初始化项目，由ModelHub XC社区提供模型

Model: Nexusflow/NexusRaven-13B
Source: Original Platform

non_langchain_example.py

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+from typing import Literal
+
+import math
+
+import inspect
+
+from transformers import pipeline
+
+
+##########################################################
+# Step 1: Define the functions you want to articulate. ###
+##########################################################
+
+
+def calculator(
+    input_a: float,
+    input_b: float,
+    operation: Literal["add", "subtract", "multiply", "divide"],
+):
+    """
+    Computes a calculation.
+
+    Args:
+    input_a (float) : Required. The first input.
+    input_b (float) : Required. The second input.
+    operation (string): The operation. Choices include: add to add two numbers, subtract to subtract two numbers, multiply to multiply two numbers, and divide to divide them.
+    """
+    match operation:
+        case "add":
+            return input_a + input_b
+        case "subtract":
+            return input_a - input_b
+        case "multiply":
+            return input_a * input_b
+        case "divide":
+            return input_a / input_b
+
+
+def cylinder_volume(radius, height):
+    """
+    Calculate the volume of a cylinder.
+
+    Parameters:
+    - radius (float): The radius of the base of the cylinder.
+    - height (float): The height of the cylinder.
+
+    Returns:
+    - float: The volume of the cylinder.
+    """
+    if radius < 0 or height < 0:
+        raise ValueError("Radius and height must be non-negative.")
+
+    volume = math.pi * (radius**2) * height
+    return volume
+
+
+#############################################################
+# Step 2: Let's define some utils for building the prompt ###
+#############################################################
+
+
+def format_functions_for_prompt(*functions):
+    formatted_functions = []
+    for func in functions:
+        source_code = inspect.getsource(func)
+        docstring = inspect.getdoc(func)
+        formatted_functions.append(
+            f"OPTION:\n<func_start>{source_code}<func_end>\n<docstring_start>\n{docstring}\n<docstring_end>"
+        )
+    return "\n".join(formatted_functions)
+
+
+##############################
+# Step 3: Construct Prompt ###
+##############################
+
+
+def construct_prompt(user_query: str):
+    formatted_prompt = format_functions_for_prompt(calculator, cylinder_volume)
+    formatted_prompt += f"\n\nUser Query: Question: {user_query}\n"
+
+    prompt = (
+        "<human>:\n"
+        + formatted_prompt
+        + "Please pick a function from the above options that best answers the user query and fill in the appropriate arguments.<human_end>"
+    )
+    return prompt
+
+
+#######################################
+# Step 4: Execute the function call ###
+#######################################
+
+
+def execute_function_call(model_output):
+    # Ignore everything after "Reflection" since that is not essential.
+    function_call = (
+        model_output[0]["generated_text"]
+        .strip()
+        .split("\n")[1]
+        .replace("Initial Answer:", "")
+        .strip()
+    )
+
+    try:
+        return eval(function_call)
+    except Exception as e:
+        return str(e)
+
+
+if __name__ == "__main__":
+    # Build the model
+    text_gen = pipeline(
+        "text-generation",
+        model="Nexusflow/NexusRaven-13B",
+        device="cuda",
+    )
+
+    # Comp[ute a Simple equation
+    prompt = construct_prompt("What is 1+10?")
+    model_output = text_gen(
+        prompt, do_sample=False, max_new_tokens=400, return_full_text=False
+    )
+    result = execute_function_call(model_output)
+
+    print("Model Output:", model_output)
+    print("Execution Result:", result)
+
+    prompt = construct_prompt(
+        "I have a cake that is about 3 centimenters high and 200 centimeters in diameter. How much cake do I have?"
+    )
+    model_output = text_gen(
+        prompt,
+        do_sample=False,
+        max_new_tokens=400,
+        return_full_text=False,
+        stop=["\nReflection:"],
+    )
+    result = execute_function_call(model_output)
+
+    print("Model Output:", model_output)
+    print("Execution Result:", result)