🚀 Vecless RAG (Vectorless Retrieval-Augmented Generation)

📌 Overview

Vecless RAG is an end-to-end Retrieval-Augmented Generation (RAG) system that eliminates the need for traditional vector databases. Instead of embeddings, it uses PageIndex for structured document retrieval and Groq LLMs for fast, context-aware response generation.

This approach simplifies architecture, reduces computational overhead, and enables efficient document querying.

Get Your API's By Creating Account.

• 🔑 Get your PageIndex API key from: https://dash.pageindex.ai/api-keys
• 🔑 Get your groq API key from: https://platform.openai.com

⚙️ Features

• 🔍 Vectorless document retrieval (no embeddings required)
• ⚡ Fast inference using Groq LLM APIs
• 🧠 Context-aware response generation
• 📄 Structured document indexing via PageIndex
• 🛠️ Lightweight and scalable architecture

🏗️ Architecture

• Ingestion → Documents are indexed using PageIndex
• Query Input → User provides a natural language query
• Relevant Node Retrieval → PageIndex fetches relevant sections
• LLM Processing → Groq LLM generates final response

🛠️ Tech Stack

• Python
• Groq API (LLM inference)
• PageIndex API (document retrieval)
• JSON processing

📂 Project Structure

vecless_rag/
│── vectorless_rag.ipynb   # Main Code File
│── constants.py         # Stores API key
│── requirements.txt     # Dependencies
└── README.md            # Documentation

🔑 Setup & Installation

• 1️⃣ Clone the repository

git clone https://github.com/your-username/vecless_rag.git
cd vecless_rag

• 2️⃣ Install dependencies

uv add -r requirements.txt

• 3️⃣ Add API Keys -- Create a constants.py file:

Groq_API_KEY = "your_groq_api_key"
PAGEINDEX_API_KEY = "your_pageindex_api_key

• ▶️ Usage -- Run the notebook:

jupyter notebook vecless_rag.ipynb

Steps:

1. Load your document/index
2. Enter a query
3. Retrieve relevant nodes
4. Generate response using LLM

📊 Example

• Input Query:

What is Handwriting Recognition?

• Output:

Handwriting recognition is a machine learning application that converts handwritten text into digital format...

✅ Advantages

• No need for vector databases (FAISS, Pinecone, etc.)
• Lower cost and complexity
• Faster setup and deployment
• Easier debugging and transparency

🚧 Future Improvements

• Add evaluation metrics for retrieval quality
• Integrate UI (Streamlit / React)
• Support multiple document formats (PDF, DOCX)
• Add caching for faster responses

🤝 Contributing

Contributions are welcome!

• Fork the repository
• Create a new branch
• Submit a pull request

👤 Author

Vrushank Dhande Aspiring Data Scientist | Machine Learning Engineer

⭐ Support

If you found this project useful, please give it a ⭐ on GitHub!