Create VisionaryAgent

VisionaryAgent

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+import os
+from phi.agent import Agent
+from phi.tools.firecrawl import FirecrawlTools
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.model.openai import OpenAIChat
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.embedder.openai import OpenAIEmbedder
+from phi.vectordb.lancedb import LanceDb, SearchType
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.common.keys import Keys
+import helium
+from typing import List
+from pydantic import BaseModel, Field
+from fastapi import UploadFile
+
+# Load environment variables (API keys, etc.)
+from dotenv import load_dotenv
+load_dotenv()
+
+#####################################################################################
+#                                    PHASE 1                                        #
+#####################################################################################
+
+
+##############################
+# 1️⃣ Company Search Agent   #
+##############################
+company_search_agent = Agent(
+    name="Company Search Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[DuckDuckGo()],
+    description="Finds company details based on name using web search.",
+    instructions=["Always include sources in search results."],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def search_company(company_name: str):
+    query = f"Find detailed company information for {company_name}. Extract its official website, mission, services, and any AI-related initiatives. Prioritize official sources and provide links where available."
+    return company_search_agent.print_response(query)
+
+
+##############################
+# 2️⃣ Website Scraper Agent   #
+##############################
+firecrawl_agent = Agent(
+    name="Website Scraper Agent",
+    tools=[FirecrawlTools(scrape=True, crawl=False)],
+    description="Extracts content from company websites.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def scrape_website(url: str):
+    return firecrawl_agent.print_response(f"Extract all relevant business information from {url}, including mission statement, services, case studies, and AI-related content. Provide structured output.")
+
+# Helium for dynamic websites
+chrome_options = webdriver.ChromeOptions()
+chrome_options.add_argument("--headless")
+driver = helium.start_chrome(headless=True, options=chrome_options)
+
+def scrape_dynamic_website(url: str):
+    helium.go_to(url)
+    text = helium.get_driver().page_source
+    return text
+
+
+##############################
+# 3️⃣ Text Processing Agent   #
+##############################
+class CompanySummary(BaseModel):
+    summary: str = Field(..., description="Summarized company details based on user input.")
+
+text_processing_agent = Agent(
+    model=OpenAIChat(id="gpt-4o"),
+    description="Summarizes user-written company descriptions.",
+    response_model=CompanySummary,
+)
+
+def process_company_description(text: str):
+    return text_processing_agent.print_response(f"Summarize the following company description: {text}. Focus on key services, mission, industry, and potential AI use cases where applicable.")
+
+
+#################################
+# 4️⃣ Document Processing Agent  #
+#################################
+# LanceDB for storing extracted knowledge
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=[],  # PDFs will be dynamically added
+    vector_db=LanceDb(
+        table_name="company_docs",
+        uri="tmp/lancedb",
+        search_type=SearchType.vector,
+        embedder=OpenAIEmbedder(model="text-embedding-3-small"),
+    ),
+)
+knowledge_base.load(recreate=False)
+
+document_processing_agent = Agent(
+    model=OpenAIChat(id="gpt-4o"),
+    knowledge=knowledge_base,
+    description="Extracts and processes data from uploaded PDFs/PPTs.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def process_uploaded_document(file: UploadFile):
+    file_path = f"tmp/{file.filename}"
+    with open(file_path, "wb") as buffer:
+        buffer.write(file.file.read())
+    
+    knowledge_base.load(recreate=False)
+    return document_processing_agent.print_response(f"Analyze and extract key insights from the uploaded document: {file.filename}. Summarize business operations, AI-related discussions, financial details, and relevant strategic insights.")
+
+
+#####################################################################################
+#                                    PHASE 2                                        #
+#####################################################################################
+
+
+###########################
+# Example Usage           #
+###########################
+# if __name__ == "__main__":
+    # company_name = "Tesla"
+    # print("Company Search Results:")
+    # search_company(company_name)
+    
+    # website_url = "https://www.tesla.com"
+    # print("\nScraped Website Data:")
+    # scrape_website(website_url)
+    
+    # user_description = "We are a renewable energy startup focusing on solar solutions."
