📝 Session 9: Practical Coordination - ReAct Implementation & Communication¶

async def _execute_data_reasoning_step(
    self, context: str, step_num: int
) -> ReActStep:
    """Execute a single ReAct reasoning step for data processing"""

    # Generate thought based on current data context
    thought = await self._generate_data_processing_thought(context)

    # Determine action based on data pipeline requirements
    action_decision = await self._decide_next_data_action(thought, context)
    action_type = ActionType(action_decision['action'])
    action_input = action_decision['input']

    # Execute data action and get observation
    observation = await self._execute_data_action(action_type, action_input)

    # Calculate data quality confidence for this step
    data_quality_score = await self._calculate_data_quality_confidence(
        thought, action_type, observation
    )

    return ReActStep(
        step_number=step_num,
        thought=thought,
        action=action_type,
        action_input=action_input,
        observation=observation,
        data_quality_score=data_quality_score
    )

def _generate_data_processing_thought(self, context: str) -> str:
    """Generate systematic thought with data processing framework"""
    prompt = f"""
    Current data context: {context}
    Recent processing history: {self._get_recent_data_history_summary()}

    Think systematically about data processing:
    1. What do I understand about this data schema and quality?
    2. What data transformation gaps remain?
    3. What's the most productive next processing action?
    4. What data consistency risks should I consider?

    Provide clear reasoning for the next data processing step:
    """
    return await self.llm.generate(prompt)

class MetaDataReActAnalyzer:
    """Analyzes and improves data processing ReAct reasoning quality"""

    def __init__(self, llm_client):
        self.llm = llm_client

    async def analyze_data_reasoning_quality(
        self, reasoning_history: List[ReActStep]
    ) -> Dict[str, Any]:
        """Analyze data processing reasoning chain quality"""

        if len(reasoning_history) < 2:
            return {'quality_score': 0.5, 'issues': []}

        # Detect circular data processing patterns
        circular_analysis = await self._detect_circular_data_processing(reasoning_history)

        # Assess data transformation progress quality
        progress_analysis = await self._assess_data_progress_quality(reasoning_history)

        # Evaluate data quality confidence patterns
        quality_analysis = await self._analyze_data_quality_patterns(reasoning_history)

        return {
            'quality_score': self._calculate_overall_data_quality(
                circular_analysis, progress_analysis, quality_analysis
            ),
            'circular_processing': circular_analysis,
            'progress_quality': progress_analysis,
            'data_quality_patterns': quality_analysis,
            'recommendations': await self._generate_data_improvement_recommendations(
                reasoning_history
            )
        }

async def _detect_circular_data_processing(
    self, history: List[ReActStep]
) -> Dict[str, Any]:
    """Detect if agent is stuck in data processing loops"""
    recent_steps = history[-4:]  # Examine last 4 steps
    action_sequence = [step.action for step in recent_steps]

    # Check for repeated data processing action patterns
    if len(set(action_sequence)) <= 2 and len(action_sequence) >= 3:
        return {
            'has_circular_processing': True,
            'pattern': action_sequence,
            'severity': 'high'
        }

    return {'has_circular_processing': False}

class DataCommunicationHub:
    """Central coordination hub for multi-agent data processing communication"""

    def __init__(self):
        self.data_agents: Dict[str, 'BaseDataAgent'] = {}
        self.message_queue: List[DataAgentMessage] = []
        self.active_data_conversations: Dict[str, List[DataAgentMessage]] = {}
        self.data_lineage_tracking: Dict[str, Dict[str, Any]] = {}

    async def register_data_agent(self, agent: 'BaseDataAgent'):
        """Register data processing agent with communication hub"""
        self.data_agents[agent.agent_id] = agent
        await agent.set_data_communication_hub(self)

async def send_data_message(self, message: DataAgentMessage) -> bool:
    """Send data processing message with delivery confirmation and lineage tracking"""

    # Validate recipient exists
    if message.recipient_id not in self.data_agents:
        return False

    # Track data lineage for this message
    await self._track_data_lineage(message)

    # Add to conversation thread
    if message.conversation_id:
        if message.conversation_id not in self.active_data_conversations:
            self.active_data_conversations[message.conversation_id] = []
        self.active_data_conversations[message.conversation_id].append(message)

    # Deliver message
    recipient = self.data_agents[message.recipient_id]
    success = await recipient.receive_data_message(message)

    return success

class DataConsensusManager:
    """Basic consensus mechanisms for multi-agent data processing decisions"""

    def __init__(self, agents: List['BaseDataAgent'], threshold: float = 0.67):
        self.data_agents = agents
        self.consensus_threshold = threshold
        self.data_voting_history: List[Dict[str, Any]] = []

    async def data_schema_consensus(
        self, schema_proposal: str, data_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Schema validation consensus across data processing agents"""

        # Collect schema validation proposals from all agents
        proposals = await self._collect_schema_proposals(schema_proposal, data_context)

        # Conduct data validation voting round
        votes = await self._conduct_schema_voting_round(proposals, data_context)

        # Count votes and determine schema acceptance
        vote_counts = self._count_schema_votes(votes)
        winner = max(vote_counts.items(), key=lambda x: x[1])

