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Azure OpenAI Assistants function calling

  • Article

The Assistants API supports function calling, which allows you to describe the structure of functions to an Assistant and then return the functions that need to be called along with their arguments.

Note

  • File search can ingest up to 10,000 files per assistant - 500 times more than before. It is fast, supports parallel queries through multi-threaded searches, and features enhanced reranking and query rewriting.
    • Vector store is a new object in the API. Once a file is added to a vector store, it's automatically parsed, chunked, and embedded, made ready to be searched. Vector stores can be used across assistants and threads, simplifying file management and billing.
  • We've added support for the tool_choice parameter which can be used to force the use of a specific tool (like file search, code interpreter, or a function) in a particular run.

Function calling support

Supported models

The models page contains the most up-to-date information on regions/models where Assistants are supported.

To use all features of function calling including parallel functions, you need to use a model that was released after November 6th 2023.

API Versions

  • 2024-02-15-preview
  • 2024-05-01-preview

Example function definition

Note

  • We've added support for the tool_choice parameter which can be used to force the use of a specific tool (like file_search, code_interpreter, or a function) in a particular run.
  • Runs expire ten minutes after creation. Be sure to submit your tool outputs before this expiration.
from openai import AzureOpenAI
    
client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
    api_version="2024-02-15-preview",
    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
    )

assistant = client.beta.assistants.create(
  instructions="You are a weather bot. Use the provided functions to answer questions.",
  model="gpt-4-1106-preview", #Replace with model deployment name
  tools=[{
      "type": "function",
    "function": {
      "name": "getCurrentWeather",
      "description": "Get the weather in location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
          "unit": {"type": "string", "enum": ["c", "f"]}
        },
        "required": ["location"]
      }
    }
  }, {
    "type": "function",
    "function": {
      "name": "getNickname",
      "description": "Get the nickname of a city",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
        },
        "required": ["location"]
      }
    } 
  }]
)

Reading the functions

When you initiate a Run with a user Message that triggers the function, the Run will enter a pending status. After it processes, the run will enter a requires_action state that you can verify by retrieving the Run.

{
  "id": "run_abc123",
  "object": "thread.run",
  "assistant_id": "asst_abc123",
  "thread_id": "thread_abc123",
  "status": "requires_action",
  "required_action": {
    "type": "submit_tool_outputs",
    "submit_tool_outputs": {
      "tool_calls": [
        {
          "id": "call_abc123",
          "type": "function",
          "function": {
            "name": "getCurrentWeather",
            "arguments": "{\"location\":\"San Francisco\"}"
          }
        },
        {
          "id": "call_abc456",
          "type": "function",
          "function": {
            "name": "getNickname",
            "arguments": "{\"location\":\"Los Angeles\"}"
          }
        }
      ]
    }
  },
...

Submitting function outputs

You can then complete the Run by submitting the tool output from the function(s) you call. Pass the tool_call_id referenced in the required_action object above to match output to each function call.

from openai import AzureOpenAI
    
client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
    api_version="2024-02-15-preview",
    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
    )


run = client.beta.threads.runs.submit_tool_outputs(
  thread_id=thread.id,
  run_id=run.id,
  tool_outputs=[
      {
        "tool_call_id": call_ids[0],
        "output": "22C",
      },
      {
        "tool_call_id": call_ids[1],
        "output": "LA",
      },
    ]
)

After you submit tool outputs, the Run will enter the queued state before it continues execution.

See also