migrate-from-pinecone

What problem does it solve?

This workflow helps teams move vector indexes and associated metadata from Pinecone into Elasticsearch to consolidate search infrastructure, reduce vendor lock-in, and enable unified observability and kNN queries.

Core Features & Use Cases

• Index provisioning: Create an Elasticsearch index with a dense_vector field that matches the Pinecone dimensions and any metadata fields used for filtering.
• Bulk data migration: Export vectors and metadata from Pinecone, map them to Elasticsearch document shape, and perform efficient bulk indexing.
• Integration & validation: Replace Pinecone query calls with Elasticsearch kNN searches, optionally use an ingest pipeline for server-side embedding, and validate results by comparing top-k outputs.
• Use Case: Move a semantic search index from Pinecone to Elasticsearch for tighter integration with observability and existing data pipelines.

Quick Start

Use the migrate-from-pinecone skill to transfer vectors and metadata from my Pinecone index named 'docs' into a new Elasticsearch index with matching vector dimensions.