ragcheck-cli 0.2.2

Lighthouse for RAG systems - diagnose and fix your retrieval pipeline

ragcheck - Lighthouse for RAG Systems

One command to diagnose your RAG pipeline and get actionable fixes.

pip install ragcheck-cli
ragcheck init
ragcheck run --docs ./data --query "What is Article 370?"

What is ragcheck?

ragcheck is a lightweight, one-command diagnostic CLI that generates a beautiful, shareable HTML report analyzing why your RAG system fails and how to fix it.

Think of it as Lighthouse for RAG systems — just like Lighthouse audits web pages, ragcheck audits your retrieval pipeline.

Features

• Auto-Generated Test Suite - 50 synthetic questions from your documents
• Chunk Visualizer - See exactly where your chunking breaks
• Retrieval Heatmap - Identify dead chunks and dominant chunks
• Failure Classification - Know WHY your RAG fails, not just THAT it fails
• Actionable Recommendations - Specific fixes with predicted impact
• CI/CD Integration - Fail builds when RAG quality regresses

Quick Start

Installation

pip install ragcheck-cli

Or with uv:

uv tool install ragcheck-cli

Initialize

ragcheck init

Creates a ragcheck.yaml config file in your project.

Run Analysis

ragcheck run --docs ./data --query "Your test query"

Generates ragcheck_report.html with:

• Scorecards (retrieval accuracy, faithfulness)
• Chunk boundary visualization
• Retrieval heatmap
• Failure mode classification
• Before/after score predictions

CI Mode

ragcheck run --docs ./data --ci --min-score 0.80

Returns exit code 0/1. Use in GitHub Actions to fail builds on quality regression.

Example Report

Architecture

ragcheck CLI
    ├── Chunk Analyzer (6 strategies + benchmark)
    ├── Retriever Tester (auto-QA + dense retrieval)
    ├── Failure Classifier (4 failure modes)
    ├── Recommendation Engine (decision tree)
    └── Report Engine (Jinja2 + CSS/HTML)

Tech Stack

Component	Tool
CLI	Typer + Rich
Config	Pydantic
Embeddings	sentence-transformers
Vector DB	ChromaDB
LLM Interface	LiteLLM
Reports	Jinja2 + CSS/HTML

Configuration

ragcheck.yaml:

project_name: ragcheck
docs_path: ./data
chunking:
  strategy: recursive
  chunk_size: 512
  chunk_overlap: 128
llm:
  provider: openai
  model: gpt-3.5-turbo
retrieval:
  top_k: 5
  similarity_threshold: 0.7
report:
  format: html
  include_heatmap: true

Development

git clone https://github.com/pranay7863/ragcheck.git
cd ragcheck
uv sync
uv run pytest
uv run ruff check .
uv run mypy ragcheck/

Contributing

See CONTRIBUTING.md

License

MIT — see LICENSE

Roadmap

• v0.2.0 — Offline reports, NLI faithfulness, scaled auto-QA, chunk viz
• v0.3.0 — More vector DBs (Pinecone, Weaviate)
• v0.3.0 — SaaS API for teams
• v0.4.0 — Enterprise features (SSO, audit logs)

Support

• GitHub
• Twitter: @mane_pranay

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Built with discipline. Read the blueprint that started it all.