RobotFramework-AI

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.

Setup

To setup the library:

1. Start by cloning this repository.

2. Open a terminal and locate the robotframework-ai folder.

3. Use the following command to create the package.

    python setup.py sdist bdist_wheel
   

   This requires the wheel package to be installed, which can be installed with:

    pip install wheel
   

4. To install the library using the package just created, use the following command.

    pip install .\dist\RobotFrameworkAI-0.0.1-py3-none-any.whl
   

5. Setup the API key for the AI you want to use. This can be done by creating a new environment variable called OPENAI_KEY with your key as the value. More information about this in the AI models header below.

6. The library is now properly setup, to confirm it works, run the following code in a robot file. This will generate 3 addresses and create a log.html file.

    *** Settings ***
    Library   RobotFrameworkAI
   
   
    *** Test Cases ***
    Exec Test
        [Documentation]    Test
        ${response}    Generate Test Data    openai    address
        Log    ${response}
   

   Opening this testlog file in a browser and opening the suite Test, the test Exec test and the keyword Log will reveal a list with the 3 addresses in the INFO section.

   

AI models

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. At the moment only OpenAI is supported.

Example API keys

• OPENAI_KEY=278bxw4m89monwxmu89wm98ufx8hwxfhqwifmxou09qwxp09jmx
• GEMINI_KEY=cavhjbcZCJKnvmzxcnzkcjkczckzcskjnjn7h38nwd923hdnind

RealTestDataGenerator

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.

Addresses

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

Examples

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

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.

Examples

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"}   

Setters

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

Examples

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

Logging

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.

Enabling Logging

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.

Examples

To log to both the console and a file:

Setup Logging

To log for tests without console logging:

Setup Logging    True    True    False