Specialized skill for ingesting rag content
Automated process for indexing new documents into the factory's Qdrant vector store using standardized Parent-Child retrieval patterns.
cursor-factory env active.D:/ebooks or target document path.antigravity-rag MCP server active (configured in %USERPROFILE%\.gemini\antigravity\mcp_config.json).@tool mcp_antigravity-rag_ingest_document with the absolute path.list_library_sourcesscripts/validate_ingestion.py: Runs a health check on the Qdrant ebook_library collection.scripts/ai/rag/rag_optimized.py: The core ingestion engine logic.references/rag-architecture.md: Explains the Parent-Child chunking strategy and Qdrant storage schema.$env:PYTHONIOENCODING="utf-8") are set.BAAI/bge-small-en-v1.5 (via FastEmbed) for performance.scripts/validate_ingestion.py after bulk ingestion.