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>
3.3 KiB
Purpose
Define the autonomous agent-driven extraction pipeline — structured field extraction from natural language, schema-based validation via tool calling, autonomous retry logic, and human-in-the-loop clarification.
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