# darrencxl0301/StageRAG

A blueprint for building production-ready RAG systems that minimize hallucination, featuring switchable 3-step (Speed) and 4-step (Precision) pipelines.

Repository: https://github.com/darrencxl0301/StageRAG
Canonical: https://ross.abutalabs.com/products/stagerag
Language: Python
License: MIT
License Family: permissive
Last push: 2025-10-13T03:46:08+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 46, release rhythm 35, longevity 24
- inputs: {"age_days": 336, "days_push": 324, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1040, forks 90 (observed 2026-08-28T04:03:20.364675+00:00)

## What it is
StageRAG is a Python framework/blueprint for building production-ready RAG applications with switchable 3-step (Speed) and 4-step (Precision) pipelines. It includes built-in confidence scoring to reduce hallucinations and runs efficiently on small Llama 3.2 models with 4-bit quantization.

## Use cases
- build a production-ready RAG system
- reduce hallucinations in LLM answers
- build a question-answering chatbot over my own documents
- trade off response speed vs answer accuracy in RAG
- run RAG on small models with limited GPU memory
- add confidence scoring to retrieval-augmented answers
- index a JSONL knowledge base for retrieval

## When to choose
- you want a lightweight, hackable RAG blueprint with dual speed/precision modes
- you need programmatic confidence scores to handle uncertain answers
- you're constrained to small models (1B/3B) and 5-10GB GPU memory
- your knowledge base is already in JSONL conversation format

## When to avoid
- you need a turnkey enterprise RAG platform with a UI and connectors
- you require multi-tenant, distributed, or cloud-managed retrieval infrastructure
- you don't want gated Llama model dependencies or a GPU requirement
- you need very long-context or agentic RAG workflows beyond simple pipelines

## Facets
- artifact type: framework
- maturity: active
- function: rag, llm-inference, machine-learning, search-engine
- domain: large-language-models, artificial-intelligence
- platform: python, cross-platform
- tags: hallucination-reduction, confidence-scoring, dual-mode-pipeline, llama-3-2, quantization, vector-index, production-ready, blueprint, retrieval-augmented-generation, natural-language-processing, gpu, docker

## Member repositories
- darrencxl0301/StageRAG (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.364675+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 2026-08-30T07:03:29.379452+00:00, confidence not recorded.
  - readme: https://github.com/darrencxl0301/StageRAG (fetched 2026-08-28T04:03:20.364675+00:00, sha 17b048f5da95)
- Data as of 2026-08-30T08:39:29.467469+00:00.