langchain.js
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    Deepseek chat model integration.

    The Deepseek API is compatible to the OpenAI API with some limitations.

    Setup: Install @langchain/deepseek and set an environment variable named DEEPSEEK_API_KEY.

    npm install @langchain/deepseek
    export DEEPSEEK_API_KEY="your-api-key"

    Runtime args can be passed as the second argument to any of the base runnable methods .invoke. .stream, .batch, etc. They can also be passed via .withConfig, or the second arg in .bindTools, like shown in the examples below:

    // When calling `.withConfig`, call options should be passed via the first argument
    const llmWithArgsBound = llm.withConfig({
    stop: ["\n"],
    tools: [...],
    });

    // When calling `.bindTools`, call options should be passed via the second argument
    const llmWithTools = llm.bindTools(
    [...],
    {
    tool_choice: "auto",
    }
    );
    Instantiate
    import { ChatDeepSeek } from '@langchain/deepseek';

    const llm = new ChatDeepSeek({
    model: "deepseek-reasoner",
    temperature: 0,
    // other params...
    });

    Invoking
    const input = `Translate "I love programming" into French.`;

    // Models also accept a list of chat messages or a formatted prompt
    const result = await llm.invoke(input);
    console.log(result);
    AIMessage {
      "content": "The French translation of \"I love programming\" is \"J'aime programmer\". In this sentence, \"J'aime\" is the first person singular conjugation of the French verb \"aimer\" which means \"to love\", and \"programmer\" is the French infinitive for \"to program\". I hope this helps! Let me know if you have any other questions.",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "tokenUsage": {
          "completionTokens": 82,
          "promptTokens": 20,
          "totalTokens": 102
        },
        "finish_reason": "stop"
      },
      "tool_calls": [],
      "invalid_tool_calls": []
    }
    

    Streaming Chunks
    for await (const chunk of await llm.stream(input)) {
    console.log(chunk);
    }
    AIMessageChunk {
      "content": "",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": "The",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": " French",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": " translation",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": " of",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": " \"",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": "I",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": " love",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    ...
    AIMessageChunk {
      "content": ".",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": null
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    AIMessageChunk {
      "content": "",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": "stop"
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    

    Aggregate Streamed Chunks
    import { AIMessageChunk } from '@langchain/core/messages';
    import { concat } from '@langchain/core/utils/stream';

    const stream = await llm.stream(input);
    let full: AIMessageChunk | undefined;
    for await (const chunk of stream) {
    full = !full ? chunk : concat(full, chunk);
    }
    console.log(full);
    AIMessageChunk {
      "content": "The French translation of \"I love programming\" is \"J'aime programmer\". In this sentence, \"J'aime\" is the first person singular conjugation of the French verb \"aimer\" which means \"to love\", and \"programmer\" is the French infinitive for \"to program\". I hope this helps! Let me know if you have any other questions.",
      "additional_kwargs": {
        "reasoning_content": "...",
      },
      "response_metadata": {
        "finishReason": "stop"
      },
      "tool_calls": [],
      "tool_call_chunks": [],
      "invalid_tool_calls": []
    }
    

    Bind tools
    import { z } from 'zod';

    const llmForToolCalling = new ChatDeepSeek({
    model: "deepseek-chat",
    temperature: 0,
    // other params...
    });

    const GetWeather = {
    name: "GetWeather",
    description: "Get the current weather in a given location",
    schema: z.object({
    location: z.string().describe("The city and state, e.g. San Francisco, CA")
    }),
    }

    const GetPopulation = {
    name: "GetPopulation",
    description: "Get the current population in a given location",
    schema: z.object({
    location: z.string().describe("The city and state, e.g. San Francisco, CA")
    }),
    }

    const llmWithTools = llmForToolCalling.bindTools([GetWeather, GetPopulation]);
    const aiMsg = await llmWithTools.invoke(
    "Which city is hotter today and which is bigger: LA or NY?"
    );
    console.log(aiMsg.tool_calls);
    [
      {
        name: 'GetWeather',
        args: { location: 'Los Angeles, CA' },
        type: 'tool_call',
        id: 'call_cd34'
      },
      {
        name: 'GetWeather',
        args: { location: 'New York, NY' },
        type: 'tool_call',
        id: 'call_68rf'
      },
      {
        name: 'GetPopulation',
        args: { location: 'Los Angeles, CA' },
        type: 'tool_call',
        id: 'call_f81z'
      },
      {
        name: 'GetPopulation',
        args: { location: 'New York, NY' },
        type: 'tool_call',
        id: 'call_8byt'
      }
    ]
    

    Structured Output
    import { z } from 'zod';

    const Joke = z.object({
    setup: z.string().describe("The setup of the joke"),
    punchline: z.string().describe("The punchline to the joke"),
    rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
    }).describe('Joke to tell user.');

    const structuredLlm = llmForToolCalling.withStructuredOutput(Joke, { name: "Joke" });
    const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
    console.log(jokeResult);
    {
      setup: "Why don't cats play poker in the wild?",
      punchline: 'Because there are too many cheetahs.'
    }
    

    Hierarchy

    Index

    Constructors

    Properties

    lc_namespace: string[] = ...
    lc_serializable: boolean = true

    Accessors

    • get lc_secrets(): undefined | { [key: string]: string }

      Returns undefined | { [key: string]: string }

    Methods

    • Returns string

    • Type Parameters

      • RunOutput extends Record<string, any> = Record<string, any>

      Parameters

      • outputSchema: any
      • Optionalconfig: any

      Returns Runnable<BaseLanguageModelInput, RunOutput>

    • Type Parameters

      • RunOutput extends Record<string, any> = Record<string, any>

      Parameters

      • outputSchema: any
      • Optionalconfig: any

      Returns Runnable<BaseLanguageModelInput, { parsed: RunOutput; raw: BaseMessage }>

    • Type Parameters

      • RunOutput extends Record<string, any> = Record<string, any>

      Parameters

      • outputSchema: any
      • Optionalconfig: any

      Returns any

    • Returns string