"""DeepSeek chat models.""" from __future__ import annotations import json from collections.abc import Callable, Iterator, Sequence from json import JSONDecodeError from typing import Any, Literal, TypeAlias, cast import openai from langchain_core.callbacks import ( CallbackManagerForLLMRun, ) from langchain_core.language_models import ( LangSmithParams, LanguageModelInput, ModelProfile, ModelProfileRegistry, ) from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage from langchain_core.outputs import ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool 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 from langchain_deepseek.data._profiles import _PROFILES DEFAULT_API_BASE = "https://api.deepseek.com/v1" DEFAULT_BETA_API_BASE = "https://api.deepseek.com/beta" _DictOrPydanticClass: TypeAlias = dict[str, Any] | type[BaseModel] _DictOrPydantic: TypeAlias = dict[str, Any] | BaseModel _MODEL_PROFILES = cast("ModelProfileRegistry", _PROFILES) def _get_default_model_profile(model_name: str) -> ModelProfile: default = _MODEL_PROFILES.get(model_name) or {} return default.copy() 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 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") model_with_tools = model.bind_tools([GetWeather, GetPopulation]) ai_msg = model_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: ```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: int | None = Field(description="How funny the joke is, from 1 to 10") structured_model = model.with_structured_output(Joke) structured_model.invoke("Tell me a joke about cats") ``` See `ChatDeepSeek.with_structured_output()` for more. Token usage: ```python ai_msg = model.invoke(messages) ai_msg.usage_metadata ``` ```python {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33} ``` Response metadata: ```python ai_msg = model.invoke(messages) ai_msg.response_metadata ``` """ # noqa: E501 model_name: str = Field(alias="model") """The name of the model""" api_key: SecretStr | None = 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"} def _get_ls_params( self, stop: list[str] | None = None, **kwargs: Any, ) -> LangSmithParams: ls_params = super()._get_ls_params(stop=stop, **kwargs) ls_params["ls_provider"] = "deepseek" return ls_params @model_validator(mode="after") def validate_environment(self) -> Self: """Validate necessary environment vars and client params.""" if self.api_base == DEFAULT_API_BASE and not ( self.api_key and self.api_key.get_secret_value() ): msg = "If using default api base, DEEPSEEK_API_KEY must be set." raise ValueError(msg) 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 @model_validator(mode="after") def _set_model_profile(self) -> Self: """Set model profile if not overridden.""" if self.profile is None: self.profile = _get_default_model_profile(self.model_name) return self def _get_request_payload( self, input_: LanguageModelInput, *, stop: list[str] | None = None, **kwargs: Any, ) -> dict: payload = super()._get_request_payload(input_, stop=stop, **kwargs) for message in payload["messages"]: if message["role"] == "tool" and isinstance(message["content"], list): message["content"] = json.dumps(message["content"]) elif message["role"] == "assistant" and isinstance( message["content"], list ): # DeepSeek API expects assistant content to be a string, not a list. # Extract text blocks and join them, or use empty string if none exist. text_parts = [ block.get("text", "") for block in message["content"] if isinstance(block, dict) and block.get("type") == "text" ] message["content"] = "".join(text_parts) if text_parts else "" return payload def _create_chat_result( self, response: dict | openai.BaseModel, generation_info: dict | None = None, ) -> ChatResult: rtn = super()._create_chat_result(response, generation_info) if not isinstance(response, openai.BaseModel): return rtn for generation in rtn.generations: if generation.message.response_metadata is None: generation.message.response_metadata = {} generation.message.response_metadata["model_provider"] = "deepseek" choices = getattr(response, "choices", None) if choices and hasattr(choices[0].message, "reasoning_content"): rtn.generations[0].message.additional_kwargs["reasoning_content"] = choices[ 0 ].message.reasoning_content # Handle use via OpenRouter elif choices and hasattr(choices[0].message, "model_extra"): model_extra = choices[0].message.model_extra 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: dict | None, ) -> ChatGenerationChunk | None: 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): generation_chunk.message.response_metadata = { **generation_chunk.message.response_metadata, "model_provider": "deepseek", } if ( reasoning_content := top.get("delta", {}).get("reasoning_content") ) is not None: generation_chunk.message.additional_kwargs["reasoning_content"] = ( reasoning_content ) # Handle use via OpenRouter elif (reasoning := top.get("delta", {}).get("reasoning")) is not None: generation_chunk.message.additional_kwargs["reasoning_content"] = ( reasoning ) return generation_chunk def _stream( self, messages: list[BaseMessage], stop: list[str] | None = None, run_manager: CallbackManagerForLLMRun | None = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: try: yield from super()._stream( messages, stop=stop, run_manager=run_manager, **kwargs, ) except JSONDecodeError as e: msg = ( "DeepSeek API returned an invalid response. " "Please check the API status and try again." ) raise JSONDecodeError( msg, e.doc, e.pos, ) from e def _generate( self, messages: list[BaseMessage], stop: list[str] | None = None, run_manager: CallbackManagerForLLMRun | None = None, **kwargs: Any, ) -> ChatResult: try: return super()._generate( messages, stop=stop, run_manager=run_manager, **kwargs, ) except JSONDecodeError as e: msg = ( "DeepSeek API returned an invalid response. " "Please check the API status and try again." ) raise JSONDecodeError( msg, e.doc, e.pos, ) from e def bind_tools( self, tools: Sequence[dict[str, Any] | type | Callable | BaseTool], *, tool_choice: dict | str | bool | None = None, strict: bool | None = None, parallel_tool_calls: bool | None = None, **kwargs: Any, ) -> Runnable[LanguageModelInput, AIMessage]: """Bind tool-like objects to this chat model. Overrides parent to use beta endpoint when `strict=True`. Args: tools: A list of tool definitions to bind to this chat model. tool_choice: Which tool to require the model to call. strict: If True, uses beta API for strict schema validation. parallel_tool_calls: Set to `False` to disable parallel tool use. **kwargs: Additional parameters passed to parent `bind_tools`. Returns: A Runnable that takes same inputs as a chat model. """ # If strict mode is enabled and using default API base, switch to beta endpoint if strict is True and self.api_base == DEFAULT_API_BASE: # Create a new instance with beta endpoint beta_model = self.model_copy(update={"api_base": DEFAULT_BETA_API_BASE}) return beta_model.bind_tools( tools, tool_choice=tool_choice, strict=strict, parallel_tool_calls=parallel_tool_calls, **kwargs, ) # Otherwise use parent implementation return super().bind_tools( tools, tool_choice=tool_choice, strict=strict, parallel_tool_calls=parallel_tool_calls, **kwargs, ) def with_structured_output( self, schema: _DictOrPydanticClass | None = None, *, method: Literal[ "function_calling", "json_mode", "json_schema", ] = "function_calling", include_raw: bool = False, strict: bool | None = 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, - 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 `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 DeepSeek'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). 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'`. strict: Whether to enable strict schema adherence when generating the function call. When set to `True`, DeepSeek will use the beta API endpoint (`https://api.deepseek.com/beta`) for strict schema validation. This ensures model outputs exactly match the defined schema. !!! note DeepSeek's strict mode requires all object properties to be marked as required in the schema. kwargs: Additional keyword args aren't supported. Returns: A `Runnable` that takes same inputs as a `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'`: `BaseException | None` """ # Some applications require that incompatible parameters (e.g., unsupported # methods) be handled. if method == "json_schema": method = "function_calling" # If strict mode is enabled and using default API base, switch to beta endpoint if strict is True and self.api_base == DEFAULT_API_BASE: # Create a new instance with beta endpoint beta_model = self.model_copy(update={"api_base": DEFAULT_BETA_API_BASE}) return beta_model.with_structured_output( schema, method=method, include_raw=include_raw, strict=strict, **kwargs, ) return super().with_structured_output( schema, method=method, include_raw=include_raw, strict=strict, **kwargs, )