Source code for langchain_deepseek.chat_models

"""DeepSeek chat models."""

from json import JSONDecodeError
from typing import Any, Dict, Iterator, List, Literal, Optional, Type, TypeVar, Union

import openai
from langchain_core.callbacks import (
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.messages import AIMessageChunk, BaseMessage
from langchain_core.outputs import ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable
from langchain_core.utils import from_env, secret_from_env
from langchain_openai.chat_models.base import BaseChatOpenAI
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self

DEFAULT_API_BASE = "https://api.deepseek.com/v1"

_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM], Type]
_DictOrPydantic = Union[Dict, _BM]



[docs]
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``.

        .. code-block:: bash

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

    Key init args — completion params:
        model: str
            Name of DeepSeek model to use, e.g. "deepseek-chat".
        temperature: float
            Sampling temperature.
        max_tokens: Optional[int]
            Max number of tokens to generate.

    Key init args — client params:
        timeout: Optional[float]
            Timeout for requests.
        max_retries: int
            Max number of retries.
        api_key: Optional[str]
            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:
        .. code-block:: python

            from langchain_deepseek import ChatDeepSeek

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

    Invoke:
        .. code-block:: python

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

    Stream:
        .. code-block:: python

            for chunk in llm.stream(messages):
                print(chunk.text(), end="")

        .. code-block:: python

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

    Async:
        .. code-block:: python

            await llm.ainvoke(messages)

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

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

    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")

            llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
            ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
            ai_msg.tool_calls

        See ``ChatDeepSeek.bind_tools()`` method for more.

    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_llm = llm.with_structured_output(Joke)
            structured_llm.invoke("Tell me a joke about cats")

        See ``ChatDeepSeek.with_structured_output()`` for more.

    Token usage:
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.usage_metadata

        .. code-block:: python

            {'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}

    Response metadata
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.response_metadata

    """  # noqa: E501

    model_name: str = Field(alias="model")
    """The name of the model"""
    api_key: Optional[SecretStr] = Field(
        default_factory=secret_from_env("DEEPSEEK_API_KEY", default=None)
    )
    """DeepSeek API key"""
    api_base: str = Field(
        default_factory=from_env("DEEPSEEK_API_BASE", default=DEFAULT_API_BASE)
    )
    """DeepSeek API base URL"""

    model_config = ConfigDict(populate_by_name=True)

    @property
    def _llm_type(self) -> str:
        """Return type of chat model."""
        return "chat-deepseek"

    @property
    def lc_secrets(self) -> Dict[str, str]:
        """A map of constructor argument names to secret ids."""
        return {"api_key": "DEEPSEEK_API_KEY"}

    @model_validator(mode="after")
    def validate_environment(self) -> Self:
        if self.api_base == DEFAULT_API_BASE and not (
            self.api_key and self.api_key.get_secret_value()
        ):
            raise ValueError("If using default api base, DEEPSEEK_API_KEY must be set.")
        client_params: dict = {
            k: v
            for k, v in {
                "api_key": self.api_key.get_secret_value() if self.api_key else None,
                "base_url": self.api_base,
                "timeout": self.request_timeout,
                "max_retries": self.max_retries,
                "default_headers": self.default_headers,
                "default_query": self.default_query,
            }.items()
            if v is not None
        }

        if not (self.client or None):
            sync_specific: dict = {"http_client": self.http_client}
            self.root_client = openai.OpenAI(**client_params, **sync_specific)
            self.client = self.root_client.chat.completions
        if not (self.async_client or None):
            async_specific: dict = {"http_client": self.http_async_client}
            self.root_async_client = openai.AsyncOpenAI(
                **client_params,
                **async_specific,
            )
            self.async_client = self.root_async_client.chat.completions
        return self

    def _create_chat_result(
        self,
        response: Union[dict, openai.BaseModel],
        generation_info: Optional[Dict] = None,
    ) -> ChatResult:
        rtn = super()._create_chat_result(response, generation_info)

