mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-04 02:03:32 +00:00
452 lines
16 KiB
Python
452 lines
16 KiB
Python
"""deepinfra.com chat models wrapper"""
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from __future__ import annotations
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import json
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import logging
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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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Tuple,
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Type,
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Union,
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)
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import aiohttp
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import requests
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from langchain_core.callbacks.manager 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 (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.language_models.llms import create_base_retry_decorator
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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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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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# from langchain.llms.base import create_base_retry_decorator
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from langchain_community.utilities.requests import Requests
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logger = logging.getLogger(__name__)
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class ChatDeepInfraException(Exception):
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pass
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def _create_retry_decorator(
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llm: ChatDeepInfra,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
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return create_base_retry_decorator(
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error_types=[requests.exceptions.ConnectTimeout, ChatDeepInfraException],
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max_retries=llm.max_retries,
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run_manager=run_manager,
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)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "") 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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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") 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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else:
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additional_kwargs = {}
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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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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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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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 ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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class ChatDeepInfra(BaseChatModel):
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"""A chat model that uses the DeepInfra API."""
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# client: Any #: :meta private:
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model_name: str = Field(default="meta-llama/Llama-2-70b-chat-hf", alias="model")
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"""Model name to use."""
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deepinfra_api_token: Optional[str] = None
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request_timeout: Optional[float] = Field(default=None, alias="timeout")
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temperature: Optional[float] = 1
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Run inference with this temperature. Must by in the closed
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interval [0.0, 1.0]."""
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top_p: Optional[float] = None
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"""Decode using nucleus sampling: consider the smallest set of tokens whose
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probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
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top_k: Optional[int] = None
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"""Decode using top-k sampling: consider the set of top_k most probable tokens.
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Must be positive."""
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n: int = 1
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"""Number of chat completions to generate for each prompt. Note that the API may
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not return the full n completions if duplicates are generated."""
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max_tokens: int = 256
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streaming: bool = False
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max_retries: int = 1
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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 OpenAI API."""
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return {
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"model": self.model_name,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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"request_timeout": self.request_timeout,
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**self.model_kwargs,
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}
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@property
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def _client_params(self) -> Dict[str, Any]:
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"""Get the parameters used for the openai client."""
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return {**self._default_params}
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def completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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try:
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request_timeout = kwargs.pop("request_timeout")
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request = Requests(headers=self._headers())
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response = request.post(
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url=self._url(), data=self._body(kwargs), timeout=request_timeout
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)
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self._handle_status(response.status_code, response.text)
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return response
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except Exception as e:
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# import pdb; pdb.set_trace()
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print("EX", e)
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raise
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return _completion_with_retry(**kwargs)
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async def acompletion_with_retry(
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self,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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try:
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request_timeout = kwargs.pop("request_timeout")
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request = Requests(headers=self._headers())
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async with request.apost(
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url=self._url(), data=self._body(kwargs), timeout=request_timeout
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) as response:
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self._handle_status(response.status, response.text)
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return await response.json()
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except Exception as e:
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print("EX", e)
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raise
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return await _completion_with_retry(**kwargs)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate api key, python package exists, temperature, top_p, and top_k."""
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# For compatibility with LiteLLM
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api_key = get_from_dict_or_env(
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values,
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"deepinfra_api_key",
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"DEEPINFRA_API_KEY",
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default="",
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)
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values["deepinfra_api_token"] = get_from_dict_or_env(
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values,
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"deepinfra_api_token",
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"DEEPINFRA_API_TOKEN",
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default=api_key,
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)
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if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
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raise ValueError("temperature must be in the range [0.0, 1.0]")
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if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
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raise ValueError("top_p must be in the range [0.0, 1.0]")
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if values["top_k"] is not None and values["top_k"] <= 0:
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raise ValueError("top_k must be positive")
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return values
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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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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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)
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return self._create_chat_result(response.json())
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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gen = ChatGeneration(
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message=message,
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generation_info=dict(finish_reason=res.get("finish_reason")),
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)
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generations.append(gen)
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token_usage = response.get("usage", {})
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llm_output = {"token_usage": token_usage, "model": self.model_name}
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res = ChatResult(generations=generations, llm_output=llm_output)
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return res
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = self._client_params
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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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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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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)
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for line in _parse_stream(response.iter_lines()):
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chunk = _handle_sse_line(line)
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if chunk:
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yield ChatGenerationChunk(message=chunk, generation_info=None)
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if run_manager:
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run_manager.on_llm_new_token(str(chunk.content))
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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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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {"messages": message_dicts, "stream": True, **params, **kwargs}
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request_timeout = params.pop("request_timeout")
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request = Requests(headers=self._headers())
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async with request.apost(
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url=self._url(), data=self._body(params), timeout=request_timeout
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) as response:
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async for line in _parse_stream_async(response.content):
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chunk = _handle_sse_line(line)
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if chunk:
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yield ChatGenerationChunk(message=chunk, generation_info=None)
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if run_manager:
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await run_manager.on_llm_new_token(str(chunk.content))
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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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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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stream_iter = self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await agenerate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {"messages": message_dicts, **params, **kwargs}
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res = await self.acompletion_with_retry(run_manager=run_manager, **params)
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return self._create_chat_result(res)
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"model": self.model_name,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"top_k": self.top_k,
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"n": self.n,
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}
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@property
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def _llm_type(self) -> str:
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return "deepinfra-chat"
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def _handle_status(self, code: int, text: Any) -> None:
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if code >= 500:
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raise ChatDeepInfraException(f"DeepInfra Server: Error {code}")
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elif code >= 400:
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raise ValueError(f"DeepInfra received an invalid payload: {text}")
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elif code != 200:
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raise Exception(
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f"DeepInfra returned an unexpected response with status "
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f"{code}: {text}"
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)
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def _url(self) -> str:
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return "https://stage.api.deepinfra.com/v1/openai/chat/completions"
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def _headers(self) -> Dict:
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return {
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"Authorization": f"bearer {self.deepinfra_api_token}",
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"Content-Type": "application/json",
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}
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def _body(self, kwargs: Any) -> Dict:
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return kwargs
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def _parse_stream(rbody: Iterator[bytes]) -> Iterator[str]:
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for line in rbody:
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_line = _parse_stream_helper(line)
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if _line is not None:
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yield _line
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async def _parse_stream_async(rbody: aiohttp.StreamReader) -> AsyncIterator[str]:
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async for line in rbody:
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_line = _parse_stream_helper(line)
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if _line is not None:
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yield _line
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def _parse_stream_helper(line: bytes) -> Optional[str]:
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if line and line.startswith(b"data:"):
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if line.startswith(b"data: "):
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# SSE event may be valid when it contain whitespace
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line = line[len(b"data: ") :]
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else:
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line = line[len(b"data:") :]
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if line.strip() == b"[DONE]":
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# return here will cause GeneratorExit exception in urllib3
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# and it will close http connection with TCP Reset
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return None
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else:
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return line.decode("utf-8")
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return None
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def _handle_sse_line(line: str) -> Optional[BaseMessageChunk]:
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try:
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obj = json.loads(line)
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default_chunk_class = AIMessageChunk
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delta = obj.get("choices", [{}])[0].get("delta", {})
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return _convert_delta_to_message_chunk(delta, default_chunk_class)
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except Exception:
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return None
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