RAGForge
Chat with Your Documents.
Fully Local. Fully Private.
A professional Retrieval-Augmented Generation (RAG) system built with Streamlit, FAISS, and Ollama — no cloud, no data leaks.
$ streamlit run app.py
🚀 Launching RAGForge...
📚 Loading knowledge base: research_papers
🧠 Model: llama3.2 via Ollama
✅ FAISS index loaded: 1,247 vectors
You: What are the key findings about neural networks?
RAGForge: Based on your documents, the key findings include...
Why RAGForge?
Better than ChatGPT + uploads. Here's why developers and researchers choose us.
Privacy-First
Your documents never leave your machine. No API calls, no telemetry, no vendor lock-in.
Context-Aware Chat
Ask complex questions and get grounded answers powered by vector similarity, not keyword matching.
Multi-Knowledge Bases
Organize documents under isolated titles and chat with them independently or together.
Developer-Friendly
Clean architecture, readable code, and a full learning phase for understanding RAG from scratch.
How It Works
From document to intelligent conversation in four simple steps.
Upload Documents
Drop PDFs, text files, or paste content directly
Chunk & Embed
Documents split into chunks, embedded via Sentence-Transformers
Store in FAISS
Vectors indexed for lightning-fast similarity search
Chat & Retrieve
Query with Ollama, get grounded answers from your docs
🔍 Want the full picture? See the architecture below →
Who is RAGForge for?
Whether you're a student, developer, or professional — RAGForge adapts to your needs.
Students & Researchers
- •Chat with PDFs, notes, and books
- •Build private study copilots
- •Extract insights without cloud uploads
Developers
- •Query codebases intelligently
- •Offline documentation assistant
- •Learn RAG internals hands-on
Privacy-Conscious Users
- •No cloud dependency whatsoever
- •Safe for sensitive documents
- •Full control over your data
Knowledge Workers
- •Personal knowledge vault
- •Long-term memory assistant
- •Organize multiple knowledge bases
Get Started in Minutes
No complex setup. Just clone, install, and run.
0Prerequisites
Quick Install
git clone https://github.com/koffandaff/RAGForge.gitcd RAGForge && pip install -r requirements.txtstreamlit run app.py# Open http://localhost:8501That's it! You're ready to chat with your documents. 🎉
Built to Teach
This project is designed not just to work — but to help you understand RAG from the ground up.
LearningPhase/
Educational modules included
Every core RAG concept is broken down into isolated, heavily-commented Python modules. Perfect for students, educators, and anyone wanting to understand the internals.
RAGForge/ ├── LearningPhase/ │ ├── 01_chunking.py # Document splitting │ ├── 02_embeddings.py # Vector generation │ ├── 03_faiss_basics.py # Index operations │ └── 04_rag_pipeline.py # Full RAG flow ├── app.py # Main Streamlit app └── core/ # Production modules
Chunking & Preprocessing
Learn how documents are split into semantic chunks for optimal retrieval.
Embedding Generation
Understand how Sentence-Transformers convert text to vectors.
Vector Search Fundamentals
Master FAISS indexing and similarity search algorithms.
📖 Each module is self-contained and runnable independently.
Built for Trust
Transparency and reliability at every level.
Battle-tested libraries
Built on FAISS, Sentence-Transformers, and Ollama
Works fully offline
No internet required after initial setup
MIT Licensed
Free to use, modify, and distribute
No external APIs
Zero data leaves your machine
Open source
Fully transparent, auditable code
Community driven
Open to contributions and feedback
Ready to Get Started?
Explore the code, star the repo, and start chatting with your documents today.