ניתוח של תמיכת לקוחות באמצעות Gemini ו-CrewAI

pip install "crewai[tools]"

import os
from crewai import LLM

gemini_api_key = os.getenv("GEMINI_API_KEY")

gemini_llm = LLM(
    model='gemini/gemini-3.5-flash',
    api_key=gemini_api_key,
    temperature=1.0  # Use the Gemini 3 recommended temperature
)

from crewai.tools import BaseTool

# Placeholder tool for fetching customer support data
class CustomerSupportDataTool(BaseTool):
    name: str = "Customer Support Data Fetcher"
    description: str = (
      "Fetches recent customer support interactions, tickets, and feedback. "
      "Returns a summary string.")

    def _run(self, argument: str) -> str:
        # In a real scenario, this would query a database or API.
        # For this example, return simulated data.
        print(f"--- Fetching data for query: {argument} ---")
        return (
            """Recent Support Data Summary:
- 50 tickets related to 'login issues'. High resolution time (avg 48h).
- 30 tickets about 'billing discrepancies'. Mostly resolved within 12h.
- 20 tickets on 'feature requests'. Often closed without resolution.
- Frequent feedback mentions 'confusing user interface' for password reset.
- High volume of calls related to 'account verification process'.
- Sentiment analysis shows growing frustration with 'login issues' resolution time.
- Support agent notes indicate difficulty reproducing 'login issues'."""
        )

support_data_tool = CustomerSupportDataTool()

from crewai import Agent

# Agent 1: Data analyst
data_analyst = Agent(
    role='Customer Support Data Analyst',
    goal='Analyze customer support data to identify trends, recurring issues, and key pain points.',
    backstory=(
        """You are an expert data analyst specializing in customer support operations.
        Your strength lies in identifying patterns and quantifying problems from raw support data."""
    ),
    verbose=True,
    allow_delegation=False,  # This agent focuses on its specific task
    tools=[support_data_tool],  # Assign the data fetching tool
    llm=gemini_llm  # Use the configured Gemini LLM
)

# Agent 2: Process optimizer
process_optimizer = Agent(
    role='Process Optimization Specialist',
    goal='Identify bottlenecks and inefficiencies in current support processes based on the data analysis. Propose actionable improvements.',
    backstory=(