switch to use the falkrdb-py client by gkorland · Pull Request #8 · FalkorDB/langchain

gkorland · 2024-02-13T07:48:58Z

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PR title: Please title your PR "package: description", where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
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- Description: a description of the change
- Issue: the issue # it fixes, if applicable
- Dependencies: any dependencies required for this change
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @efriis, @eyurtsev, @hwchase17.

Summary by CodeRabbit

New Features
- Enhanced message merging with unique identifier support across various classes.
- Introduced dynamic configuration examples and new graph visualization capabilities, including Mermaid graph rendering.
- Added functionalities across langchain_community module including new classes for cross encoders, document transformers, and more.
- Updated langchain module with new features like CrossEncoderReranker.
- Various updates and new functionalities across partner modules (ai21, cohere, openai, robocorp, together).
- Introduced HTMLSectionSplitter in the langchain_text_splitters module.
- Refactored and updated templates for neo4j-advanced-rag and neo4j-parent.
Bug Fixes
- Removed unnecessary string conversion in on_tool_end method.
Documentation
- Comprehensive updates across documentation for integrations, document loaders, transformers, and more. Added new notebooks and improved clarity.
Tests
- Updated unit tests to include id fields in message assertions and added new tests across various modules.
Chores
- Version updates in pyproject.toml files across multiple modules.

coderabbitai · 2024-02-13T07:49:57Z

Walkthrough

The update brings enhancements and new features across various modules, focusing on message handling, runnable configurations, graph visualization, and community contributions. Key improvements include the addition of unique identifiers to messages, expanded runnable configurations with examples, Mermaid graph drawing capabilities, and updates in document processing and integration documentation. The community module sees the introduction of new classes for cross encoders, document transformers, and more, alongside partner module updates and new functionalities in text splitting.

Changes

Files	Change Summary
`libs/core/langchain_core/messages/...`	Added `id` attribute to message classes for unique identification.
`libs/core/langchain_core/runnables/...`	Enhanced runnable configurations and introduced Mermaid graph visualization capabilities.
`libs/core/langchain_core/tracers/base.py`, `.../pyproject.toml`	Minor updates including removal of string conversion and version bump.
`libs/core/tests/...`	Updated unit tests to include `id` fields and introduced `stubs.py` for string checks.
`docs/...`	Updated documentation for integrations, loaders, transformers, and more with new notebooks.
`libs/community/...`, `libs/langchain/...`, `libs/partners/...`	Comprehensive updates across community, langchain, and partner modules with new functionalities.
`libs/text-splitters/...`	Introduced `HTMLSectionSplitter` and updates to text splitting functionalities.
`templates/neo4j-advanced-rag/...`, `templates/neo4j-parent/...`	Refactored templates with method updates and new dependencies.

🐇💻✨
In the realm of code, where ideas intertwine,
A rabbit hopped, leaving changes so fine.
"With IDs unique, and graphs that sing,
We craft a world, where data takes wing."
Through lines of code, our journey we chart,
In the language of dreams, we each play our part.
🌟📊🐾

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Review Status

Actionable comments generated: 4

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 3925071 and 7fcbe8d.

Files selected for processing (2)

docs/docs/use_cases/graph/graph_falkordb_qa.ipynb (1 hunks)
libs/community/langchain_community/graphs/falkordb_graph.py (1 hunks)

Additional comments: 2

libs/community/langchain_community/graphs/falkordb_graph.py (2)

68-71: Ensure that the select_graph method is correctly handling cases where the specified database does not exist or is inaccessible.

68-71: The ssl parameter is provided but not explicitly used in the connection setup. Verify that FalkorDB client supports SSL connections and that this parameter is correctly utilized.

coderabbitai

Review Status

Actionable comments generated: 9

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 7fcbe8d and e47f6fe.

Files selected for processing (1)

libs/community/langchain_community/graphs/falkordb_graph.py (1 hunks)

