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Function calling (also called “tool use”) lets models invoke structured functions you define. The model decides when to call a function, generates the arguments, and you execute the function and return the result.
OpenAI-Compatible Function Calling Use the tools parameter with the Chat Completions endpoint:
{ "model" : "Qwen/Qwen2.5-VL-72B-Instruct-together" , "messages" : [ { "role" : "user" , "content" : "What's the weather in San Francisco?" } ], "tools" : [ { "type" : "function" , "function" : { "name" : "get_weather" , "description" : "Get the current weather for a location" , "parameters" : { "type" : "object" , "properties" : { "location" : { "type" : "string" , "description" : "City and state, e.g. 'San Francisco, CA'" }, "unit" : { "type" : "string" , "enum" : [ "celsius" , "fahrenheit" ], "description" : "Temperature unit" } }, "required" : [ "location" ] } } } ], "tool_choice" : "auto" }
When the model decides to use a tool, the response includes a tool_calls array:
{ "id" : "chat-abc123" , "choices" : [ { "index" : 0 , "message" : { "role" : "assistant" , "content" : null , "tool_calls" : [ { "id" : "call_abc123" , "type" : "function" , "function" : { "name" : "get_weather" , "arguments" : "{ \" location \" : \" San Francisco, CA \" , \" unit \" : \" fahrenheit \" }" } } ] }, "finish_reason" : "tool_calls" } ] }
Send the tool result back to continue the conversation:
{ "model" : "Qwen/Qwen2.5-VL-72B-Instruct-together" , "messages" : [ { "role" : "user" , "content" : "What's the weather in San Francisco?" }, { "role" : "assistant" , "content" : null , "tool_calls" : [ { "id" : "call_abc123" , "type" : "function" , "function" : { "name" : "get_weather" , "arguments" : "{ \" location \" : \" San Francisco, CA \" , \" unit \" : \" fahrenheit \" }" } } ] }, { "role" : "tool" , "tool_call_id" : "call_abc123" , "content" : "{ \" temperature \" : 62, \" unit \" : \" fahrenheit \" , \" condition \" : \" foggy \" }" } ] }
Full Python Example import json from openai import OpenAI client = OpenAI( api_key = "YOUR_MODELSLAB_API_KEY" , base_url = "https://modelslab.com/api/v7/llm" , ) # Define your tools tools = [ { "type" : "function" , "function" : { "name" : "get_weather" , "description" : "Get the current weather for a location" , "parameters" : { "type" : "object" , "properties" : { "location" : { "type" : "string" , "description" : "City name" } }, "required" : [ "location" ] } } } ] # Your actual function implementations def get_weather ( location : str ) -> str : # Replace with real weather API call return json.dumps({ "temperature" : 62 , "condition" : "foggy" }) # Step 1: Send message with tools response = client.chat.completions.create( model = "Qwen/Qwen2.5-VL-72B-Instruct-together" , messages = [{ "role" : "user" , "content" : "What's the weather in Paris?" }], tools = tools, tool_choice = "auto" , ) message = response.choices[ 0 ].message # Step 2: Check if the model wants to call a function if message.tool_calls: # Execute the function tool_call = message.tool_calls[ 0 ] args = json.loads(tool_call.function.arguments) result = get_weather( ** args) # Step 3: Send the result back final_response = client.chat.completions.create( model = "Qwen/Qwen2.5-VL-72B-Instruct-together" , messages = [ { "role" : "user" , "content" : "What's the weather in Paris?" }, message, { "role" : "tool" , "tool_call_id" : tool_call.id, "content" : result, }, ], tools = tools, ) print (final_response.choices[ 0 ].message.content) else : print (message.content)
Use the tools parameter with the Messages endpoint:
{ "model" : "Qwen/Qwen2.5-VL-72B-Instruct-together" , "max_tokens" : 1024 , "tools" : [ { "name" : "get_weather" , "description" : "Get the current weather for a location" , "input_schema" : { "type" : "object" , "properties" : { "location" : { "type" : "string" , "description" : "City name" } }, "required" : [ "location" ] } } ], "messages" : [ { "role" : "user" , "content" : "What's the weather in San Francisco?" } ] }
{ "id" : "msg_abc123" , "type" : "message" , "role" : "assistant" , "content" : [ { "type" : "tool_use" , "id" : "toolu_abc123" , "name" : "get_weather" , "input" : { "location" : "San Francisco" } } ], "stop_reason" : "tool_use" }
{ "model" : "Qwen/Qwen2.5-VL-72B-Instruct-together" , "max_tokens" : 1024 , "tools" : [ ... ], "messages" : [ { "role" : "user" , "content" : "What's the weather in San Francisco?" }, { "role" : "assistant" , "content" : [ { "type" : "tool_use" , "id" : "toolu_abc123" , "name" : "get_weather" , "input" : { "location" : "San Francisco" } } ] }, { "role" : "user" , "content" : [ { "type" : "tool_result" , "tool_use_id" : "toolu_abc123" , "content" : "{ \" temperature \" : 62, \" condition \" : \" foggy \" }" } ] } ] }
Control when the model uses tools:
Value Behavior "auto"Model decides whether to call a tool (default) "none"Model will not call any tools "required"Model must call at least one tool {"type": "function", "function": {"name": "get_weather"}}Force a specific tool
Tips
Write clear tool descriptions
The model uses the description field to decide when to call a tool. Be specific about what the tool does and when it should be used.
Use JSON Schema for parameters
Define parameters with JSON Schema including types, descriptions, enums, and required fields. The more precise your schema, the better the model’s arguments will be.
Handle multiple tool calls
Models may want to call multiple tools in a single response. Always check for and handle all tool calls in the tool_calls array.
Some models support calling multiple tools in parallel within a single response. Process all tool calls and return all results before sending the next message.
