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AI Memory with Elasticsearch

ModuleCrestApps.OrchardCore.AI.Memory.Elasticsearch
Feature NameAI Memory indexing using Elasticsearch

Provides Elasticsearch indexing and vector search support for AI Memory.

When to enable this module​

Enable this module when you want authenticated user memories to be stored in the core AI Memory feature and indexed into Elasticsearch for semantic lookup.

This module depends on:

  • AI Memory
  • OrchardCore.Indexing
  • OrchardCore.Elasticsearch

What this provider adds​

  • an Elasticsearch-backed memory index profile type
  • vector indexing for saved user memories
  • semantic memory search using the embedding deployment selected for the memory index
  • the provider-specific wiring needed by the shared AI Memory tools and preemptive retrieval pipeline

Setup​

  1. Enable AI Memory indexing using Elasticsearch.
  2. Enable the Orchard Core Elasticsearch feature that provides search indexes.
  3. Open Search -> Indexing and create an AI Memory (Elasticsearch) index.
  4. Select the embedding deployment that should be used for memory embeddings and queries.
  5. Go to Settings -> Artificial Intelligence -> Memory and choose that index as the Index profile.

Operational notes​

  • The embedding deployment is chosen when the memory index is created and then treated as stable so the stored vectors keep the expected dimensions.
  • Changing the master memory index after production data exists usually requires a full re-index or data migration plan.
  • All reads and writes remain scoped to the current authenticated user even though the vectors are stored in a shared search service.