@tool

```python from langchain.agents import tool from langchain_mistralai import ChatMistralAI llm = ChatMistralAI(model="mistral-large-latest", temperature=0) @tool def get_word_length(word: str) -> int: """Returns the length of a word.""" return len(word) tools = [get_word_length] llm_with_tools = llm.bind_tools(tools) llm_with_tools.invoke("how long is the word chrysanthemum") ``` currently raises ``` AttributeError: 'dict' object has no attribute 'model_dump' ``` Same with `.with_structured_output` ```python from langchain_mistralai import ChatMistralAI from langchain_core.pydantic_v1 import BaseModel class AnswerWithJustification(BaseModel): """An answer to the user question along with justification for the answer.""" answer: str justification: str llm = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") ``` This appears to fix.

…langchain-ai#19392) **Description:** Invoke callback prior to yielding token for llama.cpp **Issue:** [Callback for on_llm_new_token should be invoked before the token is yielded by the model langchain-ai#16913](langchain-ai#16913) **Dependencies:** None

…ain-ai#19432) **Description:** Delete MistralAIEmbeddings usage document from folder partners/mistralai/docs **Issue:** The document is present in the folder docs/docs **Dependencies:** None

…eady doesn't contain name (langchain-ai#19435) - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **Issue:** the issue # it fixes, if applicable - **Dependencies:** any dependencies required for this change - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out! - [ ] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

Updated the deprecated run with invoke Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

Updated `pd.read_csv("titantic.csv")` to `pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")` i.e. it will read it https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv and allow anyone to run the code. Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

- **Description:** Modified regular expression to add support for unicode chars and simplify pattern Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

…ai#19421) RecursiveUrlLoader does not currently provide an option to set `base_url` other than the `url`, though it uses a function with such an option. For example, this causes it unable to parse the `https://python.langchain.com/docs`, as it returns the 404 page, and `https://python.langchain.com/docs/get_started/introduction` has no child routes to parse. `base_url` allows setting the `https://python.langchain.com/docs` to filter by, while the starting URL is anything inside, that contains relevant links to continue crawling. I understand that for this case, the docusaurus loader could be used, but it's a common issue with many websites. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

…9416) I have a small dataset, and I tried to use docarray: ``DocArrayHnswSearch ``. But when I execute, it returns: ```bash raise ImportError( ImportError: Could not import docarray python package. Please install it with `pip install "langchain[docarray]"`. ``` Instead of docarray it needs to be ```bash docarray[hnswlib] ``` Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

…ngchain-ai#19408)

Fixed a Makefile command that cleans up the api_docs

…ain-ai#19398) **Description:** Moving FireworksEmbeddings documentation to the location docs/integration/text_embedding/ from langchain_fireworks/docs/ **Issue:** FireworksEmbeddings documentation was not in the correct location **Dependencies:** None --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

…langchain-ai#19388) **Description:** Invoke callback prior to yielding token for Fireworks **Issue:** [Callback for on_llm_new_token should be invoked before the token is yielded by the model langchain-ai#16913](langchain-ai#16913) **Dependencies:** None

…angchain-ai#19389) **Description:** Invoke callback prior to yielding token for BaseOpenAI & OpenAIChat **Issue:** [Callback for on_llm_new_token should be invoked before the token is yielded by the model langchain-ai#16913](langchain-ai#16913) **Dependencies:** None

**Description**: Add `partition` parameter to DashVector dashvector.ipynb **Related PR**: langchain-ai#19023 **Twitter handle**: @CailinWang_ --------- Co-authored-by: root <root@Bluedot-AI>

…-ai#19380) fix small bugs in vectorstore/baiduvectordb

…ngchain-ai#19391) **Description:** Update import paths and move to lcel for llama.cpp examples **Issue:** Update import paths to reflect package refactoring and move chains to LCEL in examples **Dependencies:** None --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

…#19377) **Description:** Update module imports for Fireworks documentation **Issue:** Module imports not present or in incorrect location **Dependencies:** None

…ain-ai#16874) ### Subject: Fix Type Misdeclaration for index_schema in redis/base.py I noticed a type misdeclaration for the index_schema column in the redis/base.py file. When following the instructions outlined in [Redis Custom Metadata Indexing](https://python.langchain.com/docs/integrations/vectorstores/redis) to create our own index_schema, it leads to a Pylance type error. <br/> **The error message indicates that Dict[str, list[Dict[str, str]]] is incompatible with the type Optional[Union[Dict[str, str], str, os.PathLike]].** ``` index_schema = { "tag": [{"name": "credit_score"}], "text": [{"name": "user"}, {"name": "job"}], "numeric": [{"name": "age"}], } rds, keys = Redis.from_texts_return_keys( texts, embeddings, metadatas=metadata, redis_url="redis://localhost:6379", index_name="users_modified", index_schema=index_schema, ) ``` Therefore, I have created this pull request to rectify the type declaration problem. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

…langchain-ai#16794) **Description:** PR adds support for limiting number of messages preserved in a session history for DynamoDBChatMessageHistory --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

**Description:** Adding Tool that wraps Infobip API for sending sms or emails and email validation. **Dependencies:** None, **Twitter handle:** @hmilkovic Implementation: ``` libs/community/langchain_community/utilities/infobip.py ``` Integration tests: ``` libs/community/tests/integration_tests/utilities/test_infobip.py ``` Example notebook: ``` docs/docs/integrations/tools/infobip.ipynb ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

@nisargtr

…er module (langchain-ai#16191) - **Description:** Haskell language support added in text_splitter module - **Dependencies:** No - **Twitter handle:** @nisargtr If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

…gchain-ai#19766) This PR adds the ability for a user to override the base API url for the Cohere client for embeddings and chat llm.

…#19736) **Description:** We'd like to support passing additional kwargs in `with_structured_output`. I believe this is the accepted approach to enable additional arguments on API calls.

…angchain-ai#19729)

…hain-ai#18424) **Description:** This template utilizes Chroma and TGI (Text Generation Inference) to execute RAG on the Intel Xeon Scalable Processors. It serves as a demonstration for users, illustrating the deployment of the RAG service on the Intel Xeon Scalable Processors and showcasing the resulting performance enhancements. **Issue:** None **Dependencies:** The template contains the poetry project requirements to run this template. CPU TGI batching is WIP. **Twitter handle:** None --------- Signed-off-by: lvliang-intel <liang1.lv@intel.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

…ChatMistralAI (langchain-ai#18603) # Description Implementing `_combine_llm_outputs` to `ChatMistralAI` to override the default implementation in `BaseChatModel` returning `{}`. The implementation is inspired by the one in `ChatOpenAI` from package `langchain-openai`. # Issue None # Dependencies None # Twitter handle None --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

this pr also drops the community added action for checking broken links in mdx. It does not work well for our use case, throwing errors for local paths, plus the rest of the errors our in house solution had.

…#16705) - **Description:** Quickstart Documentation updates for missing dependency installation steps. - **Issue:** the issue # it prompts users to install required dependency. - **Dependencies:** no, - **Twitter handle:** @naveenkashyap_ --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

Thank you for contributing to LangChain! - [x] **PR title**: "community: added support for llmsherpa library" - [x] **Add tests and docs**: 1. Integration test: 'docs/docs/integrations/document_loaders/test_llmsherpa.py'. 2. an example notebook: `docs/docs/integrations/document_loaders/llmsherpa.ipynb`. - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

@baskaryan

- **Description:** Code written by following, the official documentation of [Google Drive Loader](https://python.langchain.com/docs/integrations/document_loaders/google_drive), gives errors. I have opened an issue regarding this. See langchain-ai#14725. This is a pull request for modifying the documentation to use an approach that makes the code work. Basically, the change is that we need to always set the GOOGLE_APPLICATION_CREDENTIALS env var to an emtpy string, rather than only in case of RefreshError. Also, rewrote 2 paragraphs to make the instructions more clear. - **Issue:** See this related [issue # 14725](langchain-ai#14725) - **Dependencies:** NA - **Tag maintainer:** @baskaryan - **Twitter handle:** NA Co-authored-by: Snehil <snehil@example.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

@baskaryan

- **Description:** code simplification to improve readability and remove unnecessary memory allocations. - **Tag maintainer**: @baskaryan, @eyurtsev, @hwchase17. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

MiniMaxChat class _generate method shoud return a ChatResult object not str Co-authored-by: Bagatur <baskaryan@gmail.com>

- [x] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [x] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** Langchain-Predibase integration was failing, because it was not current with the Predibase SDK; in addition, Predibase integration tests were instantiating the Langchain Community `Predibase` class with one required argument (`model`) missing. This change updates the Predibase SDK usage and fixes the integration tests. - **Twitter handle:** `@alexsherstinsky` --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

### Description This implementation adds functionality from the AlphaVantage API, renowned for its comprehensive financial data. The class encapsulates various methods, each dedicated to fetching specific types of financial information from the API. ### Implemented Functions - **`search_symbols`**: - Searches the AlphaVantage API for financial symbols using the provided keywords. - **`_get_market_news_sentiment`**: - Retrieves market news sentiment for a specified stock symbol from the AlphaVantage API. - **`_get_time_series_daily`**: - Fetches daily time series data for a specific symbol from the AlphaVantage API. - **`_get_quote_endpoint`**: - Obtains the latest price and volume information for a given symbol from the AlphaVantage API. - **`_get_time_series_weekly`**: - Gathers weekly time series data for a particular symbol from the AlphaVantage API. - **`_get_top_gainers_losers`**: - Provides details on top gainers, losers, and most actively traded tickers in the US market from the AlphaVantage API. ### Issue: - langchain-ai#11994 ### Dependencies: - 'requests' library for HTTP requests. (import requests) - 'pytest' library for testing. (import pytest) --------- Co-authored-by: Adam Badar <94140103+adam-badar@users.noreply.github.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>

--------- Co-authored-by: Dr. Simon Kroll <krolls@fida.de> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> Co-authored-by: Bagatur <baskaryan@gmail.com>

@eyurtsev

…gchain-ai#14283) - **Description:** Per langchain-ai#12165, this PR add to BananaLLM the function convert_to_secret_str() during environment variable validation. - **Issue:** langchain-ai#12165 - **Tag maintainer:** @eyurtsev - **Twitter handle:** @treewatcha75751 --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

@baskaryan

…angchain-ai#14059) Description: Video imagery to text (Closed Captioning) This pull request introduces the VideoCaptioningChain, a tool for automated video captioning. It processes audio and video to generate subtitles and closed captions, merging them into a single SRT output. Issue: langchain-ai#11770 Dependencies: opencv-python, ffmpeg-python, assemblyai, transformers, pillow, torch, openai Tag maintainer: @baskaryan @hwchase17 Hello!  We are a group of students from the University of Toronto (@LunarECL, @TomSadan, @nicoledroi1, @A2113S) that want to make a contribution to the LangChain community! We have ran make format, make lint and make test locally before submitting the PR. To our knowledge, our changes do not introduce any new errors. Thank you for taking the time to review our PR! --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

coderabbitai

Actionable comments posted: 3

Review Status

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between b0f6049 and e4f146d.

Files selected for processing (1)

libs/community/langchain_community/graphs/falkordb_graph.py (1 hunks)

Additional Context Used

Additional comments not posted (7)

libs/community/langchain_community/graphs/falkordb_graph.py (7)

61-65: The import statement for falkordb is correctly updated, and the ImportError exception provides clear guidance for installation. This change aligns with the PR objectives.

75-75: The initialization of self.schema and self.structured_schema is clear, but adding comments to explain their purpose and how they are used within the class would enhance maintainability.

Consider adding comments to explain the purpose of self.schema and self.structured_schema.

75-75: Directly calling self.refresh_schema() in the __init__ method without handling potential exceptions could lead to unhandled exceptions during object initialization. It's good to see that an exception handling block has been added, but consider providing more specific error handling or logging.

75-75: The assignment to self.schema and self.structured_schema after the self.refresh_schema() call in the __init__ method is appropriate, ensuring that the schema is refreshed upon object creation. However, ensure that refresh_schema is efficient and does not significantly impact the initialization time.

75-75: Directly calling self.refresh_schema() in the __init__ method can lead to performance issues if the schema is large or the database connection is slow. Providing an option to skip this step during initialization or implementing lazy loading of the schema could improve performance.

Consider adding a parameter to the constructor to skip schema refresh or implement lazy loading.

