I added pgvector support to my .NET AI memory library

#dotnet #postgressql #vectordatabase #ai

BlazorMemory has had EF Core storage from the start. It works. But there is a problem with how it handles search.

The current EfCoreMemoryStore loads every memory for a user into C#, then runs cosine similarity in a loop. For a user with 50 memories that is fine. For a user with 5,000 it is a full table scan on every query.

v0.6.0 ships a new package that fixes this properly.

BlazorMemory.Storage.Pgvector

The pgvector adapter moves vector search into PostgreSQL. Instead of loading everything and computing in C#, it runs a single SQL query using the <=> cosine distance operator.

SELECT * FROM "Memories"
WHERE "UserId" = @userId
ORDER BY "Embedding" <=> @queryVector
LIMIT 5;

PostgreSQL handles the math. The .NET code just maps the results.

Setup

dotnet add package BlazorMemory.Storage.Pgvector

You need PostgreSQL with the pgvector extension. If you are on a fresh database:

CREATE EXTENSION IF NOT EXISTS vector;

Then wire it up:

builder.Services
    .AddBlazorMemory()
    .UsePgvectorStorage<AppDbContext>()
    .UseOpenAiEmbeddings(apiKey)
    .UseOpenAiExtractor(apiKey);

Your AppDbContext needs to extend PgvectorMemoryDbContext or apply the configuration manually:

public class AppDbContext : PgvectorMemoryDbContext
{
    public AppDbContext(DbContextOptions<PgvectorMemoryDbContext> options)
        : base(options, dimensions: 1536) { }
}

The dimensions parameter must match your embedding model. OpenAI text-embedding-3-small uses 1536. Adjust for other providers.

The HNSW index

The package creates an HNSW index on the embedding column automatically via EF migrations:

entity.HasIndex(e => e.Embedding)
      .HasMethod("hnsw")
      .HasOperators("vector_cosine_ops")
      .HasDatabaseName("IX_Memories_Embedding_Hnsw");

HNSW is approximate nearest neighbour search. It trades a small amount of recall for much faster query times at scale. For memory retrieval this is the right tradeoff. You do not need perfect recall, you need fast and good enough.