"""
Pattern 173: Telemetry Pattern with LangChain/LangGraph

Purpose: Collect detailed operational data for analysis and optimization with LLM insights.
"""

from typing import Dict, Any, List, Optional, TypedDict
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from collections import defaultdict
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import statistics
import uuid


class EventType(Enum):
    """Telemetry event types."""
    USER_ACTION = "user_action"
    SYSTEM_EVENT = "system_event"
    ERROR = "error"
    PERFORMANCE = "performance"
    BUSINESS = "business"


class TraceLevel(Enum):
    """Trace detail levels."""
    CRITICAL = "critical"
    ERROR = "error"
    WARNING = "warning"
    INFO = "info"
    DEBUG = "debug"


class AggregationPeriod(Enum):
    """Data aggregation periods."""
    MINUTE = "minute"
    HOUR = "hour"
    DAY = "day"
    WEEK = "week"


@dataclass
class TelemetryEvent:
    """Telemetry event."""
    event_id: str
    event_type: EventType
    agent_id: str
    properties: Dict[str, Any]
    severity: TraceLevel = TraceLevel.INFO
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Span:
    """Distributed tracing span."""
    span_id: str
    parent_id: Optional[str]
    operation: str
    start_time: datetime
    end_time: Optional[datetime] = None
    tags: Dict[str, Any] = field(default_factory=dict)
    status: str = "in_progress"
    error: Optional[str] = None
    
    def duration_ms(self) -> float:
        """Get span duration in milliseconds."""
        if self.end_time:
            return (self.end_time - self.start_time).total_seconds() * 1000
        return 0.0


@dataclass
class MetricPoint:
    """Custom metric data point."""
    metric_name: str
    value: float
    dimensions: Dict[str, str]
    unit: str = ""
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Trace:
    """Distributed trace containing multiple spans."""
    trace_id: str
    root_span_id: str
    spans: List[Span]
    start_time: datetime
    end_time: Optional[datetime] = None
    service_name: str = ""


@dataclass
class AggregatedMetrics:
    """Aggregated metrics summary."""
    period: AggregationPeriod
    start_time: datetime
    end_time: datetime
    metrics: Dict[str, Dict[str, float]]
    event_counts: Dict[EventType, int]
    error_rate: float


class TelemetryState(TypedDict):
    """State for telemetry analysis workflow."""
    events: List[Dict[str, Any]]
    traces: List[Dict[str, Any]]
    metrics: Dict[str, Any]
    insights: str
    recommendations: List[str]
    anomalies: List[str]


class TelemetryCollector:
    """
    Advanced telemetry collector with LangChain/LangGraph integration.
    """
    
    def __init__(self, model_name: str = "gpt-4", temperature: float = 0.3):
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
        self.events: List[TelemetryEvent] = []
        self.spans: Dict[str, Span] = {}
        self.traces: Dict[str, Trace] = {}
        self.metrics: Dict[str, List[MetricPoint]] = defaultdict(list)
        self.active_spans: Dict[str, Span] = {}
        self.workflow = self._build_telemetry_workflow()
        
        # Statistics
        self.stats = {
            "total_events": 0,
            "total_spans": 0,
            "total_traces": 0,
            "total_metrics": 0,
            "events_by_type": {event_type: 0 for event_type in EventType},
            "errors_tracked": 0,
            "avg_span_duration_ms": 0.0
        }
    
    def _build_telemetry_workflow(self) -> StateGraph:
        """Build LangGraph workflow for telemetry analysis."""
        workflow = StateGraph(TelemetryState)
        
        # Add nodes
        workflow.add_node("collect_data", self._collect_data_node)
        workflow.add_node("analyze_patterns", self._analyze_patterns_node)
        workflow.add_node("detect_anomalies", self._detect_anomalies_node)
        workflow.add_node("generate_insights", self._generate_insights_node)
        workflow.add_node("provide_recommendations", self._provide_recommendations_node)
        
