d01a626b76Re-implementation of C# interceptors SDK with clearer focus on server-side / gateway interceptors. Fixes a lot of issues with the existing implementation. Still more boilerplate than I'm happy with, but this is better than what was there. Will refine further in later PRs.
52 lines
1.9 KiB
C#
52 lines
1.9 KiB
C#
using System.Text.Json.Nodes;
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using Microsoft.Extensions.AI;
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using ModelContextProtocol.Interceptors.Protocol;
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using ModelContextProtocol.Interceptors.Server;
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namespace AvatarMoodInterceptorSample;
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/// <summary>
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/// A sink interceptor that classifies conversational mood from each
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/// <c>llm/completion</c> response using a smaller, secondary model (Haiku) and
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/// updates a console avatar. Demonstrates that sink interceptors can do
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/// real work — driving downstream state — without gating the main conversation.
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/// </summary>
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[McpServerInterceptorType]
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public sealed class AvatarInterceptors
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{
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private readonly IChatClient _haiku;
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private readonly string _haikuModelId;
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private readonly AvatarState _state;
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public AvatarInterceptors(IChatClient haiku, string haikuModelId, AvatarState state)
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{
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_haiku = haiku;
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_haikuModelId = haikuModelId;
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_state = state;
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}
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[McpServerInterceptor(
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Name = "avatar-mood",
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Description = "Classifies conversation mood via a secondary model and updates the avatar state.",
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Type = InterceptorType.Sink,
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Events = [InterceptionEvents.LlmCompletion],
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Phase = InterceptorPhase.Response)]
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public SinkInterceptorResult OnLlmCompletion(
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JsonNode payload,
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string @event,
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InterceptorPhase phase,
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InvokeInterceptorContext? context,
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CancellationToken cancellationToken)
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{
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// Sink contract: parallel, fire-and-forget, failures swallowed.
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// We return immediately so the main conversation never waits on Haiku.
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_ = Task.Run(async () =>
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{
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var verdict = await MoodClassifier.ClassifyAsync(_haiku, payload, cancellationToken);
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_state.Update(verdict.Label, verdict.Confidence, _haikuModelId);
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AvatarRenderer.Render(_state);
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}, cancellationToken);
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return new SinkInterceptorResult { Recorded = true };
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}
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}
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