mirror of
https://github.com/hwchase17/langchain.git
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453 lines
16 KiB
Python
453 lines
16 KiB
Python
"""DeepSeek chat models."""
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from __future__ import annotations
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import json
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from collections.abc import Iterator
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from json import JSONDecodeError
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from typing import Any, Literal, TypeAlias
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import openai
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from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import LangSmithParams, LanguageModelInput
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from langchain_core.messages import AIMessageChunk, BaseMessage
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from langchain_core.outputs import ChatGenerationChunk, ChatResult
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from langchain_core.runnables import Runnable
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from langchain_core.utils import from_env, secret_from_env
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from langchain_openai.chat_models.base import BaseChatOpenAI
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from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
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from typing_extensions import Self
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DEFAULT_API_BASE = "https://api.deepseek.com/v1"
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_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[BaseModel]
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_DictOrPydantic: TypeAlias = dict[str, Any] | BaseModel
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class ChatDeepSeek(BaseChatOpenAI):
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"""DeepSeek chat model integration to access models hosted in DeepSeek's API.
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Setup:
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Install `langchain-deepseek` and set environment variable `DEEPSEEK_API_KEY`.
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```bash
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pip install -U langchain-deepseek
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export DEEPSEEK_API_KEY="your-api-key"
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```
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Key init args — completion params:
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model:
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Name of DeepSeek model to use, e.g. `"deepseek-chat"`.
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temperature:
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Sampling temperature.
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max_tokens:
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Max number of tokens to generate.
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Key init args — client params:
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timeout:
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Timeout for requests.
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max_retries:
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Max number of retries.
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api_key:
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DeepSeek API key. If not passed in will be read from env var `DEEPSEEK_API_KEY`.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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```python
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from langchain_deepseek import ChatDeepSeek
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model = ChatDeepSeek(
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model="...",
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temperature=0,
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max_tokens=None,
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timeout=None,
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max_retries=2,
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# api_key="...",
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# other params...
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)
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```
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Invoke:
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```python
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messages = [
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("system", "You are a helpful translator. Translate the user sentence to French."),
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("human", "I love programming."),
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]
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model.invoke(messages)
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```
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Stream:
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```python
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for chunk in model.stream(messages):
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print(chunk.text, end="")
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```
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```python
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stream = model.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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```
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Async:
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```python
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await model.ainvoke(messages)
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# stream:
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# async for chunk in (await model.astream(messages))
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# batch:
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# await model.abatch([messages])
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```
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Tool calling:
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```python
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from pydantic import BaseModel, Field
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class GetWeather(BaseModel):
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'''Get the current weather in a given location'''
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location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
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class GetPopulation(BaseModel):
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'''Get the current population in a given location'''
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location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
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model_with_tools = model.bind_tools([GetWeather, GetPopulation])
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ai_msg = model_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
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ai_msg.tool_calls
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```
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See `ChatDeepSeek.bind_tools()` method for more.
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Structured output:
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```python
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from typing import Optional
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from pydantic import BaseModel, Field
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class Joke(BaseModel):
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'''Joke to tell user.'''
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setup: str = Field(description="The setup of the joke")
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punchline: str = Field(description="The punchline to the joke")
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rating: int | None = Field(description="How funny the joke is, from 1 to 10")
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structured_model = model.with_structured_output(Joke)
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structured_model.invoke("Tell me a joke about cats")
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```
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See `ChatDeepSeek.with_structured_output()` for more.
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Token usage:
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```python
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ai_msg = model.invoke(messages)
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ai_msg.usage_metadata
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```
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```python
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{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
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```
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Response metadata
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```python
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ai_msg = model.invoke(messages)
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ai_msg.response_metadata
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```
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""" # noqa: E501
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model_name: str = Field(alias="model")
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"""The name of the model"""
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api_key: SecretStr | None = Field(
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default_factory=secret_from_env("DEEPSEEK_API_KEY", default=None),
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)
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"""DeepSeek API key"""
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api_base: str = Field(
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default_factory=from_env("DEEPSEEK_API_BASE", default=DEFAULT_API_BASE),
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)
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"""DeepSeek API base URL"""
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model_config = ConfigDict(populate_by_name=True)
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "chat-deepseek"
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@property
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def lc_secrets(self) -> dict[str, str]:
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"""A map of constructor argument names to secret ids."""
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return {"api_key": "DEEPSEEK_API_KEY"}
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def _get_ls_params(
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self,
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stop: list[str] | None = None,
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**kwargs: Any,
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) -> LangSmithParams:
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ls_params = super()._get_ls_params(stop=stop, **kwargs)
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ls_params["ls_provider"] = "deepseek"
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return ls_params
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate necessary environment vars and client params."""
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if self.api_base == DEFAULT_API_BASE and not (
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self.api_key and self.api_key.get_secret_value()
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):
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msg = "If using default api base, DEEPSEEK_API_KEY must be set."
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raise ValueError(msg)
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client_params: dict = {
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k: v
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for k, v in {
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"api_key": self.api_key.get_secret_value() if self.api_key else None,
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"base_url": self.api_base,
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"timeout": self.request_timeout,
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"max_retries": self.max_retries,
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"default_headers": self.default_headers,
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"default_query": self.default_query,
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}.items()
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if v is not None
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}
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if not (self.client or None):
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sync_specific: dict = {"http_client": self.http_client}
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self.root_client = openai.OpenAI(**client_params, **sync_specific)
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self.client = self.root_client.chat.completions
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if not (self.async_client or None):
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async_specific: dict = {"http_client": self.http_async_client}
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self.root_async_client = openai.AsyncOpenAI(
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**client_params,
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**async_specific,
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)
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self.async_client = self.root_async_client.chat.completions
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return self
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def _get_request_payload(
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self,
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input_: LanguageModelInput,
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*,
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stop: list[str] | None = None,
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**kwargs: Any,
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) -> dict:
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payload = super()._get_request_payload(input_, stop=stop, **kwargs)
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for message in payload["messages"]:
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if message["role"] == "tool" and isinstance(message["content"], list):
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message["content"] = json.dumps(message["content"])
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elif message["role"] == "assistant" and isinstance(
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message["content"], list
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):
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# DeepSeek API expects assistant content to be a string, not a list.
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# Extract text blocks and join them, or use empty string if none exist.
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text_parts = [
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block.get("text", "")
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for block in message["content"]
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if isinstance(block, dict) and block.get("type") == "text"
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]
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message["content"] = "".join(text_parts) if text_parts else ""
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return payload
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def _create_chat_result(
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self,
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response: dict | openai.BaseModel,
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generation_info: dict | None = None,
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) -> ChatResult:
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rtn = super()._create_chat_result(response, generation_info)
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if not isinstance(response, openai.BaseModel):
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return rtn
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for generation in rtn.generations:
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if generation.message.response_metadata is None:
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generation.message.response_metadata = {}
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generation.message.response_metadata["model_provider"] = "deepseek"
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choices = getattr(response, "choices", None)
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if choices and hasattr(choices[0].message, "reasoning_content"):
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rtn.generations[0].message.additional_kwargs["reasoning_content"] = choices[
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0
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].message.reasoning_content
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# Handle use via OpenRouter
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elif choices and hasattr(choices[0].message, "model_extra"):
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model_extra = choices[0].message.model_extra
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if isinstance(model_extra, dict) and (
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reasoning := model_extra.get("reasoning")
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):
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rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
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reasoning
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)
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return rtn
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def _convert_chunk_to_generation_chunk(
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self,
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chunk: dict,
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default_chunk_class: type,
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base_generation_info: dict | None,
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) -> ChatGenerationChunk | None:
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generation_chunk = super()._convert_chunk_to_generation_chunk(
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chunk,
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default_chunk_class,
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base_generation_info,
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)
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if (choices := chunk.get("choices")) and generation_chunk:
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top = choices[0]
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if isinstance(generation_chunk.message, AIMessageChunk):
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generation_chunk.message.response_metadata = {
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**generation_chunk.message.response_metadata,
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"model_provider": "deepseek",
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}
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if (
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reasoning_content := top.get("delta", {}).get("reasoning_content")
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) is not None:
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generation_chunk.message.additional_kwargs["reasoning_content"] = (
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reasoning_content
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)
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# Handle use via OpenRouter
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elif (reasoning := top.get("delta", {}).get("reasoning")) is not None:
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generation_chunk.message.additional_kwargs["reasoning_content"] = (
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reasoning
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)
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return generation_chunk
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def _stream(
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self,
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messages: list[BaseMessage],
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stop: list[str] | None = None,
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run_manager: CallbackManagerForLLMRun | None = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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try:
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yield from super()._stream(
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messages,
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stop=stop,
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run_manager=run_manager,
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**kwargs,
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)
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except JSONDecodeError as e:
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msg = (
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"DeepSeek API returned an invalid response. "
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"Please check the API status and try again."
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)
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raise JSONDecodeError(
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msg,
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e.doc,
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e.pos,
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) from e
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def _generate(
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self,
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messages: list[BaseMessage],
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stop: list[str] | None = None,
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run_manager: CallbackManagerForLLMRun | None = None,
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**kwargs: Any,
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) -> ChatResult:
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try:
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return super()._generate(
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messages,
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stop=stop,
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run_manager=run_manager,
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**kwargs,
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)
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except JSONDecodeError as e:
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msg = (
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"DeepSeek API returned an invalid response. "
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"Please check the API status and try again."
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)
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raise JSONDecodeError(
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msg,
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e.doc,
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e.pos,
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) from e
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def with_structured_output(
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self,
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schema: _DictOrPydanticClass | None = None,
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*,
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method: Literal[
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"function_calling",
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"json_mode",
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"json_schema",
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] = "function_calling",
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include_raw: bool = False,
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strict: bool | None = None,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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Args:
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schema: The output schema. Can be passed in as:
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- an OpenAI function/tool schema,
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- a JSON Schema,
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- a `TypedDict` class,
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- or a Pydantic class.
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If `schema` is a Pydantic class then the model output will be a
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Pydantic instance of that class, and the model-generated fields will be
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validated by the Pydantic class. Otherwise the model output will be a
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dict and will not be validated.
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See `langchain_core.utils.function_calling.convert_to_openai_tool` for
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more on how to properly specify types and descriptions of schema fields
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when specifying a Pydantic or `TypedDict` class.
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method: The method for steering model generation, one of:
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- `'function_calling'`:
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Uses DeepSeek's [tool-calling features](https://api-docs.deepseek.com/guides/function_calling).
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- `'json_mode'`:
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Uses DeepSeek's [JSON mode feature](https://api-docs.deepseek.com/guides/json_mode).
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include_raw:
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If `False` then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If `True`
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then both the raw model response (a `BaseMessage`) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well.
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The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
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`'parsing_error'`.
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strict:
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Whether to enable strict schema adherence when generating the function
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call. This parameter is included for compatibility with other chat
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models, and if specified will be passed to the Chat Completions API
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in accordance with the OpenAI API specification. However, the DeepSeek
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API may ignore the parameter.
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kwargs: Additional keyword args aren't supported.
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Returns:
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A `Runnable` that takes same inputs as a
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`langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is
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`False` and `schema` is a Pydantic class, `Runnable` outputs an instance
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of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is
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`False` then `Runnable` outputs a `dict`.
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If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys:
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- `'raw'`: `BaseMessage`
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- `'parsed'`: `None` if there was a parsing error, otherwise the type
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depends on the `schema` as described above.
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- `'parsing_error'`: `BaseException | None`
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"""
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# Some applications require that incompatible parameters (e.g., unsupported
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# methods) be handled.
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if method == "json_schema":
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method = "function_calling"
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return super().with_structured_output(
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schema,
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method=method,
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include_raw=include_raw,
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strict=strict,
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**kwargs,
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)
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