VisionStream-IO 🚀

A high-performance, event-driven streaming vision-language ingestion pipeline designed for real-time AI assistance, built using FastAPI, Asynchronous Python (Asyncio), and OpenCV.

This architecture implements the structural paradigms of state-of-the-art streaming interaction models: a Dual-Loop Asynchronous Handoff Policy and a Hierarchical, Compressed Multi-Tier Memory Layer.

System Architecture Overview

1. The Real-Time Ingestion Loop (1 Hz): Captures visual frames at a strict frequency constraint using OpenCV, converting and encoding visual metrics to Base64 payloads.
2. Hierarchical Memory Tiering: Prevents KV-cache explosion over continuous video streams by dynamically compressing short-term frame buffers into text summaries, which are then archived cleanly into long-term history blocks.
3. Asynchronous Background Delegation Loop: Intercepts complex visual query triggers mid-stream to seamlessly dispatch heavy tasks to background worker tasks without stalling or lagging the primary live streaming video feed.

Local Execution Instructions

1. Prerequisites & Environment Setup

# Clone the repository
git clone [https://github.com/YOUR_GITHUB_USERNAME/VisionStream-IO.git](https://github.com/YOUR_GITHUB_USERNAME/VisionStream-IO.git)
cd VisionStream-IO

# Setup and activate your isolated virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install architectural dependencies
pip install -r requirements.txt