+    # print("\nProcessed Company Description:")
+    # process_company_description(user_description)
+    
+    # Example of handling an uploaded file
+    # process_uploaded_document(uploaded_file)
+
+
+    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 typing import List
+from pydantic import BaseModel, Field
+
+# Load environment variables (API keys, etc.)
+from dotenv import load_dotenv
+load_dotenv()
+
+##############################
+# 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):
+    query = f"Find the latest AI advancements, innovations, and emerging technologies in the {industry} sector. Include breakthroughs, adoption trends, and notable implementations by leading companies. Provide references and insights from credible sources."
+    return industry_trends_agent.print_response(query)
+
+
+##################################
+# 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):
+    query = f"Identify the most impactful AI use cases in the {industry} sector. Include real-world applications, automation improvements, cost-saving innovations, and data-driven decision-making processes. Provide case studies and examples of successful AI implementation."
+    return ai_use_case_agent.print_response(query)
+
+
+####################################
+# 3️⃣ Competitive Analysis Agent   #
+####################################
+competitive_analysis_agent = Agent(
+    name="Competitive Analysis Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[DuckDuckGo(), ExaTools(include_domains=["techcrunch.com", "forbes.com", "businessinsider.com"])],
+    description="Analyzes how competitors are using AI in their businesses.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def get_competitor_ai_strategies(company_name: str):
+    query = f"Analyze how {company_name} is leveraging AI in its business operations. Find recent reports, product innovations, automation strategies, and AI-driven transformations. Highlight competitive advantages gained through AI adoption. Provide references and sources."
+    return competitive_analysis_agent.print_response(query)
+
+
+###########################
+# Example Usage           #
+###########################
+# if __name__ == "__main__":
+#     industry = "Healthcare"
+#     print("Industry Trends:")
+#     get_industry_trends(industry)
+    
+#     print("\nAI Use Cases:")
+#     get_ai_use_cases(industry)
+    
+#     competitor = "Pfizer"
+#     print("\nCompetitor AI Strategies:")
+#     get_competitor_ai_strategies(competitor)
+
+
+#####################################################################################
+#                                    PHASE 3                                        #
+#####################################################################################
+
+
+##############################
+# 1️⃣ Reasoning Agent        #
+##############################
+reasoning_agent = Agent(
+    name="Reasoning Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    description="Processes all collected data and generates structured AI adoption strategies.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def generate_ai_strategy(company_data: str, industry_trends: str, ai_use_cases: str, competitor_analysis: str):
+    query = f"""
+    You are an AI business strategist analyzing a company's potential AI adoption. Given the following:
+    
+    - **Company Overview:** {company_data}
+    - **Industry Trends:** {industry_trends}
+    - **AI Use Cases:** {ai_use_cases}
+    - **Competitor AI Strategies:** {competitor_analysis}
+    
+    Generate a structured AI adoption strategy that includes:
+    1. **AI Opportunities**: Identify key areas where AI can enhance operations, customer experience, or business efficiency.
+    2. **Technology Fit**: Recommend specific AI tools, models, or methodologies that fit this company's needs.
+    3. **Implementation Roadmap**: Step-by-step guidance on integrating AI, considering costs, scalability, and ROI.
+    4. **Future Scalability**: How AI adoption can evolve over time for long-term growth.
+    
+    Provide structured insights with a logical flow and avoid generic statements. Use industry benchmarks where possible.
+    """
+    return reasoning_agent.print_response(query)
+
+
+##############################
+# 2️⃣ AI Integration Advisor  #
+##############################
+ai_integration_agent = Agent(
+    name="AI Integration Advisor",
+    model=OpenAIChat(id="gpt-4o"),
+    description="Suggests AI implementation strategies based on industry insights and company operations.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def suggest_ai_integration(company_data: str, ai_strategy: str):
+    query = f"""
+    Based on the AI adoption strategy:
+    
+    - **Company Context:** {company_data}
+    - **AI Strategy Summary:** {ai_strategy}
+    
+    Provide a structured AI implementation plan:
+    1. **Step-by-step AI Integration**: List phases of AI adoption, from pilot testing to full deployment.
