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Migration workflow from Pinecone to Elasticsearch
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Based on SOC occupation classification
Debug and analyze LLM eval runs — view traces, compare runs, investigate failures, track costs. Use when debugging @kbn/evals failures, comparing eval runs, or analyzing LLM performance.
Start your security session with a personalized briefing — attacks, alerts, cases, rules, threat intel. Use as the first thing when starting security work.
Guide users from zero to a working Elastic cluster — Cloud or on-prem, connection config, first queries, and next steps.
Interactive guide for creating an APM service overview dashboard — discovers service data, presents metrics, and creates a tailored dashboard.
Interactive guide for creating SLOs from discovered APM and metric data — identifies candidates, lets user configure targets, and creates SLOs.
Create, configure, and manage Elasticsearch indices — mappings, settings, templates, data streams, and lifecycle policies.
| name | migrate-from-pinecone |
| description | Migration workflow from Pinecone to Elasticsearch |
Use when the user wants to move from Pinecone to Elasticsearch for vector search.
create_index: include a dense_vector field (same dimensions as Pinecone) and any metadata fields they filter on.create_ingest_pipeline with inference for embeddings if they want server-side embedding; otherwise app-side.bulk_index to load data into Elasticsearch. Use same embedding model for queries.search tool using kNN query.