ChatTongyi

Setup:

Install dashscope and set environment variables DASHSCOPE_API_KEY.

.. code-block:: bash

pip install dashscope
export DASHSCOPE_API_KEY="your-api-key"

Key init args — completion params: model: str Name of Qianfan model to use. top_p: float Total probability mass of tokens to consider at each step. streaming: bool Whether to stream the results or not.

Key init args — client params: api_key: Optional[str] Dashscope API KEY. If not passed in will be read from env var DASHSCOPE_API_KEY. max_retries: int Maximum number of retries to make when generating.

See full list of supported init args and their descriptions in the params section.

Instantiate:

.. code-block:: python

from langchain_community.chat_models import ChatTongyi

tongyi_chat = ChatTongyi( model="qwen-max", # top_p="...", # api_key="...", # 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。"),
]
tongyi_chat.invoke(messages)

.. code-block:: python

AIMessage(
    content='I enjoy programming.',
    response_metadata={
        'model_name': 'qwen-max',
        'finish_reason': 'stop',
        'request_id': '0bd14853-4abc-9593-8642-8dbb915bd4df',
        'token_usage': {
            'input_tokens': 30,
            'output_tokens': 4,
            'total_tokens': 34
        }
    },
    id='run-533b3688-d12b-40c6-a2f7-52f291f8fa0a-0'
)

Stream:

.. code-block:: python

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

.. code-block:: python

content='I' id='run-8fbcce63-42fc-4208-9399-da46ac40c967'
content=' enjoy' id='run-8fbcce63-42fc-4208-9399-da46ac40c967'
content=' programming' id='run-8fbcce63-42fc-4208-9399-da46ac40c967'
content='.' response_metadata={'finish_reason': 'stop', 'request_id': '67aec2b5-72bf-96a4-ae29-5bfebd2e7305', 'token_usage': {'input_tokens': 30, 'output_tokens': 4, 'total_tokens': 34}} id='run-8fbcce63-42fc-4208-9399-da46ac40c967'

Async:

.. code-block:: python

await tongyi_chat.ainvoke(messages)

# stream:
# async for chunk in tongyi_chat.astream(messages):
#    print(chunk)

# batch:
# await tongyi_chat.abatch([messages])

.. code-block:: python

AIMessage(
    content='I enjoy programming.',
    response_metadata={
        'model_name': 'qwen-max',
        'finish_reason': 'stop',
        'request_id': 'a55a2d6c-a876-9789-9dd9-7b52bf8adde0',
        'token_usage': {
            'input_tokens': 30,
            'output_tokens': 4,
            'total_tokens': 34
        }
    },
    id='run-3bffa3ec-e8d9-4043-b57d-348e047d64de-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 = tongyi_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': 'Los Angeles, CA'}, 'id': '' } ]

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 = tongyi_chat.with_structured_output(Joke)
structured_chat.invoke("Tell me a joke about cats")

.. code-block:: python

Joke(
    setup='Why did the cat join the band?',
    punchline='Because it wanted to be a solo purr-sonality!',
    rating=None
)

Response metadata .. code-block:: python

ai_msg = tongyi_chat.invoke(messages)
ai_msg.response_metadata

.. code-block:: python

{
    'model_name': 'qwen-max',
    'finish_reason': 'stop',
    'request_id': '32a13e4c-370e-99cb-8f9b-4c999d98c57d',
    'token_usage': {
        'input_tokens': 30,
        'output_tokens': 4,
        'total_tokens': 34
    }
}