: node?.data?.label, + inputs, + inputParams: (node?.data?.inputParams || []).map((param) => ({ + name: param?.name, + label: param?.label + })) + } + } + }) + .filter((node) => Object.keys(node?.data?.inputs || {}).length > 0) + +const hasDataprepNode = (nodes = []) => + nodes.some((node) => String(node?.data?.name || '').startsWith('opea_service@prepare_doc_redis_prep')) + +const buildRunName = ({ sandboxId, datasetId, modelName }) => { + const sandboxLabel = sandboxId || 'sandbox' + const datasetLabel = datasetId || 'dataset' + const modelLabel = modelName || 'model' + + return `${sandboxLabel} · dataset ${datasetLabel} · ${modelLabel}` +} + +const createConfigurationSnapshot = async ({ sandboxId, systemPrompt, temperature, maxTokens }) => { + const snapshot = { + captured_at: new Date().toISOString(), + request: { + system_prompt: systemPrompt, + temperature, + max_tokens: maxTokens, + request_model: 'NA' + }, + workflow_nodes: [], + data_management: { + status: 'idle', + files: [], + error: null + } + } + + const chatflowId = String(sandboxId || '').replace(/^sandbox-/, '') + + if (!chatflowId) { + snapshot.data_management.status = 'not-applicable' + return snapshot + } + + try { + const cfRes = await chatflowsApi.getSpecificChatflow(chatflowId) + const flowData = cfRes.data?.flowData + const parsed = typeof flowData === 'string' ? JSON.parse(flowData) : flowData + const nodes = parsed?.nodes || [] + + snapshot.workflow_nodes = sanitizeWorkflowNodes(nodes) + + if (!hasDataprepNode(nodes)) { + snapshot.data_management.status = 'not-applicable' + return snapshot + } + + try { + const filesRes = await evaluationApi.getSandboxDataManagementFiles({ + sandbox_id: sandboxId + }) + snapshot.data_management.status = 'done' + snapshot.data_management.files = Array.isArray(filesRes.data?.files) ? filesRes.data.files : [] + } catch (filesErr) { + snapshot.data_management.status = 'error' + snapshot.data_management.error = + filesErr?.response?.data?.message || + filesErr?.response?.data?.detail || + filesErr?.message || + 'Unknown error' + } + } catch (chatflowErr) { + snapshot.data_management.status = 'error' + snapshot.data_management.error = + chatflowErr?.response?.data?.message || + chatflowErr?.response?.data?.detail || + chatflowErr?.message || + 'Failed to load workflow configuration' + } + + return snapshot +} + +const CreateRunModal = ({ open, onClose, onRunCreated }) => { + const [sandboxes, setSandboxes] = useState([]) + const [datasets, setDatasets] = useState([]) + const [models, setModels] = useState([]) + const [loadingOptions, setLoadingOptions] = useState(false) + + const [sandboxId, setSandboxId] = useState('') + const [datasetId, setDatasetId] = useState('') + const [judgeModel, setJudgeModel] = useState('') + const [selectedMetrics, setSelectedMetrics] = useState(['AnswerRelevancy', 'Faithfulness']) + const [submitting, setSubmitting] = useState(false) + const [error, setError] = useState('') + const [showAdvanced, setShowAdvanced] = useState(false) + const [systemPrompt, setSystemPrompt] = useState('You are a helpful assistant') + const [temperature, setTemperature] = useState(0.4) + const [maxTokens, setMaxTokens] = useState(100) + + const fetchModels = () => { + evaluationApi.getModels().then((res) => setModels(res.data || [])).catch(() => {}) + } + + useEffect(() => { + if (!open) return + setLoadingOptions(true) + setError('') + Promise.all([ + evaluationApi.getSandboxes(), + evaluationApi.getDatasets(), + evaluationApi.getModels() + ]) + .then(([sbRes, dsRes, mdRes]) => { + setSandboxes(sbRes.data?.sandboxes || []) + setDatasets((dsRes.data || []).filter((ds) => ds.status === 'completed')) + setModels(mdRes.data || []) + }) + .catch(() => setError('Failed to load options.')) + .finally(() => setLoadingOptions(false)) + }, [open]) + + const handleMetricToggle = (metric) => { + setSelectedMetrics((prev) => + prev.includes(metric) ? prev.filter((m) => m !== metric) : [...prev, metric] + ) + } + + const handleSubmit = async () => { + if (!sandboxId || !datasetId || !judgeModel || selectedMetrics.length === 0) { + setError('Please fill in all fields and select at least one metric.') + return + } + setSubmitting(true) + setError('') + try { + const parsedTemperature = parseFloat(temperature) + const parsedMaxTokens = parseInt(maxTokens, 10) + const configurationSnapshot = await createConfigurationSnapshot({ + sandboxId, + systemPrompt, + temperature: parsedTemperature, + maxTokens: parsedMaxTokens + }) + + const res = await evaluationApi.createRun({ + name: buildRunName({ + sandboxId, + datasetId, + modelName: judgeModel + }), + sandbox_id: sandboxId, + dataset_id: datasetId, + model_name: judgeModel, + metrics: selectedMetrics, + system_prompt: systemPrompt, + temperature: parsedTemperature, + max_tokens: parsedMaxTokens, + configuration_snapshot: configurationSnapshot + }) + onRunCreated && onRunCreated(res.data) + handleClose() + } catch (err) { + const detail = err?.response?.data?.detail + + if (Array.isArray(detail) && detail.length > 0) { + setError(detail.map((item) => item.msg).join(' ')) + } else { + setError(err?.response?.data?.message || err?.response?.data?.detail || 'Failed to create run.') + } + } finally { + setSubmitting(false) + } + } + + const handleClose = () => { + setSandboxId('') + setDatasetId('') + setJudgeModel('') + setSelectedMetrics(['AnswerRelevancy', 'Faithfulness']) + setShowAdvanced(false) + setSystemPrompt('You are a helpful assistant') + setTemperature(0.4) + setMaxTokens(100) + setError('') + onClose() + } + + return ( + + + New Evaluation Run + + + + + + + {loadingOptions ? ( + + + + ) : ( + + {/* Sandbox */} + + Sandbox + + + + {/* Dataset */} + + Dataset + + + + {/* Judge Model */} + + + {/* Metrics */} + + + Metrics + + + {METRICS.map((metric) => ( + handleMetricToggle(metric)} + /> + } + label={{metric}} + sx={{ width: '50%' }} + /> + ))} + + + + {/* Advanced settings toggle */} + + + + + + setSystemPrompt(e.target.value)} + helperText='System message sent to the app-backend for every question.' + /> + + setTemperature(e.target.value)} + inputProps={{ min: 0, max: 2, step: 0.05 }} + sx={{ flex: 1 }} + /> + setMaxTokens(e.target.value)} + inputProps={{ min: 1, step: 1 }} + InputProps={{ + endAdornment: tok + }} + sx={{ flex: 1 }} + /> + + + + + + {error && ( + + {error} + + )} + + )} + + + + + + + + ) +} + +CreateRunModal.propTypes = { + open: PropTypes.bool.isRequired, + onClose: PropTypes.func.isRequired, + onRunCreated: PropTypes.func +} + +export default CreateRunModal diff --git a/studio-frontend/packages/ui/src/views/evaluation/DatasetDetailDialog.jsx b/studio-frontend/packages/ui/src/views/evaluation/DatasetDetailDialog.jsx new file mode 100644 index 0000000..28257c0 --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/DatasetDetailDialog.jsx @@ -0,0 +1,951 @@ +import { useState, useEffect, useCallback } from 'react' +import PropTypes from 'prop-types' + +// material-ui +import { + Alert, + Box, + Button, + Chip, + CircularProgress, + Dialog, + DialogActions, + DialogContent, + DialogTitle, + Divider, + Drawer, + IconButton, + Paper, + Stack, + Tab, + Table, + TableBody, + TableCell, + TableContainer, + TableHead, + TablePagination, + TableRow, + Tabs, + TextField, + Tooltip, + Typography +} from '@mui/material' +import { styled } from '@mui/material/styles' +import { tableCellClasses } from '@mui/material/TableCell' + +// icons +import { IconCheck, IconPencil, IconPlus, IconTrash, IconX } from '@tabler/icons-react' + +// API +import evaluationApi from '@/api/evaluation' + +// ── Constants ───────────────────────────────────────────────────────────────── + +const ROWS_PER_PAGE = 10 + +const STATUS_COLOR = { + pending: 'default', + synthesizing: 'warning', + completed: 'success', + failed: 'error' +} + +// ── Styled components (same pattern as DatasetTab) ──────────────────────────── + +const StyledTableCell = styled(TableCell)(({ theme }) => ({ + borderColor: theme.palette.grey[900] + 25, + [`&.${tableCellClasses.head}`]: { + color: theme.palette.grey[900] + }, + [`&.${tableCellClasses.body}`]: { + fontSize: 14 + } +})) + +const StyledTableRow = styled(TableRow)(() => ({ + '&:last-child td, &:last-child th': { + border: 0 + } +})) + +// ── Helpers ─────────────────────────────────────────────────────────────────── + +const contextToText = (context) => { + if (!context || context.length === 0) return '' + return context.join('\n') +} + +const textToContext = (text) => { + if (!text.trim()) return [] + return text + .split('\n') + .map((s) => s.trim()) + .filter(Boolean) +} + +function TabPanel(props) { + const { children, value, index, ...other } = props + + return ( + + ) +} + +TabPanel.propTypes = { + children: PropTypes.node, + index: PropTypes.number.isRequired, + value: PropTypes.number.isRequired +} + +// ── DatasetDetailDialog ─────────────────────────────────────────────────────── + +const DatasetDetailDialog = ({ open, datasetId, onClose, onDatasetChanged }) => { + const [loading, setLoading] = useState(false) + const [dataset, setDataset] = useState(null) + + // Editable metadata + const [name, setName] = useState('') + const [description, setDescription] = useState('') + const [savingMeta, setSavingMeta] = useState(false) + const [metaError, setMetaError] = useState('') + + // Per-row edit state + // editingRow: null | { id: number|null, input, expected_output, contextText } + // id === null means new entry + const [editingRow, setEditingRow] = useState(null) + const [savingRow, setSavingRow] = useState(false) + const [rowError, setRowError] = useState('') + + // Entry deletion + const [confirmDeleteId, setConfirmDeleteId] = useState(null) + const [deletingEntry, setDeletingEntry] = useState(false) + + // Pagination + const [page, setPage] = useState(0) + + // Focused row detail drawer + const [selectedEntry, setSelectedEntry] = useState(null) + const [drawerOpen, setDrawerOpen] = useState(false) + const [detailTabValue, setDetailTabValue] = useState(0) + + // ── Data loading ────────────────────────────────────────────────────────── + + const loadDataset = useCallback(async (id) => { + setLoading(true) + try { + const res = await evaluationApi.getDataset(id) + const ds = res.data + setDataset(ds) + setName(ds.name || '') + setDescription(ds.description || '') + } catch { + setDataset(null) + } finally { + setLoading(false) + } + }, []) + + useEffect(() => { + if (open && datasetId) { + setPage(0) + setEditingRow(null) + setConfirmDeleteId(null) + setSelectedEntry(null) + setDrawerOpen(false) + setDetailTabValue(0) + setMetaError('') + setRowError('') + setDataset(null) + loadDataset(datasetId) + } + }, [open, datasetId, loadDataset]) + + // ── Metadata ────────────────────────────────────────────────────────────── + + const metaDirty = + dataset !== null && + (name !== (dataset.name || '') || description !== (dataset.description || '')) + + const handleSaveMeta = async () => { + if (!name.trim()) { + setMetaError('Name is required.') + return + } + setSavingMeta(true) + setMetaError('') + try { + const res = await evaluationApi.updateDataset(datasetId, { + name: name.trim(), + description: description.trim() + }) + const updated = res.data + setDataset((prev) => ({ ...prev, name: updated.name, description: updated.description })) + onDatasetChanged && onDatasetChanged({ id: datasetId, name: updated.name, description: updated.description }) + } catch (err) { + setMetaError(err?.response?.data?.message || 'Failed to save metadata.') + } finally { + setSavingMeta(false) + } + } + + // ── Entry editing ───────────────────────────────────────────────────────── + + const handleEditRow = (entry) => { + setDrawerOpen(false) + setSelectedEntry(null) + setRowError('') + setEditingRow({ + id: entry.id, + input: entry.input || '', + expected_output: entry.expected_output || '', + contextText: contextToText(entry.context) + }) + } + + const handleAddRow = () => { + setDrawerOpen(false) + setSelectedEntry(null) + setRowError('') + setEditingRow({ id: null, input: '', expected_output: '', contextText: '' }) + // Navigate to the page where the new row will appear + if (dataset?.entries) { + const newPage = Math.max(0, Math.ceil((dataset.entries.length + 1) / ROWS_PER_PAGE) - 1) + setPage(newPage) + } + } + + const handleCancelEdit = () => { + setEditingRow(null) + setRowError('') + } + + const handleOpenDrawer = (entry, entryNumber) => { + setDetailTabValue(0) + setSelectedEntry({ ...entry, _entryNumber: entryNumber }) + setDrawerOpen(true) + } + + const handleCloseDrawer = () => { + setDrawerOpen(false) + setSelectedEntry(null) + } + + const handleDetailTabChange = (event, newValue) => { + setDetailTabValue(newValue) + } + + const handleSaveRow = async () => { + if (!editingRow.input.trim()) { + setRowError('Input is required.') + return + } + setSavingRow(true) + setRowError('') + try { + const payload = { + input: editingRow.input.trim(), + expected_output: editingRow.expected_output.trim() || null, + context: textToContext(editingRow.contextText) + } + + if (editingRow.id !== null) { + // Update existing entry + const res = await evaluationApi.updateEntry(datasetId, editingRow.id, payload) + const updated = res.data + setDataset((prev) => ({ + ...prev, + entries: prev.entries.map((e) => (e.id === updated.id ? updated : e)) + })) + } else { + // Add new entry + const res = await evaluationApi.addEntries(datasetId, { entries: [payload] }) + const newEntries = res.data.entries || [] + const updatedEntries = [...