        # Define edges
        workflow.set_entry_point("collect_data")
        workflow.add_edge("collect_data", "analyze_patterns")
        workflow.add_edge("analyze_patterns", "detect_anomalies")
        workflow.add_edge("detect_anomalies", "generate_insights")
        workflow.add_edge("generate_insights", "provide_recommendations")
        workflow.add_edge("provide_recommendations", END)
        
        return workflow.compile()
    
    def _collect_data_node(self, state: TelemetryState) -> TelemetryState:
        """Collect and organize telemetry data."""
        # Get recent events
        recent_events = self.events[-50:]
        state["events"] = [
            {
                "event_id": e.event_id,
                "type": e.event_type.value,
                "agent_id": e.agent_id,
                "severity": e.severity.value,
                "timestamp": e.timestamp.isoformat()
            }
            for e in recent_events
        ]
        
        # Get recent traces
        recent_traces = list(self.traces.values())[-20:]
        state["traces"] = [
            {
                "trace_id": t.trace_id,
                "spans": len(t.spans),
                "duration_ms": sum(s.duration_ms() for s in t.spans),
                "service": t.service_name
            }
            for t in recent_traces
        ]
        
        # Get metrics summary
        metrics_summary = {}
        for metric_name, points in self.metrics.items():
            recent_points = points[-20:]
            if recent_points:
                metrics_summary[metric_name] = {
                    "count": len(recent_points),
                    "avg": statistics.mean(p.value for p in recent_points),
                    "min": min(p.value for p in recent_points),
                    "max": max(p.value for p in recent_points)
                }
        
        state["metrics"] = metrics_summary
        return state
    
    def _analyze_patterns_node(self, state: TelemetryState) -> TelemetryState:
        """Analyze telemetry patterns using LLM."""
        events = state.get("events", [])
        traces = state.get("traces", [])
        metrics = state.get("metrics", {})
        
        # Use LLM to analyze patterns
        prompt = ChatPromptTemplate.from_template(
            "Analyze the following telemetry data and identify patterns:\n"
            "Recent Events: {events_summary}\n"
            "Trace Data: {traces_summary}\n"
            "Metrics: {metrics_summary}\n\n"
            "Identify key patterns, trends, and noteworthy observations."
        )
        
        chain = prompt | self.llm
        response = chain.invoke({
            "events_summary": f"{len(events)} events tracked",
            "traces_summary": f"{len(traces)} traces with avg {statistics.mean([t['duration_ms'] for t in traces]):.2f}ms" if traces else "No traces",
            "metrics_summary": str(metrics)
        })
        
        state["insights"] = str(response.content)
        return state
    
    def _detect_anomalies_node(self, state: TelemetryState) -> TelemetryState:
        """Detect anomalies in telemetry data."""
        anomalies = []
        
        # Detect error spikes
        recent_events = state.get("events", [])
        error_events = [e for e in recent_events if e.get("type") == EventType.ERROR.value]
        if len(error_events) > len(recent_events) * 0.1:
            anomalies.append(f"High error rate: {len(error_events)}/{len(recent_events)} events are errors")
        
        # Detect slow traces
        traces = state.get("traces", [])
        if traces:
            durations = [t["duration_ms"] for t in traces]
            avg_duration = statistics.mean(durations)
            stdev_duration = statistics.stdev(durations) if len(durations) > 1 else 0
            
            slow_traces = [t for t in traces if t["duration_ms"] > avg_duration + 2 * stdev_duration]
            if slow_traces:
                anomalies.append(f"Detected {len(slow_traces)} slow traces (>2 std dev)")
        
        # Detect metric anomalies
        metrics = state.get("metrics", {})
        for metric_name, stats in metrics.items():
            if stats["max"] > stats["avg"] * 3:
                anomalies.append(f"Metric '{metric_name}' has outlier: max={stats['max']:.2f}, avg={stats['avg']:.2f}")
        
        state["anomalies"] = anomalies
        return state
    
    def _generate_insights_node(self, state: TelemetryState) -> TelemetryState:
        """Generate insights from telemetry data."""
        insights = state.get("insights", "")
        anomalies = state.get("anomalies", [])
        
