Phase 1: schema

trustgraph-flow/trustgraph/agent/confidence/__init__.py

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+"""
+TrustGraph Confidence-Based Agent
+
+This module implements a confidence-based agent architecture that provides
+enhanced reliability and auditability compared to traditional ReAct agents.
+
+The agent uses structured execution plans with confidence-based control flow,
+ensuring high-quality outputs through systematic evaluation and retry mechanisms.
+"""
+
+__all__ = [
+    "ConfidenceAgent",
+    "ConfidenceMetrics", 
+    "ExecutionStep",
+    "ExecutionPlan",
+    "StepResult"
+]

trustgraph-flow/trustgraph/agent/confidence/types.py

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+"""
+Internal type definitions for the confidence-based agent.
+
+These types are used internally between confidence agent modules for
+structured data flow, execution state, and metrics tracking.
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Any
+from enum import Enum
+
+
+@dataclass
+class ConfidenceMetrics:
+    """Confidence evaluation metrics for execution results."""
+    
+    score: float  # Confidence score (0.0 to 1.0)
+    reasoning: str  # Explanation of score calculation
+    retry_count: int  # Number of retries attempted
+
+
+@dataclass
+class ExecutionStep:
+    """Individual step in an execution plan."""
+    
+    id: str  # Unique step identifier
+    function: str  # Tool/function to execute
+    arguments: Dict[str, Any]  # Arguments for the function
+    dependencies: List[str]  # IDs of prerequisite steps
+    confidence_threshold: float  # Minimum acceptable confidence
+    timeout_ms: int  # Execution timeout
+
+
+@dataclass
+class ExecutionPlan:
+    """Complete execution plan with ordered steps."""
+    
+    id: str  # Plan identifier
+    steps: List[ExecutionStep]  # Ordered execution steps
+    context: Dict[str, Any]  # Global context for plan
+
+
+@dataclass
+class StepResult:
+    """Result of executing a single step."""
+    
+    step_id: str  # Reference to ExecutionStep
+    success: bool  # Execution success status
+    output: str  # Step execution output
+    confidence: ConfidenceMetrics  # Confidence evaluation
+    execution_time_ms: int  # Actual execution time
+
+
+class ExecutionStatus(Enum):
+    """Status of plan or step execution."""
+    
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    RETRYING = "retrying"
+
+
+@dataclass
+class PlanExecution:
+    """Tracks execution state of an entire plan."""
+    
+    plan_id: str
+    status: ExecutionStatus
+    current_step: Optional[str]  # ID of currently executing step
+    completed_steps: List[str]  # IDs of completed steps
+    failed_steps: List[str]  # IDs of failed steps
+    results: List[StepResult]  # All step results
+    total_execution_time_ms: int
+    
+    
+@dataclass
+class AgentConfig:
+    """Configuration for the confidence agent."""
+    
+    default_confidence_threshold: float = 0.75
+    max_retries: int = 3
+    retry_backoff_factor: float = 2.0
+    override_enabled: bool = True
+    step_timeout_ms: int = 30000
+    parallel_execution: bool = False
+    max_iterations: int = 15
+    
+    # Tool-specific thresholds
+    tool_thresholds: Dict[str, float] = None
+    
+    def __post_init__(self):
+        if self.tool_thresholds is None:
+            self.tool_thresholds = {
+                "GraphQuery": 0.8,
+                "TextCompletion": 0.7, 
+                "McpTool": 0.6,
+                "Prompt": 0.7
+            }
+
+
+@dataclass
+class ContextEntry:
+    """Entry in the execution context/memory."""
+    
+    key: str
+    value: Any
+    step_id: str  # Step that created this entry
+    timestamp: int  # Unix timestamp
+    ttl_seconds: Optional[int] = None  # Time to live