(dataset?.entries || []), ...newEntries] + setDataset((prev) => ({ ...prev, entries: updatedEntries })) + onDatasetChanged && onDatasetChanged({ id: datasetId, entry_count: updatedEntries.length }) + } + setEditingRow(null) + } catch (err) { + setRowError(err?.response?.data?.message || 'Failed to save entry.') + } finally { + setSavingRow(false) + } + } + + // ── Entry deletion ──────────────────────────────────────────────────────── + + const handleDeleteEntry = async (entryId) => { + setDeletingEntry(true) + try { + await evaluationApi.deleteEntry(datasetId, entryId) + const updatedEntries = (dataset?.entries || []).filter((e) => e.id !== entryId) + setDataset((prev) => ({ ...prev, entries: updatedEntries })) + if (selectedEntry?.id === entryId) { + handleCloseDrawer() + } + onDatasetChanged && onDatasetChanged({ id: datasetId, entry_count: updatedEntries.length }) + setConfirmDeleteId(null) + // Adjust page down if we removed the last entry on the current page + const maxPage = Math.max(0, Math.ceil(updatedEntries.length / ROWS_PER_PAGE) - 1) + setPage((prev) => Math.min(prev, maxPage)) + } catch { + // silently fail — user can retry + } finally { + setDeletingEntry(false) + } + } + + // ── Derived values ──────────────────────────────────────────────────────── + + const entries = dataset?.entries || [] + const pageEntries = entries.slice(page * ROWS_PER_PAGE, page * ROWS_PER_PAGE + ROWS_PER_PAGE) + const showNewRow = editingRow !== null && editingRow.id === null + const isSynthesizing = dataset?.status === 'synthesizing' + + // ── Render ──────────────────────────────────────────────────────────────── + + return ( + + + Dataset Detail + + + + + + + {loading ? ( + + + + ) : !dataset ? ( + Failed to load dataset. + ) : ( + + {/* ── Metadata section ── */} + + + + + Metadata + + + + {entries.length} {entries.length === 1 ? 'entry' : 'entries'} + + + + + setName(e.target.value)} + sx={{ flex: 1 }} + required + /> + setDescription(e.target.value)} + sx={{ flex: 2 }} + /> + + + + {metaError && ( + + {metaError} + + )} + + + + {/* ── Entries table ── */} + + + + Entries + + + + + + + + + {rowError && ( + + {rowError} + + )} + + + + + + # + Input + + Expected Output + + Context + + Actions + + + + + {pageEntries.length === 0 && !showNewRow ? ( + + + + No entries yet. Click "Add Entry" to create one. + + + + ) : ( + pageEntries.map((entry, idx) => { + const globalIdx = page * ROWS_PER_PAGE + idx + 1 + const isEditing = editingRow?.id === entry.id + const isConfirmingDelete = confirmDeleteId === entry.id + + return ( + handleOpenDrawer(entry, globalIdx) + : undefined + } + sx={ + !isEditing && !isConfirmingDelete + ? { cursor: 'pointer' } + : undefined + } + > + {globalIdx} + + {isEditing ? ( + <> + + + setEditingRow((r) => ({ + ...r, + input: e.target.value + })) + } + multiline + minRows={2} + maxRows={6} + size='small' + fullWidth + placeholder='Input (required)' + /> + + + + setEditingRow((r) => ({ + ...r, + expected_output: e.target.value + })) + } + multiline + minRows={2} + maxRows={6} + size='small' + fullWidth + placeholder='Expected output' + /> + + + + setEditingRow((r) => ({ + ...r, + contextText: e.target.value + })) + } + multiline + minRows={2} + maxRows={6} + size='small' + fullWidth + placeholder='One context item per line' + helperText='Each line → one context string' + /> + + + + + + { + event.stopPropagation() + handleSaveRow() + }} + disabled={savingRow} + > + {savingRow ? ( + + ) : ( + + )} + + + + + { + event.stopPropagation() + handleCancelEdit() + }} + disabled={savingRow} + > + + + + + + + ) : isConfirmingDelete ? ( + <> + + + Delete this entry? This cannot be undone. + + + + + + + + + + ) : ( + <> + + + {entry.input || '—'} + + + + + {entry.expected_output || '—'} + + + + + {entry.context && entry.context.length > 0 + ? entry.context.join('\n') + : '—'} + + + + + + + { + event.stopPropagation() + handleEditRow(entry) + }} + disabled={ + !!editingRow || isSynthesizing + } + > + + + + + + + { + event.stopPropagation() + handleCloseDrawer() + setConfirmDeleteId(entry.id) + }} + disabled={ + !!editingRow || isSynthesizing + } + > + + + + + + + + )} + + ) + }) + )} + + {/* New entry row — always shown at bottom when adding */} + {showNewRow && ( + + {entries.length + 1} + + + setEditingRow((r) => ({ ...r, input: e.target.value })) + } + multiline + minRows={2} + maxRows={6} + size='small' + fullWidth + placeholder='Input (required)' + autoFocus + /> + + + + setEditingRow((r) => ({ + ...r, + expected_output: e.target.value + })) + } + multiline + minRows={2} + maxRows={6} + size='small' + fullWidth + placeholder='Expected output' + /> + + + + setEditingRow((r) => ({ + ...r, + contextText: e.target.value + })) + } + multiline + minRows={2} + maxRows={6} + size='small' + fullWidth + placeholder='One context item per line' + helperText='Each line → one context string' + /> + + + + + + + {savingRow ? ( + + ) : ( + + )} + + + + + + + + + + + + )} + +
+ + + {entries.length > ROWS_PER_PAGE && ( + { + setEditingRow(null) + setConfirmDeleteId(null) + setPage(newPage) + }} + /> + )} + + + )} + + + + + + + theme.zIndex.modal + 10 }} + PaperProps={{ + sx: { + width: { xs: '100%', sm: 520, md: 680 }, + p: 3, + bgcolor: 'background.paper' + } + }} + > + {selectedEntry && ( + + + + Entry #{selectedEntry._entryNumber} Details + + Entry ID: {selectedEntry.id} + + + + + + + + + + + + + + + + + + + + + + {selectedEntry.input || '—'} + + + + + + + + {selectedEntry.expected_output || '—'} + + + + + + + {selectedEntry.context && selectedEntry.context.length > 0 ? ( + selectedEntry.context.map((contextItem, index) => ( + + {contextItem} + + )) + ) : ( + — + )} + + + + + )} + + + ) +} + +DatasetDetailDialog.propTypes = { + open: PropTypes.bool.isRequired, + datasetId: PropTypes.oneOfType([PropTypes.number, PropTypes.string]), + onClose: PropTypes.func.isRequired, + onDatasetChanged: PropTypes.func +} + +export default DatasetDetailDialog diff --git a/studio-frontend/packages/ui/src/views/evaluation/DatasetTab.jsx b/studio-frontend/packages/ui/src/views/evaluation/DatasetTab.jsx new file mode 100644 index 0000000..000c316 --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/DatasetTab.jsx @@ -0,0 +1,1095 @@ +import { useState, useEffect, useCallback, useRef } from 'react' +import PropTypes from 'prop-types' + +// material-ui +import { + Box, + Button, + Checkbox, + Chip, + CircularProgress, + Dialog, + DialogActions, + DialogContent, + DialogTitle, + FormControlLabel, + FormControl, + IconButton, + LinearProgress, + Menu, + MenuItem, + Paper, + Stack, + Switch, + Table, + TableBody, + TableCell, + TableContainer, + TableHead, + TableRow, + TableSortLabel, + TextField, + Tooltip, + Typography +} from '@mui/material' +import { styled } from '@mui/material/styles' +import { tableCellClasses } from '@mui/material/TableCell' + +// icons +import { IconChevronDown, IconEye, IconPlus, IconRefresh, IconTrash, IconWand, IconX, IconPlayerStop } from '@tabler/icons-react' + +// API +import evaluationApi from '@/api/evaluation' +import DatasetDetailDialog from './DatasetDetailDialog' +import FileUploadArea from '@/ui-component/file/FileUploadArea' +import ModelSelect from './ModelSelect' + +import { StyledButton } from '@/ui-component/button/StyledButton' + +const DATASET_UPLOAD_TYPES = ['.json', '.jsonl'] +const SYNTHESIZE_UPLOAD_TYPES = ['.pdf', '.docx'] + +const DEFAULT_SYNTHESIS_OPTIONS = { + targetGoldens: 10, + maxGoldensPerDocument: 5, + maxContexts: 5, + minContexts: 1, + averageChunksPerContext: 3, + chunkSize: 1024, + chunkOverlap: 64, + numEvolutions: 1, + inputQuality: 0.4, + llmTimeout: 0, + asyncMode: false, + maxConcurrent: 1 +} + +const getAcceptedFileLabel = (types) => types.join(', ') + +const deriveNameFromFile = (filename) => { + const lastDot = filename.lastIndexOf('.') + return lastDot > 0 ? filename.slice(0, lastDot) : filename +} + +const parseManualEntries = (entriesText) => { + const trimmed = entriesText.trim() + if (!trimmed) return [] + + try { + const parsed = JSON.parse(trimmed) + return Array.isArray(parsed) ? parsed : [parsed] + } catch { + return trimmed + .split('\n') + .map((line) => line.trim()) + .filter(Boolean) + .map((line) => JSON.parse(line)) + } +} + +const StyledTableCell = styled(TableCell)(({ theme }) => ({ + borderColor: theme.palette.grey[900] + 25, + [`&.${tableCellClasses.head}`]: { + color: theme.palette.grey[900] + }, + [`&.${tableCellClasses.body}`]: { + fontSize: 14, + height: 56 + } +})) + +const StyledTableRow = styled(TableRow)(() => ({ + '&:last-child td, &:last-child th': { + border: 0 + } +})) + +const formatDate = (dateStr) => { + if (!dateStr) return '—' + try { + const dt = new Date(dateStr) + if (isNaN(dt.getTime())) return dateStr + return dt.toLocaleString() + } catch { + return dateStr + } +} + +const normalizeDatasetName = (value) => (value || '').trim().toLowerCase() + +const statusColor = (status) => { + switch (status) { + case 'completed': + return 'success' + case 'failed': + return 'error' + case 'synthesizing': + return 'primary' + case 'pending': + default: + return 'default' + } +} + +const STATUS_CONFIG = { + pending: { label: 'pending' }, + synthesizing: { label: 'synthesizing' }, + completed: { label: 'completed' }, + failed: { label: 'failed' }, + stopped: { label: 'stopped' }, +} + +const StatusChip = ({ status, error }) => { + const cfg = STATUS_CONFIG[status] || { label: status } + const isInProgress = status === 'synthesizing' || status === 'pending' + const chip = ( + : undefined} + /> + ) + if (status === 'failed' && error) { + return ( + + {chip} + + ) + } + return chip +} + +StatusChip.propTypes = { + status: PropTypes.string, + error: PropTypes.string +} + +// ── New Dataset Modal ───────────────────────────────────────────────────────── + +const NewDatasetModal = ({ open, onClose, onCreated, existingDatasetNames }) => { + const missingUploadError = 'Upload a .json or .jsonl file, or paste dataset entries manually.' + const [name, setName] = useState('') + const [description, setDescription] = useState('') + const [entries, setEntries] = useState('') + const [file, setFile] = useState(null) + const [submitting, setSubmitting] = useState(false) + const [error, setError] = useState('') + + const handleClose = () => { + setName('') + setDescription('') + setEntries('') + setFile(null) + setError('') + onClose() + } + + const handleFileChange = (nextFile) => { + setFile(nextFile) + setError('') + if (nextFile && !name.trim()) { + setName(deriveNameFromFile(nextFile.name)) + } + } + + const handleSubmit = async () => { + if (!name.trim()) { + setError('Dataset name is required.') + return + } + + const normalizedName = normalizeDatasetName(name) + const hasDuplicateName = existingDatasetNames.some( + (existingName) => normalizeDatasetName(existingName) === normalizedName + ) + if (hasDuplicateName) { + setError('A dataset with this name already exists. Please choose a different name.') + return + } + + if (!file && !entries.trim()) { + setError(missingUploadError) + return + } + + setSubmitting(true) + setError('') + try { + let res + + if (file) { + const formData = new FormData() + formData.append('file', file) + formData.append('name', name.trim()) + if (description.trim()) formData.append('description', description.trim()) + res = await evaluationApi.createDataset(formData) + } else { + res = await evaluationApi.createDataset({ + name: name.trim(), + description: description.trim(), + entries: parseManualEntries(entries) + }) + } + + onCreated && onCreated(res.data) + handleClose() + } catch (err) { + setError(err?.response?.data?.message || err?.response?.data?.detail || 'Failed to create dataset.') + } finally { + setSubmitting(false) + } + } + + return ( + + + Import Dataset + + + + + + + + setName(e.target.value)} + required + /> + setDescription(e.target.value)} + /> + {error && ( + + {error} + + )} + + + + + + + + ) +} + +NewDatasetModal.propTypes = { + open: PropTypes.bool.isRequired, + onClose: PropTypes.func.isRequired, + onCreated: PropTypes.func, + existingDatasetNames: PropTypes.arrayOf(PropTypes.string) +} + +// ── Synthesize Modal ────────────────────────────────────────────────────────── + +const SynthesizeModal = ({ open, onClose, onCreated, existingDatasetNames }) => { + const [models, setModels] = useState([]) + const [datasetName, setDatasetName] = useState('') + const [description, setDescription] = useState('') + const [modelName, setModelName] = useState('') + const [sourceFile, setSourceFile] = useState(null) + const [options, setOptions] = useState(DEFAULT_SYNTHESIS_OPTIONS) + const [totalGoldensEnabled, setTotalGoldensEnabled] = useState(true) + const [showAdvanced, setShowAdvanced] = useState(false) + const [loadingOptions, setLoadingOptions] = useState(false) + const [submitting, setSubmitting] = useState(false) + const [error, setError] = useState('') + + const fetchModels = () => { + evaluationApi.getModels().then((res) => setModels(res.data || [])).catch(() => {}) + } + + useEffect(() => { + if (!open) return + setLoadingOptions(true) + setError('') + evaluationApi + .getModels() + .then((res) => setModels(res.data || [])) + .catch(() => setError('Failed to load judge models.')) + .finally(() => setLoadingOptions(false)) + }, [open]) + + const handleClose = () => { + setDatasetName('') + setDescription('') + setModelName('') + setSourceFile(null) + setOptions(DEFAULT_SYNTHESIS_OPTIONS) + setTotalGoldensEnabled(true) + setShowAdvanced(false) + setError('') + onClose() + } + + const updateOption = (key, value) => { + setOptions((previous) => ({ + ...previous, + [key]: value + })) + } + + const missingSrcError = 'A source file is required to synthesize a dataset.' + + const handleFileChange = (nextFile) => { + setSourceFile(nextFile) + setError('') + if (nextFile && !datasetName.trim()) { + setDatasetName(deriveNameFromFile(nextFile.name)) + } + } + + const