        if anomalies:
            # Use LLM to interpret anomalies
            prompt = ChatPromptTemplate.from_template(
                "Based on the following telemetry anomalies, provide detailed insights:\n"
                "Anomalies: {anomalies}\n"
                "Context: {insights}\n\n"
                "Provide actionable insights about what these anomalies might indicate."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "anomalies": "\n".join(anomalies),
                "insights": insights
            })
            
            state["insights"] = str(response.content)
        
        return state
    
    def _provide_recommendations_node(self, state: TelemetryState) -> TelemetryState:
        """Provide recommendations based on telemetry analysis."""
        insights = state.get("insights", "")
        anomalies = state.get("anomalies", [])
        
        recommendations = []
        
        if anomalies or insights:
            # Use LLM to generate recommendations
            prompt = ChatPromptTemplate.from_template(
                "Based on the telemetry analysis, provide 3-5 specific recommendations:\n"
                "Insights: {insights}\n"
                "Anomalies: {anomalies}\n\n"
                "Provide actionable recommendations for improvement."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "insights": insights,
                "anomalies": "\n".join(anomalies) if anomalies else "No anomalies detected"
            })
            
            # Parse recommendations
            recommendations = [
                line.strip()
                for line in str(response.content).split('\n')
                if line.strip() and len(line.strip()) > 10
            ][:5]
        
        state["recommendations"] = recommendations
        return state
    
    def track_event(
        self,
        event_type: EventType,
        agent_id: str,
        properties: Dict[str, Any],
        severity: TraceLevel = TraceLevel.INFO
    ) -> TelemetryEvent:
        """Track a telemetry event."""
        event = TelemetryEvent(
            event_id=str(uuid.uuid4()),
            event_type=event_type,
            agent_id=agent_id,
            properties=properties,
            severity=severity
        )
        
        self.events.append(event)
        self.stats["total_events"] += 1
        self.stats["events_by_type"][event_type] += 1
        
        if event_type == EventType.ERROR:
            self.stats["errors_tracked"] += 1
        
        return event
    
    def start_span(
        self,
        operation: str,
        parent_id: Optional[str] = None,
        trace_id: Optional[str] = None,
        tags: Optional[Dict[str, Any]] = None
    ) -> Span:
        """Start a new tracing span."""
        span = Span(
            span_id=str(uuid.uuid4()),
            parent_id=parent_id,
            operation=operation,
            start_time=datetime.now(),
            tags=tags or {}
        )
        
        self.spans[span.span_id] = span
        self.active_spans[span.span_id] = span
        self.stats["total_spans"] += 1
        
        # Create or update trace
        if trace_id:
            if trace_id in self.traces:
                self.traces[trace_id].spans.append(span)
            else:
                self.traces[trace_id] = Trace(
                    trace_id=trace_id,
                    root_span_id=span.span_id,
                    spans=[span],
                    start_time=span.start_time
                )
                self.stats["total_traces"] += 1
        
        return span
    
    def end_span(self, span_id: str, status: str = "success", error: Optional[str] = None):
        """End a tracing span."""
        if span_id in self.active_spans:
            span = self.active_spans[span_id]
            span.end_time = datetime.now()
            span.status = status
            span.error = error
            
            # Update average duration
            total_durations = sum(s.duration_ms() for s in self.spans.values() if s.end_time)
            completed_spans = sum(1 for s in self.spans.values() if s.end_time)
            if completed_spans > 0:
                self.stats["avg_span_duration_ms"] = total_durations / completed_spans
            
            del self.active_spans[span_id]
    
    def record_metric(
        self,
        metric_name: str,
        value: float,
        dimensions: Optional[Dict[str, str]] = None,
        unit: str = ""
    ):
        """Record a custom metric."""
        metric = MetricPoint(
            metric_name=metric_name,
            value=value,
            dimensions=dimensions or {},
            unit=unit
        )
        
        self.metrics[metric_name].append(metric)
        self.stats["total_metrics"] += 1
    
    def get_aggregated_metrics(
        self,
        period: AggregationPeriod,
        start_time: Optional[datetime] = None,
        end_time: Optional[datetime] = None
    ) -> AggregatedMetrics:
        """Get aggregated metrics for a period."""
        if not end_time:
            end_time = datetime.now()
        if not start_time:
            # Default to last hour
            start_time = end_time - timedelta(hours=1)
        
