Custom retrievers

To create your own retriever, you need to extend the BaseRetriever class and implement a _getRelevantDocuments method that takes a string as its first parameter and an optional runManager for tracing. This method should return an array of Documents fetched from some source. This process can involve calls to a database or to the web using fetch. Note the underscore before _getRelevantDocuments() - the base class wraps the non-prefixed version in order to automatically handle tracing of the original call.

Here's an example of a custom retriever that returns static documents:

import {
  BaseRetriever,
  type BaseRetrieverInput,
} from "@langchain/core/retrievers";
import type { CallbackManagerForRetrieverRun } from "@langchain/core/callbacks/manager";
import { Document } from "@langchain/core/documents";

export interface CustomRetrieverInput extends BaseRetrieverInput {}

export class CustomRetriever extends BaseRetriever {
  lc_namespace = ["langchain", "retrievers"];

  constructor(fields?: CustomRetrieverInput) {
    super(fields);
  }

  async _getRelevantDocuments(
    query: string,
    runManager?: CallbackManagerForRetrieverRun
  ): Promise<Document[]> {
    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing
    // const additionalDocs = await someOtherRunnable.invoke(params, runManager?.getChild());
    return [
      // ...additionalDocs,
      new Document({
        pageContent: `Some document pertaining to ${query}`,
        metadata: {},
      }),
      new Document({
        pageContent: `Some other document pertaining to ${query}`,
        metadata: {},
      }),
    ];
  }
}

Then, you can call .invoke() as follows:

const retriever = new CustomRetriever({});

await retriever.invoke("LangChain docs");

[
  Document {
    pageContent: 'Some document pertaining to LangChain docs',
    metadata: {}
  },
  Document {
    pageContent: 'Some other document pertaining to LangChain docs',
    metadata: {}
  }
]