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*   How to create a custom chat model class

On this page

How to create a custom chat model class
=======================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Chat models](/v0.2/docs/concepts/#chat-models)

This notebook goes over how to create a custom chat model wrapper, in case you want to use your own chat model or a different wrapper than one that is directly supported in LangChain.

There are a few required things that a chat model needs to implement after extending the [`SimpleChatModel` class](https://v02.api.js.langchain.com/classes/langchain_core_language_models_chat_models.SimpleChatModel.html):

*   A `_call` method that takes in a list of messages and call options (which includes things like `stop` sequences), and returns a string.
*   A `_llmType` method that returns a string. Used for logging purposes only.

You can also implement the following optional method:

*   A `_streamResponseChunks` method that returns an `AsyncGenerator` and yields [`ChatGenerationChunks`](https://v02.api.js.langchain.com/classes/langchain_core_outputs.ChatGenerationChunk.html). This allows the LLM to support streaming outputs.

Let's implement a very simple custom chat model that just echoes back the first `n` characters of the input.

    import {  SimpleChatModel,  type BaseChatModelParams,} from "@langchain/core/language_models/chat_models";import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";import { AIMessageChunk, type BaseMessage } from "@langchain/core/messages";import { ChatGenerationChunk } from "@langchain/core/outputs";export interface CustomChatModelInput extends BaseChatModelParams {  n: number;}export class CustomChatModel extends SimpleChatModel {  n: number;  constructor(fields: CustomChatModelInput) {    super(fields);    this.n = fields.n;  }  _llmType() {    return "custom";  }  async _call(    messages: BaseMessage[],    options: this["ParsedCallOptions"],    runManager?: CallbackManagerForLLMRun  ): Promise<string> {    if (!messages.length) {      throw new Error("No messages provided.");    }    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing    // await subRunnable.invoke(params, runManager?.getChild());    if (typeof messages[0].content !== "string") {      throw new Error("Multimodal messages are not supported.");    }    return messages[0].content.slice(0, this.n);  }  async *_streamResponseChunks(    messages: BaseMessage[],    options: this["ParsedCallOptions"],    runManager?: CallbackManagerForLLMRun  ): AsyncGenerator<ChatGenerationChunk> {    if (!messages.length) {      throw new Error("No messages provided.");    }    if (typeof messages[0].content !== "string") {      throw new Error("Multimodal messages are not supported.");    }    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing    // await subRunnable.invoke(params, runManager?.getChild());    for (const letter of messages[0].content.slice(0, this.n)) {      yield new ChatGenerationChunk({        message: new AIMessageChunk({          content: letter,        }),        text: letter,      });      // Trigger the appropriate callback for new chunks      await runManager?.handleLLMNewToken(letter);    }  }}

We can now use this as any other chat model:

    const chatModel = new CustomChatModel({ n: 4 });await chatModel.invoke([["human", "I am an LLM"]]);

    AIMessage {  content: 'I am',  additional_kwargs: {}}

And support streaming:

    const stream = await chatModel.stream([["human", "I am an LLM"]]);for await (const chunk of stream) {  console.log(chunk);}

    AIMessageChunk {  content: 'I',  additional_kwargs: {}}AIMessageChunk {  content: ' ',  additional_kwargs: {}}AIMessageChunk {  content: 'a',  additional_kwargs: {}}AIMessageChunk {  content: 'm',  additional_kwargs: {}}

Richer outputs[​](#richer-outputs "Direct link to Richer outputs")
------------------------------------------------------------------

