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Refactor README for EasyAppDev.Blazor.AutoComplete.Generators: Update features, installation instructions, and source generator details; add diagnostic codes for expression validation. Add icon for OData package.
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‎.gitignore‎

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appsettings.json
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claude_workspace/
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.claude-workspace/
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docs/
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# docs/
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*.nupkg
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*.snupkg
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############################
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Thumbs.db
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samples/AutoComplete.Playground/appsettings.json
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docs/blog.md

‎Directory.Build.props‎

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<PackageTags>blazor;autocomplete;component;ai;semantic-search;performance;trimming;aot;wcag;accessibility;theming;virtualization</PackageTags>
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<PackageReadmeFile>README.md</PackageReadmeFile>
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<PackageIcon>icon.png</PackageIcon>
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<Version>1.0.5</Version>
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<PackageReleaseNotes>Version 1.0.5 - Added OData integration package (EasyAppDev.Blazor.AutoComplete.OData) with support for OData v3/v4 endpoints, automatic $filter generation, multi-field search, and all filter strategies (StartsWith, Contains, FuzzyFallback). Enhanced demo pages with interactive search hints.</PackageReleaseNotes>
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<Version>1.0.6</Version>
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<PackageReleaseNotes>Version 1.0.6 - Updated all package READMEs with focused documentation for each package. Added READMEs for vector database providers (PostgreSQL, Azure AI Search, Pinecone, Qdrant, CosmosDB).</PackageReleaseNotes>
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<Copyright>Copyright (c) 2025 EasyAppDev</Copyright>
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<!-- Documentation -->

‎src/EasyAppDev.Blazor.AutoComplete.AI.AzureSearch/EasyAppDev.Blazor.AutoComplete.AI.AzureSearch.csproj‎

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<NoWarn>$(NoWarn);IL2026;IL3050</NoWarn>
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</PropertyGroup>
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<!-- Package README and Icon -->
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<ItemGroup>
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<None Include="README.md" Pack="true" PackagePath="\" />
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<None Include="icon.png" Pack="true" PackagePath="\" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\EasyAppDev.Blazor.AutoComplete.AI\EasyAppDev.Blazor.AutoComplete.AI.csproj" />
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</ItemGroup>

‎src/EasyAppDev.Blazor.AutoComplete.AI.AzureSearch/README.md‎

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# EasyAppDev.Blazor.AutoComplete.AI.AzureSearch
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Azure AI Search integration for semantic search with the Blazor AutoComplete component.
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[![NuGet](https://img.shields.io/nuget/v/EasyAppDev.Blazor.AutoComplete.AI.AzureSearch.svg)](https://www.nuget.org/packages/EasyAppDev.Blazor.AutoComplete.AI.AzureSearch/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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## Features
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- **Hybrid Search**: Combines vector + keyword search
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- **Semantic Ranking**: AI-powered result re-ranking
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- **Managed Service**: No infrastructure to maintain
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- **Enterprise Ready**: Built-in security, scaling, and SLA
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## Installation
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```bash
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dotnet add package EasyAppDev.Blazor.AutoComplete.AI.AzureSearch
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```
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## Quick Start
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### 1. Configure Services
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```csharp
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// Program.cs
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builder.Services.AddAutoCompleteAzureSearch<Product>(
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endpoint: "https://mysearch.search.windows.net",
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apiKey: "your-admin-key",
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indexName: "products",
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options => {
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options.EnableSemanticRanking = true;
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options.SemanticConfigurationName = "my-semantic-config";
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},
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textSelector: p => $"{p.Name} {p.Description}",
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idSelector: p => p.Id.ToString());
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// Register embedding generator
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builder.Services.AddAutoCompleteVectorSearch<Product>(
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openAiApiKey: "sk-...");
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```
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### 2. Use the Component
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```razor
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@using EasyAppDev.Blazor.AutoComplete.AI
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<VectorAutoComplete TItem="Product"
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TextField="@(p => p.Name)"
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@bind-Value="@selectedProduct"
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Placeholder="Semantic search..." />
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```
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## Configuration Options
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| Option | Description | Default |
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|--------|-------------|---------|
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| `Endpoint` | Azure Search endpoint | Required |
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| `ApiKey` | Admin API key | Required |
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| `IndexName` | Search index name | Required |
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| `EnableSemanticRanking` | Use semantic ranker | `false` |
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| `VectorFieldName` | Vector field in index | `embedding` |
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| `TopK` | Max results to return | `10` |
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## Hybrid Search
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Azure AI Search combines vector similarity with traditional keyword matching:
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```csharp
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options.EnableHybridSearch = true;
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options.HybridSearchWeight = 0.5f; // 50% vector, 50% keyword
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```
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## Creating an Index
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Use Azure Portal or SDK to create an index with a vector field:
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```json
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{
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"name": "products",
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"fields": [
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{ "name": "id", "type": "Edm.String", "key": true },
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{ "name": "name", "type": "Edm.String", "searchable": true },
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{ "name": "embedding", "type": "Collection(Edm.Single)", "dimensions": 1536, "vectorSearchProfile": "default" }
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]
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}
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```
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## License
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MIT

‎src/EasyAppDev.Blazor.AutoComplete.AI.AzureSearch/icon.png‎

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‎src/EasyAppDev.Blazor.AutoComplete.AI.CosmosDb/EasyAppDev.Blazor.AutoComplete.AI.CosmosDb.csproj‎

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<NoWarn>$(NoWarn);IL2026;IL3050</NoWarn>
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</PropertyGroup>
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<!-- Package README and Icon -->
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<ItemGroup>
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<None Include="README.md" Pack="true" PackagePath="\" />
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<None Include="icon.png" Pack="true" PackagePath="\" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\EasyAppDev.Blazor.AutoComplete.AI\EasyAppDev.Blazor.AutoComplete.AI.csproj" />
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</ItemGroup>

‎src/EasyAppDev.Blazor.AutoComplete.AI.CosmosDb/README.md‎

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# EasyAppDev.Blazor.AutoComplete.AI.CosmosDb
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Azure Cosmos DB integration for semantic search with the Blazor AutoComplete component.
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[![NuGet](https://img.shields.io/nuget/v/EasyAppDev.Blazor.AutoComplete.AI.CosmosDb.svg)](https://www.nuget.org/packages/EasyAppDev.Blazor.AutoComplete.AI.CosmosDb/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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## Features
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- **Multi-Model Database**: NoSQL + vector search
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- **Global Distribution**: Low latency worldwide
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- **Integrated NoSQL**: Store documents with embeddings
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- **Enterprise SLA**: 99.999% availability
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## Installation
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```bash
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dotnet add package EasyAppDev.Blazor.AutoComplete.AI.CosmosDb
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```
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## Quick Start
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### 1. Configure Services
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```csharp
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// Program.cs
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builder.Services.AddAutoCompleteCosmosDb<Product>(
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endpoint: "https://myaccount.documents.azure.com:443/",
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key: "your-cosmos-key",
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databaseName: "myapp",
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containerName: "products",
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options => {
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options.VectorIndexType = VectorIndexType.DiskANN;
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options.Dimensions = 1536;
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},
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textSelector: p => $"{p.Name} {p.Description}",
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idSelector: p => p.Id.ToString());
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// Register embedding generator
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builder.Services.AddAutoCompleteVectorSearch<Product>(
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openAiApiKey: "sk-...");
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```
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### 2. Use the Component
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```razor
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@using EasyAppDev.Blazor.AutoComplete.AI
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<VectorAutoComplete TItem="Product"
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TextField="@(p => p.Name)"
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@bind-Value="@selectedProduct"
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Placeholder="Semantic search..." />
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```
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## Configuration Options
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| Option | Description | Default |
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|--------|-------------|---------|
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| `Endpoint` | Cosmos DB endpoint | Required |
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| `Key` | Primary key | Required |
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| `DatabaseName` | Database name | Required |
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| `ContainerName` | Container name | Required |
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| `VectorIndexType` | Index type | `DiskANN` |
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| `Dimensions` | Vector dimensions | `1536` |
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| `PartitionKeyPath` | Partition key | `/id` |
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## Vector Index Types
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| Type | Description |
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|------|-------------|
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| `Flat` | Exact search, smaller datasets |
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| `QuantizedFlat` | Compressed, balanced |
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| `DiskANN` | Large scale, best performance |
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## Container Setup
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Enable vector search on container:
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```csharp
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var containerProperties = new ContainerProperties("products", "/id")
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{
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VectorEmbeddingPolicy = new VectorEmbeddingPolicy(
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new Collection<Embedding> {
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new Embedding {
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Path = "/embedding",
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DataType = VectorDataType.Float32,
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Dimensions = 1536,
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DistanceFunction = DistanceFunction.Cosine
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}
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})
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};
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```
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## Integrated Document Storage
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Store full documents with embeddings:
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```csharp
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public class ProductDocument
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{
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public string Id { get; set; }
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public string Name { get; set; }
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public string Description { get; set; }
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public float[] Embedding { get; set; } // Auto-populated
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}
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```
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## License
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MIT

