Agent skill · software engineering · davila7
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Why this skill is useful
Provides specific commands and patterns for integrating with the Pinecone vector database that the AI wouldn't reliably generate on its own.
What it needs
Requires pinecone-client installed locally. About 4k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Pinecone - Managed Vector Database The vector database for production AI applications. When to use Pinecone Use when: Need managed, serverless vector database Production RAG applications Auto-scaling required Low latency critical (<100ms) Don't want to manage infrastructure Need hybrid search (dense + sparse vectors) Metrics: Fully managed SaaS Auto-scales to billions of vectors p95 latency <100ms 99.9% uptime SLA Use alternatives instead: Chroma: Self-hosted, open-source FAISS: Offline, pure similarity search Weaviate: Self-hosted with more features Quick start Installation Basic usage Core operations Create index Upsert vectors Query vectors Metadata filtering Namespaces Hybrid search (dense + sparse) LangChain integration LlamaIndex integration Index management Delete vectors Best practices 1. Use serverless - Auto-scaling, cost-effective 2. Batch upserts - More efficient (100-200 per batch) 3. Add metadata - Enable filtering 4. Use namespaces - Isolate data by user/tenant 5. Monitor usage - Check Pinecone dashboard 6. Optimize filters - Index frequently filtered fields 7. Test with free tier - 1 index, 100K vectors free 8. Use hybrid search - Better quality 9. Set appropriate dimensions - Match embedding model 10. Regular backups - Export important data Performance Operation Latency Notes ----------- --------- ------- Upsert ~50-100ms Per batch Query (p50) ~50ms Depends on index size Query (p95) ~100ms SLA target Metadata filter ~+10-20ms Additional overhead Pricing (as of 2025) Serverless: $0.096 per million read units $0.06 per million write units $0.06 per GB storage/month Free tier: 1 serverless index 100K vectors (1536 dimensions) Great for prototyping Resources Website: https://www.pinecone.io Docs: https://docs.pinecone.io Console: https://app.pinecone.io Pricing: https://www.pinecone.io/pricing
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/rag-pinecone