        # Check if data consensus threshold met
        total_votes = sum(vote_counts.values())
        if winner[1] / total_votes >= self.consensus_threshold:
            return {
                'consensus_reached': True,
                'schema_decision': winner[0],
                'vote_counts': vote_counts,
                'data_confidence': winner[1] / total_votes
            }
        else:
            return {
                'consensus_reached': False,
                'vote_counts': vote_counts,
                'reason': 'Data validation threshold not met'
            }

async def _collect_schema_proposals(
    self, schema_proposal: str, data_context: Dict[str, Any]
) -> List[Dict[str, Any]]:
    """Collect initial schema proposals from all data agents"""
    proposal_tasks = []
    for agent in self.data_agents:
        task = self._get_agent_schema_proposal(agent, schema_proposal, data_context)
        proposal_tasks.append(task)

    proposals = await asyncio.gather(*proposal_tasks, return_exceptions=True)

    # Filter out failed schema proposals
    valid_proposals = []
    for i, proposal in enumerate(proposals):
        if not isinstance(proposal, Exception):
            valid_proposals.append({
                'agent_id': self.data_agents[i].agent_id,
                'schema_proposal': proposal,
                'data_quality_score': proposal.get('data_quality_score', 0.5),
                'timestamp': datetime.now()
            })

    return valid_proposals

class HierarchicalDataCoordinator:
    """Implements hierarchical multi-agent coordination patterns for data processing"""

    def __init__(self):
        self.coordinator_agents: Dict[str, 'DataCoordinatorAgent'] = {}
        self.worker_agents: Dict[str, 'DataWorkerAgent'] = {}
        self.data_delegation_rules: Dict[str, List[str]] = {}

    async def create_data_coordination_hierarchy(
        self, data_task: str, complexity_analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create hierarchical data processing coordination structure"""

        # Analyze data processing decomposition requirements
        decomposition = await self._analyze_data_task_decomposition(data_task, complexity_analysis)

        # Create coordinator for high-level data pipeline planning
        coordinator = await self._create_data_task_coordinator(decomposition)

        # Create workers for data processing execution
        workers = await self._create_data_worker_agents(decomposition)

        # Establish data processing delegation relationships
        delegation_map = await self._establish_data_delegation_hierarchy(
            coordinator, workers, decomposition
        )

        return {
            'data_coordinator': coordinator,
            'data_workers': workers,
            'delegation_map': delegation_map,
            'processing_depth': decomposition['required_levels']
        }

async def execute_hierarchical_data_task(
    self, data_task: str, hierarchy: Dict[str, Any]
) -> Dict[str, Any]:
    """Execute data processing task using hierarchical coordination"""

    # Phase 1: High-level data pipeline planning
    high_level_plan = await self._create_data_pipeline_plan(
        data_task, hierarchy['data_coordinator']
    )

    # Phase 2: Data processing delegation and parallel execution
    delegation_results = await self._execute_delegated_data_tasks(
        high_level_plan, hierarchy['delegation_map']
    )

    # Phase 3: Data result aggregation and validation
    final_result = await self._aggregate_hierarchical_data_results(
        delegation_results, hierarchy['data_coordinator']
    )

    return {
        'data_task': data_task,
        'processing_result': final_result,
        'execution_success': True
    }

class DataProcessingAuctionCoordinator:
    """Basic auction-based data processing task allocation"""

    def __init__(self, agents: List['BaseDataAgent']):
        self.data_agents = agents
        self.data_auction_history: List[Dict[str, Any]] = []

    async def conduct_data_processing_auction(
        self, data_task: str, processing_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Conduct sealed-bid auction for data processing task allocation"""

        # Phase 1: Assess agent data processing capabilities
        capability_assessments = await self._assess_data_processing_capabilities(
            data_task, processing_requirements
        )

        # Filter eligible data processing agents
        eligible_agents = [
            agent for agent, assessment in capability_assessments.items()
            if assessment['meets_data_requirements']
        ]

        if not eligible_agents:
            return {'success': False, 'reason': 'No eligible data processing agents'}

        # Phase 2: Collect data processing bids
        bids = await self._collect_data_processing_bids(data_task, eligible_agents, processing_requirements)

        # Phase 3: Select winner (best cost/performance ratio)
        winner = await self._select_data_auction_winner(bids, processing_requirements)

        if winner:
            return {
                'success': True,
                'winner': winner['agent_id'],
                'winning_bid': winner['bid'],
                'data_task': data_task,
                'expected_processing_time': winner['bid']['estimated_processing_time']
            }
        else:
            return {'success': False, 'reason': 'No valid data processing bids received'}

# Your task: Complete this ReAct implementation
class CSVDataReActAgent(DataProcessingReActAgent):
    async def process_csv_file(self, file_path: str) -> Dict[str, Any]:
        """Process CSV file using ReAct pattern"""
        # TODO: Implement ReAct reasoning for CSV processing
        # 1. Schema analysis step
        # 2. Data validation step
        # 3. Transformation planning step
        # 4. Processing execution step
        pass

# Your task: Implement data quality consensus
class DataQualityConsensusManager(DataConsensusManager):
    async def quality_threshold_consensus(
        self, dataset_name: str, proposed_thresholds: Dict[str, float]
    ) -> Dict[str, Any]:
        """Reach consensus on data quality thresholds"""
        # TODO: Implement consensus algorithm
        # 1. Collect agent assessments of proposed thresholds
        # 2. Conduct voting on threshold values
        # 3. Apply consensus rules and return decision
        pass

# Your task: Implement hierarchical ETL coordination
class ETLHierarchicalCoordinator(HierarchicalDataCoordinator):
    async def coordinate_etl_pipeline(
        self, pipeline_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Coordinate complex ETL pipeline using hierarchical agents"""
        # TODO: Implement hierarchical coordination
        # 1. Analyze ETL complexity and create hierarchy
        # 2. Delegate extraction, transformation, and loading tasks
        # 3. Monitor execution and aggregate results
        pass