Individual skill
orchardcore-custom-indexing-azureai
Skill for building a custom Azure AI Search 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 AddAzureAISearchIndexingSource, IndexProfileHandlerBase mappings with AzureAISearchIndexMetadata, AzureAISearchIndexMap with IndexingKey and AzureFieldKey, vector fields with AzureAISearchIndexMapVectorInfo and VectorSearchMappings, normalizing managed mappings in LoadedAsync, an embedding deployment from the embedding slot stored in DataSourceIndexProfileMetadata, IDocumentIndexHandler mapping, a custom indexing service with the keyed IDocumentIndexManager, deferred lifecycle indexing, and re-sync through SynchronizedAsync. Use it for custom Azure AI Search indexing of non-content data, vector search over custom records, or questions about CrestApps.OrchardCore.AI.Memory.AzureAI, OrchardCore.Indexing, OrchardCore.AzureAI, IIndexProfileStore, IndexProfileEmbeddingMetadataAccessor, and VectorizedQuery.
src/CrestApps.AgentSkills/crestapps-orchardcore/orchardcore-custom-indexing-azureai/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 Azure AI Search
## Index arbitrary records into Azure AI Search
You are an Orchard Core expert. Generate code that indexes custom records (not content items) into an Azure AI Search index profile, optionally with vectors. Model it on `CrestApps.OrchardCore.AI.Memory` (records, settings, indexing service) and `CrestApps.OrchardCore.AI.Memory.AzureAI` (provider registration and mappings).
### When to use it
- Records you store yourself (CRM rows, generated summaries, user-scoped facts, imported data) that need full-text, filtered, or vector search.
- For Orchard content items use the built-in content index (see `orchardcore-azure-ai-search`). For RAG over an index you already have, an AI data source is usually enough (see `orchardcore-ai-data-sources`).
### Architecture
The shared module (records, neutral document, master-index setting, indexing service, lifecycle handler, re-sync handler) is identical to the Elasticsearch variant; build it as shown in `orchardcore-custom-indexing-elasticsearch`. This skill covers the Azure AI Search provider module and what differs.