        # Filter events in time range
        period_events = [
            e for e in self.events
            if start_time <= e.timestamp  0 else 0.0
        
        # Aggregate metrics
        metrics_summary = {}
        for metric_name, points in self.metrics.items():
            period_points = [
                p for p in points
                if start_time <= p.timestamp  1 else 0.0
                }
        
        return AggregatedMetrics(
            period=period,
            start_time=start_time,
            end_time=end_time,
            metrics=metrics_summary,
            event_counts=event_counts,
            error_rate=error_rate
        )
    
    def analyze_telemetry(self) -> Dict[str, Any]:
        """Analyze telemetry data using workflow."""
        initial_state: TelemetryState = {
            "events": [],
            "traces": [],
            "metrics": {},
            "insights": "",
            "recommendations": [],
            "anomalies": []
        }
        
        # Run workflow
        result = self.workflow.invoke(initial_state)
        
        return {
            "insights": result["insights"],
            "anomalies": result["anomalies"],
            "recommendations": result["recommendations"],
            "events_analyzed": len(result["events"]),
            "traces_analyzed": len(result["traces"])
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get telemetry statistics."""
        return {
            "total_events": self.stats["total_events"],
            "total_spans": self.stats["total_spans"],
            "total_traces": self.stats["total_traces"],
            "total_metrics": self.stats["total_metrics"],
            "events_by_type": {
                event_type.value: count
                for event_type, count in self.stats["events_by_type"].items()
            },
            "errors_tracked": self.stats["errors_tracked"],
            "avg_span_duration_ms": round(self.stats["avg_span_duration_ms"], 2),
            "active_spans": len(self.active_spans)
        }


class TelemetryPattern:
    """
    Comprehensive telemetry pattern coordinator with LangChain/LangGraph.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.collector = TelemetryCollector(model_name=model_name)
    
    def track(
        self,
        event_type: EventType,
        agent_id: str,
        properties: Dict[str, Any]
    ) -> str:
        """Track an event."""
        event = self.collector.track_event(event_type, agent_id, properties)
        return event.event_id
    
    def trace_operation(
        self,
        operation: str,
        tags: Optional[Dict[str, Any]] = None
    ) -> Span:
        """Start tracing an operation."""
        return self.collector.start_span(operation, tags=tags)
    
    def complete_trace(self, span: Span, success: bool = True):
        """Complete a trace."""
        status = "success" if success else "error"
        self.collector.end_span(span.span_id, status)
    
    def measure(
        self,
        metric_name: str,
        value: float,
        dimensions: Optional[Dict[str, str]] = None
    ):
        """Measure a metric."""
        self.collector.record_metric(metric_name, value, dimensions)
    
    def analyze(self) -> Dict[str, Any]:
        """Analyze telemetry data."""
        return self.collector.analyze_telemetry()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get telemetry statistics."""
        return self.collector.get_statistics()



if __name__ == "__main__":
    print("=== Telemetry Pattern with LangChain/LangGraph Demo ===\n")
    
    # Note: This demo shows the structure. In production, you'd need:
    # - export OPENAI_API_KEY="your-key-here"
    # - Actual LLM calls would be made
    
    print("Scenario 1: Event Tracking")
    print("-" * 50)
    telemetry = TelemetryPattern(model_name="gpt-4")
    
    # Track various events
    events = [
        (EventType.USER_ACTION, "user-123", {"action": "login", "success": True}),
        (EventType.SYSTEM_EVENT, "api-server", {"event": "startup", "version": "1.2.3"}),
        (EventType.PERFORMANCE, "database", {"query_time_ms": 45.2, "query": "SELECT"}),
        (EventType.ERROR, "payment-service", {"error": "timeout", "retry_count": 3}),
        (EventType.BUSINESS, "checkout", {"order_id": "ORD-123", "amount": 99.99}),
    ]
    
    print("Tracking events:")
    for event_type, agent_id, properties in events:
        event_id = telemetry.track(event_type, agent_id, properties)
        print(f"  ✓ Tracked {event_type.value} event {event_id[:8]}...")
    
    print("\nScenario 2: Distributed Tracing")
    print("-" * 50)
    
    # Trace a complex operation
    print("Tracing order processing:")
    order_span = telemetry.trace_operation("process_order", tags={"order_id": "ORD-456"})
    print(f"  Started trace: {order_span.span_id[:8]}...")
    