handleSubmit = async () => { + if (!sourceFile) { + setError(missingSrcError) + return + } + if (!datasetName.trim() || !modelName) { + setError('Dataset name and judge model are required.') + return + } + + const normalizedName = normalizeDatasetName(datasetName) + const hasDuplicateName = existingDatasetNames.some( + (existingName) => normalizeDatasetName(existingName) === normalizedName + ) + if (hasDuplicateName) { + setError('A dataset with this name already exists. Please choose a different name.') + return + } + + const parsedTargetGoldens = Number(options.targetGoldens) + if (totalGoldensEnabled && (!Number.isInteger(parsedTargetGoldens) || parsedTargetGoldens < 1 || parsedTargetGoldens > 200)) { + setError('Total Goldens must be an integer between 1 and 200.') + return + } + + setSubmitting(true) + setError('') + try { + const formData = new FormData() + formData.append('file', sourceFile) + formData.append('name', datasetName.trim()) + formData.append('model_name', modelName) + formData.append('embed_model_name', 'nomic-embed-text') + if (totalGoldensEnabled) formData.append('num_goldens', String(parsedTargetGoldens)) + formData.append('max_goldens_per_document', String(options.maxGoldensPerDocument)) + formData.append('max_contexts', String(options.maxContexts)) + formData.append('min_contexts', String(options.minContexts)) + formData.append('average_chunks_per_context', String(options.averageChunksPerContext)) + formData.append('chunk_size', String(options.chunkSize)) + formData.append('chunk_overlap', String(options.chunkOverlap)) + formData.append('num_evolutions', String(options.numEvolutions)) + formData.append('input_quality', String(options.inputQuality)) + formData.append('llm_timeout', String(options.llmTimeout)) + formData.append('async_mode', String(options.asyncMode)) + formData.append('max_concurrent', String(options.maxConcurrent)) + if (description.trim()) formData.append('description', description.trim()) + + const res = await evaluationApi.synthesizeDataset(formData) + // Server returns 202 immediately — close the modal and let polling update status + onCreated && onCreated(res.data) + handleClose() + } catch (err) { + setError(err?.response?.data?.message || err?.response?.data?.detail || 'Failed to start synthesis.') + } finally { + setSubmitting(false) + } + } + + return ( + + + Synthesize Dataset + + + + + + {loadingOptions ? ( + + + + ) : ( + + + setDatasetName(e.target.value)} + required + /> + setDescription(e.target.value)} + /> + + + setTotalGoldensEnabled(e.target.checked)} + size='small' + /> + } + label='Set Total Goldens' + /> + { + const nextValue = e.target.value + updateOption('targetGoldens', nextValue === '' ? '' : Number(nextValue)) + }} + disabled={!totalGoldensEnabled} + helperText={ + totalGoldensEnabled + ? 'The synthesized dataset will contain exactly this many goldens.' + : 'Goldens will be up to Max Contexts × Max Goldens Per Document (see Advanced Settings), reduced by quality filtering.' + } + /> + + + + {showAdvanced && ( + + updateOption('maxGoldensPerDocument', Number(e.target.value))} + /> + updateOption('maxContexts', Number(e.target.value))} + /> + updateOption('minContexts', Number(e.target.value))} + /> + updateOption('averageChunksPerContext', Number(e.target.value))} + /> + updateOption('chunkSize', Number(e.target.value))} + helperText='Applied during uploaded document splitting before contexts are sent to studio-eval.' + /> + updateOption('chunkOverlap', Number(e.target.value))} + /> + updateOption('numEvolutions', Number(e.target.value))} + /> + updateOption('inputQuality', Number(e.target.value))} + /> + updateOption('llmTimeout', Number(e.target.value))} + helperText='0 disables the Ollama request timeout for this synthesis request.' + /> + updateOption('asyncMode', e.target.checked)} />} + label='Enable Async Generation' + /> + updateOption('maxConcurrent', Number(e.target.value))} + disabled={!options.asyncMode} + /> + + Omitted from the UI: output path and Ollama base URL are deployment-managed; context quality scoring is not available in this flow. + + + )} + + {error && ( + + {error} + + )} + + )} + + + + + + + ) +} + +SynthesizeModal.propTypes = { + open: PropTypes.bool.isRequired, + onClose: PropTypes.func.isRequired, + onCreated: PropTypes.func, + existingDatasetNames: PropTypes.arrayOf(PropTypes.string) +} + +// ── DatasetTab ──────────────────────────────────────────────────────────────── + +const POLL_INTERVAL_MS = 5000 +const IN_PROGRESS_STATUSES = new Set(['pending', 'synthesizing']) + +const DatasetTab = ({ isVisible }) => { + const [datasets, setDatasets] = useState([]) + const [isLoading, setIsLoading] = useState(true) + const [error, setError] = useState(null) + const [order, setOrder] = useState('desc') + const [orderBy, setOrderBy] = useState('created_at') + const [newDatasetOpen, setNewDatasetOpen] = useState(false) + const [synthesizeOpen, setSynthesizeOpen] = useState(false) + const [createMenuAnchor, setCreateMenuAnchor] = useState(null) + const [detailDatasetId, setDetailDatasetId] = useState(null) + const [detailOpen, setDetailOpen] = useState(false) + const [deleteConfirmId, setDeleteConfirmId] = useState(null) + const [deletingDataset, setDeletingDataset] = useState(false) + const [stoppingDataset, setStoppingDataset] = useState(null) + const pollTimerRef = useRef(null) + + const loadDatasets = useCallback(async () => { + try { + setIsLoading(true) + setError(null) + const res = await evaluationApi.getDatasets() + setDatasets(res.data || []) + } catch (err) { + setError(err?.response?.data?.message || 'Failed to load datasets.') + } finally { + setIsLoading(false) + } + }, []) + + // Poll individual in-progress datasets without a full list reload + const pollInProgress = useCallback(async (currentDatasets) => { + const inProgress = currentDatasets.filter((ds) => IN_PROGRESS_STATUSES.has(ds.status)) + if (inProgress.length === 0) return + + const updates = await Promise.allSettled( + inProgress.map((ds) => evaluationApi.getDataset(ds.id)) + ) + + setDatasets((prev) => { + const updated = [...prev] + updates.forEach((result, idx) => { + if (result.status === 'fulfilled') { + const fresh = result.value.data + const i = updated.findIndex((d) => d.id === fresh.id) + if (i !== -1) { + updated[i] = { + ...updated[i], + status: fresh.status, + error: fresh.error, + entry_count: fresh.entries ? fresh.entries.length : (updated[i].entry_count || 0), + completed_contexts: fresh.completed_contexts, + total_contexts: fresh.total_contexts, + completed_goldens: fresh.completed_goldens, + target_goldens: fresh.target_goldens, + updated_at: fresh.updated_at, + } + } + } + }) + return updated + }) + }, []) + + // Manage polling lifecycle + useEffect(() => { + const hasInProgress = datasets.some((ds) => IN_PROGRESS_STATUSES.has(ds.status)) + + if (hasInProgress && isVisible) { + if (!pollTimerRef.current) { + pollTimerRef.current = setInterval(() => { + setDatasets((current) => { + pollInProgress(current) + return current + }) + }, POLL_INTERVAL_MS) + } + } else { + if (pollTimerRef.current) { + clearInterval(pollTimerRef.current) + pollTimerRef.current = null + } + } + + return () => { + if (pollTimerRef.current) { + clearInterval(pollTimerRef.current) + pollTimerRef.current = null + } + } + }, [datasets, isVisible, pollInProgress]) + + useEffect(() => { + if (isVisible) loadDatasets() + }, [isVisible, loadDatasets]) + + const handleDatasetCreated = (ds) => { + setDatasets((prev) => [ds, ...prev]) + } + + const handleCreateMenuOpen = (event) => { + setCreateMenuAnchor(event.currentTarget) + } + + const handleCreateMenuClose = () => { + setCreateMenuAnchor(null) + } + + const handleOpenNewDataset = () => { + handleCreateMenuClose() + setNewDatasetOpen(true) + } + + const handleOpenSynthesize = () => { + handleCreateMenuClose() + setSynthesizeOpen(true) + } + + const handleOpenDataset = (id) => { + setDetailDatasetId(id) + setDetailOpen(true) + } + + const handleDatasetChanged = (patch) => { + setDatasets((prev) => prev.map((ds) => (ds.id === patch.id ? { ...ds, ...patch } : ds))) + } + + const handleDeleteDataset = async () => { + if (!deleteConfirmId) return + setDeletingDataset(true) + try { + const dsToDelete = datasets.find((ds) => ds.id === deleteConfirmId) + if (dsToDelete && IN_PROGRESS_STATUSES.has(dsToDelete.status)) { + await evaluationApi.stopDataset(deleteConfirmId).catch(() => {}) + } + await evaluationApi.deleteDataset(deleteConfirmId) + setDatasets((prev) => prev.filter((ds) => ds.id !== deleteConfirmId)) + setDeleteConfirmId(null) + } catch { + // ignore — user can retry + } finally { + setDeletingDataset(false) + } + } + + const handleStopDataset = async (e, id) => { + e.stopPropagation() + setStoppingDataset(id) + try { + await evaluationApi.stopDataset(id) + setDatasets((prev) => prev.map((ds) => (ds.id === id ? { ...ds, status: 'stopped' } : ds))) + } catch { + // ignore + } finally { + setStoppingDataset(null) + } + } + + const handleRequestSort = (property) => { + const isAsc = orderBy === property && order === 'asc' + setOrder(isAsc ? 'desc' : 'asc') + setOrderBy(property) + } + + const sortedDatasets = [...datasets].sort((a, b) => { + let cmp = 0 + if (orderBy === 'created_at') { + cmp = new Date(a.created_at) - new Date(b.created_at) + } else if (orderBy === 'name') { + cmp = String(a.name).localeCompare(String(b.name)) + } else if (orderBy === 'entry_count') { + cmp = (a.entry_count || 0) - (b.entry_count || 0) + } + return order === 'asc' ? cmp : -cmp + }) + + const columns = [ + { id: 'name', label: 'Name' }, + { id: 'description', label: 'Description', sortable: false }, + { id: 'status', label: 'Status', sortable: false }, + { id: 'entry_count', label: 'Entries' }, + { id: 'created_at', label: 'Created At' }, + { id: 'actions', label: 'Actions', sortable: false } + ] + + const existingDatasetNames = datasets.map((ds) => ds.name).filter(Boolean) + + const isCreateMenuOpen = Boolean(createMenuAnchor) + + return ( + + + } + endIcon={} + onClick={handleCreateMenuOpen} + sx={{ borderRadius: 2, height: 40 }} + aria-controls={isCreateMenuOpen ? 'dataset-create-menu' : undefined} + aria-haspopup='menu' + aria-expanded={isCreateMenuOpen ? 'true' : undefined} + > + Add Dataset + + + + + + Import Dataset + + + + + + Synthesize Dataset + + + + + : } + sx={{ borderRadius: 2, height: 40 }} + > + Refresh + + + + + {error && ( + + {error} + + )} + + + + + + {columns.map((col) => ( + + {col.sortable === false ? ( + col.label + ) : ( + handleRequestSort(col.id)} + > + {col.label} + + )} + + ))} + + + + {isLoading ? ( + + + + + + ) : sortedDatasets.length === 0 ? ( + + + + No datasets yet. Create one manually or synthesize from an uploaded source file. + + + + ) : ( + sortedDatasets.map((ds) => ( + ds.status === 'completed' && handleOpenDataset(ds.id)} + sx={{ cursor: ds.status === 'completed' ? 'pointer' : 'default' }} + > + + + {ds.name} + + + + + {ds.description || '—'} + + + + + + {IN_PROGRESS_STATUSES.has(ds.status) && (() => { + const total = ds.target_goldens ?? null + const completed = ds.completed_goldens ?? 0 + const pct = total !== null && total > 0 ? Math.round((completed / total) * 100) : null + return ( + + + + + {pct !== null + ? `${completed}/${total} goldens (${pct}%)` + : `${completed} goldens`} + + + + ) + })()} + + + {ds.entry_count ?? '—'} + {formatDate(ds.created_at)} + + + + + { e.stopPropagation(); handleOpenDataset(ds.id) }} + disabled={ds.status !== 'completed'} + > + + + + + {IN_PROGRESS_STATUSES.has(ds.status) && ( + + handleStopDataset(e, ds.id)} + disabled={stoppingDataset === ds.id} + > + {stoppingDataset === ds.id ? ( + + ) : ( + + )} + + + )} + + { e.stopPropagation(); setDeleteConfirmId(ds.id) }} + > + + + + + + + )) + )} + +
