mirror of
https://github.com/humanlayer/humanlayer.git
synced 2025-08-20 19:01:22 +03:00
94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
import langchain_core.tools as langchain_tools
|
|
from channels import (
|
|
dm_with_ceo,
|
|
dm_with_head_of_marketing,
|
|
dm_with_summer_intern,
|
|
)
|
|
from dotenv import load_dotenv
|
|
from langchain_core.prompts import ChatPromptTemplate
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain.agents import AgentExecutor, create_tool_calling_agent
|
|
|
|
from humanlayer.core.approval import (
|
|
HumanLayer,
|
|
)
|
|
|
|
load_dotenv()
|
|
|
|
hl = HumanLayer(
|
|
verbose=True,
|
|
# run_id is optional -it can be used to identify the agent in approval history
|
|
run_id="langchain-human-as-tool-onboarding",
|
|
)
|
|
|
|
task_prompt = """
|
|
You are the email onboarding assistant. You check on the progress customers
|
|
are making and get other information, then based on that info, you
|
|
send friendly and encouraging emails to customers to help them fully onboard
|
|
into the product.
|
|
|
|
Before sending an email, you check with the head of marketing for feedback,
|
|
and incorporate that feedback into your email before sending. You repeat the
|
|
feedback process until the head of marketing approves the request
|
|
|
|
Your task is to prepare an email to send to the customer danny@metacorp.com
|
|
"""
|
|
|
|
|
|
def get_info_about_customer(customer_email: str) -> str:
|
|
"""get info about a customer"""
|
|
return """
|
|
This customer has completed most of the onboarding steps,
|
|
but still needs to invite a few team members before they can be
|
|
considered fully onboarded
|
|
"""
|
|
|
|
|
|
# require CEO approval to send an email
|
|
@hl.require_approval(contact_channel=dm_with_ceo)
|
|
def send_email(to: str, subject: str, body: str) -> str:
|
|
"""Send an email to a user"""
|
|
return f"Email sent to {to} with subject: {subject}"
|
|
|
|
|
|
tools = [
|
|
langchain_tools.StructuredTool.from_function(get_info_about_customer),
|
|
langchain_tools.StructuredTool.from_function(send_email),
|
|
langchain_tools.StructuredTool.from_function(
|
|
# allow the agent to contact the head of marketing for help
|
|
hl.human_as_tool(
|
|
contact_channel=dm_with_head_of_marketing,
|
|
)
|
|
),
|
|
langchain_tools.StructuredTool.from_function(
|
|
# allow the agent to contact the summer intern
|
|
hl.human_as_tool(
|
|
contact_channel=dm_with_summer_intern,
|
|
)
|
|
),
|
|
]
|
|
|
|
llm = ChatOpenAI(model="gpt-4o", temperature=0)
|
|
|
|
# Prompt for creating Tool Calling Agent
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"You are a helpful assistant.",
|
|
),
|
|
("placeholder", "{chat_history}"),
|
|
("human", "{input}"),
|
|
("placeholder", "{agent_scratchpad}"),
|
|
]
|
|
)
|
|
|
|
# Construct the Tool Calling Agent
|
|
agent = create_tool_calling_agent(llm, tools, prompt)
|
|
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
|
|
|
|
if __name__ == "__main__":
|
|
result = agent_executor.invoke({"input": task_prompt})
|
|
print("\n\n----------Result----------\n\n")
|
|
print(result)
|