feat(corrections): Add automatic COACHING integration for Correction Packs
- Add CorrectionPackIntegration class to bridge learning components - Modify TrainingDataCollector to auto-propagate corrections to packs - Modify FeedbackProcessor to capture corrections on INCORRECT/PARTIAL feedback - Add convenience functions: get_correction_pack_integration(), capture_coaching_correction() - Add 19 integration tests (all passing) Corrections made during COACHING mode are now automatically captured into a dedicated "auto_captured_corrections" pack for cross-workflow reuse. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Dom
@@ -16,6 +16,11 @@ from .models import (
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from .correction_repository import CorrectionRepository
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from .aggregator import CorrectionAggregator
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from .correction_pack_service import CorrectionPackService
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from .integration import (
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CorrectionPackIntegration,
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get_correction_pack_integration,
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capture_coaching_correction
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)
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__all__ = [
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'CorrectionKey',
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@@ -26,5 +31,9 @@ __all__ = [
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'CorrectionPackMetadata',
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'CorrectionRepository',
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'CorrectionAggregator',
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'CorrectionPackService'
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'CorrectionPackService',
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# Integration
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'CorrectionPackIntegration',
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'get_correction_pack_integration',
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'capture_coaching_correction'
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]
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288
core/corrections/integration.py
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288
core/corrections/integration.py
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@@ -0,0 +1,288 @@
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"""
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Correction Pack Integration - Bridge between learning components and Correction Packs.
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Automatically captures corrections from COACHING mode and propagates them
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to the Correction Pack system for cross-workflow learning.
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"""
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import logging
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from typing import Dict, Any, Optional, Callable
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from datetime import datetime
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from .correction_pack_service import CorrectionPackService
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from .models import CorrectionType
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logger = logging.getLogger(__name__)
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class CorrectionPackIntegration:
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"""
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Integration layer between learning components and Correction Packs.
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Captures corrections from TrainingDataCollector and FeedbackProcessor
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and automatically forwards them to the Correction Pack system.
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"""
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# Default pack name for auto-captured corrections
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DEFAULT_PACK_NAME = "auto_captured_corrections"
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DEFAULT_PACK_DESCRIPTION = "Corrections automatiquement capturées pendant les sessions COACHING"
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def __init__(
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self,
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service: Optional[CorrectionPackService] = None,
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auto_create_pack: bool = True,
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default_pack_id: Optional[str] = None
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):
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"""
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Initialize the integration.
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Args:
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service: CorrectionPackService instance (lazy-loaded if None)
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auto_create_pack: Create default pack if it doesn't exist
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default_pack_id: ID of pack to use (auto-detected if None)
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"""
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self._service = service
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self._auto_create_pack = auto_create_pack
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self._default_pack_id = default_pack_id
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self._initialized = False
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@property
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def service(self) -> CorrectionPackService:
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"""Lazy-load service."""
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if self._service is None:
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self._service = CorrectionPackService()
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return self._service
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def _ensure_initialized(self) -> str:
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"""
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Ensure the default pack exists and return its ID.
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Returns:
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Pack ID to use for storing corrections
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"""
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if self._initialized and self._default_pack_id:
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return self._default_pack_id
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# If specific pack ID provided, verify it exists
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if self._default_pack_id:
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pack = self.service.get_pack(self._default_pack_id)
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if pack:
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self._initialized = True
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return self._default_pack_id
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else:
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logger.warning(f"Pack {self._default_pack_id} not found, creating default")
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# Search for existing auto-capture pack
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packs = self.service.list_packs()
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for pack in packs:
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# Handle both dict and object formats
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pack_name = pack.get('name') if isinstance(pack, dict) else pack.name
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pack_id = pack.get('id') if isinstance(pack, dict) else pack.id
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if pack_name == self.DEFAULT_PACK_NAME:
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self._default_pack_id = pack_id
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self._initialized = True
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logger.info(f"Using existing auto-capture pack: {pack_id}")
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return self._default_pack_id
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# Create new pack if auto_create enabled
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if self._auto_create_pack:
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pack = self.service.create_pack(
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name=self.DEFAULT_PACK_NAME,
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description=self.DEFAULT_PACK_DESCRIPTION,
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tags=["auto", "coaching"],
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category="learning"
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)
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# Handle both dict and object formats
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pack_id = pack.get('id') if isinstance(pack, dict) else pack.id
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self._default_pack_id = pack_id
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self._initialized = True
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logger.info(f"Created auto-capture pack: {pack_id}")
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return self._default_pack_id
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raise RuntimeError("No default pack available and auto_create_pack is disabled")
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def capture_correction(
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self,
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correction_data: Dict[str, Any],
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session_id: Optional[str] = None,
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workflow_id: Optional[str] = None,
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node_id: Optional[str] = None,
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pack_id: Optional[str] = None
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) -> Optional[str]:
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"""
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Capture a correction and add it to a Correction Pack.
