I want to use structured output with Azure OpenAI.
I tried the following code, based on the code given in https://openai.com/index/introducing-structured-outputs-in-the-api/:
from pydantic import BaseModel
from openai import AzureOpenAI
class Step(BaseModel):
explanation: str
output: str
class MathResponse(BaseModel):
steps: list[Step]
final_answer: str
client = AzureOpenAI(api_key='[redacted]',
api_version='2024-05-01-preview',
azure_endpoint='[redacted]')
completion = client.beta.chat.completions.parse(
model="gpt-4omini-2024-07-18-name",
messages=[
{"role": "system", "content": "You are a helpful math tutor."},
{"role": "user", "content": "solve 8x + 31 = 2"},
],
response_format=MathResponse,
)
message = completion.choices[0].message
if message.parsed:
print(message.parsed.steps)
print(message.parsed.final_answer)
else:
print(message.refusal)
I get the error:
openai.BadRequestError: Error code: 400:
{
"error": {
"message": "Invalid parameter: response_format must be one of json_object, text.",
"type": "invalid_request_error",
"param": "response_format",
"code": "None"
}
}
How to fix it?
I ran pip install -U openai: I use openai==1.40.1 and Python 3.11.
I also tried https://cookbook.openai.com/examples/structured_outputs_intro using using Azure+ GPT-4o mini (2024-07-18), it didn’t work either, same error message:
from openai import AzureOpenAI
# Replace these variables with your Azure OpenAI endpoint and API key
endpoint = "https://<your-resource-name>.openai.azure.com"
api_key = "<your-api-key>"
deployment_name = "<your-deployment-name>" # Replace with your deployment name
MODEL = deployment_name
# API endpoint for the completion request
api_url = f"{endpoint}/openai/deployments/{deployment_name}/chat/completions?api-version=2024-06-01"
client = AzureOpenAI(api_key='[redacted]',
api_version='2024-07-01-preview',
azure_endpoint='https://[redacted].openai.azure.com/')
math_tutor_prompt = '''
You are a helpful math tutor. You will be provided with a math problem,
and your goal will be to output a step by step solution, along with a final answer.
For each step, just provide the output as an equation use the explanation field to detail the reasoning.
'''
def get_math_solution(question):
response = client.chat.completions.create(
model=MODEL,
messages=[
{
"role": "system",
"content": math_tutor_prompt
},
{
"role": "user",
"content": question
}
],
response_format={
"type": "json_schema",
"json_schema": {
"name": "math_reasoning",
"schema": {
"type": "object",
"properties": {
"steps": {
"type": "array",
"items": {
"type": "object",
"properties": {
"explanation": {"type": "string"},
"output": {"type": "string"}
},
"required": ["explanation", "output"],
"additionalProperties": False
}
},
"final_answer": {"type": "string"}
},
"required": ["steps", "final_answer"],
"additionalProperties": False
},
"strict": True
}
}
)
return response.choices[0].message
# Testing with an example question
question = "how can I solve 8x + 7 = -23"
result = get_math_solution(question)
print(result.content)
7
it’s not available yet on Azure, but if you want to see what it’s like. you can try this:
it works for all models support function calling.
it’s not an ideal solution, I am just going to wait for the actual structured ouputs to be available.
import os
import json
from openai import AzureOpenAI
from pprint import pprint
# Initialize the Azure OpenAI client
client = AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version=os.getenv("OpenAI_API_VERSION")
)
deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_ID")
# Initial user message
messages = [
{"role": "system", "content": "You are a helpful math tutor. that solve math questions step by step."},
{"role": "user", "content": "solve 8x + 31 = 2"}
]
# Define the JSON schema for structured output
tools = [
{
"type": "function",
"function": {
"name": "math_response",
"description": "help solve math questions step by step following defined format.",
"parameters": {
"type": "object",
"properties": {
"steps": {
"type": "array",
"items": {
"type": "object",
"properties": {
"explanation": {"type": "string"},
"output": {"type": "string"}
},
"required": ["explanation", "output"]
}
},
"final_answer": {"type": "string"}
},
"required": ["steps", "final_answer"],
}
}
}
]
# First API call: Ask the model to use the function
response = client.chat.completions.create(
model=deployment_name,
messages=messages,
tools=tools,
# tool_choice="none",
)
# Process the model's response
response_message = response.choices[0].message
# Handle function calls
if response_message.tool_calls:
for tool_call in response_message.tool_calls:
function_args = json.loads(tool_call.function.arguments)
function_args
here is the output
{'steps': [{'explanation': 'Start with the equation 8x + 31 = 2.',
'output': '8x + 31 = 2'},
{'explanation': 'Subtract 31 from both sides to isolate the term with x.',
'output': '8x = 2 - 31'},
{'explanation': 'Simplify the right side: 2 - 31 = -29.',
'output': '8x = -29'},
{'explanation': 'Divide both sides by 8 to solve for x.',
'output': 'x = -29/8'},
{'explanation': 'Simplifying -29/8 gives the final answer.',
'output': 'x = -3.625'}],
'final_answer': 'x = -3.625'}
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