5b027eb0db
Overhaul extraction pipeline with new TradeItem model, conversation flow, and dedicated extraction endpoint. Add sidebar navigation with NavMenu component and landing page. Introduce few-shot prompting service and tests. Add prompt settings and email upload specs. Update OpenSpec tooling with improved export-spec and extract-feature commands. Archive completed changes and export full specs. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
86 lines
4.5 KiB
Markdown
86 lines
4.5 KiB
Markdown
## Purpose
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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.
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## Requirements
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### Requirement: Structured field extraction from natural language
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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.
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#### Scenario: All fields extracted successfully
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- **WHEN** the user sends a message containing natural language with all required information
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- **THEN** the agent returns a JSON object with all predefined fields populated from the text
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#### Scenario: Partial extraction
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- **WHEN** the user sends a message that contains some but not all required fields
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- **THEN** the agent extracts available fields and leaves missing fields as null
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### Requirement: Predefined extraction schema
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The system SHALL define the extraction schema as a `TradeItem` class with fields: valuedate, counterparty, legal_entity, trade_id, display_ccy, pv, breakclause. Extraction output SHALL be wrapped in an `ExtractionResult` containing a `List<TradeItem>`. All extraction output MUST conform to this schema.
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#### Scenario: Output conforms to schema
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- **WHEN** the agent produces extracted fields from an email
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- **THEN** every item in the output is a valid TradeItem with all required fields matching expected types
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#### Scenario: Multiple items from one email
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- **WHEN** the agent extracts data from an email containing multiple trade legs
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- **THEN** the output ExtractionResult contains one TradeItem per trade leg
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### Requirement: Autonomous validation via tool calling
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The agent SHALL validate extracted fields by calling external API tools exposed as Semantic Kernel functions. Validation tools include counterparty lookup, trade validation, currency validation, and schema validation. Each tool returns structured results that the agent reasons about.
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#### Scenario: Validation passes
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- **WHEN** the agent calls the schema validation tool with a complete and correct ExtractionResult
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- **THEN** the tool returns a success result and the agent returns the final output to the user
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#### Scenario: Validation fails with fixable errors
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- **WHEN** a validation tool returns errors for missing or malformed fields
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- **THEN** the agent re-reads the source text and attempts to fix the extraction without user intervention
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#### Scenario: Counterparty disambiguation required
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- **WHEN** the counterparty lookup tool returns multiple candidate (counterparty, legal_entity) tuples
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- **THEN** the agent presents the candidates to the user as a numbered list in the chat and waits for the user to select one before completing the extraction
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### Requirement: Autonomous retry with iteration cap
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The agent SHALL retry extraction autonomously up to 3 times when validation fails. After exhausting retries, the agent MUST escalate to the user.
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#### Scenario: Agent retries and succeeds
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- **WHEN** validation fails on the first attempt but the error is recoverable
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- **THEN** the agent retries extraction and calls validation again, up to 3 total attempts
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#### Scenario: Agent exhausts retries and escalates
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- **WHEN** validation fails after 3 attempts
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- **THEN** the agent sends a natural language message to the user identifying the specific fields it could not resolve and asking for clarification
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### Requirement: Human-in-the-loop clarification
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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. Disambiguation of counterparty/legal_entity tuples is a specific case of human-in-the-loop clarification.
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#### Scenario: User provides clarification
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- **WHEN** the agent asks for clarification about missing fields and the user responds
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- **THEN** the agent incorporates the user's response into the conversation context and produces an updated extraction
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#### Scenario: User selects counterparty from candidates
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- **WHEN** the agent presents a numbered list of counterparty/legal_entity candidates and the user replies with a selection
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- **THEN** the agent populates the `legal_entity` field on all relevant TradeItems and proceeds with validation
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#### Scenario: Clarification via normal chat
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- **WHEN** the agent escalates for clarification
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- **THEN** the clarification request appears as a regular assistant message in the chat UI, and the user responds via the normal chat input
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