mirror of
https://github.com/hwchase17/langchain.git
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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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GitHub
399
libs/community/langchain_community/adapters/openai.py
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399
libs/community/langchain_community/adapters/openai.py
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from __future__ import annotations
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import importlib
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from typing import (
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Any,
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AsyncIterator,
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Dict,
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Iterable,
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List,
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Mapping,
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Sequence,
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Union,
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overload,
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)
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from langchain_core.chat_sessions import ChatSession
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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FunctionMessage,
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HumanMessage,
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SystemMessage,
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ToolMessage,
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)
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from langchain_core.pydantic_v1 import BaseModel
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from typing_extensions import Literal
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async def aenumerate(
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iterable: AsyncIterator[Any], start: int = 0
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) -> AsyncIterator[tuple[int, Any]]:
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"""Async version of enumerate function."""
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i = start
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async for x in iterable:
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yield i, x
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i += 1
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class IndexableBaseModel(BaseModel):
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"""Allows a BaseModel to return its fields by string variable indexing"""
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)
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class Choice(IndexableBaseModel):
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message: dict
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class ChatCompletions(IndexableBaseModel):
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choices: List[Choice]
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class ChoiceChunk(IndexableBaseModel):
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delta: dict
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class ChatCompletionChunk(IndexableBaseModel):
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choices: List[ChoiceChunk]
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def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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"""Convert a dictionary to a LangChain message.
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Args:
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_dict: The dictionary.
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Returns:
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The LangChain message.
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"""
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "") or ""
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additional_kwargs: Dict = {}
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if _dict.get("function_call"):
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additional_kwargs["function_call"] = dict(_dict["function_call"])
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if _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = _dict["tool_calls"]
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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elif role == "tool":
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return ToolMessage(content=_dict["content"], tool_call_id=_dict["tool_call_id"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a LangChain message to a dictionary.
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Args:
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message: The LangChain message.
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Returns:
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The dictionary.
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"""
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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# If function call only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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if "tool_calls" in message.additional_kwargs:
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message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
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# If tool calls only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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elif isinstance(message, ToolMessage):
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message_dict = {
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"role": "tool",
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"content": message.content,
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"tool_call_id": message.tool_call_id,
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}
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else:
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raise TypeError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMessage]:
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"""Convert dictionaries representing OpenAI messages to LangChain format.
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Args:
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messages: List of dictionaries representing OpenAI messages
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Returns:
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List of LangChain BaseMessage objects.
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"""
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return [convert_dict_to_message(m) for m in messages]
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def _convert_message_chunk(chunk: BaseMessageChunk, i: int) -> dict:
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_dict: Dict[str, Any] = {}
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if isinstance(chunk, AIMessageChunk):
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if i == 0:
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# Only shows up in the first chunk
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_dict["role"] = "assistant"
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if "function_call" in chunk.additional_kwargs:
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_dict["function_call"] = chunk.additional_kwargs["function_call"]
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# If the first chunk is a function call, the content is not empty string,
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# not missing, but None.
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if i == 0:
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_dict["content"] = None
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else:
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_dict["content"] = chunk.content
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else:
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raise ValueError(f"Got unexpected streaming chunk type: {type(chunk)}")
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# This only happens at the end of streams, and OpenAI returns as empty dict
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if _dict == {"content": ""}:
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_dict = {}
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return _dict
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def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
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_dict = _convert_message_chunk(chunk, i)
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return {"choices": [{"delta": _dict}]}
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class ChatCompletion:
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"""Chat completion."""
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@overload
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@staticmethod
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def create(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[False] = False,
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**kwargs: Any,
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) -> dict:
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...
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@overload
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@staticmethod
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def create(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[True],
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**kwargs: Any,
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) -> Iterable:
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...
