Function calling

Some of our models are compatible with openai function calling, so they support using the openai sdk to use the function calling capability

Currently supported models include:

• inf-chat-int-v1

Example

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.infly.cn/v1",
    api_key=os.getenv("INF_OPENAPI_API_KEY"),
)

def get_weather(params: dict):
    fake_response = {
        "location": params["location"],
        "weather_state": "晴",
        "temperature": 25 if params["unit"] == "c" else 77,
    }
    
    return fake_response


tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["c", "f"]},
                },
                "required": ["location", "unit"],
                "additionalProperties": False,
            },
        },
    }
]


model="inf-chat-int-v1"

# First model call, get tool call
completion = client.chat.completions.create(
  model=model,
  messages=[{"role": "user", "content": "上海市徐汇滨江今天天气怎么样？"}],
  tools=tools,
)

print(completion.choices[0].message.tool_calls)

# will print:
#
# [ChatCompletionMessageToolCall(id='chatcmpl-tool-55d74479b50b4d5da957b25a4c50ccc2', function=Function(arguments='{"location": "\\u4e0a\\u6d77\\u5f90\\u6c47\\u6ee8\\u6c5f", "unit": "c"}', name='get_weather'), type='function')]
# 


# extract the arguments of the tool call
if completion.choices[0].message.tool_calls:
    tool_call = completion.choices[0].message.tool_calls[0]
    tool_name = tool_call.function.name
    tool_args = eval(tool_call.function.arguments)
    
    # Call the function with the extracted arguments
    tool = globals()[tool_name]

    if not tool:
        raise ValueError(f"Tool {tool_name} not found")

    result = tool(tool_args)
    
    # Send the result back to the model
    follow_up_completion = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "user", "content": "上海徐汇滨江今天天气怎么样?"},
            {
                "role": "tool",
                "content": str(result),
            },
        ],
    )
    
    print(follow_up_completion.choices[0].message.content)

# will print:
# 今天上海市徐汇滨江天气晴，温度25摄氏度

streaming

import os
from openai import OpenAI
import json

client = OpenAI(
    base_url="https://api.infly.cn/v1",
    api_key=os.getenv("INF_OPENAPI_API_KEY"),
)


model="inf-chat-int-v1"


def get_weather(params: dict):
    fake_response = {
        "location": params["location"],
        "weather_state": "晴",
        "temperature": 25 if params.get("unit", "c") == "c" else 77,
    }
    
    return fake_response


tools = [
  {
      "type": "function",
      "function": {
          "name": "get_weather",
          "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["c", "f"]},
                },
                "required": ["location", "unit"],
                "additionalProperties": False,
            },
      },
  }
]

completion = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "上海徐汇滨江今天天气怎么样?"}],
        tools=tools,
        stream=True,
        stream_options={"include_usage": True},
    )

function_arguments = ""
function_name = ""
is_collecting_function_args = False

tool_responses = []

tool_call_content = ""

for part in  completion:
    if not part.choices:
        continue
    delta = part.choices[0].delta
    finish_reason = part.choices[0].finish_reason

    # Process assistant content
    if 'content' in delta:
        tool_call_content += delta.content or ""

    if delta.tool_calls:
        is_collecting_function_args = True
        tool_call = delta.tool_calls[0]

        if not tool_call.function:
            continue

        if tool_call.function.name:
            function_name = tool_call.function.name
            print(f"Function name: '{function_name}'")
        
        # Process function arguments delta
        if tool_call.function.arguments:
            function_arguments += tool_call.function.arguments
            print(f"Arguments: {function_arguments}")

    # Process tool call with complete arguments
    if finish_reason == "tool_calls" and is_collecting_function_args:
        print(f"Function call '{function_name}' is complete.")
        args = json.loads(function_arguments)
        print("Complete function arguments:")
        print(json.dumps(args, indent=2))


        # Call the function with the extracted arguments
        tool = globals()[function_name]

        if not tool:
            raise ValueError(f"Tool {function_name} not found")


        result = tool(eval(function_arguments))

        tool_responses.append(json.dumps(result))

    
        # Reset for the next potential function call
        function_arguments = ""
        function_name = ""
        is_collecting_function_args = False

tool_response_msgs = [{"role": "tool", "content": response} for response in tool_responses]
        
# Send the result back to the model
follow_up_completion = client.chat.completions.create(
    model=model,
    messages=[
        {"role": "user", "content": "上海徐汇滨江今天天气怎么样?"},
        {
            "role": "assistant",
            "content": tool_call_content,
        },
        *tool_response_msgs
    ],
)

print(follow_up_completion.choices[0].message.content)

The above code will print the following output:

Function name: 'get_weather'
Arguments: {"location": "
Arguments: {"location": "上海
Arguments: {"location": "上海徐
Arguments: {"location": "上海徐汇
Arguments: {"location": "上海徐汇滨江
Arguments: {"location": "上海徐汇滨江", "unit": "
Arguments: {"location": "上海徐汇滨江", "unit": "c
Arguments: {"location": "上海徐汇滨江", "unit": "c"}
Function call 'get_weather' is complete.
Complete function arguments:
{
  "location": "上海徐汇滨江",
  "unit": "c"
}
上海徐汇滨江今天天气晴朗，温度为25摄氏度。