Vector database engineer

Skill tranhieutt/software_development_department/.claude/skills/vector-database-engineer

Software Development Department

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npx -y skills add tranhieutt/software_development_department --skill vector-database-engineer

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Provides vector database and semantic search patterns for Pinecone, Weaviate, Qdrant, Milvus, and pgvector in RAG and recommendation systems. Use when implementing vector search or when the user mentions vector database, semantic search, embeddings, or similarity search.

SKILL.md

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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

When to Use

Use when Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar