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"""
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Schemas for Talk Structurally, Act Hierarchically Framework
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This module defines the Pydantic schemas used in the TalkHierarchical framework
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for structured communication and hierarchical evaluation.
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"""
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from typing import Dict, List, Optional, Union
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from pydantic import BaseModel, Field
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from enum import Enum
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class CommunicationType(str, Enum):
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"""Types of communication in the structured protocol"""
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MESSAGE = "message" # M_ij: Specific task instructions
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BACKGROUND = "background" # B_ij: Context and problem background
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INTERMEDIATE_OUTPUT = "intermediate_output" # I_ij: Intermediate results
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class AgentRole(str, Enum):
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"""Roles for agents in the hierarchical system"""
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SUPERVISOR = "supervisor"
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GENERATOR = "generator"
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EVALUATOR = "evaluator"
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REFINER = "refiner"
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COORDINATOR = "coordinator"
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class StructuredMessageSchema(BaseModel):
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"""Schema for structured communication messages"""
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message: str = Field(
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description="Specific task instructions (M_ij)",
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min_length=1
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)
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background: str = Field(
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description="Context and problem background (B_ij)",
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default=""
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)
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intermediate_output: str = Field(
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description="Intermediate results (I_ij)",
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default=""
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)
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sender: str = Field(
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description="Name of the sending agent",
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min_length=1
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)
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recipient: str = Field(
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description="Name of the receiving agent",
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min_length=1
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)
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timestamp: Optional[str] = Field(
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description="Timestamp of the message",
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default=None
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)
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communication_type: CommunicationType = Field(
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description="Type of communication",
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default=CommunicationType.MESSAGE
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)
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class HierarchicalOrderSchema(BaseModel):
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"""Schema for hierarchical task orders"""
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agent_name: str = Field(
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description="Name of the agent to receive the task",
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min_length=1
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)
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task: str = Field(
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description="Specific task description",
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min_length=1
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)
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communication_type: CommunicationType = Field(
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description="Type of communication to use",
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default=CommunicationType.MESSAGE
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)
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background_context: str = Field(
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description="Background context for the task",
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default=""
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)
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intermediate_output: str = Field(
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description="Intermediate output to pass along",
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default=""
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)
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priority: int = Field(
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description="Task priority (1-10, higher is more important)",
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default=5,
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ge=1,
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le=10
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)
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class EvaluationCriterionSchema(BaseModel):
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"""Schema for evaluation criteria"""
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name: str = Field(
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description="Name of the evaluation criterion",
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min_length=1
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)
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description: str = Field(
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description="Description of what this criterion evaluates",
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min_length=1
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)
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weight: float = Field(
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description="Weight of this criterion in overall evaluation (0-1)",
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default=1.0,
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ge=0.0,
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le=1.0
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)
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scale: str = Field(
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description="Scale for evaluation (e.g., '0-10', 'A-F', 'Poor-Excellent')",
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default="0-10"
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)
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class EvaluationResultSchema(BaseModel):
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"""Schema for evaluation results"""
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evaluator_name: str = Field(
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description="Name of the evaluator",
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min_length=1
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)
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criterion: str = Field(
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description="Evaluation criterion",
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min_length=1
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)
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score: float = Field(
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description="Evaluation score",
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ge=0.0
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)
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feedback: str = Field(
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description="Detailed feedback",
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min_length=1
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)
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confidence: float = Field(
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description="Confidence in evaluation (0-1)",
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ge=0.0,
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le=1.0
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)
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reasoning: str = Field(
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description="Reasoning behind the evaluation",
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default=""
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)
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suggestions: List[str] = Field(
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description="Suggestions for improvement",
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default=[]
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)
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class EvaluationSummarySchema(BaseModel):
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"""Schema for evaluation summaries"""
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overall_score: float = Field(
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description="Overall evaluation score",
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ge=0.0
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)
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criterion_scores: Dict[str, float] = Field(
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description="Scores for each criterion",
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default={}
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)
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strengths: List[str] = Field(
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description="Identified strengths",
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default=[]
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)
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weaknesses: List[str] = Field(
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description="Identified weaknesses",
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default=[]
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)
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recommendations: List[str] = Field(
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description="Recommendations for improvement",
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default=[]
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)
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confidence: float = Field(
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description="Overall confidence in evaluation",
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ge=0.0,
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le=1.0
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)
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class AgentConfigSchema(BaseModel):
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"""Schema for agent configuration"""
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name: str = Field(
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description="Name of the agent",
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min_length=1
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)
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role: AgentRole = Field(
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description="Role of the agent in the system"
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)
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model_name: str = Field(
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description="Model to use for this agent",
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default="gpt-4o-mini"
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)
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system_prompt: str = Field(
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description="System prompt for the agent",
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min_length=1
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)
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tools: List[str] = Field(
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description="Tools available to this agent",
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default=[]
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)
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capabilities: List[str] = Field(
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description="Capabilities of this agent",
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default=[]
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)
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evaluation_criteria: List[str] = Field(
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description="Evaluation criteria this agent can assess",
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default=[]
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)
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class TalkHierarchicalConfigSchema(BaseModel):
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"""Schema for TalkHierarchical swarm configuration"""
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name: str = Field(
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description="Name of the swarm",
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default="TalkHierarchicalSwarm"
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)
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description: str = Field(
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description="Description of the swarm",
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default="Talk Structurally, Act Hierarchically Framework"
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)
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max_loops: int = Field(
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description="Maximum number of refinement loops",
