diff --git a/packages/lmi/pyproject.toml b/packages/lmi/pyproject.toml index 002b1914..44f02779 100644 --- a/packages/lmi/pyproject.toml +++ b/packages/lmi/pyproject.toml @@ -27,7 +27,7 @@ dependencies = [ "coredis>=3.0.1", # Lower pin v3 for Redis class, pin v3.0.1 for fix in https://github.com/alisaifee/coredis/commit/7e9b1a1b384cd97725cab479d2ce091e3b0823d2 "fhaviary>=0.14.0", # For multi-image support "limits[async-redis]>=4.8", # Specify 'async-redis' since that's what we use. Lower pin for RedisBridge.key_prefix. - "litellm>=1.81.10,<=1.82.6", # Lower pin for MAX_CALLBACKS refactor from https://github.com/BerriAI/litellm/pull/20781, upper pin for supply chain attack + "litellm>=1.83.10", # Lower pin for MAX_CALLBACKS refactor from https://github.com/BerriAI/litellm/pull/20781, upper pin for supply chain attack "openai>=2", # Pin to keep recent "orjson", # Required by litellm for Responses API "pydantic~=2.0,>=2.10.1", diff --git a/packages/lmi/src/lmi/config.py b/packages/lmi/src/lmi/config.py new file mode 100644 index 00000000..1cf0b9d6 --- /dev/null +++ b/packages/lmi/src/lmi/config.py @@ -0,0 +1,279 @@ +"""Configuration types for LMI. + +An `LLMConfig` is an ordered chain of `ModelSpec` entries. `models[0]` is the +primary model; `models[1:]` are fallbacks tried in order when the primary +fails in ways that another model might handle. + +`LLMConfig.from_legacy_dict` accepts the dict-shaped configuration +(`{model_list, fallbacks, router_kwargs}`) that mirrors litellm's Router layout. +""" + +from __future__ import annotations + +from collections.abc import Awaitable, Callable +from typing import Annotated, Any + +import litellm +from pydantic import BaseModel, BeforeValidator, ConfigDict, Field, SecretStr + +from lmi.constants import DEFAULT_VERTEX_SAFETY_SETTINGS +from lmi.types import LLMResult + +ResponseValidator = Callable[[LLMResult], Awaitable[None] | None] + +_DEFAULT_TEMPERATURE = 1.0 +_OPENAI_ONLY_PARAMS = frozenset({"logprobs", "top_logprobs"}) + +# Per-call retry kwargs that LiteLLM honors via its own internal retry loop. LMI +# owns retries through `_run_with_fallbacks` + `ModelSpec.max_retries`, so these +# must never reach `litellm.acompletion`/`litellm.aresponses` regardless of how +# they ended up in `ModelSpec.extra_params`. +_LITELLM_RETRY_KWARGS = frozenset({"num_retries", "max_retries"}) + + +class ModelSpec(BaseModel): + """One model in an `LLMConfig` chain.""" + + model_config = ConfigDict(extra="forbid") + + name: str = Field( + description=( + "LiteLLM model string, e.g. 'gpt-4o-mini' or 'claude-3-5-sonnet-20241022'." + ), + ) + api_base: str | None = None + api_key: SecretStr | None = None + timeout: float = Field(default=60.0, description="Per-request timeout in seconds.") + max_retries: int = Field( + default=3, + description=( + "Retries against this model before falling over to the next entry" + " in the chain." + ), + ) + extra_params: dict[str, Any] = Field( + default_factory=dict, + description=( + "Pass-through kwargs for litellm.acompletion / litellm.aresponses," + " e.g. temperature, max_tokens, safety_settings, vertex_project." + ), + ) + responses_api: bool = Field( + default=False, + description=( + "If True, dispatch this model via OpenAI's stateful Responses API" + " (`litellm.aresponses`) instead of the Chat Completions API" + " (`litellm.acompletion`)." + ), + ) + + @classmethod + def from_name(cls, name: str, **overrides: Any) -> ModelSpec: + """Build a `ModelSpec` with provider-aware defaults for `extra_params`. + + Applies: Gemini default safety settings; `temperature` / `max_tokens` + defaults; and silent drop of `logprobs` / `top_logprobs` for non-OpenAI + providers (which don't support them). Explicit values in `overrides` + always win over the defaults. + + `overrides` may set any `ModelSpec` field, plus request-shape kwargs + (`temperature`, `max_tokens`, `n`, `logprobs`, `top_logprobs`, + `safety_settings`, ...) which are merged into `extra_params`. + """ + is_openai = _is_openai_provider(name) + + extra: dict[str, Any] = {} + if "gemini" in name: + extra["safety_settings"] = DEFAULT_VERTEX_SAFETY_SETTINGS + extra["temperature"] = _DEFAULT_TEMPERATURE + + spec_field_overrides: dict[str, Any] = {} + extra_overrides: dict[str, Any] = {} + for key, value in overrides.items(): + if key in cls.model_fields: + spec_field_overrides[key] = value + elif key in _OPENAI_ONLY_PARAMS and not is_openai: + raise ValueError( + f"{key!r} is only supported on OpenAI models; got {name!r}." + ) + else: + extra_overrides[key] = value + + spec_field_overrides.setdefault("name", name) + merged_extra = ( + extra | extra_overrides | spec_field_overrides.pop("extra_params", {}) + ) + return cls(extra_params=merged_extra, **spec_field_overrides) + + def to_litellm_kwargs(self) -> dict[str, Any]: + """Flatten into kwargs for `litellm.acompletion` / `litellm.aresponses`.""" + sanitized_extra = { + k: v for k, v in self.extra_params.items() if k not in _LITELLM_RETRY_KWARGS + } + out: dict[str, Any] = { + "model": self.name, + "timeout": self.timeout, + } | sanitized_extra + if self.api_base is not None: + out["api_base"] = self.api_base + if self.api_key is not None: + out["api_key"] = self.api_key.get_secret_value() + return out + + +class LLMConfig(BaseModel): + """Ordered model chain: `models[0]` is primary, `models[1:]` are fallbacks.""" + + model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True) + + models: list[ModelSpec] = Field( + min_length=1, + description=( + "Ordered list of models. The first entry is the primary; subsequent" + " entries are tried in order when earlier models fail in ways that" + " another model might handle (context overflow, content policy," + " model-unavailable, or exhausted retries)." + ), + ) + response_validator: ResponseValidator | None = Field( + default=None, + exclude=True, + description=( + "Optional callable invoked on each successful `LLMResult`. Raises" + " any exception to reject the response; we convert that into" + " `ResponseValidationError` and let the retry/fallback loop" + " handle it." + ), + ) + + @classmethod + def coerce(cls, v: Any) -> LLMConfig: + """Accept an `LLMConfig` or any dict shape LMI knows about. + + Supported inputs: + + - an `LLMConfig` instance (passes through) + - a dict with `"models"` — the typed-dict form of `LLMConfig` + - a dict with `"model_list"` — the legacy litellm-Router shape; see + `from_legacy_dict` + - a dict with `"name"` — a bare model name plus flat request-shape + kwargs (e.g. `temperature`, `max_tokens`); built via + `ModelSpec.from_name` + """ + if isinstance(v, cls): + return v + if not isinstance(v, dict): + raise TypeError(f"Cannot build an LLMConfig from {type(v).__name__}") + if "models" in v: + return cls.model_validate(v) + if "model_list" in v: + return cls.from_legacy_dict(v) + if "name" in v: + kwargs = dict(v) + name = kwargs.pop("name") + return cls(models=[ModelSpec.from_name(name, **kwargs)]) + raise ValueError( + "Can't infer LLMConfig shape from dict; expected 'models'," + " 'model_list', or 'name' key" + ) + + def with_extra_params(self, **params: Any) -> LLMConfig: + """Return a copy where every `ModelSpec.extra_params` has `params` merged in. + + Useful for chain-wide request-shape additions like stop sequences: the + caller doesn't have to rebuild each spec individually, and the original + `LLMConfig` is left untouched. + """ + return self.model_copy( + update={ + "models": [ + m.model_copy(update={"extra_params": m.extra_params | params}) + for m in self.models + ] + } + ) + + @classmethod + def from_legacy_dict(cls, legacy: dict[str, Any]) -> LLMConfig: + """Build an `LLMConfig` from the legacy dict-shaped configuration. + + The legacy shape is `{model_list: [{model_name, litellm_params}, ...], + fallbacks: [{primary_name: [fallback_names, ...]}, ...], router_kwargs: {...}}`. + The `fallbacks` list is flattened into the ordering of `models`; any + entries in `model_list` not reached by the primary's fallback chain are + appended at the end. + """ + model_list = legacy.get("model_list") or [] + if not model_list: + raise ValueError("Legacy config has empty or missing model_list") + + fallback_map: dict[str, list[str]] = {} + for entry in legacy.get("fallbacks") or []: + fallback_map.update(entry) + + params_by_name: dict[str, dict[str, Any]] = { + m["model_name"]: dict(m.get("litellm_params", {})) for m in model_list + } + + primary_name = model_list[0]["model_name"] + ordered: list[str] = [primary_name, *fallback_map.get(primary_name, [])] + for name in params_by_name: + if name not in ordered: + ordered.append(name) + + router_kwargs = legacy.get("router_kwargs") or {} + default_timeout = router_kwargs.get("timeout", 60.0) + default_retries = router_kwargs.get("num_retries", 3) + + return cls( + models=[ + _spec_from_legacy_params( + params_by_name.get(name, {}), + default_timeout=default_timeout, + default_retries=default_retries, + ) + for name in ordered + ] + ) + + +def _is_openai_provider(name: str) -> bool: + try: + return "openai" in litellm.get_llm_provider(name) + except litellm.BadRequestError: + return False + + +_RESERVED_LEGACY_PARAMS = frozenset({ + "model", + "api_base", + "api_key", + "timeout", + "max_retries", +}) + + +def _spec_from_legacy_params( + params: dict[str, Any], + *, + default_timeout: float, + default_retries: int, +) -> ModelSpec: + api_key = params.get("api_key") + return ModelSpec( + name=params["model"], + api_base=params.get("api_base"), + api_key=SecretStr(api_key) if api_key is not None else None, + timeout=params.get("timeout", default_timeout), + max_retries=params.get("max_retries", default_retries), + extra_params={ + k: v for k, v in params.items() if k not in _RESERVED_LEGACY_PARAMS + }, + ) + + +# Pydantic field annotation that accepts any input `LLMConfig.coerce` supports. +# Use in place of a bare `LLMConfig` when you want the model to accept both +# typed instances and the dict shapes LMI recognises, without writing a +# `@field_validator` on every class. +LLMConfigField = Annotated[LLMConfig, BeforeValidator(LLMConfig.coerce)] diff --git a/packages/lmi/src/lmi/constants.py b/packages/lmi/src/lmi/constants.py index c6692e5a..863b4abd 100644 --- a/packages/lmi/src/lmi/constants.py +++ b/packages/lmi/src/lmi/constants.py @@ -1,8 +1,5 @@ -import os from sys import version_info -USE_RESPONSES_API = os.environ.get("USE_RESPONSES_API", "").lower() in {"1", "true"} - # Estimate from OpenAI's FAQ # https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them CHARACTERS_PER_TOKEN_ASSUMPTION: float = 4.0 diff --git a/packages/lmi/src/lmi/exceptions.py b/packages/lmi/src/lmi/exceptions.py index aea488df..1e17b442 100644 --- a/packages/lmi/src/lmi/exceptions.py +++ b/packages/lmi/src/lmi/exceptions.py @@ -1,2 +1,46 @@ +from typing import Any + + class JSONSchemaValidationError(ValueError): """Raised when the completion does not match the specified schema.""" + + +class ModelRefusalError(RuntimeError): + """Raised when an LLM declines to complete a request (e.g. content filter). + + Carries the raw provider response so callers that choose to handle the + refusal (rather than fall back) can still inspect it. + """ + + def __init__( + self, + message: str, + *, + model: str, + finish_reason: str | None, + response: Any = None, + ) -> None: + super().__init__(message) + self.model = model + self.finish_reason = finish_reason + self.response = response + + +class ResponseValidationError(RuntimeError): + """Raised when an `LLMConfig.response_validator` rejects an `LLMResult`. + + Treated as transient by the retry/fallback loop so the validator gets a + fresh attempt at the same model (up to `ModelSpec.max_retries`) before + advancing to the next model. + """ + + +class AllModelsExhaustedError(RuntimeError): + """Raised when every model in an `LLMConfig.models` chain has failed or been skipped.""" + + def __init__(self, last_exc: BaseException | None = None) -> None: + super().__init__( + "All models in the LLMConfig chain failed." + + (f" Last error: {last_exc!r}" if last_exc is not None else "") + ) + self.last_exc = last_exc diff --git a/packages/lmi/src/lmi/litellm_patches.py b/packages/lmi/src/lmi/litellm_patches.py index 256ef522..adb14468 100644 --- a/packages/lmi/src/lmi/litellm_patches.py +++ b/packages/lmi/src/lmi/litellm_patches.py @@ -5,12 +5,9 @@ Patches applied: 1. OpenAI BaseModel.model_dump pydantic v2 fix (by_alias=None issue) -2. Provider-specific 400 error retry (Anthropic 100-image limit) -3. Vertex AI context caching fix (tools + cachedContent conflict) +2. Vertex AI context caching fix (tools + cachedContent conflict) """ -import litellm - # Patch 1: OpenAI BaseModel.model_dump pydantic v2 fix # @@ -39,41 +36,7 @@ def _patched_model_dump(self, *args, by_alias=None, **kwargs): _apply_model_dump_patch() -# Patch 2: Provider-specific 400 error retry -# -# Issue: Anthropic has a 100-image limit that returns 400 Bad Request. litellm's -# default behavior doesn't retry 400 errors, so requests with >100 images fail -# without trying Gemini/OpenAI which could handle them. -# -# This patch checks for specific provider-limit error messages and allows retry -# only for those, not for genuine client errors (malformed requests, etc.). -def _apply_should_retry_patch(): - # Error messages that indicate provider-specific limits (not client bugs) - # Matches: "Too much media: 0 document pages + 108 images > 100" (specific to Anthropic) - PROVIDER_LIMIT_PATTERNS = ("too much media",) - - original_should_retry_this_error = litellm.Router.should_retry_this_error - - def _patched_should_retry_this_error( - self, error: litellm.ContextWindowExceededError, **kwargs - ): - # Check if this is a 400 error with a provider-specific limit message - # Note: Not all error types have status_code (e.g., RouterRateLimitError) - status_code = getattr(error, "status_code", None) - if status_code == 400: # noqa: PLR2004 - error_message = str(error).lower() - if any(pattern in error_message for pattern in PROVIDER_LIMIT_PATTERNS): - # Don't raise - allow fallback cascade to continue - return None - return original_should_retry_this_error(self, error, **kwargs) - - litellm.Router.should_retry_this_error = _patched_should_retry_this_error # type: ignore[method-assign,assignment] - - -_apply_should_retry_patch() - - -# Patch 3: Vertex AI context caching fix +# Patch 2: Vertex AI context caching fix # # Bug: LiteLLM sends both cachedContent AND tools/system_instruction in Gemini # generateContent requests. Gemini's API requires that when using cached content, diff --git a/packages/lmi/src/lmi/llms.py b/packages/lmi/src/lmi/llms.py index 0af40519..ef76b4f6 100644 --- a/packages/lmi/src/lmi/llms.py +++ b/packages/lmi/src/lmi/llms.py @@ -14,7 +14,6 @@ import asyncio import contextlib -import copy import functools import json import logging @@ -69,17 +68,26 @@ model_validator, ) +from lmi.config import LLMConfig, ModelSpec from lmi.constants import ( CHARACTERS_PER_TOKEN_ASSUMPTION, DEFAULT_VERTEX_SAFETY_SETTINGS, IS_PYTHON_BELOW_312, - USE_RESPONSES_API, ) from lmi.cost_tracker import track_costs, track_costs_iter -from lmi.exceptions import JSONSchemaValidationError +from lmi.exceptions import ( + AllModelsExhaustedError, + JSONSchemaValidationError, + ModelRefusalError, + ResponseValidationError, +) from lmi.rate_limiter import GLOBAL_LIMITER +from lmi.retry import ( + backoff_seconds, + should_fallback, + should_retry, +) from lmi.types import LLMResult -from lmi.utils import get_litellm_retrying_config from . import ( litellm_patches as _litellm_patches, # noqa: F401 - In-place apply patches at import @@ -348,10 +356,26 @@ class CommonLLMNames(StrEnum): ) -class OverrideRouterConfig(BaseModel): - model_list: list[dict[str, Any]] - router_kwargs: dict[str, Any] - fallbacks: list[dict[str, Any]] +async def _commit_stream(gen: AsyncIterable[LLMResult]) -> AsyncIterable[LLMResult]: + """Advance `gen` to its first yield and return an iterator that replays it. + + Exceptions raised before the first yield propagate to the caller. Once the + first chunk has been produced the returned iterator yields it and then + forwards the rest of `gen` verbatim; any mid-stream error surfaces + unmodified to the consumer. + """ + iterator = aiter(gen) + try: + first = await anext(iterator) + except StopAsyncIteration as exc: + raise RuntimeError("Stream closed before producing any output.") from exc + + async def replay() -> AsyncIterable[LLMResult]: + yield first + async for item in iterator: + yield item + + return replay() def sum_logprobs(choice: litellm.utils.Choices | list[float]) -> float | None: @@ -477,16 +501,23 @@ class LLMModel(ABC, BaseModel): ) config: dict = Field(default_factory=dict) - async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult]: - """Return the completion as string and the number of tokens in the prompt and completion.""" + async def acompletion( + self, messages: list[Message], *, spec: ModelSpec | None = None, **kwargs + ) -> list[LLMResult]: + """Issue one completion request against the model given by `spec`. + + `spec` supplies the model name and per-request kwargs (api_base, + api_key, timeout, extra_params). When None, subclasses default to the + primary entry in `llm_config`. + """ raise NotImplementedError async def acompletion_iter( - self, messages: list[Message], **kwargs + self, messages: list[Message], *, spec: ModelSpec | None = None, **kwargs ) -> AsyncIterable[LLMResult]: - """Return an async generator that yields completions. + """Stream one completion from the model given by `spec`. - Only the last tuple will be non-zero. + See `acompletion` for the `spec` contract. """ raise NotImplementedError @@ -553,16 +584,17 @@ async def call( # noqa: C901, PLR0915 ValueError: If the LLM type is unknown. """ messages = self._maybe_patch_gemini3_tool_response_messages(messages) - chat_kwargs = copy.deepcopy(kwargs) + # Shallow copy because downstream code only mutates top-level keys. + # Trying to avoid a deepcopy if not needed. If a future edit adds + # a nested in-place mutation here, this needs revisiting. + chat_kwargs = kwargs.copy() # if using the config for an LLMModel, # there may be a nested 'config' key # that can't be used by chat chat_kwargs.pop("config", None) - n = chat_kwargs.get("n") or self.config.get("n", 1) + n = chat_kwargs.get("n", self.config.get("n", 1)) if n < 1: raise ValueError("Number of completions (n) must be >= 1.") - if "fallbacks" not in chat_kwargs and "fallbacks" in self.config: - chat_kwargs["fallbacks"] = self.config.get("fallbacks", []) # deal with tools if tools: @@ -583,7 +615,7 @@ async def call( # noqa: C901, PLR0915 # deal with specifying output type if isinstance(output_type, Mapping): # Use structured outputs - model_name: str = chat_kwargs.get("model") or self.name + model_name: str = chat_kwargs.get("model", self.name) if not litellm.supports_response_schema(model_name, None): raise ValueError(f"Model {model_name} does not support JSON schema.") @@ -628,71 +660,41 @@ async def call( # noqa: C901, PLR0915 ) for m in messages ] - results: list[LLMResult] = [] start_clock = asyncio.get_running_loop().time() - if USE_RESPONSES_API: - previous_response_id, messages_to_send = _extract_previous_response_id( - messages - ) - tools_for_api = chat_kwargs.pop("tools", None) - override_config = chat_kwargs.pop("override_config", None) - if override_config: - override_config = OverrideRouterConfig(**override_config) - router = self.get_router(override_config) # type: ignore[attr-defined] - if callbacks is not None: - sync_callbacks = [f for f in callbacks if not is_coroutine_callable(f)] - async_callbacks = [f for f in callbacks if is_coroutine_callable(f)] - stream_results = self._aresponses_iter( # type: ignore[attr-defined] - messages_to_send, - router, - tools_for_api, - previous_response_id=previous_response_id, - **chat_kwargs, - ) - async for result in stream_results: - if result.text: - if result.seconds_to_first_token == 0: - result.seconds_to_first_token = ( - asyncio.get_running_loop().time() - start_clock - ) - await do_callbacks( - async_callbacks, sync_callbacks, result.text, name - ) - results.append(result) - else: - results = await self._aresponses( # type: ignore[attr-defined] - messages_to_send, - router, - tools_for_api, - previous_response_id=previous_response_id, - **chat_kwargs, - ) - elif callbacks is None: - results = await self.acompletion(messages, **chat_kwargs) - else: + streaming = callbacks is not None + if streaming: if tools: raise NotImplementedError("Using tools with callbacks is not supported") - n = chat_kwargs.get("n") or self.config.get("n", 1) if n > 1: raise NotImplementedError( "Multiple completions with callbacks is not supported" ) + + dispatch_result = await self._run_with_fallbacks( # type: ignore[attr-defined] + self._dispatch, # type: ignore[attr-defined] + messages=messages, + streaming=streaming, + **chat_kwargs, + ) + + if streaming: + assert callbacks is not None sync_callbacks = [f for f in callbacks if not is_coroutine_callable(f)] async_callbacks = [f for f in callbacks if is_coroutine_callable(f)] - stream_results = await self.acompletion_iter(messages, **chat_kwargs) - text_result = [] - async for result in stream_results: + results: list[LLMResult] = [] + async for result in dispatch_result: if result.text: if result.seconds_to_first_token == 0: result.seconds_to_first_token = ( asyncio.get_running_loop().time() - start_clock ) - text_result.append(result.text) await do_callbacks( async_callbacks, sync_callbacks, result.text, name ) results.append(result) + else: + results = dispatch_result for result in results: if not result.completion_count and result.text is not None: @@ -929,22 +931,21 @@ class LiteLLMModel(LLMModel): config: dict = Field( default_factory=dict, description=( - "Configuration of this model containing several important keys. The" - " optional `model_list` key stores a list of all model configurations" - " (SEE: https://docs.litellm.ai/docs/routing). The optional" - " `router_kwargs` key is keyword arguments to pass to the Router class." - " Inclusion of a key `pass_through_router` with a truthy value will lead" - " to using not using LiteLLM's Router, instead just LiteLLM's free" - f" functions (see {PassThroughRouter.__name__}). Rate limiting applies" - " regardless of `pass_through_router` being present. The optional" - " `rate_limit` key is a dictionary keyed by model group name with values" - " of type limits.RateLimitItem (in tokens / minute) or valid" - " limits.RateLimitItem string for parsing. The optional `request_limit`" - " key is a dictionary keyed by model group name with values representing" - " the maximum number of requests per minute." + "Legacy dict-shaped configuration for backward compatibility. Accepts" + " a `model_list` entry (litellm Router layout) plus optional" + " `rate_limit` / `request_limit` dicts keyed by model group name for" + " tokens-per-minute and requests-per-minute throttling. New code" + " should use `llm_config` instead." + ), + ) + llm_config: LLMConfig | None = Field( + default=None, + description=( + "Typed model chain. When unset, this is synthesized from `config`" + " (via `LLMConfig.from_legacy_dict`) during validation, so callers" + " passing the legacy `config` dict still work." ), ) - _router: litellm.Router | None = None @model_validator(mode="before") @classmethod @@ -955,7 +956,12 @@ def maybe_set_config_attribute(cls, input_data: dict[str, Any]) -> dict[str, Any If name is not provided, uses the default name. If a user only gives a name, make a sensible config dict for them. """ - data = copy.deepcopy(input_data) + # Shallow copy: we only mutate top-level keys (`update` / `pop` on `data["config"]`); everything + # nested below is read or replaced wholesale. If a future edit adds + # a nested in-place mutation here, this needs revisiting. + data = input_data.copy() + if "config" in data: + data["config"] = data["config"].copy() # unnest the config key if it's nested if "config" in data and "config" in data["config"]: @@ -1013,16 +1019,6 @@ def maybe_set_config_attribute(cls, input_data: dict[str, Any]) -> dict[str, Any ], } | data["config"] - if "router_kwargs" not in data["config"]: - data["config"]["router_kwargs"] = {} - data["config"]["router_kwargs"] = ( - get_litellm_retrying_config() | data["config"]["router_kwargs"] - ) - if not data["config"].get("pass_through_router"): - data["config"]["router_kwargs"] = {"retry_after": 5} | data["config"][ - "router_kwargs" - ] - if "tool_parser" in data["config"]: data["tool_parser"] = data["config"].pop("tool_parser") @@ -1038,6 +1034,12 @@ def maybe_set_config_attribute(cls, input_data: dict[str, Any]) -> dict[str, Any return data + @model_validator(mode="after") + def _populate_llm_config(self) -> "LiteLLMModel": + if self.llm_config is None: + self.llm_config = LLMConfig.from_legacy_dict(self.config) + return self + # SEE: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice # > `none` means the model will not call any tool and instead generates a message. # > `auto` means the model can pick between generating a message or calling one or more tools. @@ -1048,49 +1050,6 @@ def maybe_set_config_attribute(cls, input_data: dict[str, Any]) -> dict[str, Any # None means we won't provide a tool_choice to the LLM API UNSPECIFIED_TOOL_CHOICE: ClassVar[None] = None - def __getstate__(self): - # Prevent _router from being pickled, SEE: https://stackoverflow.com/a/2345953 - state = super().__getstate__() - state["__dict__"] = state["__dict__"].copy() - state["__dict__"].pop("_router", None) - return state - - def get_router( - self, override_config: OverrideRouterConfig | None = None - ) -> litellm.Router: - """Get the router, optionally with an override configuration. - - Args: - override_config: Optional configuration to override the default router settings. - Should contain 'model_list' and 'router_kwargs' keys. - - Returns: - A litellm.Router instance. - """ - if override_config: - override_model_list = override_config.model_list - override_router_kwargs = override_config.router_kwargs - - can_override = override_model_list and override_router_kwargs - if not can_override: - logger.warning( - "Cannot override router with provided config. Will use default config." - ) - else: - return litellm.Router( - model_list=override_model_list, **override_router_kwargs - ) - - if self._router is None: - router_kwargs: dict = self.config.get("router_kwargs", {}) - if self.config.get("pass_through_router"): - self._router = PassThroughRouter(**router_kwargs) - else: - self._router = litellm.Router( - model_list=self.config["model_list"], **router_kwargs - ) - return self._router - async def check_request_limit(self, **kwargs) -> None: """Check if the request is within the request rate limit.""" if "request_limit" in self.config: @@ -1110,60 +1069,112 @@ async def check_rate_limit(self, token_count: float, **kwargs) -> None: **kwargs, ) - async def _handle_refusal_via_fallback( - self, messages: list[Message], kwargs: dict[str, Any] - ) -> list[LLMResult]: - # Let's remove the current model from the configuration - current_model = self.name - override_config = OverrideRouterConfig( - model_list=kwargs.pop("model_list", self.config["model_list"]), - router_kwargs=kwargs.pop("router_kwargs", self.config["router_kwargs"]), - fallbacks=kwargs.pop("fallbacks", self.config["fallbacks"]), + async def _dispatch( + self, + spec: ModelSpec, + *, + messages: list[Message], + streaming: bool = False, + **chat_kwargs, + ) -> list[LLMResult] | AsyncIterable[LLMResult]: + """Dispatch one request to `spec`, choosing Chat vs Responses per `spec.responses_api`. + + Non-streaming paths return a list of `LLMResult`s. Streaming paths + return an async iterator that has already produced its first chunk; + errors before the first chunk (stream-open failure, an immediate + refusal) surface as exceptions from this coroutine, while mid-stream + errors propagate unmodified when the caller iterates the result. + """ + logger.info( + "Dispatching %s via %s%s", + spec.name, + "responses" if spec.responses_api else "chat", + " (stream)" if streaming else "", ) + if spec.responses_api: + previous_response_id, messages = _extract_previous_response_id(messages) + tools = chat_kwargs.pop("tools", None) + if streaming: + gen = await self._aresponses_iter( + messages, tools, previous_response_id, spec=spec, **chat_kwargs + ) + return await _commit_stream(gen) + return await self._aresponses( + messages, tools, previous_response_id, spec=spec, **chat_kwargs + ) - new_model_list = [ - model - for model in override_config.model_list - if model.get("model_name") != current_model - ] - - new_fallbacks = [] - for fallback in override_config.fallbacks: - if current_model not in fallback: - new_fallbacks.append(fallback) - else: - target_model = fallback[current_model][0] - new_fallbacks.append({target_model: fallback[current_model][1:]}) - - # Now we update the configuration - # Here I am resetting all the configuration I think the user needs to set - # But it is possible there is a user misconfiguration that I'd be fixing here - # We use model name to make the request. Will reset it after the request - self.name = new_model_list[0].get("model_name") or "" - if "fallbacks" in kwargs: - kwargs["fallbacks"] = new_fallbacks - kwargs["override_config"] = { - "model_list": new_model_list, - "router_kwargs": override_config.router_kwargs - | { - "fallbacks": new_fallbacks, - }, - "fallbacks": new_fallbacks, - } + # Chat Completions path: `tools` stays in chat_kwargs. + if streaming: + gen = await self.acompletion_iter(messages, spec=spec, **chat_kwargs) + return await _commit_stream(gen) + return await self.acompletion(messages, spec=spec, **chat_kwargs) - results = await self.acompletion(messages, **kwargs) - # Put the object back to the original state - self.name = current_model - return results + async def _run_with_fallbacks( + self, + attempt: Callable[..., Coroutine[Any, Any, Any]], + /, + *args: Any, + **kwargs: Any, + ) -> Any: + """Drive a single-attempt coroutine across the `LLMConfig.models` chain. + + `attempt(spec, *args, **kwargs)` must run one try at the model described + by `spec` and raise on failure. Retries within a single model use + exponential jitter backoff (see `retry.backoff_seconds`). Exceptions + classified by `should_fallback` (or retry exhaustion on a retryable + exception) advance to the next spec; anything else propagates. + """ + llm_config = cast("LLMConfig", self.llm_config) # populated by after-validator + last_exc: BaseException | None = None + for spec_idx, spec in enumerate(llm_config.models): + for i in range(spec.max_retries + 1): + try: + result = await attempt(spec, *args, **kwargs) + except Exception as exc: + last_exc = exc + if should_retry(exc) and i < spec.max_retries: + delay = backoff_seconds(i) + logger.info( + "Retrying %s (attempt %d/%d) after %s in %.2fs", + spec.name, + i + 2, + spec.max_retries + 1, + type(exc).__name__, + delay, + ) + await asyncio.sleep(delay) + continue + if should_retry(exc) or should_fallback(exc): + if spec_idx + 1 < len(llm_config.models): + logger.info( + "Advancing from %s to %s after %s", + spec.name, + llm_config.models[spec_idx + 1].name, + type(exc).