darrencxl0301/StageRAG

Health v2 · maintenance only

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Adoption not part of the score

1040 stars · 90 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

framework · maturity active

rag llm-inference machine-learning search-engine large-language-models artificial-intelligence python cross-platform 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

For agents

markdown · JSON · MCP: product_card(name="darrencxl0301/StageRAG")