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110 | using LLamaSharp.KernelMemory;
using Microsoft.KernelMemory;
using Microsoft.KernelMemory.Configuration;
using System.Diagnostics;
namespace LLama.Examples.Examples
{
// This example is from Microsoft's official kernel memory "custom prompts" example:
// https://github.com/microsoft/kernel-memory/blob/6d516d70a23d50c6cb982e822e6a3a9b2e899cfa/examples/101-dotnet-custom-Prompts/Program.cs#L1-L86
// Microsoft.KernelMemory has more features than Microsoft.SemanticKernel.
// See https://microsoft.github.io/kernel-memory/ for details.
public class KernelMemory
{
public static async Task Run()
{
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine(
"""
This program uses the Microsoft.KernelMemory package to ingest documents
and answer questions about them in an interactive chat prompt.
""");
// Setup the kernel memory with the LLM model
string modelPath = UserSettings.GetModelPath();
IKernelMemory memory = CreateMemory(modelPath);
// Ingest documents (format is automatically detected from the filename)
string[] filesToIngest = [
Path.GetFullPath(@"./Assets/sample-SK-Readme.pdf"),
Path.GetFullPath(@"./Assets/sample-KM-Readme.pdf"),
];
for (int i = 0; i < filesToIngest.Length; i++)
{
string path = filesToIngest[i];
Stopwatch sw = Stopwatch.StartNew();
Console.ForegroundColor = ConsoleColor.Blue;
Console.WriteLine($"Importing {i + 1} of {filesToIngest.Length}: {path}");
await memory.ImportDocumentAsync(path, steps: Constants.PipelineWithoutSummary);
Console.WriteLine($"Completed in {sw.Elapsed}\n");
}
// Ask a predefined question
Console.ForegroundColor = ConsoleColor.Green;
string question1 = "What formats does KM support";
Console.WriteLine($"Question: {question1}");
await AnswerQuestion(memory, question1);
// Let the user ask additional questions
while (true)
{
Console.ForegroundColor = ConsoleColor.Green;
Console.Write("Question: ");
string question = Console.ReadLine()!;
if (string.IsNullOrEmpty(question))
return;
await AnswerQuestion(memory, question);
}
}
private static IKernelMemory CreateMemory(string modelPath)
{
Common.InferenceParams infParams = new() { AntiPrompts = ["\n\n"] };
LLamaSharpConfig lsConfig = new(modelPath) { DefaultInferenceParams = infParams };
SearchClientConfig searchClientConfig = new()
{
MaxMatchesCount = 1,
AnswerTokens = 100,
};
TextPartitioningOptions parseOptions = new()
{
MaxTokensPerParagraph = 300,
MaxTokensPerLine = 100,
OverlappingTokens = 30
};
return new KernelMemoryBuilder()
.WithLLamaSharpDefaults(lsConfig)
.WithSearchClientConfig(searchClientConfig)
.With(parseOptions)
.Build();
}
private static async Task AnswerQuestion(IKernelMemory memory, string question)
{
Stopwatch sw = Stopwatch.StartNew();
Console.ForegroundColor = ConsoleColor.DarkGray;
Console.WriteLine($"Generating answer...");
MemoryAnswer answer = await memory.AskAsync(question);
Console.WriteLine($"Answer generated in {sw.Elapsed}");
Console.ForegroundColor = ConsoleColor.Gray;
Console.WriteLine($"Answer: {answer.Result}");
foreach (var source in answer.RelevantSources)
{
Console.WriteLine($"Source: {source.SourceName}");
}
Console.WriteLine();
}
}
}
|