mirror of
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141 lines
3.9 KiB
Python
141 lines
3.9 KiB
Python
from datetime import datetime
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import langchain_core.tools as langchain_tools
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from dotenv import load_dotenv
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from langchain.agents import AgentType, initialize_agent
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from langchain_openai import ChatOpenAI
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from pydantic import BaseModel
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from channels import (
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dm_with_ceo,
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)
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from humanlayer.core.approval import (
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HumanLayer,
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)
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load_dotenv()
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hl = HumanLayer()
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task_prompt = """
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You are the linkedin inbox assistant. You check on
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the CEO's linkedin inbox and decide if there are any messages
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that seem interesting, then contact the human in slack with a summary.
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don't provide detail on spam-looking messages, or messages
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that appear to be selling a service or software
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You can offer to perform actions like schedule time.
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Example slack dm to send:
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Your inbox for today includes 4 spam messages,
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and 1 message from Devin who seems interested in your
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product - [here's the link](https://linkedin.com/in/devin).
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Terri has still not responded to your question about scheduling an onboarding call.
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Would you like me to respond to Devin with your availability?
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"""
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class LinkedInMessage(BaseModel):
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from_name: str
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date: str
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message: str
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class LinkedInThread(BaseModel):
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thread_id: str
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thread_url: str
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with_name: str
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messages: list[LinkedInMessage]
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def get_time() -> str:
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"""get the current time"""
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return datetime.now().isoformat()
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def get_linkedin_threads() -> list[LinkedInThread]:
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"""get the linkedin threads in the inbox"""
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return [
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LinkedInThread(
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thread_id="123",
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thread_url="https://linkedin.com/in/msg/123",
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with_name="Danny",
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messages=[
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LinkedInMessage(
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message="Hello, i am wondering if you are interested to try our excellent offshore "
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"developer service",
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from_name="Danny",
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date="2024-08-17",
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)
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],
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),
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LinkedInThread(
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thread_id="124",
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with_name="Sarah",
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thread_url="https://linkedin.com/in/msg/124",
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messages=[
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LinkedInMessage(
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message="Hello, I am interested in your product, what's the best way to get started",
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from_name="Sarah",
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date="2024-08-16",
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)
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],
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),
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LinkedInThread(
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thread_id="125",
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with_name="Terri",
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thread_url="https://linkedin.com/in/msg/125",
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messages=[
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LinkedInMessage(
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message="Hello, I am interested in your product, what's the best way to get started",
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from_name="Terri",
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date="2024-08-12",
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),
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LinkedInMessage(
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message="I would be happy to give you a demo - please let me know when you're "
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"available, or you can book time at http://calendly.com/im-the-ceo",
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from_name="you",
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date="2024-08-12",
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),
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],
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),
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]
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@hl.require_approval(contact_channel=dm_with_ceo)
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def send_linkedin_message(thread_id: str, to_name: str, msg: str) -> str:
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"""send a message in a thread in LinkedIn"""
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return f"message successfully sent to {to_name}"
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tools = [
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langchain_tools.StructuredTool.from_function(get_linkedin_threads),
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langchain_tools.StructuredTool.from_function(send_linkedin_message),
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langchain_tools.StructuredTool.from_function(
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# allow the agent to contact the CEO
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hl.human_as_tool(
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contact_channel=dm_with_ceo,
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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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agent = initialize_agent(
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tools=tools,
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llm=llm,
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agent=AgentType.OPENAI_FUNCTIONS,
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verbose=True,
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handle_parsing_errors=True,
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)
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if __name__ == "__main__":
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result = agent.run(task_prompt)
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print("\n\n----------Result----------\n\n")
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print(result)
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