.Net: AzureCognitiveSearchMemoryRecord EmbeddingField is not configurable

TL;DR

Semantic Kernel - Describe the bug During Azure cognitive search search connection and search in the collection the vector embedding name cannot be configured.

Steps

1. If Semantic Kernel's Azure AI Search connector fails with 'Unknown field Embedding in vector field list', it is because the connector assumes its own predefined schema. That works when SK created the index, but not when the index was created elsewhere (for example the Azure portal, where the vector field is contentVector). The maintainer's guidance: bypass the memory-store connector with a custom search plugin against your own schema (see the MyAzureAISearchPlugin sample), or wait for the new vector-store abstractions that accept any schema.

When to use

You are seeing this: That works when SK created the index, but not when the index was created elsewhere (for example the Azure portal, where the vector field is contentVector). Use this skill when you run into ".Net: AzureCognitiveSearchMemoryRecord EmbeddingField is not configurable".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Sources

• https://github.com/microsoft/semantic-kernel/issues/3014

In gh:microsoft/semantic-kernel#3014 (https://github.com/microsoft/semantic-kernel/issues/3014), the issue reported: Describe the bug During Azure cognitive search search connection and search in the collection the vector embedding name cannot be configured. In Azure Cognitive Search the Embeddings Name is "contentVector" while in Semantic Kernel the embedding vector name is set to public const string EmbeddingField = "Embedding"; from namespace Microsoft.SemanticKernel.Connectors.Memory.AzureCognitiveSearch; AzureCognitiveSearchMemoryStore line 167 GetNearestMatches ``` SearchQueryVector vectorQuery = new() { KNearestNeighborsCount = limit, Fields = { AzureCognitiveSearchMemoryRecord. The verified answer: If Semantic Kernel's Azure AI Search connector fails with 'Unknown field Embedding in vector field list', it is because the connector assumes its own predefined schema. That works when SK created the index, but not when the index was created elsewhere (for example the Azure portal, where the vector field is contentVector). The maintainer's guidance: bypass the memory-store connector with a custom search plugin against your own schema (see the MyAzureAISearchPlugin sample), or wait for the new vector-store abstractions that accept any schema.