mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
369 lines
13 KiB
Python
369 lines
13 KiB
Python
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import json
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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from aiohttp import ClientSession
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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 (
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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.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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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 Extra, 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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from langchain_community.utilities.requests import Requests
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def _message_role(type: str) -> str:
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role_mapping = {"ai": "assistant", "human": "user", "chat": "user"}
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if type in role_mapping:
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return role_mapping[type]
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else:
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raise ValueError(f"Unknown type: {type}")
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def _format_edenai_messages(messages: List[BaseMessage]) -> Dict[str, Any]:
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system = None
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formatted_messages = []
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text = messages[-1].content
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for i, message in enumerate(messages[:-1]):
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if message.type == "system":
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if i != 0:
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raise ValueError("System message must be at beginning of message list.")
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system = message.content
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else:
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formatted_messages.append(
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{
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"role": _message_role(message.type),
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"message": message.content,
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}
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)
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return {
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"text": text,
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"previous_history": formatted_messages,
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"chatbot_global_action": system,
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}
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class ChatEdenAI(BaseChatModel):
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"""`EdenAI` chat large language models.
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`EdenAI` is a versatile platform that allows you to access various language models
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from different providers such as Google, OpenAI, Cohere, Mistral and more.
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To get started, make sure you have the environment variable ``EDENAI_API_KEY``
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set with your API key, or pass it as a named parameter to the constructor.
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Additionally, `EdenAI` provides the flexibility to choose from a variety of models,
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including the ones like "gpt-4".
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatEdenAI
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from langchain_core.messages import HumanMessage
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# Initialize `ChatEdenAI` with the desired configuration
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chat = ChatEdenAI(
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provider="openai",
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model="gpt-4",
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max_tokens=256,
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temperature=0.75)
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# Create a list of messages to interact with the model
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messages = [HumanMessage(content="hello")]
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# Invoke the model with the provided messages
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chat.invoke(messages)
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`EdenAI` goes beyond mere model invocation. It empowers you with advanced features :
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- **Multiple Providers**: access to a diverse range of llms offered by various
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providers giving you the freedom to choose the best-suited model for your use case.
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- **Fallback Mechanism**: Set a fallback mechanism to ensure seamless operations
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even if the primary provider is unavailable, you can easily switches to an
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alternative provider.
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- **Usage Statistics**: Track usage statistics on a per-project
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and per-API key basis.
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This feature allows you to monitor and manage resource consumption effectively.
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- **Monitoring and Observability**: `EdenAI` provides comprehensive monitoring
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and observability tools on the platform.
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Example of setting up a fallback mechanism:
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.. code-block:: python
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# Initialize `ChatEdenAI` with a fallback provider
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chat_with_fallback = ChatEdenAI(
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provider="openai",
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model="gpt-4",
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max_tokens=256,
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temperature=0.75,
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fallback_provider="google")
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you can find more details here : https://docs.edenai.co/reference/text_chat_create
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"""
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provider: str = "openai"
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"""chat provider to use (eg: openai,google etc.)"""
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model: Optional[str] = None
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"""
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model name for above provider (eg: 'gpt-4' for openai)
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available models are shown on https://docs.edenai.co/ under 'available providers'
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"""
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max_tokens: int = 256
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"""Denotes the number of tokens to predict per generation."""
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temperature: Optional[float] = 0
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"""A non-negative float that tunes the degree of randomness in generation."""
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streaming: bool = False
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"""Whether to stream the results."""
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fallback_providers: Optional[str] = None
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"""Providers in this will be used as fallback if the call to provider fails."""
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edenai_api_url: str = "https://api.edenai.run/v2"
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edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token")
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key exists in environment."""
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values["edenai_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "edenai_api_key", "EDENAI_API_KEY")
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)
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return values
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@staticmethod
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def get_user_agent() -> str:
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from langchain_community import __version__
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return f"langchain/{__version__}"
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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 "edenai-chat"
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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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"""Call out to EdenAI's chat endpoint."""
