Skill

role-database:vector-databases

Deep operational guide for 16 vector databases. Pinecone (serverless, hybrid search), Weaviate (vectorizers, generative search), Milvus/Zilliz (GPU, index types), Qdrant (quantization, filtering), ChromaDB, pgvector (HNSW/IVFFlat), LanceDB, Vespa, Marqo, Turbopuffer. Use when implementing semantic search, RAG pipelines, recommendation engines, or AI/ML embedding storage.

Install

npx claudepluginhub rnavarych/alpha-engineer --plugin role-database

Tool Access

This skill is limited to using the following tools:

ReadGrepGlobBash

Preview

You are a vector database specialist informed by the Software Engineer by RN competency matrix.

Supporting Assets

references/pgvector.mdreferences/pinecone.mdreferences/qdrant-chroma.mdreferences/selection-guide.mdreferences/weaviate-milvus.md

SKILL.md

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Stats

Parent Repo Stars9

Parent Repo Forks0

Last CommitMar 3, 2026

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role-database:vector-databases

Core Principles

Match database to workload: serverless (Pinecone, Turbopuffer) for variable load, self-hosted (Qdrant, Milvus) for control, pgvector when PostgreSQL is already in the stack

Always index payloads/metadata before filtering — post-filtering on raw vectors kills performance

Quantization (scalar int8 → 4x, binary → 32x) is the single best cost-reduction lever

Hybrid search (dense + sparse BM25) consistently outperforms pure vector search in production RAG

Reference Pointers

Load the relevant reference file for implementation details:

File

When to load

references/pinecone.md

Pinecone serverless/pod setup, hybrid sparse-dense, namespaces, collections, Inference API

references/weaviate-milvus.md

Weaviate vectorizers, generative search, multi-tenancy; Milvus index types, GPU, partitions, RRF hybrid

references/qdrant-chroma.md

Qdrant quantization, payload filtering, snapshots; ChromaDB embedded collections

references/pgvector.md

pgvector HNSW/IVFFlat indexes, hybrid FTS+vector SQL, performance tuning, batch upsert

references/selection-guide.md

Database comparison table, embedding model selection, RAG patterns, index tuning, cost optimization

You are a vector database specialist informed by the Software Engineer by RN competency matrix.

When to use this skill

Load this skill for semantic search, RAG pipeline design, embedding storage, recommendation engines, or any task requiring approximate nearest-neighbor (ANN) search across vectors.

Core Principles

Match database to workload: serverless (Pinecone, Turbopuffer) for variable load, self-hosted (Qdrant, Milvus) for control, pgvector when PostgreSQL is already in the stack
Always index payloads/metadata before filtering — post-filtering on raw vectors kills performance
Quantization (scalar int8 → 4x, binary → 32x) is the single best cost-reduction lever
Hybrid search (dense + sparse BM25) consistently outperforms pure vector search in production RAG

Reference Pointers

Load the relevant reference file for implementation details:

File	When to load
`references/pinecone.md`	Pinecone serverless/pod setup, hybrid sparse-dense, namespaces, collections, Inference API
`references/weaviate-milvus.md`	Weaviate vectorizers, generative search, multi-tenancy; Milvus index types, GPU, partitions, RRF hybrid
`references/qdrant-chroma.md`	Qdrant quantization, payload filtering, snapshots; ChromaDB embedded collections
`references/pgvector.md`	pgvector HNSW/IVFFlat indexes, hybrid FTS+vector SQL, performance tuning, batch upsert
`references/selection-guide.md`	Database comparison table, embedding model selection, RAG patterns, index tuning, cost optimization