+ + + setNewDatasetOpen(false)} + onCreated={handleDatasetCreated} + existingDatasetNames={existingDatasetNames} + /> + setSynthesizeOpen(false)} + onCreated={handleDatasetCreated} + existingDatasetNames={existingDatasetNames} + /> + setDetailOpen(false)} + onDatasetChanged={handleDatasetChanged} + /> + + {/* Delete dataset confirmation */} + setDeleteConfirmId(null)} maxWidth='xs' fullWidth> + + Delete Dataset + + + + Are you sure you want to delete this dataset? This cannot be undone. + + {deleteConfirmId && IN_PROGRESS_STATUSES.has(datasets.find((ds) => ds.id === deleteConfirmId)?.status) && ( + + Synthesis is currently in progress and will be stopped before being deleted. + + )} + + + + + + + + ) +} + +DatasetTab.propTypes = { + isVisible: PropTypes.bool +} + +export default DatasetTab diff --git a/studio-frontend/packages/ui/src/views/evaluation/ExecutionTab.jsx b/studio-frontend/packages/ui/src/views/evaluation/ExecutionTab.jsx new file mode 100644 index 0000000..2112e2a --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/ExecutionTab.jsx @@ -0,0 +1,609 @@ +import { useState, useEffect, useRef, useCallback } from 'react' +import PropTypes from 'prop-types' + +// material-ui +import { + Box, + Button, + Chip, + CircularProgress, + Dialog, + DialogActions, + DialogContent, + DialogTitle, + IconButton, + LinearProgress, + Paper, + Stack, + Table, + TableBody, + TableCell, + TableContainer, + TableHead, + TableRow, + TableSortLabel, + Tooltip, + Typography +} from '@mui/material' +import { useTheme, styled } from '@mui/material/styles' +import { tableCellClasses } from '@mui/material/TableCell' + +// icons +import { IconPlus, IconRefresh, IconEye, IconTrash, IconPlayerStop } from '@tabler/icons-react' + +// API +import evaluationApi from '@/api/evaluation' + +// components +import CreateRunModal from './CreateRunModal' +import RunDetailsModal from './RunDetailsModal' + +import { StyledButton } from '@/ui-component/button/StyledButton' + +const POLL_INTERVAL_MS = 30000 +const ACTIVE_STATUSES = ['pending', 'running'] +const CANCELLED_STATUSES = ['stopped', 'cancelled', 'canceled'] + +const StyledTableCell = styled(TableCell)(({ theme }) => ({ + borderColor: theme.palette.grey[900] + 25, + verticalAlign: 'middle', + [`&.${tableCellClasses.head}`]: { + color: theme.palette.grey[900] + }, + [`&.${tableCellClasses.body}`]: { + fontSize: 14 + } +})) + +const OverflowTypography = styled(Typography)(() => ({ + overflow: 'hidden', + textOverflow: 'ellipsis', + whiteSpace: 'nowrap', + display: 'block' +})) + + +const StyledTableRow = styled(TableRow)(() => ({ + '&:last-child td, &:last-child th': { + border: 0 + } +})) + +const GRADE_CONFIG = [ + { grade: 'A', min: 0.90, color: '#2e7d32', label: 'Excellent', range: '≥ 0.90' }, + { grade: 'B', min: 0.75, color: '#558b2f', label: 'Good', range: '0.75 – 0.89' }, + { grade: 'C', min: 0.60, color: '#f57c00', label: 'Acceptable', range: '0.60 – 0.74' }, + { grade: 'D', min: 0.50, color: '#e64a19', label: 'Poor', range: '0.50 – 0.59' }, + { grade: 'E', min: 0.40, color: '#c62828', label: 'Very Poor', range: '0.40 – 0.49' }, + { grade: 'F', min: -Infinity, color: '#7f0000', label: 'Failed', range: '< 0.40 or no score' }, +] + +const getGrade = (avgScore) => { + if (avgScore === null || avgScore === undefined) return GRADE_CONFIG[GRADE_CONFIG.length - 1] + for (const cfg of GRADE_CONFIG) { + if (avgScore >= cfg.min) return cfg + } + return GRADE_CONFIG[GRADE_CONFIG.length - 1] +} + +const effectiveRunStatus = (run) => { + if (CANCELLED_STATUSES.includes(run?.status)) { + return 'stopped' + } + return run?.status +} + +const isCancelledRun = (run) => CANCELLED_STATUSES.includes(run?.status) + +const formatDate = (dateStr) => { + if (!dateStr) return '—' + try { + const dt = new Date(dateStr) + if (isNaN(dt.getTime())) return dateStr + return dt.toLocaleString() + } catch { + return dateStr + } +} + +const computeMetricAverages = (run) => { + const metrics = run.metrics || [] + if (metrics.length === 0) return [] + + const results = run.results || [] + return metrics.map((metricName) => { + const scores = results + .map((r) => r.metric_scores?.[metricName]?.score) + .filter((s) => s !== null && s !== undefined && typeof s === 'number') + const avg = scores.length > 0 ? scores.reduce((a, b) => a + b, 0) / scores.length : null + return { metric: metricName, avg } + }) +} + +const getRunId = (run) => run?.id ?? run?.run_id + +const ExecutionTab = ({ isVisible }) => { + const theme = useTheme() + + const [runs, setRuns] = useState([]) + const [datasetMap, setDatasetMap] = useState({}) + const [isLoading, setIsLoading] = useState(true) + const [error, setError] = useState(null) + const [order, setOrder] = useState('desc') + const [orderBy, setOrderBy] = useState('created_at') + const [createModalOpen, setCreateModalOpen] = useState(false) + const [detailsModalOpen, setDetailsModalOpen] = useState(false) + const [selectedRunId, setSelectedRunId] = useState(null) + const [deleteConfirmOpen, setDeleteConfirmOpen] = useState(false) + const [runToDelete, setRunToDelete] = useState(null) + const [deleting, setDeleting] = useState(false) + const [stoppingRuns, setStoppingRuns] = useState(new Set()) + + // Track interval ids per run_id + const pollTimersRef = useRef({}) + + const stopPolling = useCallback((runId) => { + if (pollTimersRef.current[runId]) { + clearInterval(pollTimersRef.current[runId]) + delete pollTimersRef.current[runId] + } + }, []) + + const startPolling = useCallback( + (runId) => { + if (pollTimersRef.current[runId]) return + pollTimersRef.current[runId] = setInterval(async () => { + try { + const res = await evaluationApi.getRun(runId) + const updated = res.data + setRuns((prev) => + prev.map((r) => (getRunId(r) === runId ? { ...r, ...updated } : r)) + ) + if (!ACTIVE_STATUSES.includes(updated?.status)) { + stopPolling(runId) + } + } catch { + stopPolling(runId) + } + }, POLL_INTERVAL_MS) + }, + [stopPolling] + ) + + const loadRuns = useCallback(async () => { + try { + setIsLoading(true) + setError(null) + const [runsRes, datasetsRes] = await Promise.all([ + evaluationApi.getRuns(), + evaluationApi.getDatasets().catch(() => ({ data: [] })) + ]) + let data = runsRes.data || [] + const dsMap = {} + ;(datasetsRes.data || []).forEach((ds) => { dsMap[ds.id] = ds.name }) + setDatasetMap(dsMap) + + // Start polling for any active runs + data.forEach((run) => { + if (ACTIVE_STATUSES.includes(run.status)) { + startPolling(getRunId(run)) + } + }) + + // Wait for full details of completed runs so we can detect per-entry failures + const completedRuns = data.filter((r) => r.status === 'completed') + if (completedRuns.length > 0) { + const responses = await Promise.all( + completedRuns.map((r) => + evaluationApi.getRun(getRunId(r)).catch(() => null) + ) + ) + const updates = {} + responses.forEach((resp, idx) => { + if (resp?.data) updates[getRunId(completedRuns[idx])] = resp.data + }) + if (Object.keys(updates).length > 0) { + data = data.map((r) => (updates[getRunId(r)] ? { ...r, ...updates[getRunId(r)] } : r)) + } + } + + setRuns(data) + } catch (err) { + setError(err?.response?.data?.message || 'Failed to load evaluation runs.') + } finally { + setIsLoading(false) + } + }, [startPolling]) + + useEffect(() => { + if (isVisible) loadRuns() + }, [isVisible, loadRuns]) + + // Cleanup all poll timers on unmount + useEffect(() => { + return () => { + Object.keys(pollTimersRef.current).forEach((id) => clearInterval(pollTimersRef.current[id])) + pollTimersRef.current = {} + } + }, []) + + const handleRunCreated = (newRun) => { + setRuns((prev) => [newRun, ...prev]) + if (ACTIVE_STATUSES.includes(newRun.status)) { + startPolling(getRunId(newRun)) + } + } + + const handleOpenDetails = (runId) => { + setSelectedRunId(runId) + setDetailsModalOpen(true) + } + + const handleCloseDetails = () => { + setDetailsModalOpen(false) + setSelectedRunId(null) + } + + const handleOpenDeleteConfirm = (e, run) => { + e.stopPropagation() + setRunToDelete(run) + setDeleteConfirmOpen(true) + } + + const handleStopRun = async (e, run) => { + e.stopPropagation() + const runId = getRunId(run) + setStoppingRuns((prev) => new Set(prev).add(runId)) + try { + await evaluationApi.stopRun(runId) + setRuns((prev) => prev.map((r) => (getRunId(r) === runId ? { ...r, status: 'stopped' } : r))) + stopPolling(runId) + } catch (err) { + setError(err?.response?.data?.detail || 'Failed to stop run.') + } finally { + setStoppingRuns((prev) => { + const next = new Set(prev) + next.delete(runId) + return next + }) + } + } + + const handleConfirmDelete = async () => { + if (!runToDelete) return + try { + setDeleting(true) + const runId = getRunId(runToDelete) + if (ACTIVE_STATUSES.includes(runToDelete.status)) { + await evaluationApi.stopRun(runId).catch(() => {}) + } + await evaluationApi.deleteRun(runId) + setRuns((prev) => prev.filter((r) => getRunId(r) !== runId)) + stopPolling(runId) + setDeleteConfirmOpen(false) + setRunToDelete(null) + } catch (err) { + setError(err?.response?.data?.detail || 'Failed to delete run.') + } finally { + setDeleting(false) + } + } + + const handleRequestSort = (property) => { + const isAsc = orderBy === property && order === 'asc' + setOrder(isAsc ? 'desc' : 'asc') + setOrderBy(property) + } + + const sortedRuns = [...runs].sort((a, b) => { + let cmp = 0 + if (orderBy === 'created_at') { + cmp = new Date(a.created_at) - new Date(b.created_at) + } else if (orderBy === 'run_id') { + cmp = String(getRunId(a)).localeCompare(String(getRunId(b))) + } else if (orderBy === 'status') { + cmp = String(a.status).localeCompare(String(b.status)) + } + return order === 'asc' ? cmp : -cmp + }) + + const columns = [ + { id: 'run_id', label: 'Run ID' }, + { id: 'sandbox_id', label: 'Sandbox', sortable: false }, + { id: 'dataset_id', label: 'Dataset', sortable: false }, + { id: 'model_name', label: 'Generator Model', sortable: false }, + { id: 'status', label: 'Status' }, + { id: 'eval_score', label: 'Eval Score', sortable: false }, + { id: 'created_at', label: 'Created At' }, + { id: 'actions', label: 'Actions', sortable: false } + ] + + return ( + + + } + onClick={() => setCreateModalOpen(true)} + sx={{ borderRadius: 2, height: 40 }} + > + Create New Run + + + : } + sx={{ borderRadius: 2, height: 40 }} + > + Refresh + + + + + {error && ( + + {error} + + )} + + + + + + {columns.map((col) => ( + + {col.sortable === false ? ( + col.label + ) : ( + handleRequestSort(col.id)} + > + {col.label} + + )} + + ))} + + + + {isLoading ? ( + + + + + + ) : sortedRuns.length === 0 ? ( + + + + No evaluation runs yet. Click "Create New Run" to get started. + + + + ) : ( + sortedRuns.map((run) => ( + handleOpenDetails(getRunId(run))} + sx={{ cursor: 'pointer' }} + > + + + {getRunId(run)} + + + + + {run.sandbox_id || '—'} + + + + + {run.dataset_name_snapshot ?? datasetMap[run.dataset_id] ?? (run.dataset_id ? String(run.dataset_id) : '—')} + + + + + {run.model_name || '—'} + + + + {(() => { + const effStatus = effectiveRunStatus(run) + const statusLabel = { + running: 'running', + pending: 'pending', + completed: 'completed', + failed: 'failed', + stopped: 'cancelled', + }[effStatus] ?? effStatus + const statusColor = { + running: 'primary', + pending: 'default', + completed: 'success', + failed: 'error', + stopped: 'default', + }[effStatus] ?? 'default' + return ( + : undefined} + /> + ) + })()} + + + {(() => { + if (isCancelledRun(run)) { + return N/A + } + const effStatus = effectiveRunStatus(run) + if (ACTIVE_STATUSES.includes(effStatus)) { + const total = run.total_count ?? run.dataset_entries_snapshot?.length ?? 0 + const completed = run.completed_count ?? run.results?.length ?? 0 + const pct = total > 0 ? Math.round((completed / total) * 100) : null + return ( + + + {pct !== null && ( + + {completed}/{total} ({pct}%) + + )} + + ) + } + const metricAvgs = computeMetricAverages(run) + const validAvgs = metricAvgs.map((m) => m.avg).filter((a) => a !== null) + const overallAvg = validAvgs.length > 0 + ? validAvgs.reduce((acc, val) => acc + val, 0) / validAvgs.length + : null + const gradeCfg = getGrade(overallAvg) + return ( + + + + Grade {gradeCfg.grade} + + + {metricAvgs.length > 0 ? ( + + {metricAvgs.map(({ metric, avg }) => ( + + {metric}: {avg !== null ? avg.toFixed(3) : 'N/A'} + + ))} + + ) : ( + N/A + )} + + ) + })()} + + + + {formatDate(run.created_at)} + + + e.stopPropagation()}> + + + handleOpenDetails(getRunId(run))} + > + + + + {ACTIVE_STATUSES.includes(run.status) && ( + + handleStopRun(e, run)} + disabled={stoppingRuns.has(getRunId(run))} + > + {stoppingRuns.has(getRunId(run)) ? ( + + ) : ( + + )} + + + )} + + handleOpenDeleteConfirm(e, run)} + > + + + + + + + )) + )} + +
+ + + setCreateModalOpen(false)} + onRunCreated={handleRunCreated} + /> + + + + {/* Delete Confirmation Dialog */} + setDeleteConfirmOpen(false)}> + Delete Run? + + + Are you sure you want to delete this evaluation run? This action cannot be undone. + + {runToDelete && ACTIVE_STATUSES.includes(runToDelete.status) && ( + + This run is currently in progress and will be stopped before being deleted. + + )} + {runToDelete && ( + + Run ID: {getRunId(runToDelete).substring(0, 12)}... + + )} + + + + + + + + ) +} + +ExecutionTab.propTypes = { + isVisible: PropTypes.bool +} + +export default ExecutionTab diff --git a/studio-frontend/packages/ui/src/views/evaluation/ModelSelect.jsx b/studio-frontend/packages/ui/src/views/evaluation/ModelSelect.jsx new file mode 100644 index 0000000..c5cf40a --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/ModelSelect.jsx @@ -0,0 +1,589 @@ +import { useState, useRef, useCallback, useEffect } from 'react' +import PropTypes from 'prop-types' +import { + Box, + CircularProgress, + ClickAwayListener, + Divider, + InputAdornment, + List, + ListItemButton, + Paper, + TextField, + Tooltip, + Typography +} from '@mui/material' +import { IconSearch, IconDownload, IconAlertCircle, IconCheck } from '@tabler/icons-react' +import evaluationApi from '@/api/evaluation' + +// Hide embedding models from judge/synthesize dropdowns +const HIDDEN_MODEL_PATTERN = /nomic.embed/i + +// How often to poll for pull completion (ms) +const POLL_INTERVAL_MS = 1200 + +const EMPTY_PULL_STATE = { + modelName: '', + status: '', + detail: '', + error: '', + completed: null, + total: null +} + +const deriveProgressValue = (completed, total) => { + if (!Number.isFinite(completed) || !Number.isFinite(total) || total <= 0) { + return null + } + + return Math.min(100, Math.max(0, (completed / total) * 100)) +} + +/** + * Searchable judge model selector with an inline "Pull New Model" action + * and real-time pull-progress polling. + * + * Props: + * - models {Array} – list of model objects with `name` (or `id`) field + * - value {string} – currently selected model name + * - onChange {function} – called with the new model name string + * - onModelsRefresh {function} – called after a successful pull so parent can re-fetch + * - disabled {boolean} – disables the control + */ +const ModelSelect = ({ models, value, onChange, onModelsRefresh, disabled, label }) => { + const [open, setOpen] = useState(false) + const [search, setSearch] = useState('') + + // Pull-progress state — persists even after dropdown closes + const [pullState, setPullState] = useState(EMPTY_PULL_STATE) + const [statusOffset, setStatusOffset] = useState(null) + const [isReadyFading, setIsReadyFading] = useState(false) + + const pollTimerRef = useRef(null) + const clearBannerTimerRef = useRef(null) + const fadeReadyTimerRef = useRef(null) + const anchorRef = useRef(null) + const inputRef = useRef(null) + + const