linkMinimal Working Example

Here we will walk you through a minimal working example of using MTLLM to generate translate a sentence from English to a target language.

linkSetup

Before we start, make sure you have installed MTLLM & Jaclang. If not, follow the instructions here. Following code snippet will be our starting point:

translator.jac
1linkcan translate(eng_sentence: str, target_lang: str) -> str {

2link    """Normally this would include the translation logic such as calling an API.

3link    For the sake of this example, we will return a dummy translated sentence."""

4link

5link    return "Hola Mundo";

6link}

7link

8linkwith entry {

9link    print(translate("Hello World", "es"));

10link}

Assuming we went with API based translation, target_lang would be the language code of the target language. For example, es for Spanish, fr for French, etc. But Assume that you don't know the language code for the target language, or you would like to provide a context to target_lang instead of a language code. for example Spanish instead of es or Language spoken in Somalia. This is where you need the help of LLMs.

linkUsing MTLLM

linkImport the LLM You Want to Use

For this example, we will use OpenAI's GPT-3.5-turbo (default).

translator.jac
1linkimport:py from mtllm.llms, OpenAI;

2link

3linkllm = OpenAI();

4link

5link# Rest of the code

linkRemove the Ability Body and Add by LLM keyword

translator.jac
1linkimport:py from mtllm.llms, OpenAI;

2link

3linkllm = OpenAI();

4link

5linkcan translate(eng_sentence: str, target_lang: str) -> str by llm;

6link

7linkwith entry {

8link    print(translate("Hello World", "Language spoken in Somalia"));

9link}

Thats it! 🎊

Now you can run the code and see the translated sentence by running the following command: Makesure to export your OpenAI API key as an environment variable OPENAI_API_KEY before running the code.

1link$jac run translator.jac

linkAdding Additional Support to the LLMs

In this example, we dont need to add any additional support to the LLMs. But if you want to add additional support, you can do so by adding SemStrings to variables, output type hint and abilities the following code snippet:

translator.jac
1linkimport:py from mtllm.llms, OpenAI;

2link

3linkllm = OpenAI();

4link

5linkcan 'