A production-style Retrieval-Augmented Generation (RAG) system that allows users to upload documents (PDFs) and ask intelligent questions with context-aware AI responses.
Built using FAISS vector search, OpenAI embeddings, and GPT-4o-mini, this project demonstrates how modern AI applications retrieve and reason over private documents.
https://your-streamlit-app-link.streamlit.app
- Upload and analyze PDF documents
- Semantic search using embeddings
- FAISS vector database for fast retrieval
- Context-aware AI question answering
- Multi-chunk retrieval (top-k context)
- Retrieval-Augmented Generation (RAG pipeline)
- Clean Streamlit UI
PDF Upload
↓
Text Extraction
↓
Chunking
↓
Embeddings (OpenAI)
↓
FAISS Vector Search
↓
Top-K Relevant Context
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LLM (GPT-4o-mini)
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Answer Generation
Tech Stack
- Python
- Streamlit
- OpenAI API
- FAISS (Vector Database)
- NumPy
- PyPDF
What This Project Demonstrates
- Retrieval-Augmented Generation (RAG)
- Vector similarity search
- Embedding-based AI systems
- LLM orchestration
- Real-world AI application design
Example Use Cases
- Resume Q&A assistant
- Legal document analysis
- Study material assistant
- Company knowledge base chatbot
Future Improvements
- Page-level citations
- Multi-document chat memory
- Streaming responses
- Authentication system
- Cloud deployment (SaaS version)
What I learned:
- How RAG systems work in production
- Vector databases (FAISS)
- Embedding-based semantic search
- Designing LLM-powered applications beyond simple prompting
- Turning AI models into real product workflows
Why this project matters
This project demonstrates how modern AI applications move beyond simple prompting into retrieval-based reasoning systems, similar to production tools like ChatGPT’s file upload, Notion AI, and enterprise knowledge assistants.