🧠 Qdrant Cloud Document Indexer

This Node.js project demonstrates how to embed and index documents into Qdrant Cloud using local embeddings and the Qdrant REST client. Ideal for building AI-powered search, recommendation systems, or RAG pipelines.

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📦 Tech Stack

• Node.js
• Qdrant Cloud
• @xenova/transformers – Local embedding pipeline
• dotenv – For environment variable management

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🚀 Getting Started

1. Clone and install dependencies

git clone https://github.com/matheussf/qdrant.git
cd qdrant
npm install

2. Set up environment variables

Create a .env file in the root of the qdrant/ directory:

QDRANT_URL=https://your-cluster-name.cloud.qdrant.io
QDRANT_API_KEY=your_qdrant_api_key

🔐 You can find both in your Qdrant Cloud dashboard.

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📂 Project Structure

qdrant/
├── index.js            # Main script: loads model, embeds & indexes docs
├── documents.json      # List of example documents
├── .env                # Your Qdrant credentials
├── package.json        # Project metadata and dependencies
└── README.md           # You're here!

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📄 Sample Document Format

The documents.json file should look like:

[
  {
    "id": "doc1",
    "title": "First Document",
    "text": "This is the content of the first document about AI."
  }
]

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⚙️ Running the Indexer

node index.js

This will:

1. Load the local embedding model (all-MiniLM-L6-v2)
2. Embed all documents
3. Push them to your Qdrant Cloud collection

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📌 Notes

• The embedding model runs locally via @xenova/transformers, no Python needed.
• Be sure your QDRANT_API_KEY has write access to the collection.

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