Customer Service

pip install praisonaiagents

export OPENAI_API_KEY=your_api_key_here

from praisonaiagents import Agent, Task, PraisonAIAgents
import time
from typing import Dict, List

def classify_query():
    """Simulates query classification"""
    query_types = [
        {"type": "technical", "priority": "high", "complexity": "complex"},
        {"type": "billing", "priority": "medium", "complexity": "simple"},
        {"type": "general", "priority": "low", "complexity": "simple"}
    ]
    return query_types[int(time.time()) % 3]

def handle_query(query: Dict):
    """Simulates query handling"""
    responses = {
        "technical": "Technical support solution provided",
        "billing": "Billing inquiry resolved",
        "general": "General information provided"
    }
    return responses.get(query["type"], "Query forwarded to specialist")

def evaluate_satisfaction():
    """Simulates satisfaction evaluation"""
    scores = ["satisfied", "neutral", "unsatisfied"]
    return scores[int(time.time()) % 3]

def optimize_response(satisfaction: str):
    """Simulates response optimization"""
    optimizations = {
        "satisfied": "maintain_approach",
        "neutral": "minor_adjustments",
        "unsatisfied": "major_revision"
    }
    return optimizations.get(satisfaction, "review_process")

# Create specialized agents
classifier = Agent(
    name="Query Classifier",
    role="Query Classification",
    goal="Classify incoming customer queries",
    instructions="Analyze and categorize customer queries",
    tools=[classify_query]
)

handler = Agent(
    name="Query Handler",
    role="Query Resolution",
    goal="Handle customer queries appropriately",
    instructions="Provide appropriate responses to queries",
    tools=[handle_query]
)

evaluator = Agent(
    name="Satisfaction Evaluator",
    role="Satisfaction Assessment",
    goal="Evaluate customer satisfaction",
    instructions="Assess response effectiveness",
    tools=[evaluate_satisfaction]
)

optimizer = Agent(
    name="Response Optimizer",
    role="Service Optimization",
    goal="Optimize service based on feedback",
    instructions="Improve response strategies",
    tools=[optimize_response]
)

# Create workflow tasks
classification_task = Task(
    name="classify_query",
    description="Classify customer query",
    expected_output="Query classification",
    agent=classifier,
    is_start=True,
    task_type="decision",
    condition={
        "high": ["handle_query", "evaluate_satisfaction"],
        "medium": ["handle_query", "evaluate_satisfaction"],
        "low": ["handle_query"]
    }
)

handling_task = Task(
    name="handle_query",
    description="Handle customer query",
    expected_output="Query response",
    agent=handler,
    next_tasks=["evaluate_satisfaction"]
)

evaluation_task = Task(
    name="evaluate_satisfaction",
    description="Evaluate customer satisfaction",
    expected_output="Satisfaction level",
    agent=evaluator,
    next_tasks=["optimize_response"]
)

optimization_task = Task(
    name="optimize_response",
    description="Optimize response strategy",
    expected_output="Optimization recommendations",
    agent=optimizer,
    task_type="decision",
    condition={
        "major_revision": ["classify_query"],
        "minor_adjustments": "",
        "maintain_approach": ""
    }
)

# Create workflow
workflow = PraisonAIAgents(
    agents=[classifier, handler, evaluator, optimizer],
    tasks=[classification_task, handling_task, evaluation_task, optimization_task],
    process="workflow",
    verbose=True
)

def main():
    print("\nStarting Customer Service Optimization Workflow...")
    print("=" * 50)
    
    # Run workflow
    results = workflow.start()
    
    # Print results
    print("\nCustomer Service Results:")
    print("=" * 50)
    for task_id, result in results["task_results"].items():
        if result:
            print(f"\nTask: {task_id}")
            print(f"Result: {result.raw}")
            print("-" * 50)

if __name__ == "__main__":
    main()

python app.py