visibleModels = models.filter((m) => !HIDDEN_MODEL_PATTERN.test(m.name || m.id || '')) + + const filteredModels = visibleModels.filter((m) => { + if (!search.trim()) return true + return (m.name || m.id || '').toLowerCase().includes(search.trim().toLowerCase()) + }) + + const searchTag = search.trim() + const showPull = Boolean(searchTag) + const alreadyInList = visibleModels.some( + (m) => (m.name || m.id || '').toLowerCase() === searchTag.toLowerCase() + ) + const progressValue = deriveProgressValue(pullState.completed, pullState.total) + const isPulling = pullState.status === 'pulling' + + // ── Polling ────────────────────────────────────────────────────────────── + + const stopPolling = useCallback(() => { + if (pollTimerRef.current) { + clearTimeout(pollTimerRef.current) + pollTimerRef.current = null + } + }, []) + + const stopClearBannerTimer = useCallback(() => { + if (clearBannerTimerRef.current) { + clearTimeout(clearBannerTimerRef.current) + clearBannerTimerRef.current = null + } + }, []) + + const stopFadeReadyTimer = useCallback(() => { + if (fadeReadyTimerRef.current) { + clearTimeout(fadeReadyTimerRef.current) + fadeReadyTimerRef.current = null + } + }, []) + + const clearPullStateSoon = useCallback(() => { + setIsReadyFading(false) + stopFadeReadyTimer() + stopClearBannerTimer() + fadeReadyTimerRef.current = setTimeout(() => { + setIsReadyFading(true) + }, 2200) + clearBannerTimerRef.current = setTimeout(() => { + setIsReadyFading(false) + setPullState(EMPTY_PULL_STATE) + }, 3000) + }, [stopClearBannerTimer, stopFadeReadyTimer]) + + const applyPullEntry = useCallback( + (modelName, entry) => { + if (!entry) return false + + stopClearBannerTimer() + + setPullState({ + modelName, + status: entry.status || '', + detail: entry.detail || '', + error: entry.error || '', + completed: Number.isFinite(entry.completed) ? entry.completed : null, + total: Number.isFinite(entry.total) ? entry.total : null + }) + + if (entry.status === 'ready') { + stopPolling() + onModelsRefresh && onModelsRefresh() + clearPullStateSoon() + return true + } + + if (entry.status === 'error') { + stopPolling() + return true + } + + return false + }, + [clearPullStateSoon, onModelsRefresh, stopClearBannerTimer, stopPolling] + ) + + const pollPullStatus = useCallback( + async (modelName) => { + try { + const res = await evaluationApi.getPullStatus() + const statusMap = res.data || {} + const entry = statusMap[modelName] + + if (!entry) { + pollTimerRef.current = setTimeout(() => pollPullStatus(modelName), POLL_INTERVAL_MS) + return + } + + const done = applyPullEntry(modelName, entry) + if (!done) { + pollTimerRef.current = setTimeout(() => pollPullStatus(modelName), POLL_INTERVAL_MS) + } + } catch { + pollTimerRef.current = setTimeout(() => pollPullStatus(modelName), POLL_INTERVAL_MS) + } + }, + [applyPullEntry] + ) + + const startPolling = useCallback( + (modelName) => { + stopPolling() + pollPullStatus(modelName) + }, + [pollPullStatus, stopPolling] + ) + + // Clean up on unmount + useEffect( + () => () => { + stopPolling() + stopClearBannerTimer() + stopFadeReadyTimer() + }, + [stopClearBannerTimer, stopFadeReadyTimer, stopPolling] + ) + + // ── Dropdown helpers ────────────────────────────────────────────────────── + + const handleOpen = useCallback(() => { + if (disabled) return + setSearch('') + setOpen(true) + }, [disabled]) + + const handleClickAway = useCallback(() => { + setOpen(false) + setSearch('') + }, []) + + const handleSelect = useCallback( + (modelName) => { + onChange(modelName) + setOpen(false) + setSearch('') + }, + [onChange] + ) + + // ── Pull action ─────────────────────────────────────────────────────────── + + const handlePull = useCallback(async () => { + if (!searchTag || isPulling) return + + setIsReadyFading(false) + stopFadeReadyTimer() + stopClearBannerTimer() + setPullState({ + modelName: searchTag, + status: 'pulling', + detail: 'Submitting pull request', + error: '', + completed: null, + total: null + }) + setOpen(false) + setSearch('') + // Pre-select so the field shows the model name immediately + onChange(searchTag) + + try { + await evaluationApi.pullModel({ model_name: searchTag }) + setPullState((current) => ({ + ...current, + detail: 'Waiting for Ollama progress…' + })) + startPolling(searchTag) + } catch (err) { + const msg = + err?.response?.data?.detail || + err?.response?.data?.message || + 'Failed to initiate model pull.' + setPullState({ + modelName: searchTag, + status: 'error', + detail: 'Pull failed', + error: msg, + completed: null, + total: null + }) + } + }, [isPulling, onChange, searchTag, startPolling, stopClearBannerTimer, stopFadeReadyTimer]) + + useEffect(() => { + if (!value) return + if (pullState.modelName) return + + let active = true + + const hydratePullState = async () => { + try { + const res = await evaluationApi.getPullStatus() + const entry = res.data?.[value] + if (!active || !entry) return + if (entry.status !== 'pulling') return + applyPullEntry(value, entry) + startPolling(value) + } catch { + // Ignore hydration failures; explicit pulls still start polling. + } + } + + hydratePullState() + + return () => { + active = false + } + }, [applyPullEntry, pullState.modelName, startPolling, value]) + + // Display: when open show search query; when closed show selected value + const inputValue = open ? search : value + const statusAnchorText = inputValue || pullState.modelName + const hasInlineStatus = isPulling || pullState.status === 'ready' || pullState.status === 'error' + + const updateStatusOffset = useCallback(() => { + if (!hasInlineStatus || !statusAnchorText || !anchorRef.current || !inputRef.current || typeof window === 'undefined') { + setStatusOffset(null) + return + } + + const canvas = document.createElement('canvas') + const context = canvas.getContext('2d') + if (!context) { + setStatusOffset(null) + return + } + + const wrapperRect = anchorRef.current.getBoundingClientRect() + const inputRect = inputRef.current.getBoundingClientRect() + const computedStyle = window.getComputedStyle(inputRef.current) + const indicatorSize = 22 + const gap = 8 + + context.font = computedStyle.font + const textWidth = context.measureText(statusAnchorText).width + const baseLeft = inputRect.left - wrapperRect.left + const minLeft = baseLeft + 2 + const maxLeft = wrapperRect.width - indicatorSize - 18 + const nextOffset = Math.min(Math.max(minLeft + textWidth + gap, minLeft), maxLeft) + + setStatusOffset(nextOffset) + }, [hasInlineStatus, statusAnchorText]) + + useEffect(() => { + updateStatusOffset() + }, [updateStatusOffset]) + + useEffect(() => { + if (!hasInlineStatus || typeof window === 'undefined') return undefined + + const handleResize = () => updateStatusOffset() + + window.addEventListener('resize', handleResize) + + return () => { + window.removeEventListener('resize', handleResize) + } + }, [hasInlineStatus, updateStatusOffset]) + + const renderInlineStatus = () => { + if (!hasInlineStatus || statusOffset === null) return null + + const indicatorSx = { + position: 'absolute', + left: statusOffset, + top: '50%', + transform: 'translateY(-50%)', + display: 'flex', + alignItems: 'center', + justifyContent: 'center', + width: 22, + height: 22, + zIndex: 2 + } + + if (isPulling) { + return ( + + + + {progressValue !== null && ( + + + {`${Math.round(progressValue)}%`} + + + )} + + + ) + } + + if (pullState.status === 'ready') { + return ( + + + + + + ) + } + + if (pullState.status === 'error') { + return ( + + + + + + + + ) + } + + return null + } + + return ( + + + setSearch(e.target.value)} + disabled={disabled} + inputRef={inputRef} + InputProps={{ + sx: hasInlineStatus + ? { + '& input': { + pr: 5 + } + } + : undefined, + startAdornment: ( + + + + ) + }} + /> + {renderInlineStatus()} + + {open && ( + + + {filteredModels.length === 0 && !showPull && ( + + + No models available. + + + )} + + {filteredModels.map((m) => { + const name = m.name || m.id || '' + return ( + handleSelect(name)} + sx={{ py: 1, px: 2 }} + > + + {name} + + + ) + })} + + + {/* Pull new model action */} + {showPull && ( + <> + + + {isPulling ? ( + + ) : ( + + + + )} + + + {alreadyInList ? 'Re-pull Model' : 'Pull New Model'} + + + {searchTag} + + + + + )} + + )} + + + ) +} + +ModelSelect.propTypes = { + models: PropTypes.arrayOf( + PropTypes.shape({ + name: PropTypes.string, + id: PropTypes.string + }) + ).isRequired, + value: PropTypes.string.isRequired, + onChange: PropTypes.func.isRequired, + onModelsRefresh: PropTypes.func, + disabled: PropTypes.bool, + label: PropTypes.string +} + +ModelSelect.defaultProps = { + onModelsRefresh: null, + disabled: false, + label: 'Model' +} + +export default ModelSelect diff --git a/studio-frontend/packages/ui/src/views/evaluation/RunDetailsModal.jsx b/studio-frontend/packages/ui/src/views/evaluation/RunDetailsModal.jsx new file mode 100644 index 0000000..1639553 --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/RunDetailsModal.jsx @@ -0,0 +1,1022 @@ +import { useState, useEffect } from 'react' +import PropTypes from 'prop-types' + +// material-ui +import { + Box, + Button, + Chip, + CircularProgress, + Dialog, + DialogActions, + DialogContent, + DialogTitle, + Divider, + Drawer, + IconButton, + Paper, + Stack, + Tab, + Tabs, + Table, + TableBody, + TableCell, + TableContainer, + TableHead, + TableRow, + Tooltip, + Typography, + styled +} from '@mui/material' +import { useTheme } from '@mui/material/styles' +import { tableCellClasses } from '@mui/material/TableCell' + +// icons +import { IconX, IconAlertTriangle, IconInfoCircle } from '@tabler/icons-react' + +// API +import evaluationApi from '@/api/evaluation' +import chatflowsApi from '@/api/chatflows' + +const StyledTableCell = styled(TableCell)(({ theme }) => ({ + borderColor: theme.palette.grey[900] + 25, + [`&.${tableCellClasses.head}`]: { + color: theme.palette.grey[900], + fontWeight: 600 + }, + [`&.${tableCellClasses.body}`]: { + fontSize: 13, + padding: '8px 12px' + } +})) + +const StyledTableRow = styled(TableRow)(() => ({ + '&:last-child td': { + border: 0 + } +})) + +const GRADE_CONFIG = [ + { grade: 'A', min: 0.90, color: '#2e7d32', label: 'Excellent', range: '≥ 0.90' }, + { grade: 'B', min: 0.75, color: '#558b2f', label: 'Good', range: '0.75 – 0.89' }, + { grade: 'C', min: 0.60, color: '#f57c00', label: 'Acceptable', range: '0.60 – 0.74' }, + { grade: 'D', min: 0.50, color: '#e64a19', label: 'Poor', range: '0.50 – 0.59' }, + { grade: 'E', min: 0.40, color: '#c62828', label: 'Very Poor', range: '0.40 – 0.49' }, + { grade: 'F', min: -Infinity, color: '#7f0000', label: 'Failed', range: '< 0.40 or no score' }, +] +const CANCELLED_STATUSES = ['stopped', 'cancelled', 'canceled'] + +const getGrade = (avgScore) => { + if (avgScore === null || avgScore === undefined) return GRADE_CONFIG[GRADE_CONFIG.length - 1] + for (const cfg of GRADE_CONFIG) { + if (avgScore >= cfg.min) return cfg + } + return GRADE_CONFIG[GRADE_CONFIG.length - 1] +} + +const formatDate = (dateStr) => { + if (!dateStr) return '—' + try { + const dt = new Date(dateStr) + if (isNaN(dt.getTime())) return dateStr + return dt.toLocaleString() + } catch { + return dateStr + } +} + +const truncateText = (text, maxLength = 100) => { + if (!text) return '—' + if (text.length <= maxLength) return text + return text.substring(0, maxLength) + '...' +} + +const formatMetricScore = (score) => { + if (score === null || score === undefined) return 'N/A' + if (typeof score === 'number') return score.toFixed(3) + return String(score) +} + +const applyConfigurationSnapshot = (snapshot, setters) => { + if (!snapshot || typeof snapshot !== 'object') { + return false + } + + const hasSnapshotData = Array.isArray(snapshot?.workflow_nodes) || snapshot?.data_management + if (!hasSnapshotData) { + return false + } + + const workflowNodes = Array.isArray(snapshot?.workflow_nodes) ? snapshot.workflow_nodes : [] + const dataManagement = snapshot?.data_management || {} + + setters.setWorkflowNodes(workflowNodes) + setters.setDataManagementFiles(Array.isArray(dataManagement.files) ? dataManagement.files : []) + setters.setDataFilesError(dataManagement.error || '') + + if (dataManagement.status) { + setters.setDataFilesStatus(dataManagement.status) + return true + } + + setters.setDataFilesStatus(workflowNodes.length > 0 ? 