# 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** ```bash git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-sravnitelnyy-obzor-3.git cd povtornyy-ekzamen-2-sravnitelnyy-obzor-3 ``` 2. **Install dependencies** ```bash npm install ``` 3. **Set up environment variables** Create a `.env` file or export the following variables in your shell: ```bash 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** ```js 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 --- Feel free to extend the client or add more detailed tests. Happy coding!