MiniMaxChat()MiniMax chat model integration.
Setup:
To use, you should have the environment variableMINIMAX_API_KEY set with
your API KEY.
.. code-block:: bash
export MINIMAX_API_KEY="your-api-key"
Key init args — completion params: model: Optional[str] Name of MiniMax model to use. max_tokens: Optional[int] Max number of tokens to generate. temperature: Optional[float] Sampling temperature. top_p: Optional[float] Total probability mass of tokens to consider at each step. streaming: Optional[bool] Whether to stream the results or not.
Key init args — client params: api_key: Optional[str] MiniMax API key. If not passed in will be read from env var MINIMAX_API_KEY. base_url: Optional[str] Base URL for API requests.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_community.chat_models import MiniMaxChat
chat = MiniMaxChat( api_key=api_key, model='abab6.5-chat', # temperature=..., # other params... )
Invoke:
.. code-block:: python
messages = [
("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
("human", "我喜欢编程。"),
]
chat.invoke(messages)
.. code-block:: python
AIMessage(
content='I enjoy programming.',
response_metadata={
'token_usage': {'total_tokens': 48},
'model_name': 'abab6.5-chat',
'finish_reason': 'stop'
},
id='run-42d62ba6-5dc1-4e16-98dc-f72708a4162d-0'
)
Stream:
.. code-block:: python
for chunk in chat.stream(messages):
print(chunk)
.. code-block:: python
content='I' id='run-a5837c45-4aaa-4f64-9ab4-2679bbd55522'
content=' enjoy programming.' response_metadata={'finish_reason': 'stop'} id='run-a5837c45-4aaa-4f64-9ab4-2679bbd55522'
.. code-block:: python
stream = chat.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(
content='I enjoy programming.',
response_metadata={'finish_reason': 'stop'},
id='run-01aed0a0-61c4-4709-be22-c6d8b17155d6'
)
Async:
.. code-block:: python
await chat.ainvoke(messages)
# stream
# async for chunk in chat.astream(messages):
# print(chunk)
# batch
# await chat.abatch([messages])
.. code-block:: python
AIMessage(
content='I enjoy programming.',
response_metadata={
'token_usage': {'total_tokens': 48},
'model_name': 'abab6.5-chat',
'finish_reason': 'stop'
},
id='run-c263b6f1-1736-4ece-a895-055c26b3436f-0'
)
Tool calling:
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
chat_with_tools = chat.bind_tools([GetWeather, GetPopulation])
ai_msg = chat_with_tools.invoke(
"Which city is hotter today and which is bigger: LA or NY?"
)
ai_msg.tool_calls
.. code-block:: python
[
{
'name': 'GetWeather',
'args': {'location': 'LA'},
'id': 'call_function_2140449382',
'type': 'tool_call'
}
]
Structured output:
.. code-block:: python
from typing import Optional
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_chat = chat.with_structured_output(Joke)
structured_chat.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(
setup='Why do cats have nine lives?',
punchline='Because they are so cute and cuddly!',
rating=None
)
Response metadata .. code-block:: python
ai_msg = chat.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{'token_usage': {'total_tokens': 48},
'model_name': 'abab6.5-chat',
'finish_reason': 'stop'}