gurrt 1.0.3

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.

gurrt is an intelligent video understanding system, an open-source alternative to monolithic Large Video Language Models built out of frustration.

One cannot work with Large Video Language Models :

• 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.

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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

The idea is how can i just get the job done with minimal efforts yielding high end results

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.

🌿 Quick Start Guide for pypi package

1. Installation

Set up gurrt using uv. Note: This project requires Python 3.12.

# 1. Install uv and set Python version
pip install uv
uv venv
uv python pin 3.12

# 2. Activate environment
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 3. Install gurrt (Standard/CPU)
uv pip install gurrt

# 4. OR Install with GPU Support
uv pip install gurrt[cuda] --extra-index-url https://download.pytorch.org/whl/cu121

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2. Commands

Command	Description
gurrt init	Configure API keys (Groq, Supermemory, Ollama).
gurrt models-download	Download and cache AI models locally.
gurrt index <path>	Extract frames and audio for search.
gurrt index-ollama <path> <model>	Index using a specific Ollama model.
gurrt ask "<query>"	Query your indexed video content.

The tool automatically optimizes performance by disabling unnecessary logging and tokenizer parallelism to ensure a clean CLI experience yet some logs do appear of Moviepy will resolve it in future iterations.

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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