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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
234
libs/community/langchain_community/chat_models/cohere.py
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234
libs/community/langchain_community/chat_models/cohere.py
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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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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ChatMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_community.llms.cohere import BaseCohere
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def get_role(message: BaseMessage) -> str:
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"""Get the role of the message.
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Args:
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message: The message.
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Returns:
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The role of the message.
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Raises:
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ValueError: If the message is of an unknown type.
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"""
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if isinstance(message, ChatMessage) or isinstance(message, HumanMessage):
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return "User"
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elif isinstance(message, AIMessage):
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return "Chatbot"
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elif isinstance(message, SystemMessage):
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return "System"
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else:
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raise ValueError(f"Got unknown type {message}")
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def get_cohere_chat_request(
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messages: List[BaseMessage],
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*,
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connectors: Optional[List[Dict[str, str]]] = None,
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**kwargs: Any,
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) -> Dict[str, Any]:
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"""Get the request for the Cohere chat API.
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Args:
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messages: The messages.
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connectors: The connectors.
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**kwargs: The keyword arguments.
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Returns:
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The request for the Cohere chat API.
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"""
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documents = (
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None
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if "source_documents" not in kwargs
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else [
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{
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"snippet": doc.page_content,
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"id": doc.metadata.get("id") or f"doc-{str(i)}",
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}
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for i, doc in enumerate(kwargs["source_documents"])
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]
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)
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kwargs.pop("source_documents", None)
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maybe_connectors = connectors if documents is None else None
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# by enabling automatic prompt truncation, the probability of request failure is
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# reduced with minimal impact on response quality
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prompt_truncation = (
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"AUTO" if documents is not None or connectors is not None else None
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)
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return {
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"message": messages[-1].content,
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"chat_history": [
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{"role": get_role(x), "message": x.content} for x in messages[:-1]
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],
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"documents": documents,
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"connectors": maybe_connectors,
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"prompt_truncation": prompt_truncation,
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**kwargs,
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}
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class ChatCohere(BaseChatModel, BaseCohere):
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"""`Cohere` chat large language models.
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To use, you should have the ``cohere`` python package installed, and the
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environment variable ``COHERE_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatCohere
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from langchain_core.messages import HumanMessage
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chat = ChatCohere(model="foo")
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result = chat([HumanMessage(content="Hello")])
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print(result.content)
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"""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "cohere-chat"
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Cohere API."""
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return {
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"temperature": self.temperature,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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stream = self.client.chat(**request, stream=True)
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for data in stream:
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if data.event_type == "text-generation":
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delta = data.text
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yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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run_manager.on_llm_new_token(delta)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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stream = await self.async_client.chat(**request, stream=True)
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async for data in stream:
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if data.event_type == "text-generation":
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delta = data.text
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yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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await run_manager.on_llm_new_token(delta)
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def _get_generation_info(self, response: Any) -> Dict[str, Any]:
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"""Get the generation info from cohere API response."""
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return {
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"documents": response.documents,
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"citations": response.citations,
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"search_results": response.search_results,
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"search_queries": response.search_queries,
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"token_count": response.token_count,
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}
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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response = self.client.chat(**request)
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message = AIMessage(content=response.text)
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generation_info = None
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if hasattr(response, "documents"):
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generation_info = self._get_generation_info(response)
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return ChatResult(
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generations=[
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ChatGeneration(message=message, generation_info=generation_info)
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]
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)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await agenerate_from_stream(stream_iter)
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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response = self.client.chat(**request, stream=False)
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message = AIMessage(content=response.text)
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generation_info = None
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if hasattr(response, "documents"):
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generation_info = self._get_generation_info(response)
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return ChatResult(
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generations=[
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ChatGeneration(message=message, generation_info=generation_info)
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]
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)
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def get_num_tokens(self, text: str) -> int:
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"""Calculate number of tokens."""
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return len(self.client.tokenize(text).tokens)
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