LLamaSharp/LLama.SemanticKernel
xbotter a2b26faa7a
🔧 Refactor chat completion implementation
- Refactored the chat completion implementation in `LLamaSharpChatCompletion.cs` to use `StatelessExecutor` instead of `InteractiveExecutor`.
- Updated the chat history prompt in `LLamaSharpChatCompletion.cs` to include a conversation between the assistant and the user.
- Modified the `HistoryTransform` class in `HistoryTransform.cs` to append the assistant role to the chat history prompt.
- Updated the constructor of `LLamaSharpChatCompletion` to accept optional parameters for `historyTransform` and `outputTransform`.
- Modified the `GetChatCompletionsAsync` and `GetChatCompletions` methods in `LLamaSharpChatCompletion.cs` to use the new `StatelessExecutor` and `outputTransform`.
- Updated the `ExtensionMethods.cs` file to include the assistant and system roles in the list of anti-prompts.
2023-12-01 21:39:31 +08:00
..
ChatCompletion 🔧 Refactor chat completion implementation 2023-12-01 21:39:31 +08:00
TextCompletion Added a converter similar to the Open AI one 2023-11-18 21:42:34 -06:00
TextEmbedding bump semantic kernel 1.0.0-beta8 2023-11-17 21:17:27 +08:00
ExtensionMethods.cs 🔧 Refactor chat completion implementation 2023-12-01 21:39:31 +08:00
LLamaSharp.SemanticKernel.csproj bump semantic kernel 1.0.0-beta8 2023-11-17 21:17:27 +08:00
README.md Bump example, readme 2023-09-02 14:21:02 +09:00

README.md

LLamaSharp.SemanticKernel

LLamaSharp.SemanticKernel are connections for SemanticKernel: an SDK for intergrating 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();