__name__, + ) + break + raise + else: + if i > 0 or spec_idx > 0: + logger.info( + "%s succeeded (spec %d/%d, attempt %d/%d)", + spec.name, + spec_idx + 1, + len(llm_config.models), + i + 1, + spec.max_retries + 1, + ) + return result + raise AllModelsExhaustedError(last_exc) from last_exc # the order should be first request and then rate(token) @request_limited @rate_limited - async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult]: # noqa: C901 - override_config = kwargs.pop("override_config", None) - if override_config: - override_config = OverrideRouterConfig(**override_config) - router = self.get_router(override_config) + async def acompletion( # noqa: C901 + self, messages: list[Message], *, spec: ModelSpec | None = None, **kwargs + ) -> list[LLMResult]: + if spec is None: + spec = cast("LLMConfig", self.llm_config).models[0] tools = kwargs.get("tools") if not tools: # OpenAI, Anthropic and potentially other LLM providers @@ -1186,9 +1197,12 @@ async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult ) kwargs["tool_choice"] = self.NO_TOOL_CHOICE - completions = await track_costs(router.acompletion)( - self.name, prompts, **kwargs - ) + call_kwargs = {**spec.to_litellm_kwargs(), **kwargs, "messages": prompts} + try: + completions = await track_costs(litellm.acompletion)(**call_kwargs) + except Exception: + logger.exception("acompletion attempt failed on %s.", spec.name) + raise finish_reason = ( getattr(completions.choices[0], "finish_reason", None) @@ -1196,26 +1210,16 @@ async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult else None ) if completions.choices and finish_reason in REFUSAL_REASON: - logger.warning( - f"The LLM request was refused with finish reason '{finish_reason}' " - f"for model {self.name}. " - "Attempting to fallback to next model in the list." - ) - if override_config is not None: - # Case we had a previous refusal - kwargs["model_list"] = override_config.model_list - kwargs["router_kwargs"] = override_config.router_kwargs - kwargs["fallbacks"] = override_config.fallbacks - - model_list = kwargs.get("model_list") or self.config.get("model_list", None) - if model_list and len(model_list) > 1: - return await self._handle_refusal_via_fallback(messages, kwargs) - logger.warning( - f"No fallback models available after refusal for model {self.name}. " - "Will return a LLMResult with the refusal completion." + refusal = ModelRefusalError( + f"Model {spec.name} refused with finish_reason={finish_reason!r}.", + model=spec.name, + finish_reason=finish_reason, + response=completions, ) + logger.error("Model %s refused.", spec.name, exc_info=refusal) + raise refusal - used_model = completions.model or self.name + used_model = completions.model results: list[LLMResult] = [] # Use getattr because ModelResponse.usage not in LiteLLM's type hints @@ -1283,18 +1287,37 @@ async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult finish_reason=choice.finish_reason, ) ) + await self._maybe_validate(results) return results + async def _maybe_validate(self, results: list[LLMResult]) -> None: + """Run `llm_config.response_validator` against each result, if attached. + + Any exception from the validator is wrapped as `ResponseValidationError` + so the retry/fallback loop can re-attempt the same model (up to + `ModelSpec.max_retries`) and then advance to the next. + """ + validator = cast("LLMConfig", self.llm_config).response_validator + if not validator: + return + for result in results: + try: + outcome = validator(result) + if isawaitable(outcome): + await outcome + except Exception as exc: + raise ResponseValidationError( + f"response_validator {validator!r} rejected {result!r}" + ) from exc + # the order should be first request and then rate(token) @request_limited @rate_limited - async def acompletion_iter( - self, messages: list[Message], **kwargs + async def acompletion_iter( # noqa: C901 + self, messages: list[Message], *, spec: ModelSpec | None = None, **kwargs ) -> AsyncIterable[LLMResult]: - override_config = kwargs.pop("override_config", None) - if override_config: - override_config = OverrideRouterConfig(**override_config) - router = self.get_router(override_config) + if spec is None: + spec = cast("LLMConfig", self.llm_config).models[0] # cast is necessary for LiteLLM typing bug: https://github.com/BerriAI/litellm/issues/7641 prompts = cast( "list[litellm.types.llms.openai.AllMessageValues]", @@ -1307,13 +1330,20 @@ async def acompletion_iter( if kwargs.get("include_reasoning"): stream_options["include_reasoning"] = True - stream_completions = await track_costs_iter(router.acompletion)( - self.name, - prompts, - stream=True, - stream_options=stream_options, + call_kwargs = { + **spec.to_litellm_kwargs(), **kwargs, - ) + "messages": prompts, + "stream": True, + "stream_options": stream_options, + } + try: + stream_completions = await track_costs_iter(litellm.acompletion)( + **call_kwargs + ) + except Exception: + logger.exception("acompletion_iter failed to open stream on %s.", spec.name) + raise start_clock = asyncio.get_running_loop().time() outputs = [] logprobs = [] @@ -1323,7 +1353,7 @@ async def acompletion_iter( used_model = None async for completion in stream_completions: if not used_model: - used_model = completion.model or self.name + used_model = completion.model choice = completion.choices[0] delta = choice.delta # logprobs can be None, or missing a content attribute, @@ -1372,6 +1402,16 @@ async def acompletion_iter( asyncio.get_running_loop().time() - start_clock ) + if finish_reason in REFUSAL_REASON: + refusal = ModelRefusalError( + f"Model {spec.name} refused with finish_reason={finish_reason!r}.", + model=spec.name, + finish_reason=finish_reason, + response=result, + ) + logger.error("Model %s refused.", spec.name, exc_info=refusal) + raise refusal + yield result @request_limited @@ -1379,27 +1419,35 @@ async def acompletion_iter( async def _aresponses( self, messages: list[Message], - router: litellm.Router | PassThroughRouter, tools: list[dict] | None, previous_response_id: str | None = None, + *, + spec: ModelSpec | None = None, **kwargs, ) -> list[LLMResult]: """Call the Responses API (non-streaming).""" + if spec is None: + spec = cast("LLMConfig", self.llm_config).models[0] responses_input = _convert_to_responses_input(messages) responses_tools = _convert_tools_for_responses(tools) call_kwargs: dict[str, Any] = { - "model": self.name, + **spec.to_litellm_kwargs(), "input": responses_input, "tools": responses_tools, "store": True, - } | kwargs + **kwargs, + } if previous_response_id is not None: call_kwargs["previous_response_id"] = previous_response_id - response = await track_costs(router.aresponses)(**call_kwargs) + try: + response = await track_costs(litellm.aresponses)(**call_kwargs) + except Exception: + logger.exception("aresponses attempt failed on %s.", spec.name) + raise - used_model = response.model or self.name + used_model = response.model usage = getattr(response, "usage", None) prompt_count = usage.input_tokens if usage else None completion_count = usage.output_tokens if usage else None @@ -1430,7 +1478,9 @@ async def _aresponses( ] def _build_result_from_response( - self, response: ResponsesAPIResponse, messages: list[Message] + self, + response: ResponsesAPIResponse, + messages: list[Message], ) -> LLMResult: """Build an LLMResult from a completed Responses API response.""" text, output_messages = _parse_responses_output(response.output) # type: ignore[arg-type] @@ -1440,7 +1490,7 @@ def _build_result_from_response( msg.info = {**(msg.info or {}), "response_id": response.id} return LLMResult( - model=response.model or self.name, + model=response.model, text=text, messages=output_messages or [Message(role="assistant", content=text)], prompt=messages, @@ -1451,35 +1501,43 @@ def _build_result_from_response( @request_limited @rate_limited - async def _aresponses_iter( + async def _aresponses_iter( # noqa: C901 self, messages: list[Message], - router: litellm.Router | PassThroughRouter, tools: list[dict] | None, previous_response_id: str | None = None, + *, + spec: ModelSpec | None = None, **kwargs, ) -> AsyncIterable[LLMResult]: """Stream results from the Responses API.""" + if spec is None: + spec = cast("LLMConfig", self.llm_config).models[0] responses_input = _convert_to_responses_input(messages) responses_tools = _convert_tools_for_responses(tools) call_kwargs: dict[str, Any] = { - "model": self.name, + **spec.to_litellm_kwargs(), "input": responses_input, "tools": responses_tools, "store": True, "stream": True, - } | kwargs + **kwargs, + } if previous_response_id is not None: call_kwargs["previous_response_id"] = previous_response_id - stream = await track_costs_iter(router.aresponses)(**call_kwargs) + try: + stream = await track_costs_iter(litellm.aresponses)(**call_kwargs) + except Exception: + logger.exception("aresponses_iter failed to open stream on %s.", spec.name) + raise completed_response: ResponsesAPIResponse | None = None incomplete_response: ResponsesAPIResponse | None = None async for event in stream: if isinstance(event, OutputTextDeltaEvent): - yield LLMResult(model=self.name, text=event.delta, prompt=messages) + yield LLMResult(model=spec.name, text=event.delta, prompt=messages) elif isinstance(event, ResponseCompletedEvent): completed_response = event.response elif isinstance(event, ResponseIncompleteEvent): @@ -1501,7 +1559,7 @@ async def _aresponses_iter( yield self._build_result_from_response(completed_response, messages) elif incomplete_response: logger.warning( - f"Responses API returned incomplete response for model {self.name}." + f"Responses API returned incomplete response for model {spec.name}." ) yield self._build_result_from_response(incomplete_response, messages) else: @@ -1523,7 +1581,12 @@ async def select_tool( self, *selection_args, **selection_kwargs ) -> ToolRequestMessage: """Shim to aviary.core.ToolSelector that supports tool schemae.""" + primary = cast("LLMConfig", self.llm_config).models[0] + + async def _acompletion(**kw: Any) -> Any: + return await litellm.acompletion(**primary.to_litellm_kwargs(), **kw) + tool_selector = ToolSelector( - model_name=self.name, acompletion=track_costs(self.get_router().acompletion) + model_name=self.name, acompletion=track_costs(_acompletion) ) return await tool_selector(*selection_args, **selection_kwargs) diff --git a/packages/lmi/src/lmi/retry.py b/packages/lmi/src/lmi/retry.py new file mode 100644 index 00000000..363d5da2 --- /dev/null +++ b/packages/lmi/src/lmi/retry.py @@ -0,0 +1,65 @@ +"""Retry and fallback policy for LMI. + +LMI attempts each model in an `LLMConfig.models` chain. Within a single model +it retries on transient errors (rate limits, timeouts, 5xx, transport blips) +using `BACKOFF_INITIAL` / `BACKOFF_CAP` exponential backoff with full jitter. +When retries are exhausted or the error indicates that another model might +succeed, it advances to the next entry in the chain. + +Classification is delegated to litellm's exception hierarchy: `_RETRYABLE` +covers transient failures; `_FALLBACKABLE` covers errors that are stable +against retry but may resolve on a different model. +""" + +from __future__ import annotations + +import random + +import litellm + +from lmi.exceptions import ModelRefusalError, ResponseValidationError + +BACKOFF_INITIAL = 1.0 +BACKOFF_CAP = 30.0 + + +_RETRYABLE: tuple[type[BaseException], ...] = ( + litellm.RateLimitError, + litellm.Timeout, + litellm.APIConnectionError, + litellm.InternalServerError, + litellm.ServiceUnavailableError, + ResponseValidationError, +) + +_FALLBACKABLE: tuple[type[BaseException], ...] = ( + litellm.ContextWindowExceededError, + litellm.NotFoundError, + litellm.ContentPolicyViolationError, + # Auth/permission failures: when a user configures a fallback chain, they + # likely have distinct credentials per entry, so a 401/403 on one model + # shouldn't block the others. + litellm.AuthenticationError, + litellm.PermissionDeniedError, + # Providers reject requests for schema reasons that differ across + # providers (e.g. image count limits, unsupported field combinations); a + # sibling model may accept the same input. + litellm.BadRequestError, + ModelRefusalError, +) + + +def should_retry(exc: BaseException) -> bool: + """True if the same model might succeed on another try.""" + return isinstance(exc, _RETRYABLE) + + +def should_fallback(exc: BaseException) -> bool: + """True if another model might succeed where this one failed.""" + return isinstance(exc, _FALLBACKABLE) + + +def backoff_seconds(attempt: int) -> float: + """Exponential backoff with full jitter. `attempt` is 0-indexed.""" + ceiling = min(BACKOFF_CAP, BACKOFF_INITIAL * (2**attempt)) + return random.uniform(0, ceiling) diff --git a/packages/lmi/src/lmi/utils.py b/packages/lmi/src/lmi/utils.py index 90514acd..446944f2 100644 --- a/packages/lmi/src/lmi/utils.py +++ b/packages/lmi/src/lmi/utils.py @@ -28,12 +28,13 @@ def configure_llm_logs() -> None: # Set sane default LiteLLM logging configuration # SEE: https://docs.litellm.ai/docs/observability/telemetry litellm.telemetry = False - if ( + verbose = ( logging.getLevelNamesMapping().get( os.environ.get("LITELLM_LOG", ""), logging.WARNING ) < logging.WARNING - ): + ) + if verbose: # If LITELLM_LOG is DEBUG or INFO, don't change the LiteLLM log levels litellm_loggers_config: dict[str, Any] = {} else: @@ -51,6 +52,10 @@ def configure_llm_logs() -> None: "httpx": {"level": "WARNING"}, "httpcore.connection": {"level": "WARNING"}, # For TCP connection events "httpcore.http11": {"level": "WARNING"}, # For request send/receive events + # Surface our own retry/fallback orchestration logs when the user + # opts into verbose mode via LITELLM_LOG; otherwise rely on + # root/WARNING defaults. + "lmi": {"level": "INFO" if verbose else "WARNING"}, } | litellm_loggers_config, }) diff --git a/packages/lmi/tests/cassettes/TestLiteLLMModel.test_call_single.yaml b/packages/lmi/tests/cassettes/TestLiteLLMModel.test_call_single.yaml new file mode 100644 index 00000000..b20e6049 --- /dev/null +++ b/packages/lmi/tests/cassettes/TestLiteLLMModel.test_call_single.yaml @@ -0,0 +1,1046 @@ +interactions: + - request: + body: + '{"messages":[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"The + duck says"}],"model":"gpt-4o-mini-2024-07-18","logprobs":true,"max_tokens":56,"n":1,"stream":true,"stream_options":{"include_usage":true},"temperature":0}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - "256" + content-type: + - application/json + host: + - api.openai.com + user-agent: + - AsyncOpenAI/Python 2.30.