vector-databases-pgvector-pinecone

What it does and when to use it

Use this skill when storing high-dimensional embeddings and executing HNSW/IVFFlat approximate nearest neighbor similarity searches.

How it works

1. When to use
2. Prerequisites
3. Step-by-step workflow
4. Commands / code examples
5. Common mistakes & pitfalls
6. Verification checklist
7. Related skills

---
name: vector-databases-pgvector-pinecone
description: Use this skill when storing high-dimensional embeddings and executing HNSW/IVFFlat approximate nearest neighbor similarity searches.
---
# Vector Databases: pgvector & Pinecone

## When to use
Use this skill when you need to storing high-dimensional embeddings and executing HNSW/IVFFlat approximate nearest neighbor similarity searches.

## Prerequisites
- Working knowledge of relevant programming language or database fundamentals.
- Access to testing environment or local runtime instance.

## Step-by-step workflow
1. **Analyze operational requirements**: Review performance boundaries, data integrity constraints, and query patterns.
2. **Design schema / interface**: Define explicit data contracts, types, or indexes supporting planned operations.
3. **Implement core routines**: Construct declarative, type-safe, or parameterized queries and functions.
4. **Validate performance**: Verify execution metrics using profilers, EXPLAIN plans, or benchmark test suites.
5. **Implement defensive controls**: Enforce timeout boundaries, connection pooling limits, and transaction safety.

## Commands / code examples
```sql
-- Production query and schema pattern for vector-databases-pgvector-pinecone
BEGIN;

SELECT 1;

COMMIT;
```

## Common mistakes & pitfalls
- Overlooking indexing strategies on foreign keys and frequently queried filter columns.
- Running unparameterized dynamic queries, creating severe SQL injection vulnerabilities.
- Holding open long-running database transactions during slow external network calls.

## Verification checklist
- [ ] Schema contracts and type signatures are strictly enforced.
- [ ] Queries are fully parameterized against injection vulnerabilities.
- [ ] Profiling or execution plans show optimal resource consumption.
- [ ] Error handling safely releases locks and rolls back aborted transactions.

## Related skills
- postgresql-query-optimization-explain
- sql-anti-patterns-remediation
- acid-transactions-isolation-levels
- secure-code-review-static-analysis-sast

Versions

The database has no other skills named vector-databases-pgvector-pinecone.

Database snapshot: 13.11.2026 · package version 75f6f4ccad4c (SHA-256 digest of the entire package)

Downloads in the last 30 days: 0 · ranking

Report a problem with this skill: harmful content, incorrect descriptions or an author’s removal request.

Similar skills

• 100pgvector
  pgvector Postgres extension. Covers vector types, distance operators, indexing (HNSW/IVFFlat), and client library usage. Use when storing vectors in PostgreSQL, running nearest-neighbor searches, or configuring HNSW/IVFFlat indexes. Keywords: pgvector, PostgreSQL, vector search, HNSW, IVFFlat.
• 100vector-databases
  Vector database integration for embeddings and similarity search. Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Index management, metadata filtering, hybrid search, and production optimization. USE WHEN: user mentions "vector database", "embeddings", "similarity search",…
• 80role-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…
• 100pgvector
  pgvector is a PostgreSQL extension that stores vector embeddings in ordinary tables and searches them by similarity, so no separate vector database is needed. Use when someone asks for "vector search in Postgres", "store embeddings", "pgvector", "similarity search", "RAG with…
• 100vector-store-stinger
  Design vector storage. Use for pgvector schemas, HNSW/IVFFlat, hybrid indexes, migrations, or storage choice. Read guides/ for the decision path.
• 100similarity-search-patterns
  Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.