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| import os | |
| from phi.agent import Agent | |
| from phi.tools.duckduckgo import DuckDuckGo | |
| from phi.tools.exa import ExaTools | |
| from phi.model.openai import OpenAIChat | |
| from dotenv import load_dotenv | |
| # Load environment variables (API keys, etc.) | |
| load_dotenv() | |
| ##################################################################################### | |
| # PHASE 2 # | |
| ##################################################################################### | |
| ############################## | |
| # 1️⃣ Industry Trends Agent # | |
| ############################## | |
| industry_trends_agent = Agent( | |
| name="Industry Trends Agent", | |
| model=OpenAIChat(id="gpt-4o"), | |
| tools=[ExaTools(include_domains=["cnbc.com", "reuters.com", "bloomberg.com"])], | |
| description="Finds the latest AI advancements in a given industry.", | |
| show_tool_calls=True, | |
| markdown=True, | |
| ) | |
| def get_industry_trends(industry: str) -> dict: | |
| """ Fetches the latest AI advancements, trends, and emerging technologies in a given industry. """ | |
| query = f"Find recent AI advancements in the {industry} sector, including major breakthroughs and adoption trends." | |
| response = industry_trends_agent.run(query) | |
| return {"industry": industry, "trends": response.content} | |
| ################################## | |
| # 2️⃣ AI Use Case Discovery Agent # | |
| ################################## | |
| ai_use_case_agent = Agent( | |
| name="AI Use Case Discovery Agent", | |
| model=OpenAIChat(id="gpt-4o"), | |
| tools=[DuckDuckGo()], | |
| description="Identifies AI applications relevant to a given industry.", | |
| show_tool_calls=True, | |
| markdown=True, | |
| ) | |
| def get_ai_use_cases(industry: str) -> dict: | |
| """ Identifies key AI use cases, automation improvements, and cost-saving innovations for an industry. """ | |
| query = f"Identify the most impactful AI use cases in the {industry} sector. Include real-world examples and benefits." | |
| response = ai_use_case_agent.run(query) | |
| return {"industry": industry, "use_cases": response.content} | |