RobotFramework-AI is a library that adds AI functionality to the Robot Framework.
It can generate test data for you using the RealTestDataGenerator but also reply to your
messages with the Chatbot.
RealTestDataGenerator can generate test data for the Robot Framework similar to
the library Faker. The RealTestDataGenerator however, generates real existing data, using AI.
To generate test data simply import the package and use the keyword: Generate Test Data
This keyword takes various arguments, some being specific for the generation of certain
types of test data.
The following arguments can be used (arguments prefixed by an * are required):
- *ai_model: str: The AI model to be used, e.g. "openai", "gemini", "copilot", etc. Currently supporting: "openai"
- *type: str: The type of test data to create, e.g. "address", "user_data", etc. Currently supporting: "address"
- model: str: AI model specfic. The model of the AI model to be used. E.g. "gpt-3.5-turbo" when using the "openai" AI model.
Default per AI model:
- "openai" = "gpt-3.5-turbo"
- amount: int = 3 The amount of rows of test data to generate.
- format: str = None The format in which the test data will be given. If None, will return a 2 dimensional list.
- max_tokens: int = 256 The token limit for a conversation. Both prompt and response tokens will count towards this limit.
- temperature: float = 1 This value determines the creativity of the AI model. Can be anything from 0-2.
- top_p: float = 1 Similar to temperature. Determines the selection of tokens before selecting one. The higher the value the more less likely tokens get added to the selection. Can be anything from 0-2. At 1, only the top 50% of tokens will be used when selecting a token at 0 all tokens will be taken into consideration
- frequency_penalty: float = 0 Penalizes more frequent token reducing the chance of it reappearing. Negative values encourage it to reuse tokens. Can be anything from -2 to 2.
- presence_penalty: float = 0 Exact same as frequency_penalty except its scope is reduced to the immediate context. The immediate context can be seen as one or more paragrahps about a singular subject. Can be anything from -2 to 2.
- kwargs: dict: Additional arguments can be supplied for specific types of test data. These will be explained in per type below
NOTE: Be careful with changing the temperature, top p, frequency- and presence penalty as it will likely deviate from the format we expect it to return.
When generating addresses additional argument are available. These arguments are as follows:
- Country:str: The country from which to create addresses. If None, will generate an address from anywhere. Default = None
Generate 3 addresses from anywhere using OpenAI:
Generate Test Data openai address
Generate 10 addresses from Finland using the gpt-3.5-turbo from OpenAI in the default format with a token limit at 1024, temperature at 1, top p at .5 and frequency- and presence penalty at 0
Generate Test Data openai address gpt-3.5-turbo 10 None 1024 1 .5 0 0 country=finland
Chatbot is a simple response generating library for Robot Framework similar to
ChatGPT on the web. You can ask it a question or give it a task to have it automatically
reply to your emails.
The following arguments can be used (arguments prefixed by an * are required):
-
*ai_model: str: The AI model to be used, e.g. "openai", "gemini", "copilot", etc. Currently supporting: "openai"
-
*message: str: The message you want to send to the AI model, e.g. "What is the weather today?"
-
model: str: AI model specfic. The model of the AI model to be used. E.g. "gpt-3.5-turbo" when using the "openai" AI model. Default depends on AI model:
- "openai" = "gpt-3.5-turbo"
-
max_tokens: int = 256 The token limit for a conversation. Both prompt and response tokens will count towards this limit.
-
temperature: float = 1 This value determines the creativity of the AI model. Can be anything from 0-2.
-
top_p: float = 1 Similar to temperature. Determines the selection of tokens before selecting one. The higher the value the more less likely tokens get added to the selection. Can be anything from 0-2. At 1, only the top 50% of tokens will be used when selecting a token at 0 all tokens will be taken into consideration
-
frequency_penalty: float = 0 Penalizes more frequent token reducing the chance of it reappearing. Negative values encourage it to reuse tokens. Can be anything from -2 to 2.
-
presence_penalty: float = 0 Exact same as frequency_penalty except its scope is reduced to the immediate context. The immediate context can be seen as one or more paragrahps about a singular subject. Can be anything from -2 to 2.
