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] 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 ) yield ChatGenerationChunk(message=chunk, generation_info=generation_info) if run_manager: await run_manager.on_llm_new_token(content)