Document Savvy AI Agent

The Document-Savvy AI Agent is a full-stack application powered by OpenAI and Pinecone. It is designed to ingest, index, and respond to document-based queries using advanced natural language processing (NLP) techniques. The platform provides an intuitive interface to manage and interact with large document repositories efficiently.
SECTOR
Technology
PROJECT TYPE
Full Stack + OpenAI
Technologies
ReactJS
NodeJS
ExpressJS
OpenAI API
Pinecone
PostgreSQL
AWS Lambda

Goal of the project

The project aimed to:

  • Build an AI-powered system that understands and retrieves precise information from document repositories.
  • Enable efficient document indexing using vector databases for semantic search capabilities.
  • Provide a responsive, user-friendly interface for querying and managing documents.

Project Execution

Architecture

The application was built using a modular architecture to ensure scalability and flexibility:

Architecture Diagram
  1. Frontend:
    • Developed using React.js for a responsive and interactive user interface.
    • Features included document upload, query input, and response visualization.
  2. Backend:
    • Built with Node.js, handling API requests and orchestrating AI-powered document query processing.
    • Integrated with OpenAI API for generating natural language responses based on user queries.
  3. Vector Database:
    • Used Pinecone for vector storage and similarity search.
    • Processed and indexed documents into embeddings using OpenAI’s Embedding API.
  4. Document Processing:
    • Preprocessed and segmented documents for efficient embedding and indexing.
    • Stored metadata and file references for retrieval.
  5. Cloud Integration:
    • AWS S3 for storing uploaded documents securely.
    • AWS Lambda for asynchronous tasks like embedding generation and indexing.

Tech Stack

  • Frontend: React.js, Material-UI
  • Backend: Node.js, Express.js
  • AI Integration: OpenAI API (ChatGPT and Embedding models)
  • Database: Pinecone (Vector Database)
  • Cloud Services: AWS S3, AWS Lambda
  • Tools Used:
    • Postman for API testing.
    • Docker for containerization.
    • GitHub for version control.

Timelines

The project was executed over 10 weeks:

  • Week 1-2: Requirement gathering, architecture design, and tech stack finalization.
  • Week 3-5: Backend development, including integration with OpenAI and Pinecone.
  • Week 6-7: Frontend development and user interface design.
  • Week 8: Cloud integration for document storage and embedding processing.
  • Week 9-10: Testing, performance optimization, and deployment.