mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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133 lines
3.8 KiB
Python
133 lines
3.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#!/usr/bin/env python3
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"""Example 5: Tool/Function Calling.
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Demonstrates tool calling with function definitions and responses.
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Note: This requires a compatible model (e.g., Qwen3, GPT-OSS, Kimi K2).
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"""
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import json
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from openai import OpenAI
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# Initialize the client
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="tensorrt_llm",
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)
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# Get the model name from the server
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models = client.models.list()
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model = models.data[0].id
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TOOL_CALL_SUPPORTED_MODELS = ["Qwen3", "GPT-OSS", "Kimi K2"]
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print("=" * 80)
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print("Example 5: Tool/Function Calling")
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print("=" * 80)
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print()
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print(
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f"Note: Tool calling requires compatible models (e.g. {', '.join(TOOL_CALL_SUPPORTED_MODELS)})\n"
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)
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# Define the available tools
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tools = [
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{
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"name": "get_weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "City and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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"type": "function",
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"description": "Get the current weather in a location",
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}
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]
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def get_weather(location: str, unit: str = "fahrenheit") -> dict:
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return {"location": location, "temperature": 68, "unit": unit, "conditions": "sunny"}
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def process_tool_call(response) -> tuple[dict, str]:
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function_name = None
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function_arguments = None
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tool_call_id = None
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for output in response.output:
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if output.type == "function_call":
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function_name = output.name
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function_arguments = json.loads(output.arguments)
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tool_call_id = output.call_id
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break
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try:
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print(
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f"Get tool call result:\n\ttool_name: {function_name}\n\tparameters: {function_arguments})"
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)
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result = eval(f"{function_name}(**{function_arguments})")
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except Exception as e:
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print(f"Error processing tool call: {e}")
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return None, None
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return result, tool_call_id
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print("Available tools:")
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print(json.dumps(tools, indent=2))
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print("\nUser query: What is the weather in San Francisco?\n")
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try:
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# Initial request with tools
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response = client.responses.create(
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model=model,
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input="What is the weather in San Francisco?",
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tools=tools,
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tool_choice="auto",
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max_output_tokens=4096,
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)
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tool_call_result, tool_call_id = process_tool_call(response)
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call_input = [
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{
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"type": "function_call_output",
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"call_id": tool_call_id,
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"output": json.dumps(tool_call_result),
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}
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]
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prev_response_id = response.id
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response = client.responses.create(
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model=model,
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input=call_input,
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previous_response_id=prev_response_id,
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tools=tools,
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)
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print(f"Final response: {response.output_text}")
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except Exception as e:
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print(
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f"Note: Tool calling requires model support (e.g. {', '.join(TOOL_CALL_SUPPORTED_MODELS)})"
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)
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print(f"Error: {e}")
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