🔍 RAG-Application

RAG‑Application is a Retrieval-Augmented Generation (RAG) demo powered by LangChain, designed to help you build a robust end-to-end pipeline that:

1. Ingests documents (e.g. PDFs, text)
2. Splits them into searchable chunks
3. Embeds chunks into a vector store (e.g. FAISS, Chroma)
4. Serves queries by retrieving relevant chunks and augmenting an LLM prompt
5. Generates accurate, context-aware responses

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🧩 Architecture

• Ingestion & Chunking: Convert PDFs/text into semantically meaningful vector chunks.
• Vector Store: Use FAISS, Chroma, or similar for fast similarity retrieval.
• Retriever: Fetch top‑K similar chunks given a query.
• LLM Prompting: Craft prompts that combine user query with retrieved context.
• Answer Generation: Output answers with citations or metadata attached.

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🚀 Quick Start

▶️ Clone & setup

git clone https://github.com/mehul-lll/RAG-Application.git
cd RAG-Application
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
fastapi dev app/main.py
streamlit run app.py