Retrieval-Augmented Generation (RAG) with Qdrant and OpenAI

This project demonstrates how to perform Retrieval-Augmented Generation (RAG) using Qdrant for vector storage and OpenAI for language generation. The setup involves loading documents, generating embeddings, storing them in Qdrant, and querying them to generate responses using OpenAI.

Table of Contents

• Installation
• Setup
• Ingesting Documents
• Running the RAG Script
• Configuration
• License

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/rag-with-qdrant.git
   cd rag-with-qdrant

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate

3. Install the dependencies:

   pip install -r requirements.txt

Setup

1. Ensure you have a running instance of Qdrant. You can use Docker to run Qdrant:

   docker compose up -d

2. Create a .env file in the project directory and add your OpenAI API key:

   OPENAI_KEY=your-openai-api-key

Ingesting Documents

Use the ingest.py script to load and process documents, generate embeddings, and store them in Qdrant.

python ingest.py

Running the RAG Script

Use the rag.py script to query the vector database and generate responses using OpenAI.

python rag.py

Configuration

• Qdrant: Ensure Qdrant is running on http://localhost:6333.
• OpenAI API Key: Store your OpenAI API key in a .env file in the project directory.

License

This project is licensed under the MIT License. See the LICENSE file for details.