LLamaSharp/LLama.SemanticKernel
Martin Evans c9c8cd0d62 - Swapped embeddings generator to use `llama_decode`
- Modified `GetEmbeddings` method to be async
2024-01-31 20:28:53 +00:00
..
ChatCompletion bump sk-1.0.0-rc4 2023-12-14 09:47:32 +08:00
TextCompletion bump sk-1.0.0-rc4 2023-12-14 09:47:32 +08:00
TextEmbedding - Swapped embeddings generator to use `llama_decode` 2024-01-31 20:28:53 +00:00
ExtensionMethods.cs bump sk-1.0.0-rc4 2023-12-14 09:47:32 +08:00
LLamaSharp.SemanticKernel.csproj bump sk & km 2024-01-22 19:03:28 +08:00
README.md Fix typos in SemanticKernel README file 2024-01-05 22:21:53 +03:00

README.md

LLamaSharp.SemanticKernel

LLamaSharp.SemanticKernel are connections for SemanticKernel: an SDK for integrating various LLM interfaces into a single implementation. With this, you can add local LLaMa queries as another connection point with your existing connections.

For reference on how to implement it, view the following examples:

ITextCompletion

using var model = LLamaWeights.LoadFromFile(parameters);
// LLamaSharpTextCompletion can accept ILLamaExecutor. 
var ex = new StatelessExecutor(model, parameters);
var builder = new KernelBuilder();
builder.WithAIService<ITextCompletion>("local-llama", new LLamaSharpTextCompletion(ex), true);

IChatCompletion

using var model = LLamaWeights.LoadFromFile(parameters);
using var context = model.CreateContext(parameters);
// LLamaSharpChatCompletion requires InteractiveExecutor, as it's the best fit for the given command.
var ex = new InteractiveExecutor(context);
var chatGPT = new LLamaSharpChatCompletion(ex);

ITextEmbeddingGeneration

using var model = LLamaWeights.LoadFromFile(parameters);
var embedding = new LLamaEmbedder(model, parameters);
var kernelWithCustomDb = Kernel.Builder
    .WithLoggerFactory(ConsoleLogger.LoggerFactory)
    .WithAIService<ITextEmbeddingGeneration>("local-llama-embed", new LLamaSharpEmbeddingGeneration(embedding), true)
    .WithMemoryStorage(new VolatileMemoryStore())
    .Build();