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default=3,
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ge=1
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)
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enable_structured_communication: bool = Field(
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description="Enable structured communication protocol",
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default=True
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)
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enable_hierarchical_evaluation: bool = Field(
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description="Enable hierarchical evaluation system",
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default=True
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)
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shared_memory: bool = Field(
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description="Enable shared memory between agents",
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default=True
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)
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evaluation_criteria: List[EvaluationCriterionSchema] = Field(
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description="Evaluation criteria for the system",
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default=[]
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)
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agents: List[AgentConfigSchema] = Field(
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description="Configuration for all agents",
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default=[]
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)
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quality_threshold: float = Field(
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description="Quality threshold for stopping refinement",
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default=8.0,
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ge=0.0,
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le=10.0
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)
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class TalkHierarchicalStateSchema(BaseModel):
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"""Schema for TalkHierarchical swarm state"""
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conversation_history: List[StructuredMessageSchema] = Field(
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description="History of structured messages",
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default=[]
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)
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intermediate_outputs: Dict[str, str] = Field(
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description="Intermediate outputs from agents",
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default={}
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)
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evaluation_results: List[EvaluationResultSchema] = Field(
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description="Results from evaluations",
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default=[]
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)
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current_loop: int = Field(
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description="Current refinement loop",
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default=0
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)
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task: str = Field(
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description="Current task being processed",
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default=""
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)
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final_result: Optional[str] = Field(
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description="Final result of the workflow",
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default=None
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)
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class TalkHierarchicalResponseSchema(BaseModel):
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"""Schema for TalkHierarchical swarm response"""
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final_result: str = Field(
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description="Final result of the workflow"
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)
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total_loops: int = Field(
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description="Total number of loops executed",
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ge=1
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)
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conversation_history: List[StructuredMessageSchema] = Field(
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description="Complete conversation history"
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)
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evaluation_results: List[EvaluationResultSchema] = Field(
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description="All evaluation results"
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)
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intermediate_outputs: Dict[str, str] = Field(
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description="All intermediate outputs"
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)
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evaluation_summary: Optional[EvaluationSummarySchema] = Field(
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description="Summary of evaluations",
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default=None
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)
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performance_metrics: Dict[str, Union[float, int, str]] = Field(
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description="Performance metrics",
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default={}
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)
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class GraphNodeSchema(BaseModel):
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"""Schema for graph nodes in agent orchestration"""
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node_id: str = Field(
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description="Unique identifier for the node",
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min_length=1
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)
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agent_name: str = Field(
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description="Name of the agent at this node",
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min_length=1
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)
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role: AgentRole = Field(
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description="Role of the agent"
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)
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capabilities: List[str] = Field(
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description="Capabilities of this node",
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default=[]
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)
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connections: List[str] = Field(
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description="IDs of connected nodes",
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default=[]
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)
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conditions: Dict[str, str] = Field(
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description="Conditions for routing to this node",
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default={}
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)
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class GraphSchema(BaseModel):
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"""Schema for agent orchestration graph"""
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nodes: List[GraphNodeSchema] = Field(
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description="Nodes in the graph",
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default=[]
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)
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start_node: str = Field(
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description="ID of the start node",
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min_length=1
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)
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end_nodes: List[str] = Field(
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description="IDs of end nodes",
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default=[]
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)
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routing_rules: Dict[str, str] = Field(
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description="Rules for routing between nodes",
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default={}
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)
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max_depth: int = Field(
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description="Maximum depth of the graph",
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default=10,
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ge=1
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)
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# Response schemas for different agent types
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class GeneratorResponseSchema(BaseModel):
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"""Schema for generator agent responses"""
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content: str = Field(
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description="Generated content",
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min_length=1
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)
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intermediate_output: str = Field(
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description="Intermediate output for next agent",
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default=""
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)
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reasoning: str = Field(
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description="Reasoning behind the generation",
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default=""
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)
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confidence: float = Field(
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description="Confidence in the generated content",
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ge=0.0,
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le=1.0
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)
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class EvaluatorResponseSchema(BaseModel):
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"""Schema for evaluator agent responses"""
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criterion: str = Field(
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description="Evaluation criterion",
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min_length=1
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)
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score: float = Field(
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description="Evaluation score",
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ge=0.0
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)
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feedback: str = Field(
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description="Detailed feedback",
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min_length=1
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)
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confidence: float = Field(
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description="Confidence in evaluation",
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ge=0.0,
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le=1.0
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)
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reasoning: str = Field(
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description="Reasoning behind the evaluation",
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default=""
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)
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suggestions: List[str] = Field(
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description="Suggestions for improvement",
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default=[]
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)
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class RefinerResponseSchema(BaseModel):
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"""Schema for refiner agent responses"""
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refined_content: str = Field(
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description="Refined content",
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min_length=1
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)
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changes_made: List[str] = Field(
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description="List of changes made",
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default=[]
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)
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reasoning: str = Field(
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description="Reasoning behind refinements",
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default=""
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)
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confidence: float = Field(
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description="Confidence in refinements",
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ge=0.0,
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le=1.0
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)
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feedback_addressed: List[str] = Field(
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description="Feedback points addressed",
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default=[]
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)
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