Roadmap & Accomplishments¶

This page documents the development milestones, implementation status, and recent accomplishments of the NassaQ graduation project.

Graduation Project Milestones¶

NassaQ has achieved all its core research and engineering targets. The platform has successfully transitioned from a local, single-pipeline mockup into a highly resilient, enterprise-ready dual-architecture platform ready for real-world deployments.

Implementation Status¶

pie title Implementation Accomplishments
    "Fully Implemented" : 20
    "Active & Integrated" : 3
    "Planned / Under Review" : 2

Key Accomplishments Breakdown¶

1. Unified RAG and Semantic Ingestion¶

We successfully implemented a premium Retrieval-Augmented Generation (RAG) and Semantic Search ecosystem: - Embedding Generation: Core backend uses Azure OpenAI to convert parsed markdown chunks into high-density 1536-dimension vector embeddings. - Precision Search: Standard queries run through a broad recall phase, followed by a Cohere Rerank v4.0 Fast cross-attention pass to elevate top-relevance chunks. - Sourced Answers: The final context is compiled for gpt-4.1-mini to answer questions bilingual (Arabic/English) based only on the user's uploaded documents with precise page citations.

2. Dual Message Broker Architecture¶

The application handles message brokerage seamlessly across both local development and cloud production configurations: - Dev Mode: Uses AMQP to queue tasks asynchronously inside a local containerized RabbitMQ instance. - Prod Mode: Automatically activates the AzureServiceBusBroker SDK client to route document tasks directly through managed Azure Service Bus queues, eliminating infrastructure operations and guaranteeing highly durable scaling.

3. Cosmos DB NoSQL Persistence¶

The planned migration from local flat-file worker disks is fully completed: - Extracted JSON schemas, text chunks, and OCR pipeline performance markers are written directly to Azure Cosmos DB (MongoDB API). - This keeps our primary SQL database extremely lean, saving parsed document blobs inside a distributed, horizontally-scalable NoSQL cluster indexed directly against SQL records via the mongo_doc_id field.

4. Relational Database Sync & Audit Trails¶

The platform’s transaction histories and analytical audit trails are now fully functional: - Ocr_Results Table: Synced automatically after worker ingestion to track layout confidence, categorized folders, language labels, and exact Azure cognitive/OpenAI costs in USD. - Logs Table: Populates instantly on auth actions (user registrations, logins), administrative modifications (role changes, user activations), and document cycles (uploads, RAG ingestions, vector removals).

Planned Future Research¶

While all core targets for the graduation project defense are fully satisfied, the team holds these avenues for post-graduation research:

1. Individual Permissions Enforcement
2. The Individual_Permissions table is successfully structured. Future updates will wire these SQL rows directly into the FastAPI authorization route chain to support granular document sharing and path-inheritance controls.
3. Refresh Token Rotation (RTR)
4. Future security updates will implement single-use refresh token rotation and server-side revocation lists to harden the authentication gateway.
5. Advanced Rate Limiting
6. Add token-bucket rate limiting on the /auth/login and /auth/register endpoints to protect database clusters against brute-force attacks.