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EasyAppDev.Blazor.AutoComplete.AI.Pinecone 1.0.7

EasyAppDev.Blazor.AutoComplete.AI.Pinecone

Pinecone integration for semantic search with the Blazor AutoComplete component.

👁 NuGet
👁 License: MIT

Features

• Serverless: No infrastructure to manage
• Namespaces: Organize vectors by category
• Automatic Scaling: Handles any volume
• Low Latency: Optimized for real-time search

Installation

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

Quick Start

1. Configure Services

// Program.cs
builder.Services.AddAutoCompletePinecone<Product>(
 apiKey: "your-pinecone-api-key",
 environment: "us-east-1-aws",
 indexName: "products",
 options => {
 options.Namespace = "production";
 options.TopK = 10;
 },
 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

Namespaces

Organize vectors by category or tenant:

options.Namespace = "category-electronics";
// or
options.Namespace = $"tenant-{tenantId}";

Metadata Filtering

Filter results by metadata:

options.Filter = new Dictionary<string, object> {
 { "category", "Electronics" },
 { "inStock", true }
};

Creating an Index

Use Pinecone console or SDK:

import pinecone

pinecone.create_index(
 name="products",
 dimension=1536,
 metric="cosine"
)

License

MIT