Government schemes and policy documents often contain large amounts of unstructured information that can be difficult and time-consuming to navigate. Users frequently need to review lengthy documents to understand eligibility criteria, benefits, application procedures, and required documentation.
This project automates document understanding by combining semantic search, vector embeddings, and retrieval-augmented generation techniques. Users can upload or reference scheme documents, ask natural-language questions, and receive contextually relevant answers derived from the source content.
The platform extracts information, generates structured summaries, and enables efficient knowledge retrieval through vector similarity search powered by FAISS and transformer-based embeddings.
Document Input ↓ Text Extraction ↓ Chunking & Preprocessing ↓ Embedding Generation ↓ FAISS Vector Store ↓ Semantic Retrieval ↓ LLM Response Generation ↓ User Query Interface
- Retrieval-Augmented Generation (RAG)
- Document Chunking Strategies
- Vector Embeddings
- Semantic Similarity Search
- Information Retrieval
- Vector Database Indexing with FAISS
- LLM-Powered Question Answering
- Unstructured Data Processing