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 2.30.0 + x-stainless-raw-response: + - "true" + x-stainless-read-timeout: + - "60.0" + x-stainless-retry-count: + - "0" + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.7 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: + 'data: {"id":"chatcmpl-DVm26X7HYwJOI2xedPdJWY7Tc5JzL","object":"chat.completion.chunk","created":1776465178,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_93a2164d78","choices":[{"index":0,"delta":{"role":"assistant","content":"","refusal":null},"logprobs":{"content":[],"refusal":null},"finish_reason":null}],"usage":null,"obfuscation":""} + + + data: 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- payment=(self "https://checkout.stripe.com" "https://connect-js.stripe.com" - "https://js.stripe.com" "https://*.js.stripe.com" "https://hooks.stripe.com") - Referrer-Policy: - - no-referrer, strict-origin-when-cross-origin - Server: - - cloudflare - Transfer-Encoding: - - chunked - Vary: - - Accept-Encoding - X-Content-Type-Options: - - nosniff - status: - code: 200 - message: OK -version: 1 diff --git a/packages/lmi/tests/test_config.py b/packages/lmi/tests/test_config.py new file mode 100644 index 00000000..f7d4011a --- /dev/null +++ b/packages/lmi/tests/test_config.py @@ -0,0 +1,405 @@ +from typing import Any + +import pytest +from pydantic import BaseModel, Field, SecretStr, ValidationError + +from lmi.config import LLMConfig, LLMConfigField, ModelSpec +from lmi.constants import DEFAULT_VERTEX_SAFETY_SETTINGS +from lmi.llms import CommonLLMNames, LiteLLMModel + + +class TestModelSpec: + def test_minimal(self) -> None: + spec = ModelSpec(name="gpt-4o-mini") + assert spec.name == "gpt-4o-mini" + assert spec.timeout == 60.0 + assert spec.max_retries == 3 + assert not spec.extra_params + assert spec.api_key is None + assert spec.api_base is None + + def test_to_litellm_kwargs_strips_none_and_unwraps_secret(self) -> None: + spec = ModelSpec( + name="gpt-4o-mini", + api_base="https://example.com", + api_key=SecretStr("sk-abc"), + timeout=10.0, + extra_params={"temperature": 0.3}, + ) + assert spec.to_litellm_kwargs() == { + "model": "gpt-4o-mini", + "timeout": 10.0, + "temperature": 0.3, + "api_base": "https://example.com", + "api_key": "sk-abc", # pragma: allowlist secret + } + + def test_to_litellm_kwargs_omits_unset_optional_fields(self) -> None: + kwargs = ModelSpec(name="gpt-4o-mini").to_litellm_kwargs() + assert "api_key" not in kwargs + assert "api_base" not in kwargs + + def test_to_litellm_kwargs_strips_litellm_retry_kwargs(self) -> None: + # `num_retries` / `max_retries` are LiteLLM's internal retry knobs; LMI + # owns retries, so they must not flow through to litellm.acompletion + # even when callers stuff them into extra_params via legacy configs. + kwargs = ModelSpec( + name="gpt-4o-mini", + extra_params={"num_retries": 5, "max_retries": 7, "temperature": 0.3}, + ).to_litellm_kwargs() + assert "num_retries" not in kwargs + assert "max_retries" not in kwargs + assert kwargs["temperature"] == 0.3 + + def test_extra_forbidden(self) -> None: + with pytest.raises(ValidationError): + ModelSpec(name="gpt-4o-mini", unknown_field=1) # type: ignore[call-arg] + + def test_from_name_openai_defaults(self) -> None: + spec = ModelSpec.from_name("gpt-4o-mini") + assert spec.extra_params == {"temperature": 1.0} + + def test_from_name_gemini_adds_safety_settings(self) -> None: + spec = ModelSpec.from_name("gemini-1.5-pro") + assert spec.extra_params["safety_settings"] == DEFAULT_VERTEX_SAFETY_SETTINGS + + def test_from_name_rejects_logprobs_for_non_openai(self) -> None: + with pytest.raises(ValueError, match="only supported on OpenAI"): + ModelSpec.from_name("claude-3-5-sonnet-20241022", logprobs=True) + with pytest.raises(ValueError, match="only supported on OpenAI"): + ModelSpec.from_name("claude-3-5-sonnet-20241022", top_logprobs=5) + + def test_from_name_keeps_logprobs_for_openai(self) -> None: + spec = ModelSpec.from_name("gpt-4o-mini", logprobs=True, top_logprobs=5) + assert spec.extra_params["logprobs"] is True + assert spec.extra_params["top_logprobs"] == 5 + + def test_from_name_override_wins_over_default(self) -> None: + spec = ModelSpec.from_name("gpt-4o-mini", temperature=0.0) + assert spec.extra_params["temperature"] == 0.0 + + def test_from_name_routes_spec_field_overrides(self) -> None: + spec = ModelSpec.from_name("gpt-4o-mini", timeout=5.0, max_retries=7) + assert spec.timeout == 5.0 + assert spec.max_retries == 7 + assert "timeout" not in spec.extra_params + assert "max_retries" not in spec.extra_params + + +class TestLLMConfigFromLegacy: + def test_requires_model_list(self) -> None: + with pytest.raises(ValueError, match="empty or missing model_list"): + LLMConfig.from_legacy_dict({}) + + def test_single_model_primary_only(self) -> None: + legacy = { + "model_list": [ + { + "model_name": "gpt-4o-mini", + "litellm_params": { + "model": "gpt-4o-mini", + "temperature": 0.5, + "max_tokens": 200, + }, + } + ] + } + cfg = LLMConfig.from_legacy_dict(legacy) + assert len(cfg.models) == 1 + assert cfg.models[0].name == "gpt-4o-mini" + assert cfg.models[0].extra_params == {"temperature": 0.5, "max_tokens": 200} + + def test_fallback_chain_ordered_from_primary(self) -> None: + legacy = { + "model_list": [ + {"model_name": "A", "litellm_params": {"model": "gpt-4o-mini"}}, + { + "model_name": "B", + "litellm_params": {"model": "claude-3-5-sonnet-20241022"}, + }, + { + "model_name": "C", + "litellm_params": {"model": "gemini-1.5-pro"}, + }, + ], + "fallbacks": [{"A": ["B", "C"]}], + } + cfg = LLMConfig.from_legacy_dict(legacy) + assert [s.name for s in cfg.models] == [ + "gpt-4o-mini", + "claude-3-5-sonnet-20241022", + "gemini-1.5-pro", + ] + + def test_unreferenced_models_appended(self) -> None: + legacy = { + "model_list": [ + {"model_name": "A", "litellm_params": {"model": "gpt-4o-mini"}}, + { + "model_name": "B", + "litellm_params": {"model": "claude-3-5-sonnet-20241022"}, + }, + { + "model_name": "C", + "litellm_params": {"model": "gemini-1.5-pro"}, + }, + ], + "fallbacks": [{"A": ["B"]}], + } + cfg = LLMConfig.from_legacy_dict(legacy) + # A -> B (from fallbacks), then C is orphan and appended at end. + assert [s.name for s in cfg.models] == [ + "gpt-4o-mini", + "claude-3-5-sonnet-20241022", + "gemini-1.5-pro", + ] + + def test_router_kwargs_feed_timeout_and_retries(self) -> None: + legacy = { + "model_list": [ + {"model_name": "A", "litellm_params": {"model": "gpt-4o-mini"}} + ], + "router_kwargs": {"timeout": 12.5, "num_retries": 7}, + } + cfg = LLMConfig.from_legacy_dict(legacy) + assert cfg.models[0].timeout == 12.5 + assert cfg.models[0].max_retries == 7 + + def test_per_model_overrides_beat_router_defaults(self) -> None: + legacy = { + "model_list": [ + { + "model_name": "A", + "litellm_params": { + "model": "gpt-4o-mini", + "timeout": 3.0, + "max_retries": 1, + }, + } + ], + "router_kwargs": {"timeout": 12.5, "num_retries": 7}, + } + cfg = LLMConfig.from_legacy_dict(legacy) + assert cfg.models[0].timeout == 3.0 + assert cfg.models[0].max_retries == 1 + + def test_api_key_lifts_to_secret(self) -> None: + legacy = { + "model_list": [ + { + "model_name": "A", + "litellm_params": { + "model": "gpt-4o-mini", + "api_key": "sk-xyz", # pragma: allowlist secret + }, + } + ] + } + cfg = LLMConfig.from_legacy_dict(legacy) + assert isinstance(cfg.models[0].api_key, SecretStr) + assert cfg.models[0].api_key.get_secret_value() == "sk-xyz" + assert "api_key" not in cfg.models[0].extra_params + + +class TestLiteLLMModelPopulatesLLMConfig: + """New and legacy construction paths should propagate to the same `llm_config`.""" + + def test_bare_name_produces_single_model_chain(self) -> None: + model = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value) + assert model.llm_config is not None + assert len(model.llm_config.models) == 1 + assert model.llm_config.models[0].name == CommonLLMNames.OPENAI_TEST.value + + def test_legacy_dict_and_explicit_llm_config_agree(self) -> None: + name = CommonLLMNames.OPENAI_TEST.value + legacy_model = LiteLLMModel( + config={ + "model_list": [ + { + "model_name": name, + "litellm_params": { + "model": name, + "temperature": 0.3, + "max_tokens": 128, + }, + } + ] + } + ) + + explicit_model = LiteLLMModel( + name=name, + llm_config=LLMConfig( + models=[ + ModelSpec( + name=name, + extra_params={"temperature": 0.3, "max_tokens": 128}, + ) + ] + ), + ) + + assert legacy_model.llm_config is not None + assert explicit_model.llm_config is not None + legacy_specs = legacy_model.llm_config.models + explicit_specs = explicit_model.llm_config.models + assert len(legacy_specs) == len(explicit_specs) == 1 + assert legacy_specs[0].name == explicit_specs[0].name + assert legacy_specs[0].extra_params == explicit_specs[0].extra_params + + def test_legacy_fallbacks_become_ordered_chain(self) -> None: + model = LiteLLMModel( + config={ + "model_list": [ + { + "model_name": "primary", + "litellm_params": {"model": CommonLLMNames.OPENAI_TEST.value}, + }, + { + "model_name": "backup", + "litellm_params": { + "model": CommonLLMNames.ANTHROPIC_TEST.value + }, + }, + ], + "fallbacks": [{"primary": ["backup"]}], + } + ) + assert model.llm_config is not None + assert [s.name for s in model.llm_config.models] == [ + CommonLLMNames.OPENAI_TEST.value, + CommonLLMNames.ANTHROPIC_TEST.value, + ] + + def test_config_dict_preserved_alongside_llm_config(self) -> None: + model = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value) + assert "model_list" in model.config + assert model.llm_config is not None + # Both sources of truth should reference the same primary name. + assert ( + model.config["model_list"][0]["model_name"] + == model.llm_config.models[0].name + ) + + @pytest.mark.parametrize( + ("config_overrides", "expected_temp", "expected_max_tokens"), + [ + pytest.param( + {"temperature": 0, "max_tokens": 56}, + 0, + 56, + id="explicit_zero_temperature", + ), + pytest.param( + {"temperature": 0.5, "max_tokens": 100}, + 0.5, + 100, + id="nonzero_overrides", + ), + ], + ) + def test_config_overrides_propagate_to_llm_config( + self, + config_overrides: dict[str, Any], + expected_temp: float, + expected_max_tokens: int, + ) -> None: + model = LiteLLMModel( + name=CommonLLMNames.OPENAI_TEST.value, config=config_overrides + ) + assert model.llm_config is not None + extras = model.llm_config.models[0].extra_params + assert extras["temperature"] == expected_temp + assert extras["max_tokens"] == expected_max_tokens + + +class TestLLMConfigCoerce: + def test_passthrough_llmconfig_instance(self) -> None: + cfg = LLMConfig(models=[ModelSpec(name="gpt-4o-mini")]) + assert LLMConfig.coerce(cfg) is cfg + + def test_typed_dict(self) -> None: + cfg = LLMConfig.coerce({"models": [{"name": "gpt-4o-mini"}]}) + assert [m.name for m in cfg.models] == ["gpt-4o-mini"] + + def test_legacy_router_dict(self) -> None: + cfg = LLMConfig.coerce({ + "model_list": [ + { + "model_name": "primary", + "litellm_params": {"model": "gpt-4o-mini", "temperature": 0.5}, + }, + ] + }) + assert cfg.models[0].name == "gpt-4o-mini" + assert cfg.models[0].extra_params == {"temperature": 0.5} + + def test_name_plus_flat_kwargs(self) -> None: + cfg = LLMConfig.coerce({ + "name": "gpt-4o-mini", + "temperature": 0.1, + "timeout": 30.0, + }) + spec = cfg.models[0] + assert spec.name == "gpt-4o-mini" + assert spec.timeout == 30.0 + assert spec.extra_params["temperature"] == 0.1 + + def test_name_plus_flat_does_not_mutate_input(self) -> None: + src = {"name": "gpt-4o-mini", "temperature": 0.1} + LLMConfig.coerce(src) + # coerce() must not mutate the caller's dict. + assert src == {"name": "gpt-4o-mini", "temperature": 0.1} + + def test_empty_dict_raises(self) -> None: + with pytest.raises(ValueError, match="Can't infer"): + LLMConfig.coerce({}) + + def test_non_dict_non_llmconfig_raises(self) -> None: + with pytest.raises(TypeError, match="Cannot build"): + LLMConfig.coerce(42) + + +class TestLLMConfigField: + """Pydantic fields annotated with `LLMConfigField` coerce all supported input shapes.""" + + class _Holder(BaseModel): + cfg: LLMConfigField = Field( + default_factory=lambda: LLMConfig(models=[ModelSpec(name="gpt-4o-mini")]) + ) + + def test_accepts_instance(self) -> None: + expected = LLMConfig(models=[ModelSpec(name="x")]) + h = self._Holder(cfg=expected) + # Pydantic copies the instance through BeforeValidator but preserves contents. + assert [m.name for m in h.cfg.models] == ["x"] + + def test_accepts_typed_dict(self) -> None: + h = self._Holder(cfg={"models": [{"name": "y"}]}) + assert [m.name for m in h.cfg.models] == ["y"] + + def test_accepts_legacy_dict(self) -> None: + h = self._Holder( + cfg={ + "model_list": [ + { + "model_name": "primary", + "litellm_params": {"model": "gpt-4o-mini"}, + } + ] + } + ) + assert h.cfg.models[0].name == "gpt-4o-mini" + + def test_accepts_name_plus_flat(self) -> None: + h = self._Holder(cfg={"name": "gpt-4o-mini", "temperature": 0.1}) + assert h.cfg.models[0].extra_params["temperature"] == 0.1 + + def test_rejects_unrecognized_dict(self) -> None: + with pytest.raises(ValidationError): + self._Holder(cfg={"unknown_key": "value"}) + + def test_rejects_wrong_type(self) -> None: + # `TypeError` raised inside `BeforeValidator` propagates unwrapped. + with pytest.raises(TypeError, match="Cannot build"): + self._Holder(cfg=42) diff --git a/packages/lmi/tests/test_cost_tracking.py b/packages/lmi/tests/test_cost_tracking.py index b7ef7c15..fcb14e99 100644 --- a/packages/lmi/tests/test_cost_tracking.py +++ b/packages/lmi/tests/test_cost_tracking.py @@ -129,35 +129,20 @@ async def ac(x) -> None: await llm.call(messages, [ac]) @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) - @pytest.mark.parametrize( - "config", - [ - pytest.param( - { - "model_list": [ - { - "model_name": CommonLLMNames.OPENAI_TEST.value, - "litellm_params": { - "model": CommonLLMNames.OPENAI_TEST.value, - "temperature": 0, - "max_tokens": 56, - }, - } - ] - }, - id="with-router", - ), - pytest.param( - { - "pass_through_router": True, - "router_kwargs": {"temperature": 0, "max_tokens": 56}, - }, - id="without-router", - ), - ], - ) @pytest.mark.asyncio - async def test_cost_call_single(self, config: dict[str, Any]) -> None: + async def test_cost_call_single(self) -> None: + config = { + "model_list": [ + { + "model_name": CommonLLMNames.OPENAI_TEST.value, + "litellm_params": { + "model": CommonLLMNames.OPENAI_TEST.value, + "temperature": 0, + "max_tokens": 56, + }, + } + ] + } with cost_tracking_ctx(), assert_costs_increased(): llm = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value, config=config) diff --git a/packages/lmi/tests/test_dispatch.py b/packages/lmi/tests/test_dispatch.py new file mode 100644 index 00000000..a6931924 --- /dev/null +++ b/packages/lmi/tests/test_dispatch.py @@ -0,0 +1,422 @@ +"""Tests for the retry/fallback loop, streaming first-chunk commit, and dispatch. + +End-to-end tests mock `litellm.acompletion` to drive `LiteLLMModel.call()` through +retry-same-model → advance-to-next-model → all-exhausted scenarios without any +network access. Unit-level tests exercise `_run_with_fallbacks` and +`_commit_stream` directly. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import Any +from unittest.mock import AsyncMock, patch + +import litellm +import pytest +from aviary.core import Message +from litellm.types.utils import Choices, ModelResponse, Usage +from litellm.types.utils import Message as LiteLLMMessage + +from lmi.config import LLMConfig, ModelSpec +from lmi.exceptions import AllModelsExhaustedError, ModelRefusalError +from lmi.llms import LiteLLMModel, _commit_stream +from lmi.types import LLMResult + + +def _chunk(text: str) -> LLMResult: + return LLMResult(model=PRIMARY, text=text) + + +PRIMARY = "gpt-5-mini" +FALLBACK = "claude-sonnet-4-6" + + +def _two_model_chain(primary: str = PRIMARY, fallback: str = FALLBACK) -> LLMConfig: + return LLMConfig( + models=[ + ModelSpec(name=primary, max_retries=2, timeout=5.0), + ModelSpec(name=fallback, max_retries=1, timeout=5.0), + ] + ) + + +def _model(cfg: LLMConfig | None = None) -> LiteLLMModel: + return LiteLLMModel(name=PRIMARY, llm_config=cfg or _two_model_chain()) + + +def _rate_limit_exc(model: str = PRIMARY) -> litellm.RateLimitError: + return litellm.RateLimitError( + message="rate limited", model=model, llm_provider="openai" + ) + + +def _context_overflow_exc(model: str = PRIMARY) -> litellm.ContextWindowExceededError: + return litellm.ContextWindowExceededError( + message="too many tokens", model=model, llm_provider="openai" + ) + + +@pytest.fixture(autouse=True) +def _no_backoff(monkeypatch: pytest.MonkeyPatch) -> None: + """Zero out the exponential-jitter sleep so tests don't wait.""" + monkeypatch.setattr("lmi.llms.backoff_seconds", lambda _attempt: 0.0) + + +class TestRunWithFallbacks: + @pytest.mark.asyncio + async def test_returns_first_success(self) -> None: + model = _model() + attempt = AsyncMock(return_value="ok") + result = await model._run_with_fallbacks(attempt) + assert result == "ok" + assert attempt.call_count == 1 + + @pytest.mark.asyncio + async def test_retries_same_model_on_transient(self) -> None: + model = _model() + attempt = AsyncMock(side_effect=[_rate_limit_exc(), _rate_limit_exc(), "ok"]) + result = await model._run_with_fallbacks(attempt) + assert result == "ok" + # All three calls used the primary spec (same model) + assert attempt.call_count == 3 + specs = [call.args[0] for call in attempt.await_args_list] + assert all(s.name == PRIMARY for s in specs) + + @pytest.mark.asyncio + async def test_retries_exhausted_advances_to_fallback(self) -> None: + model = _model() + # Primary: max_retries=2 -> 3 attempts, all rate-limited. Then fallback succeeds. + attempt = AsyncMock( + side_effect=[ + _rate_limit_exc(), + _rate_limit_exc(), + _rate_limit_exc(), + "ok", + ] + ) + result = await model._run_with_fallbacks(attempt) + assert result == "ok" + specs = [call.args[0] for call in attempt.await_args_list] + assert [s.name for s in specs] == [PRIMARY, PRIMARY, PRIMARY, FALLBACK] + + @pytest.mark.asyncio + async def test_fallbackable_exception_advances_immediately(self) -> None: + model = _model() + # Context overflow on primary -> no retries, go straight to fallback. + attempt = AsyncMock(side_effect=[_context_overflow_exc(), "ok"]) + result = await model._run_with_fallbacks(attempt) + assert result == "ok" + specs = [call.args[0] for call in attempt.await_args_list] + assert [s.name for s in specs] == [PRIMARY, FALLBACK] + + @pytest.mark.asyncio + async def test_model_refusal_advances_to_fallback(self) -> None: + model = _model() + refusal = ModelRefusalError( + "safety", model=PRIMARY, finish_reason="content_filter" + ) + attempt = AsyncMock(side_effect=[refusal, "ok"]) + result = await model._run_with_fallbacks(attempt) + assert result == "ok" + specs = [call.args[0] for call in attempt.await_args_list] + assert [s.name for s in specs] == [PRIMARY, FALLBACK] + + @pytest.mark.asyncio + async def test_fatal_exception_propagates(self) -> None: + model = _model() + # An uncategorized exception (caller bug, malformed Message, etc.) is + # neither retryable nor fallback-able; it propagates immediately. + attempt = AsyncMock(side_effect=[ValueError("caller bug")]) + with pytest.raises(ValueError, match="caller bug"): + await model._run_with_fallbacks(attempt) + # Did not fall back to the second model. + assert attempt.call_count == 1 + + @pytest.mark.asyncio + async def test_auth_error_advances_to_fallback(self) -> None: + model = _model() + auth_err = litellm.AuthenticationError( + message="bad key", model=PRIMARY, llm_provider="openai" + ) + attempt = AsyncMock(side_effect=[auth_err, "ok"]) + result = await model._run_with_fallbacks(attempt) + assert result == "ok" + specs = [call.args[0] for call in attempt.await_args_list] + assert [s.name for s in specs] == [PRIMARY, FALLBACK] + + @pytest.mark.asyncio + async def test_all_models_exhausted_raises(self) -> None: + model = _model() + # Every attempt rate-limited on both models. + attempt = AsyncMock( + side_effect=[ + _rate_limit_exc(), + _rate_limit_exc(), + _rate_limit_exc(), + _rate_limit_exc(FALLBACK), + _rate_limit_exc(FALLBACK), + ] + ) + with pytest.raises(AllModelsExhaustedError) as excinfo: + await model._run_with_fallbacks(attempt) + assert attempt.call_count == 5 # 3 on primary + 2 on fallback + assert isinstance(excinfo.value.last_exc, litellm.RateLimitError) + + @pytest.mark.asyncio + async def test_passes_args_and_kwargs_to_attempt(self) -> None: + model = _model() + attempt = AsyncMock(return_value="ok") + await model._run_with_fallbacks(attempt, "a", "b", path="chat", extra=1) + # First positional is the spec; the rest are the forwarded args. + call = attempt.await_args_list[0] + assert call.args[1:] == ("a", "b") + assert call.kwargs == {"path": "chat", "extra": 1} + + +class TestCommitStream: + @pytest.mark.asyncio + async def test_replays_all_chunks(self) -> None: + async def gen() -> AsyncIterator[LLMResult]: # noqa: RUF029 + for chunk in ("a", "b", "c"): + yield _chunk(chunk) + + committed = await _commit_stream(gen()) + collected = [r.text async for r in committed] + assert collected == ["a", "b", "c"] + + @pytest.mark.asyncio + async def test_pre_first_chunk_error_propagates(self) -> None: + async def gen() -> AsyncIterator[LLMResult]: # noqa: RUF029 + raise litellm.RateLimitError( + message="pre-first-chunk", model=PRIMARY, llm_provider="openai" + ) + yield _chunk("unreachable") # type: ignore[unreachable] # pragma: no cover + + with pytest.raises(litellm.RateLimitError): + await _commit_stream(gen()) + + @pytest.mark.asyncio + async def test_empty_stream_raises_runtime_error(self) -> None: + async def gen() -> AsyncIterator[LLMResult]: # noqa: RUF029 + return + yield _chunk("unreachable") # type: ignore[unreachable] # pragma: no cover + + with pytest.raises(RuntimeError, match="Stream closed before"): + await _commit_stream(gen()) + + @pytest.mark.asyncio + async def test_mid_stream_error_surfaces_unmodified(self) -> None: + async def gen() -> AsyncIterator[LLMResult]: # noqa: RUF029 + yield _chunk("a") + raise litellm.APIConnectionError( + message="mid-stream", model=PRIMARY, llm_provider="openai" + ) + + committed = await _commit_stream(gen()) + first = await anext(aiter(committed)) + # First chunk is delivered; the error only surfaces on the *next* pull. + assert first.text == "a" + with pytest.raises(litellm.APIConnectionError): + async for _ in committed: + pass + + +def _fake_chat_response( + model: str = PRIMARY, + text: str = "hello", + finish_reason: str = "stop", +) -> ModelResponse: + """Build a minimal litellm ModelResponse for non-streaming chat.""" + return ModelResponse( + id="chatcmpl-test", + model=model, + choices=[ + Choices( + finish_reason=finish_reason, + message=LiteLLMMessage(content=text), + ) + ], + usage=Usage(prompt_tokens=5, completion_tokens=3, total_tokens=8), + ) + + +class TestCallEndToEnd: + """Drive LiteLLMModel.call() with a mocked litellm.acompletion.""" + + @pytest.mark.asyncio + async def test_retries_then_succeeds_on_primary(self) -> None: + model = _model() + responses: list[Any] = [_rate_limit_exc(), _fake_chat_response()] + with patch( + "litellm.acompletion", AsyncMock(side_effect=responses) + ) as mock_call: + results = await model.call([Message(content="hi")]) + + assert len(results) == 1 + assert results[0].text == "hello" + # 2 attempts, both on primary. + assert mock_call.await_count == 2 + assert all( + call.kwargs["model"] == PRIMARY for call in mock_call.await_args_list + ) + + @pytest.mark.asyncio + async def test_falls_back_to_secondary_on_context_overflow(self) -> None: + model = _model() + responses: list[Any] = [ + _context_overflow_exc(), + _fake_chat_response(model=FALLBACK), + ] + with patch( + "litellm.acompletion", AsyncMock(side_effect=responses) + ) as mock_call: + results = await model.call([Message(content="hi")]) + + assert len(results) == 1 + assert results[0].model == FALLBACK + models_tried = [call.kwargs["model"] for call in mock_call.await_args_list] + assert models_tried == [PRIMARY, FALLBACK] + + @pytest.mark.asyncio + async def test_refusal_triggers_fallback(self) -> None: + model = _model() + refusal_resp = _fake_chat_response(finish_reason="content_filter", text="no") + ok_resp = _fake_chat_response(model=FALLBACK, text="ok") + with patch( + "litellm.acompletion", AsyncMock(side_effect=[refusal_resp, ok_resp]) + ) as mock_call: + results = await model.call([Message(content="hi")]) + + assert len(results) == 1 + assert results[0].model == FALLBACK + assert results[0].text == "ok" + models_tried = [call.kwargs["model"] for call in mock_call.await_args_list] + assert models_tried == [PRIMARY, FALLBACK] + + @pytest.mark.asyncio + async def test_all_models_exhausted_raises(self) -> None: + model = _model() + # 3 attempts on primary (max_retries=2) + 2 on fallback (max_retries=1) = 5 + side_effects = [_rate_limit_exc()] * 5 + with ( + patch( + "litellm.acompletion", AsyncMock(side_effect=side_effects) + ) as mock_call, + pytest.raises(AllModelsExhaustedError), + ): + await model.call([Message(content="hi")]) + assert mock_call.await_count == 5 + + @pytest.mark.asyncio + async def test_fatal_exception_propagates_unwrapped(self) -> None: + model = _model() + # Uncategorized exceptions (caller bug, Python-level error) propagate + # without being wrapped in AllModelsExhaustedError. + with ( + patch( + "litellm.acompletion", AsyncMock(side_effect=[ValueError("caller bug")]) + ) as mock_call, + pytest.raises(ValueError, match="caller bug"), + ): + await model.call([Message(content="hi")]) + assert mock_call.await_count == 1 + + @pytest.mark.asyncio + async def test_spec_extra_params_flow_to_litellm(self) -> None: + cfg = LLMConfig( + models=[ + ModelSpec( + name=PRIMARY, + max_retries=0, + timeout=7.5, + extra_params={"temperature": 0.2, "max_tokens": 42}, + ) + ] + ) + model = LiteLLMModel(name=PRIMARY, llm_config=cfg) + with patch( + "litellm.acompletion", + AsyncMock(return_value=_fake_chat_response()), + ) as mock_call: + await model.call([Message(content="hi")]) + + call_kwargs = mock_call.await_args_list[0].kwargs + assert call_kwargs["model"] == PRIMARY + assert call_kwargs["timeout"] == 7.5 + assert call_kwargs["temperature"] == 0.2 + assert call_kwargs["max_tokens"] == 42 + + +class TestDispatchPrimitiveSelection: + """Verify `_dispatch` routes to the right primitive. + + Routing depends on `spec.responses_api` and whether `streaming=True` was passed. + """ + + @pytest.mark.asyncio + async def test_chat_non_streaming(self, monkeypatch: pytest.MonkeyPatch) -> None: + model = _model() + seen: dict[str, Any] = {} + + async def fake_acompletion( # noqa: RUF029 + _self: LiteLLMModel, + messages: Any, # noqa: ARG001 + *, + spec: ModelSpec, + **_kwargs: Any, + ) -> list[Any]: + seen["primitive"] = "acompletion" + seen["spec"] = spec + return [] + + monkeypatch.setattr(LiteLLMModel, "acompletion", fake_acompletion) + await model.call([Message(content="hi")]) + assert seen["primitive"] == "acompletion" + assert seen["spec"].name == PRIMARY + + @pytest.mark.asyncio + async def test_chat_streaming(self, monkeypatch: pytest.MonkeyPatch) -> None: + model = _model() + seen: dict[str, Any] = {} + + async def one_chunk() -> AsyncIterator[LLMResult]: # noqa: RUF029 + yield _chunk("ok") + + async def fake_acompletion_iter( # noqa: RUF029 + _self: LiteLLMModel, + messages: Any, # noqa: ARG001 + *, + spec: ModelSpec, # noqa: ARG001 + **_kwargs: Any, + ) -> AsyncIterator[LLMResult]: + seen["primitive"] = "acompletion_iter" + return one_chunk() + + monkeypatch.setattr(LiteLLMModel, "acompletion_iter", fake_acompletion_iter) + await model.call([Message(content="hi")], callbacks=[lambda *_a, **_k: None]) + assert seen["primitive"] == "acompletion_iter" + + @pytest.mark.asyncio + async def test_responses_non_streaming( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + cfg = LLMConfig(models=[ModelSpec(name=PRIMARY, responses_api=True)]) + model = LiteLLMModel(name=PRIMARY, llm_config=cfg) + seen: dict[str, Any] = {} + + async def fake_aresponses( # noqa: RUF029 + _self: LiteLLMModel, + messages: Any, # noqa: ARG001 + tools: Any, # noqa: ARG001 + previous_response_id: Any = None, # noqa: ARG001 + *, + spec: ModelSpec, # noqa: ARG001 + **_kwargs: Any, + ) -> list[Any]: + seen["primitive"] = "_aresponses" + return [] + + monkeypatch.setattr(LiteLLMModel, "_aresponses", fake_aresponses) + await model.call([Message(content="hi")]) + assert seen["primitive"] == "_aresponses" diff --git a/packages/lmi/tests/test_litellm_patches.py b/packages/lmi/tests/test_litellm_patches.py index ecb052c0..1c29da23 100644 --- a/packages/lmi/tests/test_litellm_patches.py +++ b/packages/lmi/tests/test_litellm_patches.py @@ -4,7 +4,6 @@ requiring actual API calls. """ -import litellm from litellm.llms.vertex_ai.gemini import transformation @@ -24,51 +23,6 @@ class TestModel(OpenAIBaseModel): assert "test_field" in model.model_dump(by_alias=None) -class TestProviderRetryPatch: - """Tests for the provider-specific 400 error retry patch.""" - - def test_should_retry_is_patched(self): - """Verify Router.should_retry_this_error is patched.""" - method = litellm.Router.should_retry_this_error - # The patched version should have a closure containing original function - assert hasattr(method, "__closure__") - assert method.__closure__ is not None - - def test_allows_retry_on_provider_limit_400(self): - """Verify 400 errors with provider limit messages allow retry.""" - router = litellm.Router(model_list=[]) - - # Create a mock 400 error with provider limit message - error = litellm.BadRequestError( - message="Too much media: 0 document pages + 108 images > 100", - model="anthropic/claude-3-5-sonnet", - llm_provider="anthropic", - ) - error.status_code = 400 - - # Should return None to allow retry cascade - result = router.should_retry_this_error(error) - assert result is None - - def test_does_not_retry_generic_400(self): - """Verify generic 400 errors are NOT retried.""" - router = litellm.Router(model_list=[]) - - error = litellm.BadRequestError( - message="Invalid request format", - model="anthropic/claude-3-5-sonnet", - llm_provider="anthropic", - ) - error.status_code = 400 - - # Method should either raise or return non-None to stop retry - try: - result = router.should_retry_this_error(error) - except Exception: - return - assert result is not None - - class TestVertexCachingPatch: """Tests for the Vertex AI context caching fix.""" diff --git a/packages/lmi/tests/test_llms.py b/packages/lmi/tests/test_llms.py index b4a009ec..23f07ff0 100644 --- a/packages/lmi/tests/test_llms.py +++ b/packages/lmi/tests/test_llms.py @@ -18,7 +18,7 @@ ) from pydantic import BaseModel, Field, TypeAdapter, computed_field -from lmi.exceptions import JSONSchemaValidationError +from lmi.exceptions import AllModelsExhaustedError, JSONSchemaValidationError from lmi.llms import ( CommonLLMNames, LiteLLMModel, @@ -386,38 +386,23 @@ async def ac(x) -> None: assert result.completion_count > 0 assert result.cost > 0 - @pytest.mark.parametrize( - "config", - [ - pytest.param( - { - "name": CommonLLMNames.OPENAI_TEST.value, - "model_list": [ - { - "model_name": CommonLLMNames.OPENAI_TEST.value, - "litellm_params": { - "model": CommonLLMNames.OPENAI_TEST.value, - "temperature": 0, - "max_tokens": 56, - "logprobs": True, - }, - } - ], - }, - id="with-router", - ), - pytest.param( - { - "pass_through_router": True, - "router_kwargs": {"temperature": 0, "max_tokens": 56}, - }, - id="without-router", - ), - ], - ) @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) @pytest.mark.asyncio - async def test_call_single(self, config: dict[str, Any], subtests) -> None: + async def test_call_single(self, subtests) -> None: + config = { + "name": CommonLLMNames.OPENAI_TEST.value, + "model_list": [ + { + "model_name": CommonLLMNames.OPENAI_TEST.value, + "litellm_params": { + "model": CommonLLMNames.OPENAI_TEST.value, + "temperature": 0, + "max_tokens": 56, + "logprobs": True, + }, + } + ], + } llm = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value, config=config) outputs = [] @@ -485,48 +470,23 @@ def mock_call(messages, *_, **__): await llm.call_single("Test message") @pytest.mark.vcr - @pytest.mark.parametrize( - ("config", "bypassed_router"), - [ - pytest.param( - { - "model_list": [ - { - "model_name": CommonLLMNames.OPENAI_TEST.value, - "litellm_params": { - "model": CommonLLMNames.OPENAI_TEST.value, - "max_tokens": 3, - }, - } - ] - }, - False, - id="with-router", - ), - pytest.param( - {"pass_through_router": True, "router_kwargs": {"max_tokens": 3}}, - True, - id="without-router", - ), - ], - ) @pytest.mark.asyncio - async def test_max_token_truncation( - self, config: dict[str, Any], bypassed_router: bool - ) -> None: - llm = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value, config=config) - with patch( - "litellm.Router.acompletion", - side_effect=litellm.Router.acompletion, - autospec=True, - ) as mock_completion: - completions = await llm.acompletion([ - Message(content="Please tell me a story") - ]) - if bypassed_router: - mock_completion.assert_not_awaited() - else: - mock_completion.assert_awaited_once() + async