Vectorizer.Sdk 1.3.1

Vectorizer C# SDK

High-performance C# SDK for Vectorizer vector database.

Package: Vectorizer.Sdk
Version: 1.3.0
NuGet: https://www.nuget.org/packages/Vectorizer.Sdk

Features

• ✅ Async/Await Support: Full async/await support for high performance
• ✅ .NET 8.0+: Modern C# with latest language features
• ✅ Type Safety: Strong typing with comprehensive models
• ✅ Collection Management: CRUD operations for collections
• ✅ Vector Operations: Insert, search, update, delete vectors
• ✅ Semantic Search: Text and vector similarity search
• ✅ Intelligent Search: Advanced multi-query search with domain expansion
• ✅ Semantic Search: High-precision semantic search with reranking
• ✅ Hybrid Search: Combine dense and sparse vectors for improved search quality
• ✅ Batch Operations: Efficient bulk operations
• ✅ Error Handling: Comprehensive exception handling
• ✅ IDisposable: Proper resource management

Installation

dotnet add package Vectorizer.Sdk

# Or via NuGet Package Manager
Install-Package Vectorizer.Sdk

# Or specific version
dotnet add package Vectorizer.Sdk --version 1.3.0

Quick Start

using Vectorizer;
using Vectorizer.Models;

// Create client
var client = new VectorizerClient(new ClientConfig
{
    BaseUrl = "http://localhost:15002",
    ApiKey = "your-api-key"
});

try
{
    // Health check
    await client.HealthAsync();
    Console.WriteLine("✓ Server is healthy");

    // Create collection
    var collection = await client.CreateCollectionAsync(new CreateCollectionRequest
    {
        Name = "documents",
        Config = new CollectionConfig
        {
            Dimension = 384,
            Metric = DistanceMetric.Cosine
        }
    });
    Console.WriteLine($"✓ Created collection: {collection.Name}");

    // Insert text
    var result = await client.InsertTextAsync("documents", "Hello, world!", null);
    Console.WriteLine($"✓ Inserted vector ID: {result.Id}");

    // Search
    var results = await client.SearchTextAsync("documents", "hello", new SearchOptions
    {
        Limit = 10
    });
    Console.WriteLine($"✓ Found {results.Count} results");

    // Intelligent search
    var intelligentResults = await client.IntelligentSearchAsync(new IntelligentSearchRequest
    {
        Query = "machine learning algorithms",
        Collections = new List<string> { "documents" },
        MaxResults = 15,
        DomainExpansion = true,
        TechnicalFocus = true,
        MMREnabled = true,
        MMRLambda = 0.7
    });
    Console.WriteLine($"✓ Intelligent search found {intelligentResults.Count} results");

    // Semantic search
    var semanticResults = await client.SemanticSearchAsync(
        "documents",
        "neural networks",
        maxResults: 10,
        semanticReranking: true
    );
    Console.WriteLine($"✓ Semantic search found {semanticResults.Count} results");
}
catch (VectorizerException ex)
{
    Console.WriteLine($"Error: {ex.ErrorType} - {ex.Message}");
}
finally
{
    client.Dispose();
}

Configuration

Basic Configuration

var client = new VectorizerClient(new ClientConfig
{
    BaseUrl = "http://localhost:15002",
    ApiKey = "your-api-key",
    TimeoutSeconds = 30
});

Custom HTTP Client

var httpClient = new HttpClient
{
    Timeout = TimeSpan.FromSeconds(60)
};

var client = new VectorizerClient(new ClientConfig
{
    BaseUrl = "http://localhost:15002",
    ApiKey = "your-api-key",
    HttpClient = httpClient
});

API Reference

Collection Management

// List collections
var collections = await client.ListCollectionsAsync();

// Get collection info
var info = await client.GetCollectionInfoAsync("documents");

// Create collection
var collection = await client.CreateCollectionAsync(new CreateCollectionRequest
{
    Name = "documents",
    Config = new CollectionConfig
    {
        Dimension = 384,
        Metric = DistanceMetric.Cosine
    }
});

// Delete collection
await client.DeleteCollectionAsync("documents");

Vector Operations

// Insert text (with automatic embedding)
var result = await client.InsertTextAsync("documents", "Hello, world!", new Dictionary<string, object>
{
    ["source"] = "example.txt"
});

// Get vector
var vector = await client.GetVectorAsync("documents", "vector-id");

// Update vector
await client.UpdateVectorAsync("documents", "vector-id", new Vector
{
    Id = "vector-id",
    Data = new float[] { 0.1f, 0.2f, 0.3f },
    Payload = new Dictionary<string, object>
    {
        ["updated"] = true
    }
});

// Delete vector
await client.DeleteVectorAsync("documents", "vector-id");

// Vector search
var results = await client.SearchAsync("documents", new float[] { 0.1f, 0.2f, 0.3f }, new SearchOptions
{
    Limit = 10
});

// Text search
var textResults = await client.SearchTextAsync("documents", "query", new SearchOptions
{
    Limit = 10,
    Filter = new Dictionary<string, object>
    {
        ["category"] = "AI"
    }
});

Intelligent Search

// Intelligent search with multi-query expansion
var results = await client.IntelligentSearchAsync(new IntelligentSearchRequest
{
    Query = "machine learning algorithms",
    Collections = new List<string> { "documents", "research" },
    MaxResults = 15,
    DomainExpansion = true,
    TechnicalFocus = true,
    MMREnabled = true,
    MMRLambda = 0.7
});

Semantic Search

// Semantic search with reranking
var results = await client.SemanticSearchAsync(
    collectionName: "documents",
    query: "neural networks",
    maxResults: 10,
    semanticReranking: true,
    similarityThreshold: 0.6
);

Batch Operations

// Batch insert
var batchResult = await client.BatchInsertAsync("documents", new BatchInsertRequest
{
    Texts = new List<string>
    {
        "Machine learning algorithms",
        "Deep learning neural networks",
        "Natural language processing"
    }
});

// Batch search
var batchSearchResult = await client.BatchSearchAsync("documents", new BatchSearchRequest
{
    Queries = new List<string>
    {
        "machine learning",
        "neural networks",
        "NLP techniques"
    },
    Limit = 5
});

Error Handling

try
{
    var collection = await client.CreateCollectionAsync(new CreateCollectionRequest
    {
        Name = "documents",
        Config = new CollectionConfig
        {
            Dimension = 384,
            Metric = DistanceMetric.Cosine
        }
    });
}
catch (VectorizerException ex)
{
    if (ex.IsNotFound)
    {
        Console.WriteLine("Collection not found");
    }
    else if (ex.IsUnauthorized)
    {
        Console.WriteLine("Authentication failed");
    }
    else if (ex.IsValidationError)
    {
        Console.WriteLine($"Validation error: {ex.Message}");
    }
    else
    {
        Console.WriteLine($"Error: {ex.ErrorType} - {ex.Message} (status: {ex.StatusCode})");
    }
}

Examples

See Examples directory for more usage examples:

• Basic Example - Basic operations
• More examples coming soon

Development

# Restore dependencies
dotnet restore

# Build
dotnet build

# Run tests
dotnet test

# Build release
dotnet build -c Release

# Pack NuGet package
dotnet pack -c Release

Requirements

• .NET 8.0 or later
• System.Text.Json 9.0.0+

License

Apache License 2.0 - see LICENSE for details.

Support

• Documentation: Vectorizer Documentation
• Issues: GitHub Issues
• Discussions: GitHub Discussions