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164 | using LLama.Common;
using Microsoft.SemanticKernel.Memory;
using LLamaSharp.SemanticKernel.TextEmbedding;
namespace LLama.Examples.Examples
{
public class SemanticKernelMemory
{
private const string MemoryCollectionName = "SKGitHub";
public static async Task Run()
{
string modelPath = UserSettings.GetModelPath();
Console.WriteLine("This example is from: \n" +
"https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/KernelSyntaxExamples/Example14_SemanticMemory.cs");
var seed = 1337u;
// Load weights into memory
var parameters = new ModelParams(modelPath)
{
Seed = seed,
EmbeddingMode = true
};
using var model = LLamaWeights.LoadFromFile(parameters);
var embedding = new LLamaEmbedder(model, parameters);
Console.WriteLine("====================================================");
Console.WriteLine("======== Semantic Memory (volatile, in RAM) ========");
Console.WriteLine("====================================================");
/* You can build your own semantic memory combining an Embedding Generator
* with a Memory storage that supports search by similarity (ie semantic search).
*
* In this example we use a volatile memory, a local simulation of a vector DB.
*
* You can replace VolatileMemoryStore with Qdrant (see QdrantMemoryStore connector)
* or implement your connectors for Pinecone, Vespa, Postgres + pgvector, SQLite VSS, etc.
*/
var memory = new MemoryBuilder()
.WithTextEmbeddingGeneration(new LLamaSharpEmbeddingGeneration(embedding))
.WithMemoryStore(new VolatileMemoryStore())
.Build();
await RunExampleAsync(memory);
}
private static async Task RunExampleAsync(ISemanticTextMemory memory)
{
await StoreMemoryAsync(memory);
await SearchMemoryAsync(memory, "How do I get started?");
/*
Output:
Query: How do I get started?
Result 1:
URL: : https://github.com/microsoft/semantic-kernel/blob/main/README.md
Title : README: Installation, getting started, and how to contribute
Result 2:
URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/dotnet-jupyter-notebooks/00-getting-started.ipynb
Title : Jupyter notebook describing how to get started with the Semantic Kernel
*/
await SearchMemoryAsync(memory, "Can I build a chat with SK?");
/*
Output:
Query: Can I build a chat with SK?
Result 1:
URL: : https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT
Title : Sample demonstrating how to create a chat skill interfacing with ChatGPT
Result 2:
URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/apps/chat-summary-webapp-react/README.md
Title : README: README associated with a sample chat summary react-based webapp
*/
await SearchMemoryAsync(memory, "Jupyter notebook");
await SearchMemoryAsync(memory, "README: README associated with a sample chat summary react-based webapp");
await SearchMemoryAsync(memory, "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function");
}
private static async Task SearchMemoryAsync(ISemanticTextMemory memory, string query)
{
Console.WriteLine("\nQuery: " + query + "\n");
var memories = memory.SearchAsync(MemoryCollectionName, query, limit: 10, minRelevanceScore: 0.5);
int i = 0;
await foreach (MemoryQueryResult result in memories)
{
Console.WriteLine($"Result {++i}:");
Console.WriteLine(" URL: : " + result.Metadata.Id);
Console.WriteLine(" Title : " + result.Metadata.Description);
Console.WriteLine(" Relevance: " + result.Relevance);
Console.WriteLine();
}
Console.WriteLine("----------------------");
}
private static async Task StoreMemoryAsync(ISemanticTextMemory memory)
{
/* Store some data in the semantic memory.
*
* When using Azure Cognitive Search the data is automatically indexed on write.
*
* When using the combination of VolatileStore and Embedding generation, SK takes
* care of creating and storing the index
*/
Console.WriteLine("\nAdding some GitHub file URLs and their descriptions to the semantic memory.");
var githubFiles = SampleData();
var i = 0;
foreach (var entry in githubFiles)
{
var result = await memory.SaveReferenceAsync(
collection: MemoryCollectionName,
externalSourceName: "GitHub",
externalId: entry.Key,
description: entry.Value,
text: entry.Value);
Console.WriteLine($"#{++i} saved.");
Console.WriteLine(result);
}
Console.WriteLine("\n----------------------");
}
private static Dictionary<string, string> SampleData()
{
return new Dictionary<string, string>
{
["https://github.com/microsoft/semantic-kernel/blob/main/README.md"]
= "README: Installation, getting started, and how to contribute",
["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks/02-running-prompts-from-file.ipynb"]
= "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function",
["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks//00-getting-started.ipynb"]
= "Jupyter notebook describing how to get started with the Semantic Kernel",
["https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT"]
= "Sample demonstrating how to create a chat skill interfacing with ChatGPT",
["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/VolatileMemoryStore.cs"]
= "C# class that defines a volatile embedding store",
["https://github.com/microsoft/semantic-kernel/blob/main/samples/dotnet/KernelHttpServer/README.md"]
= "README: How to set up a Semantic Kernel Service API using Azure Function Runtime v4",
["https://github.com/microsoft/semantic-kernel/blob/main/samples/apps/chat-summary-webapp-react/README.md"]
= "README: README associated with a sample chat summary react-based webapp",
};
}
}
}
|