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85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
import langchain_core.tools as langchain_tools
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from dotenv import load_dotenv
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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from humanlayer import (
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HumanLayer,
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)
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from channels import (
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dm_with_head_of_marketing,
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dm_with_summer_intern,
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)
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load_dotenv()
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hl = HumanLayer(
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verbose=True,
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# run_id is optional -it can be used to identify the agent in approval history
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run_id="langchain-approvals-and-humans-composite",
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)
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task_prompt = """
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You are the email onboarding assistant. You check on the progress customers
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are making and get other information, then based on that info, you
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send friendly and encouraging emails to customers to help them
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Before sending an email, you check with the head of marketing for feedback,
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and incorporate that feedback into your email before sending. You repeat the
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feedback process until the head of marketing approves the request
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Your task is to prepare an email to send to the customer danny@metacorp.com
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"""
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def get_info_about_customer(customer_email: str) -> str:
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"""get info about a customer"""
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return """
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This customer has completed most of the onboarding steps,
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but still needs to invite a few team members before they can be considered fully onboarded
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"""
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def send_email(email: str, message: str) -> str:
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"""Send an email to a user"""
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return f"Email sent to {email} with message: {message}"
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tools = [
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langchain_tools.StructuredTool.from_function(get_info_about_customer),
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langchain_tools.StructuredTool.from_function(send_email),
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langchain_tools.StructuredTool.from_function(
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# allow the agent to contact the head of marketing,
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# but require approval from the summer intern before sending
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hl.require_approval(contact_channel=dm_with_summer_intern).wrap(
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hl.human_as_tool(contact_channel=dm_with_head_of_marketing)
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)
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),
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]
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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# Prompt for creating Tool Calling Agent
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful assistant.",
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),
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("placeholder", "{chat_history}"),
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("human", "{input}"),
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("placeholder", "{agent_scratchpad}"),
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]
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)
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# Construct the Tool Calling Agent
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agent = create_tool_calling_agent(llm, tools, prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
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if __name__ == "__main__":
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result = agent_executor.invoke({"input": task_prompt})
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print("\n\n----------Result----------\n\n")
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print(result)
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