---

name: vector-database description: "Use for all vector database work — pgvector schema design, Weaviate collection creation, RAG pipeline scaffolding, embedding model selection, index tuning (HNSW vs IVFFlat), and embedding model migration. Triggers on: 'vector search', 'pgvector', 'weaviate', 'embedding', 'RAG pipeline', 'semantic search', 'hybrid search', 'nearest neighbor', 'vector index', 're-embedding', 'ANN index'. Use whenever vectors, embeddings, or similarity search are involved, even if not explicitly named." allowed-tools: Read, Glob, Grep metadata: triggers: pgvector, weaviate, embedding, vector search, RAG pipeline, semantic search, hybrid search, vector index, re-embedding, ANN index, HNSW, IVFFlat, quantization, INT8, product quantization, binary quantization, HNSW benchmark, vector memory, index benchmarking related-skills: weaviate, weaviate-cookbooks, agentic-ai-dev, database-schema-designer domain: backend role: specialist scope: implementation output-format: code last-reviewed: "2026-03-15"

Vector Database Skill

Iron Law

Pin dimensions at model selection. The embedding dimension must match the model exactly and is set once — changing models requires a full re-embedding migration. Store embedding_model varchar(100) alongside every vector column so the model is always auditable.

---

Embedding Model Selection

Choose model before writing any schema. Dimension determines index shape.

Model	Dims	Notes
text-embedding-3-small (OpenAI)	1536	Best cost/quality for most cases
text-embedding-3-large (OpenAI)	3072	Max OpenAI quality, 2× cost
embed-english-v3.0 (Cohere)	1024	Native Weaviate integration
voyage-3-large (Voyage AI)	1024	Top retrieval benchmarks
nomic-embed-text (local)	768	Free, on-prem, no API call

Rule: Once a dimension is written into schema, it cannot change without /migrate-embedding-model.

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pgvector

When to Use

• Data lives in PostgreSQL (tickets, vendors, invoices, tenant records)
• You need vector search + relational JOINs in a single query
• Filtering on structured columns (city, category, rating) alongside semantic search

Vector Column Declaration

-- ALWAYS enable extension in migration up
CREATE EXTENSION IF NOT EXISTS vector;

-- Column: dimension must match model exactly
ALTER TABLE vendors ADD COLUMN embedding vector(1536);
ALTER TABLE vendors ADD COLUMN embedding_model varchar(100) DEFAULT 'text-embedding-3-small';

Distance Operators

Operator	Distance Type	Use When
<->	L2 (Euclidean)	Default; requires L2-normalized embeddings
<#>	Negative inner product	Dot-product similarity (fast for normalized vecs)
<=>	Cosine	Cosine similarity; works unnormalized

Critical: The operator used in queries MUST match the operator used in CREATE INDEX. Mismatch = full table scan silently.

Index Selection

	HNSW	IVFFlat
Build speed	Slow, high memory	Fast, needs existing data
Query recall	Higher (~0.99)	Tunable via probes
Use when	>100K rows, production	Dev / budget-constrained / <100K rows
Update behavior	Good — no rebuild needed	Degrades; needs ANALYZE after bulk inserts

-- HNSW (production default)
CREATE INDEX ON vendors USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 64);

-- IVFFlat (dev/budget)
CREATE INDEX ON vendors USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);
-- After bulk insert: ANALYZE vendors;

Example Query Pattern (vendor matching)

SELECT v.id, v.name, v.city, v.rating,
       1 - (v.embedding <=> $1::vector) AS similarity
FROM vendors v
WHERE v.city = $2
  AND v.category = $3
  AND v.rating >= $4
  AND v.embedding IS NOT NULL
ORDER BY v.embedding <=> $1::vector
LIMIT 10;

For full migration template → see references/pgvector-migration-template.md

---

Weaviate

When to Use

• Data lives outside PostgreSQL (scraped text, PDFs, docs, external reviews)
• You need native hybrid search (BM25 + vector) with fusion control
• Multi-tenancy (one collection shared across tenants)
• Phase 2+ RAG against GCS / external document stores

Collection Schema Essentials

import weaviate
import weaviate.classes as wvc

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=os.environ["WEAVIATE_URL"],
    auth_credentials=wvc.init.Auth.api_key(os.environ["WEAVIATE_API_KEY"]),
)

