import json import logging from contextlib import asynccontextmanager from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, Type import requests 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, HumanMessage, HumanMessageChunk, SystemMessage, SystemMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, ) logger = logging.getLogger(__name__) DEFAULT_API_BASE = "https://api.baichuan-ai.com/v1/chat/completions" def _convert_message_to_dict(message: BaseMessage) -> dict: message_dict: Dict[str, Any] if isinstance(message, ChatMessage): message_dict = {"role": message.role, "content": message.content} elif isinstance(message, HumanMessage): message_dict = {"role": "user", "content": message.content} elif isinstance(message, AIMessage): message_dict = {"role": "assistant", "content": message.content} elif isinstance(message, SystemMessage): message_dict = {"role": "system", "content": message.content} else: raise TypeError(f"Got unknown type {message}") return message_dict def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: role = _dict["role"] if role == "user": return HumanMessage(content=_dict["content"]) elif role == "assistant": return AIMessage(content=_dict.get("content", "") or "") elif role == "system": return SystemMessage(content=_dict.get("content", "")) else: return ChatMessage(content=_dict["content"], role=role) def _convert_delta_to_message_chunk( _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk] ) -> BaseMessageChunk: role = _dict.get("role") content = _dict.get("content") or "" if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content) elif role == "assistant" or default_class == AIMessageChunk: return AIMessageChunk(content=content) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content) 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] @asynccontextmanager async def aconnect_httpx_sse( client: Any, method: str, url: str, **kwargs: Any ) -> AsyncIterator: """Async context manager for connecting to an SSE stream. Args: client: The httpx client. method: The HTTP method. url: The URL to connect to. kwargs: Additional keyword arguments to pass to the client. Yields: An EventSource object. """ from httpx_sse import EventSource async with client.stream(method, url, **kwargs) as response: yield EventSource(response) class ChatBaichuan(BaseChatModel): """Baichuan chat model integration. Setup: To use, you should have the environment variable``BAICHUAN_API_KEY`` set with your API KEY. .. code-block:: bash export BAICHUAN_API_KEY="your-api-key" Key init args — completion params: model: Optional[str] Name of Baichuan model to use. max_tokens: Optional[int] Max number of tokens to generate. streaming: Optional[bool] Whether to stream the results or not. temperature: Optional[float] Sampling temperature. top_p: Optional[float] What probability mass to use. top_k: Optional[int] What search sampling control to use. Key init args — client params: api_key: Optional[str] MiniMax API key. If not passed in will be read from env var BAICHUAN_API_KEY. base_url: Optional[str] Base URL for API requests. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_community.chat_models import ChatBaichuan chat = ChatBaichuan( api_key=api_key, model='Baichuan4', # temperature=..., # other params... ) Invoke: .. code-block:: python messages = [ ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"), ("human", "我喜欢编程。"), ] chat.invoke(messages) .. code-block:: python AIMessage( content='I enjoy programming.', response_metadata={ 'token_usage': { 'prompt_tokens': 93, 'completion_tokens': 5, 'total_tokens': 98 }, 'model': 'Baichuan4' }, id='run-944ff552-6a93-44cf-a861-4e4d849746f9-0' ) Stream: .. code-block:: python for chunk in chat.stream(messages): print(chunk) .. code-block:: python content='I' id='run-f99fcd6f-dd31-46d5-be8f-0b6a22bf77d8' content=' enjoy programming.' id='run-f99fcd6f-dd31-46d5-be8f-0b6a22bf77d8 .. code-block:: python stream = chat.stream(messages) full = next(stream) for chunk in stream: full += chunk full .. code-block:: python AIMessageChunk( content='I like programming.', id='run-74689970-dc31-461d-b729-3b6aa93508d2' ) Async: .. code-block:: python await chat.ainvoke(messages) # stream # async for chunk in chat.astream(messages): # print(chunk) # batch # await chat.abatch([messages]) .. code-block:: python AIMessage( content='I enjoy programming.', response_metadata={ 'token_usage': { 'prompt_tokens': 93, 'completion_tokens': 5, 'total_tokens': 98 }, 'model': 'Baichuan4' }, id='run-952509ed-9154-4ff9-b187-e616d7ddfbba-0' ) Response metadata .. code-block:: python ai_msg = chat.invoke(messages) ai_msg.response_metadata .. code-block:: python { 'token_usage': { 'prompt_tokens': 93, 'completion_tokens': 5, 'total_tokens': 98 }, 'model': 'Baichuan4' } """ # noqa: E501 @property def lc_secrets(self) -> Dict[str, str]: return { "baichuan_api_key": "BAICHUAN_API_KEY", } @property def lc_serializable(self) -> bool: return True baichuan_api_base: str = Field(default=DEFAULT_API_BASE, alias="base_url") """Baichuan custom endpoints""" baichuan_api_key: SecretStr = Field(alias="api_key") """Baichuan API Key""" baichuan_secret_key: Optional[SecretStr] = None """[DEPRECATED, keeping it for for backward compatibility] Baichuan Secret Key""" streaming: bool = False """Whether to stream the results or not.""" max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" request_timeout: int = Field(default=60, alias="timeout") """request timeout for chat http requests""" model: str = "Baichuan2-Turbo-192K" """model name of Baichuan, default is `Baichuan2-Turbo-192K`, other options include `Baichuan2-Turbo`""" temperature: Optional[float] = Field(default=0.3) """What sampling temperature to use.""" top_k: int = 5 """What search sampling control to use.""" top_p: float = 0.85 """What probability mass to use.""" with_search_enhance: bool = False """[DEPRECATED, keeping it for for backward compatibility], Whether to use search enhance, default is False.