If you want to take advantage of LangChain's callback system for functionality like token tracking, you can extend the [`BaseChatModel`](https://v02.api.js.langchain.com/classes/langchain_core_language_models_chat_models.BaseChatModel.html) class and implement the lower level `_generate` method. It also takes a list of `BaseMessage`s as input, but requires you to construct and return a `ChatGeneration` object that permits additional metadata. Here's an example:

    import { AIMessage, BaseMessage } from "@langchain/core/messages";import { ChatResult } from "@langchain/core/outputs";import {  BaseChatModel,  BaseChatModelCallOptions,  BaseChatModelParams,} from "@langchain/core/language_models/chat_models";import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";export interface AdvancedCustomChatModelOptions  extends BaseChatModelCallOptions {}export interface AdvancedCustomChatModelParams extends BaseChatModelParams {  n: number;}export class AdvancedCustomChatModel extends BaseChatModel<AdvancedCustomChatModelOptions> {  n: number;  static lc_name(): string {    return "AdvancedCustomChatModel";  }  constructor(fields: AdvancedCustomChatModelParams) {    super(fields);    this.n = fields.n;  }  async _generate(    messages: BaseMessage[],    options: this["ParsedCallOptions"],    runManager?: CallbackManagerForLLMRun  ): Promise<ChatResult> {    if (!messages.length) {      throw new Error("No messages provided.");    }    if (typeof messages[0].content !== "string") {      throw new Error("Multimodal messages are not supported.");    }    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing    // await subRunnable.invoke(params, runManager?.getChild());    const content = messages[0].content.slice(0, this.n);    const tokenUsage = {      usedTokens: this.n,    };    return {      generations: [{ message: new AIMessage({ content }), text: content }],      llmOutput: { tokenUsage },    };  }  _llmType(): string {    return "advanced_custom_chat_model";  }}

This will pass the additional returned information in callback events and in the \`streamEvents method:

    const chatModel = new AdvancedCustomChatModel({ n: 4 });const eventStream = await chatModel.streamEvents([["human", "I am an LLM"]], {  version: "v1",});for await (const event of eventStream) {  if (event.event === "on_llm_end") {    console.log(JSON.stringify(event, null, 2));  }}

    {  "event": "on_llm_end",  "name": "AdvancedCustomChatModel",  "run_id": "b500b98d-bee5-4805-9b92-532a491f5c70",  "tags": [],  "metadata": {},  "data": {    "output": {      "generations": [        [          {            "message": {              "lc": 1,              "type": "constructor",              "id": [                "langchain_core",                "messages",                "AIMessage"              ],              "kwargs": {                "content": "I am",                "additional_kwargs": {}              }            },            "text": "I am"          }        ]      ],      "llmOutput": {        "tokenUsage": {          "usedTokens": 4        }      }    }  }}

Tracing (advanced)[​](#tracing-advanced "Direct link to Tracing (advanced)")
----------------------------------------------------------------------------

If you are implementing a custom chat model and want to use it with a tracing service like [LangSmith](https://smith.langchain.com/), you can automatically log params used for a given invocation by implementing the `invocationParams()` method on the model.

This method is purely optional, but anything it returns will be logged as metadata for the trace.

Here's one pattern you might use:

    export interface CustomChatModelOptions extends BaseChatModelCallOptions {  // Some required or optional inner args  tools: Record<string, any>[];}export interface CustomChatModelParams extends BaseChatModelParams {  temperature: number;}export class CustomChatModel extends BaseChatModel<CustomChatModelOptions> {  temperature: number;  static lc_name(): string {    return "CustomChatModel";  }  constructor(fields: CustomChatModelParams) {    super(fields);    this.temperature = fields.temperature;  }  // Anything returned in this method will be logged as metadata in the trace.  // It is common to pass it any options used to invoke the function.  invocationParams(options?: this["ParsedCallOptions"]) {    return {      tools: options?.tools,      n: this.n,    };  }  async _generate(    messages: BaseMessage[],    options: this["ParsedCallOptions"],    runManager?: CallbackManagerForLLMRun  ): Promise<ChatResult> {    if (!messages.length) {      throw new Error("No messages provided.");    }    if (typeof messages[0].content !== "string") {      throw new Error("Multimodal messages are not supported.");    }    const additionalParams = this.invocationParams(options);    const content = await someAPIRequest(messages, additionalParams);    return {      generations: [{ message: new AIMessage({ content }), text: content }],      llmOutput: {},    };  }  _llmType(): string {    return "advanced_custom_chat_model";  }}

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