DOC.AI is an end-to-end RAG (Retrieval-Augmented Generation) system that lets you interact with documents like never before. Instead of just summarizing, it enables context-aware Q&A, structured insights, and multi-document analysis — built for real-world use cases like research, legal docs, and enterprise knowledge systems.
- 📂 Upload PDF, DOCX, TXT
- 🧠 Ask questions → get context-grounded answers
- 📑 Generate section-wise summaries
- 🔍 Compare multiple documents
- 💬 Maintain conversation memory
- 🎙️ Voice support (speech ↔ text)
- Documents are parsed & chunked
- Chunks → converted into embeddings
- Stored for fast retrieval
- Query → matched with relevant chunks
- LLM generates accurate, context-based response
👉 No generic answers — everything is grounded in your data.
- Frontend: HTML, CSS, JavaScript
- Backend: Python (RAG pipeline)
- AI: Embeddings + LLM APIs
- Extras: Speech-to-Text, Text-to-Speech
git clone https://github.com/Yashasvaddi/DOC.AI.git
cd DOC.AI
pip install -r requirements.txt
python app.py- Retrieval quality depends on chunking & embeddings
- No formal evaluation metrics yet
- Slows down with large datasets
- Multi-doc comparison = retrieval-based (not true reasoning)
- Hybrid search (semantic + keyword)
- Re-ranking for better accuracy
- Caching & latency optimization
- Fine-tuned domain models
- Scalable backend deployment
DOC.AI is not just a chatbot — it’s a system-level AI application designed to turn static documents into interactive intelligence.