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url = f"{self.edenai_api_url}/text/chat/stream"
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headers = {
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"Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
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"User-Agent": self.get_user_agent(),
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}
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formatted_data = _format_edenai_messages(messages=messages)
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payload: Dict[str, Any] = {
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"providers": self.provider,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"fallback_providers": self.fallback_providers,
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**formatted_data,
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**kwargs,
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}
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payload = {k: v for k, v in payload.items() if v is not None}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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request = Requests(headers=headers)
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response = request.post(url=url, data=payload, stream=True)
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response.raise_for_status()
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for chunk_response in response.iter_lines():
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chunk = json.loads(chunk_response.decode())
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token = chunk["text"]
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chat_generatio_chunk = ChatGenerationChunk(
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message=AIMessageChunk(content=token)
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)
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yield chat_generatio_chunk
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if run_manager:
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run_manager.on_llm_new_token(token, chunk=chat_generatio_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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url = f"{self.edenai_api_url}/text/chat/stream"
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headers = {
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"Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
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"User-Agent": self.get_user_agent(),
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}
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formatted_data = _format_edenai_messages(messages=messages)
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payload: Dict[str, Any] = {
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"providers": self.provider,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"fallback_providers": self.fallback_providers,
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**formatted_data,
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**kwargs,
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}
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payload = {k: v for k, v in payload.items() if v is not None}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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async with ClientSession() as session:
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async with session.post(url, json=payload, headers=headers) as response:
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response.raise_for_status()
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async for chunk_response in response.content:
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chunk = json.loads(chunk_response.decode())
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token = chunk["text"]
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chat_generation_chunk = ChatGenerationChunk(
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message=AIMessageChunk(content=token)
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)
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yield chat_generation_chunk
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if run_manager:
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await run_manager.on_llm_new_token(
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token=chunk["text"], chunk=chat_generation_chunk
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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 EdenAI's chat endpoint."""
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if self.streaming:
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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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url = f"{self.edenai_api_url}/text/chat"
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headers = {
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"Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
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"User-Agent": self.get_user_agent(),
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}
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formatted_data = _format_edenai_messages(messages=messages)
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payload: Dict[str, Any] = {
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"providers": self.provider,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"fallback_providers": self.fallback_providers,
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**formatted_data,
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**kwargs,
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}
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payload = {k: v for k, v in payload.items() if v is not None}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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request = Requests(headers=headers)
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response = request.post(url=url, data=payload)
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response.raise_for_status()
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data = response.json()
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provider_response = data[self.provider]
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if self.fallback_providers:
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fallback_response = data.get(self.fallback_providers)
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if fallback_response:
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provider_response = fallback_response
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if provider_response.get("status") == "fail":
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err_msg = provider_response.get("error", {}).get("message")
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raise Exception(err_msg)
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return ChatResult(
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generations=[
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ChatGeneration(
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message=AIMessage(content=provider_response["generated_text"])
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)
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],
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llm_output=data,
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)
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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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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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url = f"{self.edenai_api_url}/text/chat"
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headers = {
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"Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
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"User-Agent": self.get_user_agent(),
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}
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formatted_data = _format_edenai_messages(messages=messages)
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payload: Dict[str, Any] = {
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"providers": self.provider,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"fallback_providers": self.fallback_providers,
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**formatted_data,
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**kwargs,
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}
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payload = {k: v for k, v in payload.items() if v is not None}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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async with ClientSession() as session:
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async with session.post(url, json=payload, headers=headers) as response:
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response.raise_for_status()
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data = await response.json()
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provider_response = data[self.provider]
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if self.fallback_providers:
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fallback_response = data.get(self.fallback_providers)
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if fallback_response:
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provider_response = fallback_response
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if provider_response.get("status") == "fail":
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err_msg = provider_response.get("error", {}).get("message")
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raise Exception(err_msg)
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return ChatResult(
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generations=[
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ChatGeneration(
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message=AIMessage(
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content=provider_response["generated_text"]
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)
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)
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],
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llm_output=data,
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)
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