ChatDeepSeek Class — langchain Architecture

Source Code

libs/partners/deepseek/langchain_deepseek/chat_models.py lines 46–542

class ChatDeepSeek(BaseChatOpenAI):
    """DeepSeek chat model integration to access models hosted in DeepSeek's API.

    Setup:
        Install `langchain-deepseek` and set environment variable `DEEPSEEK_API_KEY`.

        ```bash
        pip install -U langchain-deepseek
        export DEEPSEEK_API_KEY="your-api-key"
        ```

    Key init args — completion params:
        model:
            Name of DeepSeek model to use, e.g. `'deepseek-chat'`.
        temperature:
            Sampling temperature.
        max_tokens:
            Max number of tokens to generate.

    Key init args — client params:
        timeout:
            Timeout for requests.
        max_retries:
            Max number of retries.
        api_key:
            DeepSeek API key. If not passed in will be read from env var `DEEPSEEK_API_KEY`.

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

    Instantiate:
        ```python
        from langchain_deepseek import ChatDeepSeek

        model = ChatDeepSeek(
            model="...",
            temperature=0,
            max_tokens=None,
            timeout=None,
            max_retries=2,
            # api_key="...",
            # other params...
        )
        ```

    Invoke:
        ```python
        messages = [
            ("system", "You are a helpful translator. Translate the user sentence to French."),
            ("human", "I love programming."),
        ]
        model.invoke(messages)
        ```

    Stream:
        ```python
        for chunk in model.stream(messages):
            print(chunk.text, end="")
        ```
        ```python
        stream = model.stream(messages)
        full = next(stream)
        for chunk in stream:
            full += chunk
        full
        ```

    Async:
        ```python
        await model.ainvoke(messages)

        # stream:
        # async for chunk in (await model.astream(messages))

        # batch:
        # await model.abatch([messages])
        ```

    Tool calling:
        ```python
        from pydantic import BaseModel, Field