-
keep_history: bool = False A flag to keep the chat history of previous messages. When settings this to True, your previous prompt and the response by the AI will be saved for the next message. This feature will keep the previous message, so if you want to send two messages and refer to your first message from the second message, you need to set this flag to True in the second message. Leaving this on for the third message aswell will keep both the first and second message.
NOTE: This works by incorporating the previous messages into the prompt, this will charge you again for both the prompt and response. So leaving this on, could quickly drain all your tokens.
-
response_format: dict = None Can be used to make the response compile to JSON. Set this to { "type": "json_object" } to make the response compile to JSON or None if it shouldn't necessarily.
Generate a response to a question using OpenAI
Generate Response openai What is the wheather today?
Declare a rule in the first message and refer to it in the second message
Generate Response openai If I say water you say fire
Generate Response openai Water keep_history=True
Generate the meaning of life in a json format using the gpt-3.5-turbo from OpenAI in the default format with a token limit at 1024, temperature at 2, top p at .5 and frequency- and presence penalty at 0 without using the previous messages in the response.
Generate Response openai What is the meaning of life? In json. gpt-3.5-turbo 1024 2 .5 0 0 False {"type": "json_object"}
Each module in the RobotFramework-AI library can support multiple different AI models. Each AI model needs an API key for the generation of test data. This key gets read directly from your environment variables. Each AI model has their own API key. To define a key, create a new variable with the name of the AI model capitalized followed by "_KEY". Then set this variable to your key.
Example API keys OPENAI_KEY=278bxw4m89monwxmu89wm98ufx8hwxfhqwifmxou09qwxp09jmx GEMINI_KEY=cavhjbcZCJKnvmzxcnzkcjkczckzcskjnjn7h38nwd923hdnind
Instead of providing all arguments through this keyword, it is also possible to set each argument beforehand. This way, when making repeated calls, arguments do not have to be supplied each time. After setting these arguments they will remain untill set again. When arguments are set and the keyword also has arguments supplied, then the supplied arguments will take priority.
NOTE: Setting arguments will impact other modules aswell. This means that when setting the temperature to 2, that both the RealTestDataGenerator and the Chatbot will use this temperature from then on. This is only the case when both modules share arguments, the arguments that are shared are as followed:
ai_model, model, max_tokens, temperature, top_p,
frequency_penalty, presence_penalty, response_format
Each argument has its own setter, the name of the keyword is 'set' plus the name of the argument e.g. Set AI Model for AI Model. In the case of kwargs, use the setter to set individual kwargs, set to None to unset it. The setter takes 2 arguments, the name of the kwarg to set and its value. To set a kwarg use:
Set Kwarg country Czechia
Generate 3 addresses from anywhere using OpenAI:
Set AI Model openai
Set Type address
Generate Test Data
Generate 10 addresses from Finland using the gpt-3.5-turbo from OpenAI in the default format with a token limit at 1024, temperature at 1, top p at .5 and frequency- and presence penalty at 0
Set AI Model openai
Set Type address
Set Model gpt-3.5-turbo
Set Amount 10
Set Format None
Set Max Tokens 1024
Set Temperature 1
Set Top P .5
Set Frequency Penalty 0
Set Presence Penalty 0
Set Kwarg country finland
Generate Test Data
The RobotFramework-AI library includes configurable logging capabilities to assist with debugging and monitoring. This logging setup ensures that log messages are handled appropriately, including support for Unicode characters.
To enable logging, use the Setup Logging keyword provided by the RobotFramework-AI library. This keyword allows you to configure console and file logging, and to enable or disable logging as needed.
The Setup Logging keyword takes the following arguments:
- enabled: bool = True Determines whether logging should be turned on.
- for_test: bool = False Can be used to log logs to a different folder specific for test logs. Will log to the logs_test folder instead of logs if set to True.
- console_logging: bool = True Determines whether logs should be printed in the console.
- file_logging: bool = True Determines whether logs should be logged to a file.
To log to both the console and a file:
Setup Logging
To log for tests without console logging:
Setup Logging True True False