🎥 VISIONFLOW – Intelligent Video-to-Docs AI System

VISIONFLOW is a powerful, end-to-end solution that transforms meeting recordings into structured, readable documentation using the latest in LLM-based transcription, semantic understanding, and Retrieval-Augmented Generation (RAG).

From raw audio to detailed summaries and visualized logic flows - this system enables faster collaboration, insight extraction, and productivity across teams.

✨ Features

• 🎙️ Automatic Transcription using Whisper
• 🧠 Semantic Understanding of conversations using LLMs
• 📄 Document Generation in DOCX format
• 🔍 RAG Pipeline with ChromaDB + nomic embeddings
• 📈 Auto-generated Visualizations of meeting logic
• 🛠️ Modular, production-ready architecture
• ⚡ Streamlit interface for easy interaction

🧰 Tech Stack

Layer	Tools
Transcription	faster-whisper
Semantic Parsing	OpenAI / LLM (custom prompts)
Vector DB	ChromaDB
Embeddings	nomic-embed-text
Visualization	Matplotlib / Graphviz
UI	Streamlit
Audio	Pydub
Document Output	Python-docx

📂 Project Structure

visionflow/
├── transcription/       # Audio extraction, whisper transcription
├── semantic_analysis/   # LLM-based content parsing & summarization
├── rag_engine/          # ChromaDB setup, retrieval, and generation
├── doc_generation/      # Auto DOCX report creation
├── visualizer/          # Logic flow visualization
├── ui/                  # Streamlit interface
├── utils/               # Error handling, logging, helpers
├── main.py              # Entrypoint script
└── requirements.txt

🚀 Getting Started

1. Clone the Repo

git clone https://github.com/king04aman/visionflow.git
cd visionflow

2. Setup Environment

python -m venv venv
source venv/bin/activate  # or .\venv\Scripts\activate on Windows
pip install -r requirements.txt

3. Install FFmpeg

Required for audio processing (Pydub):

• macOS: brew install ffmpeg
• Ubuntu: sudo apt install ffmpeg
• Windows: Install Guide

4. Run the App

streamlit run ui/app.py

🧪 Sample Output

Coming Soon — Example DOCX + Logic Flow Diagram

🔍 How It Works

1. Audio is extracted from the video file using FFmpeg + Pydub
2. Whisper transcribes the audio with timestamps
3. LLM parses the transcript, identifies decisions, actions, topics
4. ChromaDB stores and retrieves embeddings for context-aware generation
5. DOCX reports and logic flow diagrams are created for end users

⚙️ Configuration & CLI

You can also run components via CLI:

python main.py --input path/to/video.mp4 --output_dir results/

Options:

• --fast: Use lightweight model for quick testing
• --visualize: Generate logic diagrams
• --debug: Enable verbose logs

📌 To-Do / Roadmap

• Add multi-language support
• Export to PDF / Markdown
• Real-time meeting integration (Zoom, Teams)
• Web dashboard & history tracking

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you’d like to change.

💬 Questions?

Feel free to open an Issue or connect with me on LinkedIn.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.