Source code for langchain_community.chat_models.baidu_qianfan_endpoint

from __future__ import annotations

import logging
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, cast

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    ChatMessage,
    FunctionMessage,
    HumanMessage,
    SystemMessage,
)
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

logger = logging.getLogger(__name__)


[docs]def convert_message_to_dict(message: BaseMessage) -> dict: """Convert a message to a dictionary that can be passed to the API.""" 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} if "function_call" in message.additional_kwargs: message_dict["function_call"] = message.additional_kwargs["function_call"] # If function call only, content is None not empty string if message_dict["content"] == "": message_dict["content"] = None elif isinstance(message, FunctionMessage): message_dict = { "role": "function", "content": message.content, "name": message.name, } else: raise TypeError(f"Got unknown type {message}") return message_dict
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage: content = _dict.get("result", "") or "" if _dict.get("function_call"): additional_kwargs = {"function_call": dict(_dict["function_call"])} if "thoughts" in additional_kwargs["function_call"]: # align to api sample, which affects the llm function_call output additional_kwargs["function_call"].pop("thoughts") else: additional_kwargs = {} return AIMessage( content=content, additional_kwargs={**_dict.get("body", {}), **additional_kwargs}, )
[docs]class QianfanChatEndpoint(BaseChatModel): """Baidu Qianfan chat models. To use, you should have the ``qianfan`` python package installed, and the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with your API key and Secret Key. ak, sk are required parameters which you could get from https://cloud.baidu.com/product/wenxinworkshop Example: .. code-block:: python from langchain_community.chat_models import QianfanChatEndpoint qianfan_chat = QianfanChatEndpoint(model="ERNIE-Bot", endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk") """ model_kwargs: Dict[str, Any] = Field(default_factory=dict) client: Any qianfan_ak: Optional[SecretStr] = None qianfan_sk: Optional[SecretStr] = None streaming: Optional[bool] = False """Whether to stream the results or not.""" request_timeout: Optional[int] = 60 """request timeout for chat http requests""" top_p: Optional[float] = 0.8 temperature: Optional[float] = 0.95 penalty_score: Optional[float] = 1 """Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. In the case of other model, passing these params will not affect the result. """ model: str = "ERNIE-Bot-turbo" """Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `model` will be ignored if `endpoint` is set. Default is ERNIE-Bot-turbo. """ endpoint: Optional[str] = None """Endpoint of the Qianfan LLM, required if custom model used.""" @root_validator() def validate_environment(cls, values: Dict) -> Dict: values["qianfan_ak"] = convert_to_secret_str( get_from_dict_or_env( values, "qianfan_ak", "QIANFAN_AK", ) ) values["qianfan_sk"] = convert_to_secret_str( get_from_dict_or_env( values, "qianfan_sk", "QIANFAN_SK", ) ) params = { "ak": values["qianfan_ak"].get_secret_value(), "sk": values["qianfan_sk"].get_secret_value(), "model": values["model"], "stream": values["streaming"], } if values["endpoint"] is not None and values["endpoint"] != "": params["endpoint"] = values["endpoint"] try: import qianfan values["client"] = qianfan.ChatCompletion(**params) except ImportError: raise ValueError( "qianfan package not found, please install it with " "`pip install qianfan`" ) return values @property def _identifying_params(self) -> Dict[str, Any]: return { **{"endpoint": self.endpoint, "model": self.model}, **super()._identifying_params, } @property def _llm_type(self) -> str: """Return type of chat_model.""" return "baidu-qianfan-chat" @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Qianfan API.""" normal_params = { "model": self.model, "endpoint": self.endpoint, "stream": self.streaming, "request_timeout": self.request_timeout, "top_p": self.top_p, "temperature": self.temperature, "penalty_score": self.penalty_score, } return {**normal_params, **self.model_kwargs} def _convert_prompt_msg_params( self, messages: List[BaseMessage], **kwargs: Any, ) -> Dict[str, Any]: """ Converts a list of messages into a dictionary containing the message content and default parameters. Args: messages (List[BaseMessage]): The list of messages. **kwargs (Any): Optional arguments to add additional parameters to the resulting dictionary. Returns: Dict[str, Any]: A dictionary containing the message content and default parameters. """ messages_dict: Dict[str, Any] = { "messages": [ convert_message_to_dict(m) for m in messages if not isinstance(m, SystemMessage) ] } for i in [i for i, m in enumerate(messages) if isinstance(m, SystemMessage)]: if "system" not in messages_dict: messages_dict["system"] = "" messages_dict["system"] += cast(str, messages[i].content) + "\n" return { **messages_dict, **self._default_params, **kwargs, } def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: """Call out to an qianfan models endpoint for each generation with a prompt. Args: messages: The messages to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = qianfan_model("Tell me a joke.") """ if self.streaming: completion = "" for chunk in self._stream(messages, stop, run_manager, **kwargs): completion += chunk.text lc_msg = AIMessage(content=completion, additional_kwargs={}) gen = ChatGeneration( message=lc_msg, generation_info=dict(finish_reason="stop"), ) return ChatResult( generations=[gen], llm_output={"token_usage": {}, "model_name": self.model}, ) params = self._convert_prompt_msg_params(messages, **kwargs) response_payload = self.client.do(**params) lc_msg = _convert_dict_to_message(response_payload) gen = ChatGeneration( message=lc_msg, generation_info={ "finish_reason": "stop", **response_payload.get("body", {}), }, ) token_usage = response_payload.get("usage", {}) llm_output = {"token_usage": token_usage, "model_name": self.model} return ChatResult(generations=[gen], llm_output=llm_output) async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: if self.streaming: completion = "" token_usage = {} async for chunk in self._astream(messages, stop, run_manager, **kwargs): completion += chunk.text lc_msg = AIMessage(content=completion, additional_kwargs={}) gen = ChatGeneration( message=lc_msg, generation_info=dict(finish_reason="stop"), ) return ChatResult( generations=[gen], llm_output={"token_usage": {}, "model_name": self.model}, ) params = self._convert_prompt_msg_params(messages, **kwargs) response_payload = await self.client.ado(**params) lc_msg = _convert_dict_to_message(response_payload) generations = [] gen = ChatGeneration( message=lc_msg, generation_info={ "finish_reason": "stop", **response_payload.get("body", {}), }, ) generations.append(gen) token_usage = response_payload.get("usage", {}) llm_output = {"token_usage": token_usage, "model_name": self.model} return ChatResult(generations=generations, llm_output=llm_output) def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: params = self._convert_prompt_msg_params(messages, **kwargs) for res in self.client.do(**params): if res: msg = _convert_dict_to_message(res) chunk = ChatGenerationChunk( text=res["result"], message=AIMessageChunk( content=msg.content, role="assistant", additional_kwargs=msg.additional_kwargs, ), ) yield chunk if run_manager: run_manager.on_llm_new_token(chunk.text, chunk=chunk) async def _astream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: params = self._convert_prompt_msg_params(messages, **kwargs) async for res in await self.client.ado(**params): if res: msg = _convert_dict_to_message(res) chunk = ChatGenerationChunk( text=res["result"], message=AIMessageChunk( content=msg.content, role="assistant", additional_kwargs=msg.additional_kwargs, ), ) yield chunk if run_manager: await run_manager.on_llm_new_token(chunk.text, chunk=chunk)