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585 lines
23 KiB
Python
585 lines
23 KiB
Python
import logging
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import uuid
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from operator import itemgetter
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Sequence,
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Type,
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Union,
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cast,
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)
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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 import LanguageModelInput
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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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ToolMessage,
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)
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from langchain_core.output_parsers.base import OutputParserLike
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from langchain_core.output_parsers.openai_tools import (
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JsonOutputKeyToolsParser,
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PydanticToolsParser,
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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 BaseModel, Field, SecretStr, root_validator
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from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
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from langchain_core.tools import BaseTool
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_core.utils.function_calling import convert_to_openai_tool
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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, ToolMessage)):
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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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additional_kwargs: Mapping[str, Any] = {}
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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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additional_kwargs = {**_dict.get("body", {}), **additional_kwargs}
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msg_additional_kwargs = dict(
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finish_reason=additional_kwargs.get("finish_reason", ""),
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request_id=additional_kwargs["id"],
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object=additional_kwargs.get("object", ""),
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search_info=additional_kwargs.get("search_info", []),
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)
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if additional_kwargs.get("function_call", {}):
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msg_additional_kwargs["function_call"] = additional_kwargs.get(
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"function_call", {}
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)
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msg_additional_kwargs["tool_calls"] = [
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{
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"type": "function",
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"function": additional_kwargs.get("function_call", {}),
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"id": str(uuid.uuid4()),
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}
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]
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return AIMessage(
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content=content,
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additional_kwargs=msg_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 #: :meta private:
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qianfan_ak: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Qianfan API KEY"""
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qianfan_sk: Optional[SecretStr] = Field(default=None, alias="secret_key")
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"""Qianfan SECRET KEY"""
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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] = Field(60, alias="timeout")
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"""request timeout for chat http requests"""
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top_p: Optional[float] = 0.8
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"""What probability mass to use."""
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temperature: Optional[float] = 0.95
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"""What sampling temperature to use."""
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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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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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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 ImportError(
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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.invoke("Tell me a joke.")
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"""
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if self.streaming:
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completion = ""
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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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params["stop"] = stop
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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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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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params["stop"] = stop
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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["stop"] = stop
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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( # type: ignore[call-arg]
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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["stop"] = stop
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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( # type: ignore[call-arg]
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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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def bind_tools(
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self,
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tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, BaseMessage]:
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"""Bind tool-like objects to this chat model.
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Assumes model is compatible with OpenAI tool-calling API.
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Args:
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tools: A list of tool definitions to bind to this chat model.
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Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
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models, callables, and BaseTools will be automatically converted to
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their schema dictionary representation.
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**kwargs: Any additional parameters to pass to the
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:class:`~langchain.runnable.Runnable` constructor.
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"""
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formatted_tools = [convert_to_openai_tool(tool)["function"] for tool in tools]
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return super().bind(functions=formatted_tools, **kwargs)
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|
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def with_structured_output(
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self,
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schema: Union[Dict, Type[BaseModel]],
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*,
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include_raw: bool = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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|
|
Args:
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schema: The output schema as a dict or a Pydantic class. If a Pydantic class
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then the model output will be an object of that class. If a dict then
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the model output will be a dict. With a Pydantic class the returned
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attributes will be validated, whereas with a dict they will not be. If
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`method` is "function_calling" and `schema` is a dict, then the dict
|
|
must match the OpenAI function-calling spec.
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include_raw: If False then only the parsed structured output is returned. If
|
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an error occurs during model output parsing it will be raised. If True
|
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then both the raw model response (a BaseMessage) and the parsed model
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response will be returned. If an error occurs during output parsing it
|
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will be caught and returned as well. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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Returns:
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A Runnable that takes any ChatModel input and returns as output:
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If include_raw is True then a dict with keys:
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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If include_raw is False then just _DictOrPydantic is returned,
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where _DictOrPydantic depends on the schema:
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If schema is a Pydantic class then _DictOrPydantic is the Pydantic
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class.
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If schema is a dict then _DictOrPydantic is a dict.
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|
|
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
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.. code-block:: python
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from langchain_mistralai import QianfanChatEndpoint
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from langchain_core.pydantic_v1 import BaseModel
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|
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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|
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llm = QianfanChatEndpoint(endpoint="ernie-3.5-8k-0329")
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
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# )
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Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
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.. code-block:: python
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|
|
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from langchain_mistralai import QianfanChatEndpoint
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from langchain_core.pydantic_v1 import BaseModel
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|
|
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class AnswerWithJustification(BaseModel):
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|
'''An answer to the user question along with justification for the answer.'''
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answer: str
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|
justification: str
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|
|
|
llm = QianfanChatEndpoint(endpoint="ernie-3.5-8k-0329")
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structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
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|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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|
# -> {
|
|
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
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# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
Example: Function-calling, dict schema (method="function_calling", include_raw=False):
|
|
.. code-block:: python
|
|
|
|
from langchain_mistralai import QianfanChatEndpoint
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.utils.function_calling import convert_to_openai_tool
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
dict_schema = convert_to_openai_tool(AnswerWithJustification)
|
|
llm = QianfanChatEndpoint(endpoint="ernie-3.5-8k-0329")
|
|
structured_llm = llm.with_structured_output(dict_schema)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
# -> {
|
|
# 'answer': 'They weigh the same',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
|
|
# }
|
|
|
|
""" # noqa: E501
|
|
if kwargs:
|
|
raise ValueError(f"Received unsupported arguments {kwargs}")
|
|
is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
|
|
llm = self.bind_tools([schema])
|
|
if is_pydantic_schema:
|
|
output_parser: OutputParserLike = PydanticToolsParser(
|
|
tools=[schema], # type: ignore[list-item]
|
|
first_tool_only=True, # type: ignore[list-item]
|
|
)
|
|
else:
|
|
key_name = convert_to_openai_tool(schema)["function"]["name"]
|
|
output_parser = JsonOutputKeyToolsParser(
|
|
key_name=key_name, first_tool_only=True
|
|
)
|
|
|
|
if include_raw:
|
|
parser_assign = RunnablePassthrough.assign(
|
|
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
|
|
)
|
|
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
|
|
parser_with_fallback = parser_assign.with_fallbacks(
|
|
[parser_none], exception_key="parsing_error"
|
|
)
|
|
return RunnableMap(raw=llm) | parser_with_fallback
|
|
else:
|
|
return llm | output_parser
|