Pinecone -- Vector Search & Embeddings

Stop building vector search without understanding chunking strategies and metadata filtering. This skill gives your agent production-grade Pinecone integration patterns for semantic search and RAG pipelines.

Pinecone's vector database requires understanding embedding dimensions, similarity metrics, metadata filtering with complex operators, and index types that have trade-offs between speed and recall. Production challenges: chunking strategies that balance context size and search granularity, metadata filter composition, bulk upsertion with error recovery, and handling embedding model changes.

This skill encodes the patterns that survive production: index design and configuration, embedding pipeline with chunking strategies, metadata schema design, query patterns with filters, bulk upsertion, and integration with embedding APIs for document ingestion.

What You Get:

• Index Configuration -- vector dimensions, similarity metrics, index types
• Embedding Pipeline -- chunking strategies, embedding generation, bulk upsertion
• Metadata Design -- schema design, filter composition, query optimization
• RAG Integration -- document ingestion, retrieval patterns, hybrid search

Pairs well with OpenAI and Anthropic for RAG and semantic search applications.

Drop this skill in and your agent builds vector search like a data engineer. No setup, no dependencies -- just install and go.