Add comprehensive documentation and templates for LangChain skill development
- Introduced a new reference document for streaming output issues, detailing the differences between streaming APIs and providing solutions for common problems. - Created a structured output issues reference, outlining the use of `with_structured_output`, `response_format`, and schema enforcement strategies. - Added a user query convention guide to standardize structured questions for skill authors, including block types for queries and data gathering. - Implemented a template for a chat model, encapsulating API key management, request payload construction, and response handling. - Established a symlink for the LangChain dev guide in the Claude skills directory for easier access. - Initialized a skills lock file to manage dependencies and versions for the LangChain dev guide.
This commit is contained in:
yuxuanhui
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from __future__ import annotations
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from json import JSONDecodeError
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from typing import Any, Self, cast
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import openai
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from langchain_core.language_models import (
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LanguageModelInput,
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ModelProfile,
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ModelProfileRegistry,
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)
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from langchain_core.messages import AIMessage, AIMessageChunk
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from langchain_core.outputs import ChatGenerationChunk, ChatResult
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from langchain_core.utils import from_env, secret_from_env
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from langchain_openai.chat_models.base import BaseChatOpenAI, _convert_message_to_dict
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from pydantic import Field, SecretStr, model_validator
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_INVALID_RESPONSE_ERROR_MSG = (
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"Provider API returned an invalid response. "
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"Please check your API key, network connection, or API base URL."
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)
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def _get_default_model_profile(model_name: str) -> ModelProfile:
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try:
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from .data._profiles import _PROFILES # type: ignore[import-untyped]
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except ImportError:
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return {}
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_MODEL_PROFILES = cast("ModelProfileRegistry", _PROFILES)
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default = _MODEL_PROFILES.get(model_name) or {}
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return default.copy()
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class ChatModel(BaseChatOpenAI):
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api_key: SecretStr | None = Field(
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default_factory=secret_from_env("PROVIDER_API_KEY", default=None),
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)
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api_base: str = Field(
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default_factory=from_env(
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"PROVIDER_API_BASE",
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default="PROVIDER_API_BASE_URL",
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),
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)
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@property
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def _llm_type(self) -> str:
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return "chat-provider"
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@property
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def lc_secrets(self) -> dict[str, str]:
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return {"api_key": "PROVIDER_API_KEY"}
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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if not (self.api_key and self.api_key.get_secret_value()):
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msg = "PROVIDER_API_KEY must be set."
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raise ValueError(msg)
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client_params: dict = {
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"api_key": self.api_key.get_secret_value() if self.api_key else None,
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"base_url": self.api_base,
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"timeout": self.request_timeout,
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"max_retries": self.max_retries,
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"default_headers": self.default_headers,
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"default_query": self.default_query,
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}
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client_params = {k: v for k, v in client_params.items() if v is not None}
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if not (self.client or None):
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sync_specific: dict = {"http_client": self.http_client}
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self.root_client = openai.OpenAI(**client_params, **sync_specific)
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self.client = self.root_client.chat.completions
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if not (self.async_client or None):
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async_specific: dict = {"http_client": self.http_async_client}
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self.root_async_client = openai.AsyncOpenAI(
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**client_params, **async_specific,
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)
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self.async_client = self.root_async_client.chat.completions
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return self
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def _resolve_model_profile(self) -> ModelProfile | None:
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return _get_default_model_profile(self.model_name) or None
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def _get_request_payload(
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self,
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input_: LanguageModelInput,
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*,
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stop: list[str] | None = None,
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**kwargs: Any,
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) -> dict:
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payload = super()._get_request_payload(input_, stop=stop, **kwargs)
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messages = self._convert_input(input_).to_messages()
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payload_messages = []
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for m in messages:
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if isinstance(m, AIMessage):
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msg_dict = _convert_message_to_dict(m)
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if m.additional_kwargs.get("reasoning_content"):
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msg_dict["reasoning_content"] = m.additional_kwargs.get(
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"reasoning_content",
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)
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payload_messages.append(msg_dict)
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else:
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payload_messages.append(_convert_message_to_dict(m))
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payload["messages"] = payload_messages
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if "tools" in payload and len(payload["tools"]) == 0:
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payload.pop("tools")
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return payload
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def _create_chat_result(
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self,
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response: dict | openai.BaseModel,
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generation_info: dict | None = None,
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) -> ChatResult:
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rtn = super()._create_chat_result(response, generation_info)
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if not isinstance(response, openai.BaseModel):
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return rtn
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for generation in rtn.generations:
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if generation.message.response_metadata is None:
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generation.message.response_metadata = {}
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generation.message.response_metadata["model_provider"] = "provider-name"
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choices = getattr(response, "choices", None)
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if choices and hasattr(choices[0].message, "reasoning_content"):
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rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
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choices[0].message.reasoning_content
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)
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return rtn
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def _convert_chunk_to_generation_chunk(
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self,
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chunk: dict,
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default_chunk_class: type,
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base_generation_info: dict | None,
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) -> ChatGenerationChunk | None:
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generation_chunk = super()._convert_chunk_to_generation_chunk(
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chunk,
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default_chunk_class,
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base_generation_info,
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)
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if (choices := chunk.get("choices")) and generation_chunk:
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top = choices[0]
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if isinstance(generation_chunk.message, AIMessageChunk):
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generation_chunk.message.response_metadata = {
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**generation_chunk.message.response_metadata,
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"model_provider": "provider-name",
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}
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if (
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reasoning_content := top.get("delta", {}).get("reasoning_content")
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) is not None:
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generation_chunk.message.additional_kwargs["reasoning_content"] = (
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reasoning_content
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)
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return generation_chunk
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def _stream(self, messages, stop=None, run_manager=None, **kwargs):
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kwargs["stream_options"] = {"include_usage": True}
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try:
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yield from super()._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs,
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)
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except JSONDecodeError as e:
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raise JSONDecodeError(
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_INVALID_RESPONSE_ERROR_MSG,
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e.doc,
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e.pos,
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) from e
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async def _astream(self, messages, stop=None, run_manager=None, **kwargs):
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kwargs["stream_options"] = {"include_usage": True}
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try:
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async for chunk in super()._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs,
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):
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yield chunk
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except JSONDecodeError as e:
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raise JSONDecodeError(
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_INVALID_RESPONSE_ERROR_MSG,
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e.doc,
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e.pos,
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) from e
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def _generate(self, messages, stop=None, run_manager=None, **kwargs):
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try:
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return super()._generate(
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messages, stop=stop, run_manager=run_manager, **kwargs,
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)
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except JSONDecodeError as e:
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raise JSONDecodeError(
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_INVALID_RESPONSE_ERROR_MSG,
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e.doc,
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e.pos,
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) from e
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async def _agenerate(self, messages, stop=None, run_manager=None, **kwargs):
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try:
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return await super()._agenerate(
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messages, stop=stop, run_manager=run_manager, **kwargs,
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)
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except JSONDecodeError as e:
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raise JSONDecodeError(
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_INVALID_RESPONSE_ERROR_MSG,
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e.doc,
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e.pos,
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) from e
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