Source code for langchain_community.chat_models.baichuan

import json
import logging
from typing import Any, Dict, Iterator, List, Mapping, Optional, Type

import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import (
    BaseChatModel,
    generate_from_stream,
)
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    ChatMessage,
    ChatMessageChunk,
    HumanMessage,
    HumanMessageChunk,
)
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}
    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 "")
    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 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 ChatBaichuan(BaseChatModel): """Baichuan chat models API by Baichuan Intelligent Technology. For more information, see https://platform.baichuan-ai.com/docs/api """ @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) """Baichuan custom endpoints""" baichuan_api_key: Optional[SecretStr] = Field(default=None, 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.""" request_timeout: int = Field(default=60, alias="timeout") """request timeout for chat http requests""" model = "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 """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() 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", "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, "with_search_enhance": self.with_search_enhance, "stream": self.streaming, } 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, **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 def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response: parameters = {**self._default_params, **kwargs} model = parameters.pop("model") headers = parameters.pop("headers", {}) temperature = parameters.pop("temperature", 0.3) top_k = parameters.pop("top_k", 5) top_p = parameters.pop("top_p", 0.85) 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, } url = self.baichuan_api_base api_key = "" if self.baichuan_api_key: api_key = self.baichuan_api_key.get_secret_value() res = requests.post( url=url, timeout=self.request_timeout, headers={ "Content-Type": "application/json", "Authorization": f"Bearer {api_key}", **headers, }, json=payload, stream=self.streaming, ) return res 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"