python-agents-tutorial/3_state.py at main · run-llama/python-agents-tutorial

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from dotenv import load_dotenv

load_dotenv()

from llama_index.llms.openai import OpenAI

from llama_index.core.agent.workflow import AgentWorkflow

from llama_index.tools.yahoo_finance import YahooFinanceToolSpec

from llama_index.core.workflow import Context

from llama_index.core.workflow import JsonPickleSerializer, JsonSerializer

def multiply(a: float, b: float) -> float:

"""Multiply two numbers and returns the product"""

return a * b

def add(a: float, b: float) -> float:

"""Add two numbers and returns the sum"""

return a + b

llm = OpenAI(model="gpt-4o-mini")

finance_tools = YahooFinanceToolSpec().to_tool_list()

finance_tools.extend([multiply, add])

workflow = AgentWorkflow.from_tools_or_functions(

finance_tools,

llm=llm,

system_prompt="You are an agent that can perform basic mathematical operations using tools."

)

# configure a context to work with our workflow

ctx = Context(workflow)

async def main():

response = await workflow.run(user_msg="Hi, my name is Laurie!",ctx=ctx)

print(response)

response2 = await workflow.run(user_msg="What's my name?",ctx=ctx)

print(response2)

# convert our Context to a dictionary object

ctx_dict = ctx.to_dict(serializer=JsonSerializer())

# create a new Context from the dictionary

restored_ctx = Context.from_dict(

workflow, ctx_dict, serializer=JsonSerializer()

)

response3 = await workflow.run(user_msg="What's my name?",ctx=restored_ctx)

print(response3)

if __name__ == "__main__":

import asyncio

asyncio.run(main())

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