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for API call not explicitly specified.""" class Config: """Configuration for this pydantic object.""" allow_population_by_field_name = True @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) for field_name in list(values): if field_name in extra: raise ValueError(f"Found {field_name} supplied twice.") if field_name not in all_required_field_names: logger.warning( f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. Please confirm that {field_name} is what you intended.""" ) extra[field_name] = values.pop(field_name) invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " f"Instead they were passed in as part of `model_kwargs` parameter." ) values["model_kwargs"] = extra return values @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: values["baichuan_api_base"] = get_from_dict_or_env( values, "baichuan_api_base", "BAICHUAN_API_BASE", DEFAULT_API_BASE, ) values["baichuan_api_key"] = convert_to_secret_str( get_from_dict_or_env( values, ["baichuan_api_key", "api_key"], "BAICHUAN_API_KEY", ) ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Baichuan API.""" normal_params = { "model": self.model, "temperature": self.temperature, "top_p": self.top_p, "top_k": self.top_k, "stream": self.streaming, "max_tokens": self.max_tokens, } return {**normal_params, **self.model_kwargs} 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=messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) res = self._chat(messages, **kwargs) if res.status_code != 200: raise ValueError(f"Error from Baichuan api response: {res}") response = res.json() 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]: res = self._chat(messages, stream=True, **kwargs) if res.status_code != 200: raise ValueError(f"Error from Baichuan api response: {res}") default_chunk_class = AIMessageChunk for chunk in res.iter_lines(): chunk = chunk.decode("utf-8").strip("\r\n") parts = chunk.split("data: ", 1) chunk = parts[1] if len(parts) > 1 else None if chunk is None: continue if chunk == "[DONE]": break response = json.loads(chunk) for m in response.get("choices"): chunk = _convert_delta_to_message_chunk( m.get("delta"), default_chunk_class ) default_chunk_class = chunk.__class__ cg_chunk = ChatGenerationChunk(message=chunk) if run_manager: run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk) yield cg_chunk 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) headers = self._create_headers_parameters(**kwargs) payload = self._create_payload_parameters(messages, **kwargs) import httpx async with httpx.AsyncClient( headers=headers, timeout=self.request_timeout ) as client: response = await client.post(self.baichuan_api_base, json=payload) response.raise_for_status() return self._create_chat_result(response.json()) async def _astream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: headers = self._create_headers_parameters(**kwargs) payload = self._create_payload_parameters(messages, stream=True, **kwargs) import httpx async with httpx.AsyncClient( headers=headers, timeout=self.request_timeout ) as client: async with aconnect_httpx_sse( client, "POST", self.baichuan_api_base, json=payload ) as event_source: async for sse in event_source.aiter_sse(): chunk = json.loads(sse.data) if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] chunk = _convert_delta_to_message_chunk( choice["delta"], AIMessageChunk ) finish_reason = choice.get("finish_reason", None) generation_info = ( {"finish_reason": finish_reason} if finish_reason is not None else None ) chunk = ChatGenerationChunk( message=chunk, generation_info=generation_info ) if run_manager: await run_manager.on_llm_new_token(chunk.text, chunk=chunk) yield chunk if finish_reason is not None: break def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response: payload = self._create_payload_parameters(messages, **kwargs) url = self.baichuan_api_base headers = self._create_headers_parameters(**kwargs) res = requests.post( url=url, timeout=self.request_timeout, headers=headers, json=payload, stream=self.streaming, ) return res def _create_payload_parameters( # type: ignore[no-untyped-def] self, messages: List[BaseMessage], **kwargs ) -> Dict[str, Any]: parameters = {**self._default_params, **kwargs} temperature = parameters.pop("temperature", 0.3) top_k = parameters.pop("top_k", 5) top_p = parameters.pop("top_p", 0.85) model = parameters.pop("model") with_search_enhance = parameters.pop("with_search_enhance", False) stream = parameters.pop("stream", False) payload = { "model": model, "messages": [_convert_message_to_dict(m) for m in messages], "top_k": top_k, "top_p": top_p, "temperature": temperature, "with_search_enhance": with_search_enhance, "stream": stream, } return payload def _create_headers_parameters(self, **kwargs) -> Dict[str, Any]: # type: ignore[no-untyped-def] parameters = {**self._default_params, **kwargs} default_headers = parameters.pop("headers", {}) api_key = "" if self.baichuan_api_key: api_key = self.baichuan_api_key.get_secret_value() headers = { "Content-Type": "application/json", "Authorization": f"Bearer {api_key}", **default_headers, } return headers def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult: generations = [] for c in response["choices"]: message = _convert_dict_to_message(c["message"]) gen = ChatGeneration(message=message) generations.append(gen) token_usage = response["usage"] llm_output = {"token_usage": token_usage, "model": self.model} return ChatResult(generations=generations, llm_output=llm_output) @property def _llm_type(self) -> str: return "baichuan-chat"