Semantic Document Search using RAG

This project implements a semantic document search system using text embeddings and a FAISS vector database.

Architecture

Document → Text Extraction → Chunking → Embeddings → FAISS Vector Database → Semantic Search

Tech Stack

Python Sentence Transformers FAISS NumPy

Features

• PDF document ingestion
• Text chunking
• Embedding generation
• Vector similarity search
• Semantic search over documents

Run the Project

Install dependencies

pip install -r requirements.txt

Run the application

python app.py

Semantic Document Search using RAG

• Built a Retrieval-Augmented Generation (RAG) system using Sentence Transformers embeddings and FAISS vector search.
• Integrated Llama3 via Ollama to generate context-aware answers using retrieved document content.
• Designed a semantic search pipeline enabling efficient retrieval of relevant document chunks for AI-assisted knowledge discovery.

Tech Stack: Python, Sentence Transformers, FAISS, Llama3 (Ollama)