Gemma3でtoolsを使用する(Ollama, ローカルLLM)

ollama pull gemma3:27b

ollama show gemma3:27b --modelfile > gemma3_27b.modelfile

# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
FROM gemma3:27b
TEMPLATE """{{- if or .System .Tools }}<start_of_turn>user
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>

You may receive a response from the tool you called. The response will be within <tool_response></tool_response> XML tags.
And you can use the tool response to generate a response to the original user question.
<tool_response>
Tool response content
</tool_response>

{{- end }}<end_of_turn>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
{{- if eq .Role "user" }}<start_of_turn>user
{{ .Content }}<end_of_turn>
{{- else if eq .Role "assistant" }}<start_of_turn>model
{{- if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{- end }}</tool_call>
{{- end }}
{{- if not $last }}<end_of_turn>
{{- end }}
{{- else if eq .Role "tool" }}<start_of_turn>user
<tool_response>
{{ .Content }}
</tool_response><end_of_turn>
{{- end }}  
{{ if and (ne .Role "assistant") $last }}<start_of_turn>model
{{ end }}
{{- end }}"""

PARAMETER stop <end_of_turn>
PARAMETER temperature 0.1
LICENSE """Gemma Terms of Use 
... 以下略 ...

ollama create gemma3-tools:27b -f .\gemma3_27b.modelfile  

import openai
from openai.types.chat import ChatCompletionToolMessageParam
import base64
import json

# Set up the client with your configuration
client = openai.OpenAI(
    base_url="http://localhost:11434/v1/",
    api_key="ollama"
)
model = "gemma3-tools:27b"

# Define the tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "add_numbers",
            "description": "Add two numbers",
            "parameters": {
                "type": "object",
                "properties": {
                    "a": {"type": "number", "description": "The first number"},
                    "b": {"type": "number", "description": "The second number"}
                },
                "required": ["a", "b"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "multiply_numbers",
            "description": "Multiply two numbers",
            "parameters": {
                "type": "object",
                "properties": {
                    "a": {"type": "number", "description": "The first number"},
                    "b": {"type": "number", "description": "The second number"}
                },
                "required": ["a", "b"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "power_numbers",
            "description": "Raise a number to the power of another",
            "parameters": {
                "type": "object",
                "properties": {
                    "base": {"type": "number", "description": "The base number"},
                    "exponent": {"type": "number", "description": "The exponent"}
                },
                "required": ["base", "exponent"]
            }
        }
    },
]

# Example function to handle the tool
def add_numbers(a, b):
    return a + b

# Example function to handle the tool
def multiply_numbers(a, b):
    return a * b

# Example function to handle the tool
def power_numbers(base, exponent):
    return base ** exponent

def completion_with_tool(messages):
    # Call the model with the message string and tools
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
        tools=tools,
        tool_choice = 'auto',
    )

    # Check if the model called a tool
    if completion.choices[0].message.tool_calls:
        tool_call = completion.choices[0].message.tool_calls[0]
        tool_name = tool_call.function.name
        arguments = json.loads(tool_call.function.arguments)
        print("Tool called:", tool_name, "with parameters:", arguments)

        # Call the actual function with the parameters
        if tool_name == "add_numbers":
            result = add_numbers(**arguments)
            print("Result:", result)
        elif tool_name == "multiply_numbers":
            result = multiply_numbers(**arguments)
            print("Result:", result)
        elif tool_name == "power_numbers":
            result = power_numbers(**arguments)
            print("Result:", result)
        else:
            print("Unknown tool:", tool_name)
            raise ValueError(f"Unknown tool: {tool_name}")

        # Send the result back to the model
        toolMessage: ChatCompletionToolMessageParam = {
            'role': "tool",
            'content': f"{result}",
            'tool_call_id': tool_call.id,
        }
        completion = client.chat.completions.create(
            model=model,
            messages=[
                *messages,
                toolMessage
            ],
            tools=tools,
            tool_choice = 'none',
        )
    return completion.choices[0].message.content

# Main function to demonstrate tool usage
if __name__ == "__main__":
    # Example message string to trigger the tool
    response = completion_with_tool([{"role": "user", "content": "What is 5 + 3?"}])
    print("Response:", response)

    # Example message string to trigger another tool
    response = completion_with_tool([{"role": "user", "content": "What is 4 * 7?"}])
    print("Response:", response)

    # Example message string to trigger another tool
    response = completion_with_tool([{"role": "user", "content": "What is 2 ^ 3?"}])
    print("Response:", response)

    # Example message string to trigger another tool with Image
    with open("2^10.png", "rb") as f:
        response = completion_with_tool([
            {
                "role": "user", 
                "content": [
                    {"type": "text", "text": "What is answer of this image?" },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64.b64encode(f.read()).decode('utf-8')}"
                        },
                    }
                ]
            }
        ])
        print("Response:", response)

Tool called: add_numbers with parameters: {'a': 5, 'b': 3}
Result: 8
Response: 
The answer is 8.

Tool called: multiply_numbers with parameters: {'a': 4, 'b': 7}
Result: 28
Response: 
4 multiplied by 7 is 28.

Tool called: power_numbers with parameters: {'base': 2, 'exponent': 3}
Result: 8
Response: 
2 raised to the power of 3 is 8.

Tool called: power_numbers with parameters: {'base': 2, 'exponent': 10}
Result: 1024
Response: 
The answer to the image, which represents 2 raised to the power of 10, is 1024.