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Audit RAG Chunks Before Vector Database Ingestion
Created by
Michael Olmos
Find empty, stale, undersized, and duplicate RAG chunks before indexing them in Pinecone, Weaviate, Qdrant, or another vector database.
RAG Dataset Quality Auditorgifted_wagon/rag-dataset-quality-auditor
Quality score
Grade
Document ID
Title
+9 fieldsTextNumberBooleanListObject
Input
Inline documents
Document ID:guide-1+3
Source URL:https://example.com/refunds+1
Document title:Refund policy+3
Document text:Customers may request a refund within 30 days of purchase. Requests must include the order number and the email address used at checkout. Approved refunds return to the original payment method within five to ten business days. Digital products that have been substantially consumed may not qualify. Contact support before filing a payment dispute so the team can investigate and document the request.+3
Updated at:2026-08-01T00:00:00.000Z+2
Maximum documents:100
Minimum useful words:50
Maximum useful words:2000
Stale after days:365
Near-duplicate similarity:0.92
Include content preview:false
Output fields
Quality score
Grade
Document ID
Title
Source URL
Words
Issues
Critical
High
Exact duplicate of
Near duplicate of
Similarity
Issue codes
Sign up on Apify01
Create your Apify account to access the RAG Dataset Quality Auditor.
Start the run02
The Actor will start running based on the input automatically.
Receive the output03
Monitor the progress in real-time. You will be notified as soon as your dataset is complete and ready for review.
Integrate into your workflow04
The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow.
