FileOps + Calculator 示例：读取、计算并写回¶

import json
import os

from cicada.core.model import MultiModalModel
from cicada.core.utils import cprint
from dotenv import load_dotenv

from toolregistry import ToolRegistry
from toolregistry.hub import Calculator, FileOps

load_dotenv()

# Initialize LLM model
model_name = os.getenv("MODEL", "deepseek-v3")
API_KEY = os.getenv("API_KEY", "your-api-key")
BASE_URL = os.getenv("BASE_URL", "https://api.deepseek.com/")
stream = os.getenv("STREAM", "True").lower() == "true"

llm = MultiModalModel(
    api_key=API_KEY,
    api_base_url=BASE_URL,
    model_name=model_name,
    stream=stream,
)

# Initialize tool registry and register Calculator static methods
tool_registry = ToolRegistry()
tool_registry.register_from_class(Calculator, namespace=True)
tool_registry.register_from_class(FileOps, namespace=True)
print(tool_registry.get_available_tools())

input_file = "examples/hub_related/concurrent_raw_results.txt"
output_file = "examples/hub_related/concurrent_average_results.txt"
# drop existing output file
if os.path.exists(output_file):
    os.remove(output_file)

# Example instruction to compute the averages
instruction = f"""
I have a few test results from multiple runs.
Please compute the averages of the metrics for each category. Attention to the EXEC_MODE, there are two different types. Compute average metrics separately. So there should be 8 results The input is at {input_file}. Write your output to {output_file}.
"""

# Query LLM to get result
response = llm.query(instruction, tools=tool_registry, stream=stream)
cprint(json.dumps(response, indent=2))

import os

from dotenv import load_dotenv
from openai import OpenAI

from toolregistry import ToolRegistry
from toolregistry.hub import Calculator, FileOps

# Load environment variables from .env file
load_dotenv()


model_name = os.getenv("MODEL", "deepseek-v3")
stream = os.getenv("STREAM", "True").lower() == "true"

API_KEY = os.getenv("API_KEY", "your-api-key")
BASE_URL = os.getenv("BASE_URL", "https://api.deepseek.com/")

# Initialize tool registry and register Calculator static methods
tool_registry = ToolRegistry()
tool_registry.register_from_class(Calculator, namespace=True)
tool_registry.register_from_class(FileOps, namespace=True)
print(tool_registry.get_available_tools())

input_file = "examples/hub_related/concurrent_raw_results.txt"
output_file = "examples/hub_related/concurrent_average_results.txt"
# drop existing output file
if os.path.exists(output_file):
    os.remove(output_file)

# Set up OpenAI client
client = OpenAI(
    api_key=os.getenv("API_KEY", "your-api-key"),
    base_url=os.getenv("BASE_URL", "https://api.deepseek.com/"),
)


messages = [
    {
        "role": "user",
        "content": f"""
I have a few test results from multiple runs.
Please compute the averages of the metrics for each category. Attention to the EXEC_MODE, there are two different types. Compute average metrics separately. So there should be 8 results The input is at {input_file}. Write your output to {output_file}. Use your available tools at hand to do this.
""",
    }
]
# Make the chat completion request
response = client.chat.completions.create(
    model=model_name,
    messages=messages,
    tools=tool_registry.get_schemas(),
    tool_choice="auto",
)


def handle_tool_calls(response, messages):
    """Handle tool calls in a loop until no more tool calls are needed"""
    while response.choices[0].message.tool_calls:
        tool_calls = response.choices[0].message.tool_calls
        print("Tool calls:", tool_calls)

        # Execute tool calls
        tool_responses = tool_registry.execute_tool_calls(tool_calls)

        # Construct assistant messages with results
        assistant_tool_messages = tool_registry.build_tool_call_messages(
            tool_calls, tool_responses
        )

        messages.extend(assistant_tool_messages)

        # Send the results back to the model
        response = client.chat.completions.create(
            model=model_name,
            messages=messages,
            tools=tool_registry.get_schemas(),
            tool_choice="auto",
        )
    return response


# Handle tool calls using the new function (without iteration limit)
response = handle_tool_calls(response, messages)

# Print final response
if response.choices[0].message.content:
    print(response.choices[0].message.content)