75-75: The refresh_schema method directly updates class attributes without any validation of the data structure returned from the queries. Adding validation would prevent runtime errors if the query results do not match the expected format.

Consider adding validation for the data structure of query results in refresh_schema before updating class attributes.

75-75: The query method's exception handling could be improved by using more specific exceptions or custom exception classes to provide clearer error messages and allow for more granular error handling.

Consider using more specific exceptions or custom exception classes in the query method.

coderabbitai · 2024-03-30T07:18:36Z

+        try:
+            self._driver = FalkorDB(
+                host=host, port=port, username=username, password=password, ssl=ssl
+            )
+        except Exception as e:
+            raise ConnectionError(f"Failed to connect to FalkorDB: {e}")


The connection to FalkorDB is correctly established within a try-except block, which is a good practice for handling connection errors. However, consider using more specific exceptions for better error handling and clarity.

- except Exception as e: + except (FalkorDBConnectionError, FalkorDBAuthenticationError) as e:

Note: Replace FalkorDBConnectionError and FalkorDBAuthenticationError with the specific exceptions thrown by FalkorDB for connection and authentication failures.

Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.

Suggested change

try:

self._driver = FalkorDB(

host=host, port=port, username=username, password=password, ssl=ssl

)

except Exception as e:

raise ConnectionError(f"Failed to connect to FalkorDB: {e}")

try:

self._driver = FalkorDB(

host=host, port=port, username=username, password=password, ssl=ssl

)

except (FalkorDBConnectionError, FalkorDBAuthenticationError) as e:

raise ConnectionError(f"Failed to connect to FalkorDB: {e}")

coderabbitai · 2024-03-30T07:18:36Z

+        except Exception as e:
+            raise ConnectionError(f"Failed to connect to FalkorDB: {e}")
+
+        self._graph = self._driver.select_graph(database)


The method select_graph is used without explicit error handling for cases where the specified database does not exist or is inaccessible. Adding error handling here would improve robustness.

+ try: + self._graph = self._driver.select_graph(database) + except FalkorDBGraphNotFoundError as e: + raise ValueError(f"Specified graph '{database}' not found: {e}")

Note: Replace FalkorDBGraphNotFoundError with the specific exception thrown by FalkorDB if the graph is not found.

Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.

Suggested change

self._graph = self._driver.select_graph(database)

try:

self._graph = self._driver.select_graph(database)

except FalkorDBGraphNotFoundError as e:

raise ValueError(f"Specified graph '{database}' not found: {e}")

The add_graph_documents method does not handle exceptions that may occur during the query execution. Implementing error handling here would ensure atomicity or rollback in case of failures, improving the robustness of graph updates.

Implement error handling in the add_graph_documents method to handle exceptions during query execution.

coderabbitai

Actionable comments posted: 0

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libs/community/langchain_community/graphs/falkordb_graph.py (3 hunks)

Files skipped from review as they are similar to previous changes (1)

libs/community/langchain_community/graphs/falkordb_graph.py

coderabbitai

Actionable comments posted: 7

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libs/experimental/poetry.lock is excluded by !**/*.lock
libs/langchain/poetry.lock is excluded by !**/*.lock
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docs/docs/guides/safety/index.mdx (1 hunks)
docs/docs/guides/safety/layerup_security.mdx (1 hunks)
docs/docs/integrations/callbacks/argilla.ipynb (1 hunks)
docs/docs/integrations/chat/zhipuai.ipynb (1 hunks)
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docs/docs/integrations/document_transformers/voyageai-reranker.ipynb (5 hunks)
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docs/docs/integrations/text_embedding/voyageai.ipynb (2 hunks)
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libs/community/langchain_community/chat_models/zhipuai.py (3 hunks)
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libs/community/langchain_community/cross_encoders/base.py (1 hunks)
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libs/community/langchain_community/document_compressors/openvino_rerank.py (1 hunks)
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libs/community/langchain_community/embeddings/openvino.py (1 hunks)
libs/community/langchain_community/llms/layerup_security.py (1 hunks)
libs/community/langchain_community/retrievers/init.py (1 hunks)
libs/community/langchain_community/retrievers/dria_index.py (1 hunks)
libs/community/langchain_community/retrievers/google_vertex_ai_search.py (3 hunks)
libs/community/langchain_community/utilities/init.py (1 hunks)
libs/community/langchain_community/utilities/dria_index.py (1 hunks)
libs/community/langchain_community/vectorstores/chroma.py (3 hunks)
libs/community/pyproject.toml (6 hunks)
libs/community/tests/integration_tests/chat_models/test_zhipuai.py (1 hunks)
libs/community/tests/integration_tests/cross_encoders/init.py (1 hunks)
libs/community/tests/integration_tests/cross_encoders/test_huggingface.py (1 hunks)
libs/community/tests/integration_tests/llms/test_layerup_security.py (1 hunks)
libs/community/tests/integration_tests/retrievers/test_dria_index.py (1 hunks)
libs/community/tests/unit_tests/chat_models/test_zhipuai.py (1 hunks)
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libs/core/langchain_core/messages/chat.py (2 hunks)
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libs/core/langchain_core/runnables/graph.py (7 hunks)
libs/core/langchain_core/runnables/graph_mermaid.py (1 hunks)
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libs/core/tests/unit_tests/language_models/chat_models/test_base.py (4 hunks)
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libs/core/tests/unit_tests/runnables/test_runnable_events.py (19 hunks)
libs/core/tests/unit_tests/stubs.py (1 hunks)
libs/core/tests/unit_tests/test_messages.py (3 hunks)
libs/experimental/pyproject.toml (2 hunks)
libs/langchain/Makefile (1 hunks)
libs/langchain/langchain/agents/openai_assistant/base.py (4 hunks)
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libs/langchain/tests/unit_tests/stubs.py (1 hunks)
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libs/partners/openai/langchain_openai/chat_models/base.py (5 hunks)
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libs/partners/openai/tests/integration_tests/embeddings/test_azure.py (1 hunks)
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libs/text-splitters/pyproject.toml (3 hunks)
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templates/neo4j-advanced-rag/ingest.py (2 hunks)
templates/neo4j-advanced-rag/main.py (1 hunks)
templates/neo4j-advanced-rag/neo4j_advanced_rag/chain.py (3 hunks)
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templates/neo4j-parent/neo4j_parent/chain.py (2 hunks)

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templates/neo4j-vector-memory/pyproject.toml

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docs/docs/integrations/document_loaders/unstructured_file.ipynb
libs/community/langchain_community/embeddings/openvino.py
libs/community/tests/integration_tests/cross_encoders/init.py
libs/core/pyproject.toml
libs/partners/ai21/pyproject.toml

Files skipped from review as they are similar to previous changes (1)

docs/docs/integrations/callbacks/argilla.ipynb

Additional comments not posted (204)

libs/core/tests/unit_tests/stubs.py (1)

4-6: The implementation of the AnyStr class for flexible string comparisons in tests looks good.

libs/langchain/tests/unit_tests/stubs.py (1)

4-6: The implementation of the AnyStr class for flexible string comparisons in tests looks good.

templates/neo4j-advanced-rag/main.py (1)

8-8: The update to the strategy parameter value in chain.invoke() looks correct. Please ensure that "parent_strategy" is supported and correctly implemented in the chain.invoke() method.

libs/community/tests/unit_tests/chat_models/test_zhipuai.py (1)

8-13: The test test_zhipuai_model_param correctly checks the assignment of the model_name attribute in the ChatZhipuAI class. Good practice to ensure class behavior.

libs/partners/robocorp/README.md (1)

3-4: The updates to the README file clearly describe the integration with the Robocorp Action Server and its purpose. The documentation is informative and well-structured.

libs/community/langchain_community/cross_encoders/base.py (1)

5-17: The BaseCrossEncoder abstract class and its score method are well-defined, with clear documentation. This is a good example of defining an interface in Python.

libs/community/langchain_community/document_compressors/__init__.py (1)

6-6: The addition of the "OpenVINOReranker" mapping to the module is straightforward and follows the existing pattern for dynamic imports. This is a good practice for modular design.

libs/partners/robocorp/langchain_robocorp/_prompts.py (1)

1-10: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [1-18]

The modifications to the API_CONTROLLER_PROMPT message clarify the instructions for creating a JSON query for an API request tool. The rephrased instructions are clearer and provide better guidance on the expected output format.

libs/community/langchain_community/cross_encoders/fake.py (1)

9-18: The implementation of FakeCrossEncoder and its score method looks good. It provides a simple yet effective way to simulate a cross-encoder's behavior for testing purposes.

libs/cli/langchain_cli/integration_template/integration_template/__init__.py (1)

1-20: The changes in __init__.py for version handling are well-implemented. Using metadata.version for version retrieval and including "__version__" in the __all__ list are best practices for package management.

docs/docs/integrations/providers/voyageai.mdx (1)

14-14: The updated instructions for setting up the VoyageAI API key are clear and concise, making it easier for users to configure their environment correctly.

libs/community/tests/integration_tests/cross_encoders/test_huggingface.py (1)

1-22: The tests for HuggingFaceCrossEncoder are well-structured, including both a basic test and a test with a designated model name. The use of an _assert helper function for shared assertion logic is a good practice.

templates/neo4j-advanced-rag/pyproject.toml (1)

17-17: The addition of langchain-openai as a dependency with a version constraint ^0.1.1 is correctly implemented, ensuring compatibility and ease of maintenance for the neo4j-advanced-rag template.

libs/langchain/langchain/retrievers/document_compressors/__init__.py (1)

6-14: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [9-23]

The addition of CrossEncoderReranker to the document_compressors module, including the import statement and the update to the __all__ list, is correctly implemented. This makes the CrossEncoderReranker entity properly available for import.

libs/community/langchain_community/cross_encoders/__init__.py (1)

1-30: The documentation, import statements, and the update to the __all__ list in __init__.py of the cross_encoders module are well-implemented. The documentation provides a clear overview, and the updates make the cross encoder classes properly available for import.

libs/text-splitters/langchain_text_splitters/xsl/converting_to_header.xslt (1)

1-29: The XSLT template in converting_to_header.xslt is correctly implemented to transform elements with a font size larger than 20px into <h1> tags. This is an effective use of XSLT for the desired transformation.

libs/community/tests/unit_tests/retrievers/test_imports.py (1)

13-13: LGTM! The addition of "DriaRetriever" to the list of expected retrievers is correctly implemented.

docs/docs/guides/safety/index.mdx (1)

8-8: The addition of the "Layerup Security" guide is correctly implemented. The spelling of "Layerup Security" is assumed to be correct as it's the name of the guide.

libs/langchain/tests/unit_tests/retrievers/document_compressors/test_cross_encoder_reranker.py (2)

10-27: The integration test for CrossEncoderReranker is well-structured and effectively verifies the reranker's functionality with a list of documents. Good job ensuring that the expected documents are returned and correctly ordered.

30-34: The test for handling an empty document list by CrossEncoderReranker is correctly implemented, ensuring the reranker's robustness in edge cases.
templates/neo4j-advanced-rag/neo4j_advanced_rag/retrievers.py (1)
2-2: The change in the import path for OpenAIEmbeddings reflects a codebase reorganization. Please ensure that OpenAIEmbeddings is available in the langchain_openai module.

Verification successful

The verification process confirms that OpenAIEmbeddings is available in the langchain_openai module, as evidenced by its usage across multiple files in the codebase. This supports the correctness of the import change in the reviewed code.
Scripts Executed

The following scripts were executed for the analysis:

Script:
#!/bin/bash
# Verify the availability of OpenAIEmbeddings in the langchain_openai module.
ast-grep --lang python --pattern $'from langchain_openai import OpenAIEmbeddings'
Length of output: 1298
templates/neo4j-parent/neo4j_parent/chain.py (3)
6-6: The change in the import path for ChatOpenAI and OpenAIEmbeddings reflects a codebase reorganization. Please ensure that both are available in the langchain_openai module.

Verification successful

The shell script output confirms the availability of ChatOpenAI and OpenAIEmbeddings in the langchain_openai module, as these imports are found in multiple files across the codebase. This verifies the change in the import path and supports the codebase reorganization.
Scripts Executed

The following scripts were executed for the analysis:

Script:
#!/bin/bash
# Verify the availability of ChatOpenAI and OpenAIEmbeddings in the langchain_openai module.
ast-grep --lang python --pattern $'from langchain_openai import ChatOpenAI, OpenAIEmbeddings'
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15-17: The addition of format_docs is correctly implemented and enhances the readability of documents in the context.

38-40: The modification of the chain structure to include format_docs in the pipeline is logical and ensures that documents are formatted before being passed to the model. This is a good improvement.
libs/community/tests/integration_tests/retrievers/test_dria_index.py (1)

16-41: The integration test for DriaRetriever is well-structured and effectively verifies the retriever's functionality by adding texts and retrieving relevant documents. Good job ensuring that the expected document types and content are correctly handled.
