Abstract
Chipper is an open-source project for Retrieval-Augmented Generation (RAG), designed for the development, experimentation, and deployment of agentic AI systems. By integrating retrieval-enhanced reasoning, tooling for document processing, web scraping, Chipper facilitates the creation of autonomous AI workflows capable of dynamically retrieving, analyzing, and executing actions on structured knowledge. Engineered with a focus on extensibility and modification, Chipper provides a fully containerized, self-discoverable environment, balancing rigorous AI experimentation with practical application development. This paper delineates Chipper’s architecture, deployment methodologies, and its significance as a robust platform for agentic AI research and development.
URL: https://github.com/TilmanGriesel/chipper
1. Introduction
Agentic AI systems necessitate modular and adaptable architectures that enable autonomous retrieval, advanced reasoning, and dynamic execution. Existing AI frameworks often present a dichotomy: they are either overly intricate, limiting experimentation, or excessively simplistic, rendering them unsuitable for real-world deployment. Chipper addresses this gap by offering:
- Modular, customizable workflows tailored for retrieval-augmented AI decision-making.
- Professional-grade containerization using Docker to ensure scalability and reproducibility.
- Self-discoverable, modifiable components designed to facilitate learning and hands-on AI development.
Rather than serving as a monolithic AI product, Chipper functions as an experimental sandbox, providing AI researchers, developers, and innovators with an open, extensible system for constructing and refining agentic AI workflows.
2. System Architecture
Chipper is architected to optimize flexibility and extensibility, enabling users to experiment with diverse AI workflows. Its core components include:
- Retrieval System: Efficient vector-based search leveraging ElasticSearch.
- Document Processing: Intelligent document chunking and embedding to optimize structured knowledge retrieval.
- Model Integration: Seamless support for local AI models (e.g., Ollama) and cloud-based inference (e.g., Hugging Face API).
- Web Scraping & Data Collection: Automated knowledge base construction to enhance AI reasoning capabilities.
- Web & CLI Interfaces: Intuitive user interfaces and command-line accessibility.
- API Proxy: Extends Ollama’s API to enable retrieval-augmented AI workflows.
Each of these modules is designed to function independently, allowing for modular experimentation without being constrained by rigid workflows.
2.1 Containerized Deployment
Chipper employs a professional containerization strategy to ensure seamless deployment and reproducibility, featuring:
- Comprehensive Dockerization to guarantee consistent execution across various environments.
- Automated Setup Scripts to streamline installation and initialization.
- Modular Service Deployment that facilitates customized AI pipelines.
- Distributed Processing Capabilities to enhance scalability and computational efficiency.
These attributes position Chipper as an optimal environment for both AI research and real-world application development.
3. Learning and Experimentation
Chipper functions as a hands-on AI experimentation platform, fostering learning and development in:
- Retrieval-Augmented Generation (RAG) workflows for AI-driven knowledge retrieval and synthesis.
- Model inference and fine-tuning using both local and cloud-based architectures.
- Containerized AI deployment incorporating modern DevOps best practices.
Primary Target Audience:
- Academics & Educators: Facilitating AI pedagogy through interactive experimentation.
- Researchers & Developers: Accelerating prototyping and deployment of retrieval-augmented AI applications.
- AI Enthusiasts & Tinkerers: Providing a flexible environment for exploring and customizing agentic AI workflows.
4. Conclusion
Chipper represents a scalable, modular, and agent-oriented AI framework that empowers users to design, refine, and deploy retrieval-augmented AI agents. By integrating autonomous knowledge retrieval, multi-step reasoning, and robust containerization, Chipper bridges the gap between experimental AI research and real-world automation.
Designed with modification and exploration in mind, Chipper provides a critical link between lightweight AI scripting and full-scale AI platforms, making it an indispensable tool for students, developers, and researchers seeking to advance agentic AI systems.