js

langchain-sourcey

Implement Ask AI over a published Sourcey docs site.

langchain-sourcey reads Sourcey's generated search and LLM artefacts and returns canonical page URLs for citation.

Sourcey already emits the files a retriever needs:

• search-index.json for candidate discovery
• llms-full.txt for full-page hydration
• canonical page URLs for citations

No hosted index is required. Point siteUrl at the docs root and use it.

Install

npm

npm install langchain-sourcey @langchain/core

yarn

yarn add langchain-sourcey @langchain/core

pnpm

pnpm add langchain-sourcey @langchain/core

Quickstart

import { SourceyRetriever } from "langchain-sourcey";

const retriever = new SourceyRetriever({
  siteUrl: "https://sourcey.com/docs",
  topK: 3,
});

const docs = await retriever.invoke("mcp integration");

for (const doc of docs) {
  console.log(doc.metadata.title);
  console.log(doc.metadata.source);
  console.log(doc.pageContent.slice(0, 160));
  console.log();
}

For a runnable script, see examples/live-quickstart.ts.

More context: Sourcey guide

Implement Ask AI

Install a chat model package. This example uses OpenAI:

npm

npm install @langchain/openai

yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

import { StringOutputParser } from "@langchain/core/output_parsers";
import type { Document } from "@langchain/core/documents";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { ChatOpenAI } from "@langchain/openai";
import { SourceyRetriever } from "langchain-sourcey";

const retriever = new SourceyRetriever({
  siteUrl: "https://sourcey.com/docs",
  topK: 3,
});

const prompt = ChatPromptTemplate.fromTemplate(
  `Answer the question using the documentation context below.

{context}

Question: {question}`
);

const formatDocs = (docs: Document[]) =>
  docs.map((doc) => doc.pageContent).join("\n\n");

const chain = RunnableSequence.from([
  {
    context: retriever.pipe(formatDocs),
    question: new RunnablePassthrough(),
  },
  prompt,
  new ChatOpenAI({ model: "gpt-4.1-mini" }),
  new StringOutputParser(),
]);

const answer = await chain.invoke("How does Sourcey document MCP servers?");

console.log(answer);

For a fuller example, see examples/rag-chain.ts.

Sourcey Output Contract

For predictable retrieval, the published Sourcey site should expose:

• publish search-index.json
• publish llms-full.txt
• set siteUrl in sourcey.config.ts so citations are canonical

search-index.json is required.

llms-full.txt is strongly recommended. If it is missing, the retriever falls back to the matched page HTML.