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https://github.com/hwchase17/langchain
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docs: Add MiniMaxChat docstrings (#24026)
- **Description:** Add MiniMaxChat rich docstrings. - **Issue:** the issue #22296
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@ -116,18 +116,133 @@ def _convert_delta_to_message_chunk(
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class MiniMaxChat(BaseChatModel):
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"""MiniMax large language models.
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"""MiniMax chat model integration.
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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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Setup:
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To use, you should have the environment variable``MINIMAX_API_KEY`` set with
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your API KEY.
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Example:
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.. code-block:: bash
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export MINIMAX_API_KEY="your-api-key"
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Key init args — completion params:
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model: Optional[str]
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Name of MiniMax model to use.
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max_tokens: Optional[int]
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Max number of tokens to generate.
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temperature: Optional[float]
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Sampling temperature.
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top_p: Optional[float]
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Total probability mass of tokens to consider at each step.
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streaming: Optional[bool]
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Whether to stream the results or not.
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Key init args — client params:
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api_key: Optional[str]
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MiniMax API key. If not passed in will be read from env var MINIMAX_API_KEY.
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base_url: Optional[str]
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Base URL for API requests.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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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="abab5-chat")
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"""
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chat = MiniMaxChat(
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api_key=api_key,
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model='abab6.5-chat',
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# temperature=...,
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# other params...
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)
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Invoke:
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.. code-block:: python
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messages = [
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("system", "你是一名专业的翻译家,可以将用户的中文翻译为英文。"),
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("human", "我喜欢编程。"),
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]
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chat.invoke(messages)
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.. code-block:: python
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AIMessage(
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content='I enjoy programming.',
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response_metadata={
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'token_usage': {'total_tokens': 48},
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'model_name': 'abab6.5-chat',
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'finish_reason': 'stop'
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},
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id='run-42d62ba6-5dc1-4e16-98dc-f72708a4162d-0'
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)
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Stream:
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.. code-block:: python
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for chunk in chat.stream(messages):
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print(chunk)
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.. code-block:: python
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content='I' id='run-a5837c45-4aaa-4f64-9ab4-2679bbd55522'
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content=' enjoy programming.' response_metadata={'finish_reason': 'stop'} id='run-a5837c45-4aaa-4f64-9ab4-2679bbd55522'
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.. code-block:: python
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stream = chat.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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.. code-block:: python
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AIMessageChunk(
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content='I enjoy programming.',
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response_metadata={'finish_reason': 'stop'},
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id='run-01aed0a0-61c4-4709-be22-c6d8b17155d6'
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)
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Async:
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.. code-block:: python
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await chat.ainvoke(messages)
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# stream
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# async for chunk in chat.astream(messages):
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# print(chunk)
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# batch
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# await chat.abatch([messages])
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.. code-block:: python
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AIMessage(
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content='I enjoy programming.',
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response_metadata={
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'token_usage': {'total_tokens': 48},
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'model_name': 'abab6.5-chat',
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'finish_reason': 'stop'
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},
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id='run-c263b6f1-1736-4ece-a895-055c26b3436f-0'
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)
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Response metadata
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.. code-block:: python
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ai_msg = chat.invoke(messages)
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ai_msg.response_metadata
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.. code-block:: python
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{'token_usage': {'total_tokens': 48},
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'model_name': 'abab6.5-chat',
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'finish_reason': 'stop'}
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""" # noqa: E501conj
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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