RAG-Qdrant-pipeline

Overview

A powerful implementation of a Retrieval-Augmented Generation (RAG) model leveraging Qdrant as a vector store and Google Gemini for generative AI. This project enables intelligent document retrieval and response generation based on the context extracted from various PDF documents. 📄

Architecture:

Table of Contents

• Workflow
• Technologies Used
• Installation
• Usage
• Example
• Hybrid Search
• Contributing
• License

Workflow

• 📥 Load and process PDF documents from a specified directory.
• ✂️ Split documents into manageable chunks for efficient processing.
• 🔗 Use SentenceTransformers for generating embeddings.
• 💾 Store embeddings in Qdrant for fast retrieval.
• 🤖 Leverage Google Gemini for advanced question answering.
• 🔍 Hybrid search implementation combining vector similarity and keyword matching.
• 📝 Detailed context-aware responses based on user queries.

Technologies Used

• Langchain: Framework for working with LLMs and document loaders.
• Qdrant: Vector database for efficient storage and retrieval of embeddings.
• Google Gemini: Generative AI model for producing intelligent responses.
• Sentence Transformers: Used for creating document embeddings.
• PyMuPDF: For loading and processing PDF files.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/AtharvaKulkarniIT/rag-qdrant-pipeline.git
   cd rag-qdrant-pipeline-main

2. Install the required packages:

   pip install langchain PyMuPDF
   pip install langchain_google_genai
   pip install sentence-transformers

3. Set up your API keys:

   Make sure to add your Qdrant and Gemini API keys to the environment variables or replace them in the code directly.

Usage

1. Load Documents: Ensure your PDF documents are in the specified data folder.
2. Run the Script: Execute the notebook to start the RAG processing.

Example

To get a response about the rivers in Maharashtra, you can use the following input:

input_text = "Describe the rivers in Maharashtra"
response = get_gemini_response(input_text)
print(response)

Hybrid Search

The hybrid search combines results from both Qdrant's vector similarity search and keyword searches on the original documents. The results are ranked using Reciprocal Rank Fusion (RRF). 🔄

Contributing

🤝 Contributions are welcome! If you have suggestions for improvements or want to add new features, feel free to create an issue or submit a pull request.

License

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