004-dotnet-serverless-custom-pipeline

Example: serverless, custom ingestion pipeline

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

The example uses memory.Orchestrator.AddHandler to set which handlers are available:

memory.Orchestrator.AddHandler<TextExtractionHandler>("extract_text");
memory.Orchestrator.AddHandler<TextPartitioningHandler>("split_text_in_partitions");
memory.Orchestrator.AddHandler<GenerateEmbeddingsHandler>("generate_embeddings");
memory.Orchestrator.AddHandler<SummarizationHandler>("summarize");
memory.Orchestrator.AddHandler<SaveRecordsHandler>("save_memory_records");

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

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"),
    index: "user-id-1",
    steps: new[]
    {
        "extract_text",
        "split_text_in_partitions",
        "generate_embeddings",
        "save_memory_records"
    });

Note that as soon as ImportDocumentAsync is done, the memories are available for queries.

Run the example

To run the example, first set the OPENAI_API_KEY environment variable with your OpenAI API key, or adjust the memory builder code to use Azure or other LLMs, then execute dotnet run from this folder.