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84 lines
2.0 KiB
84 lines
2.0 KiB
import os
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from dotenv import load_dotenv
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from swarms.models import Anthropic, OpenAIChat
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from swarms.prompts.accountant_swarm_prompts import (
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DECISION_MAKING_PROMPT,
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DOC_ANALYZER_AGENT_PROMPT,
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SUMMARY_GENERATOR_AGENT_PROMPT,
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)
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from swarms.structs import Agent
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from swarms.utils.pdf_to_text import pdf_to_text
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# Environment variables
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load_dotenv()
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anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
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openai_api_key = os.getenv("OPENAI_API_KEY")
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# Base llms
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llm1 = OpenAIChat(
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openai_api_key=openai_api_key,
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max_tokens=5000,
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)
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llm2 = Anthropic(
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anthropic_api_key=anthropic_api_key,
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max_tokens=5000,
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)
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# Agents
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doc_analyzer_agent = Agent(
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llm=llm2,
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sop=DOC_ANALYZER_AGENT_PROMPT,
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max_loops=1,
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autosave=True,
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saved_state_path="doc_analyzer_agent.json",
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)
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summary_generator_agent = Agent(
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llm=llm2,
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sop=SUMMARY_GENERATOR_AGENT_PROMPT,
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max_loops=1,
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autosave=True,
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saved_state_path="summary_generator_agent.json",
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)
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decision_making_support_agent = Agent(
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llm=llm2,
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sop=DECISION_MAKING_PROMPT,
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max_loops=1,
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saved_state_path="decision_making_support_agent.json",
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)
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pdf_path = "bankstatement.pdf"
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fraud_detection_instructions = "Detect fraud in the document"
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summary_agent_instructions = (
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"Generate an actionable summary of the document with action steps"
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" to take"
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)
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decision_making_support_agent_instructions = (
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"Provide decision making support to the business owner:"
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)
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# Transform the pdf to text
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pdf_text = pdf_to_text(pdf_path)
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print(pdf_text)
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# Detect fraud in the document
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fraud_detection_agent_output = doc_analyzer_agent.run(
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f"{fraud_detection_instructions}: {pdf_text}"
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)
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# Generate an actionable summary of the document
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summary_agent_output = summary_generator_agent.run(
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f"{summary_agent_instructions}: {fraud_detection_agent_output}"
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)
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# Provide decision making support to the accountant
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decision_making_support_agent_output = decision_making_support_agent.run(
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f"{decision_making_support_agent_instructions}:"
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f" {summary_agent_output}"
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)
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