WebVoyager is a research project that builds an end-to-end web agent using large language models (LLMs). It leverages LLMs to navigate the internet and retrieve information in response to user queries. This implementation is inspired by the concepts presented in the paper "WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models" (citation to be added once available).
Description
WebVoyager empowers users to explore the web through the power of LLMs. It operates by:
- Understanding Your Query: The LLM analyzes your query to grasp its intent and identify relevant information to retrieve.
- Navigating Websites: By simulating user interaction, the LLM explores web pages, following hyperlinks to gather pertinent details.
- Processing Information: The LLM interprets the collected data in the context of your query.
- Generating a Response: WebVoyager provides a response that summarizes or answers your question based on the information it found.
Setup
- Create a Virtual Environment (Recommended):
- Isolate project dependencies using
venv:python3 -m venv webvoyager_env source webvoyager_env/bin/activate # Activate on Linux/macOS venv\Scripts\activate.bat # Activate on Windows
- Isolate project dependencies using
- Clone the Repository:
git clone https://github.com/your-username/webvoyager.git cd webvoyager - Install Dependencies:
pip install -r requirements.txt
Getting Started
- Run the Setup Script:
./setup.sh # Assuming setup.sh is in the project directory- This script (if provided) might perform tasks like downloading additional resources or preparing the environment.
- Launch WebVoyager: Execute the provided Jupyter Notebook file (e.g.,
webvoyager.ipynb). - Interact with WebVoyager: Within the notebook, provide queries and observe the results generated by the LLM.
Disclaimer
WebVoyager is a research project. Its functionalities may be limited. Use it responsibly, adhering to website terms of service and respecting user privacy. To learn more about web voyager check out webvoyagers github page.