title: Intelex
emoji: 🎓
colorFrom: indigo
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
🧠 InteleX: Intelligence Extended
The Ultimate Multi-Source RAG Ecosystem
InteleX is a production-grade Retrieval-Augmented Generation (RAG) platform that transforms static data into an interactive, high-velocity knowledge base. Built for speed and accuracy, it leverages Groq's LPU™ technology and semantic vector indexing to provide instant insights from diverse data sources.
🚀 The Vision
InteleX is designed to solve the "Knowledge Fragmentation" problem. Instead of searching through countless PDFs, web pages, and video transcripts, InteleX unifies them into a single, semantically aware interface. It doesn't just answer questions—it cites its sources, understands context, and delivers sub-second responses.
🌟 Key Pillars
- 🌐 Universal Ingestion: Seamlessly process PDFs, Live Websites, and YouTube Transcripts.
- ⚡ Neural Inference: Powered by Groq LLaMA 3.3, delivering the world's fastest token-per-second performance.
- 🔍 Deep Memory: Utilizes Pinecone for high-dimensional vector search and Aiven MySQL for metadata persistence.
- 🛡️ Secure & Private: A fully containerized architecture designed to handle proprietary research data safely.
🏛️ System Architecture
Our hybrid architecture is engineered for low latency and high reliability, balancing heavy cloud inference with optimized vector retrieval.
Hybrid Retrieval: InteleX uses a dual-pass retrieval system. First, it performs a semantic search via Pinecone, followed by a cross-encoder reranking pass to ensure the most relevant context is provided to the LLM.
🔄 The Intelligence Pipeline
Witness the journey of a single byte of data as it is transformed into actionable intelligence.
🛠️ Technical Breakdown
- Ingestion Engine: Extracts raw text from PDFs (OCR-ready), dynamic websites (BS4/Playwright), and YouTube (API-based transcripts).
- Semantic Chunking: Implements recursive character splitting with context-aware overlap to prevent "context loss" at the edges.
- Vectorization: Text is embedded using
intfloat/multilingual-e5-large, creating a 1024-dimensional semantic fingerprint. - Indexing & Storage: Vectors are committed to Pinecone with HNSW indexing for $O(\log n)$ search speeds.
- Contextual Synthesis: The top-ranked chunks are injected into a specialized prompt engineering template and streamed via Groq.
🛠️ Technology Stack
| Layer | Technology |
|---|---|
| Interface | React 18, Tailwind CSS, Framer Motion |
| Backend Engine | FastAPI (Python 3.11), Pydantic |
| Inference | Groq LPU™ (LLaMA 3.3 70B) |
| Vector Memory | Pinecone (Serverless) |
| Relational Memory | Aiven Managed MySQL |
| DevOps | Docker, Vercel Edge, HF Spaces |
⚙️ Deployment & Setup
Production Ecosystem
- Frontend: Hosted on intelex.vercel.app (Edge Optimized).
- Backend: Containerized on Hugging Face Spaces.
- Infrastructure: Secured via GitHub Private Repository and Environment Secret Injection.
Quick Start (Local Development)
# Clone the private repository
git clone https://github.com/yakoob-md/InteleX.git
cd InteleX
# Setup Backend
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload
# Setup Frontend
cd ../frontend
npm install
npm run dev