Serverless memory (.NET only)

Kernel Memory works and scales at best when running as a service, allowing to ingest thousands of documents and information without blocking your app. However, you can also embed the MemoryServerless class in your app, using KernelMemoryBuilder.

MemoryServerless and MemoryWebClient implement the same interface and offer the same API, so you can easily switch from one to the other.

The embedded serverless mode is available only for .NET applications, by running the KM codebase inside the same process of your .NET application.

By default the serverless memory is volatile and keeps all data only in memory, without persistence on disk. Follow the configuration instructions to persist memory across multiple executions.

Importing documents into your Kernel Memory can be as simple as this:

var memory = new KernelMemoryBuilder()
    .WithOpenAIDefaults(Env.Var("OPENAI_API_KEY"))
    .Build<MemoryServerless>();

// Import a file
await memory.ImportDocumentAsync("meeting-transcript.docx", tags: new() { { "user", "Blake" } });

// Import multiple files and apply multiple tags
await memory.ImportDocumentAsync(new Document("file001")
    .AddFile("business-plan.docx")
    .AddFile("project-timeline.pdf")
    .AddTag("user", "Blake")
    .AddTag("collection", "business")
    .AddTag("collection", "plans")
    .AddTag("fiscalYear", "2023"));

Asking questions:

var answer1 = await memory.AskAsync("How many people attended the meeting?");

var answer2 = await memory.AskAsync("what's the project timeline?", filter: new MemoryFilter().ByTag("user", "Blake"));

The code leverages the default documents ingestion pipeline:

  1. Extract text: recognize the file format and extract the information
  2. Partition the text in small chunks, to optimize search
  3. Extract embedding using an LLM embedding generator
  4. Save embedding into a vector index such as Azure AI Search, Qdrant or other DBs.

Documents are organized by users, safeguarding their private information. Furthermore, memories can be categorized and structured using tags, enabling efficient search and retrieval through faceted navigation.

Topics


Table of contents