The project is not production-ready but serves as a tool for education and exploration, aiming to be approachable for beginners and useful for experts. Installation involves setting up Docker and Ollama, with a quick start guide provided. Chipper's philosophy emphasizes education and innovation, offering a platform for learning AI concepts and experimentation. The roadmap includes enhancing the web UI, improving linting and formatting, and adding features like a React-based web app and smart document chunking. Contributions and improvements to the project are encouraged.
Key takeaways:
- Chipper provides a web interface, CLI, and a simple architecture for embedding pipelines, document chunking, web scraping, and query workflows.
- It is built with Haystack, Ollama, Docker, Tailwind, and ElasticSearch, and can run locally or as a Dockerized service.
- The project is a research initiative aimed at using local RAG and LLMs for creative exploration without sharing proprietary details with cloud services.
- Chipper offers features like building a knowledge base with ElasticSearch, document splitting via Haystack, web scraping, and audio transcription.