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Args:
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correction_data: Correction dict (from TrainingDataCollector format)
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session_id: Source session ID
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workflow_id: Source workflow ID
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node_id: Source node ID
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pack_id: Specific pack to add to (uses default if None)
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Returns:
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Correction ID if successfully added, None otherwise
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"""
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try:
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target_pack_id = pack_id or self._ensure_initialized()
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# Enrich correction data with source info
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enriched = {
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**correction_data,
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'node_id': node_id or correction_data.get('node_id')
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}
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# Determine session_id
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final_session_id = session_id or correction_data.get('session_id', 'unknown')
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final_workflow_id = workflow_id or correction_data.get('workflow_id')
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# Add to pack
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correction = self.service.add_correction_from_session(
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pack_id=target_pack_id,
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session_correction=enriched,
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session_id=final_session_id,
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workflow_id=final_workflow_id
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)
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if correction:
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# Handle both dict and object formats
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correction_id = correction.get('id') if isinstance(correction, dict) else correction.id
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logger.info(
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f"Captured correction {correction_id} from "
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f"session={final_session_id}, workflow={final_workflow_id}"
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)
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return correction_id
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else:
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logger.warning("Failed to add correction to pack")
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return None
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except Exception as e:
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logger.error(f"Error capturing correction: {e}")
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return None
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def capture_feedback_correction(
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self,
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workflow_id: str,
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execution_id: str,
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corrections: Dict[str, Any],
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context: Optional[Dict[str, Any]] = None
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) -> Optional[str]:
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"""
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Capture a correction from FeedbackProcessor.
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Args:
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workflow_id: Workflow ID
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execution_id: Execution ID (used as session_id)
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corrections: Correction dict from feedback
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context: Additional context (failure_reason, element_type, etc.)
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Returns:
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Correction ID if successfully added
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"""
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if not corrections:
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return None
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# Build correction data in standard format
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correction_data = {
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'action_type': corrections.get('action_type', corrections.get('type', 'unknown')),
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'element_type': corrections.get('element_type', context.get('element_type', 'unknown') if context else 'unknown'),
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'failure_reason': corrections.get('failure_reason', context.get('failure_reason', '') if context else ''),
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'correction_type': self._infer_correction_type(corrections),
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'original_target': corrections.get('original_target'),
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'corrected_target': corrections.get('corrected_target', corrections.get('new_target')),
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'original_params': corrections.get('original_params'),
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'corrected_params': corrections.get('corrected_params', corrections.get('new_params')),
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'description': corrections.get('description', f"Correction from feedback {execution_id}")
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}
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return self.capture_correction(
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correction_data=correction_data,
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session_id=execution_id,
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workflow_id=workflow_id
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)
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def _infer_correction_type(self, corrections: Dict[str, Any]) -> str:
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"""Infer correction type from correction data."""
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if corrections.get('correction_type'):
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return corrections['correction_type']
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# Infer from data
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if corrections.get('corrected_target') or corrections.get('new_target'):
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return CorrectionType.TARGET_CHANGE.value
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if corrections.get('corrected_params') or corrections.get('new_params'):
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return CorrectionType.PARAMETER_CHANGE.value
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if corrections.get('wait_time') or corrections.get('timing'):
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return CorrectionType.TIMING_ADJUST.value
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if corrections.get('coordinates') or corrections.get('offset'):
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return CorrectionType.COORDINATES_ADJUST.value
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return CorrectionType.OTHER.value
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def create_hook_for_collector(self) -> Callable[[Dict[str, Any]], None]:
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"""
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Create a hook function for TrainingDataCollector.
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Returns:
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Callback function that captures corrections
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Usage:
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integration = CorrectionPackIntegration()
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collector.on_correction = integration.create_hook_for_collector()
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"""
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def hook(correction: Dict[str, Any]) -> None:
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self.capture_correction(correction)
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return hook
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def get_statistics(self) -> Dict[str, Any]:
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"""Get statistics about captured corrections."""
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try:
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pack_id = self._ensure_initialized()
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return self.service.get_pack_statistics(pack_id)
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except Exception as e:
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logger.error(f"Error getting statistics: {e}")
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return {'error': str(e)}
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# Global instance for easy access
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_global_integration: Optional[CorrectionPackIntegration] = None
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def get_correction_pack_integration() -> CorrectionPackIntegration:
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"""
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Get the global CorrectionPackIntegration instance.
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Creates a new instance if one doesn't exist.
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Returns:
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CorrectionPackIntegration instance
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"""
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global _global_integration
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if _global_integration is None:
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_global_integration = CorrectionPackIntegration()
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return _global_integration
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def capture_coaching_correction(
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correction_data: Dict[str, Any],
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session_id: Optional[str] = None,
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workflow_id: Optional[str] = None,
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node_id: Optional[str] = None
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) -> Optional[str]:
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"""
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Convenience function to capture a correction.
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Args:
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correction_data: Correction dict
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session_id: Source session ID
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workflow_id: Source workflow ID
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node_id: Source node ID
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Returns:
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Correction ID if successful
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"""
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integration = get_correction_pack_integration()
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return integration.capture_correction(
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correction_data=correction_data,
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session_id=session_id,
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workflow_id=workflow_id,
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node_id=node_id
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)
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