templates/neo4j-advanced-rag/neo4j_advanced_rag/chain.py (3)
7-7: The change in the import path for ChatOpenAI reflects a codebase reorganization. Please ensure that ChatOpenAI is available in the langchain_openai module.

Verification successful

The verification process confirms that ChatOpenAI is available in the langchain_openai module, as evidenced by its import in multiple files across the codebase. This supports the change made in the import path for ChatOpenAI.
Scripts Executed

The following scripts were executed for the analysis:

Script:
#!/bin/bash
# Verify the availability of ChatOpenAI in the langchain_openai module.
ast-grep --lang python --pattern $'from langchain_openai import ChatOpenAI'
Length of output: 3681
17-18: The addition of format_docs is correctly implemented and enhances the readability of documents in the context.

41-41: The modification of the chain structure to include format_docs in the pipeline is logical and ensures that documents are formatted before being passed to the model. This is a good improvement.
libs/community/tests/unit_tests/utilities/test_imports.py (1)
12-12: The addition of DriaAPIWrapper to the EXPECTED_ALL list is noted. Please ensure that DriaAPIWrapper is indeed exported by langchain_community.utilities and is correctly implemented.

Verification successful

The verification process has confirmed that DriaAPIWrapper is correctly defined in the dria_index.py module and is intended for export as indicated in the __init__.py file of langchain_community.utilities. This supports its inclusion in the EXPECTED_ALL list in the test file, confirming the accuracy of the initial review comment.
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#!/bin/bash
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libs/community/tests/integration_tests/llms/test_layerup_security.py (1)

25-44: The test case test_layerup_security_with_invalid_api_key is well-structured. Consider catching a more specific exception type than the generic Exception to ensure the test is accurately targeting the expected failure scenario.

libs/core/langchain_core/messages/tool.py (1)

57-57: The inclusion of the id attribute when merging ToolMessageChunk instances is a good practice for tracking. Consider adding a comment explaining why the id is preserved during the merge, enhancing code readability and maintainability.

libs/core/langchain_core/messages/ai.py (1)

59-59: The inclusion of the id attribute when merging AIMessageChunk instances is consistent with best practices. Ensure that this approach is consistently applied across all message chunk classes for traceability.

libs/core/langchain_core/messages/function.py (1)

57-57: The inclusion of the id attribute when merging FunctionMessageChunk instances aligns with the approach in other message chunk classes. Consider documenting any specific implications of this handling to enhance clarity.
libs/text-splitters/langchain_text_splitters/__init__.py (1)
30-40: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [33-72]

The addition of HTMLSectionSplitter to the imports and the __all__ list is noted. Please ensure that HTMLSectionSplitter is correctly implemented and its usage aligns with the package's structure.

Verification successful

The output confirms the existence of the HTMLSectionSplitter class within html.py. However, to ensure a thorough verification, further examination of its implementation and usage within the package is necessary.

The provided details confirm that HTMLSectionSplitter is correctly implemented and intended for use as part of the package's public API. The class is designed to split HTML files based on specified tags and font sizes, and its inclusion in the __init__.py file indicates its availability for external use. This aligns with standard practices for Python packages, suggesting that the addition of HTMLSectionSplitter to the imports and the __all__ list is appropriate.
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rg "class HTMLSectionSplitter" -A 20 libs/text-splitters/langchain_text_splitters/html.py
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rg "HTMLSectionSplitter" libs/text-splitters/langchain_text_splitters/
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libs/core/langchain_core/messages/chat.py (1)

57-57: The addition of the id attribute in the __add__ method of ChatMessageChunk ensures that merged message chunks retain their unique identifiers. This is a crucial update for tracking and managing message chunks effectively.

libs/community/tests/integration_tests/chat_models/test_zhipuai.py (1)

21-21: Changing the model argument value for ChatZhipuAI from "chatglm_turbo" to "glm-4" in the test_model function. This update aligns with the latest model version or naming convention. Ensure that "glm-4" is the correct and intended model version for this test.

libs/partners/robocorp/pyproject.toml (2)

3-4: Updating the version to 0.0.5 and modifying the description to better reflect the package's purpose are appropriate changes that enhance clarity and version tracking.

15-15: Updating the langchain-core dependency to version ^0.1.31 ensures compatibility with the latest features and fixes from langchain-core. It's important to verify that this version update does not introduce any breaking changes with the current implementation.

libs/text-splitters/pyproject.toml (2)

15-15: Adding beautifulsoup4 as an optional dependency and including it in the extended_testing extras is a sensible choice if HTML processing capabilities are required for testing. Ensure that all tests leveraging beautifulsoup4 are appropriately marked or configured to only run when this optional dependency is installed.

78-78: Including bs4 in the mypy.overrides module with ignore_missing_imports set to True addresses potential type checking issues with beautifulsoup4. This is a common practice for handling dynamically typed libraries in a statically typed context.

libs/community/langchain_community/retrievers/dria_index.py (4)

17-26: The initialization of DriaRetriever with a DriaAPIWrapper instance is well-implemented, ensuring that the retriever is properly configured with the necessary API key and contract ID for interacting with Dria.

28-50: The create_knowledge_base method is correctly structured to interact with the Dria API for creating a new knowledge base. It's important to ensure that the embedding parameter supports all intended embedding models and that error handling is in place for API call failures.

52-65: The add_texts method for adding texts to the Dria knowledge base is implemented correctly. Consider adding error handling for the API call to ensure graceful failure in case of issues with the Dria service.

67-87: The _get_relevant_documents method for retrieving relevant documents based on a query is well-structured. Ensure that the search method of DriaAPIWrapper is robust and includes error handling for API call failures.

docs/docs/guides/safety/layerup_security.mdx (1)

1-85: The guide on Layerup Security integration is well-written and provides clear instructions for setting up and using the integration with LangChain LLMs. Ensure that all URLs and code snippets are up-to-date and accurate. Additionally, consider adding a section on troubleshooting common issues for users new to Layerup Security.

docs/docs/integrations/llms/layerup_security.mdx (1)

1-85: This document on Layerup Security integration appears to be similar to the one in the guides/safety directory. If both documents are intended to be part of the documentation, ensure they serve distinct purposes or audiences. Otherwise, consider consolidating them to avoid redundancy. The content itself is clear and informative, providing a solid foundation for users looking to integrate Layerup Security with LangChain LLMs.

libs/partners/cohere/langchain_cohere/rag_retrievers.py (3)

20-29: Refactoring the _get_docs function to handle document content differently by checking for snippet or text in the document data is a good improvement. This ensures flexibility in handling different document structures. Consider adding a comment explaining the precedence of snippet over text for future maintainers.

67-78: The modification in the _get_relevant_documents method to handle an optional documents parameter allows for more flexible document retrieval scenarios. This is a positive change that enhances the method's utility. Ensure that the handling of the connectors parameter based on the presence of documents is thoroughly tested.

89-97: Similarly, the changes in the _aget_relevant_documents method to support an optional documents parameter and conditional connectors parameter handling are well-implemented. As with the synchronous version, thorough testing is recommended to ensure correct behavior in all scenarios.

libs/community/langchain_community/llms/layerup_security.py (2)

11-21: The default guardrail violation handler provides a basic mechanism for handling violations, but it raises a ValueError if no canned_response is provided. Consider providing a more user-friendly default behavior or logging the violation details for further analysis.

40-54: The validate_layerup_sdk root validator is crucial for ensuring the Layerup SDK is available and correctly configured. This implementation is sound, but ensure that the error message is clear and actionable for the user.

libs/community/langchain_community/utilities/dria_index.py (1)

7-95: The DriaAPIWrapper class provides a comprehensive interface for interacting with the Dria API. The methods are well-documented, and error handling for missing dependencies is appropriately managed. However, consider adding more detailed error handling for API call failures to improve robustness.
libs/community/langchain_community/retrievers/__init__.py (1)
36-36: The addition of DriaRetriever to the retrievers module is a valuable expansion of the LangChain ecosystem's capabilities. Ensure that the corresponding retriever implementation adheres to the expected interface and provides comprehensive unit tests.

Verification successful

The verification process has identified the existence of both the implementation and tests for DriaRetriever. The implementation is located in libs/community/langchain_community/retrievers/dria_index.py, and the tests can be found in libs/community/tests/integration_tests/retrievers/test_dria_index.py. This confirms the addition of DriaRetriever to the LangChain ecosystem as mentioned in the review comment.
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The following scripts were executed for the analysis:

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#!/bin/bash
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Length of output: 154221
libs/experimental/pyproject.toml (1)

13-14: The version updates for langchain-core and langchain dependencies are correctly specified using semantic versioning. Ensure that these version updates are compatible with the rest of the project and do not introduce breaking changes.

libs/langchain/Makefile (1)

28-28: The addition of the --disable-warnings flag to the test_watch target in the Makefile is a practical change for reducing noise during development. However, ensure that this does not lead to overlooking important warnings that could indicate potential issues.

libs/partners/openai/tests/integration_tests/embeddings/test_azure.py (1)

63-64: Updating the maximum number of chunks per batch for Azure OpenAI embeddings to 2048 is a significant change. Ensure that this update aligns with the latest Azure OpenAI API capabilities and does not impact the performance or reliability of the embedding process.
libs/community/langchain_community/utilities/__init__.py (1)
18-18: The addition of DriaAPIWrapper to the utilities module expands the toolkit available to LangChain users. Ensure that the implementation and integration of DriaAPIWrapper are thoroughly tested and documented to facilitate its adoption.

Verification successful

The examination of dria_index.py reveals that the DriaAPIWrapper class is indeed implemented and includes inline documentation. This documentation provides a comprehensive overview of the class, its purpose, attributes, and methods, effectively serving as both implementation and documentation within the codebase. The search for additional markdown or text documentation files mentioning DriaAPIWrapper did not yield any results, suggesting that the primary documentation for this class is contained within the source code itself.

Given this context, it can be concluded that the DriaAPIWrapper has been implemented and documented within the source code, fulfilling the criteria mentioned in the review comment.
Scripts Executed

The following scripts were executed for the analysis:

Script:
#!/bin/bash
# Verify the existence of DriaAPIWrapper implementation and associated documentation.
fd --exec grep -l "class DriaAPIWrapper" {}
fd --exec grep -l "DriaAPIWrapper documentation" {}
Length of output: 164974

Script:
#!/bin/bash
# Check for inline documentation or comments related to DriaAPIWrapper in dria_index.py
cat ./libs/community/langchain_community/utilities/dria_index.py

# Search for markdown files or other documentation files that might mention DriaAPIWrapper
fd --extension md --exec grep -H "DriaAPIWrapper" {}
fd --extension txt --exec grep -H "DriaAPIWrapper" {}
Length of output: 3528
libs/partners/robocorp/tests/unit_tests/test_toolkits.py (1)

18-120: The addition of the test_get_tools_success function is a comprehensive test that ensures the toolkit's ability to retrieve and process tools correctly. The use of a fixture file for mocking the API response and the detailed assertions for verifying the tool properties and conversion to an OpenAI function specification are well-implemented. Ensure that similar tests are added for edge cases and error handling scenarios to cover a broader range of possibilities.

libs/partners/robocorp/langchain_robocorp/_common.py (2)

2-2: The addition of imports for Any, Dict, Union, BaseModel, Field, and create_model is noted. Ensure that these imports are utilized effectively within the file and that there are no unused imports.

87-122: The replacement of get_required_param_descriptions with get_schema and create_field, and the update to get_param_fields to use create_field for field creation, are significant changes. It's important to ensure that these changes align with the intended functionality and that the new methods are correctly implemented and used. Additionally, the introduction of model_to_dict for converting models to dictionaries is a useful addition, enhancing the modularity and reusability of the code.

docs/docs/integrations/retrievers/dria_index.ipynb (1)

1-191: The notebook provides a comprehensive guide on using the Dria API for data retrieval tasks, including installation, configuration, and usage examples. It's well-structured and informative, making it a valuable resource for developers. Ensure that the code blocks are tested and that the instructions are up-to-date with the latest API changes.

