OpenAI Connector
The OpenAI Connector is an outbound Connector that allows you to use ChatGPT or Moderation API in your BPMN process.
Prerequisites
To use the OpenAI Connector, create an OpenAI account and create an API key.
Refer to the OpenAI Platform documentation for a detailed setup guide.
Use Camunda secrets to avoid exposing your sensitive data, such as your OpenAI API key, as plain text. Follow our documentation on managing secrets to learn more.
Create an OpenAI Connector task
A Connector can be applied to a task or an event using the append menu. Access the append menu using any of the three methods below:
- From the canvas: Select an element and click the Change element icon to change an existing element, or use the append feature to add a new element to the diagram.
- From the properties panel: Navigate to the Template section and click Select.
- From the side palette: Click the Create element icon.
Once you have applied a Connector to your element, follow the configuration steps or read our guide on using Connectors to learn more.
Make your OpenAI Connector executable
To work with the OpenAI Connector, fill all mandatory fields.
Authentication
To use the OpenAI Connector, obtain an API key from OpenAI. To create an OpenAI account and learn more about API keys, visit the OpenAI Platform documentation.
Create a new Connector secret
Keep your API key safe and avoid exposing it in the BPMN xml file by creating a secret:
- Follow our guide for creating secrets.
- Name your secret (i.e
OPENAI_API_KEY) so you can reference it later in the Connector.
Configure the API key
Select the OpenAI API key field in the Authentication section and set it to the secret you created (e.g. {{secrets.OPENAI_API_TOKEN}}).
Operations
The OpenAI Connector currently supports two operation types in the Operation dropdown list: Chat and Moderation.
Chat
With the Chat operation, you can interact with OpenAI chat-based language models.
Model
The Model dropdown list allows you to select the model.
Refer to the Models section of OpenAI documentation for detailed information about models.
Custom model version
The Model dropdown list does not contain all available models.
To use a model that is not listed, use the Custom option and provide the model name in the Custom model version field that appears.
Selection of models is user-specific and depends on your account privileges. For this reason, GPT-4 may appear as non-existing when you attempt to use it, although it is defined in the element template.
Temperature
The Temperature field controls the randomness of the model's output. Lower temperatures make the model more deterministic and less random, while higher temperatures make the model more random.
Accepted values are between 0 and 2 (add a leading 0 for values less than 1), and you can use two digits after the decimal point.
System message
The System message field allows you to provide initial instructions for the model, and helps set the behavior of the assistant.
For example, if you want ChatGPT to translate the prompt into a different language instead of interpreting the questions contained in the prompt, you can set the System message to
You are a translator bot. You provide literal translation of inputs from English into German. You do not interpret the input.
Chat history
OpenAI API doesn't store message history for ChatGPT. Therefore, it is up to you as a process developer to decide if and how you should retain the chat history.
The Chat history input field may contain the history of previous messages or examples of the desired behavior. Following the translation example above, you can provide some translation examples to make the expectations clearer.
Chat history consumed by this Connector follows the chat format described in the corresponding part of OpenAI documentation.
Prompt
While System message and Chat history fields are optional and provide the model with additional context, Prompt is the actual input. This is the query that is used to trigger the model output.
To use the System message, Chat history, and Prompt together, you would follow this format:
The example below illustrates how you can use System message, Chat history, and Prompt together.
System message
You are a helpful assistant.
Chat history
= [
{"role": "user", "content": "Who won the world series in 2020?"},
{"role": "assistant", "content": "The Los Angeles Dodgers the World series in 2020."}
]
Prompt
Where was it played?
In this example, the chat history provides the context of a user asking who won the World Series in 2020, and the assistant providing the correct answer that the Los Angeles Dodgers won. The prompt, "Where was it played?" is the follow-up question that seeks additional information about the location where the World Series took place in 2020.
Find more complex examples of prompt engineering and sample real-life use cases of ChatGPT on the OpenAI examples page.
Choices to generate
The numeric Choices to generate field determines how many alternative answers the model returns in the API response.
Sample chat output
You can use an output mapping to map the response:
- Use Result Variable to store the response in a process variable. For example,
myResultVariable. - Use Result Expression to map fields from the response into process variables.
{
"status": 200,
"headers": {
# response headers
},
"body": {
"id": "chatcmpl-6ws27w7nADFLWp7KD3dhjiUmP0kfu",
"object": "chat.completion",
"created": 1679488747,
"model": "gpt-3.5-turbo-0301",
"usage": {
"prompt_tokens": 16,
"completion_tokens": 79,
"total_tokens": 95
},
"choices": [
{
"message": {
"role": "assistant",
"content": "ChatGPT has gained significant attention in recent years, especially with the development and advancement of Natural Language Processing (NLP) tools used in chatbots and virtual assistants."
},
"finish_reason": "stop",
"index": 0
}
]
}
}
Moderation
It is recommended to use the Moderation API to sanitize inputs and outputs of the language model. You will be able to prevent violation of OpenAI policies and displaying the potentially unsafe content in your system.
Evaluation input
Sample moderation output
Output contains the evaluation result broken down by violation categories. To learn more about Moderation output, visit the OpenAI documentation.
{
"status": 200,
"headers": {
# response headers
},
"body": {
"id": "modr-6wtH8E1f2W533qdQAzq8dUpmKRVCV",
"model": "text-moderation-004",
"results": [
{
"flagged": false,
"categories": {
"sexual": false,
"hate": false,
"violence": false,
"self-harm": false,
"sexual/minors": false,
"hate/threatening": false,
"violence/graphic": false
},
"category_scores": {
"sexual": 1.0084246241603978E-5,
"hate": 5.5422344303224236E-5,
"violence": 8.184280159184709E-5,
"self-harm": 1.3117542607687938E-7,
"sexual/minors": 4.457491709075612E-9,
"hate/threatening": 9.144552337581047E-10,
"violence/graphic": 1.770446012017146E-8
}
}
]
}
}