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Example: async memory, custom ingestion pipeline

This is a more advanced example, showing how to customize the way documents are processed and turned into memories when using asynchronous memory pipelines.

The example uses AddHandlerAsHostedService to set which handlers to run as services in the hosting application:

host.Services.AddHandlerAsHostedService<TextExtractionHandler>("extract_text");
host.Services.AddHandlerAsHostedService<TextPartitioningHandler>("split_text_in_partitions");
host.Services.AddHandlerAsHostedService<SummarizationHandler>("summarize");
host.Services.AddHandlerAsHostedService<GenerateEmbeddingsHandler>("generate_embeddings");
host.Services.AddHandlerAsHostedService<SaveRecordsHandler>("save_memory_records");

and the steps parameter to choose which handlers to use during the ingestion:

string docId = await memory.ImportDocumentAsync(
    new Document("inProcessTest")
        .AddFile("file1-Wikipedia-Carbon.txt")
        .AddFile("file2-Wikipedia-Moon.txt")
        .AddFile("file3-lorem-ipsum.docx")
        .AddFile("file4-KM-Readme.pdf")
        .AddFile("file5-NASA-news.pdf")
        .AddTag("testName", "example3"),
    steps: new[]
    {
        "extract_text",
        "split_text_in_partitions",
        "generate_embeddings",
        "save_memory_records"
    });

Please see https://learn.microsoft.com/aspnet/core/fundamentals/host/hosted-services for more information about .NET background tasks.

Note that after calling ImportDocumentAsync the code checks the asynchronous memory to see when the background tasks are complete.

Run the example

To run the example, either set the OPENAI_API_KEY environment variable with your OpenAI API key, or adjust the memory builder code to use Azure or other LLMs.