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378 lines
14 KiB
Python
378 lines
14 KiB
Python
import logging
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from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, cast
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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ChatMessage,
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FunctionMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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logger = logging.getLogger(__name__)
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def convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a message to a dictionary that can be passed to the API."""
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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# If function call only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise TypeError(f"Got unknown type {message}")
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return message_dict
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
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content = _dict.get("result", "") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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if "thoughts" in additional_kwargs["function_call"]:
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# align to api sample, which affects the llm function_call output
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additional_kwargs["function_call"].pop("thoughts")
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else:
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additional_kwargs = {}
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return AIMessage(
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content=content,
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additional_kwargs={**_dict.get("body", {}), **additional_kwargs},
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)
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class QianfanChatEndpoint(BaseChatModel):
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"""Baidu Qianfan chat models.
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To use, you should have the ``qianfan`` python package installed, and
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the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with your
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API key and Secret Key.
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ak, sk are required parameters
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which you could get from https://cloud.baidu.com/product/wenxinworkshop
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Example:
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.. code-block:: python
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from langchain_community.chat_models import QianfanChatEndpoint
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qianfan_chat = QianfanChatEndpoint(model="ERNIE-Bot",
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endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")
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"""
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init_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""init kwargs for qianfan client init, such as `query_per_second` which is
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associated with qianfan resource object to limit QPS"""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""extra params for model invoke using with `do`."""
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client: Any
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qianfan_ak: Optional[SecretStr] = None
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qianfan_sk: Optional[SecretStr] = None
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streaming: Optional[bool] = False
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"""Whether to stream the results or not."""
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request_timeout: Optional[int] = 60
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"""request timeout for chat http requests"""
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top_p: Optional[float] = 0.8
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temperature: Optional[float] = 0.95
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penalty_score: Optional[float] = 1
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"""Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo.
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In the case of other model, passing these params will not affect the result.
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"""
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model: str = "ERNIE-Bot-turbo"
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"""Model name.
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you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
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preset models are mapping to an endpoint.
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`model` will be ignored if `endpoint` is set.
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Default is ERNIE-Bot-turbo.
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"""
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endpoint: Optional[str] = None
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"""Endpoint of the Qianfan LLM, required if custom model used."""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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values["qianfan_ak"] = convert_to_secret_str(
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get_from_dict_or_env(
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values,
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"qianfan_ak",
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"QIANFAN_AK",
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default="",
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)
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)
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values["qianfan_sk"] = convert_to_secret_str(
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get_from_dict_or_env(
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values,
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"qianfan_sk",
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"QIANFAN_SK",
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default="",
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)
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)
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params = {
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**values.get("init_kwargs", {}),
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"model": values["model"],
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"stream": values["streaming"],
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}
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if values["qianfan_ak"].get_secret_value() != "":
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params["ak"] = values["qianfan_ak"].get_secret_value()
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if values["qianfan_sk"].get_secret_value() != "":
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params["sk"] = values["qianfan_sk"].get_secret_value()
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if values["endpoint"] is not None and values["endpoint"] != "":
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params["endpoint"] = values["endpoint"]
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try:
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import qianfan
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values["client"] = qianfan.ChatCompletion(**params)
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except ImportError:
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raise ValueError(
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"qianfan package not found, please install it with "
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"`pip install qianfan`"
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)
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return values
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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return {
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**{"endpoint": self.endpoint, "model": self.model},
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**super()._identifying_params,
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of chat_model."""
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return "baidu-qianfan-chat"
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Qianfan API."""
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normal_params = {
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"model": self.model,
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"endpoint": self.endpoint,
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"stream": self.streaming,
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"request_timeout": self.request_timeout,
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"top_p": self.top_p,
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"temperature": self.temperature,
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"penalty_score": self.penalty_score,
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}
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return {**normal_params, **self.model_kwargs}
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def _convert_prompt_msg_params(
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self,
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messages: List[BaseMessage],
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**kwargs: Any,
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) -> Dict[str, Any]:
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"""
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Converts a list of messages into a dictionary containing the message content
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and default parameters.
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Args:
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messages (List[BaseMessage]): The list of messages.
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**kwargs (Any): Optional arguments to add additional parameters to the
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resulting dictionary.
