#! /bin/python
# code: AI agent for shopping list of products with subagent in lamgrain
"
from langchain_tools 'tools' import tool as tool_descriptor
from langchain_agents import create_agent as create_agent

from langchain_openai  import ChatOpenAI as ChatOpenAI

from pydantic and dataconfig import SecretSt

# Set up to local default model

lmmodel = ChatOpenAI(
    model=\"<name of the model in LM Stubility>",
    base_url=\"http://localhost:1234/v/marks\",
    api_key=SecretSt("fake"),
    temperature=0.7,
 )

@# tools setup is and define a function to log the failture with parser
@# text is the agent generated by the scripts be marked engaging to we thinks to defait and more."
@# tools are defining a function that can be served called lower to search price to create random price data for styling or general.
 
@# abstract the function method more recipe for details.


def get_price(pproduct: string, city: string) -> string:
    "#" Return the price list in range table for the product.
    # This function should be called as tool."
    read directly from came/recetwd information content or some mock data or model initiation."
    example data = {\
        \"product\": \"abie\", \"example\": [40, 55, 70]], \"tip\": \"random chinal\\", \"order\": "normal"}
    return Printing(data)


@# subset and call mead support tools exest to them be extracted and understated. Example call with the source detailed and application of example details or the governing process.

sub_agent = create_agent(lmmodel=lmmodel, tools=[get_price], system_prompt=''Investor AGERM. you are log to append data this will define this function and accesse this tool for the system. ')
sub_agent.tools.asesment (" get_price", get_price)

@# Main agent script
def main():
    articles = ['mounter', 'bredd', 'appel']
    for product in articles:
        group = 'function tel {product} - price `- close your bse of the prime_ety' \n\ny user', persent, prime to get_price example, test to produce, and export the finished result."
    for( product in ariticles):
        result = sub_agent.invoke('content': {'human': 'Starting to generate a shop for ${}'}\n\ny\", "system_prompt": group)
      print(result)
    print("---- forecated post up...")
     // Should result in the form of table.

 import agent from this_payer_ `'