初始化项目，由ModelHub XC社区提供模型
Model: Salesforce/xLAM-1b-fc-r Source: Original Platform
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ModelHub XC
161
examples/test_prompt_template.py
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161
examples/test_prompt_template.py
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import argparse
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import json
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from typing import Dict
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from jinja2 import Template
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from transformers import AutoTokenizer
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# Default prompts
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TASK_INSTRUCTION = """
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You are an expert in composing functions. You are given a question and a set of possible functions.
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Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
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If none of the functions can be used, point it out and refuse to answer.
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If the given question lacks the parameters required by the function, also point it out.
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""".strip()
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FORMAT_INSTRUCTION = """
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The output MUST strictly adhere to the following JSON format, and NO other text MUST be included.
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The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make tool_calls an empty list '[]'
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```
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{
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"tool_calls": [
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{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
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... (more tool calls as required)
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]
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}
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```
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""".strip()
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class PromptAssembler:
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def __init__(self, model: str):
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tokenizer = AutoTokenizer.from_pretrained(model)
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self.chat_template = tokenizer.chat_template
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@staticmethod
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def apply_chat_template(template, messages):
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jinja_template = Template(template)
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return jinja_template.render(messages=messages)
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def assemble_prompt(self, query: str, tools: Dict, task_instruction: str, format_instruction: str):
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# Convert tools to XLAM format
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xlam_tools = self.convert_to_xlam_tool(tools)
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# Build the input prompt
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prompt = self.build_prompt(query, xlam_tools, task_instruction, format_instruction)
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messages = [
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{"role": "user", "content": prompt}
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]
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formatted_prompt = self.apply_chat_template(self.chat_template, messages)
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return formatted_prompt
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def convert_to_xlam_tool(self, tools):
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if isinstance(tools, dict):
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return {
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"name": tools["name"],
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"description": tools["description"],
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"parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()}
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}
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elif isinstance(tools, list):
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return [self.convert_to_xlam_tool(tool) for tool in tools]
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else:
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return tools
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def build_prompt(self, query, tools, task_instruction=TASK_INSTRUCTION, format_instruction=FORMAT_INSTRUCTION):
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prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
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prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(tools)}\n[END OF AVAILABLE TOOLS]\n\n"
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prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
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prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
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return prompt
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def print_prompt_template(self):
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template = self.chat_template.replace("{{", "{").replace("}}", "}")
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print("Prompt Template with Placeholders:")
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print(template)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Assemble prompts using chat template")
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parser.add_argument("--model", required=True, help="Name of the model (for chat template)")
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args = parser.parse_args()
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# Initialize the PromptAssembler
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assembler = PromptAssembler(args.model)
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# Print the prompt template with placeholders
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assembler.print_prompt_template()
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# Test case 1: Weather API, follows the OpenAI format: https://platform.openai.com/docs/guides/function-calling
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weather_api = {
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"name": "get_weather",
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"description": "Get the current weather for a location",
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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": "The city and state, e.g. San Francisco, CA"
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The unit of temperature to return"
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}
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},
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"required": ["location"]
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}
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}
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# Test queries
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test_queries = [
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"What's the weather like in New York?",
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"Tell me the temperature in London in Celsius",
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"What's the weather forecast for Tokyo?",
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"What is the stock price of CRM?", # the model should return an empty list
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"What's the current temperature in Paris in Fahrenheit?"
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]
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# Run test cases
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for query in test_queries:
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print(f"\nQuery: {query}")
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formatted_prompt = assembler.assemble_prompt(query, weather_api, TASK_INSTRUCTION, FORMAT_INSTRUCTION)
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print("Formatted Prompt:")
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print(formatted_prompt)
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print("-" * 50)
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# Test case 2: Multiple APIs, follows the OpenAI format: https://platform.openai.com/docs/guides/function-calling
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calculator_api = {
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"name": "calculate",
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"description": "Perform a mathematical calculation",
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"parameters": {
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"type": "object",
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"properties": {
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"operation": {
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"type": "string",
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"enum": ["add", "subtract", "multiply", "divide"],
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"description": "The mathematical operation to perform"
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},
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"x": {
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"type": "number",
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"description": "The first number"
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},
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"y": {
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"type": "number",
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"description": "The second number"
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}
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},
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"required": ["operation", "x", "y"]
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}
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}
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multi_api_query = "What's the weather in Miami and what's 15 multiplied by 7?"
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print(f"\nMulti-API Query: {multi_api_query}")
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multi_api_formatted_prompt = assembler.assemble_prompt(
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multi_api_query,
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[weather_api, calculator_api],
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TASK_INSTRUCTION,
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FORMAT_INSTRUCTION
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)
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print("Formatted Prompt:")
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print(multi_api_formatted_prompt)
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