RAG Starter: Minimal Python Boilerplate for Retrieval-Augmented Generation
Minimal Python RAG boilerplate with vector database adapters. Build retrieval-augmented generation systems with a clean, modular, and extensible architecture.
RAG Starter is a minimal, adapter-first Python boilerplate designed to help developers build retrieval-augmented generation systems quickly and clearly.
The project focuses on structure over complexity. Instead of locking you into a specific stack, it provides a clean foundation that separates core RAG logic from vector database implementations. This makes it easier to experiment, swap components, and understand how each part of the system works.
It is intended for developers who want a practical starting point for building AI-powered applications that combine language models with external knowledge.
View the repository and get started:
brandonhimpfen
Why This Project Exists
Many RAG examples are either too abstract or too tightly coupled to a specific vendor.
RAG Starter takes a different approach. It emphasizes clarity, modularity, and portability. The goal is to provide a reference implementation that is easy to read, extend, and reuse across projects.
By using an adapter-based architecture, the project allows you to integrate different vector databases without changing your core logic. This keeps your system flexible as tools and requirements evolve.
Key Features
RAG Starter includes a lightweight but complete pipeline for building RAG systems.
It provides simple utilities for chunking text, generating embeddings, retrieving relevant context, and constructing prompts for language models.
The project also introduces a vector store abstraction layer, along with adapter stubs for popular databases such as Chroma, Qdrant, and Pinecone. An in-memory implementation is included for quick testing and local development.
The result is a clean, working baseline that can be expanded into more advanced systems.
How It Fits Into Your Workflow
RAG Starter is designed to be used as a starting point rather than a finished framework.
You can clone the repository, plug in your preferred embedding model and vector database, and begin building immediately. The structure encourages you to evolve the system based on your needs, whether that means adding evaluation layers, improving retrieval strategies, or integrating with APIs and user interfaces.
Because the core components are loosely coupled, it also works well as a teaching tool or internal reference for teams exploring retrieval-based AI systems.
Use Cases
This project is well suited for developers building:
- AI assistants that rely on private or structured knowledge.
- Internal tools for document search and question answering.
- Prototypes for knowledge-driven applications.
- Experiments with different retrieval and embedding strategies.
It is equally useful for learning how RAG systems work under the hood without the overhead of a large framework.
Positioning
RAG Starter is not a full framework and does not aim to replace production-ready platforms.
Instead, it acts as a clean foundation and reference architecture. It helps bridge the gap between simple examples and real-world implementations by showing how to structure a system in a way that remains flexible over time.