Vector Database Engineer

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.
• If detailed examples are required, open resources/implementation-playbook.md.

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

1. Analyze data characteristics and query patterns
2. Select appropriate embedding model
3. Design chunking and preprocessing pipeline
4. Choose vector database and index type
5. Configure metadata schema for filtering
6. Implement hybrid search if needed
7. Optimize for latency/recall tradeoffs
8. 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

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

# Check for prior backend/API context before starting
python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Vector Database Engineer"

Storing Results

After completing work, store backend/API decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \
  --type decision --project <project> \
  --tags vector-database-engineer backend

Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>

Agent Team: Code Review

After implementation, dispatch code_review_team for two-stage review (spec compliance + code quality) before merging.