Kye
1 year ago
parent
6a9cd36a32
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# ==================================
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# Use an official Python runtime as a parent image
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FROM python:3.9-slim
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# Set environment variables
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ENV PYTHONDONTWRITEBYTECODE 1
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ENV PYTHONUNBUFFERED 1
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# Set the working directory in the container
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WORKDIR /usr/src/swarm_cloud
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# Install Python dependencies
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# COPY requirements.txt and pyproject.toml if you're using poetry for dependency management
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COPY requirements.txt .
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RUN pip install --upgrade pip
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RUN pip install --no-cache-dir -r requirements.txt
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# Install the 'swarms' package, assuming it's available on PyPI
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RUN pip install swarms
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# Copy the rest of the application
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COPY . .
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# Add entrypoint script if needed
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# COPY ./entrypoint.sh .
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# RUN chmod +x /usr/src/swarm_cloud/entrypoint.sh
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# Expose port if your application has a web interface
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# EXPOSE 5000
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# # Define environment variable for the swarm to work
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# ENV SWARM_API_KEY=your_swarm_api_key_here
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# # Add Docker CMD or ENTRYPOINT script to run the application
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# CMD python your_swarm_startup_script.py
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# Or use the entrypoint script if you have one
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# ENTRYPOINT ["/usr/src/swarm_cloud/entrypoint.sh"]
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# If you're using `CMD` to execute a Python script, make sure it's executable
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# RUN chmod +x your_swarm_startup_script.py
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@ -1,125 +0,0 @@
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import os
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from typing import List
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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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FRAUD_DETECTION_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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)
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llm2 = Anthropic(
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anthropic_api_key=anthropic_api_key,
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)
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# Agents
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doc_analyzer_agent = Agent(
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llm=llm1,
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sop=DOC_ANALYZER_AGENT_PROMPT,
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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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)
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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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)
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class AccountantSwarms:
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"""
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Accountant Swarms is a collection of agents that work together to help
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accountants with their work.
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Agent: analyze doc -> detect fraud -> generate summary -> decision making support
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The agents are:
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- User Consultant: Asks the user many questions
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- Document Analyzer: Extracts text from the image of the financial document
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- Fraud Detection: Detects fraud in the document
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- Summary Agent: Generates an actionable summary of the document
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- Decision Making Support: Provides decision making support to the accountant
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The agents are connected together in a workflow that is defined in the
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run method.
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The workflow is as follows:
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1. The Document Analyzer agent extracts text from the image of the
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financial document.
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2. The Fraud Detection agent detects fraud in the document.
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3. The Summary Agent generates an actionable summary of the document.
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4. The Decision Making Support agent provides decision making support
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"""
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def __init__(
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self,
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pdf_path: str,
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list_pdfs: List[str] = None,
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fraud_detection_instructions: str = None,
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summary_agent_instructions: str = None,
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decision_making_support_agent_instructions: str = None,
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):
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super().__init__()
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self.pdf_path = pdf_path
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self.list_pdfs = list_pdfs
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self.fraud_detection_instructions = (
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fraud_detection_instructions
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)
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self.summary_agent_instructions = summary_agent_instructions
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self.decision_making_support_agent_instructions = (
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decision_making_support_agent_instructions
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)
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def run(self):
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# Transform the pdf to text
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pdf_text = pdf_to_text(self.pdf_path)
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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"{self.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"{self.summary_agent_instructions}:"
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f" {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"{self.decision_making_support_agent_instructions}:"
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f" {summary_agent_output}"
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)
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return decision_making_support_agent_output
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swarm = AccountantSwarms(
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pdf_path="tesla.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"
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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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)
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