EasyAppDev.Blazor.AutoComplete.AI.AzureSearch

Azure AI Search integration for semantic search with the Blazor AutoComplete component.

Features

• Hybrid Search: Combines vector + keyword search
• Semantic Ranking: AI-powered result re-ranking
• Managed Service: No infrastructure to maintain
• Enterprise Ready: Built-in security, scaling, and SLA

Installation

dotnet add package EasyAppDev.Blazor.AutoComplete.AI.AzureSearch

Quick Start

1. Configure Services

// Program.cs builder.Services.AddAutoCompleteAzureSearch<Product>( endpoint: "https://mysearch.search.windows.net", apiKey: "your-admin-key", indexName: "products", options => { options.EnableSemanticRanking = true; options.SemanticConfigurationName = "my-semantic-config"; }, textSelector: p => $"{p.Name} {p.Description}", idSelector: p => p.Id.ToString()); // Register embedding generator builder.Services.AddAutoCompleteVectorSearch<Product>( openAiApiKey: "sk-...");

2. Use the Component

@using EasyAppDev.Blazor.AutoComplete.AI <VectorAutoComplete TItem="Product" TextField="@(p => p.Name)" @bind-Value="@selectedProduct" Placeholder="Semantic search..." />

Configuration Options

Option	Description	Default
Endpoint	Azure Search endpoint	Required
ApiKey	Admin API key	Required
IndexName	Search index name	Required
EnableSemanticRanking	Use semantic ranker	false
VectorFieldName	Vector field in index	embedding
TopK	Max results to return	10

Hybrid Search

Azure AI Search combines vector similarity with traditional keyword matching:

options.EnableHybridSearch = true; options.HybridSearchWeight = 0.5f; // 50% vector, 50% keyword

Creating an Index

Use Azure Portal or SDK to create an index with a vector field:

{ "name": "products", "fields": [ { "name": "id", "type": "Edm.String", "key": true }, { "name": "name", "type": "Edm.String", "searchable": true }, { "name": "embedding", "type": "Collection(Edm.Single)", "dimensions": 1536, "vectorSearchProfile": "default" } ] }

License

MIT