d3300c7db9
Two new OpenSpec skills for porting features to sandboxed codebases: - /opsx:extract-feature generates minimal, printable code recipes - /opsx:export-spec generates compact specs for AI-assisted reimplementation Both support cumulative dependency analysis across archived changes. Includes first export of migrate-to-semantic-kernel in all three formats: code recipe (~120 lines), portable spec (~40 lines), OpenSpec variant (~25 lines). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
64 lines
2.8 KiB
Markdown
64 lines
2.8 KiB
Markdown
# Feature: SK Chat Endpoint with Tool Calling
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## Target: ApplicationX (ASP.NET Core + Blazor WASM + MudBlazor)
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**Included**: wire-responses-api (superseded — SK version used), migrate-to-semantic-kernel
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**Lines to type**: ~40 | **Code equivalent**: ~120 lines | **Compression**: 3x
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## Packages
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- Microsoft.SemanticKernel 1.74.0
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- Microsoft.SemanticKernel.Connectors.OpenAI 1.74.0
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## Architecture
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POST /api/chat accepts conversation messages, runs them through Semantic
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Kernel's chat completion with auto tool calling, streams response as SSE.
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An ExtractionPlugin lets the LLM validate structured data it extracts
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from natural language, retrying autonomously before escalating to the user.
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## Components
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### ChatController: Controllers/ChatController.cs
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- [ApiController] POST endpoint, injects Kernel via DI
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- Converts List<ChatMessage> to SK ChatHistory (user/assistant roles)
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- Imports ExtractionPlugin per-request via _kernel.ImportPluginFromObject
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- Uses OpenAIPromptExecutionSettings with FunctionChoiceBehavior.Auto()
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- Streams via GetStreamingChatMessageContentsAsync, skips empty chunks
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- SSE output: `data: {"text":"..."}\n\n` per chunk, `data: [DONE]\n\n` at end
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- Error: `data: {"error":"..."}\n\n` then [DONE]
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- Catches TaskCanceledException silently (client disconnect)
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### ExtractionPlugin: Plugins/ExtractionPlugin.cs
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- [KernelFunction("validate_extracted_fields")]
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- [Description] tells LLM: validates extracted fields against required schema
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- Accepts string fieldsJson, deserializes to ExtractedFields
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- Checks required fields non-null/non-empty, decimals > 0
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- Returns JSON: {"IsValid": bool, "Errors": ["..."]}
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### ExtractedFields: Shared/Models/ExtractedFields.cs
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- Required: Client(string?), Project(string?), Hours(decimal?), Rate(decimal?), Currency(string?), Date(string?)
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- Optional: Description(string?), PoNumber(string?)
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### ValidationResult: Shared/Models/ValidationResult.cs
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- IsValid(bool), Errors(List<string>)
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### ChatRequest + ChatMessage: Shared/Models/
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- ChatRequest: Messages(List<ChatMessage>)
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- ChatMessage: Role(string), Content(string), Timestamp(DateTime)
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## Wiring (Program.cs, after AddControllers)
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1. `using Microsoft.SemanticKernel;` at top (required for extension methods)
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2. Read BaseUrl and Model from config section "ResponsesApi"
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3. AddOpenAIChatCompletion(modelId, endpoint: new Uri(baseUrl), apiKey)
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4. AddKernel()
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5. AddSingleton<ExtractionPlugin>()
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6. CORS policy if Blazor client on different port
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## Config (appsettings.json)
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- ResponsesApi:BaseUrl = "http://localhost:8317/v1"
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- ResponsesApi:Model = "claude-sonnet-4-6"
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## Gotchas
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- Base URL MUST include /v1 — OpenAI SDK appends chat/completions directly
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- Plugin imported per-request, not at startup (avoids kernel state leaks)
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- SK has built-in auto-invoke limit — no need to set max retries
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- JsonSerializerOptions needs PropertyNameCaseInsensitive = true for deserialization
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