Individual skill
orchardcore-custom-indexing-elasticsearch
Skill for building a custom Elasticsearch index for arbitrary records in Orchard Core 4.0, following the CrestApps AI Memory pattern of CrestApps.OrchardCore 3.0. Covers a custom index profile type with AddElasticsearchIndexingSource, IndexProfileHandlerBase mappings with ElasticsearchIndexMetadata and dense vectors, an embedding deployment chosen from the embedding slot and stored in DataSourceIndexProfileMetadata, IDocumentIndexHandler mapping, a custom indexing service with the keyed IDocumentIndexManager and BuildDocumentIndexContext, deferred lifecycle indexing with ShellScope, re-sync through SynchronizedAsync, and a master index site setting read through IOptionsMonitor. Use it for custom Elasticsearch indexing of non-content data, vector search over custom records, or questions about CrestApps.OrchardCore.AI.Memory.Elasticsearch, OrchardCore.Indexing, OrchardCore.Elasticsearch, IIndexProfileStore, IndexProfileEmbeddingMetadataAccessor, and IAIClientFactory.CreateEmbeddingGeneratorAsync.
src/CrestApps.AgentSkills/crestapps-orchardcore/orchardcore-custom-indexing-elasticsearch/SKILL.md
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- Last checked
- Nov 6, 2026
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- Repository last updated
- Oct 21, 2026
- License
- MIT
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- agent-skills Repository topic
- ai Repository topic
- ai-agents-framework Repository topic
- dotnet Repository topic
- mcp Repository topic
- mcp-server Repository topic
- orchardcore Repository topic
- writing Derived category
- data Derived category
- devops Derived category
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# Orchard Core Custom Indexing for Elasticsearch
## Index arbitrary records into Elasticsearch
You are an Orchard Core expert. Generate code that indexes custom records (not content items) into an Elasticsearch index profile, optionally with embeddings for vector search. Model it on `CrestApps.OrchardCore.AI.Memory` (records, settings, indexing service) and `CrestApps.OrchardCore.AI.Memory.Elasticsearch` (provider registration and mappings).
### When to use it
- Records you store yourself: CRM or ERP rows, generated summaries, user-scoped facts, imported business data.
- Full-text, filtered, or vector search over them through Orchard Core's index profiles.
- For Orchard content items use the built-in content index instead (see `orchardcore-elasticsearch`). For retrieval-augmented chat over an index you already have, an AI data source is usually enough (see `orchardcore-ai-data-sources`).
### Architecture
