Vector Search Comparison Project

This repository demonstrates a simple integration with Qdrant and provides a markdown table comparing three popular search and vector store services: Tavily, Qdrant, and Pinecone.

Features

• Qdrant Integration
  A lightweight wrapper (src/qdrantIntegration.js) that allows you to:

  • Create and delete collections
  • Upsert points (vectors + payload)
  • Perform similarity search

• Markdown Comparison
  The comparison.md file contains a side‑by‑side table highlighting key differences and use cases for each service.

Getting Started

1. Clone the repository

   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-sravnitelnyy-obzor-3.git
   cd povtornyy-ekzamen-2-sravnitelnyy-obzor-3
   

2. Install dependencies

   npm install
   

3. Set up environment variables

   Create a .env file or export the following variables in your shell:

   export QDRANT_URL="https://your-qdrant-instance.com"
   export QDRANT_API_KEY="your_api_key"   # optional if your instance is public
   

4. Use the Qdrant client

   const QdrantClient = require('./src/qdrantIntegration');
   
   const client = new QdrantClient({
     url: process.env.QDRANT_URL,
     apiKey: process.env.QDRANT_API_KEY,
   });
   
   async function demo() {
     await client.createCollection('demo', { size: 1536, distance: 'Cosine' });
     await client.upsertPoints('demo', [
       { id: 1, vector: Array(1536).fill(0.1), payload: { title: 'Example' } },
     ]);
     const results = await client.searchPoints('demo', Array(1536).fill(0.1));
     console.log(results);
   }
   
   demo().catch(console.error);
   

Documentation

• Qdrant Integration – src/qdrantIntegration.js
  Contains the QdrantClient class with methods for CRUD operations and search.

• Comparison Table – comparison.md
  Provides a concise comparison of Tavily, Qdrant, and Pinecone.

License

MIT © Your Name

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Feel free to extend the client or add more detailed tests. Happy coding!