YouTubeGPT is a web app that can be run fully locally and lets you summarize and chat (Q&A) with YouTube videos. You can either use OpenAI's API or a (local) Ollama instance.
YouTubeGPT's features include:
โ๏ธ Provide a custom prompt for summaries VIEW DEMO
- you can tailor the summary to your needs by providing a custom prompt or just use the default summarization
โ Get answers to questions about the video content VIEW DEMO
- part of the application is designed and optimized specifically for question answering tasks (Q&A)
- the summaries and answers can be saved to a library accessible at a separate page!
- additionally, summaries and answers can be exported/downloaded as Markdown files!
- choose between OpenAI's API or a (local) Ollama instance
- currently available: ChatGPT 4-5 (incl. nano & mini) and continuously updated with new models
- by choosing a different model, you can summarize even longer videos and get better responses
- adjust the temperature and top P of the model
- go to the three dots in the upper right corner, select settings and choose either light, dark or my aesthetic custom theme
If you want to use OpenAI's API, you will first need to get an OpenAI API-Key. This is very straightforward and free. Have a look at their instructions to get started.
If you want to use Ollama, you need to have an Ollama server running locally or remotely. You can download Ollama for macOS, Linux, or Windows on their website. Make sure the server is reachable either on the default port 11434 or set the OLLAMA_HOST environment variable to point to your Ollama server. Also, you need to pull the models you want to use.
Note: Ollama limits the context window to 4k tokens by default. I strongly recommend to adjust it to at least 16k tokens. This can be done in the Ollama app settings.
- set the
OPENAI_API_KEYenvironment variable either in docker-compose.yml or by runningexport OPENAI_API_KEY=<your-actual-key>in your terminal - execute the following command:
# pull from docker hub
docker-compose up -d
# or build image locally
docker-compose up --build -dThe app will be accessible in the browser under http://localhost:8501.
โน๏ธ For the best user-experience, you need to be in
Tier 1usage tier, which requires a one-time payment of 5$. However it's worth it, since then, you'll have access to all models and higher rate limits.
You can customize the default settings using environment variables. These are optional and will use built-in defaults if not set:
| Environment Variable | Description | Default | Example |
|---|---|---|---|
YTGPT_LLM_PROVIDER |
Default LLM provider | OpenAI |
OpenAI or Ollama |
YTGPT_DEFAULT_GPT_MODEL |
Default language model | gpt-4.1-nano |
gpt-4o, llama3 |
YTGPT_DEFAULT_EMBEDDINGS_MODEL |
Default embeddings model | text-embedding-3-small |
text-embedding-3-large, nomic-embed-text:latest |
YTGPT_TEMPERATURE |
Model temperature (0.0-2.0) | 1.0 |
0.7 |
YTGPT_TOP_P |
Model top-p (0.0-1.0) | 1.0 |
0.9 |
Example usage:
# Set environment variables
export YTGPT_LLM_PROVIDER=Ollama
export YTGPT_DEFAULT_GPT_MODEL=llama3
export YTGPT_TEMPERATURE=0.7
export YTGPT_TOP_P=0.9
# Run the app
streamlit run main.pyOr with Docker:
# Add to docker-compose.yml or set before running
export YTGPT_LLM_PROVIDER=OpenAI
export YTGPT_DEFAULT_GPT_MODEL=gpt-4o
docker-compose up -dNote: These settings apply at startup and set the initial values in the UI. Users can still change these settings in the sidebar during their session.
Iโm working on adding more features and am open to feedback and contributions. Don't hesitate to create an issue or a pull request. Also, if you are enjoying the app or find it useful, please consider giving the repository a star โญ
This is a small side-project and it's easy to get started! If you want to contribute, hereโs the gist to get your changes rolling:
- Fork & clone: Fork the repo and clone your fork to start.
- Pick an issue or suggest One: Choose an open issue to work on, or suggest a new feature or bug fix by creating an issue for discussion.
- Develop: Make your changes.
- Ensure your code is clean and documented. Test the changes at least exploratively. Make sure to test 'edge cases'.
- Commit your changes with clear, descriptive messages, using conventional commits.
- Stay updated: Keep your branch in sync with the main branch to avoid merge conflicts.
- Pull Request: Push your changes to your fork and submit a pull request (PR) to the main repository. Describe your changes and any relevant details.
- Engage: Respond to feedback on your PR to finalize your contribution.
If you don't have uv installed, please follow the installation instructions in the official documentation before running the commands below.
# create a virtual environment and install dependencies
uv sync
# you'll need an API key
export OPENAI_API_KEY=<your-openai-api-key>
# run chromadb (necessary for chat)
docker-compose up -d chromadb
# run app
uv run streamlit run main.pyNote: pyproject.toml and uv.lock are the source of truth for dependencies. requirements.txt is kept for legacy pip users.
The app will be accessible in the browser under http://localhost:8501 and the ChromaDB API under http://localhost:8000/docs.
The project is built using some amazing libraries:
- The project uses YouTube Transcript API for fetching transcripts.
- LangChain is used to create a prompt, submit it to an LLM and process it's response.
- The UI is built using Streamlit.
- ChromaDB is used as a vector store for embeddings.
This project is licensed under the MIT License - see the LICENSE file for details.