def test_max_token_truncation(self) -> None: + llm = LiteLLMModel( + name=CommonLLMNames.OPENAI_TEST.value, + config={ + "model_list": [ + { + "model_name": CommonLLMNames.OPENAI_TEST.value, + "litellm_params": { + "model": CommonLLMNames.OPENAI_TEST.value, + "max_tokens": 3, + }, + } + ] + }, + ) + completions = await llm.acompletion([Message(content="Please tell me a story")]) assert isinstance(completions, list) completion = completions[0] assert completion.completion_count == 3 @@ -556,10 +516,7 @@ def test_pickling(self, tmp_path: pathlib.Path) -> None: rehydrated_llm = pickle.load(f) assert llm.name == rehydrated_llm.name assert llm.config == rehydrated_llm.config - assert ( - llm.get_router().deployment_names - == rehydrated_llm.get_router().deployment_names - ) + assert llm.llm_config == rehydrated_llm.llm_config @pytest.mark.asyncio async def test_acompletion_iter_logprobs_edge_cases(self) -> None: @@ -592,60 +549,35 @@ def _build_mock_completion( usage=usage, ) - # Mock the router to return different logprobs scenarios - with patch.object(model, "_router") as mock_router: - # Mock completion with None logprobs - mock_completion_none = _build_mock_completion(delta_content="Hello") - - # Mock completion with logprobs but no content - mock_completion_no_content = _build_mock_completion( - logprobs=Mock(content=None), - delta_content=" world", - ) - - # Mock completion with empty content list - mock_completion_empty = _build_mock_completion( - logprobs=Mock(content=[]), delta_content="!" - ) - - # Mock completion with valid logprobs - mock_completion_valid = _build_mock_completion( - logprobs=Mock(content=[Mock(logprob=-0.5)]) - ) - - # Mock completion with usage info (final chunk has finish_reason) - mock_completion_usage = _build_mock_completion( + chunks = [ + _build_mock_completion(delta_content="Hello"), + _build_mock_completion(logprobs=Mock(content=None), delta_content=" world"), + _build_mock_completion(logprobs=Mock(content=[]), delta_content="!"), + _build_mock_completion(logprobs=Mock(content=[Mock(logprob=-0.5)])), + # Final chunk: includes usage and the terminal finish_reason. + _build_mock_completion( usage=Mock(prompt_tokens=10, completion_tokens=5), finish_reason="stop", - ) - - # Create async generator that yields mock completions - async def mock_stream(): # noqa: RUF029 - async def mock_stream_iter(): # noqa: RUF029 - yield mock_completion_none - yield mock_completion_no_content - yield mock_completion_empty - yield mock_completion_valid - yield mock_completion_usage - - return mock_stream_iter() + ), + ] - mock_router.acompletion.return_value = mock_stream() + async def mock_stream() -> AsyncIterator[Any]: # noqa: RUF029 + for chunk in chunks: + yield chunk - # Test that the method doesn't raise exceptions + with patch("litellm.acompletion", AsyncMock(return_value=mock_stream())): async_iterable = await model.acompletion_iter(messages) results = [result async for result in async_iterable] - # Verify we got one final result - assert len(results) == 1 - result = results[0] - assert isinstance(result, LLMResult) - assert result.text == "Hello world!" - assert result.model == "test-model" - assert result.logprob == -0.5 - assert result.prompt_count == 10 - assert result.completion_count == 5 - assert result.finish_reason == "stop" + assert len(results) == 1 + result = results[0] + assert isinstance(result, LLMResult) + assert result.text == "Hello world!" + assert result.model == "test-model" + assert result.logprob == -0.5 + assert result.prompt_count == 10 + assert result.completion_count == 5 + assert result.finish_reason == "stop" class DummyOutputSchema(BaseModel): @@ -884,9 +816,12 @@ async def test_multiple_completion(self, model_name: str, request) -> None: Message(content="Hello, how are you?"), ] if request.node.callspec.id == "anthropic": - # Anthropic does not support multiple completions - with pytest.raises(litellm.BadRequestError, match="anthropic"): + # Anthropic does not support multiple completions; the single-model + # chain exhausts on `UnsupportedParamsError` (a BadRequestError). + with pytest.raises(AllModelsExhaustedError) as excinfo: await model.call(messages) + assert isinstance(excinfo.value.last_exc, litellm.BadRequestError) + assert "anthropic" in str(excinfo.value.last_exc) else: results = await model.call(messages) # noqa: FURB120 assert len(results) == self.NUM_COMPLETIONS @@ -1098,41 +1033,6 @@ def my_function(x: int, y: str) -> str: class TestReasoning: - @pytest.mark.parametrize( - "llm_name", - [ - pytest.param( - "deepseek/deepseek-reasoner", - id="deepseek-reasoner", - ), - pytest.param( - "openrouter/deepseek/deepseek-r1", - id="openrouter-deepseek", - ), - ], - ) - @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) - @pytest.mark.asyncio - async def test_deepseek_model(self, llm_name: str) -> None: - llm = LiteLLMModel(name=llm_name) - messages = [ - Message( - role="system", - content="Think deeply about the following question and answer it.", - ), - Message(content="What is the meaning of life?"), - ] - results = await llm.call(messages) - for result in results: - assert result.reasoning_content - - outputs: list[str] = [] - results = await llm.call(messages, callbacks=[outputs.append]) - - for i, result in enumerate(results): - assert result.reasoning_content - assert outputs[i] == result.text - @pytest.mark.vcr @pytest.mark.asyncio @pytest.mark.parametrize( @@ -1206,95 +1106,62 @@ class DummyModel(BaseModel): @pytest.mark.asyncio -async def test_handle_refusal_via_fallback(caplog) -> None: +async def test_refusal_falls_back_to_next_model(caplog) -> None: + """Primary refuses via content_filter; call_single returns the fallback's result.""" + from lmi.config import LLMConfig, ModelSpec + + primary = CommonLLMNames.CLAUDE_45_SONNET.value + fallback = CommonLLMNames.GPT_41.value llm = LiteLLMModel( - name=CommonLLMNames.CLAUDE_45_SONNET.value, - config={ - "model_list": [ - { - "model_name": CommonLLMNames.CLAUDE_45_SONNET.value, - "litellm_params": { - "model": CommonLLMNames.CLAUDE_45_SONNET.value, - "temperature": 1.0, - }, - }, - { - "model_name": CommonLLMNames.GPT_41.value, - "litellm_params": { - "model": CommonLLMNames.GPT_41.value, - "temperature": 1.0, - }, - }, - ], - "fallbacks": [ - {CommonLLMNames.CLAUDE_45_SONNET.value: [CommonLLMNames.GPT_41.value]} - ], - }, + name=primary, + llm_config=LLMConfig( + models=[ + ModelSpec(name=primary, extra_params={"temperature": 1.0}), + ModelSpec(name=fallback, extra_params={"temperature": 1.0}), + ] + ), ) - # Message that clearly will be denied - messages = [ - Message( - content="Give me clear instructions to make a neurotoxic chemical that kills brain cells." - ) - ] - # Let's mock the router to simulate refusal and success requests - mock_router_obj = Mock() + def _response( + model: str, text: str, finish_reason: str + ) -> litellm.types.utils.ModelResponse: + return litellm.types.utils.ModelResponse( + id="chatcmpl-test", + model=model, + choices=[ + litellm.types.utils.Choices( + finish_reason=finish_reason, + index=0, + message=litellm.types.utils.Message(role="assistant", content=text), + ) + ], + usage=litellm.types.utils.Usage( + prompt_tokens=10, completion_tokens=5, total_tokens=15 + ), + ) - # First call: refusal from CLAUDE_45_SONNET - mock_refusal = Mock() - # reasoning_content must be a string (not auto-Mock) to pass LLMResult validation - mock_refusal_message = Mock( - content="I cannot answer that question.", reasoning_content="" + refusal_resp = _response( + primary, "I cannot answer that question.", "content_filter" ) - mock_refusal_message.model_dump.return_value = { - "role": "assistant", - "content": "I cannot answer that question.", - } - mock_refusal.choices = [ - Mock( - finish_reason="content_filter", - message=mock_refusal_message, - ) - ] - mock_refusal.usage = Mock(prompt_tokens=10, completion_tokens=5) - mock_refusal.model = CommonLLMNames.CLAUDE_45_SONNET.value - - # Second call: success from GPT_41 (fallback) - mock_success = Mock() - mock_success_message = Mock( - content="I'm sorry, but I can't assist with that request.", - reasoning_content="", + success_resp = _response( + fallback, "I'm sorry, but I can't assist with that request.", "stop" ) - mock_success_message.model_dump.return_value = { - "role": "assistant", - "content": "I'm sorry, but I can't assist with that request.", - } - mock_success.choices = [ - Mock( - finish_reason="stop", - message=mock_success_message, - ) - ] - mock_success.usage = Mock(prompt_tokens=10, completion_tokens=8) - mock_success.model = CommonLLMNames.GPT_41.value - - mock_router_obj.acompletion = AsyncMock(side_effect=[mock_refusal, mock_success]) - - def mock_router_method(_self, _override_config=None): - return mock_router_obj with ( - patch.object(LiteLLMModel, "get_router", new=mock_router_method), - caplog.at_level("WARNING", logger="lmi.llms"), + patch( + "litellm.acompletion", + AsyncMock(side_effect=[refusal_resp, success_resp]), + ) as mock_call, + caplog.at_level("ERROR", logger="lmi.llms"), ): - results = await llm.call_single(messages) + result = await llm.call_single([Message(content="dangerous question")]) - assert results.text == "I'm sorry, but I can't assist with that request." - assert results.model == CommonLLMNames.GPT_41.value - assert results.finish_reason == "stop" - assert "the llm request was refused" in caplog.text.lower() - assert "attempting to fallback" in caplog.text.lower() + assert result.text == "I'm sorry, but I can't assist with that request." + assert result.model == fallback + assert result.finish_reason == "stop" + models_tried = [call.kwargs["model"] for call in mock_call.await_args_list] + assert models_tried == [primary, fallback] + assert "refused" in caplog.text.lower() @pytest.mark.asyncio @@ -1572,17 +1439,28 @@ def test_parse_responses_output_tool_call(self) -> None: class TestResponsesAPIIntegration: """Tests for Responses API call-level behavior with real API calls (VCR-recorded).""" + @staticmethod + def _responses_model() -> LiteLLMModel: + from lmi.config import LLMConfig, ModelSpec + + name = CommonLLMNames.OPENAI_TEST.value + return LiteLLMModel( + name=name, + llm_config=LLMConfig( + models=[ModelSpec.from_name(name, responses_api=True)] + ), + ) + @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) @pytest.mark.asyncio async def test_basic_call(self) -> None: """First turn: full history sent, response_id returned.""" - model = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value) + model = self._responses_model() messages = [ Message(role="system", content="Respond with single words."), Message(role="user", content="What color is the sky?"), ] - with patch("lmi.llms.USE_RESPONSES_API", new=True): - results = await model.call(messages) + results = await model.call(messages) assert len(results) == 1 result = results[0] @@ -1597,14 +1475,13 @@ async def test_basic_call(self) -> None: @pytest.mark.asyncio async def test_multi_turn_stateful(self) -> None: """Second turn uses previous_response_id, sends only delta.""" - model = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value) + model = self._responses_model() messages_turn1 = [ Message(role="system", content="Respond with single words."), Message(role="user", content="What color is the sky?"), ] - with patch("lmi.llms.USE_RESPONSES_API", new=True): - results1 = await model.call(messages_turn1) + results1 = await model.call(messages_turn1) assert results1[0].response_id is not None assert results1[0].messages is not None @@ -1615,8 +1492,7 @@ async def test_multi_turn_stateful(self) -> None: first_response_msg, Message(role="user", content="And grass?"), ] - with patch("lmi.llms.USE_RESPONSES_API", new=True): - results2 = await model.call(messages_turn2) + results2 = await model.call(messages_turn2) assert len(results2) == 1 assert results2[0].text @@ -1626,7 +1502,7 @@ async def test_multi_turn_stateful(self) -> None: @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) @pytest.mark.asyncio async def test_responses_api_off_ignores_response_id(self) -> None: - """When USE_RESPONSES_API is off, response_id in Message.info is inert.""" + """When a model's `responses_api` is False, response_id in Message.info is inert.""" model = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value) messages = [ Message(role="user", content="Hello"), @@ -1637,8 +1513,7 @@ async def test_responses_api_off_ignores_response_id(self) -> None: ), Message(role="user", content="How are you?"), ] - with patch("lmi.llms.USE_RESPONSES_API", new=False): - results = await model.call(messages) + results = await model.call(messages) assert len(results) == 1 assert results[0].response_id is None diff --git a/packages/lmi/tests/test_retry.py b/packages/lmi/tests/test_retry.py new file mode 100644 index 00000000..329df43a --- /dev/null +++ b/packages/lmi/tests/test_retry.py @@ -0,0 +1,115 @@ +import litellm +import pytest + +from lmi.exceptions import ModelRefusalError +from lmi.retry import ( + BACKOFF_CAP, + BACKOFF_INITIAL, + backoff_seconds, + should_fallback, + should_retry, +) + + +def _litellm_exc(cls, message: str = "boom"): + """Instantiate a litellm exception uniformly across variants.""" + kwargs: dict[str, object] = { + "message": message, + "model": "gpt-4o-mini", + "llm_provider": "openai", + } + # PermissionDeniedError inherits from openai.PermissionDeniedError which + # requires a `response` argument. + if cls is litellm.PermissionDeniedError: + import httpx + + kwargs["response"] = httpx.Response(403, request=httpx.Request("POST", "x")) + return cls(**kwargs) + + +class TestShouldRetry: + @pytest.mark.parametrize( + "cls", + [ + litellm.RateLimitError, + litellm.Timeout, + litellm.APIConnectionError, + litellm.InternalServerError, + litellm.ServiceUnavailableError, + ], + ) + def test_transient_errors_retry(self, cls) -> None: + assert should_retry(_litellm_exc(cls)) + + @pytest.mark.parametrize( + "cls", + [ + litellm.ContextWindowExceededError, + litellm.NotFoundError, + litellm.ContentPolicyViolationError, + litellm.AuthenticationError, + litellm.PermissionDeniedError, + litellm.BadRequestError, + ], + ) + def test_terminal_errors_dont_retry(self, cls) -> None: + assert not should_retry(_litellm_exc(cls)) + + def test_model_refusal_does_not_retry(self) -> None: + exc = ModelRefusalError( + "refused", model="gpt-4o-mini", finish_reason="content_filter" + ) + assert not should_retry(exc) + + def test_generic_exception_does_not_retry(self) -> None: + assert not should_retry(ValueError("nope")) + + +class TestShouldFallback: + @pytest.mark.parametrize( + "cls", + [ + litellm.ContextWindowExceededError, + litellm.NotFoundError, + litellm.ContentPolicyViolationError, + litellm.AuthenticationError, + litellm.PermissionDeniedError, + litellm.BadRequestError, + ], + ) + def test_stable_errors_fallback(self, cls) -> None: + assert should_fallback(_litellm_exc(cls)) + + def test_model_refusal_falls_back(self) -> None: + exc = ModelRefusalError( + "refused", model="gpt-4o-mini", finish_reason="content_filter" + ) + assert should_fallback(exc) + + @pytest.mark.parametrize( + "cls", + [ + litellm.RateLimitError, + litellm.Timeout, + ], + ) + def test_retryable_errors_do_not_fall_back(self, cls) -> None: + assert not should_fallback(_litellm_exc(cls)) + + +class TestBackoff: + def test_returns_non_negative_float(self) -> None: + for attempt in range(10): + delay = backoff_seconds(attempt) + assert 0.0 <= delay <= BACKOFF_CAP + + def test_first_attempt_bounded_by_initial(self) -> None: + # attempt=0 -> ceiling = min(CAP, INITIAL * 2**0) = INITIAL + for _ in range(50): + assert backoff_seconds(0) <= BACKOFF_INITIAL + + def test_ceiling_grows_with_attempt_up_to_cap(self) -> None: + # attempt large enough that 2**attempt * INITIAL exceeds CAP + # => every draw is in [0, CAP] + for _ in range(50): + assert backoff_seconds(20) <= BACKOFF_CAP diff --git a/src/ldp/agent/react_agent.py b/src/ldp/agent/react_agent.py index f24a1794..8d2e3456 100644 --- a/src/ldp/agent/react_agent.py +++ b/src/ldp/agent/react_agent.py @@ -1,5 +1,5 @@ import logging -from typing import Any, Self, cast +from typing import Self, cast from aviary.core import ( MalformedMessageError, @@ -8,6 +8,7 @@ ToolResponseMessage, ) from lmi import CommonLLMNames +from lmi.config import LLMConfig, LLMConfigField, ModelSpec from pydantic import BaseModel, ConfigDict, Field from tenacity import ( Future, @@ -79,14 +80,18 @@ class ReActAgent(BaseModel, Agent[SimpleAgentState]): # passed around) or in the internal Ops model_config = ConfigDict(frozen=True) - llm_model: dict[str, Any] = Field( - default={ - "name": CommonLLMNames.GPT_4O.value, - "temperature": 0.1, - "logprobs": True, - "top_logprobs": 1, - "timeout": DEFAULT_LLM_COMPLETION_TIMEOUT, - }, + llm_config: LLMConfigField = Field( + default_factory=lambda: LLMConfig( + models=[ + ModelSpec.from_name( + CommonLLMNames.GPT_4O.value, + temperature=0.1, + logprobs=True, + top_logprobs=1, + timeout=DEFAULT_LLM_COMPLETION_TIMEOUT, + ) + ] + ), description="Starting configuration for the LLM model.", ) sys_prompt: str = Field( @@ -151,11 +156,11 @@ def __init__(self, **kwargs): super().