471e9ce935
Replace manual HTTP proxy in ChatController with Semantic Kernel's OpenAI chat completion service pointed at CLIProxyAPI. Add extraction plugin with validation function for structured field extraction from natural language, enabling an agentic loop with auto-retry and human-in-the-loop escalation. - Add Microsoft.SemanticKernel 1.74.0 with OpenAI connector - Create ExtractedFields schema and ValidationResult models - Create ExtractionPlugin with [KernelFunction] validation - Rewrite ChatController to use IChatCompletionService streaming - Configure FunctionChoiceBehavior.Auto() for tool calling - Preserve existing SSE contract (client unchanged) - Update tests to mock SK services, add plugin and integration tests - Archive multi-turn-conversations and migrate-to-semantic-kernel changes - Sync specs for agent-extraction, semantic-kernel-integration, chat-streaming Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1.7 KiB
1.7 KiB
ADDED Requirements
Requirement: Semantic Kernel service registration
The API backend SHALL register a Semantic Kernel Kernel instance in the ASP.NET Core DI container at startup, configured with an OpenAI chat completion connector.
Scenario: Kernel registered at startup
- WHEN the API application starts
- THEN a
Kernelinstance is available for injection into controllers
Requirement: OpenAI connector targets CLIProxyAPI proxy
The Semantic Kernel OpenAI chat completion service SHALL be configured to use the existing CLIProxyAPI proxy endpoint as its base URL, reading the URL and model name from appsettings.json.
Scenario: Connector uses configured endpoint
- WHEN the kernel makes a chat completion request
- THEN it sends the request to the URL specified in
ResponsesApi:BaseUrlconfiguration
Scenario: Model from configuration
- WHEN the kernel makes a chat completion request
- THEN it uses the model name specified in
ResponsesApi:Modelconfiguration
Requirement: Plugin registration
The API backend SHALL register extraction and validation plugins with the Kernel so they are available as tools for the LLM to invoke.
Scenario: Plugins available as tools
- WHEN the kernel is constructed
- THEN all registered plugin functions appear in the tool list sent to the LLM
Requirement: Auto function calling
The Kernel SHALL be configured with automatic function calling enabled, allowing the LLM to invoke registered plugin functions without manual dispatch code.
Scenario: LLM invokes tool automatically
- WHEN the LLM decides to call a registered function during chat completion
- THEN the kernel automatically executes the function and returns the result to the LLM