Extracting structured data from OpenAI responses in n8n workflow

Other Questions N8n
1

I’m working on an n8n workflow that uses OpenAI to process customer messages. My goal is to extract specific info like names and locations from these messages. I want the output to be in a consistent JSON format that’s easy for my system to handle.

Here’s what I’m aiming for:

{
  "response": "Hello! What should I call you?",
  "extracted_data": [
    {
      "key": "Location",
      "data": [{ "text": "Miami" }]
    },
    {
      "key": "Customer",
      "data": [{ "text": "Tom" }]
    }
  ]
}

What’s the best way to set up the OpenAI node in n8n to get this kind of structured output? How can I make sure the AI always follows this format? Any advice on making the results reliable and easy to parse would be great.

2

I’ve tackled similar challenges in my projects. One effective approach is to use a combination of prompt engineering and post-processing. In the OpenAI node, craft a detailed prompt that explicitly instructs the model to format its response in your desired JSON structure. Include examples in your prompt to guide the model. Then, add a Function node after the OpenAI node to validate and clean up the JSON if needed. This two-step process has worked well for me, ensuring more consistent and reliable outputs. Just keep in mind that you may need to fine-tune your prompt over time as you encounter edge cases. Also, consider implementing a fallback mechanism for instances where the structure isn’t perfect – it’s saved me more than once!

3

hey there! i’ve used fucntion calling in the openai node to get structured output. set up a function with the needed parameters, then parse json in a code node. defining your schema clearly helps. good luck!

4

To achieve structured output from OpenAI in n8n, I recommend utilizing the function calling feature. First, define a clear JSON schema that outlines the exact structure you want. In the OpenAI node, set up a function with parameters matching your desired output format. Instruct the model to use this function for responses. After receiving the output, use a Code node to parse and validate the JSON. This approach ensures consistency and makes data extraction straightforward. For reliability, implement error handling and fallback options in case the model deviates from the expected format. Regular testing and refinement of your prompts will help maintain accuracy over time.