Create Index for Agentic Retrieval

Build search indexes optimized for agentic retrieval workloads.

Create an Index for Agentic Retrieval

Indexes for agentic retrieval require specific configurations to support multi-query patterns and semantic reranking.

Requirements

1. Semantic Configuration

   Required for semantic reranking of subquery results

2. Vector Fields

   Recommended for hybrid search across text and embeddings

3. Filterable Fields

   Optional but useful for pre-filtering results

4. Default Scoring Profile

   Optional for custom relevance tuning

Index Schema Example

{
  "name": "products-index",
  "fields": [
    {
      "name": "productId",
      "type": "Edm.String",
      "key": true
    },
    {
      "name": "description",
      "type": "Edm.String",
      "searchable": true
    },
    {
      "name": "descriptionVector",
      "type": "Collection(Edm.Single)",
      "searchable": true,
      "dimensions": 1536,
      "vectorSearchProfile": "my-vector-profile"
    }
  ],
  "semantic": {
    "defaultConfiguration": "my-semantic-config",
    "configurations": [
      {
        "name": "my-semantic-config",
        "prioritizedFields": {
          "titleField": {"fieldName": "title"},
          "contentFields": [
            {"fieldName": "description"}
          ]
        }
      }
    ]
  }
}

Best Practices

• Use descriptive field names
• Include both text and vector fields for hybrid search
• Configure semantic ranking on most important fields
• Add filters for common query patterns

Next Steps

Create Knowledge Source

Wrap your index in a knowledge source

Vector Search

Add vector capabilities to your index