    # Simulate child operations
    validate_span = telemetry.collector.start_span(
        "validate_payment",
        parent_id=order_span.span_id,
        trace_id=order_span.span_id
    )
    telemetry.collector.end_span(validate_span.span_id, "success")
    print(f"  Completed validation: {validate_span.duration_ms():.2f}ms")
    
    inventory_span = telemetry.collector.start_span(
        "check_inventory",
        parent_id=order_span.span_id,
        trace_id=order_span.span_id
    )
    telemetry.collector.end_span(inventory_span.span_id, "success")
    print(f"  Completed inventory check: {inventory_span.duration_ms():.2f}ms")
    
    telemetry.complete_trace(order_span, success=True)
    print(f"  Total order processing: {order_span.duration_ms():.2f}ms")
    
    print("\nScenario 3: Custom Metrics")
    print("-" * 50)
    
    # Record various metrics
    metrics_data = [
        ("cpu_usage", 65.5, {"host": "server-1", "service": "api"}),
        ("memory_mb", 2048.0, {"host": "server-1", "service": "api"}),
        ("request_count", 1543.0, {"endpoint": "/api/users", "status": "200"}),
        ("response_time_ms", 125.3, {"endpoint": "/api/orders", "status": "200"}),
        ("error_rate", 2.5, {"service": "payment", "type": "timeout"}),
    ]
    
    print("Recording metrics:")
    for metric_name, value, dimensions in metrics_data:
        telemetry.measure(metric_name, value, dimensions)
        print(f"  ✓ {metric_name}: {value} {dimensions}")
    
    print("\nScenario 4: Telemetry Analysis")
    print("-" * 50)
    
    print("Analyzing telemetry data...")
    analysis = telemetry.analyze()
    
    print(f"\nInsights:")
    print(f"  {analysis['insights'][:200]}...")
    
    print(f"\nAnomalies Detected: {len(analysis['anomalies'])}")
    for anomaly in analysis['anomalies']:
        print(f"  ⚠️ {anomaly}")
    
    print(f"\nRecommendations:")
    for i, recommendation in enumerate(analysis['recommendations'][:3], 1):
        print(f"  {i}. {recommendation[:100]}...")
    
    print(f"\nEvents Analyzed: {analysis['events_analyzed']}")
    print(f"Traces Analyzed: {analysis['traces_analyzed']}")
    
    print("\nScenario 5: Aggregated Metrics")
    print("-" * 50)
    
    aggregated = telemetry.collector.get_aggregated_metrics(
        AggregationPeriod.HOUR,
        start_time=datetime.now() - timedelta(hours=1)
    )
    
    print(f"Period: {aggregated.period.value}")
    print(f"Error Rate: {aggregated.error_rate:.2f}%")
    
    print("\nEvent Counts:")
    for event_type, count in aggregated.event_counts.items():
        if count > 0:
            print(f"  {event_type.value}: {count}")
    
    print("\nMetric Aggregations:")
    for metric_name, stats in list(aggregated.metrics.items())[:3]:
        print(f"  {metric_name}:")
        print(f"    avg={stats['avg']:.2f}, min={stats['min']:.2f}, max={stats['max']:.2f}")
    
    print("\nScenario 6: Statistics")
    print("-" * 50)
    
    stats = telemetry.get_statistics()
    print(f"Total Events: {stats['total_events']}")
    print(f"Total Spans: {stats['total_spans']}")
    print(f"Total Traces: {stats['total_traces']}")
    print(f"Total Metrics: {stats['total_metrics']}")
    print(f"Errors Tracked: {stats['errors_tracked']}")
    print(f"Avg Span Duration: {stats['avg_span_duration_ms']:.2f}ms")
    
    print("\nEvents by Type:")
    for event_type, count in stats['events_by_type'].items():
        if count > 0:
            print(f"  {event_type}: {count}")
    
    print("\n" + "=" * 50)
    print("Telemetry pattern demonstration complete!")
    print("=" * 50)

