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- Add EmailState model for Flow state management - Create EmailProcessingFlow class with event-based automation - Update tools and crews for Flow integration - Add comprehensive Flow tests - Implement error handling and state tracking - Add mock implementations for testing This implementation uses CrewAI Flow features to create an event-based email processing system that can analyze emails, research senders, and generate appropriate responses using specialized AI crews. Co-Authored-By: Joe Moura <joao@crewai.com>
114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
"""
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Email analysis crew implementation using CrewAI.
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Handles comprehensive email analysis including thread history and sender research.
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"""
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from crewai import Agent, Task, Crew, Process
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from typing import Dict, List, Optional
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from datetime import datetime
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from .gmail_tool import GmailTool
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class EmailAnalysisCrew:
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"""
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Crew for analyzing emails and determining response strategy.
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"""
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def __init__(self, gmail_tool: Optional[GmailTool] = None):
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"""Initialize analysis crew with required tools"""
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self.gmail_tool = gmail_tool or GmailTool()
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self._create_agents()
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def _create_agents(self):
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"""Create specialized agents for email analysis"""
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self.context_analyzer = Agent(
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role="Email Context Analyst",
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name="Context Analyzer",
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goal="Analyze email context and history",
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backstory="Expert at understanding email threads and communication patterns",
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tools=[self.gmail_tool],
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verbose=True
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)
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self.research_specialist = Agent(
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role="Research Specialist",
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name="Research Expert",
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goal="Research sender and company background",
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backstory="Skilled at gathering and analyzing business and personal information",
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tools=[self.gmail_tool],
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verbose=True
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)
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self.response_strategist = Agent(
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role="Response Strategist",
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name="Strategy Expert",
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goal="Determine optimal response approach",
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backstory="Expert at developing communication strategies",
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tools=[self.gmail_tool],
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verbose=True
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)
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def analyze_email(self,
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email: Dict,
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thread_history: List[Dict],
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sender_info: Dict) -> Dict:
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"""
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Analyze email with comprehensive context.
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Args:
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email: Current email data
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thread_history: Previous thread messages
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sender_info: Information about the sender
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Returns:
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Dict: Analysis results including response decision
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"""
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try:
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# Create analysis crew
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crew = Crew(
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agents=[
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self.context_analyzer,
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self.research_specialist,
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self.response_strategist
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],
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tasks=[
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Task(
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description="Analyze email context and thread history",
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agent=self.context_analyzer
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),
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Task(
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description="Research sender and company background",
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agent=self.research_specialist
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),
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Task(
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description="Determine response strategy",
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agent=self.response_strategist
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)
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],
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verbose=True
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)
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# Execute analysis
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results = crew.kickoff()
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# Process results
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return {
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"email_id": email["id"],
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"thread_id": email["thread_id"],
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"response_needed": results[-1].get("response_needed", False),
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"priority": results[-1].get("priority", "low"),
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"similar_threads": results[0].get("similar_threads", []),
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"sender_context": results[1].get("sender_context", {}),
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"company_info": results[1].get("company_info", {}),
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"response_strategy": results[-1].get("strategy", {})
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}
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except Exception as e:
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print(f"Analysis error: {str(e)}")
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return {
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"email_id": email.get("id", "unknown"),
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"thread_id": email.get("thread_id", "unknown"),
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"error": f"Analysis failed: {str(e)}",
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"response_needed": False,
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"priority": "error"
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}
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