'done' : 'not-applicable') + return true +} + +function TabPanel(props) { + const { children, value, index, ...other } = props + + return ( + + ) +} +TabPanel.propTypes = { + children: PropTypes.node, + index: PropTypes.number.isRequired, + value: PropTypes.number.isRequired, +} + +const RunDetailsModal = ({ open, onClose, runId }) => { + const theme = useTheme() + + const [tabValue, setTabValue] = useState(0) + const [entryTabValue, setEntryTabValue] = useState(0) + + const handleTabChange = (event, newValue) => { + setTabValue(newValue) + } + + const handleEntryTabChange = (event, newValue) => { + setEntryTabValue(newValue) + } + + const [run, setRun] = useState(null) + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + const [datasetName, setDatasetName] = useState(null) + const [workflowNodes, setWorkflowNodes] = useState([]) + const [dataManagementFiles, setDataManagementFiles] = useState([]) + // 'idle' | 'not-applicable' | 'checking' | 'done' | 'error' + const [dataFilesStatus, setDataFilesStatus] = useState('idle') + const [dataFilesError, setDataFilesError] = useState('') + + const [selectedEntry, setSelectedEntry] = useState(null) + const [drawerOpen, setDrawerOpen] = useState(false) + + const handleOpenDrawer = (result, snapshotEntry, entryNumber) => { + setEntryTabValue(0) + setSelectedEntry({ ...result, _snapshotEntry: snapshotEntry, _entryNumber: entryNumber }) + setDrawerOpen(true) + } + + const handleCloseDrawer = () => { + setDrawerOpen(false) + setSelectedEntry(null) + } + + const getEffectiveStatus = (runData) => { + if (CANCELLED_STATUSES.includes(runData?.status)) { + return 'stopped' + } + return runData?.status + } + + const isCancelledRun = (runData) => CANCELLED_STATUSES.includes(runData?.status) + + const getOrderedMetricEntries = (runData, result) => { + if (!result?.metric_scores) return [] + + const configuredMetrics = Array.isArray(runData?.metrics) ? runData.metrics : [] + const scoredMetrics = Object.keys(result.metric_scores) + const orderedMetricNames = [ + ...configuredMetrics, + ...scoredMetrics.filter((metricName) => !configuredMetrics.includes(metricName)) + ] + + return orderedMetricNames + .map((metricName) => [metricName, result.metric_scores[metricName]]) + .filter(([, scoreDetails]) => Boolean(scoreDetails)) + } + + const getResultReasons = (runData, result) => { + const metricReasons = getOrderedMetricEntries(runData, result) + .filter(([, scoreDetails]) => scoreDetails.reason) + .map(([metricName, scoreDetails]) => `${metricName}: ${scoreDetails.reason}`) + + if (metricReasons.length > 0) { + return metricReasons + } + + if (!result?.reason) return [] + + return String(result.reason) + .split(/;\s*/) + .filter(Boolean) + } + + const getExpectedOutput = (result, snapshotEntry) => { + return result?.expected_output ?? snapshotEntry?.expected_output ?? result?.entry?.expected_output ?? null + } + + useEffect(() => { + if (!open || !runId) return + + const fetchRun = async () => { + try { + setLoading(true) + setError(null) + setDatasetName(null) + setWorkflowNodes([]) + setDataManagementFiles([]) + setDataFilesStatus('idle') + setDataFilesError('') + const res = await evaluationApi.getRun(runId) + setRun(res.data) + // Use snapshot first; fall back to live fetch only for old runs without snapshot + if (res.data.dataset_name_snapshot) { + setDatasetName(res.data.dataset_name_snapshot) + } + if ( + applyConfigurationSnapshot(res.data.configuration_snapshot, { + setWorkflowNodes, + setDataManagementFiles, + setDataFilesStatus, + setDataFilesError + }) + ) { + return + } + try { + const chatflowId = res.data.sandbox_id?.replace(/^sandbox-/, '') + if (chatflowId) { + const cfRes = await chatflowsApi.getSpecificChatflow(chatflowId).catch((e) => { + console.error('[RunDetailsModal] getSpecificChatflow failed:', e) + throw e + }) + const flowData = cfRes.data?.flowData + if (flowData) { + const parsed = typeof flowData === 'string' ? JSON.parse(flowData) : flowData + const nodes = parsed.nodes || [] + setWorkflowNodes(nodes) + + const hasDataprepNode = nodes.some((node) => + String(node?.data?.name || '').startsWith('opea_service@prepare_doc_redis_prep') + ) + + if (!hasDataprepNode) { + setDataFilesStatus('not-applicable') + } else if (res.data.sandbox_id) { + setDataFilesStatus('checking') + try { + const filesRes = await evaluationApi.getSandboxDataManagementFiles({ + sandbox_id: res.data.sandbox_id + }) + const files = Array.isArray(filesRes.data?.files) ? filesRes.data.files : [] + setDataManagementFiles(files) + setDataFilesStatus('done') + } catch (filesErr) { + console.error('[RunDetailsModal] getSandboxDataManagementFiles failed:', filesErr) + const errMsg = filesErr?.response?.data?.message || filesErr?.response?.data?.detail || filesErr?.message || 'Unknown error' + setDataManagementFiles([]) + setDataFilesError(errMsg) + setDataFilesStatus('error') + } + } + } + } + } catch { + // non-critical + } + } catch (err) { + setError(err?.response?.data?.detail || 'Failed to load run details.') + setLoading(false) + } finally { + setLoading(false) + } + } + + fetchRun() + }, [open, runId]) + + if (!open) return null + + return ( + + + Evaluation Run Details + + + + + + + {loading ? ( + + + + ) : error ? ( + {error} + ) : run ? ( + + + + + + + + + + {/* Run Metadata */} + + + Run Information + + + + + + + Run ID + + + {run.id} + + + + + Sandbox + + + {truncateText(run.sandbox_id, 50)} + + + + + Status + + + {(() => { + const effStatus = getEffectiveStatus(run) + const statusLabel = { + running: 'running', + pending: 'pending', + completed: 'completed', + failed: 'failed', + stopped: 'cancelled', + }[effStatus] ?? effStatus + const statusColor = { + running: 'primary', + pending: 'default', + completed: 'success', + failed: 'error', + stopped: 'default', + }[effStatus] ?? 'default' + const isActive = run.status === 'running' || run.status === 'pending' + return ( + : undefined} + /> + ) + })()} + + + + + Dataset + + {datasetName || run.dataset_id} + + + + Judge Model + + {run.model_name} + + + + Metrics + + + {run.metrics.map((m) => ( + + ))} + + + + + Created At + + + {formatDate(run.created_at)} + + + + + Completed At + + + {run.completed_at ? formatDate(run.completed_at) : '—'} + + + + + Average Metric Scores + + + {run.results && run.results.length > 0 ? ( + + {run.metrics.map((metricName) => { + const scores = run.results + .map((r) => r.metric_scores?.[metricName]?.score) + .filter((s) => s !== null && s !== undefined && typeof s === 'number') + const avg = scores.length > 0 ? scores.reduce((a, b) => a + b, 0) / scores.length : null + return ( + + {metricName}: {avg !== null ? avg.toFixed(3) : 'N/A'} + + ) + })} + + ) : ( + N/A + )} + + + + {run.error && ( + <> + + + + + + Error + + + {run.error} + + + + + )} + + + + + {/* Results Table */} + {run.results && run.results.length > 0 && ( + + + + Evaluation Results ({run.results.length} entries) + + + {/* Grade Distribution Summary */} + {(() => { + const gradeCounts = {} + GRADE_CONFIG.forEach((g) => { gradeCounts[g.grade] = 0 }) + run.results.forEach((result) => { + const entries = (Array.isArray(run.metrics) ? run.metrics : []) + .map((m) => [m, result.metric_scores?.[m]]) + .filter(([, s]) => Boolean(s)) + const validScores = entries + .map(([, s]) => s.score) + .filter((s) => s !== null && s !== undefined && typeof s === 'number') + const avg = validScores.length > 0 + ? validScores.reduce((a, b) => a + b, 0) / validScores.length + : null + gradeCounts[getGrade(avg).grade]++ + }) + return ( + + + Distribution: + + {GRADE_CONFIG.map((cfg) => { + const count = gradeCounts[cfg.grade] ?? 0 + return ( + + + Grade {cfg.grade} + + + {count} + + + ) + })} + + + Grading Scale (avg metric score) + + {GRADE_CONFIG.map((g) => ( + + {g.grade} + {' – '}{g.label}: {g.range} + + ))} + + } + > + + + + + + ) + })()} + {(() => { + const snapshotMap = {} + if (Array.isArray(run.dataset_entries_snapshot)) { + run.dataset_entries_snapshot.forEach((e) => { snapshotMap[e.id] = e }) + } + const hasContext = run.results.some( + (r) => { + const snapshotEntry = snapshotMap[r.entry_id] + return (r.entry?.context && r.entry.context.length > 0) + || (snapshotEntry?.context && snapshotEntry.context.length > 0) + } + ) + return ( + + + + + Entry + Input + {hasContext && Context} + Actual Output + Expected Output + + + Eval Score + + + Reason + + + + {(() => { + return run.results.map((result, index) => { + const snapshotEntry = snapshotMap[result.entry_id] + const orderedMetricEntries = getOrderedMetricEntries(run, result) + const failReasons = getResultReasons(run, result) + const failReasonText = failReasons.join('\n') + const expectedOutput = getExpectedOutput(result, snapshotEntry) + const entryNumber = index + 1 + return ( + handleOpenDrawer(result, snapshotEntry, entryNumber)} + sx={{ cursor: 'pointer' }} + > + + + #{entryNumber} + + + + + + {(snapshotEntry?.input ?? result.entry?.input) || '—'} + + ({ position: 'absolute', bottom: 0, left: 0, right: 0, height: '36px', background: `linear-gradient(to bottom, transparent, ${t.palette.background.paper})`, pointerEvents: 'none' })} /> + + + {hasContext && ( + + + + {(() => { + const ctx = snapshotEntry?.context ?? result.entry?.context + return ctx?.length > 0 ? ctx.join('\n\n') : '—' + })()} + + ({ position: 'absolute', bottom: 0, left: 0, right: 0, height: '36px', background: `linear-gradient(to bottom, transparent, ${t.palette.background.paper})`, pointerEvents: 'none' })} /> + + + )} + + + + {result.actual_output || '—'} + + ({ position: 'absolute', bottom: 0, left: 0, right: 0, height: '36px', background: `linear-gradient(to bottom, transparent, ${t.palette.background.paper})`, pointerEvents: 'none' })} /> + + + + + + {expectedOutput || '—'} + + ({ position: 'absolute', bottom: 0, left: 0, right: 0, height: '36px', background: `linear-gradient(to bottom, transparent, ${t.palette.background.paper})`, pointerEvents: 'none' })} /> + + + + {(() => { + const validScores = orderedMetricEntries + .map(([, scores]) => scores.score) + .filter((s) => s !== null && s !== undefined && typeof s === 'number') + const avgScore = validScores.length > 0 + ? validScores.reduce((a, b) => a + b, 0) / validScores.length + : null + const gradeCfg = getGrade(avgScore) + return ( + + + + Grade {gradeCfg.grade} + + + {orderedMetricEntries.length > 0 ? ( + + {orderedMetricEntries.map(([metricName, scores]) => ( + + {metricName}: {formatMetricScore(scores.score)} + + ))} + + ) : ( + — + )} + + ) + })()} + + + {failReasons.length > 0 ? ( + + {failReasonText} + + ) : ( + '—' + )} + + + ) + }) + })()} + +
+ + ) + })()} + + )} + + {(!run.results || run.results.length === 0) && ( + + No results available yet. + + )} + + + + + + + {/* Advanced / Request Settings */} + + + Advanced Settings (Request Parameters) + + + + + + System Prompt + + + {run.system_prompt || '—'} + + + + + + Temperature + + + {run.temperature !== null && run.temperature !== undefined ? run.temperature : '—'} + + + + + Max Tokens + + + {run.max_tokens !== null && run.max_tokens !== undefined ? run.max_tokens : '—'} + + + + + + + + {/* Sandbox Workflow Configuration */} + + + Sandbox Workflow Configuration + + + + {workflowNodes.length === 0 ? ( + N/A + ) : ( + + {workflowNodes + .filter((node) => node.data?.inputs && Object.keys(node.data.inputs).length > 0) + .map((node) => { + const inputs = node.data.inputs + const displayInputs = Object.entries(inputs).filter( + ([, v]) => + v !== '' && + v !== null && + v !== undefined && + !String(v).startsWith('{{') + ) + if (displayInputs.length === 0) return null + const paramLabels = {} + ;(node.data.inputParams || []).forEach((p) => { + paramLabels[p.name] = p.label + }) + return ( + + + {node.data.label} + + + {displayInputs.map(([key, val]) => ( + + + {paramLabels[key] || key}:{' '} + {String(val)} + + + ))} + + + ) + })} + + )} + + + + + + Data Management Files + + + {dataFilesStatus === 'idle' || dataFilesStatus === 'not-applicable' ? ( + N/A + ) : dataFilesStatus === 'checking' ? ( + + + + Checking... + + + ) : dataFilesStatus === 'error' ? ( + + + Unable to check (sandbox may be stopped) + + + ) : dataManagementFiles.length > 0 ? ( + + {dataManagementFiles.slice(0, 5).map((file, idx) => { + const label = typeof file === 'string' ? file : file?.name || file?.id || file?.file_name || JSON.stringify(file) + return ( + + {truncateText(label, 70)} + + ) + })} + {dataManagementFiles.length > 5 && ( + + +{dataManagementFiles.length - 5} more + + )} + + ) : ( + + No files uploaded + + )} + + + + + + + + + + ) : null} + + + + + + + {/* Entry Details Drawer */} + theme.zIndex.modal + 10 }} + PaperProps={{ sx: { width: { xs: '100%', sm: 600, md: 800 }, p: 3, bgcolor: theme.palette.mode === 'dark' ? 'grey.900' : 'background.paper' } }} + > + {selectedEntry && ( + + + Entry #{selectedEntry._entryNumber ?? selectedEntry.entry_id} Details + + + + + + {(() => { + const entries = getOrderedMetricEntries(run, selectedEntry) + const validScores = entries + .map(([, s]) => s.score) + .filter((s) => s !== null && s !== undefined && typeof s === 'number') + const avgScore = validScores.length > 0 + ? validScores.reduce((a, b) => a + b, 0) / validScores.length + : null + const gradeCfg = getGrade(avgScore) + return ( + + + + Eval Score: + + + + Grade {gradeCfg.grade} + + + + {entries.length > 0 ? ( + + {entries.map(([metricName, scoreDetails]) => ( + + {metricName}: {formatMetricScore(scoreDetails.score)} + + ))} + + ) : ( + N/A + )} + + ) + })()} + + + + + + + 0)} /> + + + + + + + + + + + {(selectedEntry._snapshotEntry?.input ?? selectedEntry.entry?.input) || '—'} + + + + + + {(() => { + const ctxList = selectedEntry._snapshotEntry?.context ?? selectedEntry.entry?.context + return ctxList && ctxList.length > 0 && ( + + {ctxList.map((ctx, idx) => ( + + {ctx} + + ))} + + ) + })()} + + + + + + {selectedEntry.actual_output || '—'} + + + + + + {getExpectedOutput(selectedEntry, selectedEntry._snapshotEntry) && ( + + + {getExpectedOutput(selectedEntry, selectedEntry._snapshotEntry)} + + + )} + + + + {getResultReasons(run, selectedEntry).length > 0 && ( + + + {getResultReasons(run, selectedEntry).join('\n')} + + + )} + + + + )} + + + ) +} + +RunDetailsModal.propTypes = { + open: PropTypes.bool.isRequired, + onClose: PropTypes.func.isRequired, + runId: PropTypes.string +} + +export default RunDetailsModal diff --git a/studio-frontend/packages/ui/src/views/evaluation/index.jsx b/studio-frontend/packages/ui/src/views/evaluation/index.jsx new file mode 100644 index 0000000..b9e44a8 --- /dev/null +++ b/studio-frontend/packages/ui/src/views/evaluation/index.jsx @@ -0,0 +1,71 @@ +import { useState } from 'react' + +// material-ui +import { Box, Tab, Tabs, Typography } from '@mui/material' +import { useTheme } from '@mui/material/styles' + +// project imports +import MainCard from '@/ui-component/cards/MainCard' + +// views +import ExecutionTab from './ExecutionTab' +import DatasetTab from './DatasetTab' + +// ==============================|| Evaluation ||============================== // + +const Evaluation = () => { + const theme = useTheme() + const [activeTab, setActiveTab] = useState(0) + + const handleTabChange = (_event, newValue) => { + setActiveTab(newValue) + } + + return ( + + + + Evaluation + + + + + + + + + + + + + ) +} + +export default Evaluation diff --git a/studio-frontend/packages/ui/src/views/finetuning/FileUploadArea.jsx b/studio-frontend/packages/ui/src/views/finetuning/FileUploadArea.jsx index e3c1d54..665d594 100644 --- a/studio-frontend/packages/ui/src/views/finetuning/FileUploadArea.jsx +++ b/studio-frontend/packages/ui/src/views/finetuning/FileUploadArea.jsx @@ -28,7 +28,10 @@ const FileUploadArea = ({ onFileUpload, acceptedTypes = ['.json', '.jsonl', '.csv'], maxSizeMB = 10, - error = null + error = null, + title = 'Drop your training dataset file here or click to browse', + subtitle, + buttonLabel = 'Choose File' }) => { const theme = useTheme() const fileInputRef = useRef(null) @@ -168,6 +171,8 @@ const FileUploadArea = ({ return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i] } + const helperText = subtitle || `Supported formats: ${acceptedTypes.join(', ')} (Max filesize: ${maxSizeMB}MB)` + return ( - Drop your training dataset file here or click to browse + {title} - Supported formats: {acceptedTypes.join(', ')} (Max filesize: {maxSizeMB}MB) + {helperText} @@ -311,7 +316,10 @@ FileUploadArea.propTypes = { onFileUpload: PropTypes.func.isRequired, acceptedTypes: PropTypes.arrayOf(PropTypes.string), maxSizeMB: PropTypes.number, - error: PropTypes.string + error: PropTypes.string, + title: PropTypes.string, + subtitle: PropTypes.string, + buttonLabel: PropTypes.string } export default FileUploadArea \ No newline at end of file diff --git a/studio-frontend/packages/ui/src/views/finetuning/FinetuningJobModal.jsx b/studio-frontend/packages/ui/src/views/finetuning/FinetuningJobModal.jsx index 03653df..dfd6fc8 100644 --- a/studio-frontend/packages/ui/src/views/finetuning/FinetuningJobModal.jsx +++ b/studio-frontend/packages/ui/src/views/finetuning/FinetuningJobModal.jsx @@ -28,7 +28,7 @@ import { useTheme } from '@mui/material/styles' import { IconX } from '@tabler/icons-react' // components -import FileUploadArea from './FileUploadArea' +import FileUploadArea from '@/ui-component/file/FileUploadArea' // API import finetuningApi from '@/api/finetuning' diff --git a/studio-frontend/packages/ui/src/views/tracer/index.jsx b/studio-frontend/packages/ui/src/views/tracer/index.jsx index d3fa4e9..68e85a4 100644 --- a/studio-frontend/packages/ui/src/views/tracer/index.jsx +++ b/studio-frontend/packages/ui/src/views/tracer/index.jsx @@ -1,264 +1,809 @@ -import { useState, useEffect } from "react"; -import { useParams } from "react-router-dom"; -import { styled } from "@mui/material/styles"; +import { useEffect, useRef, useState } from 'react' +import { useParams } from 'react-router-dom' + import { + Box, + Chip, + CircularProgress, + Drawer, + IconButton, + Paper, + Stack, + Tab, Table, TableBody, TableCell, TableContainer, TableHead, - TableRow, - Paper, - Button, - Box, - Stack, - Typography, - Divider, TablePagination, - Fade -} from "@mui/material"; + TableRow, + Tabs, + Tooltip, + Typography +} from '@mui/material' +import { styled, useTheme } from '@mui/material/styles' import { tableCellClasses } from '@mui/material/TableCell' -import ViewHeader from '@/layout/MainLayout/ViewHeader' -import chatflowsApi from '@/api/chatflows'; -import useApi from '@/hooks/useApi'; -import config from '@/config' +import { IconEye, IconRefresh, IconX } from '@tabler/icons-react' -const TraceNode = ({ node, handleSelect }) => { - console.log('TraceNode', TraceNode) - return ( - - - {node.children && node.children.length > 0 && ( - - {node.children.map((child) => ( - - ))} - - )} - -)}; +import MainCard from '@/ui-component/cards/MainCard' +import ViewHeader from '@/layout/MainLayout/ViewHeader' +import { StyledButton } from '@/ui-component/button/StyledButton' +import chatflowsApi from '@/api/chatflows' +import useApi from '@/hooks/useApi' +import config from '@/config' const StyledTableCell = styled(TableCell)(({ theme }) => ({ borderColor: theme.palette.grey[900] + 25, - + verticalAlign: 'top', [`&.${tableCellClasses.head}`]: { - color: theme.palette.grey[900] + color: theme.palette.grey[900], + fontWeight: 600 }, [`&.${tableCellClasses.body}`]: { fontSize: 14, - height: 64 + padding: '10px 12px' } })) const StyledTableRow = styled(TableRow)(() => ({ - // hide last border '&:last-child td, &:last-child th': { border: 0 } })) +const OverflowTypography = styled(Typography)(() => ({ + overflow: 'hidden', + textOverflow: 'ellipsis', + whiteSpace: 'nowrap', + display: 'block' +})) + +const formatDate = (dateStr) => { + if (!dateStr) return '—' + + try { + const date = new Date(dateStr) + if (isNaN(date.getTime())) return dateStr + return date.toLocaleString() + } catch { + return dateStr + } +} + +const truncateText = (text, maxLength = 100) => { + if (!text) return '—' + if (text.length <= maxLength) return text + return `${text.substring(0, maxLength)}...` +} + +const formatDuration = (durationNs) => { + if (durationNs === null || durationNs === undefined || durationNs === '') return '—' + + const numericDuration = Number(durationNs) + if (Number.isNaN(numericDuration)) return String(durationNs) + + const durationMs = numericDuration / 1000000 + + if (durationMs < 1) return `${(numericDuration / 1000).toFixed(2)} μs` + if (durationMs < 1000) return `${durationMs.toFixed(durationMs < 10 ? 2 : 1)} ms` + return `${(durationMs / 1000).toFixed(2)} s` +} + +const formatPayload = (payload) => { + if (!payload) return '—' + + if (typeof payload === 'object') { + return JSON.stringify(payload, null, 2) + } + + if (typeof payload !== 'string') { + return String(payload) + } + + try { + return JSON.stringify(JSON.parse(payload), null, 2) + } catch { + return payload + } +} + +const getStatusPresentation = (statusCode) => { + const normalizedStatus = String(statusCode || '').toLowerCase() + + if (normalizedStatus.includes('error') || normalizedStatus.includes('fail')) { + return { label: statusCode || 'Error', color: 'error' } + } + + if (normalizedStatus === 'ok' || normalizedStatus === 'success') { + return { label: statusCode, color: 'success' } + } + + return { label: statusCode || 'Unset', color: 'default' } +} + +const flattenSpans = (spans, depth = 0) => { + if (!Array.isArray(spans)) return [] + + return spans.flatMap((span) => [{ ...span, depth }, ...flattenSpans(span.children, depth + 1)]) +} + +const buildTraceSummary = (traceTree) => { + const rootSpans = Array.isArray(traceTree?.spans) ? traceTree.spans : [] + const flatSpans = flattenSpans(rootSpans) + const services = [...new Set(flatSpans.map((span) => span.service_name).filter(Boolean))] + const llmCalls = flatSpans.filter((span) => span.llm_input || span.llm_output).length + const durationNs = flatSpans.reduce((maxDuration, span) => Math.max(maxDuration, Number(span.duration) || 0), 0) + + let statusLabel = 'Captured' + let statusColor = 'info' + if (flatSpans.some((span) => { + const normalizedStatus = String(span.status_code || '').toLowerCase() + return normalizedStatus.includes('error') || normalizedStatus.includes('fail') + })) { + statusLabel = 'Issues' + statusColor = 'error' + } else if (flatSpans.some((span) => { + const normalizedStatus = String(span.status_code || '').toLowerCase() + return normalizedStatus === 'ok' || normalizedStatus === 'success' + })) { + statusLabel = 'OK' + statusColor = 'success' + } + + return { + rootSpanCount: rootSpans.length, + rootSpanName: rootSpans[0]?.span_name || '—', + totalSpans: flatSpans.length, + llmCalls, + durationNs, + services, + statusLabel, + statusColor + } +} + +const MetadataField = ({ label, value, monospace = false }) => ( + + + {label} + + + {value || '—'} + + +) + +const DetailSection = ({ title, value, theme }) => ( + + + {title} + + + + {value || '—'} + + + +) + export default function LLMTraces() { - const [traceList, setTraceList] = useState([]); - const [traceData, setTraceData] = useState({}); - const [selectedTrace, setSelectedTrace] = useState(null); - const [selectedSpan, setSelectedSpan] = useState(null); - const [page, setPage] = useState(0); - const [rowsPerPage, setRowsPerPage] = useState(10); - const [workflowName, setWorkflowName] = useState(''); - - const { ns } = useParams(); - console.log("ns: ", ns); - - const studio_server_url = config.studio_server_url; - const sandbox_tracer_list_endpoint = config.sandbox_tracer_list_endpoint; - const sandbox_tracer_tree_endpoint = config.sandbox_tracer_tree_endpoint; - - const getAllOpeaflowsApi = useApi(chatflowsApi.getAllOpeaflows); - - // Fetch trace ids from the server - const fetchTraces = async (ns) => { - console.log("fetchTraces", ns); - const url = `${studio_server_url}/${sandbox_tracer_list_endpoint}/${ns}`; - try { - const response = await fetch(url, { headers: { "Content-Type": "application/json" } }); - if (response.ok) { - const response_data = await response.json(); - console.log("response_data", response_data); - if (response_data && response_data.trace_ids) { - setTraceList(response_data.trace_ids); - } else { - console.error("Invalid response data structure:", response_data); - setTraceList([]); - } - } else { - console.error("Error fetching traces:", response.statusText); - setTraceList([]); - } - } catch (error) { - console.error("Error fetching traces:", error); - setTraceList([]); + const theme = useTheme() + const { ns } = useParams() + + const [traceList, setTraceList] = useState([]) + const [traceSummaryMap, setTraceSummaryMap] = useState({}) + const [traceTreeCache, setTraceTreeCache] = useState({}) + const [traceData, setTraceData] = useState(null) + const [selectedTraceId, setSelectedTraceId] = useState(null) + const [selectedSpan, setSelectedSpan] = useState(null) + const [searchValue, setSearchValue] = useState('') + const [page, setPage] = useState(0) + const [rowsPerPage, setRowsPerPage] = useState(10) + const [workflowName, setWorkflowName] = useState('') + const [isLoadingTraces, setIsLoadingTraces] = useState(true) + const [selectedPayloadTab, setSelectedPayloadTab] = useState(0) + const [isLoadingTraceDetails, setIsLoadingTraceDetails] = useState(false) + const [traceError, setTraceError] = useState(null) + const [traceDetailsError, setTraceDetailsError] = useState(null) + + const pendingSummaryIdsRef = useRef(new Set()) + + const studioServerUrl = config.studio_server_url + const sandboxTracerListEndpoint = config.sandbox_tracer_list_endpoint + const sandboxTracerTreeEndpoint = config.sandbox_tracer_tree_endpoint + + const getAllOpeaflowsApi = useApi(chatflowsApi.getAllOpeaflows) + + const filteredTraceList = traceList.filter((trace) => { + if (!searchValue.trim()) return true + return String(trace.trace_id || '').toLowerCase().includes(searchValue.trim().toLowerCase()) + }) + + const paginatedTraceList = filteredTraceList.slice(page * rowsPerPage, page * rowsPerPage + rowsPerPage) + const selectedTrace = traceList.find((trace) => trace.trace_id === selectedTraceId) || null + const selectedTraceSummary = selectedTraceId ? traceSummaryMap[selectedTraceId] || buildTraceSummary(traceData) : null + const selectedTraceSpans = flattenSpans(traceData?.spans || []) + + const fetchTraceTreeData = async (traceId) => { + const response = await fetch(`${studioServerUrl}/${sandboxTracerTreeEndpoint}/${traceId}`, { + headers: { 'Content-Type': 'application/json' } + }) + + if (!response.ok) { + const errorPayload = await response.json().catch(() => null) + throw new Error(errorPayload?.detail || 'Failed to load trace details.') } - }; - // Fetch trace tree from the server - const fetchTraceTree = async (trace_id) => { - console.log("fetchTraceTree", trace_id); - const url = `${studio_server_url}/${sandbox_tracer_tree_endpoint}/${trace_id}`; + return response.json() + } + + const loadTraces = async () => { try { - const response = await fetch(url, { headers: { "Content-Type": "application/json" } }); - const data = response.ok ? await response.json() : []; - setTraceData(data); - } catch (error) { - console.error("Error fetching trace tree:", error); - setTraceData([]); - } - }; + setIsLoadingTraces(true) + setTraceError(null) - const handleTraceSelect = (trace_id) => { - console.log("handleTraceSelect", trace_id); - setSelectedTrace(trace_id); - setSelectedSpan(null); - }; + const response = await fetch(`${studioServerUrl}/${sandboxTracerListEndpoint}/${ns}`, { + headers: { 'Content-Type': 'application/json' } + }) - const handleSpanSelect = (span) => { - setSelectedSpan(span); - }; + if (response.status === 404) { + setTraceList([]) + return + } - const handleChangePage = (event, newPage) => { - setPage(newPage); - }; + if (!response.ok) { + const errorPayload = await response.json().catch(() => null) + throw new Error(errorPayload?.detail || 'Failed to load traces.') + } - const handleChangeRowsPerPage = (event) => { - setRowsPerPage(parseInt(event.target.value, 10)); - setPage(0); - }; + const responseData = await response.json() + const nextTraceList = Array.isArray(responseData?.trace_ids) ? responseData.trace_ids : [] + + setTraceList(nextTraceList) + setPage(0) + if (selectedTraceId && !nextTraceList.some((trace) => trace.trace_id === selectedTraceId)) { + setSelectedTraceId(null) + } + } catch (error) { + setTraceError(error.message || 'Failed to load traces.') + setTraceList([]) + } finally { + setIsLoadingTraces(false) + } + } - // Use Effects useEffect(() => { - console.log("useEffect ns"); - fetchTraces(ns); - }, [studio_server_url, sandbox_tracer_list_endpoint, ns]); + setSelectedTraceId(null) + setTraceData(null) + setSelectedSpan(null) + loadTraces() + }, [ns, studioServerUrl, sandboxTracerListEndpoint]) useEffect(() => { - console.log("useEffect selectedTrace", selectedTrace); - if (selectedTrace) { - fetchTraceTree(selectedTrace); - } else { - setTraceData({}); - } - } - , [selectedTrace, studio_server_url, sandbox_tracer_tree_endpoint]); + getAllOpeaflowsApi.request() + }, []) useEffect(() => { - getAllOpeaflowsApi.request(); - }, []); + if (!getAllOpeaflowsApi.data || !ns) return + + const matchingFlow = getAllOpeaflowsApi.data.find((flow) => `sandbox-${flow.id}` === ns) + setWorkflowName(matchingFlow ? matchingFlow.name : '') + }, [getAllOpeaflowsApi.data, ns]) useEffect(() => { - if (getAllOpeaflowsApi.data && ns) { - // Namespace is usually sandbox- - const flows = getAllOpeaflowsApi.data; - const found = flows.find(flow => `sandbox-${flow.id}` === ns); - setWorkflowName(found ? found.name : ''); + const missingTraceIds = paginatedTraceList + .map((trace) => trace.trace_id) + .filter((traceId) => traceId && !traceSummaryMap[traceId] && !pendingSummaryIdsRef.current.has(traceId)) + + if (!missingTraceIds.length) return + + let isActive = true + missingTraceIds.forEach((traceId) => pendingSummaryIdsRef.current.add(traceId)) + + Promise.all( + missingTraceIds.map(async (traceId) => { + try { + const traceTree = await fetchTraceTreeData(traceId) + return { traceId, traceTree } + } catch { + return null + } + }) + ).then((results) => { + if (!isActive) return + + const nextSummaryMap = {} + const nextTraceTreeCache = {} + + results.forEach((result) => { + if (!result?.traceTree) return + + nextSummaryMap[result.traceId] = buildTraceSummary(result.traceTree) + nextTraceTreeCache[result.traceId] = result.traceTree + }) + + if (Object.keys(nextSummaryMap).length > 0) { + setTraceSummaryMap((prev) => ({ ...prev, ...nextSummaryMap })) + } + + if (Object.keys(nextTraceTreeCache).length > 0) { + setTraceTreeCache((prev) => ({ ...prev, ...nextTraceTreeCache })) + } + }).finally(() => { + missingTraceIds.forEach((traceId) => pendingSummaryIdsRef.current.delete(traceId)) + }) + + return () => { + isActive = false } - }, [getAllOpeaflowsApi.data, ns]); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [page, rowsPerPage, searchValue, traceList]) + useEffect(() => { + if (!selectedTraceId) { + setTraceData(null) + setSelectedSpan(null) + setTraceDetailsError(null) + return + } + + const cachedTrace = traceTreeCache[selectedTraceId] + if (cachedTrace) { + const flatSpans = flattenSpans(cachedTrace.spans || []) + setTraceData(cachedTrace) + setSelectedSpan((currentSelectedSpan) => { + if (currentSelectedSpan && flatSpans.some((span) => span.span_id === currentSelectedSpan.span_id)) { + return currentSelectedSpan + } + + return flatSpans[0] || null + }) + setTraceDetailsError(null) + setIsLoadingTraceDetails(false) + return + } + + let isActive = true + + setIsLoadingTraceDetails(true) + setTraceDetailsError(null) + setTraceData(null) + setSelectedSpan(null) + + fetchTraceTreeData(selectedTraceId) + .then((traceTree) => { + if (!isActive) return + + const flatSpans = flattenSpans(traceTree.spans || []) + setTraceTreeCache((prev) => ({ ...prev, [selectedTraceId]: traceTree })) + setTraceSummaryMap((prev) => ({ + ...prev, + [selectedTraceId]: prev[selectedTraceId] || buildTraceSummary(traceTree) + })) + setTraceData(traceTree) + setSelectedSpan(flatSpans[0] || null) + }) + .catch((error) => { + if (!isActive) return + setTraceDetailsError(error.message || 'Failed to load trace details.') + }) + .finally(() => { + if (!isActive) return + setIsLoadingTraceDetails(false) + }) + + return () => { + isActive = false + } + }, [selectedTraceId, traceTreeCache, sandboxTracerTreeEndpoint, studioServerUrl]) + + const handleChangePage = (_event, newPage) => { + setPage(newPage) + } + + const handleChangeRowsPerPage = (event) => { + setRowsPerPage(parseInt(event.target.value, 10)) + setPage(0) + } + + const handleSearchChange = (event) => { + setSearchValue(event.target.value) + setPage(0) + } return ( - - - - + + + : } + sx={{ borderRadius: 2, height: 40 }} > - LLM Call Traces - - {workflowName && ( - - Workflow name: {workflowName} - - )} - - {traceList.length > 0 ? ( - <> - Traces: - - - - - Trace ID - Start - End - - - - {traceList.slice(page * rowsPerPage, page * rowsPerPage + rowsPerPage).map((trace) => ( - handleTraceSelect(trace.trace_id)}> - {trace.trace_id} - {trace.start} - {trace.end} + Refresh + + + + {traceError && {traceError}} + + +
+ + + Trace ID + Root Span + Span Summary + Recorded At + Status + Actions + + + + {isLoadingTraces ? ( + + + + + + ) : paginatedTraceList.length === 0 ? ( + + + + {searchValue.trim() ? 'No traces match the current search.' : 'No traces found for this sandbox.'} + + + + ) : ( + paginatedTraceList.map((trace) => { + const traceSummary = traceSummaryMap[trace.trace_id] + + return ( + setSelectedTraceId(trace.trace_id)} + sx={{ + cursor: 'pointer', + bgcolor: + selectedTraceId === trace.trace_id + ? theme.palette.mode === 'dark' + ? 'grey.800' + : 'grey.100' + : 'inherit' + }} + > + + + {trace.trace_id} + + + + {traceSummary ? ( + + + {traceSummary.rootSpanCount > 1 + ? `${traceSummary.rootSpanName} +${traceSummary.rootSpanCount - 1} more` + : traceSummary.rootSpanName} + + + {traceSummary.services.length > 0 ? truncateText(traceSummary.services.join(', '), 50) : 'Service data unavailable'} + + + ) : ( + + Loading summary... + + )} + + + {traceSummary ? ( + + + {traceSummary.totalSpans} spans · {traceSummary.rootSpanCount} root spans + + + {traceSummary.llmCalls} LLM payloads · {formatDuration(traceSummary.durationNs)} + + + ) : ( + + )} + + + + + {formatDate(trace.start)} + + Ended: {formatDate(trace.end)} + + + + + + + + event.stopPropagation()}> + + setSelectedTraceId(trace.trace_id)}> + + + + - ))} - -
- - - - ) : ( - No traces found + ) + }) + )} + + + + + {filteredTraceList.length > 0 && ( + )} - {selectedTrace && ( - - - - Trace ID: {selectedTrace} - {traceData && traceData.spans ? ( -
- {traceData.spans.map((span) => ( - - ))} -
+ + setSelectedTraceId(null)} + sx={{ zIndex: (currentTheme) => currentTheme.zIndex.modal + 10 }} + PaperProps={{ + sx: { + width: { xs: '100%', sm: '85vw', md: '80vw' }, + p: 0, + bgcolor: theme.palette.mode === 'dark' ? 'grey.900' : 'background.paper', + display: 'flex', + flexDirection: 'column', + overflow: 'hidden' + } + }} + > + {selectedTraceId && ( + + {/* Drawer Header */} + + + Trace Details + + ID: {selectedTraceId} + + + + {selectedTraceSummary && ( + + + + + + )} + setSelectedTraceId(null)}> + + + + + + {/* Drawer Body */} + {isLoadingTraceDetails ? ( + + + + ) : traceDetailsError ? ( + + {traceDetailsError} + ) : ( -

Loading trace data...

+ + {/* Left Panel: Span List */} + + + Execution Spans ({selectedTraceSpans.length}) + + + {selectedTraceSpans.length === 0 ? ( + + No spans available for this trace. + + ) : ( + selectedTraceSpans.map((span) => { + const spanStatus = getStatusPresentation(span.status_code) + const isSelected = selectedSpan?.span_id === span.span_id + const hasPayload = Boolean(span.llm_input || span.llm_output) + + return ( + { setSelectedSpan(span); setSelectedPayloadTab(0) }} + sx={{ + ml: span.depth * 2.5, + p: 1.5, + borderRadius: 1.5, + cursor: 'pointer', + border: '1px solid', + borderColor: isSelected ? 'primary.main' : 'transparent', + borderLeft: isSelected ? '3px solid' : '1px solid', + borderLeftColor: isSelected ? 'primary.main' : 'transparent', + bgcolor: isSelected + ? theme.palette.mode === 'dark' + ? 'primary.dark' + : 'primary.50' + : theme.palette.mode === 'dark' + ? 'grey.800' + : 'background.paper', + '&:hover': { + bgcolor: isSelected + ? undefined + : theme.palette.mode === 'dark' + ? 'grey.700' + : 'grey.100' + }, + transition: 'background-color 0.15s, border-color 0.15s' + }} + > + + + {span.span_name} + + + {formatDuration(span.duration)} + + + + + {span.service_name && ( + + {span.service_name} + + )} + {hasPayload && ( + + )} + + + ) + }) + )} + + + + {/* Right Panel: Span Details */} + + {selectedSpan ? ( + + {/* Span Metadata */} + + + + + + + + + + + + + setSelectedPayloadTab(val)} + variant='fullWidth' + sx={{ borderBottom: 1, borderColor: 'divider', minHeight: 40 }} + TabIndicatorProps={{ style: { height: 2 } }} + > + + + + + + + {selectedPayloadTab === 0 && (formatPayload(selectedSpan.llm_input) || '—')} + {selectedPayloadTab === 1 && (formatPayload(selectedSpan.llm_output) || '—')} + {selectedPayloadTab === 2 && (formatPayload(selectedSpan.resource_attributes) || '—')} + + + + + ) : ( + + Select a span to inspect its details. + + )} + + )} - {selectedSpan && ( - - - {selectedSpan.span_name} - Timestamp: {selectedSpan.timestmap}ms - Duration: {selectedSpan.duration}ms - Status: {selectedSpan.status_code} - Service: {selectedSpan.service_name} - LLM Input: {selectedSpan.llm_input} - LLM Output: {selectedSpan.llm_output} - - - )} - - )} - - ); + )} + + + ) } diff --git a/studio-frontend/packages/ui/vite.config.js b/studio-frontend/packages/ui/vite.config.js index e72b1b4..de7aefd 100644 --- a/studio-frontend/packages/ui/vite.config.js +++ b/studio-frontend/packages/ui/vite.config.js @@ -9,12 +9,19 @@ export default defineConfig(async ({ mode }) => { const serverEnv = dotenv.config({ processEnv: {}, path: '../server/.env' }).parsed const serverHost = serverEnv?.['HOST'] ?? 'localhost' const serverPort = parseInt(serverEnv?.['PORT'] ?? '3000') + const studioBackendTarget = process.env.STUDIO_SERVER_URL ?? 'http://localhost:5001' proxy = { '/api': { target: `http://${serverHost}:${serverPort}`, changeOrigin: true, secure: false }, + '/studio-backend': { + target: studioBackendTarget, + changeOrigin: true, + ws: true, + secure: false + }, '/socket.io': { target: `http://${serverHost}:${serverPort}`, changeOrigin: true, @@ -39,8 +46,11 @@ export default defineConfig(async ({ mode }) => { open: true, // proxy, port: process.env.VITE_PORT ?? 8088, - host: process.env.VITE_HOST ?? '0.0.0.0' - , + host: process.env.VITE_HOST ?? '0.0.0.0', + watch: { + usePolling: true + }, + host: process.env.VITE_HOST ?? 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diff --git a/studio-frontend/turbo.json b/studio-frontend/turbo.json index 010a193..a12dd0c 100644 --- a/studio-frontend/turbo.json +++ b/studio-frontend/turbo.json @@ -1,5 +1,21 @@ { "$schema": "https://turbo.build/schema.json", + "globalEnv": [ + "DATABASE_TYPE", + "DATABASE_HOST", + "DATABASE_PORT", + "DATABASE_NAME", + "DATABASE_USER", + "DATABASE_PASSWORD", + "DATABASE_SSL", + "DATABASE_SSL_KEY_BASE64", + "FINETUNING_SERVICE_DNS", + "FINETUNING_RAY_DNS", + "STUDIO_SERVER_URL", + "PREPARE_DOC_REDIS_PREP_DNS", + "LOG_LEVEL", + "LOG_PATH" + ], "tasks": { "build": { "dependsOn": ["^build"], diff --git a/tests/playwright/studio-e2e/005_test_finetuning_rerank.spec.ts b/tests/playwright/studio-e2e/005_test_finetuning_rerank.spec.ts index 9f934ad..ed112a5 100644 --- a/tests/playwright/studio-e2e/005_test_finetuning_rerank.spec.ts +++ b/tests/playwright/studio-e2e/005_test_finetuning_rerank.spec.ts @@ -62,7 +62,8 @@ test('005_test_finetuning_rerank', async ({ browser, baseURL }) => { await expect(page.locator('div').filter({ hasText: 'Fine-tuning JobsCreate New' }).nth(3)).toContainText('rerank'); await waitForStatusText(page, 'td.MuiTableCell-root div.MuiChip-root', 'succeeded', 20, 60000); - await page.locator('button').nth(5).click(); + await page.locator('tbody tr').first().getByRole('button').nth(2).click(); await page.getByRole('menuitem', { name: 'Delete Job' }).click(); - await page.getByRole('button', { name: 'Delete', exact: true }).click(); + await page.getByRole('button', { name: 'Delete' }).click(); + await page.waitForTimeout(5000); }); \ No newline at end of file diff --git a/tests/playwright/studio-e2e/006_test_finetuning_embedding.spec.ts b/tests/playwright/studio-e2e/006_test_finetuning_embedding.spec.ts index 7886c38..891c5c0 100644 --- a/tests/playwright/studio-e2e/006_test_finetuning_embedding.spec.ts +++ b/tests/playwright/studio-e2e/006_test_finetuning_embedding.spec.ts @@ -62,7 +62,8 @@ test('006_test_finetuning_embedding', async ({ browser, baseURL }) => { await expect(page.locator('div').filter({ hasText: 'Fine-tuning JobsCreate New' }).nth(3)).toContainText('embedding'); await waitForStatusText(page, 'td.MuiTableCell-root div.MuiChip-root', 'succeeded', 20, 60000); - await page.locator('button').nth(5).click(); + await page.locator('tbody tr').first().getByRole('button').nth(2).click(); await page.getByRole('menuitem', { name: 'Delete Job' }).click(); - await page.getByRole('button', { name: 'Delete', exact: true }).click(); + await page.getByRole('button', { name: 'Delete' }).click(); + await page.waitForTimeout(5000); }); \ No newline at end of file diff --git a/tests/playwright/studio-e2e/007_test_finetuning_reasoning.spec.ts b/tests/playwright/studio-e2e/007_test_finetuning_reasoning.spec.ts index 72472ba..414dd64 100644 --- a/tests/playwright/studio-e2e/007_test_finetuning_reasoning.spec.ts +++ b/tests/playwright/studio-e2e/007_test_finetuning_reasoning.spec.ts @@ -62,7 +62,8 @@ test('007_test_finetuning_reasoning', async ({ browser, baseURL }) => { await expect(page.locator('div').filter({ hasText: 'Fine-tuning JobsCreate New' }).nth(3)).toContainText('reasoning'); await waitForStatusText(page, 'td.MuiTableCell-root div.MuiChip-root', 'succeeded', 20, 60000); - await page.locator('button').nth(5).click(); + await page.locator('tbody tr').first().getByRole('button').nth(2).click(); await page.getByRole('menuitem', { name: 'Delete Job' }).click(); - await page.getByRole('button', { name: 'Delete', exact: true }).click(); + await page.getByRole('button', { name: 'Delete' }).click(); + await page.waitForTimeout(5000); }); \ No newline at end of file