from __future__ import annotations
import logging
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Dict,
Iterator,
List,
Mapping,
Optional,
Type,
)
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,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_community.llms.gigachat import _BaseGigaChat
if TYPE_CHECKING:
import gigachat.models as gm
logger = logging.getLogger(__name__)
def _convert_dict_to_message(message: gm.Messages) -> BaseMessage:
from gigachat.models import FunctionCall, MessagesRole
additional_kwargs: Dict = {}
if function_call := message.function_call:
if isinstance(function_call, FunctionCall):
additional_kwargs["function_call"] = dict(function_call)
elif isinstance(function_call, dict):
additional_kwargs["function_call"] = function_call
if message.role == MessagesRole.SYSTEM:
return SystemMessage(content=message.content)
elif message.role == MessagesRole.USER:
return HumanMessage(content=message.content)
elif message.role == MessagesRole.ASSISTANT:
return AIMessage(content=message.content, additional_kwargs=additional_kwargs)
else:
raise TypeError(f"Got unknown role {message.role} {message}")
def _convert_message_to_dict(message: gm.BaseMessage) -> gm.Messages:
from gigachat.models import Messages, MessagesRole
if isinstance(message, SystemMessage):
return Messages(role=MessagesRole.SYSTEM, content=message.content)
elif isinstance(message, HumanMessage):
return Messages(role=MessagesRole.USER, content=message.content)
elif isinstance(message, AIMessage):
return Messages(
role=MessagesRole.ASSISTANT,
content=message.content,
function_call=message.additional_kwargs.get("function_call", None),
)
elif isinstance(message, ChatMessage):
return Messages(role=MessagesRole(message.role), content=message.content)
elif isinstance(message, FunctionMessage):
return Messages(role=MessagesRole.FUNCTION, content=message.content)
else:
raise TypeError(f"Got unknown type {message}")
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _dict.get("role")
content = _dict.get("content") or ""
additional_kwargs: Dict = {}
if _dict.get("function_call"):
function_call = dict(_dict["function_call"])
if "name" in function_call and function_call["name"] is None:
function_call["name"] = ""
additional_kwargs["function_call"] = function_call
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict["name"])
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type]
else:
return default_class(content=content) # type: ignore[call-arg]
[docs]
class GigaChat(_BaseGigaChat, BaseChatModel):
"""`GigaChat` large language models API.
To use, you should pass login and password to access GigaChat API or use token.
Example:
.. code-block:: python
from langchain_community.chat_models import GigaChat
giga = GigaChat(credentials=..., scope=..., verify_ssl_certs=False)
"""
def _build_payload(self, messages: List[BaseMessage], **kwargs: Any) -> gm.Chat:
from gigachat.models import Chat
payload = Chat(
messages=[_convert_message_to_dict(m) for m in messages],
)
payload.functions = kwargs.get("functions", None)
payload.model = self.model
if self.profanity_check is not None:
payload.profanity_check = self.profanity_check
if self.temperature is not None:
payload.temperature = self.temperature
if self.top_p is not None:
payload.top_p = self.top_p
if self.max_tokens is not None:
payload.max_tokens = self.max_tokens
if self.repetition_penalty is not None:
payload.repetition_penalty = self.repetition_penalty
if self.update_interval is not None:
payload.update_interval = self.update_interval
if self.verbose:
logger.warning("Giga request: %s", payload.dict())
return payload
def _create_chat_result(self, response: Any) -> ChatResult:
generations = []
for res in response.choices:
message = _convert_dict_to_message(res.message)
finish_reason = res.finish_reason
gen = ChatGeneration(
message=message,
generation_info={"finish_reason": finish_reason},
)
generations.append(gen)
if finish_reason != "stop":
logger.warning(
"Giga generation stopped with reason: %s",
finish_reason,
)
if self.verbose:
logger.warning("Giga response: %s", message.content)
llm_output = {"token_usage": response.usage, "model_name": response.model}
return ChatResult(generations=generations, llm_output=llm_output)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
payload = self._build_payload(messages, **kwargs)
response = self._client.chat(payload)
return self._create_chat_result(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
payload = self._build_payload(messages, **kwargs)
response = await self._client.achat(payload)
return self._create_chat_result(response)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
payload = self._build_payload(messages, **kwargs)
for chunk in self._client.stream(payload):
if not isinstance(chunk, dict):
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
content = choice.get("delta", {}).get("content", {})
chunk = _convert_delta_to_message_chunk(choice["delta"], AIMessageChunk)
finish_reason = choice.get("finish_reason")
generation_info = (
dict(finish_reason=finish_reason) if finish_reason is not None else None
)
if run_manager:
run_manager.on_llm_new_token(content)
yield ChatGenerationChunk(message=chunk, generation_info=generation_info)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
payload = self._build_payload(messages, **kwargs)
async for chunk in self._client.astream(payload):
if not isinstance(chunk, dict):
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
content = choice.get("delta", {}).get("content", {})
chunk = _convert_delta_to_message_chunk(choice["delta"], AIMessageChunk)
finish_reason = choice.get("finish_reason")
generation_info = (
dict(finish_reason=finish_reason) if finish_reason is not None else None
)
if run_manager:
await run_manager.on_llm_new_token(content)
yield ChatGenerationChunk(message=chunk, generation_info=generation_info)
