# KruxAI/ragbuilder

A toolkit to create optimal Production-readyRetrieval Augmented Generation(RAG) setup for your data

Repository: https://github.com/KruxAI/ragbuilder
Canonical: https://ross.abutalabs.com/products/ragbuilder
Homepage: https://ragbuilder.pages.dev
Language: Python
License: Apache-2.0
License Family: permissive
Topics: developer-tools, genai, rag
Last push: 2025-05-20T03:53:13+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 22, release rhythm 40, longevity 57
- inputs: {"age_days": 800, "days_push": 470, "days_rel": 610, "gap_med": 6.5, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1541, forks 127 (observed 2026-08-28T04:05:00.598684+00:00)

## What it is
RagBuilder is a Python toolkit that automatically builds an optimal, production-ready Retrieval-Augmented Generation (RAG) pipeline for your data. It uses Bayesian hyperparameter tuning over chunking strategies, embedding models, and retriever types, evaluated against test datasets, and offers pre-defined state-of-the-art RAG templates.

## Use cases
- automatically tune rag pipeline parameters for my documents
- find the best chunking strategy and chunk size for rag
- compare embedding models and retrievers for my dataset
- generate a synthetic evaluation dataset for rag testing
- deploy an optimized rag pipeline as an api
- build a production-ready rag setup from a pdf or url
- benchmark different rag configurations with bayesian optimization

## When to choose
- you want to automatically find the best RAG configuration for your data instead of hand-tuning
- you need production-grade RAG pipelines with pre-built templates like graph retriever or contextual chunker
- you want to evaluate RAG setups against a test dataset with minimal code
- you want to deploy the resulting pipeline as an API service

## When to avoid
- you need a fully managed hosted RAG service rather than a self-run Python toolkit
- your use case is simple retrieval without LLM generation
- you require a language other than Python or a framework outside the LangChain ecosystem
- you need fine-grained manual control over every pipeline component rather than automated optimization

## Facets
- artifact type: library
- maturity: active
- function: rag, machine-learning, llm-inference, search-engine, vector-database, benchmarking, developer-tools
- domain: large-language-models, artificial-intelligence, machine-learning, developer-tools
- platform: python, cross-platform
- tags: hyperparameter-tuning, bayesian-optimization, rag-templates, llm, embeddings, chunking, retrieval, api-deployment, retrieval-augmented-generation

## Member repositories
- KruxAI/ragbuilder (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.598684+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:46.932033+00:00, confidence not recorded.
  - readme: https://github.com/KruxAI/ragbuilder (fetched 2026-08-28T04:05:00.598684+00:00, sha 1244777bc27f)
  - homepage: https://ragbuilder.pages.dev (fetched 2026-08-29T11:32:13.857409+00:00, sha e2ebefd9206c)
- Data as of 2026-08-30T08:39:29.467469+00:00.