RAG-Enhanced-Chatbot-with-LoRA-Fine-Tuning is a Retrieval-Augmented Generation (RAG) based chatbot that leverages external knowledge sources and enhances generation quality using LoRA fine-tuning. It enables efficient domain-specific adaptation of large language models while combining it with semantic search for grounded, accurate, and context-rich responses.

• 🔍 Retrieval-Augmented Generation (RAG): Combines document retrieval with generative responses for factual accuracy.
• 🧠 LoRA Fine-Tuning: Efficient fine-tuning of pre-trained models with low resource requirements.
• 📄 Multi-Format Document Ingestion: Supports PDFs, text files, and scanned documents (via OCR).
• ⚙️ Modular Pipeline: Separates data ingestion, chunking, embedding, retrieval, and generation for flexibility.
• 🌐 Custom Knowledge Base: Tailor your chatbot to domain-specific knowledge.

• Language Models: Hugging Face Transformers (e.g., LLaMA, Qwen2.5VL-7B, etc.)
• Vector Database: FAISS / Pinecone / Chroma
• Embeddings: OpenAI / Hugging Face Embedding APIs
• Fine-Tuning: LoRA (via peft)
• Backend: Python
• Parsing Tools: PyMuPDF, pdfminer, pytesseract

RAG-Enhanced-Chatbot-with-LoRA-Fine-Tuning/
├── data_ingestion/          # Document parsing and preprocessing
├── chunking/                # Context-aware document chunking
├── embeddings/              # Embedding generation and storage
├── retriever/               # Vector search and retrieval logic
├── generator/               # LLM generation with context
├── finetuning/              # LoRA fine-tuning scripts
├── api/                     # REST or chat API integration
├── requirements.txt         # Python dependencies
└── README.md                # Project overview and instructions

• 💬 Customer support chatbots with company-specific knowledge
• 🧾 Legal or healthcare assistants with regulated document references
• 📚 Academic or research tools grounded in verified sources
• 🏢 Enterprise knowledge management bots

1. Clone the repository:

   git clone https://github.com/yourusername/RAG-Enhanced-Chatbot-with-LoRA-Fine-Tuning.git
   cd RAG-Enhanced-Chatbot-with-LoRA-Fine-Tuning

2. Install dependencies:

   pip install -r requirements.txt

3. Prepare your documents in the /data_ingestion/ directory.

4. Run the ingestion pipeline to chunk and embed your documents.

5. Launch the chatbot (with or without an API/UI) and start chatting!

• Python 3.8+
• CUDA-compatible GPU (recommended for LoRA fine-tuning)
• Sufficient storage for vector embeddings and model weights

Before running the project, configure the following:

• Set your API keys for embedding services (OpenAI, Hugging Face)
• Choose your preferred vector database (FAISS for local, Pinecone/Chroma for cloud)
• Select the base language model for fine-tuning
• Adjust chunking parameters based on your document types

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

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

If you encounter any issues or have questions, please open an issue on GitHub or contact the maintainer.