RAG-MultiFile-QA

📚 Multi-File Retrieval-Augmented Generation (RAG) Q&A System

This project is a Streamlit-based Q&A application that allows users to upload multiple document types (PDF, DOCX, TXT, CSV) and ask questions about their content using retrieval-augmented generation (RAG).

🔹 Features

• Upload and process multiple files at once.
• Supports PDF, DOCX, TXT, and CSV formats.
• Uses Hugging Face Embeddings and FAISS vector search for document retrieval.
• Integrates Hugging Face Inference API for generating responses.
• Maintains chat history for seamless user experience.
• Clear all button to reset uploaded files and chat history.

🛠️ Tech Stack

• Python
• Streamlit (Frontend UI)
• Langchain (Document Processing & Retrieval)
• Hugging Face Inference API (LLM-based Answer Generation)
• FAISS (Vector Store for Efficient Retrieval)
• PyPDFLoader, TextLoader, CSVLoader (File Parsing)

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/RAG-MultiFile-QA.git
   cd RAG-MultiFile-QA

2. Install dependencies:

   pip install -r requirements.txt

3. Set your Hugging Face API Key as an environment variable:

   export HUGGINGFACE_API_KEY="your_api_key"

4. Run the app:

   streamlit run main.py

📌 Notes

• Ensure your Hugging Face API Key is correctly set.
• The system works best with structured documents containing well-defined sections and tables.
• FAISS indexing helps in faster search and retrieval from large documents.

📜 License

This project is open-source and available under the MIT License.