LangChain.Databases

Database abstractions and vector store backends for the LangChain.NET ecosystem.

Philosophy

This project aligns with the Microsoft.Extensions ecosystem. For vector search, we use Microsoft.Extensions.VectorData.Abstractions (MEVA) — the industry-standard .NET vector data abstraction. For backends that have official Semantic Kernel connectors, consumers should reference those packages directly rather than using wrappers.

Packages

Vector stores (MEVA implementations)

These packages implement VectorStore / VectorStoreCollection<TKey, TRecord> from Microsoft.Extensions.VectorData:

Package	Backend	NuGet
LangChain.Databases.Postgres	PostgreSQL + pgvector
LangChain.Databases.OpenSearch	OpenSearch k-NN

Chat message history

Package	Backend	NuGet
LangChain.Databases.Abstractions	In-memory, file-based
LangChain.Databases.Redis	Redis
LangChain.Databases.Mongo	MongoDB

Use SK connectors for other backends

For backends with official Semantic Kernel connectors, use those packages directly:

Backend	SK Connector Package
In-Memory	Microsoft.SemanticKernel.Connectors.InMemory
SQLite	Microsoft.SemanticKernel.Connectors.Sqlite
Chroma	Microsoft.SemanticKernel.Connectors.Chroma
Qdrant	Microsoft.SemanticKernel.Connectors.Qdrant
Pinecone	Microsoft.SemanticKernel.Connectors.Pinecone
Weaviate	Microsoft.SemanticKernel.Connectors.Weaviate
Milvus	Microsoft.SemanticKernel.Connectors.Milvus
DuckDB	Microsoft.SemanticKernel.Connectors.DuckDB
Azure AI Search	Microsoft.SemanticKernel.Connectors.AzureAISearch
Redis (vector)	Microsoft.SemanticKernel.Connectors.Redis
MongoDB (vector)	Microsoft.SemanticKernel.Connectors.MongoDB

Quick start

1. Define a record type

using Microsoft.Extensions.VectorData;

public class DocumentRecord
{
    [VectorStoreKey]
    public string Id { get; set; } = Guid.NewGuid().ToString();

    [VectorStoreData]
    public string Text { get; set; } = string.Empty;

    [VectorStoreVector(1536)]
    public ReadOnlyMemory<float> Embedding { get; set; }
}

2. Create a store and collection

// PostgreSQL + pgvector
var store = new PostgresVectorStore(connectionString);

// Or OpenSearch
var store = new OpenSearchVectorStore(new OpenSearchVectorDatabaseOptions
{
    ConnectionUri = new Uri("http://localhost:9200"),
});

// Or SK InMemory
var store = new InMemoryVectorStore();

var collection = store.GetCollection<string, DocumentRecord>("my_collection");
await collection.EnsureCollectionExistsAsync();

3. Upsert and search

await collection.UpsertAsync(new DocumentRecord
{
    Text = "hello world",
    Embedding = embedding,
});

await foreach (var result in collection.SearchAsync(
    new ReadOnlyMemory<float>(queryEmbedding), top: 5))
{
    Console.WriteLine($"{result.Record.Text} (score: {result.Score})");
}

Migration from IVectorDatabase

The legacy IVectorDatabase / IVectorCollection interfaces have been removed in favor of MEVA. See the migration guide for detailed before/after examples.

License

This project is licensed under the MIT License.