lavague 1.0.5

Selenium code generation from text instructions

Welcome to LaVague

Copilot for devs to automate automation

🏄‍♀️ What is LaVague?

LaVague is an open-source project designed to automate automation for devs! By turning natural language queries into Python code leveraging Selenium, LaVague is designed to make it easy for users to automate express web workflows and execute them on a browser.

LaVague in Action

Here's an examples to show how LaVague can execute natural lanaguge instructions on a browser to automate interactions with a website:

LaVague interacting with Hugging Face's website.

🚀 Getting Started

Running LaVague in your local env

You can get started with LaVague in 2 steps:

1. Install LaVague & dependencies

wget https://raw.githubusercontent.com/lavague-ai/LaVague/main/setup.sh &&
sudo bash setup.sh

2. Run your LaVague command!

lavague-build --file_path tests/hf.txt --config_file examples/api/openai.py

For a step-by-step guide or to run LaVague in a Google Colab, see our quick-tour which will walk you through how to get set-up and launch LaVague with our CLI tool.

🙋 Contributing

We would love your help in making La Vague a reality.

To avoid having multiple people working on the same things & being unable to merge your work, we have outlined the following contribution process:

1. 📢 We outline tasks on our backlog: we recommend you check out issues with the help-wanted labels & good first issue labels
2. 🙋‍♀️ If you are interested in working on one of these tasks, comment on the issue!
3. 🤝 We will discuss with you and assign you the task with a community assigned label
4. 💬 We will then be available to discuss this task with you
5. ⬆️ You should submit your work as a PR
6. ✅ We will review & merge your code or request changes/give feedback

Please check out our contributing guide for a more detailed guide.

If you want to ask questions, contribute, or have proposals, please come on our Discord to chat!

✨ Features

• Natural Language Processing: Understands instructions in natural language to perform browser interactions.
• Selenium Integration: Seamlessly integrates with Selenium for automating web browsers.
• Open-Source: Built on open-source projects such as transformers and llama-index, and compatible with open-source models, either locally or remote, to ensure the transparency of the agent and ensures that it is aligned with users' interests.
• Local models for privacy and control: Supports local models like Gemma-7b so that users can fully control their AI assistant and have privacy guarantees.
• Advanced AI techniques: Uses a local embedding (bge-small-en-v1.5) first to perform RAG to extract the most relevant HTML pieces to feed the LLM answering the query. Then leverages Few-shot learning and Chain of Thought to elicit the most relevant Selenium code to perform the action without having to finetune the LLM for code generation.

🗺️ Roadmap

TO keep up to date with our project backlog here.

🚨 Disclaimer

This project executes LLM-generated code using exec. This is not considered a safe practice. We therefore recommend taking extra care when using LaVague (such as running LaVague in a sandboxed environment)!