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How to write a custom retriever class


This guide assumes familiarity with the following concepts:

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, to the web using fetch, or any other source. 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 {
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) {

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: {}

Next steps​

You've now seen an example of implementing your own custom retriever.

Next, check out the individual sections for deeper dives on specific retrievers, or the broader tutorial on RAG.

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