DocInsights: Natural Language Document Search Platform

The Challenge

In an era of vast digital information, users often struggle with document productivity. Faced with hundreds of documents, it's challenging to locate specific answers, and traditional keyword-based search falls short when the exact phrasing is unknown. The need for a platform that can "understand" document content and enable natural language queries is critical, whether for reading research papers, preparing for exams, or simply navigating large personal libraries.

My Solution

I designed and built DocInsights, a robust, end-to-end web platform engineered to boost document productivity through intelligent search. DocInsights "understands" the content of uploaded documents, allowing users to ask questions in natural language and receive precise answers with source attribution.

The platform is implemented as a microservices architecture deployed on Google Cloud, ensuring scalability, reliability, and clear separation of concerns. Key components include:

Core Backend (Python): The intelligent engine providing core logic for queries and document handling, leveraging my open-source RAGCore library for efficient Retrieval-Augmented Generation (RAG).

Web Frontend (Next.js, TypeScript): A modern, responsive user interface built with Next.js and TypeScript, designed for seamless user experience.

Vector Database (Pinecone): Utilizes Pinecone for storing document embeddings, with secure user data isolation via namespaces.

User Data Database (Firestore): A NoSQL Firestore instance for robust user account and library management.

Authentication (Firebase Authentication): Securely manages user accounts and ensures data privacy by identifying users via UIDs only.

Data Cleanup Service (Async Python): A periodic service ensuring complete data removal upon user account deletion, adhering to privacy best practices.

Users can easily upload documents, query their library in natural language, and receive sourced answers, transforming how they interact with their information.

The Outcome

Intelligent Document Interaction: Created a platform that moves beyond keyword search, enabling users to query documents using natural language and receive contextually relevant, sourced answers.

Scalable & Robust Architecture: Successfully designed and implemented a microservices-based system on Google Cloud, demonstrating expertise in modern cloud-native development and data privacy.

Enhanced Productivity: Provided a tool that significantly improves document comprehension and information retrieval, saving users time and enhancing their learning and research capabilities.

Leveraged Open Source Contributions: Integrated and showcased my own RAGCore library, demonstrating a commitment to building reusable, impactful tools within the ML ecosystem.