Create phase1_agents.py

phase1_agents.py

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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 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) -> dict:
+    """ Searches for detailed company information using web search. """
+    query = f"Find details for {company_name}, including its official website, mission, services, and AI-related initiatives. Include sources."
+    response = company_search_agent.print_response(query)
+    return {"company_name": company_name, "details": response}
+
+
+##############################
+# 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) -> dict:
+    """ Scrapes relevant company information from the given website. """
+    response = firecrawl_agent.print_response(f"Extract business details from {url}, including mission, services, and AI-related information.")
+    return {"website": url, "scraped_data": response}
+
+
+##############################
+# 3️⃣ Text Processing Agent   #
+##############################
+class CompanySummary(BaseModel):
+    summary: str = Field(..., description="Summarized company details.")
+
+text_processing_agent = Agent(
+    model=OpenAIChat(id="gpt-4o"),
+    description="Summarizes user-written company descriptions.",
+    response_model=CompanySummary,
+)
+
+def process_company_description(text: str) -> dict:
+    """ Summarizes the user-provided company description. """
+    response = text_processing_agent.print_response(f"Summarize the following description: {text}. Focus on mission, key services, industry, and AI potential.")
+    return {"user_description": text, "summary": response}
+
+
+##############################
+# 4️⃣ Document Processing Agent  #
+##############################
+def process_uploaded_document(file: UploadFile) -> dict:
+    """ Reads and processes an uploaded document, returning extracted content. """
+    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()
+
+    return {"document_name": file.filename, "content": document_text}