    

Agentic Architecture Pattern: Telemetry

Pattern 173: 173 Telemetry – Line-by-Line Explanation

Overview

Pattern 173: Telemetry Pattern with LangChain/LangGraph

Purpose: Collect detailed operational data for analysis and optimization with LLM insights.

Code Structure Analysis

File Information

• Pattern Number: 173
• Pattern Name: 173 Telemetry
• File Path: patterns\quality_performance\173_telemetry.py
• Total Lines: 687

Detailed Code Explanation

Module Docstring

"""
Pattern 173: Telemetry Pattern with LangChain/LangGraph

Purpose: Collect detailed operational data for analysis and optimization with LLM insights.
"""

Explanation: This module docstring describes the purpose and key features of the 173 Telemetry pattern.

Imports Section

from typing import Dict, Any, List, Optional, TypedDict
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from collections import defaultdict
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import statistics
import uuid  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, TypedDict: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime, timedelta: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration

Enumerations

EventType (Line 19)

class EventType(Enum):
    USER_ACTION
    SYSTEM_EVENT
    ERROR
    PERFORMANCE
    BUSINESS

Purpose: Telemetry event types.

Values: – USER_ACTION: Represents user action – SYSTEM_EVENT: Represents system event – ERROR: Error severity level – PERFORMANCE: Represents performance – BUSINESS: Represents business

TraceLevel (Line 28)

class TraceLevel(Enum):
    CRITICAL
    ERROR
    WARNING
    INFO
    DEBUG

Purpose: Trace detail levels.

Values: – CRITICAL: Represents critical – ERROR: Error severity level – WARNING: Warning severity level – INFO: Informational severity level – DEBUG: Represents debug

AggregationPeriod (Line 37)

class AggregationPeriod(Enum):
    MINUTE
    HOUR
    DAY
    WEEK

Purpose: Data aggregation periods.

Values: – MINUTE: Represents minute – HOUR: Represents hour – DAY: Represents day – WEEK: Represents week

Classes

EventType (Line 19)

Description: Telemetry event types.

TraceLevel (Line 28)

Description: Trace detail levels.

AggregationPeriod (Line 37)

Description: Data aggregation periods.

TelemetryEvent (Line 46)

Description: Telemetry event.

Span (Line 57)

Description: Distributed tracing span.

Methods:

duration_ms (Line 68)

def duration_ms(self):
    """Get span duration in milliseconds."""

Purpose: Get span duration in milliseconds.

Parameters: – self: Instance reference

MetricPoint (Line 76)

Description: Custom metric data point.

Trace (Line 86)

Description: Distributed trace containing multiple spans.

AggregatedMetrics (Line 97)

Description: Aggregated metrics summary.

TelemetryState (Line 107)

Description: State for telemetry analysis workflow.

TelemetryCollector (Line 117)

Description: Advanced telemetry collector with LangChain/LangGraph integration.

Methods:

__init__ (Line 122)

def __init__(self, model_name, temperature):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”) – temperature: Randomness in LLM responses (0.0-1.0)

_build_telemetry_workflow (Line 142)

def _build_telemetry_workflow(self):
    """Build LangGraph workflow for telemetry analysis."""

Purpose: Build LangGraph workflow for telemetry analysis.

Parameters: – self: Instance reference

_collect_data_node (Line 163)

def _collect_data_node(self, state):
    """Collect and organize telemetry data."""

Purpose: Collect and organize telemetry data.

Parameters: – self: Instance reference – state: Current workflow state

_analyze_patterns_node (Line 205)

def _analyze_patterns_node(self, state):
    """Analyze telemetry patterns using LLM."""

Purpose: Analyze telemetry patterns using LLM.

