AI Knowledge Hub

AI Knowledge Hub is a high-performance, privacy-first RAG platform that utilizes a FastAPI/Next.js 16 architecture to transform dense PDF documents into a local "Second Brain" with zero-cost scaling. click here

I’m honestly tired of people underestimating what this project does. This is AI Knowledge Hub, a full-stack engineering ecosystem that prioritizes local persistence and DevOps maturity over simple API wrappers. It combines a hardened FastAPI backend with LangChain 0.3.7 orchestration, utilizing ChromaDB for local vector storage so your data never leaves the containerized environment.

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The Concept

AI Knowledge Hub was engineered to serve as a cognitive force multiplier for researchers and students. It addresses the "Passive Reading Trap" by converting static PDFs into a multi-modal learning environment. By leveraging the long-context capabilities of Gemini 2.5 Flash, the platform provides a dual-speed intelligence layer: an instant "Executive Summary" for rapid onboarding and a "Deep Search" interface for semantic, context-aware interrogation.

Technical Implementation

The High-Integrity RAG Pipeline

The core intelligence engine is built on FastAPI and LangChain. Unlike standard RAG implementations, this project uses a specialized ingestion pipeline (rag.py) that performs semantic text splitting via RecursiveCharacterTextSplitter and generates high-dimensional embeddings using the Hugging Face all-MiniLM-L6-v2 model. These vectors are stored in a local ChromaDB instance with persistent volume mapping.

"AI Knowledge Hub bridges the gap between massive LLM reasoning and localized data privacy through clever containerized vector orchestration."

The Frontend & UX Engineering

The interface is a sophisticated Next.js 16 application utilizing Tailwind CSS 4 and Shadcn/UI. It features a keyboard-first interaction model and a custom "Spotlight Card" system built with Framer Motion that reacts to cursor proximity.

Critical Engineering Challenge: Multi-Service Container Orchestration The hardest part was engineering the seamless communication between the decoupled Next.js frontend and the FastAPI backend within a unified Docker network. I had to architect a robust CORS policy and a specialized start.sh script to manage the lifecycle of the MongoDB persistence layer alongside the Python process IDs. Ensuring that the semantic chunking in rag.py didn't block the FastAPI event loop required deep work in asynchronous task handling.

[ PDF INGESTION ] --(HuggingFace Embeds)--> [  CHROMA VECTOR DB   ]
         |                                             |
         |                                    (Local Persistence)
         v                                             |
[ NEXT.JS 16 UI ] <---(FastAPI / REST)---> [ LANGCHAIN ORCHESTRATOR ]
         |                                             |
         |---(Framer Motion)----------------> [  GEMINI 2.5 FLASH  ]
         |                                             |
[  3D FLASHCARDS  ] <------------------------ [ EXECUTIVE SUMMARIES ]