DocuMind is an intelligent Q&A platform designed to solve a critical problem with modern Large Language Models (LLMs): their inability to answer questions about private, specific documents. The project addresses the tendency of general-purpose LLMs to "hallucinate" or provide incorrect answers when queried on information they haven't been trained on.

By allowing users to upload their own files (PDFs, DOCX, TXT), DocuMind creates a secure, personalized knowledge base. It then uses a Retrieval-Augmented Generation (RAG) architecture to provide accurate, context-aware answers drawn directly from the user's content, effectively turning a general LLM into a specialized expert on your documents.

Project Overview:

Advanced RAG Pipeline: Engineered an end-to-end RAG system that improved response relevance by 30%. The pipeline processes user documents through: • Semantic Chunking: Intelligently breaking down documents into meaningful, context-rich chunks. • Vectorization & Storage: Converting text chunks into vector embeddings and storing them in a Weaviate vector database for efficient searching. • Retrieval & Re-ranking: Upon a user query, the system retrieves the most relevant chunks using semantic search and re-ranks them to prioritize the best context for the LLM.

Flexible LLM Integration: Built a modular backend with an Ollama adapter, allowing for "plug-and-play" support of various powerful, open-source LLMs like Llama 3 and Mistral.

Engineered a dynamic and responsive frontend using Next.js and TailwindCSS, creating a seamless user experience inspired by modern AI assistants. The interface features a real-time, conversational chat with streaming responses for fluid interaction. It also includes a multi-document, drag-and-drop upload system with progress indicators, allowing users to effortlessly create and query their personalized knowledge base. DocuMind is an intelligent Q&A platform designed to solve a critical problem with modern Large Language Models (LLMs): their inability to answer questions about private, specific documents. The project addresses the tendency of general-purpose LLMs to "hallucinate" or provide incorrect answers when queried on information they haven't been trained on. By allowing users to upload their own files (PDFs, DOCX, TXT), DocuMind creates a secure, personalized knowledge base. It then uses a Retrieval-Augmented Generation (RAG) architecture to provide accurate, context-aware answers drawn directly from the user's content, effectively turning a general LLM into a specialized expert on your documents. Project Overview: Advanced RAG Pipeline: Engineered an end-to-end RAG system that improved response relevance by 30%. The pipeline processes user documents through: • Semantic Chunking: Intelligently breaking down documents into meaningful, context-rich chunks. • Vectorization & Storage: Converting text chunks into vector embeddings and storing them in a Weaviate vector database for efficient searching. • Retrieval & Re-ranking: Upon a user query, the system retrieves the most relevant chunks using semantic search and re-ranks them to prioritize the best context for the LLM. Flexible LLM Integration: Built a modular backend with an Ollama adapter, allowing for "plug-and-play" support of various powerful, open-source LLMs like Llama 3 and Mistral. Engineered a dynamic and responsive frontend using Next.js and TailwindCSS, creating a seamless user experience inspired by modern AI assistants. The interface features a real-time, conversational chat with streaming responses for fluid interaction. It also includes a multi-document, drag-and-drop upload system with progress indicators, allowing users to effortlessly create and query their personalized knowledge base. Skills: Python (Programming Language) · Flask · React.js · Fast API · Ollama · Google Cloud Platform (GCP) · Docker · Large Language Models (LLM) · Next.js · React