pgvector support for C#
Supports Npgsql, Dapper, and Entity Framework Core
Follow the instructions for your database library:
Run:
dotnet add package PgvectorImport the library
using Pgvector.Npgsql;Create a connection
var dataSourceBuilder = new NpgsqlDataSourceBuilder(connString);
dataSourceBuilder.UseVector();
await using var dataSource = dataSourceBuilder.Build();
var conn = dataSource.OpenConnection();Create a table
await using (var cmd = new NpgsqlCommand("CREATE TABLE items (embedding vector(3))", conn))
{
await cmd.ExecuteNonQueryAsync();
}Insert a vector
await using (var cmd = new NpgsqlCommand("INSERT INTO items (embedding) VALUES ($1)", conn))
{
var embedding = new Vector(new float[] { 1, 1, 1 });
cmd.Parameters.AddWithValue(embedding);
await cmd.ExecuteNonQueryAsync();
}Get the nearest neighbors
await using (var cmd = new NpgsqlCommand("SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", conn))
{
var embedding = new Vector(new float[] { 1, 1, 1 });
cmd.Parameters.AddWithValue(embedding);
await using (var reader = await cmd.ExecuteReaderAsync())
{
while (await reader.ReadAsync())
{
Console.WriteLine((Vector)reader.GetValue(0));
}
}
}Add an approximate index
await using (var cmd = new NpgsqlCommand("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)", conn))
{
await cmd.ExecuteNonQueryAsync();
}Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
See a full example
Run:
dotnet add package Pgvector.DapperImport the library
using Pgvector.Dapper;
using Pgvector.Npgsql;Create a connection
SqlMapper.AddTypeHandler(new VectorTypeHandler());
var dataSourceBuilder = new NpgsqlDataSourceBuilder(connString);
dataSourceBuilder.UseVector();
await using var dataSource = dataSourceBuilder.Build();
var conn = dataSource.OpenConnection();Define a class
public class Item
{
public Vector? Embedding { get; set; }
}Create a table
conn.Execute("CREATE TABLE items (embedding vector(3))");Insert a vector
var embedding = new Vector(new float[] { 1, 1, 1 });
conn.Execute(@"INSERT INTO items (embedding) VALUES (@embedding)", new { embedding });Get the nearest neighbors
var embedding = new Vector(new float[] { 1, 1, 1 });
var items = conn.Query<Item>("SELECT * FROM items ORDER BY embedding <-> @embedding LIMIT 5", new { embedding });
foreach (Item item in items)
{
Console.WriteLine(item.Embedding);
}Add an approximate index
conn.Execute("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)");Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
See a full example
Run:
dotnet add package Pgvector.EntityFrameworkCoreImport the library
using Pgvector.EntityFrameworkCore;Configure the connection
protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
{
optionsBuilder.UseNpgsql("connString", o => o.UseVector());
}Define a model
public class Item
{
[Column(TypeName = "vector(3)")]
public Vector? Embedding { get; set; }
}Insert a vector
ctx.Items.Add(new Item { Embedding = new Vector(new float[] { 1, 1, 1 }) });
ctx.SaveChanges();Get the nearest neighbors
var embedding = new Vector(new float[] { 1, 1, 1 });
var items = await ctx.Items.FromSql($"SELECT * FROM items ORDER BY embedding <-> {embedding} LIMIT 5").ToListAsync();
foreach (Item item in items)
{
if (item.Embedding != null)
{
Console.WriteLine(item.Embedding);
}
}Add an approximate index
protected override void OnModelCreating(ModelBuilder modelBuilder)
{
modelBuilder.Entity<Item>()
.HasIndex(i => i.Embedding)
.HasMethod("ivfflat")
.HasOperators("vector_l2_ops");
}Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
See a full example
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/pgvector/pgvector-dotnet.git
cd pgvector-dotnet
createdb pgvector_dotnet_test
dotnet test