Disclaimer

This is a personal project, not intended for production use. The codebase is in need of significant improvement.

Requirements

To compile and run this project, you'll need:

• Cargo
• Trunk
• Python with duckduckgo_search installed
• Ollama installation
• nomic-embed-text for text embedding
• llama3.1:latest for other tasks
• A system capable of running playwright

Project Description

This project uses a Large Language Model (LLM) to generate search queries and scrape results from DuckDuckGo. It then ranks the results using nomic-embed-text and decides whether to provide an LLM answer or a list of links.

Key Features

• Uses cached and SQLite database for faster subsequent queries
• Integrates with DuckDuckGo to fetch search results
• Employs Nomic-embed-text for text ranking and embedding
• Decides between providing an LLM answer or a list of links based on the query