
chipper
✨ AI interface for tinkerers (Ollama, Haystack RAG, Python)
Stars: 437

Chipper provides a web interface, CLI, and architecture for pipelines, document chunking, web scraping, and query workflows. It is built with Haystack, Ollama, Hugging Face, Docker, Tailwind, and ElasticSearch, running locally or as a Dockerized service. Originally created to assist in creative writing, it now offers features like local Ollama and Hugging Face API, ElasticSearch embeddings, document splitting, web scraping, audio transcription, user-friendly CLI, and Docker deployment. The project aims to be educational, beginner-friendly, and a playground for AI exploration and innovation.
README:
Chipper provides a web interface, CLI, and a modular, hackable, and lightweight architecture for RAG pipelines, document splitting, web scraping, and query workflows, enhancing generative AI models with advanced information retrieval capabilities. It can also function as a proxy between an Ollama client, such as Enchanted or Open WebUI, and an Ollama instance. Built with Haystack, Ollama, Hugging Face, Docker, TailwindCSS, and ElasticSearch, it runs as a fully containerized service.
This project started as a personal tool to help my girlfriend with her book, using local RAG and LLMs to explore characters and creative ideas while keeping her work private and off cloud services like ChatGPT. What began as a few handy scripts soon grew into a fully dockerized, extensible service and along the way, it became a labor of love. Now, I'm excited to share it with the world.
If you find Chipper useful, leaving a star ⭐ would be lovely and will help others discover Chipper too.
- Project Site: https://chipper.tilmangriesel.com/
- Live Demo: https://demo.chipper.tilmangriesel.com/
- Installation and Setup
- Features
- Philosophy
- Demos
- Project Roadmap
- Acknowledgments
- Friends of Chipper
- Star History
- Quickstart
- Or visit the Chipper project website
- Local & Cloud Model Support - Run models locally with Ollama or connect to remote models via the Hugging Face API.
- ElasticSearch Integration - Store and retrieve vectorized data efficiently with scalable indexing.
- Document Chunking - Process and split documents into structured segments.
- Web Scraping - Extract and index content from web pages.
- Audio Transcription - Convert audio files to text.
- CLI & Web UI - Access Chipper via a command-line tool or a lightweight, self-contained web interface.
- Dockerized Deployment - Run in a fully containerized setup with minimal configuration.
- Customizable RAG Pipelines - Adjust model selection, query parameters, and system prompts as needed.
- Ollama API Proxy - Extend Ollama with retrieval capabilities, enabling interoperability with clients like Enchanted and Open WebUI.
- API Security - Proxy the Ollama API with API key-based and Baerer token service authentication.
- Offline Web UI - Works without an internet connection using vanilla JavaScript and TailwindCSS.
- Edge TTS – Listen to Chipper's output using a WebAssembly-based client-side TTS generator.
- Distributed Processing - Chain multiple Chipper instances together for workload distribution and extended processing.
Note: This is a personal project and not designed for commercial or production use. If you intend to use it in a production environment, make sure to conduct your own due diligence.
At the heart of this project lies my passion for education and exploration. I believe in creating tools that are both approachable for beginners and helpful for experts. My goal is to offer you a well-thought-out service architecture, and a stepping stone for those eager to learn and innovate.
This project wants to be more than just a technical foundation, for educators, it provides a framework to teach AI concepts in a manageable and practical way. For explorers, tinkerers and companies, it offers a playground where you can experiment, iterate, and build upon a versatile platform.
Feel free to improve, fork, copy, share or expand this project. Contributions are always very welcome!
Use Chipper's built-in web interface to set up and customize RAG pipelines with ease. Built with vanilla JavaScript and TailwindCSS, it works offline and doesn't require any framework-specific knowledge. Run the /help
command to learn how to switch models, update the embeddings index, and more.
Automatic syntax highlighting for popular programming languages in the web interface.
For models like DeepSeek-R1, Chipper suppresses the "think" output in the UI while preserving the reasoning steps in the console output.
Full support for the Ollama CLI and API, including reflection and proxy capabilities, with API key route decorations.
Enhance every third-party Ollama client with server-side knowledge base embeddings, allowing server side model selection, query parameters, and system prompt overrides. Enable RAG for any Ollama client or use Chipper as a centralized knowledge base.
- [x] Basic Functionality
- [x] Command-Line Interface (CLI)
- [x] Web-Based User Interface (UI)
- [x] Docker Containerization
- [x] Improved Web UI (Better mobile support)
- [x] Enhanced Linting and Code Formatting
- [x] Docker Hub Registry Image Publishing
- [x] Edge Inference for Text-to-Speech (TTS)
- [x] Bearer Token Authentication Support
- [x] Mirror Ollama Chat API (Enable Chipper as a drop-in middleware)
- [x] Haystack Chat Generators (
ChatPromptBuilder
&OllamaChatGenerator
) - [x] Distributed Processing (Chain multiple Chipper instances for workload distribution)
- [ ] Expanded Support for Client-Side Model Settings
- [ ] Initial Set of 3 Tutorials for New Users
- [ ] Automated Unit Testing Framework
- [ ] Smart Document Splitting and Embedding
- [ ] React-Based Web Application (Modernized UI/UX)
Check out these Chipper-compatible projects! Want to add yours? Open an issue to let me know!
- page-assist Use your locally running AI models to assist you in your web browsing
- open-webui User-friendly AI Interface
- enchanted Enchanted is iOS and macOS app for chatting with private self hosted language models.
- Ollamac Mac app for Ollama
Be sure to visit the Chipper project website for detailed setup instructions and more information.
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