feat: migrate chat backend to Semantic Kernel with tool calling support

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>

openspec/changes/archive/2026-04-04-migrate-to-semantic-kernel/specs/agent-extraction/spec.md

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+## ADDED Requirements
+
+### Requirement: Structured field extraction from natural language
+
+The agent SHALL extract a predefined set of key-value pairs from user-provided natural language text (e.g., email content) and return them as a structured JSON object.
+
+#### Scenario: All fields extracted successfully
+
+- **WHEN** the user sends a message containing natural language with all required information
+- **THEN** the agent returns a JSON object with all predefined fields populated from the text
+
+#### Scenario: Partial extraction
+
+- **WHEN** the user sends a message that contains some but not all required fields
+- **THEN** the agent extracts available fields and leaves missing fields as null
+
+### Requirement: Predefined extraction schema
+
+The system SHALL define a fixed set of known field names and types as a strongly-typed C# class. All extraction output MUST conform to this schema.
+
+#### Scenario: Output conforms to schema
+
+- **WHEN** the agent produces extracted fields
+- **THEN** every key in the output matches a field defined in the schema and values match expected types
+
+### Requirement: Autonomous validation via tool calling
+
+The agent SHALL validate extracted fields by calling a validation tool function. The validation tool checks that all required fields are present and correctly typed.
+
+#### Scenario: Validation passes
+
+- **WHEN** the agent calls the validation tool with a complete and correct extraction
+- **THEN** the tool returns a success result and the agent returns the final output to the user
+
+#### Scenario: Validation fails with fixable errors
+
+- **WHEN** the validation tool returns errors for missing or malformed fields
+- **THEN** the agent re-reads the source text and attempts to fix the extraction without user intervention
+
+### Requirement: Autonomous retry with iteration cap
+
+The agent SHALL retry extraction autonomously up to 3 times when validation fails. After exhausting retries, the agent MUST escalate to the user.
+
+#### Scenario: Agent retries and succeeds
+
+- **WHEN** validation fails on the first attempt but the error is recoverable
+- **THEN** the agent retries extraction and calls validation again, up to 3 total attempts
+
+#### Scenario: Agent exhausts retries and escalates
+
+- **WHEN** validation fails after 3 attempts
+- **THEN** the agent sends a natural language message to the user identifying the specific fields it could not resolve and asking for clarification
+
+### Requirement: Human-in-the-loop clarification
+
+When the agent escalates to the user, the user SHALL be able to provide the missing information in natural language, and the agent SHALL incorporate the clarification and re-attempt extraction.
+
+#### Scenario: User provides clarification
+
+- **WHEN** the agent asks for clarification about missing fields and the user responds
+- **THEN** the agent incorporates the user's response into the conversation context and produces an updated extraction
+
+#### Scenario: Clarification via normal chat
+
+- **WHEN** the agent escalates for clarification
+- **THEN** the clarification request appears as a regular assistant message in the chat UI, and the user responds via the normal chat input

openspec/changes/archive/2026-04-04-migrate-to-semantic-kernel/specs/chat-streaming/spec.md

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+## MODIFIED Requirements
+
+### Requirement: Chat endpoint proxies to Responses API
+
+The API backend SHALL expose `POST /api/chat` that accepts a list of messages and processes them using a Semantic Kernel chat completion service. The kernel is configured with an OpenAI connector pointed at the existing CLIProxyAPI proxy.
+
+#### Scenario: Successful chat request
+
+- **WHEN** the client sends a POST to `/api/chat` with a message list
+- **THEN** the API processes the messages through the Semantic Kernel and returns the response
+
+### Requirement: Streaming response delivery
+
+The API backend SHALL stream the Semantic Kernel's chat completion response back to the WASM client as `text/event-stream`, forwarding text content so the client can render tokens incrementally. The SSE event format MUST remain `data: {"text":"..."}\n\n` for text deltas and `data: [DONE]\n\n` for completion.
+
+#### Scenario: Tokens stream to client
+
+- **WHEN** the Semantic Kernel emits streaming chat message content
+- **THEN** the backend forwards each content chunk as an SSE event to the client containing the text fragment
+
+#### Scenario: Stream completes
+
+- **WHEN** the Semantic Kernel streaming response completes
+- **THEN** the backend signals stream completion to the client with `data: [DONE]\n\n`
+
+### Requirement: Configurable proxy target
+
+The CLIProxyAPI base URL and model name SHALL be configurable via `appsettings.json` in the API project, not hardcoded. These values are used to configure the Semantic Kernel OpenAI connector.
+
+#### Scenario: Configuration read at startup
+
+- **WHEN** the API starts
+- **THEN** it reads `ResponsesApi:BaseUrl` and `ResponsesApi:Model` from configuration to configure the Semantic Kernel
+
+### Requirement: Error propagation
+
+If the LLM service returns an error or is unreachable, the API backend SHALL return an error SSE event and the client SHALL display the error to the user.
+
+#### Scenario: LLM service unreachable
+
+- **WHEN** the CLIProxyAPI proxy is not running
+- **THEN** the client displays an error message instead of an assistant response

openspec/changes/archive/2026-04-04-migrate-to-semantic-kernel/specs/semantic-kernel-integration/spec.md

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+## 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 `Kernel` instance 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:BaseUrl` configuration
+
+#### Scenario: Model from configuration
+
+- **WHEN** the kernel makes a chat completion request
+- **THEN** it uses the model name specified in `ResponsesApi:Model` configuration
+
+### 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