A local, offline chatbot application for querying PDF content using Retrieval-Augmented Generation (RAG) with Ollama.

• Local & Offline: Complete privacy - no data leaves your machine
• PDF Processing: Upload and process PDF documents
• RAG Pipeline: Advanced retrieval-augmented generation
• Vector Search: Semantic search using ChromaDB
• Modern UI: Clean React + Tailwind interface
• Flexible Models: Support for various Ollama models

chatbot-app/
├── backend/          # FastAPI Python backend
│   ├── main.py      # API endpoints
│   ├── rag.py       # RAG pipeline
│   ├── embed.py     # Embedding service
│   └── vector_store.py  # Vector database
├── frontend/        # React + Tailwind UI
│   ├── src/components/  # Reusable components
│   └── src/pages/       # Application pages
└── docker-compose.yml   # Development setup

• Python 3.8+
• Node.js 16+
• Ollama installed and running

# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull required models
ollama pull mistral
ollama pull nomic-embed-text

# Navigate to backend
cd chatbot-app/backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Navigate to frontend
cd ../frontend

# Install dependencies
npm install

# Start Ollama service
ollama serve

# In another terminal, ensure models are available
ollama list

cd backend
source venv/bin/activate  # On Windows: venv\Scripts\activate
uvicorn main:app --reload --host 0.0.0.0 --port 8000

cd frontend
npm start

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

1. Navigate to the "Upload" tab
2. Drag & drop PDF files or click to select
3. Click "Process Documents" to ingest into vector database

1. Navigate to the "Chat" tab
2. Ask questions about your uploaded documents
3. The system will provide answers with source references

Edit .env file to customize:

# Ollama Configuration
OLLAMA_URL=http://localhost:11434
LLM_MODEL=mistral
EMBEDDING_MODEL=nomic-embed-text

# RAG Settings
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

# API Configuration
REACT_APP_API_URL=http://localhost:8000

You can use any Ollama model:

• mistral (recommended)
• llama3
• codellama
• neural-chat

# Start everything with Docker
docker-compose up --build

# Access:
# Frontend: http://localhost:3000
# Backend: http://localhost:8000

1. Document Upload: PDFs are uploaded and stored locally
2. Text Extraction: PyPDF2 extracts text from documents
3. Chunking: Text is split into overlapping chunks
4. Embedding: Chunks are embedded using Ollama/SentenceTransformers
5. Vector Storage: Embeddings stored in ChromaDB
6. Query Processing: User queries are embedded and searched
7. Response Generation: Relevant chunks sent to LLM for response

Ollama Connection Error

# Check if Ollama is running
curl http://localhost:11434/api/version

# Start Ollama if not running
ollama serve

Model Not Found

# Pull required models
ollama pull mistral
ollama pull nomic-embed-text

Port Already in Use

# Change ports in .env file
REACT_APP_API_URL=http://localhost:8001

# Start backend on different port
uvicorn main:app --port 8001

• RAM: Ensure sufficient RAM for embeddings (4GB+ recommended)
• Storage: Vector database grows with document count
• Models: Smaller models run faster but may be less accurate

1. Fork the repository
2. Create feature branch: git checkout -b feature-name
3. Commit changes: git commit -am 'Add feature'
4. Push to branch: git push origin feature-name
5. Submit pull request

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

• Ollama for local LLM inference
• ChromaDB for vector storage
• FastAPI for the backend framework
• React + Tailwind for the frontend