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What are you Looking for?
DocuMind AI – Smart Document Search & Insights
When we were brought in to build Documind, we knew we were tackling something big—a cutting-edge AI-powered document intelligence platform designed to revolutionize how businesses extract, search, and analyze information from unstructured documents.
The Mission: Smarter Document Processing
From day one, we set out to build a scalable, AI-driven SaaS solution that could seamlessly process PDFs and Word documents, generate high-quality embeddings, store them efficiently, and power blazing-fast semantic search—all while maintaining an intuitive user experience.
What We Delivered
🚀 AI-Driven Document Processing
We engineered a robust document ingestion pipeline capable of extracting text, tables, and images from PDFs and Word files with precision. Our custom-built AI modules ensured that every piece of information was properly parsed and structured for maximum usability.
🖥️ Streamlit-Powered SaaS Dashboard
We didn’t just build a backend powerhouse—we designed an elegant, fully interactive UI using Streamlit. The real-time dashboard lets users query documents, visualize results, and track performance effortlessly.
🔍 Vector-Powered Semantic Search
Leveraging OpenAI embeddings and the Supabase vector database, we created a lightning-fast search engine that retrieves relevant documents with human-like intelligence. Users can now ask questions and get precise answers in seconds, rather than manually sifting through files.
⚡ Performance & Scalability
Speed and efficiency were top priorities, so we:
- Optimized async processing for real-time responsiveness.
- Integrated Supabase vector functions to boost search performance.
- Designed a Dockerization roadmap for seamless cross-platform deployment.
🤖 Agentic RAG Workflows
To take AI-powered search to the next level, we implemented dynamic Retrieval-Augmented Generation (RAG) workflows. Our system refines queries, validates AI-generated responses, and expands searches dynamically—ensuring higher accuracy and deeper insights.
🔗 Integrations & Future-Proofing
The platform doesn’t work in isolation—it connects with third-party tools like Ottomator for workflow automation and is built to evolve. We’ve mapped out Redis caching, multi-hop reasoning, and other AI-powered enhancements to keep Documind ahead of the curve.
Tech Stack That Makes It All Happen:
- Languages & Frameworks: Python, Streamlit
- AI/ML: OpenAI API (GPT-4, text-embedding-ada-002), LangChain
- Databases: Supabase (PostgreSQL + Vector), Weaviate
- Libraries & Tools: PyPDF2, python-docx, Sentence Transformers, GitHub
- DevOps & Scaling: Docker (planned), Microsoft Visual C++ Build Tools
Development Journey
Project Inception & Ideation
- Brainstormed the concept of an AI-driven document processing system.
- Researched existing solutions and identified pain points in document retrieval and search.
- Defined core features: document extraction, vector search, and AI-powered query workflows.
- Created a high-level roadmap and timeline. 🛠️
Architecture & UI/UX Design Begins 💻
- Designed system architecture using Python, OpenAI API, and Supabase.
- Created wireframes and UI mockups for the document upload and search interface.
- Planned database schema for storing embeddings and metadata.
- Evaluated vector search solutions (Supabase vs. Weaviate). 💻
Core Development Begins 🛠️
- Built initial PDF and Word document parsing modules with PyPDF2 and python-docx.
- Developed vector embedding pipeline using OpenAI’s text-embedding-ada-002.
- Set up a Supabase PostgreSQL + Vector Extension database for efficient retrieval.
- Implemented initial backend API for document processing. 🚦
Vector Search & Retrieval MVP Completed
- Integrated semantic search capabilities using Supabase vector functions.
- Implemented document metadata filtering for better search accuracy.
- Developed query ranking logic based on vector similarity scores.
- Conducted internal testing on real-world document datasets.
Agentic RAG Workflow Implementation
- Designed dynamic query refinement using AI-driven logic.
- Implemented multi-step retrieval and validation to improve response accuracy.
- Built a feedback mechanism to enhance search results over time. 🔍
Streamlit UI Development
- Developed a user-friendly front-end in Streamlit for real-time querying.
- Implemented document upload, search results display, and performance tracking.
- Added real-time progress indicators and interactive elements. 🖥️
Performance & Scalability Enhancements
- Optimized vector search queries to improve response times.
- Implemented asynchronous processing for faster document ingestion.
- Explored Dockerization for cross-platform compatibility.
- Addressed ONNX dependency errors and streamlined model execution. 🏗️
Beta Testing & Iteration
- Launched beta version with select testers to evaluate search accuracy and UI experience.
- Gathered feedback and made improvements to query refinement and retrieval logic.
- Fixed UI inconsistencies and improved error handling mechanisms. 📢
Official Release & Deployment
- Rolled out stable version with full document processing and search capabilities.
- Implemented final security audits and performance benchmarks.
- Began onboarding users and expanding database integration. 🚀
Future Roadmap Planning
- Planned Redis caching for faster retrieval and query optimization.
- Explored multi-hop reasoning capabilities for better document understanding.
- Discussed potential integrations with third-party tools like Ottomator. 📈