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RAG Doctor — Knowledge Base Health Check & Repair for AI

Your AI is only as good as the content you feed it. RAG Doctor audits that content the way a linter audits code: it finds the contradictions, stale facts, duplicates, and broken chunks that quietly poison RAG pipelines, agents, and custom GPTs, then hands you a prioritized fix list and an optional cleaned-up version.

Most tools build a knowledge base for you. RAG Doctor fixes the one you already have.

Why this exists

Garbage in, confident garbage out. When two pages disagree, a RAG system retrieves one at random and the model states it as fact. When a chunk reads "as shown above," retrieval pulls it alone and the model fills the gap by guessing. These defects are invisible until a user gets a wrong answer. RAG Doctor surfaces them before your users do.

What it checks

Input

Point it at a site to crawl, or hand it a dataset you already extracted.

{
"startUrls":[{"url":"https://docs.example.com"}],
"maxPages":100,
"maxCrawlDepth":2,
"mode":"audit",
"checks":["staleness","duplicates","chunkHealth","deadLinks","extractability","contradictions"],
"stalenessThresholdDays":540,
"similarityThreshold":0.85,
"userQuestions":["How do I rotate my API token?"],
"anthropicApiKey":"sk-ant-...",
"llmModel":"claude-haiku-4-5-20251001"
}

Crawling and link checks run over the Apify datacenter proxy automatically; there is no proxy option to configure.

Audit content you already crawled (composes with apify/website-content-crawler):

{"datasetId":"YOUR_DATASET_ID","mode":"both"}

The LLM-backed checks (contradictions, coverage gaps) need an Anthropic API key. Without it, those two checks are skipped and every other check still runs.

Output

• Dataset — one row per finding (severity, check, issue, detail, suggested fix, URL). Sorted most-severe first.
• Key-value store
  • REPORT — a shareable HTML report with the AI-readiness score and full fix list.
  • SUMMARY / OUTPUT — the score, grade, and severity counts as JSON.
• repaired-knowledge-base dataset (repair / both modes) — duplicates collapsed, thin pages dropped, stale pages flagged, content pre-chunked and ready for a vector DB or llms.txt.

The AI-readiness score (0-100) is defect density, not raw count, so a large knowledge base isn't penalized just for having more pages.

Modes

• audit — report and fix list only.
• repair — also emit the cleaned corpus.
• both — everything.

Local development

npminstall
npm run build
apify run # or: npm start

Roadmap

• Expose as an MCP server tool (audit_knowledge_base) so an agent can call it mid-workflow before answering.
• Embedding-based duplicate and contradiction candidate selection for higher recall.
• Incremental re-audits that only re-check what changed.