.Net: Filters graduation

README.md

@@ -19,7 +19,7 @@ is an SDK that integrates Large Language Models (LLMs) like
 [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service),
 and [Hugging Face](https://huggingface.co/)
 with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this
-by allowing you to define [plugins](https://learn.microsoft.com/en-us/semantic-kernel/ai-orchestration/plugins)
+by allowing you to define [plugins](https://learn.microsoft.com/en-us/semantic-kernel/concepts/plugins)
 that can be chained together
 in just a [few lines of code](https://learn.microsoft.com/en-us/semantic-kernel/ai-orchestration/chaining-functions?tabs=Csharp#using-the-runasync-method-to-simplify-your-code).
 

dotnet/docs/EXPERIMENTS.md

@@ -26,57 +26,57 @@ You can use the following diagnostic IDs to ignore warnings or errors for a part
 
 ## Experimental Features Tracking
 
-| SKEXP​ | Features​​ | API docs​​ | Learn docs​​ | Samples​​ | Issues​​ | Implementations​ |
-|-------|----------|----------|------------|---------|--------|-----------------|
-| SKEXP0001 | Embedding services | | | | | |
-| SKEXP0001 | Image services | | | | | |
-| SKEXP0001 | Memory connectors | | | | | |
-| SKEXP0001 | Kernel filters | | | | | |
-| SKEXP0001 | Audio services | | | | | |
+| SKEXP​ | Features​​ |
+|-------|----------|
+| SKEXP0001 | Embedding services |
+| SKEXP0001 | Image services |
+| SKEXP0001 | Memory connectors |
+| SKEXP0001 | Kernel filters |
+| SKEXP0001 | Audio services |
 | | | | | | | |
-| SKEXP0010 | Azure OpenAI with your data service | | | | | |
-| SKEXP0010 | OpenAI embedding service | | | | | |
-| SKEXP0010 | OpenAI image service | | | | | |
-| SKEXP0010 | OpenAI parameters | | | | | |
-| SKEXP0010 | OpenAI chat history extension | | | | | |
-| SKEXP0010 | OpenAI file service | | | | | |
+| SKEXP0010 | Azure OpenAI with your data service |
+| SKEXP0010 | OpenAI embedding service |
+| SKEXP0010 | OpenAI image service |
+| SKEXP0010 | OpenAI parameters |
+| SKEXP0010 | OpenAI chat history extension |
+| SKEXP0010 | OpenAI file service |
 | | | | | | | |
-| SKEXP0020 | Azure AI Search memory connector | | | | | |
-| SKEXP0020 | Chroma memory connector | | | | | |
-| SKEXP0020 | DuckDB memory connector | | | | | |
-| SKEXP0020 | Kusto memory connector | | | | | |
-| SKEXP0020 | Milvus memory connector | | | | | |
-| SKEXP0020 | Qdrant memory connector | | | | | |
-| SKEXP0020 | Redis memory connector | | | | | |
-| SKEXP0020 | Sqlite memory connector | | | | | |
-| SKEXP0020 | Weaviate memory connector | | | | | |
-| SKEXP0020 | MongoDB memory connector | | | | | |
-| SKEXP0020 | Pinecone memory connector | | | | | |
-| SKEXP0020 | Postgres memory connector | | | | | |
+| SKEXP0020 | Azure AI Search memory connector |
+| SKEXP0020 | Chroma memory connector |
+| SKEXP0020 | DuckDB memory connector |
+| SKEXP0020 | Kusto memory connector |
+| SKEXP0020 | Milvus memory connector |
+| SKEXP0020 | Qdrant memory connector |
+| SKEXP0020 | Redis memory connector |
+| SKEXP0020 | Sqlite memory connector |
+| SKEXP0020 | Weaviate memory connector |
+| SKEXP0020 | MongoDB memory connector |
+| SKEXP0020 | Pinecone memory connector |
+| SKEXP0020 | Postgres memory connector |
 | | | | | | | |
-| SKEXP0040 | GRPC functions | | | | | |
-| SKEXP0040 | Markdown functions | | | | | |
-| SKEXP0040 | OpenAPI functions | | | | | |
-| SKEXP0040 | OpenAPI function extensions | | | | | |
-| SKEXP0040 | Prompty Format support | | | | | |
+| SKEXP0040 | GRPC functions |
+| SKEXP0040 | Markdown functions |
+| SKEXP0040 | OpenAPI functions |
+| SKEXP0040 | OpenAPI function extensions |
+| SKEXP0040 | Prompty Format support |
 | | | | | | | |
-| SKEXP0050 | Core plugins | | | | | |
-| SKEXP0050 | Document plugins | | | | | |
-| SKEXP0050 | Memory plugins | | | | | |
-| SKEXP0050 | Microsoft 365 plugins | | | | | |
-| SKEXP0050 | Web plugins | | | | | |
-| SKEXP0050 | Text chunker plugin | | | | | |
+| SKEXP0050 | Core plugins |
+| SKEXP0050 | Document plugins |
+| SKEXP0050 | Memory plugins |
+| SKEXP0050 | Microsoft 365 plugins |
+| SKEXP0050 | Web plugins |
+| SKEXP0050 | Text chunker plugin |
 | | | | | | | |
-| SKEXP0060 | Handlebars planner | | | | | |
-| SKEXP0060 | OpenAI Stepwise planner | | | | | |
+| SKEXP0060 | Handlebars planner |
+| SKEXP0060 | OpenAI Stepwise planner |
 | | | | | | | |
-| SKEXP0070 | Ollama AI connector | | | | | |
-| SKEXP0070 | Gemini AI connector | | | | | |
-| SKEXP0070 | Mistral AI connector | | | | | |
-| SKEXP0070 | ONNX AI connector | | | | | |
-| SKEXP0070 | Hugging Face AI connector | | | | | |
+| SKEXP0070 | Ollama AI connector |
+| SKEXP0070 | Gemini AI connector |
+| SKEXP0070 | Mistral AI connector |
+| SKEXP0070 | ONNX AI connector |
+| SKEXP0070 | Hugging Face AI connector |
 | | | | | | | |
-| SKEXP0101 | Experiment with Assistants | | | | | |
-| SKEXP0101 | Experiment with Flow Orchestration | | | | | |
+| SKEXP0101 | Experiment with Assistants |
+| SKEXP0101 | Experiment with Flow Orchestration |
 | | | | | | | |
-| SKEXP0110 | Agent Framework | | | | | |
+| SKEXP0110 | Agent Framework |

