MoonshotChat

Setup:

Install openai and set environment variables MOONSHOT_API_KEY.

.. code-block:: bash

pip install openai
export MOONSHOT_API_KEY="your-api-key"

Key init args — completion params: model: str Name of Moonshot model to use. temperature: float Sampling temperature. max_tokens: Optional[int] Max number of tokens to generate.

Key init args — client params: api_key: Optional[str] Moonshot API KEY. If not passed in will be read from env var MOONSHOT_API_KEY. api_base: 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 MoonshotChat

chat = MoonshotChat( temperature=0.5, api_key="your-api-key", model="moonshot-v1-8k", # api_base="...", # 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 like programming.',
    additional_kwargs={},
    response_metadata={
        'token_usage': {
            'completion_tokens': 5,
            'prompt_tokens': 27,
            'total_tokens': 32
        },
        'model_name': 'moonshot-v1-8k',
        'system_fingerprint': None,
        'finish_reason': 'stop',
        'logprobs': None
    },
    id='run-71c03f4e-6628-41d5-beb6-d2559ae68266-0'
)

Stream:

.. code-block:: python

for chunk in chat.stream(messages):
    print(chunk)

.. code-block:: python

content='' additional_kwargs={} response_metadata={} id='run-80d77096-8b83-4c39-a84d-71d9c746da92'
content='I' additional_kwargs={} response_metadata={} id='run-80d77096-8b83-4c39-a84d-71d9c746da92'
content=' like' additional_kwargs={} response_metadata={} id='run-80d77096-8b83-4c39-a84d-71d9c746da92'
content=' programming' additional_kwargs={} response_metadata={} id='run-80d77096-8b83-4c39-a84d-71d9c746da92'
content='.' additional_kwargs={} response_metadata={} id='run-80d77096-8b83-4c39-a84d-71d9c746da92'
content='' additional_kwargs={} response_metadata={'finish_reason': 'stop'} id='run-80d77096-8b83-4c39-a84d-71d9c746da92'

.. code-block:: python

stream = chat.stream(messages)
full = next(stream)
for chunk in stream:
    full += chunk
full

.. code-block:: python

AIMessageChunk(
    content='I like programming.',
    additional_kwargs={},
    response_metadata={'finish_reason': 'stop'},
    id='run-10c80976-7aa5-4ff7-ba3e-1251665557ef'
)

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 like programming.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 27, 'total_tokens': 32}, 'model_name': 'moonshot-v1-8k', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-2938b005-9204-4b9f-b273-1c3272fce9e5-0')]

Response metadata .. code-block:: python

ai_msg = chat.invoke(messages)
ai_msg.response_metadata

.. code-block:: python

{
    'token_usage': {
        'completion_tokens': 5,
        'prompt_tokens': 27,
        'total_tokens': 32
    },
    'model_name': 'moonshot-v1-8k',
    'system_fingerprint': None,
    'finish_reason': 'stop',
    'logprobs': None
}