Qdrant Vector Store

qdrant.tech

To run this example, you need to have a Qdrant instance running. You can run it with Docker:

docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant

Installation

npm i llamaindex @llamaindex/qdrant

Importing the modules

import fs from "node:fs/promises";
import { Document, VectorStoreIndex } from "llamaindex";
import { QdrantVectorStore } from "@llamaindex/qdrant";

Load the documents

const path = "node_modules/llamaindex/examples/abramov.txt";
const essay = await fs.readFile(path, "utf-8");

Setup Qdrant

const vectorStore = new QdrantVectorStore({
  url: "http://localhost:6333",
});

Setup the index

const document = new Document({ text: essay, id_: path });
const storageContext = await storageContextFromDefaults({ vectorStore });
  const index = await VectorStoreIndex.fromDocuments([document], {
    storageContext,
  });

Query the index

const queryEngine = index.asQueryEngine();

const response = await queryEngine.query({
  query: "What did the author do in college?",
});

// Output response
console.log(response.toString());

Full code

import fs from "node:fs/promises";
import { Document, VectorStoreIndex } from "llamaindex";
import { QdrantVectorStore } from "@llamaindex/qdrant";

async function main() {
  const path = "node_modules/llamaindex/examples/abramov.txt";
  const essay = await fs.readFile(path, "utf-8");

  const vectorStore = new QdrantVectorStore({
    url: "http://localhost:6333",
  });

  const document = new Document({ text: essay, id_: path });
  const storageContext = await storageContextFromDefaults({ vectorStore });
  const index = await VectorStoreIndex.fromDocuments([document], {
    storageContext,
  });

  const queryEngine = index.asQueryEngine();

  const response = await queryEngine.query({
    query: "What did the author do in college?",
  }); // Additional filters and params can be passed as options

  // Output response
  console.log(response.toString());
}

main().catch(console.error);

API Reference

• QdrantVectorStore