mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
63343b4987
Description: adds support for langchain_cohere --------- Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev>
252 lines
8.0 KiB
Python
252 lines
8.0 KiB
Python
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
|
|
|
|
from langchain_core._api.deprecation import deprecated
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models.chat_models import (
|
|
BaseChatModel,
|
|
agenerate_from_stream,
|
|
generate_from_stream,
|
|
)
|
|
from langchain_core.messages import (
|
|
AIMessage,
|
|
AIMessageChunk,
|
|
BaseMessage,
|
|
ChatMessage,
|
|
HumanMessage,
|
|
SystemMessage,
|
|
)
|
|
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
|
|
|
from langchain_community.llms.cohere import BaseCohere
|
|
|
|
|
|
def get_role(message: BaseMessage) -> str:
|
|
"""Get the role of the message.
|
|
|
|
Args:
|
|
message: The message.
|
|
|
|
Returns:
|
|
The role of the message.
|
|
|
|
Raises:
|
|
ValueError: If the message is of an unknown type.
|
|
"""
|
|
if isinstance(message, ChatMessage) or isinstance(message, HumanMessage):
|
|
return "User"
|
|
elif isinstance(message, AIMessage):
|
|
return "Chatbot"
|
|
elif isinstance(message, SystemMessage):
|
|
return "System"
|
|
else:
|
|
raise ValueError(f"Got unknown type {message}")
|
|
|
|
|
|
def get_cohere_chat_request(
|
|
messages: List[BaseMessage],
|
|
*,
|
|
connectors: Optional[List[Dict[str, str]]] = None,
|
|
**kwargs: Any,
|
|
) -> Dict[str, Any]:
|
|
"""Get the request for the Cohere chat API.
|
|
|
|
Args:
|
|
messages: The messages.
|
|
connectors: The connectors.
|
|
**kwargs: The keyword arguments.
|
|
|
|
Returns:
|
|
The request for the Cohere chat API.
|
|
"""
|
|
documents = (
|
|
None
|
|
if "source_documents" not in kwargs
|
|
else [
|
|
{
|
|
"snippet": doc.page_content,
|
|
"id": doc.metadata.get("id") or f"doc-{str(i)}",
|
|
}
|
|
for i, doc in enumerate(kwargs["source_documents"])
|
|
]
|
|
)
|
|
kwargs.pop("source_documents", None)
|
|
maybe_connectors = connectors if documents is None else None
|
|
|
|
# by enabling automatic prompt truncation, the probability of request failure is
|
|
# reduced with minimal impact on response quality
|
|
prompt_truncation = (
|
|
"AUTO" if documents is not None or connectors is not None else None
|
|
)
|
|
|
|
req = {
|
|
"message": messages[-1].content,
|
|
"chat_history": [
|
|
{"role": get_role(x), "message": x.content} for x in messages[:-1]
|
|
],
|
|
"documents": documents,
|
|
"connectors": maybe_connectors,
|
|
"prompt_truncation": prompt_truncation,
|
|
**kwargs,
|
|
}
|
|
|
|
return {k: v for k, v in req.items() if v is not None}
|
|
|
|
|
|
@deprecated(
|
|
since="0.0.30", removal="0.2.0", alternative_import="langchain_cohere.ChatCohere"
|
|
)
|
|
class ChatCohere(BaseChatModel, BaseCohere):
|
|
"""`Cohere` chat large language models.
|
|
|
|
To use, you should have the ``cohere`` python package installed, and the
|
|
environment variable ``COHERE_API_KEY`` set with your API key, or pass
|
|
it as a named parameter to the constructor.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.chat_models import ChatCohere
|
|
from langchain_core.messages import HumanMessage
|
|
|
|
chat = ChatCohere(model="command", max_tokens=256, temperature=0.75)
|
|
|
|
messages = [HumanMessage(content="knock knock")]
|
|
chat.invoke(messages)
|
|
"""
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
allow_population_by_field_name = True
|
|
arbitrary_types_allowed = True
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
return "cohere-chat"
|
|
|
|
@property
|
|
def _default_params(self) -> Dict[str, Any]:
|
|
"""Get the default parameters for calling Cohere API."""
|
|
return {
|
|
"temperature": self.temperature,
|
|
}
|
|
|
|
@property
|
|
def _identifying_params(self) -> Dict[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {**{"model": self.model}, **self._default_params}
|
|
|
|
def _stream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
|
|
|
|
if hasattr(self.client, "chat_stream"): # detect and support sdk v5
|
|
stream = self.client.chat_stream(**request)
|
|
else:
|
|
stream = self.client.chat(**request, stream=True)
|
|
|
|
for data in stream:
|
|
if data.event_type == "text-generation":
|
|
delta = data.text
|
|
chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(delta, chunk=chunk)
|
|
yield chunk
|
|
|
|
async def _astream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
|
|
|
|
if hasattr(self.async_client, "chat_stream"): # detect and support sdk v5
|
|
stream = self.async_client.chat_stream(**request)
|
|
else:
|
|
stream = self.async_client.chat(**request, stream=True)
|
|
|
|
async for data in stream:
|
|
if data.event_type == "text-generation":
|
|
delta = data.text
|
|
chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(delta, chunk=chunk)
|
|
yield chunk
|
|
|
|
def _get_generation_info(self, response: Any) -> Dict[str, Any]:
|
|
"""Get the generation info from cohere API response."""
|
|
return {
|
|
"documents": response.documents,
|
|
"citations": response.citations,
|
|
"search_results": response.search_results,
|
|
"search_queries": response.search_queries,
|
|
"token_count": response.token_count,
|
|
}
|
|
|
|
def _generate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
if self.streaming:
|
|
stream_iter = self._stream(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return generate_from_stream(stream_iter)
|
|
|
|
request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
|
|
response = self.client.chat(**request)
|
|
|
|
message = AIMessage(content=response.text)
|
|
generation_info = None
|
|
if hasattr(response, "documents"):
|
|
generation_info = self._get_generation_info(response)
|
|
return ChatResult(
|
|
generations=[
|
|
ChatGeneration(message=message, generation_info=generation_info)
|
|
]
|
|
)
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
if self.streaming:
|
|
stream_iter = self._astream(
|
|
messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return await agenerate_from_stream(stream_iter)
|
|
|
|
request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
|
|
response = self.client.chat(**request)
|
|
|
|
message = AIMessage(content=response.text)
|
|
generation_info = None
|
|
if hasattr(response, "documents"):
|
|
generation_info = self._get_generation_info(response)
|
|
return ChatResult(
|
|
generations=[
|
|
ChatGeneration(message=message, generation_info=generation_info)
|
|
]
|
|
)
|
|
|
|
def get_num_tokens(self, text: str) -> int:
|
|
"""Calculate number of tokens."""
|
|
return len(self.client.tokenize(text=text).tokens)
|