OpenAI Enhanced Their API With Robust Structured Output Capabilities
You might recall that OpenAI introduced structured output some time ago, particularly in the form of JSON. However, it was initially experimental and had two significant limitations. These vulnerabilities have now been addressed with this latest development.
Introduction
Previously structured output could be created via toggling the JSON mode on / off, or by making use of function calling.
Large Language Models (LLMs), much like conversational UIs in general, excel at handling unstructured data presented as natural language. This unstructured input is first organised and processed, then transformed back into natural language as a structured response.
Previously two options were available JSON Mode & Function Calling…
OpenAI JSON Mode
Enabling OpenAI’s JSON mode doesn’t ensure that the output will adhere to a specific predefined JSON schema. It only guarantees that the JSON will be valid and parse without errors.
The challenge with OpenAI’s JSON Mode lies in the significant variability of the JSON output with each inference, making it impossible to predefine a consistent JSON schema.
Function Calling
To clarify, the chat completion API itself doesn’t call any functions, but the model can generate JSON output that you can use in your code to trigger function calls.
Introducing Structured Outputs in the API
Last year OpenAI introduced JSON mode as a valuable tool for developers aiming to build reliable applications using their models.
Although JSON mode enhances the model’s ability to generate valid JSON outputs, but has I have highlighted in previous articles, it does not ensure that the responses will adhere to a specific schema. Which makes this a more experimental feature than a production ready feature.
Now, OpenAI is introducing Structured Outputs in the API, a new feature designed to guarantee that model-generated outputs will precisely match the JSON Schemas provided by developers.
Structured output is available in two formats, Function Calling & A new option for the response_format parameter.
Function Calling Python Example
The following Python code of a Function Calling example can be copied and pasted as-is into a notebook:
# Install the requests library if not already installed
!pip install requests
import requests
import json
# Define your OpenAI API key
api_key = '<You API Key Goes Here>'
# Define the API endpoint
url = "https://api.openai.com/v1/chat/completions"
# Define the headers with the API key
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# Define the data for the API request
data = {
"model": "gpt-4o-2024-08-06",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function."
},
{
"role": "user",
"content": "look up all my orders in may of last year that were fulfilled but not delivered on time"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "query",
"description": "Execute a query.",
"strict": True,
"parameters": {
"type": "object",
"properties": {
"table_name": {
"type": "string",
"enum": ["orders"]
},
"columns": {
"type": "array",
"items": {
"type": "string",
"enum": [
"id",
"status",
"expected_delivery_date",
"delivered_at",
"shipped_at",
"ordered_at",
"canceled_at"
]
}
},
"conditions": {
"type": "array",
"items": {
"type": "object",
"properties": {
"column": {
"type": "string"
},
"operator": {
"type": "string",
"enum": ["=", ">", "<", ">=", "<=", "!="]
},
"value": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "object",
"properties": {
"column_name": {
"type": "string"
}
},
"required": ["column_name"],
"additionalProperties": False
}
]
}
},
"required": ["column", "operator", "value"],
"additionalProperties": False
}
},
"order_by": {
"type": "string",
"enum": ["asc", "desc"]
}
},
"required": ["table_name", "columns", "conditions", "order_by"],
"additionalProperties": False
}
}
}
]
}
# Make the API request
response = requests.post(url, headers=headers, data=json.dumps(data))
# Print the response
print(response.status_code)
print(response.json())Finally
JSON serves as a vital tool for structuring and exchanging data between AI agents and the functions they interact with, ensuring clear, consistent, and reliable communication across various systems and platforms.
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I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
https://openai.com/index/introducing-structured-outputs-in-the-api
