Files

71 lines
2.3 KiB
Python

import json
from langchain_openai import ChatOpenAI
from pydantic import SecretStr
from langchain.tools import tool
from langchain.agents import create_agent
# Configure the local LLM
llm = ChatOpenAI(
model="gpt-4o-mini", # Replace with your LM Studio model name
base_url="http://localhost:1234/v1",
api_key=SecretStr("fake"),
temperature=0.7,
)
@tool
def get_price(product: str, city: str) -> str:
"""Get realistic price for a product in a city. Returns a markdown table."""
# Create a sub-agent that generates a price table
sub_agent = create_agent(
model=llm,
tools=[],
system_prompt=f"You are a price estimator. Provide a realistic price for {product} in {city}. Return a markdown table with columns: Product, Price (rub.), Store.",
)
# Invoke the sub-agent
result = sub_agent.invoke(
{
"messages": [
{"role": "user", "content": f"Provide price for {product} in {city}."}
]
}
)
# Extract the assistant message content
messages = result.get("messages", [])
for msg in messages:
if msg.get("role") == "assistant" and msg.get("content"):
return msg["content"]
return "No price data available."
def main():
# Main agent that uses the get_price tool
agent = create_agent(
model=llm,
tools=[get_price],
system_prompt="You are a shopping list planner. Use the get_price tool to find prices for items.",
)
# Sample user query
user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
# Invoke the agent
result = agent.invoke(
{
"messages": [
{"role": "human", "content": user_query}
]
}
)
# Print all messages, including tool calls and final answer
for msg in result.get("messages", []):
role = msg.get("role")
content = msg.get("content")
tool_calls = msg.get("tool_calls")
if content:
print(f"{role}: {content}")
elif tool_calls:
for call in tool_calls:
print(f"{role} calls {call.get('name')} with args {call.get('arguments')}")
else:
print(f"{role}: (no content)")
if __name__ == "__main__":
main()