dotnet/samples/Concepts/Optimization/FrugalGPT.cs → dotnet/samples/Concepts/Optimization/FrugalGPTWithFilters.cs

@@ -15,7 +15,7 @@ namespace Optimization;
 /// This example shows how to use FrugalGPT techniques to reduce cost and improve LLM-related task performance.
 /// More information here: https://arxiv.org/abs/2305.05176.
 /// </summary>
-public sealed class FrugalGPT(ITestOutputHelper output) : BaseTest(output)
+public sealed class FrugalGPTWithFilters(ITestOutputHelper output) : BaseTest(output)
 {
     /// <summary>
     /// One of the FrugalGPT techniques is to reduce prompt size when using few-shot prompts.

dotnet/samples/Concepts/Optimization/PluginSelection.cs → dotnet/samples/Concepts/Optimization/PluginSelectionWithFilters.cs

@@ -21,7 +21,7 @@ namespace Optimization;
 /// It also helps to handle the scenario with a general purpose chat experience for a large enterprise,
 /// where there are so many plugins, that it's impossible to share all of them with AI model in a single request.
 /// </summary>
-public sealed class PluginSelection(ITestOutputHelper output) : BaseTest(output)
+public sealed class PluginSelectionWithFilters(ITestOutputHelper output) : BaseTest(output)
 {
     /// <summary>
     /// This method shows how to select best functions to share with AI using vector similarity search.
@@ -37,7 +37,7 @@ public async Task UsingVectorSearchWithKernelAsync()
             .AddOpenAITextEmbeddingGeneration("text-embedding-3-small", TestConfiguration.OpenAI.ApiKey);
 
         // Add logging.
-        var logger = this.LoggerFactory.CreateLogger<PluginSelection>();
+        var logger = this.LoggerFactory.CreateLogger<PluginSelectionWithFilters>();
         builder.Services.AddSingleton<ILogger>(logger);
 
         // Add memory store to keep functions and search for the most relevant ones for specific request.
@@ -111,7 +111,7 @@ public async Task UsingVectorSearchWithChatCompletionAsync()
             .AddOpenAITextEmbeddingGeneration("text-embedding-3-small", TestConfiguration.OpenAI.ApiKey);
 
