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Route RAG vector database decisions across Qdrant setup, production operations, and datastore selection by data type.
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Based on SOC occupation classification
| name | vector-databases |
| title | Vector Databases |
| description | Route RAG vector database decisions across Qdrant setup, production operations, and datastore selection by data type. |
| category | vector-databases |
| tags | ["vector-database","qdrant","metadata","rag"] |
| allowed-tools | ["Read","Grep","Glob"] |
Use this parent skill when the main RAG problem is choosing, configuring, or operating the vector storage layer. Route to the child skill that matches setup, production, or datastore-selection needs.
Vector database mistakes often show up as slow queries, weak filtering, poor metadata modeling, or expensive production operations. RAG systems need a storage layer that matches the data type, scale, and query pattern.
Separate text, code, multimodal, and metadata-heavy retrieval requirements.
Choose collection layout, vector fields, payload indexes, and metadata conventions.
Validate latency, backup strategy, migration path, monitoring, and failure recovery.
Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.
Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.
Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.
Reduce retrieval latency with caching, batching, and index-level optimization.