‎src/EasyAppDev.Blazor.AutoComplete.AI.CosmosDb/icon.png‎

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‎src/EasyAppDev.Blazor.AutoComplete.AI.Pinecone/EasyAppDev.Blazor.AutoComplete.AI.Pinecone.csproj‎

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<NoWarn>$(NoWarn);IL2026;IL3050</NoWarn>
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</PropertyGroup>
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<!-- Package README and Icon -->
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<ItemGroup>
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<None Include="README.md" Pack="true" PackagePath="\" />
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<None Include="icon.png" Pack="true" PackagePath="\" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\EasyAppDev.Blazor.AutoComplete.AI\EasyAppDev.Blazor.AutoComplete.AI.csproj" />
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</ItemGroup>

‎src/EasyAppDev.Blazor.AutoComplete.AI.Pinecone/README.md‎

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# EasyAppDev.Blazor.AutoComplete.AI.Pinecone
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Pinecone integration for semantic search with the Blazor AutoComplete component.
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[![NuGet](https://img.shields.io/nuget/v/EasyAppDev.Blazor.AutoComplete.AI.Pinecone.svg)](https://www.nuget.org/packages/EasyAppDev.Blazor.AutoComplete.AI.Pinecone/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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## Features
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- **Serverless**: No infrastructure to manage
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- **Namespaces**: Organize vectors by category
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- **Automatic Scaling**: Handles any volume
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- **Low Latency**: Optimized for real-time search
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## Installation
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```bash
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dotnet add package EasyAppDev.Blazor.AutoComplete.AI.Pinecone
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```
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## Quick Start
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### 1. Configure Services
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```csharp
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// Program.cs
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builder.Services.AddAutoCompletePinecone<Product>(
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apiKey: "your-pinecone-api-key",
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environment: "us-east-1-aws",
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indexName: "products",
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options => {
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options.Namespace = "production";
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options.TopK = 10;
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},
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textSelector: p => $"{p.Name} {p.Description}",
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idSelector: p => p.Id.ToString());
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// Register embedding generator
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builder.Services.AddAutoCompleteVectorSearch<Product>(
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openAiApiKey: "sk-...");
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```
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### 2. Use the Component
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```razor
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@using EasyAppDev.Blazor.AutoComplete.AI
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<VectorAutoComplete TItem="Product"
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TextField="@(p => p.Name)"
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@bind-Value="@selectedProduct"
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Placeholder="Semantic search..." />
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```
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## Configuration Options
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| Option | Description | Default |
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|--------|-------------|---------|
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| `ApiKey` | Pinecone API key | Required |
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| `Environment` | Pinecone environment | Required |
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| `IndexName` | Index name | Required |
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| `Namespace` | Vector namespace | `""` (default) |
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| `TopK` | Max results | `10` |
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| `IncludeMetadata` | Return metadata | `true` |
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## Namespaces
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Organize vectors by category or tenant:
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```csharp
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options.Namespace = "category-electronics";
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// or
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options.Namespace = $"tenant-{tenantId}";
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```
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## Metadata Filtering
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Filter results by metadata:
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```csharp
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options.Filter = new Dictionary<string, object> {
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{ "category", "Electronics" },
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{ "inStock", true }
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};
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```
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## Creating an Index
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Use Pinecone console or SDK:
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```python
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import pinecone
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pinecone.create_index(
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name="products",
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dimension=1536,
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metric="cosine"
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)
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```
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## License
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MIT

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