Pinecone Patterns

Quick Guide: Use @pinecone-database/pinecone (v7.x) for serverless vector database operations. Target indexes by host (pc.index({ host })), not by name. Use namespaces for multi-tenant isolation (physically separate, cheaper queries). Batch upserts at 200 records (max 1,000 or 2 MB). Metadata is limited to 40 KB per record with flat key-value pairs only (no nested objects). Pinecone is eventually consistent -- vectors may not appear in queries immediately after upsert. Use describeIndexStats() to verify indexing progress. For hybrid search, use dotproduct metric with sparse+dense vectors in a single index.

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CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST target indexes by host URL, not by name -- pc.index({ host }) is the v7 API; pc.index('name') is deprecated)

(You MUST batch upserts to max 1,000 records or 2 MB per request -- exceeding either limit causes a 400 error)

(You MUST use flat key-value metadata only -- nested objects, null values, and keys starting with $ are rejected by Pinecone)

(You MUST handle eventual consistency -- vectors are not queryable immediately after upsert; use describeIndexStats() or retry logic for freshness-critical flows)