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Qdrant

Qdrant is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.

Compatibility

Only available on Node.js.

Setup​

  1. Run a Qdrant instance with Docker on your computer by following the Qdrant setup instructions.

  2. Install the Qdrant Node.js SDK.

    npm install -S @qdrant/js-client-rest

  3. Setup Env variables for Qdrant before running the code

    3.1 OpenAI

    export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE
    export QDRANT_URL=YOUR_QDRANT_URL_HERE # for example http://localhost:6333

    3.2 Azure OpenAI

    export AZURE_OPENAI_API_KEY=YOUR_AZURE_OPENAI_API_KEY_HERE
    export AZURE_OPENAI_API_INSTANCE_NAME=YOUR_AZURE_OPENAI_INSTANCE_NAME_HERE
    export AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HERE
    export AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HERE
    export AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HERE
    export AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPENAI_API_VERSION_HERE
    export AZURE_OPENAI_BASE_PATH=YOUR_AZURE_OPENAI_BASE_PATH_HERE
    export QDRANT_URL=YOUR_QDRANT_URL_HERE # for example http://localhost:6333

Usage​

Create a new index from texts​

import { QdrantVectorStore } from "langchain/vectorstores/qdrant";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
// text sample from Godel, Escher, Bach
const vectorStore = await QdrantVectorStore.fromTexts(
  [
    `Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious Little
Harmonic Labyrinth of the dreaded Majotaur?`,
    `Achilles: Yiikes! What is that?`,
    `Tortoise: They say-although I person never believed it myself-that an I
            Majotaur has created a tiny labyrinth sits in a pit in the middle of
            it, waiting innocent victims to get lost in its fears complexity.
            Then, when they wander and dazed into the center, he laughs and
            laughs at them-so hard, that he laughs them to death!`,
    `Achilles: Oh, no!`,
    `Tortoise: But it's only a myth. Courage, Achilles.`,
  ],
  [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }],
  new OpenAIEmbeddings(),
  {
    url: process.env.QDRANT_URL,
    collectionName: "goldel_escher_bach",
  }
);

const response = await vectorStore.similaritySearch("scared", 2);

console.log(response);

/*
[
  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },
  Document {
    pageContent: 'Achilles: Yiikes! What is that?',
    metadata: { id: 1 }
  }
]
*/

API Reference:

Create a new index from docs​

import { QdrantVectorStore } from "langchain/vectorstores/qdrant";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";

// Create docs with a loader
const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();

const vectorStore = await QdrantVectorStore.fromDocuments(
  docs,
  new OpenAIEmbeddings(),
  {
    url: process.env.QDRANT_URL,
    collectionName: "a_test_collection",
  }
);

// Search for the most similar document
const response = await vectorStore.similaritySearch("hello", 1);

console.log(response);
/*
[
  Document {
    pageContent: 'Foo\nBar\nBaz\n\n',
    metadata: { source: 'src/document_loaders/example_data/example.txt' }
  }
]
*/

API Reference:

Query docs from existing collection​

import { QdrantVectorStore } from "langchain/vectorstores/qdrant";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

const vectorStore = await QdrantVectorStore.fromExistingCollection(
  new OpenAIEmbeddings(),
  {
    url: process.env.QDRANT_URL,
    collectionName: "goldel_escher_bach",
  }
);

const response = await vectorStore.similaritySearch("scared", 2);

console.log(response);

/*
[
  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },
  Document {
    pageContent: 'Achilles: Yiikes! What is that?',
    metadata: { id: 1 }
  }
]
*/

API Reference: