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dotnet add package EasyAppDev.Blazor.AutoComplete.AI.PostgreSql --version 1.0.7
NuGet\Install-Package EasyAppDev.Blazor.AutoComplete.AI.PostgreSql -Version 1.0.7
<PackageReference Include="EasyAppDev.Blazor.AutoComplete.AI.PostgreSql" Version="1.0.7" />
<PackageVersion Include="EasyAppDev.Blazor.AutoComplete.AI.PostgreSql" Version="1.0.7" />Directory.Packages.props
<PackageReference Include="EasyAppDev.Blazor.AutoComplete.AI.PostgreSql" />Project file
paket add EasyAppDev.Blazor.AutoComplete.AI.PostgreSql --version 1.0.7
#r "nuget: EasyAppDev.Blazor.AutoComplete.AI.PostgreSql, 1.0.7"
#:package EasyAppDev.Blazor.AutoComplete.AI.PostgreSql@1.0.7
#addin nuget:?package=EasyAppDev.Blazor.AutoComplete.AI.PostgreSql&version=1.0.7Install as a Cake Addin
#tool nuget:?package=EasyAppDev.Blazor.AutoComplete.AI.PostgreSql&version=1.0.7Install as a Cake Tool
PostgreSQL/pgvector integration for semantic search with the Blazor AutoComplete component.
dotnet add package EasyAppDev.Blazor.AutoComplete.AI.PostgreSql
CREATE EXTENSION vector;
// Program.cs
builder.Services.AddAutoCompletePostgreSql<Product>(
connectionString: "Host=localhost;Database=myapp;Username=user;Password=pass",
options => {
options.TableName = "product_embeddings";
options.Dimensions = 1536;
options.DistanceFunction = DistanceFunction.Cosine;
},
textSelector: p => $"{p.Name} {p.Description}",
idSelector: p => p.Id.ToString());
// Register embedding generator
builder.Services.AddAutoCompleteVectorSearch<Product>(
openAiApiKey: "sk-...");
@using EasyAppDev.Blazor.AutoComplete.AI
<VectorAutoComplete TItem="Product"
TextField="@(p => p.Name)"
@bind-Value="@selectedProduct"
Placeholder="Semantic search..." />
| Option | Description | Default |
|---|---|---|
ConnectionString |
PostgreSQL connection string | Required |
TableName |
Table for embeddings | {type}_embeddings |
Dimensions |
Vector dimensions | 1536 |
DistanceFunction |
Similarity metric | Cosine |
CreateTableIfNotExists |
Auto-create table | true |
UseHnswIndex |
Enable HNSW index | true |
| Function | Use Case |
|---|---|
Cosine |
Normalized embeddings (default) |
L2 |
Euclidean distance |
DotProduct |
Inner product similarity |
L1 |
Manhattan distance |
Hamming |
Binary vectors |
Jaccard |
Set similarity |
@inject IVectorIndexer<Product> indexer
// Index all products
await indexer.IndexAsync(products);
// Re-index on update
await indexer.UpdateAsync(updatedProduct);
MIT
| Product | Versions Compatible and additional computed target framework versions. |
|---|---|
| .NET | net8.0 net8.0 is compatible. net8.0-android net8.0-android was computed. net8.0-browser net8.0-browser was computed. net8.0-ios net8.0-ios was computed. net8.0-maccatalyst net8.0-maccatalyst was computed. net8.0-macos net8.0-macos was computed. net8.0-tvos net8.0-tvos was computed. net8.0-windows net8.0-windows was computed. net9.0 net9.0 is compatible. net9.0-android net9.0-android was computed. net9.0-browser net9.0-browser was computed. net9.0-ios net9.0-ios was computed. net9.0-maccatalyst net9.0-maccatalyst was computed. net9.0-macos net9.0-macos was computed. net9.0-tvos net9.0-tvos was computed. net9.0-windows net9.0-windows was computed. net10.0 net10.0 was computed. net10.0-android net10.0-android was computed. net10.0-browser net10.0-browser was computed. net10.0-ios net10.0-ios was computed. net10.0-maccatalyst net10.0-maccatalyst was computed. net10.0-macos net10.0-macos was computed. net10.0-tvos net10.0-tvos was computed. net10.0-windows net10.0-windows was computed. |
This package is not used by any NuGet packages.
This package is not used by any popular GitHub repositories.
Version 1.0.7 - Added AOT-compatible JSON serialization overloads for all vector database providers. Fixed exception handling in GetItem to return default on deserialization failure. Added cancellation token propagation in Azure Search hybrid search. Improved robustness and trimming compatibility.