Parameters: – self: Instance reference – state: Current workflow state

_detect_anomalies_node (Line 230)

def _detect_anomalies_node(self, state):
    """Detect anomalies in telemetry data."""

Purpose: Detect anomalies in telemetry data.

Parameters: – self: Instance reference – state: Current workflow state

_generate_insights_node (Line 260)

def _generate_insights_node(self, state):
    """Generate insights from telemetry data."""

Purpose: Generate insights from telemetry data.

Parameters: – self: Instance reference – state: Current workflow state

_provide_recommendations_node (Line 284)

def _provide_recommendations_node(self, state):
    """Provide recommendations based on telemetry analysis."""

Purpose: Provide recommendations based on telemetry analysis.

Parameters: – self: Instance reference – state: Current workflow state

track_event (Line 316)

def track_event(self, event_type, agent_id, properties, severity):
    """Track a telemetry event."""

Purpose: Track a telemetry event.

Parameters: – self: Instance reference – event_type: Parameter for event_type – agent_id: Parameter for agent_id – properties: Parameter for properties – severity: Parameter for severity

start_span (Line 341)

def start_span(self, operation, parent_id, trace_id, tags):
    """Start a new tracing span."""

Purpose: Start a new tracing span.

Parameters: – self: Instance reference – operation: Parameter for operation – parent_id: Parameter for parent_id – trace_id: Parameter for trace_id – tags: Parameter for tags

end_span (Line 376)

def end_span(self, span_id, status, error):
    """End a tracing span."""

Purpose: End a tracing span.

Parameters: – self: Instance reference – span_id: Parameter for span_id – status: Parameter for status – error: Parameter for error

record_metric (Line 392)

def record_metric(self, metric_name, value, dimensions, unit):
    """Record a custom metric."""

Purpose: Record a custom metric.

Parameters: – self: Instance reference – metric_name: Parameter for metric_name – value: Parameter for value – dimensions: Parameter for dimensions – unit: Parameter for unit

get_aggregated_metrics (Line 410)

def get_aggregated_metrics(self, period, start_time, end_time):
    """Get aggregated metrics for a period."""

Purpose: Get aggregated metrics for a period.

Parameters: – self: Instance reference – period: Parameter for period – start_time: Parameter for start_time – end_time: Parameter for end_time

analyze_telemetry (Line 468)

def analyze_telemetry(self):
    """Analyze telemetry data using workflow."""

Purpose: Analyze telemetry data using workflow.

Parameters: – self: Instance reference

get_statistics (Line 490)

def get_statistics(self):
    """Get telemetry statistics."""

Purpose: Get telemetry statistics.

Parameters: – self: Instance reference

TelemetryPattern (Line 507)

Description: Comprehensive telemetry pattern coordinator with LangChain/LangGraph.

Methods:

__init__ (Line 512)

def __init__(self, model_name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

track (Line 515)

def track(self, event_type, agent_id, properties):
    """Track an event."""

Purpose: Track an event.

Parameters: – self: Instance reference – event_type: Parameter for event_type – agent_id: Parameter for agent_id – properties: Parameter for properties

trace_operation (Line 525)

def trace_operation(self, operation, tags):
    """Start tracing an operation."""

Purpose: Start tracing an operation.

Parameters: – self: Instance reference – operation: Parameter for operation – tags: Parameter for tags

complete_trace (Line 533)

def complete_trace(self, span, success):
    """Complete a trace."""

Purpose: Complete a trace.

Parameters: – self: Instance reference – span: Parameter for span – success: Parameter for success

measure (Line 538)

def measure(self, metric_name, value, dimensions):
    """Measure a metric."""

Purpose: Measure a metric.

Parameters: – self: Instance reference – metric_name: Parameter for metric_name – value: Parameter for value – dimensions: Parameter for dimensions

analyze (Line 547)

def analyze(self):
    """Analyze telemetry data."""

Purpose: Analyze telemetry data.

Parameters: – self: Instance reference

get_statistics (Line 551)

def get_statistics(self):
    """Get telemetry statistics."""

Purpose: Get telemetry statistics.

Parameters: – self: Instance reference

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = EventType(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities