🤖 RAG Assistant

A modular Retrieval-Augmented Generation (RAG) pipeline for document search, chunking, embedding, and conversational AI.

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🚀 Features

• Document Ingestion: Flexible ingestion for markdown, text, and more.
• Chunking: Smart document chunking for optimal retrieval.
• Embeddings: Integrates with state-of-the-art embedding models.
• Vector Search: Fast, scalable semantic search using ChromaDB.
• Conversational Engine: Chat interface for natural language queries.
• Extensible: Modular core for easy customization and extension.

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🗂️ Folder Structure

ai-tech-rag/
├── assets/                # Visuals, custom commands, and static assets
├── chroma_db/             # ChromaDB vector database files
├── qwerty/                # Python virtual environment
├── rag-assistant/         # Main app and core modules
│   ├── app.py             # Flask app entry point
│   ├── core/              # Core RAG logic (chat, vector, document)
│   ├── static/            # Frontend static files (CSS, JS)
│   └── templates/         # Jinja2 HTML templates
├── techcorp-docs/         # Example documents for ingestion
├── test_*.py              # Unit and integration tests
├── requirements.txt       # Python dependencies
└── README                 # This file

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🛠️ Quickstart

# 1. Clone the repo
git clone https://github.com/MansurPro/RAG-assistant.git
cd ai-tech-rag

# 2. Create and activate a virtual environment (optional)
python3 -m venv qwerty
source qwerty/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
python rag-assistant/app.py

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💡 Usage

1. Ingest Documents:
   • Place your files in techcorp-docs/ or use ingest_documents.py.
2. Search & Chat:
   • Use the web UI at http://localhost:5000 to chat and search.
3. Test the Pipeline:
   • Run pytest or use the provided test scripts.

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🌐 Visual Overview

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📦 Key Modules

• core/document_processor.py – Document loading, chunking, and preprocessing
• core/vector_engine.py – Embedding and vector search logic
• core/chat_engine.py – Conversational AI and retrieval logic
• app.py – Flask app and API endpoints

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🧪 Testing

pytest
# or run individual test files, e.g.
python test_chunking.py

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🤝 Contributing

Pull requests and issues are welcome! For major changes, please open an issue first to discuss what you would like to change.

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📄 License

MIT License. See LICENSE for details.