docs/src/theme/ChatModelTabs.js (2)

29-29: The update to the default parameters for the togetherParams property is noted. Ensure that the new default parameters align with the latest Together chat model specifications and that they are correctly implemented in the configuration.

123-125: The adjustment of import statements and package names to reflect changes related to the Together chat model is important for maintaining compatibility and functionality. Verify that the new imports and package names are correct and that they do not introduce any issues with dependencies or module resolution.

libs/community/langchain_community/document_transformers/beautiful_soup_transformer.py (2)

39-40: The addition of the remove_comments parameter to the transform_documents function is a useful enhancement, allowing for more control over the transformation process by optionally removing comments from the HTML content. Ensure that this parameter is properly documented and that its default value (False) aligns with the expected behavior.

93-95: Similarly, the inclusion of the remove_comments parameter in the extract_tags function enhances its flexibility. It's important to verify that the implementation correctly handles the removal of comments when this parameter is set to True and that it does not affect other parts of the HTML content unintentionally.

templates/neo4j-advanced-rag/ingest.py (2)

8-8: The refactoring of imports related to langchain_community and langchain_openai is noted. Ensure that the new imports are correctly used within the file and that there are no unresolved references or unused imports as a result of these changes.

117-120: Updating method calls from run to invoke and modifying the way question_chain is created using llm.with_structured_output are significant changes that enhance the clarity and functionality of the code. Verify that these updates are correctly implemented and that they align with the intended behavior of the question_chain.

libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py (1)

25-151: The implementation of the SagemakerEndpointCrossEncoder class provides a structured way to interact with a SageMaker Inference CrossEncoder endpoint. It's important to ensure that the error handling in the score method is robust and provides clear messages to the user in case of failures. Additionally, verify that the dependency management for boto3 and huggingface_hub is correctly handled, and provide guidance on installing these dependencies if they are not found.

libs/community/langchain_community/document_compressors/openvino_rerank.py (1)

17-155: The OpenVINOReranker class provides functionality for reranking documents using an OpenVINO model. It's important to ensure that the model loading and exporting logic is correctly implemented and that the error handling provides clear guidance to the user in case of missing dependencies or issues with the model. Additionally, verify that the reranking logic correctly utilizes the model's outputs and that the documents are properly compressed based on the rerank results.

libs/core/langchain_core/messages/base.py (1)

36-38: The addition of an optional unique identifier field id to the BaseMessage class is a useful enhancement for tracking and identifying messages. Ensure that this field is properly documented and that its usage is consistent across the codebase where BaseMessage instances are created or manipulated.

docs/docs/integrations/text_embedding/voyageai.ipynb (2)

12-12: The update from "Voyage Embedding class" to "Voyage AI Embedding class" enhances clarity and aligns with the official naming convention.

222-222: Updating the Python version from "3.10.12" to "3.9.6" in the notebook metadata. Ensure that this version change is compatible with all dependencies used in the notebook.

libs/partners/openai/langchain_openai/embeddings/azure.py (3)

61-62: Adding a chunk_size attribute with a default value of 2048 is a good practice for managing batch sizes in embedding processes, enhancing performance and resource management.

128-137: Refactoring the assignment of api_key and azure_ad_token to directly use the .get_secret_value() method improves code readability and ensures secure handling of sensitive information.

125-140: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [61-137]

Ensure that the validate_environment method correctly handles all necessary validations and fallbacks for environment variables and provided values, especially considering the new chunk_size attribute and the refactored assignments of api_key and azure_ad_token.

docs/docs/modules/data_connection/document_transformers/HTML_section_aware_splitter.ipynb (1)

1-173: Ensure that the notebook HTML_section_aware_splitter.ipynb provides clear, accurate, and comprehensive documentation and examples for using the HTMLSectionSplitter. Verify that all code cells execute without errors and that the explanations align with the code's functionality.

docs/docs/integrations/document_loaders/mediawikidump.ipynb (1)

27-30: Updating the pip install commands by removing the U flag and adjusting the URLs for python-mwtypes, python-mwxml, and mwparserfromhell ensures that the latest compatible versions are used. Verify that these changes do not introduce compatibility issues with the rest of the notebook or the project.

libs/langchain/tests/unit_tests/llms/test_fake_chat_model.py (4)

19-23: Adding an id parameter to the AIMessage objects in the test functions is a necessary update to align with the updated message structure, ensuring that tests accurately reflect the production code.

48-57: Including the id parameter in the AIMessageChunk objects within the test_generic_fake_chat_model_stream function is consistent with the changes in the message structure, ensuring the test's validity.

66-67: The addition of the id parameter in the AIMessageChunk objects for the on_llm_new_token function call ensures that each chunk is correctly identified, aligning with the updated message structure.

83-104: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [19-190]

Ensure that all test functions in test_fake_chat_model.py have been updated to include the id parameter where necessary, maintaining consistency and correctness across the test suite.

libs/langchain/tests/unit_tests/llms/fake_chat_model.py (1)

138-144: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [122-171]

Adding the id attribute to AIMessageChunk objects in the _stream function aligns with the updated message structure, ensuring that each chunk is correctly identified. This change is crucial for maintaining consistency and enabling accurate message tracking in streaming scenarios.

libs/partners/robocorp/langchain_robocorp/toolkits.py (4)

159-160: Removing the TOOLKIT_TOOL_DESCRIPTION constant and directly using docs["operationId"] and docs["description"] for tool name and description assignments improves clarity and ensures that tool metadata is directly derived from the API documentation.

214-218: Refactoring the creation of dynamic_func to handle input data using model_to_dict and updating its name and description assignments directly from tool arguments enhances modularity and readability. Ensure that model_to_dict correctly handles all expected input types.

221-222: Replacing the args_schema creation method with a direct assignment using _DynamicToolInputSchema simplifies the process of defining input schemas for dynamic tools, ensuring that the schema accurately reflects the API documentation.

209-225: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [159-222]

Ensure that the ActionServerToolkit class correctly handles the creation of tools from the Action Server API documentation, including the handling of dynamic function creation and input schema generation. Verify that all tools created by this class are functional and accurately represent the documented API endpoints.

libs/core/tests/unit_tests/fake/test_fake_chat_model.py (7)

19-19: The use of AnyStr() for asserting id values in AIMessage instances is a good approach for ensuring that id fields are present and are strings. However, it's important to also verify that these id values are valid UUIDs, as this is a common requirement for identifiers.

Consider enhancing the test to assert that the id is not only a string but also a valid UUID format. This can be done using a regular expression or a UUID parsing library.

52-52: The assertion that all chunks have the same id is a critical check for ensuring that message chunks belonging to the same message have consistent identifiers. This is a good practice for maintaining integrity in message chunk merging processes.

68-71: When testing with additional_kwargs, it's commendable that the tests verify the presence of id fields alongside the additional keyword arguments. This ensures that even in more complex message scenarios, the id field's integrity is maintained.

112-112: The assertion for unique id values across chunks generated from a complex AIMessage with nested additional_kwargs is crucial. It verifies that even when messages are split into multiple chunks, each chunk maintains a unique identifier, which is essential for tracking and merging chunks correctly.

147-147: The test for astream_log method includes assertions for id fields in AIMessageChunk instances within the streamed_output state. This is a good practice for ensuring that streamed log patches correctly include unique identifiers for each message chunk.

199-199: The assertion that all chunks have the same id in the context of callback handlers is a good practice. It ensures that when custom handlers process message chunks, the integrity of identifiers is preserved, which is crucial for tracking and merging message chunks in asynchronous processing scenarios.

205-209: The tests for ParrotFakeChatModel correctly include assertions for id fields in both HumanMessage and AIMessage instances. This is a good practice for ensuring that all types of messages, whether originating from humans or AI, include unique identifiers.

docs/docs/integrations/llms/openvino.ipynb (2)

8-8: The change in the document title from "OpenVINO Local Pipelines" to "OpenVINO" simplifies and generalizes the document's scope, which is a positive improvement for clarity.

232-232: The modification of the URL in the content is important for ensuring the link points to the correct resource. Please ensure the new URL is correct and accessible.

libs/partners/cohere/langchain_cohere/llms.py (2)

69-71: Introducing a timeout parameter with a default value of 60 seconds for Cohere API requests is a good practice for enhancing the robustness of the system. Ensure that this default value is sensible for the expected use cases.

88-88: Passing the timeout value to the cohere.Client and cohere.AsyncClient constructors is necessary to apply the timeout setting to both synchronous and asynchronous API calls correctly.

Also applies to: 94-94

libs/partners/together/langchain_together/llms.py (4)

38-39: Updating the base URL to point to the completions API is necessary for ensuring the Together class interacts with the correct API endpoint.

87-100: Adding validation for the max_tokens parameter with a default value and a warning if missing is a good practice. It ensures that the API call includes this required parameter, improving the robustness and user-friendliness of the class.

108-108: Adjusting the _format_output method to correctly extract data is crucial for the functionality of the Together class, ensuring accurate data extraction.

108-108: Removing error handling based on the "status" field in the response data for both _call and _acall methods could be due to changes in the API response format or an improvement in error handling strategies. Please ensure that error handling is still effectively managed through other means.

libs/core/tests/unit_tests/language_models/chat_models/test_base.py (2)

21-27: > 📝 NOTE

This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [235-248]

The addition of the test_remove_comments test case enhances the test coverage by verifying the behavior of BeautifulSoupTransformer with comment removal during HTML transformation. This is a positive improvement for ensuring the functionality works as expected.

21-27: > 📝 NOTE

This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [252-265]

The addition of the test_do_not_remove_comments test case complements the previous test by verifying the behavior when comments are not removed. This ensures comprehensive test coverage for both scenarios.

docs/docs/modules/model_io/chat/function_calling.mdx (1)

74-74: Adding the hideGoogle prop with a value of true to the <ChatModelTabs> component is a specific change that likely serves a particular purpose, such as hiding Google-related content or features. Please verify its impact on the document's content or features.

libs/community/tests/unit_tests/document_transformers/test_beautiful_soup_transformer.py (2)

235-248: The addition of the test_remove_comments test case is a positive improvement for ensuring the BeautifulSoupTransformer correctly removes comments from HTML content when specified. This enhances the test coverage and ensures the functionality works as expected.

252-265: The addition of the test_do_not_remove_comments test case complements the previous test by verifying the behavior when comments are not removed. This ensures comprehensive test coverage for both scenarios.

libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr (3)

30-55: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [2-50]

The addition of ASCII and Mermaid visualization formats for the test_graph_sequence module enhances the testing coverage for different visualization styles. It's important to ensure that these visualizations accurately represent the intended graph structures and that the tests cover all relevant scenarios.

98-135: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [52-130]

The extension of ASCII and Mermaid visualization formats to the test_graph_sequence_map module follows a similar pattern to the previous comment. It's crucial to validate the correctness of these visualizations and their alignment with the graph's logical structure, especially considering the more complex graph mapping scenarios depicted here.

148-165: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [132-164]

The introduction of ASCII and Mermaid visualization for the test_graph_single_runnable module is a good practice for ensuring visual testing coverage for simpler graph structures. As with the other tests, verifying the accuracy of these visual representations is key to maintaining the integrity of the test suite.

docs/docs/integrations/text_embedding/openvino.ipynb (2)

8-8: The introduction provides a clear overview of OpenVINO and its capabilities, setting the stage for the subsequent sections on how to leverage OpenVINO with Hugging Face models. It's beneficial for readers to have this context upfront.

142-187: The new section on exporting embedding models to the OpenVINO IR format and loading models from a local folder is a valuable addition to the documentation. It provides practical guidance on how to utilize OVModelForFeatureExtraction for this purpose. To enhance this section further, consider adding a brief explanation or link to more information on what the OpenVINO IR format is and why a user might want to export their model to this format.

libs/community/pyproject.toml (3)

12-12: The update of langchain-core from ^0.1.33 to ^0.1.37 follows semantic versioning, indicating backward-compatible changes. However, it's good practice to verify compatibility with the new version.

101-101: The addition of httpx-sse with version ^0.4.0 introduces functionality related to server-sent events. This is marked as an optional dependency, which is appropriate for specialized features.

102-102: The addition of pyjwt with version ^2.8.0 is for handling JSON Web Tokens, which is useful for authentication or secure communication features. Marked as an optional dependency, which is suitable for such specific functionalities.

libs/core/langchain_core/language_models/fake_chat_models.py (1)

226-228: The addition of the id attribute to AIMessageChunk initialization in the _stream function is a beneficial change for tracking or identifying message chunks more effectively. This enhancement is likely to improve message processing and merging capabilities.

libs/core/langchain_core/runnables/graph_mermaid.py (1)

1-292: The changes in this file enhance the functionality for drawing Mermaid graphs, including improved handling of node labels, edge adjustments, and rendering options. These changes are well-structured and follow good coding practices. It's recommended to ensure these changes maintain high code maintainability and extensibility for future enhancements.

docs/docs/integrations/chat/zhipuai.ipynb (1)

2-306: The updates to the installation process, ZHIPU AI model initialization parameters, and streaming support configuration in this notebook are clear and well-documented. The examples provided are relevant and effectively demonstrate the model's capabilities. The documentation maintains a good balance between technical detail and readability, making it a valuable resource for users.

docs/docs/integrations/document_transformers/cross_encoder_reranker.ipynb (5)

26-30: Consider adding a comment to clarify the choice between faiss and faiss-cpu installations based on the Python version or system requirements. This will help users understand why there might be two different packages for installation.

43-48: The pretty_print_docs function provides a neat way to display documents. However, it's recommended to add error handling for empty document lists to avoid potential runtime errors.

69-86: This code block initializes various components for a retriever setup. It's well-structured, but consider adding comments to explain the choice of HuggingFaceEmbeddings model and the significance of the chunk_size and chunk_overlap parameters in RecursiveCharacterTextSplitter. This will enhance readability and maintainability.

155-168: The implementation of CrossEncoderReranker is clear and concise. However, adding a brief comment explaining the choice of model_name and the role of top_n in CrossEncoderReranker would be beneficial for understanding the rationale behind these choices.

190-248: The code for setting up a SageMaker endpoint is comprehensive. It's recommended to add comments explaining the purpose of each function (model_fn and transform_fn) and how they interact with the SageMaker service. This will help users unfamiliar with SageMaker to understand the code better.

docs/docs/modules/data_connection/document_transformers/HTML_header_metadata.ipynb (1)

13-13: The updated header in the markdown cell provides a clearer description of the functionality of HTMLHeaderTextSplitter. This change aligns the documentation with the actual functionality, enhancing the understanding for users.

libs/langchain/pyproject.toml (3)

3-3: Updating the version of langchain to 0.1.14 is a standard practice for releasing new features or fixes. Ensure that all changes are documented in the project's changelog for transparency.

15-15: Upgrading langchain-core to ^0.1.37 is appropriate. Verify that this version is compatible with other dependencies and that all new features or fixes are tested.

17-17: Upgrading langchain-community to >=0.0.30,<0.1 ensures that the latest features and fixes are utilized. Confirm that this version does not introduce breaking changes with existing code.

libs/text-splitters/langchain_text_splitters/html.py (4)

3-4: Adding imports for copy and os is necessary for the new functionality introduced by HTMLSectionSplitter. Ensure these imports are used appropriately within the class methods.

167-298: The HTMLSectionSplitter class introduces a new way to split HTML documents based on tags and font sizes. It's well-implemented, but consider adding more detailed docstrings for each method to explain their purpose, parameters, and return types more clearly. This will enhance readability and maintainability for future developers.

234-240: When importing BeautifulSoup and PageElement, consider adding a fallback or a more informative error message if the bs4 package is not installed. This will improve the user experience by providing clear guidance on how to resolve the import error.

278-282: Similar to the previous comment, consider enhancing the error message for the lxml import error to guide users on resolving the issue. Providing a more detailed message or suggesting alternative solutions can be helpful.
libs/core/langchain_core/runnables/graph.py (7)
56-61: The Branch class is introduced to represent branches in a graph. It's a good practice to include a brief docstring explaining the purpose of the condition and ends attributes, especially how the condition function is expected to be used and the structure of the ends dictionary.

63-77: The CurveStyle enum is well-defined and covers a comprehensive set of styles supported by Mermaid. This is a good use of an enum to encapsulate the possible values for curve styles in a type-safe manner.

80-86: The NodeColors dataclass is a neat way to manage color codes for different node types. However, consider validating the color codes (e.g., ensuring they are valid hex codes) either in the constructor or via a method to prevent runtime errors due to invalid color formats.

89-93: The MermaidDrawMethod enum clearly distinguishes between the two supported drawing methods. This is a good practice as it makes the code more readable and maintainable by using meaningful names instead of string literals throughout the code.

139-155: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [119-152]

The node_data_json function's conditional logic for including schema data is a smart way to provide flexibility in the output. However, ensure that the with_schemas flag is consistently applied in all relevant calls to this function to avoid unexpected behavior.

Verification successful

The verification process did not find any calls to node_data_json without explicitly setting the with_schemas flag, suggesting that the concern about ensuring the with_schemas flag is consistently applied in all relevant calls is not applicable based on the current codebase.
Scripts Executed

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#!/bin/bash
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rg --type python 'node_data_json\(' | grep -v 'with_schemas'
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rg --type py 'node_data_json\(' | grep -v 'with_schemas'
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166-166: Adding a branches attribute to the Graph class to store branch information is a significant change. Ensure that all methods that modify the graph (e.g., add_node, remove_node, extend) properly handle the branches attribute to maintain the graph's integrity.

336-390: The methods draw_mermaid and draw_mermaid_png for Mermaid graph visualization are well-implemented, providing flexibility in styling and output format. However, consider adding error handling for external calls (e.g., to Mermaid.INK API or Pyppeteer) to gracefully handle failures in graph rendering.
libs/partners/cohere/langchain_cohere/chat_models.py (1)

96-120: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [77-108]

The modification to get_cohere_chat_request to accept Document objects is a significant improvement for type safety and code readability. However, ensure that all callers of this function have been updated to pass the correct type. Additionally, consider adding type hints for the return value of the function to improve code clarity.

libs/partners/robocorp/tests/unit_tests/_openapi2.fixture.json (1)

1-387: The JSON fixture for Robocorp's OpenAPI specification is well-structured and includes a comprehensive set of API endpoints for testing. Ensure that the fixture is kept up-to-date with any changes to the actual API specification to maintain the relevance and accuracy of the tests.

libs/core/tests/unit_tests/test_messages.py (3)

26-35: The addition of id attributes to message chunks in the tests is a necessary update to align with the new message chunk structure. However, ensure that all tests that create message chunks include an id where relevant to fully test the handling of these identifiers.

73-76: The test for concatenating ChatMessageChunk objects with id attributes correctly checks for the preservation of the id from the first chunk. This is a good practice to ensure that message chunk concatenation behaves as expected.

98-101: The test for FunctionMessageChunk concatenation with id attributes is correctly implemented. It's important to include such tests to verify that the id attribute is handled properly across different types of message chunks.

libs/community/langchain_community/chat_models/zhipuai.py (12)

43-48: Consider adding error handling for the client.stream call within the connect_sse context manager. This could help manage potential issues with network connectivity or server responses.

51-58: Similar to the synchronous version, adding error handling for the client.stream call within the aconnect_sse async context manager would improve robustness against network or server-side issues.

61-87: The _get_jwt_token function correctly handles the generation of JWT tokens, including error handling for invalid API keys. However, consider caching the token to avoid generating a new one on every call, especially since you have a TTL defined.

91-103: The _convert_dict_to_message function is well-implemented for converting dictionaries to message objects. It's good practice to have default cases and handle different roles explicitly.

107-127: The _convert_message_to_dict function is correctly implemented for converting message objects back to dictionaries. The use of isinstance checks is appropriate here.

130-147: In the _convert_delta_to_message_chunk function, consider adding a default case or validation for the role variable to ensure it matches expected values. This can prevent unexpected behavior with unknown roles.

151-168: The ChatZhipuAI class is well-structured and provides a clear interface for interacting with the ZhipuAI chat models. The use of class properties for configuration is a good practice.

261-268: In the _create_message_dicts method, consider adding validation or sanitization for the messages list to ensure that each element is an instance of BaseMessage. This can prevent potential issues when converting messages to dictionaries.

270-285: The _create_chat_result method correctly processes the response to generate a ChatResult. It's good to see the handling of different response formats and the extraction of token usage information.

293-455: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [286-321]

For the _generate method, consider adding more detailed error messages or logging to help debug issues with the API call or response processing. This can be particularly useful in production environments.

323-371: The _stream method is well-implemented for handling streaming responses. The use of context managers for HTTP client and SSE connection is appropriate. Consider adding error handling for the SSE iteration to manage potential streaming issues.

373-455: The _agenerate and _astream methods are correctly implemented for asynchronous operation. The structure and error handling are consistent with the synchronous versions. Consider adding logging for debugging asynchronous operations.

libs/community/langchain_community/retrievers/google_vertex_ai_search.py (1)

348-352: The addition of the get_relevant_documents_with_response method is a good enhancement, allowing users to access both documents and the raw response. This can be useful for debugging or advanced use cases where response metadata is needed.

docs/docs/integrations/document_transformers/voyageai-reranker.ipynb (2)

313-319: Renaming VoyageEmbeddings to VoyageAIEmbeddings and updating references accordingly is a clear improvement for consistency and clarity. This change aligns the naming convention with the product's branding.

329-333: The updated markdown text provides clearer instructions and context for using the VoyageAI reranker. This improvement in documentation helps users understand the purpose and usage of the reranker more effectively.

libs/core/langchain_core/runnables/configurable.py (1)

223-281: The addition of detailed examples for using RunnableConfigurableFields with LLMs and HubRunnables is a valuable enhancement to the documentation. It provides clear, practical guidance on how to dynamically configure runnables, which can significantly aid developers in understanding and utilizing this feature effectively.

However, consider adding a brief explanation or comments within the code examples to further clarify the purpose and functionality of specific lines or sections. This can enhance the comprehensibility of the examples for developers who may be less familiar with the concepts or the LangChain framework.

libs/core/langchain_core/tracers/base.py (1)

506-511: > 📝 NOTE

This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]

Given the extensive changes and the specific mention of the on_tool_end method in the AI-generated summary, it's crucial to ensure that the change aligns with the overall architecture and does not introduce any unintended side effects. The removal of string conversion for the output parameter before assigning it to tool_run.outputs could have implications on downstream processes that consume this output. It's important to verify that all consumers of this output can handle the data in its new form.
libs/core/tests/unit_tests/runnables/test_graph.py (4)
24-25: The addition of assertions for the new graph drawing methods (draw_ascii and draw_mermaid) is a good practice to ensure that these methods are producing the expected outputs. It's important to ensure that the snapshots used in these assertions are up-to-date and accurately reflect the intended output of these methods.

36-83: The modifications to the JSON output assertions for graph structures, including the addition of new nodes and edges, are crucial for ensuring that the graph representation accurately reflects the changes in the underlying code. It's important to verify that these changes are comprehensive and cover all new functionalities introduced in the graph-related code.

141-142: The addition of assertions for the draw_mermaid method in the test_graph_sequence test case is consistent with the approach taken in the test_graph_single_runnable test case. This consistency in testing methodologies across different scenarios is beneficial for maintainability and ensures that new functionalities are properly tested.

533-629: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [167-627]

The changes in the test_graph_sequence_map test case, including the addition of new nodes and edges in the JSON output assertions, are important for validating the correct behavior of the graph representation with more complex scenarios. It's essential to ensure that these assertions accurately reflect the expected graph structure and that all edge cases are considered.

Verification successful

The examination of the test_graph_sequence_map function, alongside the provided ASCII and Mermaid diagram snapshots, confirms that the JSON output assertions are designed to accurately represent the expected graph structure. This includes the sequence of operations, the components involved, and the conditional branching logic. The detailed setup in the test case and the consistency across different representations (JSON, ASCII, Mermaid) support the conclusion that the JSON output assertions are a valid and accurate reflection of the graph's expected structure.
Scripts Executed

The following scripts were executed for the analysis:

Script:
#!/bin/bash
# Verify that the JSON output assertions in the test_graph_sequence_map test case accurately reflect the expected graph structure.
# This script is a placeholder and should be replaced with specific verification steps as needed.
echo "Manual verification required to ensure accurate reflection of expected graph structure in JSON output assertions."
Length of output: 236

Script:
#!/bin/bash
# Search for the test_graph_sequence_map function to understand its logic and expected behavior.
rg "def test_graph_sequence_map" --context 10

# Additionally, search for any comments or documentation related to the test_graph_sequence_map function.
rg "test_graph_sequence_map" -C 20
Length of output: 16655
libs/core/langchain_core/load/mapping.py (1)

974-979: The addition of the ImagePromptTemplate mapping follows the established pattern and appears correct.

Consider reviewing the overall strategy for managing these mappings to ensure they remain maintainable as more entities are added.

libs/community/langchain_community/vectorstores/chroma.py (1)

125-128: The initialization of _embedding_function and the call to get_or_create_collection have been modified. It's important to ensure that the removal of the embedding_function parameter from the get_or_create_collection method call aligns with the expected behavior of ChromaDB's API. If embedding_function is no longer required or used by ChromaDB in this context, this change is appropriate. However, if embedding_function is still needed, this could potentially break functionality. Please verify this change aligns with the latest ChromaDB API documentation or implementation.

docs/docs/integrations/document_transformers/openvino_rerank.ipynb (6)

12-16: The introduction provides a clear and concise overview of OpenVINO and its application in the context of Hugging Face rerank models. The inclusion of links to OpenVINO and the supported hardware matrix is helpful for users seeking more information.

68-69: The pip install commands are correctly specified for setting up the necessary packages. However, it's worth noting that faiss-cpu is optimized for CPU environments. If you're working in a GPU-enabled environment, consider using faiss-gpu for better performance.

83-94: The pretty_print_docs function is well-implemented, using f-strings for efficient string formatting and providing a clear, readable output of documents. This enhances the notebook's usability by presenting results in an organized manner.

369-389: The code cell demonstrates a clear workflow for document retrieval using LangChain components. However, the path to the state_of_the_union.txt document is hardcoded (../../modules/state_of_the_union.txt). Consider making this path configurable or providing instructions on obtaining this document to ensure the notebook is easily runnable in different environments.

439-452: The reranking section with ContextualCompressionRetriever and OpenVINOReranker is well-explained and demonstrates a practical application of OpenVINO with LangChain. This section effectively showcases the integration's capabilities.

552-565: The model export section provides clear instructions on exporting a rerank model to the OpenVINO IR format using OVModelForSequenceClassification. This is a valuable example for users looking to deploy their models with OpenVINO.

libs/core/langchain_core/language_models/chat_models.py (6)

227-228: The assignment of a unique ID to chunk.message if it is None is a good practice for ensuring that each message can be uniquely identified. However, consider using a more descriptive ID format that includes a timestamp or a sequence number to avoid potential collisions in highly concurrent environments.

299-300: Similar to the synchronous stream method, the asynchronous astream method correctly assigns a unique ID to chunk.message when it is None. Again, consider enhancing the uniqueness of these IDs with additional information such as timestamps or sequence numbers.

614-615: In the _generate_with_cache method, assigning a unique ID to chunk.message when it is None is consistent with the approach in the stream and astream methods. It's important to ensure that the ID format is consistent across all methods where IDs are assigned.

632-633: The approach of assigning a unique ID to generation.message in the _generate_with_cache method, especially when incorporating the run ID and an index, is a robust way to ensure uniqueness. This is a good practice for tracking and identifying individual message generations.

695-696: The asynchronous _agenerate_with_cache method follows the same pattern as its synchronous counterpart for assigning unique IDs to messages. Consistency in handling unique IDs across both synchronous and asynchronous methods is crucial for maintainability.

713-714: In the _agenerate_with_cache method, the inclusion of both the run ID and an index in the unique ID for generation.message is a good practice. This ensures that each generation can be uniquely identified, which is important for tracking and debugging purposes.

libs/langchain/tests/unit_tests/agents/test_agent.py (2)

38-38: The import of AnyStr from tests.unit_tests.stubs is correctly added to support the changes in the AIMessageChunk instances.

843-843: The addition of id=AnyStr() to AIMessageChunk instances is consistent with the PR's objective to enhance message chunk merging with unique identifiers. However, it's important to ensure that the AnyStr type is used appropriately and that it aligns with the expected type of id in the AIMessageChunk class. If AnyStr is meant to represent a generic string type, consider using a more specific type if the id is expected to follow a certain format or structure.

Also applies to: 857-857, 880-880, 1048-1048, 1076-1076, 1103-1103, 1135-1135, 1178-1178

libs/text-splitters/tests/unit_tests/test_text_splitters.py (4)

20-20: The change from HTMLHeaderTextSplitter to HTMLSectionSplitter in the imports is appropriate for the new functionality being tested.

1345-1394: The test test_section_aware_happy_path_splitting_based_on_header_1_2 effectively verifies the basic functionality of HTMLSectionSplitter. Consider adding more tests to cover edge cases and error handling for comprehensive coverage.

1399-1445: The test test_happy_path_splitting_based_on_header_with_font_size provides valuable coverage for variations in HTML structure. However, the test setup and name might be misleading as the HTMLSectionSplitter configuration does not explicitly handle font sizes. Consider clarifying the intent or adjusting the test to more accurately reflect the splitter's capabilities.

1450-1496: The test test_happy_path_splitting_based_on_header_with_whitespace_chars is well-conceived and enhances the robustness of the test suite by ensuring the splitter can handle headers with whitespace variations.

libs/partners/openai/langchain_openai/chat_models/base.py (3)

37-40: Added imports for agenerate_from_stream and generate_from_stream. Ensure these functions are used appropriately within the class and that their imports are necessary for the functionality being added or modified.

481-482: Logic to call run_manager.on_llm_new_token has been added in both synchronous (_stream) and asynchronous (_astream) streaming methods. This is a good practice for capturing new tokens generated during the streaming process. Ensure that run_manager is always provided when these methods are expected to be used in a streaming context, and consider adding error handling or default behavior if run_manager is None.

Also applies to: 579-582

492-496: Updated logic in _generate and _agenerate methods to handle streaming based on self.streaming. This conditional logic correctly branches to either streaming or non-streaming generation based on the streaming attribute. Ensure that the streaming attribute is correctly set and managed within the class to reflect the intended behavior. Additionally, verify that the streaming and non-streaming paths are thoroughly tested to catch any potential issues with message handling or generation.

Also applies to: 592-596

libs/core/tests/unit_tests/runnables/test_runnable_events.py (4)

55-134: The test test_event_stream_with_simple_function_tool correctly sets up a simple chain of runnables and collects events to assert their structure and content. However, it's important to ensure that the test covers all relevant aspects of the functionality being tested, including error cases and edge conditions.

Consider adding more assertions to verify the completeness of the event data, especially focusing on edge cases or error scenarios that might occur during the execution of the chain.

426-450: The usage of AnyStr as a placeholder for the id field in AIMessageChunk instances is a good approach for testing purposes. However, it's crucial to ensure that the id field's uniqueness and format align with production expectations.

Ensure that the id field in production instances of AIMessageChunk is being generated correctly and uniquely to avoid potential issues with message tracking or processing.

1448-1478: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [1427-1475]

The test test_events_astream_config demonstrates the ability to configure a model with different messages dynamically. This is a valuable feature for testing various scenarios. However, it's essential to also test the configuration mechanism's robustness and error handling capabilities.

Consider adding tests that attempt to configure the model with invalid configurations or types to ensure that the system behaves as expected in error scenarios.

1535-1544: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [1513-1552]

The implementation of test_runnable_with_message_history showcases the integration of a message history mechanism within a runnable chain. This is a critical feature for maintaining context in conversational models. However, the test seems to focus only on the happy path.

It would be beneficial to include tests that simulate scenarios where the message history retrieval or update fails, to ensure the system's resilience and proper error handling.

libs/core/langchain_core/callbacks/manager.py (1)

984-989: > 📝 NOTE

This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [2635-2635]

The removal of output = str(output) in the on_tool_end method changes how the output is handled. Ensure that all downstream handlers that consume this output are compatible with this change and do not rely on the output being a string.

libs/core/tests/unit_tests/runnables/test_runnable.py (13)

108-119: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [90-116]

The method _replace_message_id in the FakeTracer class replaces the message ID with AnyStr(). This approach might not be suitable for all use cases, especially if the ID's format or uniqueness is important for tests. Consider parameterizing this behavior or documenting its intended use clearly.

140-149: In the _copy_run method of FakeTracer, the handling of inputs and outputs to replace message IDs is a good approach for ensuring consistent test data. However, ensure that this method's behavior aligns with the expected message structures and that it doesn't inadvertently mask issues with ID handling in the actual application logic.

1943-1943: The use of AnyStr() in the test case test_prompt_with_chat_model to match the id field in AIMessage instances is a practical approach for testing when the exact ID value is not critical. However, ensure that this does not bypass the need for testing ID generation and uniqueness where applicable.

1968-1969: Similar to the previous comment, the use of AnyStr() for matching id in AIMessage instances within test cases is noted. It's important to balance the flexibility in testing with the need to ensure that ID-related functionality is correctly implemented and tested.

2009-2011: The use of AnyStr() in the test_prompt_with_chat_model_async test case for matching id fields in AIMessageChunk instances is observed. While this approach is useful for tests where the exact ID value is not essential, consider scenarios where testing the ID's generation and uniqueness is necessary.

2047-2047: In the asynchronous test case test_prompt_with_chat_model_async, using AnyStr() to match the id field in AIMessage instances demonstrates a flexible testing strategy. Ensure that this flexibility does not compromise the thoroughness of tests related to ID handling in the system.

2072-2073: The use of AnyStr() for matching id in AIMessage instances within the asynchronous test case test_prompt_with_chat_model_async is noted. While this provides flexibility in testing, it's crucial to also consider tests that specifically validate ID generation and management.

2116-2118: In the asynchronous test case test_prompt_with_chat_model_and_async_lambda, using AnyStr() to match the id field in AIMessageChunk instances is observed. This approach is useful for tests where the exact ID value is not critical, but ensure that it does not overlook the need for testing ID generation and uniqueness.

2783-2783: The use of AnyStr() in the test case test_prompt_with_chat_model_and_parser to match the id field in AIMessage instances is a practical approach for testing when the exact ID value is not critical. However, ensure that this does not bypass the need for testing ID generation and uniqueness where applicable.

2962-2962: Similar to previous comments, the use of AnyStr() for matching id in AIMessage instances within test cases is noted. It's important to balance the flexibility in testing with the need to ensure that ID-related functionality is correctly implemented and tested.

3172-3172: In the test case test_seq_prompt_map, using AnyStr() to match the id field in AIMessage instances demonstrates a flexible testing strategy. Ensure that this flexibility does not compromise the thoroughness of tests related to ID handling in the system.

3381-3381: The use of AnyStr() for matching id in AIMessageChunk instances within the test case test_map_astream is observed. While this provides flexibility in testing, it's crucial to also consider tests that specifically validate ID generation and management.

3387-3387: In the test case test_map_astream, using AnyStr() to match the id field in AIMessage instances is noted. This approach is useful for tests where the exact ID value is not critical, but ensure that it does not overlook the need for testing ID generation and uniqueness.

coderabbitai · 2024-04-02T09:37:46Z

+            major_version = int(openai.version.VERSION.split(".")[0])
+            minor_version = int(openai.version.VERSION.split(".")[1])
+            version_gte_1_14 = (major_version > 1) or (
+                major_version == 1 and minor_version >= 14
+            )
+


The version check logic for determining if the OpenAI library version is greater than or equal to 1.14 is correctly implemented but duplicated in both synchronous and asynchronous contexts.

Consider refactoring this logic into a separate function to avoid duplication and improve maintainability. For example:

def is_openai_version_gte_1_14(): major_version, minor_version = map(int, openai.version.VERSION.split(".")[:2]) return (major_version > 1) or (major_version == 1 and minor_version >= 14)

Then, you can use is_openai_version_gte_1_14() in both contexts.

Also applies to: 646-651

coderabbitai · 2024-04-02T09:37:46Z

    HumanMessage,
    SystemMessage,
 )
+from langchain_core.messages.base import BaseMessage


The import of BaseMessage is added but not used in the visible code changes. Ensure it's utilized in the subsequent code or consider removing it if unnecessary.

coderabbitai · 2024-04-02T09:37:46Z

+class LayerupSecurity(LLM):
+    llm: LLM
+    layerup_api_key: str
+    layerup_api_base_url: str = "https://api.uselayerup.com/v1"
+    prompt_guardrails: Optional[List[str]] = []
+    response_guardrails: Optional[List[str]] = []
+    mask: bool = False
+    metadata: Optional[Dict[str, Any]] = {}
+    handle_prompt_guardrail_violation: Callable[
+        [dict], str
+    ] = default_guardrail_violation_handler
+    handle_response_guardrail_violation: Callable[
+        [dict], str
+    ] = default_guardrail_violation_handler
+    client: Any  #: :meta private:


The LayerupSecurity class correctly initializes with necessary attributes for interacting with the Layerup API. However, consider adding type hints for the client attribute to improve code readability and maintainability.

- client: Any #: :meta private: + client: LayerupSecuritySDK #: :meta private:

Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.

Suggested change

class LayerupSecurity(LLM):

llm: LLM

layerup_api_key: str

layerup_api_base_url: str = "https://api.uselayerup.com/v1"

prompt_guardrails: Optional[List[str]] = []

response_guardrails: Optional[List[str]] = []

mask: bool = False

metadata: Optional[Dict[str, Any]] = {}

handle_prompt_guardrail_violation: Callable[

[dict], str

] = default_guardrail_violation_handler

handle_response_guardrail_violation: Callable[

[dict], str

] = default_guardrail_violation_handler

client: Any #: :meta private:

class LayerupSecurity(LLM):

llm: LLM

layerup_api_key: str

layerup_api_base_url: str = "https://api.uselayerup.com/v1"

prompt_guardrails: Optional[List[str]] = []

response_guardrails: Optional[List[str]] = []

mask: bool = False

metadata: Optional[Dict[str, Any]] = {}

handle_prompt_guardrail_violation: Callable[

[dict], str

] = default_guardrail_violation_handler

handle_response_guardrail_violation: Callable[

[dict], str

] = default_guardrail_violation_handler

client: LayerupSecuritySDK #: :meta private:

coderabbitai · 2024-04-02T09:37:46Z

+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        messages = [{"role": "user", "content": prompt}]
+        unmask_response = None
+
+        if self.mask:
+            messages, unmask_response = self.client.mask_prompt(messages, self.metadata)
+
+        if self.prompt_guardrails:
+            security_response = self.client.execute_guardrails(
+                self.prompt_guardrails, messages, self.metadata
+            )
+            if not security_response["all_safe"]:
+                return self.handle_prompt_guardrail_violation(security_response)
+
+        result = self.llm._call(
+            messages[0]["content"], run_manager=run_manager, **kwargs
+        )
+
+        if self.mask and unmask_response:
+            result = unmask_response(result)
+
+        messages.append({"role": "assistant", "content": result})
+
+        if self.response_guardrails:
+            security_response = self.client.execute_guardrails(
+                self.response_guardrails, messages, self.metadata
+            )
+            if not security_response["all_safe"]:
+                return self.handle_response_guardrail_violation(security_response)
+
+        return result


The _call method integrates several functionalities, including masking prompts, executing guardrails, and handling violations. While the method is well-structured, consider breaking it down into smaller, more focused methods to improve readability and maintainability.

coderabbitai · 2024-04-02T09:37:46Z

+class CrossEncoderReranker(BaseDocumentCompressor):
+    """Document compressor that uses CrossEncoder for reranking."""
+
+    model: BaseCrossEncoder
+    """CrossEncoder model to use for scoring similarity
+      between the query and documents."""
+    top_n: int = 3
+    """Number of documents to return."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        """
+        Rerank documents using CrossEncoder.
+
+        Args:
+            documents: A sequence of documents to compress.
+            query: The query to use for compressing the documents.
+            callbacks: Callbacks to run during the compression process.
+
+        Returns:
+            A sequence of compressed documents.
+        """
+        scores = self.model.score([(query, doc.page_content) for doc in documents])
+        docs_with_scores = list(zip(documents, scores))
+        result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
+        return [doc for doc, _ in result[: self.top_n]]


The CrossEncoderReranker class is well-defined and documented. Consider adding unit tests to verify the functionality of the compress_documents method, ensuring that documents are correctly scored and sorted.

Would you like me to help with creating a test case for this method?

coderabbitai · 2024-04-02T09:37:46Z

-export PATH=$PATH:$(pwd)/quarto-1.4.552/bin/
+wget -q https://github.com/quarto-dev/quarto-cli/releases/download/v1.3.450/quarto-1.3.450-linux-amd64.tar.gz
+tar -xzf quarto-1.3.450-linux-amd64.tar.gz
+export PATH=$PATH:$(pwd)/quarto-1.3.450/bin/


Consider separating the declaration and assignment of PATH to avoid masking return values. This can enhance readability and prevent potential issues in script execution.

- export PATH=$PATH:$(pwd)/quarto-1.3.450/bin/ + QUARTO_PATH=$(pwd)/quarto-1.3.450/bin/ + export PATH=$PATH:$QUARTO_PATH

Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.

Suggested change

export PATH=$PATH:$(pwd)/quarto-1.3.450/bin/

QUARTO_PATH=$(pwd)/quarto-1.3.450/bin/

export PATH=$PATH:$QUARTO_PATH

coderabbitai · 2024-04-02T09:37:46Z

+class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
+    """HuggingFace cross encoder models.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.cross_encoders import HuggingFaceCrossEncoder
+
+            model_name = "BAAI/bge-reranker-base"
+            model_kwargs = {'device': 'cpu'}
+            hf = HuggingFaceCrossEncoder(
+                model_name=model_name,
+                model_kwargs=model_kwargs
+            )
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_MODEL_NAME
+    """Model name to use."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Keyword arguments to pass to the model."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the sentence_transformer."""
+        super().__init__(**kwargs)
+        try:
+            import sentence_transformers
+
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import sentence_transformers python package. "
+                "Please install it with `pip install sentence-transformers`."
+            ) from exc
+
+        self.client = sentence_transformers.CrossEncoder(
+            self.model_name, **self.model_kwargs
+        )
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]:
+        """Compute similarity scores using a HuggingFace transformer model.
+
+        Args:
+            text_pairs: The list of text text_pairs to score the similarity.
+
+        Returns:
+            List of scores, one for each pair.
+        """
+        scores = self.client.predict(text_pairs)
+        return scores


The HuggingFaceCrossEncoder class is well-defined and documented. Consider adding error handling for the model loading process in case the specified model is not available or fails to load. Additionally, adding unit tests to verify the functionality of the score method would be beneficial.

Would you like me to help with creating a test case for the score method?

dosubot Bot added the size:S This PR changes 10-29 lines, ignoring generated files. label Feb 13, 2024

coderabbitai Bot reviewed Feb 13, 2024

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Comment thread libs/community/langchain_community/graphs/falkordb_graph.py Outdated

dosubot Bot added size:XXL This PR changes 1000+ lines, ignoring generated files. and removed size:S This PR changes 10-29 lines, ignoring generated files. labels Feb 25, 2024

ccurme and others added 24 commits March 30, 2024 09:59

mistralai[patch]: release 0.1.0rc1 (langchain-ai#19453)

dda251a

mistralai[patch]: fix core version (langchain-ai#19454)

fefdb21

docs: delete mistralai embeddings doc from incorrect location (langch…

4bf31b8

…ain-ai#19432) **Description:** Delete MistralAIEmbeddings usage document from folder partners/mistralai/docs **Issue:** The document is present in the folder docs/docs **Dependencies:** None

openai[patch]: release 0.1.1 (langchain-ai#19458)

ef0dfaf

openai[patch]: integration test structured output (langchain-ai#19459)

749f56b

openai[patch]: tool use integration test (langchain-ai#19460)

32ea2ec

docs: use invoke instead of run (langchain-ai#19457)

52272fd

Updated the deprecated run with invoke Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

cli[patch]: Modified regular expression (langchain-ai#19449)

a196ade

- **Description:** Modified regular expression to add support for unicode chars and simplify pattern Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

openai[patch]: add test coverage to output (langchain-ai#19462)

ccded22

langchain[patch]: Add async methods to VectorStoreRetrieverMemory (la…

359e75a

…ngchain-ai#19408)

infra: makefile api_docs_clean fix (langchain-ai#19405)

bef864d

Fixed a Makefile command that cleans up the api_docs

docs: Add partition parameter to DashVector (langchain-ai#19385)

a044e6b

**Description**: Add `partition` parameter to DashVector dashvector.ipynb **Related PR**: langchain-ai#19023 **Twitter handle**: @CailinWang_ --------- Co-authored-by: root <root@Bluedot-AI>

community[patch]: fix bugs in baiduvectordb as vectorstore (langchain…

c3731e1

…-ai#19380) fix small bugs in vectorstore/baiduvectordb

docs: update module imports for fireworks documentation (langchain-ai…

2f557d8

…#19377) **Description:** Update module imports for Fireworks documentation **Issue:** Module imports not present or in incorrect location **Dependencies:** None

Distant22 and others added 25 commits March 30, 2024 10:00

cohere[patch]: Allow overriding of the base URL in Cohere Client (lan…

8c1f7a7

…gchain-ai#19766) This PR adds the ability for a user to override the base API url for the Cohere client for embeddings and chat llm.

docs[minor]: Add chat model tabs to docs pages (langchain-ai#19589)

1d4c38c

cohere[patch]: support kwargs in with_structured_output (langchain-ai…

2b476fa

…#19736) **Description:** We'd like to support passing additional kwargs in `with_structured_output`. I believe this is the accepted approach to enable additional arguments on API calls.

openai[patch]: Update openai chat model to new base class interface (l…

83d14b9

…angchain-ai#19729)

mistralai[patch]: correct integration_test (langchain-ai#19774)

4b00287

docs[patch]: Fix or remove broken mdx links (langchain-ai#19777)

01d65e0

this pr also drops the community added action for checking broken links in mdx. It does not work well for our use case, throwing errors for local paths, plus the rest of the errors our in house solution had.

langchain[patch]: Simplify ensemble retriever (langchain-ai#14427)

d5947ff

- **Description:** code simplification to improve readability and remove unnecessary memory allocations. - **Tag maintainer**: @baskaryan, @eyurtsev, @hwchase17. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>

community[patch]: Update minimax.py (langchain-ai#14384)

770e73c

MiniMaxChat class _generate method shoud return a ChatResult object not str Co-authored-by: Bagatur <baskaryan@gmail.com>

langchain[patch]: load ReadFileTool (langchain-ai#14301)

71bdc36

--------- Co-authored-by: Dr. Simon Kroll <krolls@fida.de> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> Co-authored-by: Bagatur <baskaryan@gmail.com>

dont mutate metadata/tags (langchain-ai#19742)

4d71ca2

Merge branch 'master' into falkordb-py

db7984a

fix format error

e4f146d

coderabbitai Bot reviewed Mar 30, 2024

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gkorland added 2 commits April 2, 2024 12:14

put back the depreacted redis connection for backward compatibility

9581049

Merge branch 'master' into falkordb-py

6ea9efb

coderabbitai Bot reviewed Apr 2, 2024

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	export PATH=$PATH:$(pwd)/quarto-1.3.450/bin/
	QUARTO_PATH=$(pwd)/quarto-1.3.450/bin/
	export PATH=$PATH:$QUARTO_PATH

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gkorland commented Feb 13, 2024 •

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