name: vector-database-engineer
description: Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
tags: [database, vector-db]
Vector Database Engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.
Do not use this skill when
- The task is unrelated to vector database engineer
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Capabilities
- Vector database selection and architecture
- Embedding model selection and optimization
- Index configuration (HNSW, IVF, PQ)
- Hybrid search (vector + keyword) implementation
- Chunking strategies for documents
- Metadata filtering and pre/post-filtering
- Performance tuning and scaling
Use this skill when
- Building RAG (Retrieval Augmented Generation) systems
- Implementing semantic search over documents
- Creating recommendation engines
- Building image/audio similarity search
- Optimizing vector search latency and recall
- Scaling vector operations to millions of vectors
Workflow
- Analyze data characteristics and query patterns
- Select appropriate embedding model
- Design chunking and preprocessing pipeline
- Choose vector database and index type
- Configure metadata schema for filtering
- Implement hybrid search if needed
- Optimize for latency/recall tradeoffs
- Set up monitoring and reindexing strategies
Best Practices
- Choose embedding dimensions based on use case (384-1536)
- Implement proper chunking with overlap
- Use metadata filtering to reduce search space
- Monitor embedding drift over time
- Plan for index rebuilding
- Cache frequent queries
- Test recall vs latency tradeoffs
1---2name: vector-database-engineer3description: <!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->4---5<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->6---7name: vector-database-engineer8description: Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar9tags: [database, vector-db]10---1112# Vector Database Engineer1314Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.1516## Do not use this skill when1718- The task is unrelated to vector database engineer19- You need a different domain or tool outside this scope2021## Instructions2223- Clarify goals, constraints, and required inputs.24- Apply relevant best practices and validate outcomes.25- Provide actionable steps and verification.2627## Capabilities2829- Vector database selection and architecture30- Embedding model selection and optimization31- Index configuration (HNSW, IVF, PQ)32- Hybrid search (vector + keyword) implementation33- Chunking strategies for documents34- Metadata filtering and pre/post-filtering35- Performance tuning and scaling3637## Use this skill when3839- Building RAG (Retrieval Augmented Generation) systems40- Implementing semantic search over documents41- Creating recommendation engines42- Building image/audio similarity search43- Optimizing vector search latency and recall44- Scaling vector operations to millions of vectors4546## Workflow47481. Analyze data characteristics and query patterns492. Select appropriate embedding model503. Design chunking and preprocessing pipeline514. Choose vector database and index type525. Configure metadata schema for filtering536. Implement hybrid search if needed547. Optimize for latency/recall tradeoffs558. Set up monitoring and reindexing strategies5657## Best Practices5859- Choose embedding dimensions based on use case (384-1536)60- Implement proper chunking with overlap61- Use metadata filtering to reduce search space62- Monitor embedding drift over time63- Plan for index rebuilding64- Cache frequent queries65- Test recall vs latency tradeoffs6667<!-- Source: .faos/custom/skills/ai-ml/vector-database-engineer/SKILL.md -->
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