ZoomLLM - Class Recording Q&A

A retrieval-augmented generation (RAG) application that allows you to ask questions about your class recordings. The app processes SRT subtitle files, stores embeddings in a local Chroma vector database, and uses OpenAI to generate answers based on the retrieved context.

---

Features

• Parse and process SRT subtitle files from class recordings
• Generate text embeddings using OpenAI's text-embedding-ada-002 model
• Store embeddings locally with Chroma (persistent vector database)
• Ask natural language questions about your class content
• Streamlit-based web interface for easy interaction

---

Project Structure

zoomllm/
├── main.py            # Script to process SRT files and populate the vector database
├── llm.py             # Streamlit app for Q&A interface
├── app.py             # (Optional) Additional app logic
├── requirements.txt   # Python dependencies
├── chroma_db/         # Local Chroma database (auto-generated)
└── srt_files/         # Folder containing your SRT subtitle files

---

Requirements

• Python 3.10 or higher
• OpenAI API key

---

Installation

1. Clone the repository:

   git clone https://github.com/droidmaximus/zoomllm.git
   cd zoomllm

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   venv\Scripts\activate   # Windows
   # or
   source venv/bin/activate  # macOS/Linux

3. Install dependencies:

   pip install -r requirements.txt

4. Set up your OpenAI API key:

   • For local development, create a .streamlit/secrets.toml file:

     OPENAI_API_KEY = "your-openai-api-key-here"

   • For Streamlit Community Cloud, add the secret in the app dashboard under "Secrets".

---

Usage

Step 1: Process SRT Files

Place your SRT subtitle files in the srt_files/ folder, then run:

python main.py

This will:

• Parse each SRT file
• Generate embeddings for each subtitle line
• Store the embeddings in the local Chroma database (chroma_db/)

Step 2: Run the Q&A App

Start the Streamlit app:

streamlit run llm.py

Open the provided URL in your browser, enter a question about your class recordings, and get an answer based on the stored content.

---

Deployment

Streamlit Community Cloud

1. Push your code to GitHub (ensure .streamlit/secrets.toml is in .gitignore).
2. Go to Streamlit Community Cloud and sign in.
3. Select your repository and deploy.
4. Add your OPENAI_API_KEY in the "Secrets" section of the app settings.

---

Configuration

• OpenAI Model: The app uses gpt-4.1-mini for chat completions and text-embedding-ada-002 for embeddings. You can change these in llm.py and main.py.
• Chroma Database Path: By default, the database is stored in chroma_db/. Modify the path parameter in chromadb.PersistentClient() to change this.

---

Troubleshooting

• ModuleNotFoundError: Ensure all dependencies are installed with pip install -r requirements.txt.
• API Key Errors: Verify your OpenAI API key is correctly set in .streamlit/secrets.toml or as an environment variable.
• Protobuf Errors: The requirements.txt pins protobuf==3.20.3 to avoid compatibility issues with Chroma and OpenTelemetry.

---

Author

droidmaximus