Create VisionaryAgent

VisionaryAgent

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+import os
+import faiss
+import numpy as np
+from phi.agent import Agent
+from phi.tools.firecrawl import FirecrawlTools
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.exa import ExaTools
+from phi.model.openai import OpenAIChat
+from phi.embedder.openai import OpenAIEmbedder
+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.")
+
+
+##############################
+# 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  #
+#################################
+# FAISS Index for storing extracted knowledge
+embedding_model = OpenAIEmbedder(model="text-embedding-3-small")
+dimension = 1536  # OpenAI's embedding dimension
+faiss_index = faiss.IndexFlatL2(dimension)
+
+def process_uploaded_document(file: UploadFile):
+    file_path = f"tmp/{file.filename}"
+    with open(file_path, "wb") as buffer:
+        buffer.write(file.file.read())
+    
+    with open(file_path, "r", encoding="utf-8") as f:
+        document_text = f.read()
+    
+    # Generate embedding
+    embedding = np.array(embedding_model.embed([document_text])).astype("float32")
+    faiss_index.add(embedding)
+    
+    return f"Document processed and stored in FAISS index: {file.filename}"
+
+#####################################################################################
+#                                    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):
+    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)
+
+#####################################################################################
+#                                    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):
+    query = f"""
+    Given the following:
+    - Company Overview: {company_data}
+    - Industry Trends: {industry_trends}
+    - AI Use Cases: {ai_use_cases}
+    Generate a structured AI adoption strategy, including key opportunities, recommended AI tools, implementation roadmap, and future scalability.
+    """
+    return reasoning_agent.print_response(query)