# ALWAYS set multi_tenancy at creation — cannot change later
client.collections.create(
    name="VendorReview",
    vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_openai(
        model="text-embedding-3-small",
    ),
    vector_index_config=wvc.config.Configure.VectorIndex.hnsw(
        distance_metric=wvc.config.VectorDistances.COSINE,
    ),
    multi_tenancy_config=wvc.config.Configure.multi_tenancy(enabled=True),
    properties=[
        wvc.config.Property(name="text", data_type=wvc.config.DataType.TEXT),
        wvc.config.Property(name="source", data_type=wvc.config.DataType.TEXT),
        wvc.config.Property(name="vendor_id", data_type=wvc.config.DataType.TEXT),
    ],
)

Query Patterns

collection = client.collections.get("VendorReview")

# nearText (semantic)
result = collection.query.near_text(query="plumber emergency repair", limit=5)

# Hybrid search (BM25 + vector)
# alpha=1.0 = pure vector | alpha=0.0 = pure BM25
result = collection.query.hybrid(
    query="24-hour plumber",
    alpha=0.75,
    fusion_type=wvc.query.HybridFusion.RELATIVE_SCORE,
    limit=10,
)

Fusion algorithms:

• RANKED_FUSION — rank-based merge; stable, good default
• RELATIVE_SCORE_FUSION — score-normalized merge; better when score magnitude matters

For named vectors and multi-tenant patterns → see references/weaviate-collection-patterns.md

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Two-Tier Architecture Decision

When building a system with both structured and unstructured data:

Tier	Store	Use For
1	pgvector (in Cloud SQL)	Data with a DB row — vendors, tickets, invoices, tenant records
2	Weaviate Serverless	Data outside DB — scraped reviews, PDFs in GCS, external docs

Rule: Keep data in the tier where it originates. Don't sync Postgres rows to Weaviate at MVP — async sync lag introduces consistency bugs. Move to Weaviate only when matching against external documents.

---

Slash Commands

• /design-vector-schema — pgvector migration SQL from model + row count + metric
• /design-weaviate-collection — Weaviate Python collection from vectorizer + schema
• /scaffold-rag-pipeline — Full RAG pipeline: chunk → embed → store → retrieve → rerank
• /migrate-embedding-model — Re-embedding migration plan + reversible SQL
• /tune-vector-index — HNSW vs IVFFlat recommendation + tuned params

Reference Files

File	Content	When to Use
references/pgvector-migration-template.md	Full migration SQL with up/down, index, metadata column	pgvector schema design
references/weaviate-collection-patterns.md	Named vectors, multi-tenancy, advanced queries	Weaviate collection design
references/rag-pipeline-patterns.md	Chunking strategies, retrieval, reranking, LangChain wiring	Building RAG pipelines
references/embedding-migration-guide.md	Step-by-step model switching procedure	Re-embedding migrations
references/vector-index-tuning-playbook.md	Quantization strategies, HNSW benchmarking, memory estimation, Qdrant config	Index tuning and performance optimization
references/ann-vs-knn.md	KNN vs ANN tradeoffs, HNSW/IVFFlat tuning, recall measurement, high-risk scoped verification pattern	Choosing and tuning retrieval index strategy
references/rag-ingest-checklist.md	Production ingestion checklist: embeddings, chunking, null guards, multi-tenancy	Safe data ingestion into vector stores
references/tier-1-tier-2-decision.md	Decision framework: pgvector (hot path, SQL joins) vs Weaviate (cold path, pure semantic)	Choosing the right vector store for a use case

Documentation Sources

Before generating code, consult these sources for current APIs:

Source	Tool	Purpose
pgvector	Context7 MCP (pgvector library)	Vector operators, index syntax, distance functions
Weaviate Python v4	weaviate-docs MCP	Collection API, query patterns, named vectors
OpenAI Embeddings	Context7 MCP (openai library)	text-embedding-3-small/large, dimensions, pricing
Cohere Embed	Context7 MCP (cohere library)	embed-english-v3.0, batch embedding

Post-Code Review

After writing vector database code, dispatch:

• pgvector-schema-reviewer — operator/index alignment, dimension match, null guards, reversible migrations
• weaviate-schema-reviewer — v4 client API, multi-tenancy declaration, distance metric
• rag-pipeline-reviewer — model pinning, batch embedding, silent retrieval failure risks
• postgresql-database-reviewer — for the broader migration (indexes, constraints, performance)