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Integrating the Yi family of models. (#24491)
Thank you for contributing to LangChain!
- [x] **PR title**: "community:add Yi LLM", "docs:add Yi Documentation"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** This PR adds support for the Yi model to LangChain.
- **Dependencies:**
[langchain_core,requests,contextlib,typing,logging,json,langchain_community]
- **Twitter handle:** 01.AI
- [x] **Add tests and docs**: I've added the corresponding documentation
to the relevant paths
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
This commit is contained in:
Haijian Wang
GitHub
339
libs/community/langchain_community/chat_models/yi.py
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339
libs/community/langchain_community/chat_models/yi.py
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import json
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import logging
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from contextlib import asynccontextmanager
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from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, Type
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import requests
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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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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field, SecretStr
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from langchain_core.utils import (
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convert_to_secret_str,
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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logger = logging.getLogger(__name__)
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DEFAULT_API_BASE_CN = "https://api.lingyiwanwu.com/v1/chat/completions"
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DEFAULT_API_BASE_GLOBAL = "https://api.01.ai/v1/chat/completions"
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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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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elif isinstance(message, SystemMessage):
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message_dict = {"role": "assistant", "content": message.content}
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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]) -> 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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return AIMessage(content=_dict.get("content", "") or "")
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elif role == "system":
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return AIMessage(content=_dict["content"])
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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: str = _dict["role"]
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content = _dict.get("content") or ""
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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)
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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, type=role)
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@asynccontextmanager
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async def aconnect_httpx_sse(
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client: Any, method: str, url: str, **kwargs: Any
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) -> AsyncIterator:
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from httpx_sse import EventSource
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async with client.stream(method, url, **kwargs) as response:
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yield EventSource(response)
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class ChatYi(BaseChatModel):
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"""Yi chat models API."""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {
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"yi_api_key": "YI_API_KEY",
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}
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@property
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def lc_serializable(self) -> bool:
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return True
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yi_api_base: str = Field(default=DEFAULT_API_BASE_CN)
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yi_api_key: SecretStr = Field(alias="api_key")
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region: str = Field(default="cn") # 默认使用中国区
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streaming: bool = False
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request_timeout: int = Field(default=60, alias="timeout")
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model: str = "yi-large"
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temperature: Optional[float] = Field(default=0.7)
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top_p: float = 0.7
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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class Config:
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allow_population_by_field_name = True
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def __init__(self, **kwargs: Any) -> None:
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kwargs["yi_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(
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kwargs,
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["yi_api_key", "api_key"],
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"YI_API_KEY",
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)
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)
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if kwargs.get("yi_api_base") is None:
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region = kwargs.get("region", "cn").lower()
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if region == "global":
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kwargs["yi_api_base"] = DEFAULT_API_BASE_GLOBAL
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else:
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kwargs["yi_api_base"] = DEFAULT_API_BASE_CN
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all_required_field_names = get_pydantic_field_names(self.__class__)
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extra = kwargs.get("model_kwargs", {})
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for field_name in list(kwargs):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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extra[field_name] = kwargs.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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kwargs["model_kwargs"] = extra
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super().__init__(**kwargs)
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@property
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def _default_params(self) -> Dict[str, Any]:
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return {
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"model": self.model,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"stream": self.streaming,
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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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if self.streaming:
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stream_iter = self._stream(
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messages=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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res = self._chat(messages, **kwargs)
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if res.status_code != 200:
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raise ValueError(f"Error from Yi api response: {res}")
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response = res.json()
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return self._create_chat_result(response)
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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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res = self._chat(messages, stream=True, **kwargs)
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if res.status_code != 200:
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raise ValueError(f"Error from Yi api response: {res}")
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default_chunk_class = AIMessageChunk
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for chunk in res.iter_lines():
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chunk = chunk.decode("utf-8").strip("\r\n")
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parts = chunk.split("data: ", 1)
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chunk = parts[1] if len(parts) > 1 else None
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if chunk is None:
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continue
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if chunk == "[DONE]":
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break
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response = json.loads(chunk)
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for m in response.get("choices"):
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chunk = _convert_delta_to_message_chunk(
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m.get("delta"), default_chunk_class
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)
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default_chunk_class = chunk.__class__
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cg_chunk = ChatGenerationChunk(message=chunk)
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if run_manager:
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run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk)
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yield cg_chunk
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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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headers = self._create_headers_parameters(**kwargs)
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payload = self._create_payload_parameters(messages, **kwargs)
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import httpx
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async with httpx.AsyncClient(
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headers=headers, timeout=self.request_timeout
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) as client:
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response = await client.post(self.yi_api_base, json=payload)
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response.raise_for_status()
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return self._create_chat_result(response.json())
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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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headers = self._create_headers_parameters(**kwargs)
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payload = self._create_payload_parameters(messages, stream=True, **kwargs)
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import httpx
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async with httpx.AsyncClient(
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headers=headers, timeout=self.request_timeout
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) as client:
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async with aconnect_httpx_sse(
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client, "POST", self.yi_api_base, json=payload
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) as event_source:
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async for sse in event_source.aiter_sse():
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chunk = json.loads(sse.data)
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], AIMessageChunk
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)
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finish_reason = choice.get("finish_reason", None)
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generation_info = (
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{"finish_reason": finish_reason}
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if finish_reason is not None
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else None
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)
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chunk = ChatGenerationChunk(
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message=chunk, generation_info=generation_info
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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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if finish_reason is not None:
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break
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def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
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payload = self._create_payload_parameters(messages, **kwargs)
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url = self.yi_api_base
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headers = self._create_headers_parameters(**kwargs)
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res = requests.post(
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url=url,
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timeout=self.request_timeout,
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headers=headers,
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json=payload,
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stream=self.streaming,
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)
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return res
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def _create_payload_parameters(
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self, messages: List[BaseMessage], **kwargs: Any
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) -> Dict[str, Any]:
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parameters = {**self._default_params, **kwargs}
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temperature = parameters.pop("temperature", 0.7)
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top_p = parameters.pop("top_p", 0.7)
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model = parameters.pop("model")
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stream = parameters.pop("stream", False)
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payload = {
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"model": model,
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"messages": [_convert_message_to_dict(m) for m in messages],
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"top_p": top_p,
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"temperature": temperature,
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"stream": stream,
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}
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return payload
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def _create_headers_parameters(self, **kwargs: Any) -> Dict[str, Any]:
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parameters = {**self._default_params, **kwargs}
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default_headers = parameters.pop("headers", {})
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api_key = ""
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if self.yi_api_key:
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api_key = self.yi_api_key.get_secret_value()
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}",
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**default_headers,
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}
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return headers
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for c in response["choices"]:
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message = _convert_dict_to_message(c["message"])
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gen = ChatGeneration(message=message)
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generations.append(gen)
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token_usage = response["usage"]
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llm_output = {"token_usage": token_usage, "model": self.model}
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return ChatResult(generations=generations, llm_output=llm_output)
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@property
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def _llm_type(self) -> str:
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return "yi-chat"
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