Integrating ChatGPT with Claris FileMaker

by Charlie Bailey - Technical Lead

Our goal with this post is to get you up and running with the OpenAI API as quickly as possible. We’re going to focus on a very basic technical implementation. If you’re interested in why you might want to integrate your custom Claris FileMaker solution with ChatGPT, check out this companion post by Cristos Lianides-Chin.

Getting Setup with the API

The OpenAI API documentation is a great place to start. From the main page, you can log in (or create a developer account). Once you have created your account, navigate to your account preferences, and on the Organization/Settings tab, enter an organization name, and note the Organization ID — you’ll need this later for your API request. On the Billing tab, you’ll need to set up a payment method; each API request costs a few pennies, and you’ll get a block of free requests during your introductory period. Lastly, you’ll need to generate an API key in the User/API keys tab. Create a key and note it; you will need this for your API request.

Demo Quickstart

Launch the demo, and on the “prompts” layout, enter your Organization ID and API key in the setup area. Try one of the predefined prompts or create a new record and try your own. Enter a prompt (required), add some source text (optional), and try out the davinci or GPT-4 model.

Behind the Scenes: How it Works

As we dive behind the scenes and prepare to interact with the OpenAI API, let’s take a look at the “Hello World” script in the examples folder. This script runs a script from the private folder (“private.helloWorld”) and displays the response. This “private” script assembles the API request and calls the script “private.APIRequest”, and returns the response which we display in a custom dialog. If we simply run the “Hello World” script, we should get a simple custom dialog that displays “Hello World!”, and in this case, it’s the large language model davinci-003 that generates this text for us.

To work this magic, we’ve referenced the Completions endpoint documentation and configured an appropriate POST request. According to the documentation, we need to specify which model to use, and we need a prompt. The model is the large language model that we want to use — note that there are lots of different models to explore, and not all endpoints support all models. The prompt is our question or statement that we want the model to evaluate. In this case, our prompt (say “Hello World”) is pretty simple!

The script “private.helloWorld” assembles the relevant data for the request, executes the request using the Insert from URL script step (see the subscript “private.APIRequest”), and returns the result:
If all goes well, we’ll get a response from the API that looks something like this:
"choices" :
"finish_reason" : "stop",
"index" : 0,
"logprobs" : null,
"text" : "\n\nHello World!"
"created" : 1688909468,
"id" : "cmpl-7aOnE0GYjfFrPkkY9pqTbf4atMN3U",
"model" : "text-davinci-003",
"object" : "text_completion",
"usage" :
"completion_tokens" : 5,
"prompt_tokens" : 5,
"total_tokens" : 10

There’s a lot in here to unpack, but the value we’re looking for is in the first (and only) object in the “choices” array, specifically the value for “text”. Of note, the usage object will let us know how many tokens we’ve used for this request. Armed with this JSON result, the “Hello World” example script unpacks the text result and displays “Hello World!” in a custom dialog.

A Look at Authentication

When integrating with any API, authentication is usually the first hurdle, and the OpenAI API is no exception. In this case, we’re going to pass the Organization ID and secret key as headers in each request. Our request will need to take the form:

To accomplish this, we’ll set up our cURL options in a variable and pass this as part of our request via the “Insert from URL” script step. See step 17 in the script above and the details of that script step here:
Note: The Quote() and List() functions provide a convenient way to handle the embedded quote characters and format the output for readability when debugging.

Which Model to Choose

As noted above, not all endpoints support all models so depending on what you want to accomplish, you may have a limited set of models to choose from. Models have different capabilities and significantly different fees and it’s important to choose carefully. You’ll want to experiment with the different models to ensure you’re getting the best possible response from your prompts.

You’ll be charged a fee based on how many tokens you use, and different models have different rates, but all models use this token-based fee structure. Essentially, longer prompts and longer responses will use more tokens, and your fees will be higher. At this time, pricing ranges from $0.0015 to $0.12 per 1,000 tokens depending on the model you choose.

Another Example

With the Hello World example under our belt, now we can send a more interesting prompt to the API. Let’s use the text-davinci-003 model (the same one we used for the Hello World example) to take a user prompt and return a text completion. The mechanics are virtually the same as before; we just need to assemble the prompt based on user input

In line 8, we’re ensuring that we only send 3000 characters from our sourceText field — this is just a crude way to limit the amount of data we’re sending to the API (minimizing our token usage). Of more importance is line 9 where we are URL encoding the prompt to ensure we don’t submit a malformed request. The rest is nearly identical to the Hello World example: we assemble our request, send it to the API (see the scripts “private.completion”, and “private.APIRequest” for more details), and process the response.


This demo file and examples are designed to simply get you started. Where this journey takes us is still very much an unanswered question. At Codence, we’re looking into all sorts of ways to leverage this technology to enhance the solutions that we build for our customers (and for ourselves). Summarizing long email threads, writing code, predicting outcomes, writing documentation, and assisting users are all areas that we are exploring.

How are you using this technology? We’d love to know!

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