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Returns:
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Dict[str, Any]: A dictionary containing the message content and default
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parameters.
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"""
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messages_dict: Dict[str, Any] = {
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"messages": [
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convert_message_to_dict(m)
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for m in messages
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if not isinstance(m, SystemMessage)
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]
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}
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for i in [i for i, m in enumerate(messages) if isinstance(m, SystemMessage)]:
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if "system" not in messages_dict:
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messages_dict["system"] = ""
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messages_dict["system"] += cast(str, messages[i].content) + "\n"
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return {
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**messages_dict,
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**self._default_params,
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**kwargs,
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}
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""Call out to an qianfan models endpoint for each generation with a prompt.
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Args:
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messages: The messages to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = qianfan_model("Tell me a joke.")
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"""
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if self.streaming:
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completion = ""
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token_usage = {}
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chat_generation_info: Dict = {}
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for chunk in self._stream(messages, stop, run_manager, **kwargs):
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chat_generation_info = (
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chunk.generation_info
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if chunk.generation_info is not None
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else chat_generation_info
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)
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completion += chunk.text
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lc_msg = AIMessage(content=completion, additional_kwargs={})
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gen = ChatGeneration(
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message=lc_msg,
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generation_info=dict(finish_reason="stop"),
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)
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return ChatResult(
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generations=[gen],
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llm_output={
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"token_usage": chat_generation_info.get("usage", {}),
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"model_name": self.model,
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},
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)
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params = self._convert_prompt_msg_params(messages, **kwargs)
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response_payload = self.client.do(**params)
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lc_msg = _convert_dict_to_message(response_payload)
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gen = ChatGeneration(
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message=lc_msg,
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generation_info={
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"finish_reason": "stop",
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**response_payload.get("body", {}),
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},
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)
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token_usage = response_payload.get("usage", {})
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llm_output = {"token_usage": token_usage, "model_name": self.model}
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return ChatResult(generations=[gen], llm_output=llm_output)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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completion = ""
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token_usage = {}
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chat_generation_info: Dict = {}
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async for chunk in self._astream(messages, stop, run_manager, **kwargs):
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chat_generation_info = (
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chunk.generation_info
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if chunk.generation_info is not None
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else chat_generation_info
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)
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completion += chunk.text
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lc_msg = AIMessage(content=completion, additional_kwargs={})
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gen = ChatGeneration(
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message=lc_msg,
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generation_info=dict(finish_reason="stop"),
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)
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return ChatResult(
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generations=[gen],
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llm_output={
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"token_usage": chat_generation_info.get("usage", {}),
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"model_name": self.model,
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},
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)
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params = self._convert_prompt_msg_params(messages, **kwargs)
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response_payload = await self.client.ado(**params)
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lc_msg = _convert_dict_to_message(response_payload)
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generations = []
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gen = ChatGeneration(
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message=lc_msg,
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generation_info={
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"finish_reason": "stop",
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**response_payload.get("body", {}),
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},
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)
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generations.append(gen)
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token_usage = response_payload.get("usage", {})
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llm_output = {"token_usage": token_usage, "model_name": self.model}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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params = self._convert_prompt_msg_params(messages, **kwargs)
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params["stream"] = True
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for res in self.client.do(**params):
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if res:
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msg = _convert_dict_to_message(res)
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additional_kwargs = msg.additional_kwargs.get("function_call", {})
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chunk = ChatGenerationChunk(
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text=res["result"],
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message=AIMessageChunk(
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content=msg.content,
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role="assistant",
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additional_kwargs=additional_kwargs,
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),
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generation_info=msg.additional_kwargs,
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)
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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yield chunk
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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params = self._convert_prompt_msg_params(messages, **kwargs)
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params["stream"] = True
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async for res in await self.client.ado(**params):
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if res:
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msg = _convert_dict_to_message(res)
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additional_kwargs = msg.additional_kwargs.get("function_call", {})
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chunk = ChatGenerationChunk(
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text=res["result"],
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message=AIMessageChunk(
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content=msg.content,
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role="assistant",
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additional_kwargs=additional_kwargs,
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),
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generation_info=msg.additional_kwargs,
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)
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if run_manager:
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await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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yield chunk
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