mirror of
https://github.com/hwchase17/langchain.git
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community[minor]: Implement MiniMaxChat interface (#22391)
- **Description:** Implement MiniMaxChat interface, include:
- No longer inherits the LLM class (like other chat model)
- Update request parameters (v1 -> v2)
- update `base url`
- update message role (system, user, assistant)
- add `stream` function
- no longer use `group id`
- Implement the `_stream`, `_agenerate`, and `_astream` interfaces
[minimax v2 api
document](https://platform.minimaxi.com/document/guides/chat-model/V2?id=65e0736ab2845de20908e2dd)
This commit is contained in:
maang-h
GitHub
parent
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commit
01352bb55f
@ -1,61 +1,212 @@
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"""Wrapper around Minimax chat models."""
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import json
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import logging
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from typing import Any, Dict, List, Optional, cast
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from contextlib import asynccontextmanager, contextmanager
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Type, Union
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.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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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatResult
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from langchain_community.llms.minimax import MinimaxCommon
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from langchain_community.llms.utils import enforce_stop_tokens
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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.utils import convert_to_secret_str, get_from_dict_or_env
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logger = logging.getLogger(__name__)
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def _parse_message(msg_type: str, text: str) -> Dict:
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return {"sender_type": msg_type, "text": text}
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@contextmanager
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def connect_httpx_sse(client: Any, method: str, url: str, **kwargs: Any) -> Iterator:
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from httpx_sse import EventSource
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with client.stream(method, url, **kwargs) as response:
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yield EventSource(response)
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def _parse_chat_history(history: List[BaseMessage]) -> List:
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"""Parse a sequence of messages into history."""
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chat_history = []
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for message in history:
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content = cast(str, message.content)
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if isinstance(message, HumanMessage):
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chat_history.append(_parse_message("USER", content))
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if isinstance(message, AIMessage):
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chat_history.append(_parse_message("BOT", content))
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return chat_history
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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 MiniMaxChat(MinimaxCommon, BaseChatModel):
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def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
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"""Convert a LangChain messages to Dict."""
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message_dict: Dict[str, Any]
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if 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": "system", "content": message.content}
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else:
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raise TypeError(f"Got unknown type '{message.__class__.__name__}'.")
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return message_dict
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def _convert_dict_to_message(dct: Dict[str, Any]) -> BaseMessage:
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"""Convert a dict to LangChain message."""
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role = dct.get("role")
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content = dct.get("content", "")
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if role == "assistant":
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additional_kwargs = {}
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tool_calls = dct.get("tool_calls", None)
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if tool_calls is not None:
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additional_kwargs["tool_calls"] = tool_calls
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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return ChatMessage(role=role, content=content) # type: ignore[arg-type]
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def _convert_delta_to_message_chunk(
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dct: Dict[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = dct.get("role")
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content = dct.get("content", "")
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additional_kwargs = {}
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tool_calls = dct.get("tool_call", None)
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if tool_calls is not None:
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additional_kwargs["tool_calls"] = tool_calls
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if role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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if role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type]
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return default_class(content=content) # type: ignore[call-arg]
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class MiniMaxChat(BaseChatModel):
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"""MiniMax large language models.
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To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
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``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
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the constructor.
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To use, you should have the environment variable``MINIMAX_API_KEY`` set with
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your API token, or pass it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import MiniMaxChat
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llm = MiniMaxChat(model_name="abab5-chat")
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llm = MiniMaxChat(model="abab5-chat")
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"""
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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 {**{"model": self.model}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "minimax"
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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,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"top_p": self.top_p,
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**self.model_kwargs,
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}
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_client: Any
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model: str = "abab6.5-chat"
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"""Model name to use."""
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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: float = 0.7
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"""A non-negative float that tunes the degree of randomness in generation."""
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top_p: float = 0.95
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"""Total probability mass of tokens to consider at each step."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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minimax_api_host: str = Field(
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default="https://api.minimax.chat/v1/text/chatcompletion_v2", alias="base_url"
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)
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minimax_group_id: Optional[str] = Field(default=None, alias="group_id")
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"""[DEPRECATED, keeping it for for backward compatibility] Group Id"""
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minimax_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Minimax API Key"""
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streaming: bool = False
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"""Whether to stream the results or not."""
