Welcome to the Hummingbot Helper! This repository is dedicated to exploring, testing, and refining the use of Large Language Models (LLMs) in enhancing the functionality and user experience of Hummingbot. Our primary focus is on developing preprocessing techniques and effective LLM chains to assist users in running bots and efficiently answering questions related to Hummingbot's documentation.
Hummingbot is an open-source software that enables users to create and manage automated trading bots for cryptocurrency markets. As the complexity of trading strategies and the diversity of markets grow, the need for advanced, AI-driven assistance becomes increasingly essential. This repository serves as a collaborative platform for researchers, developers, and Hummingbot enthusiasts to contribute and benefit from the integration of LLMs with Hummingbot.
Join us in this exciting journey to integrate cutting-edge LLM technologies with Hummingbot and revolutionize the way we interact with trading tools! 🚀🤖💹
First, ensure that you have Anaconda or Miniconda installed on your machine. Afterwards, you should be able to run conda command from your path and see a response similar to:
usage: conda [-h] [-V] command ...
conda is a tool for managing and deploying applications, environments and packages.
Options:
positional arguments:
command
clean Remove unused packages and caches.
compare Compare packages between conda environments.Clone the repository to your local machine:
git clone https://github.com/hummingbot/helper.gitCreate a new hummingbot-helper conda environment that install the required dependencies listed in environment.yml:
conda env createActivate the created conda environment:
conda activate hummingbot-helper- Open the
.envfile in a text editor and replace theplaceholdervariables:
OPENAI_API_KEY=placeholder # replace with your OpenAI API key: sk-123...
PERSIST_DIRECTORY=placeholder # replace with the location where you want to save the vector database: /Users/your_name/helper/vector_storesYou can get an OpenAI API key for free at https://platform.openai.com.
Check out the Explain Documentation notebooks contains a set of Jupyter notebooks. These notebooks help you create a vector database based on the current Hummingbot documentation and use it in conjunction with OpenAI models in a basic Retrieval-Augmented Generation (RAG) pipeline.
Run jupyter notebook to use the notebooks directly, or access them in Pycharm or VSCode. Explore them to understand the current state of our research. Feel free to modify them and experiment with your own questions.
After you have run through the first notebook that creates a vector database based on the Hummingbot docs, you can also test out RAG-based prompts using a test Streamlit app.
Start the app with:
streamlit run main.py
Note that your OpenAI API key and docs vector database needed to be present for the app to work.
This project is licensed under the MIT License - see the LICENSE file for details.
This repository is for research and educational purposes only. Any scripts or code should not be used for trading cryptocurrency without understanding the risks involved.
For any queries or discussions, please open an issue in this repository, and we will get back to you as soon as possible.
We warmly welcome contributions from the community! If you have ideas, code, bug reports, or enhancements, please feel free to open an issue or submit a pull request. For detailed guidelines on how to contribute, please refer to our Contribution Guidelines.