from typing import Dict, Any, Optional
import asyncio
import structlog
from datetime import datetime
from agent_system.core.base_agent import BaseAgent, AgentMessage, MessageType
from app.core.models.mcp_protocol import BusinessTask
logger = structlog.get_logger()
class DataAnalystAgent(BaseAgent):
"""Specialized agent for data analysis tasks"""
def __init__(self, agent_id: str, config: Dict[str, Any] = None):
super().__init__(
agent_id=agent_id,
name="Data Analyst",
agent_type="data_analyst",
capabilities=[
"data_processing",
"statistical_analysis",
"data_visualization",
"pattern_recognition",
"trend_analysis"
],
config=config
)
self.analysis_methods = [
"descriptive_statistics",
"correlation_analysis",
"regression_analysis",
"time_series_analysis",
"clustering",
"classification"
]
async def process_task(self, task: BusinessTask) -> Dict[str, Any]:
"""Process data analysis task"""
logger.info(
"Processing data analysis task",
agent_id=self.id,
task_id=task.id,
task_title=task.title
)
try:
# Simulate data processing
await asyncio.sleep(2.0) # Simulate processing time
# Analyze input data
input_data = task.input_data or {}
analysis_type = input_data.get("analysis_type", "descriptive_statistics")
# Perform analysis
result = await self._perform_analysis(input_data, analysis_type)
# Generate insights
insights = await self._generate_insights(result, analysis_type)
# Create recommendations
recommendations = await self._generate_recommendations(result, insights)
return {
"analysis_type": analysis_type,
"result": result,
"insights": insights,
"recommendations": recommendations,
"metadata": {
"processed_at": datetime.utcnow().isoformat(),
"data_points_processed": input_data.get("data_points", 1000),
"analysis_duration": 2.0
}
}
except Exception as e:
logger.error(
"Data analysis task failed",
agent_id=self.id,
task_id=task.id,
error=str(e)
)
raise
async def handle_message(self, message: AgentMessage) -> Optional[AgentMessage]:
"""Handle incoming messages"""
if message.message_type == MessageType.STATUS_UPDATE:
# Handle status updates from other agents
return None
elif message.message_type == MessageType.ERROR:
# Handle error messages
logger.warning(
"Received error message",
agent_id=self.id,
sender_id=message.sender_id,
error=message.payload.get("error")
)
return None
return None
async def _perform_analysis(self, data: Dict[str, Any], analysis_type: str) -> Dict[str, Any]:
"""Perform the actual data analysis"""
if analysis_type == "descriptive_statistics":
return await self._descriptive_statistics(data)
elif analysis_type == "correlation_analysis":
return await self._correlation_analysis(data)
elif analysis_type == "regression_analysis":
return await self._regression_analysis(data)
elif analysis_type == "time_series_analysis":
return await self._time_series_analysis(data)
elif analysis_type == "clustering":
return await self._clustering_analysis(data)
else:
return await self._descriptive_statistics(data)
async def _descriptive_statistics(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Perform descriptive statistics analysis"""
await asyncio.sleep(0.5) # Simulate computation
return {
"statistics": {
"mean": 45.7,
"median": 44.2,
"mode": 43.0,
"std_dev": 12.3,
"variance": 151.3,
"min": 15.2,
"max": 78.9,
"quartiles": {
"q1": 35.1,
"q2": 44.2,
"q3": 56.8
}
},
"data_quality": {
"completeness": 0.95,
"accuracy": 0.92,
"consistency": 0.88
}
}
async def _correlation_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Perform correlation analysis"""
await asyncio.sleep(0.8) # Simulate computation
return {
"correlation_matrix": {
"feature_a": {"feature_a": 1.0, "feature_b": 0.73, "feature_c": -0.21},
"feature_b": {"feature_a": 0.73, "feature_b": 1.0, "feature_c": 0.45},
"feature_c": {"feature_a": -0.21, "feature_b": 0.45, "feature_c": 1.0}
},
"significant_correlations": [
{"feature_1": "feature_a", "feature_2": "feature_b", "correlation": 0.73, "p_value": 0.001}
]
}
async def _regression_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Perform regression analysis"""
await asyncio.sleep(1.0) # Simulate computation
return {
"model": {
"type": "linear_regression",
"coefficients": {
"intercept": 12.5,
"feature_1": 0.87,
"feature_2": -0.34
},
"r_squared": 0.73,
"adjusted_r_squared": 0.71
},
"significance": {
"f_statistic": 45.2,
"p_value": 0.0001
}
}
async def _time_series_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Perform time series analysis"""
await asyncio.sleep(1.2) # Simulate computation
return {
"trend": "increasing",
"seasonality": "monthly",
"forecast": {
"next_period": 52.3,
"confidence_interval": [48.1, 56.5]
},
"decomposition": {
"trend_strength": 0.82,
"seasonal_strength": 0.65,
"noise_level": 0.23
}
}
async def _clustering_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Perform clustering analysis"""
await asyncio.sleep(0.9) # Simulate computation
return {
"clusters": [
{
"id": 1,
"size": 145,
"centroid": [45.2, 32.1, 28.7],
"characteristics": ["high_value", "frequent_buyer"]
},
{
"id": 2,
"size": 89,
"centroid": [23.4, 18.9, 41.2],
"characteristics": ["medium_value", "seasonal_buyer"]
}
],
"optimal_clusters": 3,
"silhouette_score": 0.67
}
async def _generate_insights(
self,
result: Dict[str, Any],
analysis_type: str
) -> list:
"""Generate insights from analysis results"""
insights = []
if analysis_type == "descriptive_statistics":
insights.extend([
"Data shows normal distribution with slight positive skew",
"Outliers detected in upper quartile (5% of data)",
"High data completeness suggests reliable analysis"
])
elif analysis_type == "correlation_analysis":
insights.extend([
"Strong positive correlation between feature_a and feature_b",
"Weak negative correlation between feature_a and feature_c",
"Multicollinearity should be considered in modeling"
])
elif analysis_type == "regression_analysis":
insights.extend([
"Model explains 73% of variance in target variable",
"feature_1 is the strongest predictor",
"Model assumptions are reasonably met"
])
elif analysis_type == "time_series_analysis":
insights.extend([
"Clear upward trend detected over the period",
"Monthly seasonality pattern identified",
"Forecast confidence is moderate (±15%)"
])
return insights
async def _generate_recommendations(
self,
result: Dict[str, Any],
insights: list
) -> list:
"""Generate recommendations based on analysis"""
recommendations = [
"Consider collecting more data points for improved statistical power",
"Implement data quality checks to maintain high completeness",
"Monitor outliers and investigate root causes"
]
# Add specific recommendations based on analysis type
if "correlation_matrix" in result:
recommendations.append(
"Explore causal relationships between strongly correlated variables"
)
if "forecast" in result:
recommendations.append(
"Use forecast for planning and resource allocation"
)
return recommendations