__init__(**kwargs) if self.single_prompt: self._react_module = ReActModuleSinglePrompt( - self.llm_model, self.sys_prompt, self.tool_description_method + self.llm_config, self.sys_prompt, self.tool_description_method ) else: self._react_module = ReActModule( - self.llm_model, self.sys_prompt, self.tool_description_method + self.llm_config, self.sys_prompt, self.tool_description_method ) async def init_state(self, tools: list[Tool]) -> SimpleAgentState: diff --git a/src/ldp/agent/simple_agent.py b/src/ldp/agent/simple_agent.py index fcc9b80c..2618e9c8 100644 --- a/src/ldp/agent/simple_agent.py +++ b/src/ldp/agent/simple_agent.py @@ -1,10 +1,11 @@ from collections.abc import Awaitable, Callable from itertools import chain -from typing import Any, Self +from typing import Self from aviary.core import Message, Tool, ToolRequestMessage, ToolResponseMessage from aviary.message import EnvStateMessage from lmi import CommonLLMNames +from lmi.config import LLMConfig, LLMConfigField, ModelSpec from pydantic import BaseModel, ConfigDict, Field from ldp.graph import ConfigOp, FxnOp, LLMCallOp, OpResult, compute_graph @@ -115,12 +116,16 @@ class SimpleAgent(BaseModel, Agent[SimpleAgentState]): # passed around) or in the internal Ops model_config = ConfigDict(frozen=True) - llm_model: dict[str, Any] = Field( - default={ - "name": CommonLLMNames.GPT_4O.value, - "temperature": 0.1, - "timeout": DEFAULT_LLM_COMPLETION_TIMEOUT, - }, + llm_config: LLMConfigField = Field( + default_factory=lambda: LLMConfig( + models=[ + ModelSpec.from_name( + CommonLLMNames.GPT_4O.value, + temperature=0.1, + timeout=DEFAULT_LLM_COMPLETION_TIMEOUT, + ) + ] + ), description="Starting configuration for the LLM model. Trainable.", ) sys_prompt: str | None = Field( @@ -149,7 +154,7 @@ class SimpleAgent(BaseModel, Agent[SimpleAgentState]): def __init__(self, **kwargs): super().__init__(**kwargs) - self._config_op = ConfigOp[dict](config=self.llm_model) + self._config_op = ConfigOp[LLMConfig](config=self.llm_config) self._llm_call_op = LLMCallOp() async def init_state(self, tools: list[Tool]) -> SimpleAgentState: diff --git a/src/ldp/agent/tree_of_thoughts_agent.py b/src/ldp/agent/tree_of_thoughts_agent.py index a883a43b..c1d7b69b 100644 --- a/src/ldp/agent/tree_of_thoughts_agent.py +++ b/src/ldp/agent/tree_of_thoughts_agent.py @@ -14,10 +14,10 @@ import logging import operator from collections.abc import Callable -from typing import Any from aviary.core import Message, Tool, ToolCall, ToolRequestMessage from lmi import CommonLLMNames +from lmi.config import LLMConfig, LLMConfigField, ModelSpec from pydantic import BaseModel, ConfigDict, Field from ldp.graph import FxnOp, LLMCallOp, OpResult, compute_graph @@ -42,12 +42,16 @@ class TreeofThoughtsAgent(BaseModel, Agent[SimpleAgentState]): # passed around) or in the internal Ops model_config = ConfigDict(frozen=True) - llm_model: dict[str, Any] = Field( - default={ - "name": CommonLLMNames.GPT_4O.value, - "temperature": 0.1, - "timeout": DEFAULT_LLM_COMPLETION_TIMEOUT, - }, + llm_config: LLMConfigField = Field( + default_factory=lambda: LLMConfig( + models=[ + ModelSpec.from_name( + CommonLLMNames.GPT_4O.value, + temperature=0.1, + timeout=DEFAULT_LLM_COMPLETION_TIMEOUT, + ) + ] + ), description="Starting configuration for the LLM model.", ) value_prompt_func: Callable[[str, str], str] = Field( @@ -109,7 +113,7 @@ async def get_asv( # type: ignore[override] proposal_msgs = await self._prepend_op( new_state.messages, sys_content=proposal_prompt_init ) - proposal = await self._llm_call_op(self.llm_model, msgs=proposal_msgs) + proposal = await self._llm_call_op(self.llm_config, msgs=proposal_msgs) # Append candidate paths to the current paths candidate_paths += [ path + _ + "\n" @@ -124,7 +128,9 @@ async def get_asv( # type: ignore[override] value_msgs = await self._prepend_op( new_state.messages, sys_content=value_prompt_init ) - value_outputs = await self._llm_call_op(self.llm_model, msgs=value_msgs) + value_outputs = await self._llm_call_op( + self.llm_config, msgs=value_msgs + ) values.append(eval_function(path, [value_outputs.value.content or ""])) # greedy selection diff --git a/src/ldp/graph/common_ops.py b/src/ldp/graph/common_ops.py index fa0907b8..5c124896 100644 --- a/src/ldp/graph/common_ops.py +++ b/src/ldp/graph/common_ops.py @@ -9,7 +9,6 @@ from typing import TYPE_CHECKING, Generic, TypeVar, cast, overload import numpy as np -import tenacity from aviary.core import Message, Tool, ToolRequestMessage, is_coroutine_callable from lmi import ( EmbeddingModel, @@ -19,6 +18,7 @@ SparseEmbeddingModel, ) from lmi import LiteLLMModel as LLMModel +from lmi.config import LLMConfig from pydantic import BaseModel from .memory import Memory, MemoryModel, UIndexMemoryModel @@ -211,10 +211,6 @@ def __repr__(self) -> str: return super(FxnOp, self).__repr__() -class ResponseValidationError(Exception): - """Raised when a response from the LLM does not pass user-specified validator.""" - - class LLMCallOp(Op[Message]): """An operation for LLM calls interaction.""" @@ -230,8 +226,8 @@ def __init__( num_samples_logprob_estimate: The number of samples used to estimate the partition function at T!=1. Defaults to 0 (calculation is skipped). response_validator: An optional callable (can be async) that validates the response. - It should raise an exception if the response is invalid. The Op will retry up - to `config.get('num_retries', 0)` times if validation fails. + It should raise an exception if the response is invalid. If set, forwarded to + LMI's retry/fallback loop. default_tool_choice: Default tool_choice when tools are provided and caller doesn't specify one. Defaults to "required". """ @@ -243,7 +239,7 @@ def __init__( @overload async def forward( self, - config: dict, + config: LLMConfig, msgs: list[Message], tools: list[Tool] = ..., tool_choice: Tool | str | None = ..., @@ -252,7 +248,7 @@ async def forward( @overload async def forward( self, - config: dict, + config: LLMConfig, msgs: list[Message], tools: None = None, tool_choice: str | None = ..., @@ -260,7 +256,7 @@ async def forward( async def forward( self, - config: dict, + config: LLMConfig, msgs: list[Message], tools: list[Tool] | None = None, tool_choice: Tool | str | None = None, @@ -268,7 +264,9 @@ async def forward( """Calls the LLM. Args: - config: Configuration passed to LLMModel. + config: Typed model chain used to build the `LLMModel` and to + source per-model request defaults (temperature, max_tokens, + etc. from `models[0].extra_params`). msgs: Input messages to prompt model with. tools: A list of Tools that the model may call, if supported. tool_choice: Configures how the model should choose a tool. @@ -281,7 +279,12 @@ async def forward( Returns: Output message from the model. """ - model = LLMModel(config=config) + if self.response_validator is not None: + config = config.model_copy( + update={"response_validator": self.response_validator} + ) + model = LLMModel(llm_config=config) + primary_spec = config.models[0] if not tools: # if no tools are provided, tool_choice must be 'none' @@ -289,21 +292,13 @@ async def forward( elif tool_choice is None: tool_choice = self.default_tool_choice - result = await self._call_single_and_maybe_validate( - model=model, - messages=msgs, - tools=tools, - tool_choice=tool_choice, - # Since config is shared between our LLMModel and our `call` options - # we need to ensure we remove keys which work with LLMModel - # but not `call`. - **{k: v for k, v in config.items() if k != "router_kwargs"}, + result = await model.call_single( + messages=msgs, tools=tools, tool_choice=tool_choice ) if result.messages is None: raise ValueError("No messages returned") - # if not set, assume temp = 1. TODO: when would it not be set? - temperature: float = config.get("temperature", 1.0) + temperature: float = primary_spec.extra_params.get("temperature", 1.0) # Compute a Monte Carlo estimate of the logprob of this sequence at the given temperature. logprob = await self.compute_logprob( @@ -326,39 +321,6 @@ async def forward( return result.messages[0] - async def _call_single_and_maybe_validate( - self, model: LLMModel, num_retries: int = 0, **kwargs - ) -> LLMResult: - if not self.response_validator: - # If a response validator is not supplied, then we should not do any retries here - leave - # that for LiteLLM to handle. - return await model.call_single(**kwargs) - - # NOTE: `num_retries` also gets passed to LiteLLM, so there could a maximum of - # `num_retries**2` retries. TODO: consider if we should have separate parameters - # for LiteLLM and validation retries. - @tenacity.retry( - retry=tenacity.retry_if_exception_type(ResponseValidationError), - # num_retries+1 because the first call is not a retry - stop=tenacity.stop_after_attempt(num_retries + 1), - wait=tenacity.wait_fixed(1), - ) - async def call_and_validate() -> LLMResult: - result = await model.call_single(**kwargs) - - try: - validated = cast("Callable", self.response_validator)(result) - if inspect.isawaitable(validated): - validated = await validated - except Exception as e: - raise ResponseValidationError( - f"Response validator failed: {self.response_validator!r}" - ) from e - - return result - - return await call_and_validate() - async def compute_logprob( self, raw_log_p: float | None, diff --git a/src/ldp/graph/modules/llm_call.py b/src/ldp/graph/modules/llm_call.py index aeb6ad59..4d02d59e 100644 --- a/src/ldp/graph/modules/llm_call.py +++ b/src/ldp/graph/modules/llm_call.py @@ -1,7 +1,8 @@ from collections.abc import Callable, Iterable -from typing import Any, Generic, TypeVar +from typing import Generic, TypeVar from aviary.core import Message +from lmi.config import LLMConfig from ldp.graph import ConfigOp, FxnOp, LLMCallOp, OpResult, compute_graph @@ -11,10 +12,8 @@ class ParsedLLMCallModule(Generic[TParsedMessage]): """Module for a processing-based tool selection, with a learnable configuration.""" - def __init__( - self, llm_model: dict[str, Any], parser: Callable[..., TParsedMessage] - ): - self.config_op = ConfigOp[dict](config=llm_model) + def __init__(self, llm_config: LLMConfig, parser: Callable[..., TParsedMessage]): + self.config_op = ConfigOp[LLMConfig](config=llm_config) self.llm_call_op = LLMCallOp() self.parse_msg_op = FxnOp(parser) diff --git a/src/ldp/graph/modules/react.py b/src/ldp/graph/modules/react.py index 96ad8dc7..c18966c6 100644 --- a/src/ldp/graph/modules/react.py +++ b/src/ldp/graph/modules/react.py @@ -14,6 +14,7 @@ ToolRequestMessage, ) from aviary.message import EMPTY_CONTENT_BASE_MSG +from lmi.config import LLMConfig from ldp.graph import FxnOp, LLMCallOp, OpResult, PromptOp, compute_graph from ldp.llms import prepend_sys @@ -279,16 +280,16 @@ async def _create_system_prompt(self, tools: list[Tool]) -> OpResult[str]: def __init__( self, - llm_model: dict[str, Any], + llm_config: LLMConfig, sys_prompt: str = REACT_DEFAULT_SINGLE_PROMPT_TEMPLATE, tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR, ): self.prompt_op = PromptOp(sys_prompt) self._tool_description_method = tool_description_method - llm_model["stop"] = ["Observation:"] self.package_msg_op = FxnOp(prepend_sys) self.tool_select_module = ParsedLLMCallModule[ToolRequestMessage]( - llm_model=llm_model, parser=self.parse_message + llm_config=llm_config.with_extra_params(stop=["Observation:"]), + parser=self.parse_message, ) @property @@ -323,13 +324,12 @@ def postprocess_and_concat_resoning_msg( class ReActModule(ReActModuleSinglePrompt): def __init__( self, - llm_model: dict[str, Any], + llm_config: LLMConfig, sys_prompt: str = REACT_DEFAULT_PROMPT_TEMPLATE, tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR, ): self._tool_description_method = tool_description_method - llm_model["stop"] = ["Observation:", "Action:"] - self.llm_config = llm_model + self.llm_config = llm_config.with_extra_params(stop=["Observation:", "Action:"]) self._llm_call_op = LLMCallOp() self.prompt_op = PromptOp(sys_prompt) self.package_msg_op = FxnOp(prepend_sys) diff --git a/src/ldp/graph/modules/reflect.py b/src/ldp/graph/modules/reflect.py index 821b8032..d8ddd3bc 100644 --- a/src/ldp/graph/modules/reflect.py +++ b/src/ldp/graph/modules/reflect.py @@ -1,6 +1,5 @@ -from typing import Any - from aviary.core import Message +from lmi.config import LLMConfig, LLMConfigField, ModelSpec from pydantic import BaseModel, Field from ldp.graph import ConfigOp, FxnOp, LLMCallOp, PromptOp, compute_graph @@ -11,8 +10,10 @@ class ReflectModuleConfig(BaseModel): """Configuration for the ReflectModuleConfig.""" - llm_model: dict[str, Any] = Field( - default={"name": "gpt-3.5-turbo"}, + llm_config: LLMConfigField = Field( + default_factory=lambda: LLMConfig( + models=[ModelSpec.from_name("gpt-3.5-turbo")] + ), description="Starting configuration for the LLM model.", ) @@ -28,7 +29,7 @@ def __init__(self, start_config: ReflectModuleConfig): " within tags." ) self.config_op = ConfigOp[ReflectModuleConfig](config=start_config) - self.llm_config_op = FxnOp[dict](lambda c: c.llm_model) + self.llm_config_op = FxnOp[LLMConfig](lambda c: c.llm_config) self.package_fxn = FxnOp(append_to_sys) def extract_msg(msg: Message, backup_response: str) -> str: diff --git a/src/ldp/graph/modules/thought.py b/src/ldp/graph/modules/thought.py index 400c35ab..a91230dd 100644 --- a/src/ldp/graph/modules/thought.py +++ b/src/ldp/graph/modules/thought.py @@ -2,6 +2,7 @@ from typing import Any from aviary.core import Message, ToolRequestMessage +from lmi.config import LLMConfig from ldp.graph import FxnOp, OpResult, PromptOp, compute_graph from ldp.llms import prepend_sys_and_append_sys @@ -18,13 +19,13 @@ def _downcast_to_message(message: Message | ToolRequestMessage) -> Message: return message def __init__( - self, llm_model: dict[str, Any], first_sys_prompt: str, second_sys_prompt: str + self, llm_config: LLMConfig, first_sys_prompt: str, second_sys_prompt: str ): self.first_sys_prompt_op = PromptOp(first_sys_prompt) self.second_sys_prompt_op = PromptOp(second_sys_prompt) self.package_msg_op = FxnOp(prepend_sys_and_append_sys) self.llm_call = ParsedLLMCallModule[Message]( - llm_model, parser=self._downcast_to_message + llm_config, parser=self._downcast_to_message ) @compute_graph() diff --git a/tests/test_agents.py b/tests/test_agents.py index 7badf698..51b305c8 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -133,7 +133,7 @@ class TestSimpleAgent: async def test_dummyenv(self, dummy_env: DummyEnv, model_name: str) -> None: obs, tools = await dummy_env.reset() - agent = SimpleAgent(llm_model={"name": model_name, "temperature": 0.1}) + agent = SimpleAgent(llm_config={"name": model_name, "temperature": 0.1}) agent_state = await agent.init_state(tools=tools) action, agent_state, _ = await agent.get_asv(agent_state, obs) obs, reward, done, truncated = await dummy_env.step(action.value) # noqa: RUF059 @@ -144,13 +144,16 @@ async def test_dummyenv(self, dummy_env: DummyEnv, model_name: str) -> None: # Check serialization after get_asv runs to ensure private # Ops aren't included - assert agent.model_dump() == { - "hide_old_env_states": False, - "hide_old_action_content": False, - "llm_model": {"name": model_name, "temperature": 0.1}, - "sys_prompt": None, - "sliding_window": None, + dumped = agent.model_dump() + assert dumped.keys() == { + "hide_old_env_states", + "hide_old_action_content", + "llm_config", + "sys_prompt", + "sliding_window", } + assert dumped["llm_config"]["models"][0]["name"] == model_name + assert dumped["llm_config"]["models"][0]["extra_params"]["temperature"] == 0.1 # Check we can get the LLM results to sum cost and count tokens assert action.call_id is not None, "Compute graph not attached to action." @@ -171,7 +174,7 @@ async def test_dummyenv(self, dummy_env: DummyEnv, model_name: str) -> None: async def test_agent_grad(self, dummy_env: DummyEnv, model_name: str) -> None: obs, tools = await dummy_env.reset() - agent = SimpleAgent(llm_model={"name": model_name, "temperature": 0.1}) + agent = SimpleAgent(llm_config={"name": model_name, "temperature": 0.1}) agent_state = await agent.init_state(tools=tools) action, agent_state, _ = await agent.get_asv(agent_state, obs) assert action.call_id is not None @@ -193,10 +196,9 @@ async def test_agent_grad(self, dummy_env: DummyEnv, model_name: str) -> None: ) _, g = action.ctx.get_input_grads(action.call_id) - assert isinstance(g["config"], dict), ( - "compute_grads() didn't descend into config dict" - ) - assert all(g["config"].values()), "Gradient should be non-zero" + # `config` is an LLMConfig (Pydantic object); the tree estimator treats + # it as a scalar leaf, not a nested dict. + assert g["config"] != 0, "Gradient should be non-zero" graph = to_network(action) with ( @@ -280,7 +282,8 @@ def print_story_factory(message: Message) -> ToolRequestMessage: ) agent = NoToolsSimpleAgent( - print_story_factory, llm_model={"name": model_name, "temperature": 0.1} + print_story_factory, + llm_config={"name": model_name, "temperature": 0.1}, ) agent_state = await agent.init_state(tools=tools) action, agent_state, _ = await agent.get_asv(agent_state, obs) @@ -303,7 +306,7 @@ class TestMemoryAgent: async def test_dummyenv(self, dummy_env: DummyEnv, model_name: str) -> None: obs, tools = await dummy_env.reset() - agent = MemoryAgent(llm_model={"name": model_name, "temperature": 0.1}) + agent = MemoryAgent(llm_config={"name": model_name, "temperature": 0.1}) agent_state = await agent.init_state(tools=tools) # access memory and add one to it @@ -374,10 +377,9 @@ async def test_agent_grad(self, dummy_env: DummyEnv) -> None: }, ) _, g = action.ctx.get_input_grads(action.call_id) - assert isinstance(g["config"], dict), ( - "compute_grads() didn't descend into config dict" - ) - assert all(g["config"].values()), "Action gradient should be non-zero" + # `config` is an LLMConfig (Pydantic object); the tree estimator treats + # it as a scalar leaf, not a nested dict. + assert g["config"] != 0, "Action gradient should be non-zero" memory_op = agent._memory_op mem_call_ids = list(memory_op.get_call_ids({action.call_id.run_id})) @@ -403,7 +405,7 @@ async def test_react_dummyenv( ) -> None: obs, tools = await dummy_env.reset() agent = ReActAgent( - llm_model={"name": model_name, "temperature": 0.1}, + llm_config={"name": model_name, "temperature": 0.1}, single_prompt=single_prompt, ) agent_state = await agent.init_state(tools=tools) @@ -440,7 +442,7 @@ async def test_multi_step(self, dummy_env: DummyEnv, single_prompt: bool) -> Non ] agent = ReActAgent( single_prompt=single_prompt, - llm_model={ + llm_config={ "name": CommonLLMNames.OPENAI_TEST.value, # If tools are provided, don't allow it to make parallel tool calls, since # we want to force longer trajectories. In single_prompt mode, parallel tool @@ -500,7 +502,7 @@ async def test_agent_grad( obs, tools = await dummy_env.reset() agent = ReActAgent( - llm_model={"name": model_name, "temperature": 0.1}, + llm_config={"name": model_name, "temperature": 0.1}, single_prompt=single_prompt, ) agent_state = await agent.init_state(tools=tools) diff --git a/tests/test_envs.py b/tests/test_envs.py index d0d74229..3fd7be9b 100644 --- a/tests/test_envs.py +++ b/tests/test_envs.py @@ -13,6 +13,7 @@ ToolResponseMessage, ) from lmi import CommonLLMNames +from lmi.exceptions import AllModelsExhaustedError from ldp.agent import SimpleAgent @@ -112,10 +113,7 @@ async def test_exec_tool_calls_handling(self, model_name: str) -> None: env = ParallelizedDummyEnv(right_hand_broken=True) obs, tools = await env.reset() right_hand_tool = tools[1] - agent = SimpleAgent( - llm_model=SimpleAgent.model_fields["llm_model"].default - | {"name": model_name} - ) + agent = SimpleAgent(llm_config={"name": model_name}) agent_state = await agent.init_state(tools=tools) # 1. Let's DIY create a ToolRequestMessage for test determinism @@ -137,14 +135,15 @@ async def test_exec_tool_calls_handling(self, model_name: str) -> None: raise AssertionError("Should have blown up per the test logic.") # 2. Well, it looks like both Anthropic and OpenAI don't like DIY-style - # (using a bare Message) because they expect a tool call ID and tool name - with pytest.raises( - litellm.BadRequestError, - match=( - "invalid" if version(litellm.__name__) < "1.45.0" else "tool_call_id" - ), - ): + # (using a bare Message) because they expect a tool call ID and tool name. + # Single-model chain exhausts on the provider's BadRequestError. + expected_match = ( + "invalid" if version(litellm.__name__) < "1.45.0" else "tool_call_id" + ) + with pytest.raises(AllModelsExhaustedError) as excinfo: await agent.get_asv(agent_state, obs) + assert isinstance(excinfo.value.last_exc, litellm.BadRequestError) + assert expected_match in str(excinfo.value.last_exc) # 3. Alright, let's check the agent doesn't blow up if we use a # ToolResponseMessage as Anthropic and OpenAI expect diff --git a/tests/test_modules.py b/tests/test_modules.py index 7e0ac867..607c1282 100644 --- a/tests/test_modules.py +++ b/tests/test_modules.py @@ -19,7 +19,7 @@ @pytest.mark.vcr async def test_reflect_module() -> None: config = ReflectModuleConfig( - llm_model={ + llm_config={ "name": CommonLLMNames.ANTHROPIC_TEST.value, "temperature": 0, # Lower temperature for more deterministic responses } @@ -46,12 +46,12 @@ class TestReActModule: @pytest.mark.parametrize("single_prompt", [True, False]) async def test_templating(self, dummy_env: DummyEnv, single_prompt: bool) -> None: obs, tools = await dummy_env.reset() - if single_prompt: - module = ReActModuleSinglePrompt( - ReActAgent.model_fields["llm_model"].default - ) - else: - module = ReActModule(ReActAgent.model_fields["llm_model"].default) + default_llm_config = ReActAgent().llm_config + module = ( + ReActModuleSinglePrompt(default_llm_config) + if single_prompt + else ReActModule(default_llm_config) + ) with patch( "ldp.graph.common_ops.LLMCallOp.forward", return_value=ToolRequestMessage( diff --git a/tests/test_ops.py b/tests/test_ops.py index 5351a8ca..d1a989e6 100644 --- a/tests/test_ops.py +++ b/tests/test_ops.py @@ -7,11 +7,11 @@ import litellm import numpy as np import pytest -import tenacity import tree from aviary.core import DummyEnv, Message, Tool, ToolRequestMessage from lmi import CommonLLMNames, LLMResult -from lmi import LiteLLMModel as LLMModel +from lmi.config import LLMConfig +from lmi.exceptions import AllModelsExhaustedError, ResponseValidationError from ldp.graph import ( CallID, @@ -144,7 +144,7 @@ async def generate_story() -> str: env = LLMCallingEnv() obs, tools = await env.reset() - config = {"name": model_name, "temperature": 0.1} + config = LLMConfig.coerce({"name": model_name, "temperature": 0.1}) llm_op = LLMCallOp() # Perform one step @@ -164,7 +164,7 @@ async def generate_story() -> str: async def test_empty_tools(self) -> None: llm_call_op = LLMCallOp() message_result = await llm_call_op( - LLMModel.model_fields["config"].default_factory(), # type: ignore[call-arg, misc] + LLMConfig.coerce({"name": CommonLLMNames.GPT_4O.value}), msgs=[Message(content="Hello")], tools=[], ) @@ -175,11 +175,11 @@ async def test_empty_tools(self) -> None: @pytest.mark.asyncio @pytest.mark.parametrize("temperature", [0.0, 0.5, 1.0]) async def test_compute_logprob(self, temperature) -> None: - config = { + config = LLMConfig.coerce({ "name": CommonLLMNames.OPENAI_TEST.value, "temperature": temperature, "logprobs": True, - } + }) llm_op = LLMCallOp() output = await llm_op(config, msgs=[Message(content="Hello")]) logp = llm_op.ctx.get(output.call_id, "logprob") @@ -194,16 +194,26 @@ async def test_compute_logprob(self, temperature) -> None: @pytest.mark.vcr @pytest.mark.asyncio async def test_validation(self) -> None: - config = {"name": CommonLLMNames.OPENAI_TEST.value, "num_retries": 1} + # Validator failure raises ResponseValidationError, which LMI's loop + # treats as transient: it retries against the same model up to + # ModelSpec.max_retries before exhausting the chain. + config = LLMConfig.coerce({ + "name": CommonLLMNames.OPENAI_TEST.value, + "max_retries": 1, + }) validator = StatefulValidator() llm_op = LLMCallOp(response_validator=validator) await llm_op(config, msgs=[Message(content="Hello")]) assert validator.counter == 2 # first attempt should have failed - config = {"name": CommonLLMNames.OPENAI_TEST.value, "num_retries": 0} + always_fail_config = LLMConfig.coerce({ + "name": CommonLLMNames.OPENAI_TEST.value, + "max_retries": 0, + }) llm_op = LLMCallOp(response_validator=StatefulValidator()) - with pytest.raises(tenacity.RetryError, match="ResponseValidationError"): - await llm_op(config, msgs=[Message(content="Hello")]) + with pytest.raises(AllModelsExhaustedError) as excinfo: + await llm_op(always_fail_config, msgs=[Message(content="Hello")]) + assert isinstance(excinfo.value.last_exc, ResponseValidationError) class StatefulValidator: @@ -242,12 +252,12 @@ async def test_llm_call_graph() -> None: user_prompt_op = PromptOp("What is the result of this math equation: {equation}?") package_msg_op = FxnOp(append_to_sys) - config = { + config = LLMConfig.coerce({ "name": "gpt-3.5-turbo-0125", "temperature": 0.1, "logprobs": True, "top_logprobs": 1, - } + }) config_op = ConfigOp(config=config) # Now forward pass @@ -264,9 +274,10 @@ async def test_llm_call_graph() -> None: output_grad = -2.0 # some grad accrued from result result.compute_grads([output_grad]) - # check some grads are present + # check some grads are present. `config` is an LLMConfig (Pydantic object), + # so the tree estimator treats it as a scalar leaf. _, g = llm_op.get_input_grads(result.call_id) - assert g["config"] == dict.fromkeys(config, 0.0) + assert g["config"] == 0.0 assert g["msgs"] == 0.0 _, g = config_op.get_input_grads(c.call_id) diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py index 5371fd34..fb27ebfc 100644 --- a/tests/test_optimizer.py +++ b/tests/test_optimizer.py @@ -9,6 +9,7 @@ from aviary.core import Message from lmi import CommonLLMNames from lmi import LiteLLMModel as LLMModel +from lmi.config import LLMConfig from pydantic import BaseModel, Field, JsonValue from ldp.agent import Agent, MemoryAgent, ReActAgent @@ -95,8 +96,12 @@ def backward( async def test_ape_optimizer() -> None: sys_prompt_op = PromptOp("Guess a number based on the input word.") package_msg_op = FxnOp(append_to_sys) - config = {"max_retries": 3} # we seem to be hitting rate limits frequently - llm = LLMModel(config=config) + # We seem to be hitting rate limits frequently, so bump per-model retries. + config = LLMConfig.coerce({ + "name": CommonLLMNames.GPT_4O.value, + "max_retries": 3, + }) + llm = LLMModel(llm_config=config) llm_call_op = LLMCallOp() strip_op = FxnOp(lambda x: x.content) loss_op = SquaredErrorLoss() @@ -189,11 +194,11 @@ async def __call__(self, query: str) -> tuple[OpResult[str], list[Message]]: mems = await self.mem_op(query) msgs = await self.package_msg_op(mems, query) c = await self.llm_call_op( - config={ + config=LLMConfig.coerce({ "name": "gpt-4-turbo", # this is flaky, so use a smarter model "temperature": 0, "max_retries": 3, - }, + }), msgs=msgs, ) return await self.strip_op(c), msgs.value @@ -297,7 +302,9 @@ async def test_lessons_memory_optimizer(self) -> None: This test is loosely based on Reflexion (https://arxiv.org/abs/2303.11366). """ - memory_distiller = LLMModel(config={"name": CommonLLMNames.OPENAI_TEST.value}) + memory_distiller = LLMModel( + llm_config=LLMConfig.coerce({"name": CommonLLMNames.OPENAI_TEST.value}) + ) class LessonEntry(BaseModel): """Entry for a lesson created from some example data.""" diff --git a/tests/test_rollouts.py b/tests/test_rollouts.py index a4e98221..d8ede7fa 100644 --- a/tests/test_rollouts.py +++ b/tests/test_rollouts.py @@ -8,6 +8,7 @@ import pytest from aviary.core import Environment, Frame, Message, Tool, ToolRequestMessage from litellm import AuthenticationError +from lmi.exceptions import AllModelsExhaustedError from pydantic import BaseModel from ldp.agent import Agent, SimpleAgent, SimpleAgentState @@ -127,48 +128,23 @@ async def test_rollout(training: bool) -> None: @pytest.mark.parametrize("fallback", [True, False]) @pytest.mark.asyncio async def test_fallbacks_working(fallback: bool) -> None: - AGENT_MODEL_LIST = [ - { - "model_name": "openai/gpt-4o-mini", - "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "abc123"}, + primary = { + "model_name": "openai/gpt-4o-mini", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "abc123", # pragma: allowlist secret }, - { - "model_name": "openai/gpt-4o", - "litellm_params": { - "model": "openai/gpt-4o", - }, - }, - ] - AGENT_ROUTER_KWARGS: dict[str, bool | list[dict[str, list[str]]]] = { - "set_verbose": True, } - if fallback: - AGENT_ROUTER_KWARGS["fallbacks"] = [ - { - "openai/gpt-4o-mini": [ - "openai/gpt-4o", - ] - } - ] - - AGENT_CONFIG = { - "llm_model": { - "name": "openai/gpt-4o-mini", - "config": { - "model_list": AGENT_MODEL_LIST, - "router_kwargs": AGENT_ROUTER_KWARGS, - }, - } + secondary = { + "model_name": "openai/gpt-4o", + "litellm_params": {"model": "openai/gpt-4o"}, + } + llm_config_dict: dict[str, object] = { + "model_list": [primary, secondary] if fallback else [primary], } if fallback: - AGENT_CONFIG["llm_model"]["config"]["fallbacks"] = [ # type: ignore[index] - { - "openai/gpt-4o-mini": [ - "openai/gpt-4o", - ] - } - ] - agent = SimpleAgent(**AGENT_CONFIG) + llm_config_dict["fallbacks"] = [{"openai/gpt-4o-mini": ["openai/gpt-4o"]}] + agent = SimpleAgent(llm_config=llm_config_dict) callback = DummyCallback() rollout_manager = RolloutManager( @@ -182,8 +158,11 @@ async def test_fallbacks_working(fallback: bool) -> None: environments=[env], max_steps=2 ) else: - with pytest.raises(AuthenticationError): + # With no fallback entry, the single-model chain exhausts on auth + # failure; the original AuthenticationError is chained as `last_exc`. + with pytest.raises(AllModelsExhaustedError) as excinfo: await rollout_manager.sample_trajectories(environments=[env], max_steps=2) + assert isinstance(excinfo.value.last_exc, AuthenticationError) env.close_mock.assert_awaited_once_with() diff --git a/uv.lock b/uv.lock index e0baf7d1..3af5b0bc 100644 --- a/uv.lock +++ b/uv.lock @@ -69,7 +69,7 @@ wheels = [ [[package]] name = "aiohttp" -version 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