mirror of
https://github.com/googleapis/genai-toolbox.git
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95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
import asyncio
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import re
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import json
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from typing import Callable, Any
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from toolbox_langchain import ToolboxClient
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from toolbox_core.protocol import Protocol
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from langchain_google_vertexai import ChatVertexAI
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from langchain_core.messages import ToolMessage, messages_to_dict
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from langchain.agents import create_agent
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from langchain.agents.middleware import (
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wrap_tool_call,
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AgentState
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)
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system_prompt = """
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You're a helpful hotel assistant. You handle hotel searching, booking and
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cancellations. When the user searches for a hotel, mention it's name, id,
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location and price tier. Always mention hotel ids while performing any
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searches. This is very important for any operations. For any bookings or
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cancellations, please provide the appropriate confirmation. Be sure to
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update checkin or checkout dates if mentioned by the user.
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Don't ask for confirmations from the user.
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"""
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# Pre processing
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@wrap_tool_call
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async def enforce_business_rules(request, handler):
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"""
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Business Logic Validation:
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Enforces max stay duration (e.g., max 14 days).
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"""
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tool_call = request.tool_call
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name = tool_call["name"]
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args = tool_call["args"]
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print(f"POLICY CHECK: Intercepting '{name}'")
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if name == "book-hotel":
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if "duration_days" in args and int(args["duration_days"]) > 14:
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print("BLOCKED: Stay too long")
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return ToolMessage(
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content="Error: Maximum stay duration is 14 days.",
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tool_call_id=tool_call["id"]
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)
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return await handler(request)
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# Post processing
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@wrap_tool_call
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async def enrich_response(request, handler):
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"""
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Post-Processing & Enrichment:
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Adds loyalty points information to successful bookings.
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Standardizes output format.
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"""
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result = await handler(request)
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if isinstance(result, ToolMessage):
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content = str(result.content)
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tool_name = request.tool_call["name"]
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if tool_name == "book-hotel" and "Error" not in content:
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loyalty_bonus = 500
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result.content = f"Booking Confirmed! \n You earned {loyalty_bonus} Loyalty Points with this stay.\n\nSystem Details: {content}"
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return result
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async def main():
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async with ToolboxClient("http://127.0.0.1:5000") as client:
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tools = await client.aload_toolset("my-toolset")
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model = ChatVertexAI(model="gemini-2.5-flash")
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agent = create_agent(
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system_prompt=system_prompt,
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model=model,
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tools=tools,
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middleware=[
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enforce_business_rules,
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enrich_response
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],
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)
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user_input = "Book hotel with id 3."
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response = await agent.ainvoke({"messages": [{"role": "user", "content": user_input}]})
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print("-" * 50)
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print("Final Client Response:")
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serializable_response = {
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"messages": messages_to_dict(response["messages"])
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}
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last_ai_msg = response["messages"][-1].content
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print(f"AI: {last_ai_msg}")
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if __name__ == "__main__":
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asyncio.run(main()) |