         // Add logging.
-        var logger = this.LoggerFactory.CreateLogger<PluginSelection>();
+        var logger = this.LoggerFactory.CreateLogger<PluginSelectionWithFilters>();
         builder.Services.AddSingleton<ILogger>(logger);
 
         // Add memory store to keep functions and search for the most relevant ones for specific request.

dotnet/samples/Concepts/README.md

@@ -106,8 +106,8 @@ Down below you can find the code snippets that demonstrate the usage of many Sem
 
 ## Optimization - Examples of different cost and performance optimization techniques
 
-- [FrugalGPT](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Optimization/FrugalGPT.cs)
-- [PluginSelection](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Optimization/PluginSelection.cs)
+- [FrugalGPTWithFilters](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Optimization/FrugalGPTWithFilters.cs)
+- [PluginSelectionWithFilters](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Optimization/PluginSelectionWithFilters.cs)
 
 ## Planners - Examples on using `Planners`
 

dotnet/src/SemanticKernel.Abstractions/Filters/Function/FunctionInvocationContext.cs

@@ -1,14 +1,12 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-using System.Diagnostics.CodeAnalysis;
 using System.Threading;
 
 namespace Microsoft.SemanticKernel;
 
 /// <summary>
 /// Class with data related to function invocation.
 /// </summary>
-[Experimental("SKEXP0001")]
 public class FunctionInvocationContext
 {
     /// <summary>

dotnet/src/SemanticKernel.Abstractions/Filters/Function/IFunctionInvocationFilter.cs

@@ -1,7 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.
 
 using System;
-using System.Diagnostics.CodeAnalysis;
 using System.Threading.Tasks;
 
 namespace Microsoft.SemanticKernel;
@@ -11,7 +10,6 @@ namespace Microsoft.SemanticKernel;
 /// <summary>
 /// Interface for filtering actions during function invocation.
 /// </summary>
-[Experimental("SKEXP0001")]
 public interface IFunctionInvocationFilter
 {
     /// <summary>

dotnet/src/SemanticKernel.Abstractions/Filters/Prompt/IPromptRenderFilter.cs

@@ -1,7 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.
 
 using System;
-using System.Diagnostics.CodeAnalysis;
 using System.Threading.Tasks;
 
 namespace Microsoft.SemanticKernel;
@@ -11,7 +10,6 @@ namespace Microsoft.SemanticKernel;
 /// <summary>
 /// Interface for filtering actions during prompt rendering.
 /// </summary>
-[Experimental("SKEXP0001")]
 public interface IPromptRenderFilter
 {
     /// <summary>

dotnet/src/SemanticKernel.Abstractions/Filters/Prompt/PromptRenderContext.cs

@@ -1,14 +1,11 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-using System.Diagnostics.CodeAnalysis;
 using System.Threading;
-
 namespace Microsoft.SemanticKernel;
 
 /// <summary>
 /// Class with data related to prompt rendering.
 /// </summary>
-[Experimental("SKEXP0001")]
 public sealed class PromptRenderContext
 {
     private string? _renderedPrompt;

dotnet/src/SemanticKernel.Abstractions/Kernel.cs

@@ -132,7 +132,6 @@ public Kernel Clone() =>
     /// <summary>
     /// Gets the collection of function filters available through the kernel.
     /// </summary>
-    [Experimental("SKEXP0001")]
     public IList<IFunctionInvocationFilter> FunctionInvocationFilters =>
         this._functionInvocationFilters ??
         Interlocked.CompareExchange(ref this._functionInvocationFilters, [], null) ??
@@ -141,7 +140,6 @@ public Kernel Clone() =>
     /// <summary>
     /// Gets the collection of function filters available through the kernel.
     /// </summary>
-    [Experimental("SKEXP0001")]
     public IList<IPromptRenderFilter> PromptRenderFilters =>
         this._promptRenderFilters ??
         Interlocked.CompareExchange(ref this._promptRenderFilters, [], null) ??
@@ -309,7 +307,6 @@ private void AddFilters()
         }
     }
 
-    [Experimental("SKEXP0001")]
     internal async Task<FunctionInvocationContext> OnFunctionInvocationAsync(
         KernelFunction function,
         KernelArguments arguments,
@@ -351,7 +348,6 @@ await functionFilters[index].OnFunctionInvocationAsync(context,
         }
     }
 
-    [Experimental("SKEXP0001")]
     internal async Task<PromptRenderContext> OnPromptRenderAsync(
         KernelFunction function,
         KernelArguments arguments,