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Send feedback on RAG search results — thumbs-up (+1) or thumbs-down (-1) to indicate whether the answer was helpful. Use after calling zeabur-rag-search when you can evaluate the quality of the results. Positive feedback reinforces good results; negative feedback flags poor matches for review. Requires write:feedback scope.
How this skill is triggered — by the user, by Claude, or both
Slash command
/rag-service:zeabur-rag-feedbackThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Send feedback on search results to improve knowledge base quality over time.
Send feedback on search results to improve knowledge base quality over time.
Base URL: $ZEABUR_RAG_URL
Auth: Authorization: Bearer $RAG_API_KEY
curl -s -X POST "$ZEABUR_RAG_URL/api/feedback" \
-H "Authorization: Bearer $RAG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"query": "how to deploy to zeabur",
"answer": "the generated answer text",
"chunks": ["DOC-1234", "SUP-5678"],
"score": 1,
"signal_id": "optional-signal-id",
"comment": "optional explanation"
}'
| Field | Required | Description |
|---|---|---|
query | Yes | The original search query |
answer | No | The generated RAG answer |
chunks | No | Array of chunk IDs that were returned |
score | Yes | 1 (helpful) or -1 (not helpful) |
signal_id | No | Signal ID from the search response — links feedback to the specific query signal |
comment | No | Free-text explanation of why the result was good or bad |
| Situation | Score |
|---|---|
| Answer correctly addressed the question | 1 |
| Retrieved chunks were relevant and accurate | 1 |
| Answer was wrong, incomplete, or misleading | -1 |
| Retrieved chunks were irrelevant to the query | -1 |
| No useful results were returned | -1 |
signal_id is provided, the feedback score is written back to the rag_query_signals table, linking it to the original query for analytics.data/user-feedback.jsonl as a backup log.npx claudepluginhub zeabur/rag-service --plugin rag-serviceGuides RAG evaluation: error analysis, synthetic QA/adversarial dataset building, Recall@k/Precision@k metrics for retrieval, faithfulness/relevance for generation, chunking optimization.
Build RAG systems for LLM apps using vector databases, embeddings, and retrieval strategies. Use for document Q&A, grounded chatbots, and semantic search.