        if not isinstance(response, openai.BaseModel):
            return rtn

        if hasattr(response.choices[0].message, "reasoning_content"):  # type: ignore
            rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
                response.choices[0].message.reasoning_content  # type: ignore
            )
        # Handle use via OpenRouter
        elif hasattr(response.choices[0].message, "model_extra"):  # type: ignore
            model_extra = response.choices[0].message.model_extra  # type: ignore
            if isinstance(model_extra, dict) and (
                reasoning := model_extra.get("reasoning")
            ):
                rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
                    reasoning
                )

        return rtn

    def _convert_chunk_to_generation_chunk(
        self,
        chunk: dict,
        default_chunk_class: Type,
        base_generation_info: Optional[Dict],
    ) -> Optional[ChatGenerationChunk]:
        generation_chunk = super()._convert_chunk_to_generation_chunk(
            chunk,
            default_chunk_class,
            base_generation_info,
        )
        if (choices := chunk.get("choices")) and generation_chunk:
            top = choices[0]
            if isinstance(generation_chunk.message, AIMessageChunk):
                if reasoning_content := top.get("delta", {}).get("reasoning_content"):
                    generation_chunk.message.additional_kwargs["reasoning_content"] = (
                        reasoning_content
                    )
                # Handle use via OpenRouter
                elif reasoning := top.get("delta", {}).get("reasoning"):
                    generation_chunk.message.additional_kwargs["reasoning_content"] = (
                        reasoning
                    )

        return generation_chunk

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        try:
            yield from super()._stream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
        except JSONDecodeError as e:
            raise JSONDecodeError(
                "DeepSeek API returned an invalid response. "
                "Please check the API status and try again.",
                e.doc,
                e.pos,
            ) from e

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        try:
            return super()._generate(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
        except JSONDecodeError as e:
            raise JSONDecodeError(
                "DeepSeek API returned an invalid response. "
                "Please check the API status and try again.",
                e.doc,
                e.pos,
            ) from e


[docs]
    def with_structured_output(
        self,
        schema: Optional[_DictOrPydanticClass] = None,
        *,
        method: Literal[
            "function_calling", "json_mode", "json_schema"
        ] = "function_calling",
        include_raw: bool = False,
        strict: Optional[bool] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, _DictOrPydantic]:
        """Model wrapper that returns outputs formatted to match the given schema.

        Args:
            schema:
                The output schema. Can be passed in as:

                - an OpenAI function/tool schema,
                - a JSON Schema,
                - a TypedDict class (support added in 0.1.20),
                - or a Pydantic class.

                If ``schema`` is a Pydantic class then the model output will be a
                Pydantic instance of that class, and the model-generated fields will be
                validated by the Pydantic class. Otherwise the model output will be a
                dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
                for more on how to properly specify types and descriptions of
                schema fields when specifying a Pydantic or TypedDict class.

            method: The method for steering model generation, one of:

                - "function_calling":
                    Uses DeekSeek's `tool-calling features <https://api-docs.deepseek.com/guides/function_calling>`_.
                - "json_mode":
                    Uses DeepSeek's `JSON mode feature <https://api-docs.deepseek.com/guides/json_mode>`_.

                .. versionchanged:: 0.1.3

                    Added support for ``"json_mode"``.

            include_raw:
                If False then only the parsed structured output is returned. If
                an error occurs during model output parsing it will be raised. If True
                then both the raw model response (a BaseMessage) and the parsed model
                response will be returned. If an error occurs during output parsing it
                will be caught and returned as well. The final output is always a dict
                with keys "raw", "parsed", and "parsing_error".

            kwargs: Additional keyword args aren't supported.

        Returns:
            A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.

            | If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict.

            | If ``include_raw`` is True, then Runnable outputs a dict with keys:

            - "raw": BaseMessage
            - "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
            - "parsing_error": Optional[BaseException]

        """  # noqa: E501
        # Some applications require that incompatible parameters (e.g., unsupported
        # methods) be handled.
        if method == "json_schema":
            method = "function_calling"
        return super().with_structured_output(
            schema, method=method, include_raw=include_raw, strict=strict, **kwargs
        )