Built by: kk
DocuMind is an advanced RAG (Retrieval-Augmented Generation) application that allows users to seamlessly interact with their documents using AI. By leveraging the power of Google's Gemini models, Groq's high-speed inference, and vector search, DocuMind provides accurate, context-aware answers from your uploaded files and databases.
- Frontend Application: https://querywise.vercel.app
- Backend API: https://documind-p046.onrender.com
DocuMind is built upon a highly scalable Modular RAG framework, ensuring precise context retrieval and coherent responses. The system comprises 6 Main Components:
- Orchestrator (RAG Service): Coordinates the entire workflow, managing the flow of data between user input, retrieval, and generation.
- Document Ingestion Engine: Handles the parsing, cleaning, and segmentation of various file formats (PDF, DOCX, TXT).
- Embedding Service: Transforms text chunks into high-dimensional vector embeddings for semantic understanding.
- Vector Store (Pinecone): Manages high-performance similarity search and retrieval of context.
- Parent-Child Indexing Service: Implements advanced indexing strategies to maximize retrieval quality.
- Generation Service (Gemini/Groq): Leverages state-of-the-art LLMs to synthesize answers using the retrieved context.
To overcome the limitations of standard chunking, DocuMind employs a Parent-Child Indexing strategy:
- Child Chunks: Small, dense text segments (~300 chars) responsible for high-accuracy semantic search.
- Parent Documents: Larger context blocks linked to the child chunks.
- Retrieval Logic: When a child chunk matches a user's query, the system retrieves its corresponding Parent Document. This ensures the LLM receives full, coherent context rather than fragmented snippets.
- Chat with Documents: Upload PDFs, DOCX, or text files and ask questions in natural language.
- Transparent AI: View Vector Scores (V) and Resonance Scores (R) to understand exactly why a document was retrieved.
- Strict Scoring: Uses a local Reranker (
ms-marco-MiniLM-L-12-v2) to prioritize accuracy over recall. - HyDE: Uses Hypothetical Document Embeddings to improve search relevance.
- Text-to-SQL: Connect your database and ask questions in plain English (e.g., "Show me total sales by region").
- Auto-Visualization: The system automatically detects data patterns and generates Bar, Line, or Pie charts instantly.
- Schema Awareness: Automatically extracts and understands your database structure for accurate queries.
- SQL Safety: Built-in validator prevents destructive queries and ensures syntax correctness.
- Multi-Model Support: Switch between Google Gemini 2.5 for reasoning and Groq (Llama 3) for ultra-fast responses.
- Secure Authentication: Robust user management with unique user IDs and secure session handling.
- Smart History: Persistent chat sessions allowing you to revisit previous conversations.
- Mobile Responsive: A modern, mobile-friendly interface built with React and Tailwind CSS.
- Frontend: React, Vite, Tailwind CSS, Recharts
- Backend: Python, FastAPI
- AI/LLM: Google Gemini, Groq
- Vector DB: Pinecone
- Database: MongoDB Atlas (Chat History), PostgreSQL/MySQL (Data Analysis)
- Deployment: Vercel (Frontend), Render (Backend)
This project is protected and authenticated with embedded signature verification. The signature system ensures proper attribution and project integrity.
Key Protection Features:
- 🔒 Signature Verification: Both backend and frontend verify the project signature on startup
- 🛡️ Tamper Protection: Removing or modifying the signature will prevent the application from running
- 👤 Developer Attribution: Clear attribution to the original developer throughout the application
- 🔐 License Protection: Built-in license key validation system
Protected Files:
.signature(Backend & Frontend signature files)api/lib/signature_guard.py(Backend verification service)frontend/src/utils/signatureGuard.js(Frontend verification service)