+    2. **Technology & Infrastructure**: Recommend necessary AI tools, cloud platforms, and software.
+    3. **Workforce & Training**: Suggest ways to upskill employees for AI adoption.
+    4. **Risk & Compliance Considerations**: Highlight data security, compliance, and ethical concerns.
+    5. **KPIs for Success**: Define measurable AI performance indicators.
+    
+    The output should be detailed, actionable, and specific to the business domain.
+    """
+    return ai_integration_agent.print_response(query)
+
+
+##############################
+# 3️⃣ Revenue Growth Agent    #
+##############################
+revenue_growth_agent = Agent(
+    name="Revenue Growth Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    description="Identifies AI-driven opportunities to enhance revenue and efficiency.",
+    show_tool_calls=True,
+    markdown=True,
+)
+
+def identify_revenue_opportunities(company_data: str, ai_strategy: str):
+    query = f"""
+    You are an AI business analyst tasked with identifying AI-driven revenue growth opportunities for:
+    
+    - **Company Overview:** {company_data}
+    - **AI Strategy:** {ai_strategy}
+    
+    Provide:
+    1. **AI Monetization Strategies**: Explain how AI can create new revenue streams (e.g., AI-driven products, services, or data monetization).
+    2. **Cost Reduction & Efficiency Gains**: Highlight AI automation that lowers operational costs.
+    3. **Market Expansion**: Discuss how AI can help enter new markets or scale offerings.
+    4. **Competitive Positioning**: Compare with industry leaders and suggest differentiation tactics.
+    
+    Ensure detailed, actionable insights with real-world examples where applicable.
+    """
+    return revenue_growth_agent.print_response(query)
+
+
+##############################
+# 4️⃣ Report Generation Agent #
+##############################
+def generate_report(company_name: str, ai_strategy: str, ai_integration: str, revenue_opportunities: str):
+    report_content = f"""
+    # AI Strategy Report for {company_name}
+    
+    ## AI Adoption Strategy
+    {ai_strategy}
+    
+    ## AI Implementation Plan
+    {ai_integration}
+    
+    ## Revenue Growth Opportunities
+    {revenue_opportunities}
+    
+    """
+    
+    # Convert to Markdown
+    markdown_report = markdown2.markdown(report_content)
+    
+    # Convert Markdown to PDF
+    pdfkit.from_string(markdown_report, f"{company_name}_AI_Report.pdf")
+    
+    return f"Report generated: {company_name}_AI_Report.pdf"
+
+
+###########################
+# Example Usage           #
+###########################
+# if __name__ == "__main__":
+#     company_name = "Tesla"
+#     company_data = "Tesla specializes in electric vehicles and AI-powered self-driving technology."
+#     industry_trends = "Latest AI advancements in autonomous driving and battery optimization."
+#     ai_use_cases = "AI used in predictive maintenance, customer behavior analysis, and automation."
+#     competitor_analysis = "Ford and GM are integrating AI into manufacturing and autonomous vehicle tech."
+    
+#     print("Generating AI Strategy...")
+#     ai_strategy = generate_ai_strategy(company_data, industry_trends, ai_use_cases, competitor_analysis)
+    
+#     print("\nSuggesting AI Integration Plan...")
+#     ai_integration = suggest_ai_integration(company_data, ai_strategy)
+    
+#     print("\nIdentifying Revenue Growth Opportunities...")
+#     revenue_opportunities = identify_revenue_opportunities(company_data, ai_strategy)
+    
+#     print("\nGenerating Final Report...")
+#     generate_report(company_name, ai_strategy, ai_integration, revenue_opportunities)