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An Intelligent Open-Source Video Understanding System A different path from traditional Large Video Language Models (LVLMs). Built for modularity, openness, and real-world usability.

Project description

gurrt

An intelligent video understanding system designed as an open-source alternative to monolithic Large Video Language Models

I built gurrt out of frustration.

Working with Large Video Language Models locally is:

  • Expensive to set up
  • GPU intensive
  • Slow to experiment with
  • Difficult to run on consumer hardware
  • Often closed or partially restricted

Most state-of-the-art video models require massive compute clusters and large-scale infrastructure.
They are impressive — but they are not accessible.

If meaningful video intelligence requires:

  • Multiple high-end GPUs
  • Hours of inference time
  • Proprietary model access

Then it stops feeling truly open.


A Different Philosophy

gurrt is not an attempt to compete with systems like YouTube’s internal models or other large-scale industrial LVLMs trained on massive GPU clusters.

It is an attempt to rethink the approach.

Instead of asking how to build a larger end-to-end video transformer, it explores a different path:

  • Smarter frame sampling techniques
  • Stronger and more modular vision models
  • Better structured embedding strategies
  • More efficient and grounded RAG pipelines
  • Persistent memory-driven reasoning

It represents a belief that meaningful video understanding can emerge from:

  • Thoughtful engineering
  • Smart sampling
  • Strong modular components
  • Memory-augmented retrieval

Not just from massive GPU clusters and billion-parameter models.

Architecture Overview

Video
    ├── Smart Frame Extraction
       └── Captioning + Embeddings
    ├── Audio Extraction
       └── Speech-to-Text + Embeddings
    ├── Vector Memory Store
    ├── Supermemory (Persistent Conversation Layer)
    └── LLM Reasoning Engine

Project Setup (using uv)

# Install uv if you haven't already
pip install uv

# Sync dependencies
uv sync

# Activate environment
.venv\Scripts\activate

File Structure

gurrt/
├── src/
│   |   │
│   └── videorag/                      # Core Video-RAG application package       │
│       ├── api/
│          └── server.py              # API server (exposes endpoints for querying, ingestion, etc.)       │
│       ├── cli/
│          └── main.py                # CLI entry point (init, ingest, query commands)       │
│       ├── config/
│          └── config.py              # Configuration management (API keys, paths, environment setup)       │
│       ├── core/                      # Core intelligence pipeline          ├── __init__.py
│          ├── asr.py                 # Audio extraction + speech-to-text processing          ├── embedding.py           # Embedding generation for captions & transcripts          ├── llm.py                 # LLM interaction and reasoning logic          ├── models.py              # Model loading and management utilities          ├── pipeline.py            # End-to-end ingestion + query pipeline orchestration          ├── prompts.py             # Prompt templates and structured context injection          ├── search.py              # Retrieval logic (semantic search over stored embeddings)          └── vectordb.py            # Vector database interface and storage abstraction       │
│       └── utils/
│           └── utils.py            # Shared utility functions and helpers
│
└── README.md                         # Project documentation

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