Project Description

Document Management and Query Application

This project is a secure, scalable, full-stack application designed to enable users to upload, store, and interact with documents of various formats (PDF, PPT, CSV, etc.) using advanced natural language processing (NLP) techniques. The application offers robust document management, user authentication, and a sophisticated querying system that leverages RAG (Retrieve and Generate) agents for context-aware answers to user queries.

The application supports seamless integration with cloud storage, advanced document parsing, and a highly optimized search capability. Users can easily upload documents, which are parsed and indexed, allowing them to ask questions and receive accurate, contextually relevant responses. By incorporating tools like unstructured.io for parsing and Elasticsearch for indexing, the system ensures efficient retrieval and response generation.

The platform is built with a microservices architecture, making it highly modular, scalable, and fault-tolerant. Each service is containerized using Docker and orchestrated via Kubernetes, ensuring reliable deployments and easy scaling to accommodate increased demand. Key features include:

User Authentication: Secure login and signup functionality, using JWT tokens for session management.
Document Upload and Management: Supports various file formats, with file storage in AWS S3 and metadata management in PostgreSQL.
Advanced Document Parsing: Uses unstructured.io to extract structured data from uploaded documents, making them searchable and query-ready.
NLP Querying with RAG Agents: Integrates RAG agents like LangChain/Llama for accurate, context-aware query handling.
Search and Indexing: Stores parsed document content in Elasticsearch for fast and efficient querying.
Caching and Status Management: Uses Redis for caching document status and tracking service health.
Logging and Monitoring: Structured logging and monitoring via ELK Stack (Elasticsearch, Logstash, Kibana) and optional Prometheus/Grafana for metrics visualization.

Technology Stack

Backend Services: NestJS (for Login and DMS services), Flask (for Indexing and QA services)
Frontend: Next.js
File Storage: AWS S3
Database: PostgreSQL (for metadata), Redis (for caching)
Document Parsing: unstructured.io
NLP Processing: LangChain/LlamaIndex, RAG agents for query responses
Search Engine: Elasticsearch
Containerization and Orchestration: Docker and Kubernetes
Logging and Monitoring: ELK Stack, Prometheus, Grafana (optional)

Key Functionalities

Document Upload and Storage: Allows users to securely upload files to S3, storing document metadata in PostgreSQL for tracking and categorization.
Document Parsing and Indexing: Automatically retrieves uploaded files, parses content using unstructured.io, and indexes them in Elasticsearch for efficient querying.
Natural Language Querying: RAG agents interpret user queries, retrieving relevant document content and generating accurate answers.
Caching and Real-time Status Updates: Uses Redis to track and share document processing status across services, enhancing efficiency.
Monitoring and Logging: Uses sidecar logging with ELK Stack and optional monitoring with Prometheus and Grafana for system performance visibility.

Deployment

The entire application is containerized using Docker, and Kubernetes orchestrates the deployment. Kubernetes handles service scaling, load balancing, and fault tolerance. Logging is facilitated through a sidecar service, ensuring centralized logging for all services. The project also supports optional monitoring with Prometheus and Grafana, allowing for real-time tracking of application metrics.

Goals

The primary goals of this project are to:

Provide a seamless document management experience that supports various file formats.
Enable advanced NLP-based querying that returns contextually relevant answers.
Ensure security, scalability, and reliability by employing best practices in microservices, caching, and containerization.

This project ultimately provides a comprehensive solution for document management, processing, and querying, combining cloud infrastructure, advanced NLP, and scalable architecture to meet enterprise-level requirements.