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@staticmethod
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def create(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: bool = False,
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**kwargs: Any,
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) -> Union[dict, Iterable]:
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models = importlib.import_module("langchain.chat_models")
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model_cls = getattr(models, provider)
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model_config = model_cls(**kwargs)
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converted_messages = convert_openai_messages(messages)
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if not stream:
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result = model_config.invoke(converted_messages)
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return {"choices": [{"message": convert_message_to_dict(result)}]}
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else:
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return (
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_convert_message_chunk_to_delta(c, i)
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for i, c in enumerate(model_config.stream(converted_messages))
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)
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@overload
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@staticmethod
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async def acreate(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[False] = False,
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**kwargs: Any,
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) -> dict:
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...
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@overload
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@staticmethod
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async def acreate(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[True],
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**kwargs: Any,
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) -> AsyncIterator:
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...
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@staticmethod
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async def acreate(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: bool = False,
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**kwargs: Any,
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) -> Union[dict, AsyncIterator]:
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models = importlib.import_module("langchain.chat_models")
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model_cls = getattr(models, provider)
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model_config = model_cls(**kwargs)
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converted_messages = convert_openai_messages(messages)
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if not stream:
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result = await model_config.ainvoke(converted_messages)
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return {"choices": [{"message": convert_message_to_dict(result)}]}
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else:
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return (
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_convert_message_chunk_to_delta(c, i)
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async for i, c in aenumerate(model_config.astream(converted_messages))
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)
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def _has_assistant_message(session: ChatSession) -> bool:
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"""Check if chat session has an assistant message."""
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return any([isinstance(m, AIMessage) for m in session["messages"]])
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def convert_messages_for_finetuning(
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sessions: Iterable[ChatSession],
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) -> List[List[dict]]:
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"""Convert messages to a list of lists of dictionaries for fine-tuning.
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Args:
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sessions: The chat sessions.
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Returns:
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The list of lists of dictionaries.
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"""
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return [
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[convert_message_to_dict(s) for s in session["messages"]]
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for session in sessions
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if _has_assistant_message(session)
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]
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class Completions:
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"""Completion."""
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@overload
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@staticmethod
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def create(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[False] = False,
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**kwargs: Any,
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) -> ChatCompletions:
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...
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@overload
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@staticmethod
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def create(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[True],
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**kwargs: Any,
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) -> Iterable:
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...
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@staticmethod
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def create(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: bool = False,
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**kwargs: Any,
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) -> Union[ChatCompletions, Iterable]:
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models = importlib.import_module("langchain.chat_models")
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model_cls = getattr(models, provider)
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model_config = model_cls(**kwargs)
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converted_messages = convert_openai_messages(messages)
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if not stream:
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result = model_config.invoke(converted_messages)
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return ChatCompletions(
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choices=[Choice(message=convert_message_to_dict(result))]
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)
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else:
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return (
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ChatCompletionChunk(
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choices=[ChoiceChunk(delta=_convert_message_chunk(c, i))]
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)
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for i, c in enumerate(model_config.stream(converted_messages))
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)
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@overload
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@staticmethod
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async def acreate(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[False] = False,
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**kwargs: Any,
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) -> ChatCompletions:
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...
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@overload
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@staticmethod
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async def acreate(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: Literal[True],
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**kwargs: Any,
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) -> AsyncIterator:
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...
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@staticmethod
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async def acreate(
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messages: Sequence[Dict[str, Any]],
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*,
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provider: str = "ChatOpenAI",
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stream: bool = False,
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**kwargs: Any,
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) -> Union[ChatCompletions, AsyncIterator]:
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models = importlib.import_module("langchain.chat_models")
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model_cls = getattr(models, provider)
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model_config = model_cls(**kwargs)
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converted_messages = convert_openai_messages(messages)
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if not stream:
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result = await model_config.ainvoke(converted_messages)
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return ChatCompletions(
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choices=[Choice(message=convert_message_to_dict(result))]
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)
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else:
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return (
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ChatCompletionChunk(
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choices=[ChoiceChunk(delta=_convert_message_chunk(c, i))]
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)
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async for i, c in aenumerate(model_config.astream(converted_messages))
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)
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class Chat:
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def __init__(self) -> None:
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self.completions = Completions()
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chat = Chat()
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