I’m attempting to set up the GPT-4 vision preview model to generate responses in JSON format, but I’m encountering validation errors.
According to the OpenAI documentation, setting the response format to JSON should work for specific models. However, when I try to include the JSON response format parameter in my API request, I receive the following error message:
{'error': {'message': '1 validation error for Request\nbody -> response_format\n extra fields not permitted (type=value_error.extra)', 'type': 'invalid_request_error', 'param': None, 'code': None}}
The API call functions correctly when I exclude the response format specification. Below is my current code implementation:
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
data = {
"model": "gpt-4-vision-preview",
"response_format": {"type": "json_object"},
"messages": [
{
"role": "system",
"content": "You are an AI assistant. Please provide the response in JSON format."
},
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 1500,
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=data)
print(response.json())
Has anyone managed to enable JSON mode with the vision preview model? What could I be overlooking?
gpt-4-vision-preview doesn’t support response_format yet - super annoying. I just add “respond with valid json only, no markdown or extra text” to my system prompt. Also helps to end your user message with “format as json:” so it knows what you want upfront.
Yeah, the vision preview model doesn’t support structured response_format yet. I hit this same issue a few weeks back on an image analysis project. You’re on the right track using the system message to request JSON, but you need to get way more specific about what you want. I’ve found that spelling out exact field names and data types makes a huge difference. Try something like “Return your response as valid JSON with fields ‘description’, ‘objects’, and ‘confidence_score’” instead of just asking for generic JSON. The model will try to follow your formatting instructions, but it’s definitely not as solid as the formal response_format parameter would be.
Hit this same issue building an automated invoice tool. GPT-4 Vision doesn’t support response_format - super annoying since it works fine with regular models. Tried a bunch of approaches and found that adding instructions at the end of your user message works way better than just using the system prompt. I tack on something like “Format your response as valid JSON with no extra text” right after my question. Here’s what actually works: be redundant. Put JSON formatting instructions in both system AND user messages. Also wrap your parsing in try-catch and strip any markdown that sometimes slips through before running JSON.parse(). Not pretty, but it’s solid in production.
yeah, currently gpt-4-vision-preview doesnt support the response_format param. u just gotta make sure to ask for json in your sys msg, like you’re doin. it should work but no promises!
This messy API limitation is exactly why I switched to automation workflows instead of fighting with code constantly.
Yeah, the vision model doesn’t support response_format, but there’s a way cleaner approach. Skip the hardcoded prompts and hoping the model behaves - set up an automated workflow that handles your images in multiple steps.
I run similar image analysis by creating workflows that extract visual data first, format it properly, then validate JSON structure before passing it along. Everything runs automatically and catches formatting problems before they break stuff.
You can build conditional logic that tries different prompt strategies when the first JSON attempt fails. Plus you get proper error handling and retry logic built in. Way more reliable than crossing your fingers with prompt engineering.
Set it up once and you’re done. Beats debugging API calls every time OpenAI changes something.
Had the exact same issue last month building an OCR validation system. The vision preview model doesn’t support response_format yet, which sucks since regular GPT-4 handles it fine. What worked for me was being super explicit in the prompt. Instead of just asking for JSON, I include an actual example in my system message like: “Return response as JSON matching this structure: {"analysis": "text here", "items_found": ["item1", "item2"]}”. I also add “Ensure valid JSON syntax with proper quotes and brackets” at the end - otherwise it drops quotes or adds trailing commas. You’ll probably want to add JSON validation on your end too since there’s no guarantee without proper response_format support.