RAG Chat App

This project is a Retrieval-Augmented Generation (RAG) Chat Application built with:

• Streamlit for the web interface
• Ollama for running local LLMs
• FAISS for vector search
• LangChain for orchestration
• Sentence Transformers for embeddings
• PyPDF for PDF document loading

Features

• Upload PDF documents and index them for semantic search.
• Query your documents in natural language and get context-aware answers.
• Powered by FAISS + Sentence Transformers for efficient similarity search.
• Runs locally with Ollama, no external API keys required.

Installation

git clone https://github.com/codersbranch/ragchatapp.git
cd ragchatapp
Set up virtual environment 
pip install -r requirements.txt

Usage

create folder uploaded_pdfs , vector_data upload your pdfs in the uploaded_pdfs with different categories To build vector indexes

python save_data.py

Run the Streamlit app:

streamlit run app.py

Then open the provided URL in your browser.

Requirements

See requirements.txt for dependencies.
Key packages include:

• streamlit
• ollama
• faiss-cpu
• numpy
• langchain
• sentence-transformers
• pypdf
• langchain-community