VideoRAC 0.1.1

Video Retrieval-Augmented Chunking and Q&A Generation Toolkit

🌐 VideoRAC: Retrieval-Adaptive Chunking for Lecture Video RAG

🏛️ Official CSICC 2025 Implementation

"Adaptive Chunking for VideoRAG Pipelines with a Newly Gathered Bilingual Educational Dataset"

(Presented at the 30th International Computer Society of Iran Computer Conference — CSICC 2025)

---

📊 Project Pipeline

---

📖 Overview

VideoRAC (Video Retrieval-Adaptive Chunking) provides a comprehensive framework for multimodal retrieval-augmented generation (RAG) in educational videos. This toolkit integrates visual-semantic chunking, entropy-based keyframe selection, and LLM-driven question generation to enable effective multimodal retrieval.

This repository is the official implementation of the CSICC 2025 paper by Hemmat et al.

Hemmat, A., Vadaei, K., Shirian, M., Heydari, M.H., Fatemi, A. “Adaptive Chunking for VideoRAG Pipelines with a Newly Gathered Bilingual Educational Dataset.” Proceedings of the 30th International Computer Society of Iran Computer Conference (CSICC 2025), University of Isfahan.

---

🧩 Core Components

Module	Class	Description
processing.chunking.py	HybridChunker	Detects slide transitions using CLIP embeddings and SSIM to segment videos into coherent chunks.
processing.entropy_utils.py	EntropyUtils	Computes frame entropy for selecting representative keyframes.
processing.qa_generation.py	VideoQAGenerator	Generates structured Q&A pairs using transcripts and visual frame descriptions.

---

🧠 Research Background

This framework underpins the EduViQA bilingual dataset, designed for evaluating lecture-based RAG systems in both Persian and English. The dataset and code form a unified ecosystem for multimodal question generation and retrieval evaluation.

Key Contributions:

• 🎥 Adaptive Hybrid Chunking — Combines CLIP cosine similarity with SSIM-based visual comparison.
• 🧮 Entropy-Based Keyframe Selection — Extracts high-information frames for retrieval.
• 🗣️ Transcript–Frame Alignment — Synchronizes ASR transcripts with visual semantics.
• 🔍 Multimodal Retrieval — Integrates visual and textual embeddings for RAG.
• 🧠 Benchmark Dataset — 20 bilingual educational videos with 50 QA pairs each.

---

⚙️ Installation

# Clone repository
git clone https://github.com/your-org/VideoRAC.git
cd VideoRAC

# Create environment & install
ython -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

---

🚀 Usage Example

1️⃣ Hybrid Chunking

from VideoRAC import HybridChunker

chunker = HybridChunker(alpha=0.6, threshold_embedding=0.85)
chunks, timestamps, duration = chunker.chunk("lecture.mp4")
chunker.evaluate()

2️⃣ Q&A Generation

from VideoRAC import VideoQAGenerator

def my_llm_fn(messages):
    from openai import OpenAI
    client = OpenAI()
    response = client.chat.completions.create(model="gpt-4o", messages=messages)
    return response.choices[0].message.content

urls = ["https://www.youtube.com/watch?v=2uYu8nMR5O4"]
qa = VideoQAGenerator(video_urls=urls, llm_fn=my_llm_fn)
qa.process_videos()

---

📈 Results Summary (CSICC 2025)

Method	AR	CR	F	Notes
VideoRAC (CLIP+SSIM)	0.87	0.82	0.91	Best performance overall
CLIP-only	0.80	0.75	0.83	Weaker temporal segmentation
Simple Slicing	0.72	0.67	0.76	Time-based only

Evaluated using RAGAS metrics: Answer Relevance (AR), Context Relevance (CR), and Faithfulness (F).

---

🧾 License

Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

You may share and adapt this work with attribution. Please cite our paper when using VideoRAC or EduViQA:

@inproceedings{hem2025videorac,
  title={Adaptive Chunking for VideoRAG Pipelines with a Newly Gathered Bilingual Educational Dataset},
  author={Hemmat, Arshia and Vadaei, Kianoosh and Shirian, Melika and Heydari, Mohammad Hassan and Fatemi, Afsaneh},
  booktitle={30th International Computer Society of Iran Computer Conference (CSICC 2025)},
  year={2025},
  organization={IEEE}
}

---

👥 Authors

University of Isfahan — Department of Computer Engineering

• Kianoosh Vadaei — [e-mail]
• Melika Shirian — [e-mail]
• Arshia Hemmat — [e-mail]
• Mohammad Hassan Heydari — [e-mail]
• Afsaneh Fatemi — [e-mail]

---

⭐ Official CSICC 2025 Implementation — Give it a star if you use it in your research! ⭐ Made with ❤️ at University of Isfahan