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RAGFreeOpen Source
RAGS
Create custom RAG systems from natural language
MIT
ABOUT
Building a production RAG system requires stitching together embedding models, vector databases, chunking strategies, retrieval algorithms, and prompt templates — a complex engineering task that slows down experimentation. RAGs lets developers describe their desired RAG system in natural language and generates a complete, working pipeline with configurable components, dramatically reducing the time from idea to a functional retrieval-augmented generation setup.
INSTALL
pip install ragsINTEGRATION GUIDE
1. Prototype a RAG system for internal documentation search by describing the data sources and query types in plain English
2. Generate multiple RAG pipeline variants with different chunking strategies and embedding models to compare retrieval quality
3. Quickly set up a contextual chat engine over a new dataset without manually configuring vector stores and retrieval params
4. Teach RAG concepts by letting students describe systems in natural language and inspecting the generated pipeline
TAGS
ragretrievalllama-indexllmnatural-languagepipeline