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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(allow_reuse=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["minimax_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "minimax_api_key", "MINIMAX_API_KEY")
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)
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values["minimax_group_id"] = get_from_dict_or_env(
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values, "minimax_group_id", "MINIMAX_GROUP_ID"
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)
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# Get custom api url from environment.
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values["minimax_api_host"] = get_from_dict_or_env(
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values,
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"minimax_api_host",
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"MINIMAX_API_HOST",
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values["minimax_api_host"],
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)
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return values
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def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
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generations = []
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if not isinstance(response, dict):
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response = response.dict()
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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generation_info = dict(finish_reason=res.get("finish_reason"))
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generations.append(
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ChatGeneration(message=message, generation_info=generation_info)
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)
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token_usage = response.get("usage", {})
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llm_output = {
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"token_usage": token_usage,
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"model_name": self.model,
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}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _create_payload_parameters( # type: ignore[no-untyped-def]
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self, messages: List[BaseMessage], is_stream: bool = False, **kwargs
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) -> Dict[str, Any]:
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"""Create API request body parameters."""
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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payload = self._default_params
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payload["messages"] = message_dicts
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payload.update(**kwargs)
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if is_stream:
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payload["stream"] = True
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return payload
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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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"""Generate next turn in the conversation.
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@ -64,6 +215,7 @@ class MiniMaxChat(MinimaxCommon, BaseChatModel):
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does not support context.
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stop: The list of stop words (optional).
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run_manager: The CallbackManager for LLM run, it's not used at the moment.
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stream: Whether to stream the results or not.
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Returns:
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The ChatResult that contains outputs generated by the model.
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@ -75,22 +227,148 @@ class MiniMaxChat(MinimaxCommon, BaseChatModel):
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raise ValueError(
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"You should provide at least one message to start the chat!"
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)
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history = _parse_chat_history(messages)
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payload = self._default_params
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payload["messages"] = history
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text = self._client.post(payload)
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is_stream = stream if stream is not None else self.streaming
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if is_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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payload = self._create_payload_parameters(messages, **kwargs)
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api_key = ""
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if self.minimax_api_key is not None:
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api_key = self.minimax_api_key.get_secret_value()
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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import httpx
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# This is required since the stop are not enforced by the model parameters
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text = text if stop is None else enforce_stop_tokens(text, stop)
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return ChatResult(generations=[ChatGeneration(message=AIMessage(text))]) # type: ignore[misc]
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with httpx.Client(headers=headers, timeout=60) as client:
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response = client.post(self.minimax_api_host, 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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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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"""Stream the chat response in chunks."""
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payload = self._create_payload_parameters(messages, is_stream=True, **kwargs)
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api_key = ""
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if self.minimax_api_key is not None:
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api_key = self.minimax_api_key.get_secret_value()
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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import httpx
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with httpx.Client(headers=headers, timeout=60) as client:
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with connect_httpx_sse(
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client, "POST", self.minimax_api_host, json=payload
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) as event_source:
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for sse in event_source.iter_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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yield chunk
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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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if finish_reason is not None:
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break
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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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raise NotImplementedError(
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"""Minimax AI doesn't support async requests at the moment."""
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)
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if not messages:
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raise ValueError(
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"You should provide at least one message to start the chat!"
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)
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is_stream = stream if stream is not None else self.streaming
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if is_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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payload = self._create_payload_parameters(messages, **kwargs)
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api_key = ""
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if self.minimax_api_key is not None:
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api_key = self.minimax_api_key.get_secret_value()
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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import httpx
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async with httpx.AsyncClient(headers=headers, timeout=60) as client:
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response = await client.post(self.minimax_api_host, 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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payload = self._create_payload_parameters(messages, is_stream=True, **kwargs)
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api_key = ""
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if self.minimax_api_key is not None:
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api_key = self.minimax_api_key.get_secret_value()
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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import httpx
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async with httpx.AsyncClient(headers=headers, timeout=60) as client:
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async with aconnect_httpx_sse(
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client, "POST", self.minimax_api_host, 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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yield chunk
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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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if finish_reason is not None:
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break
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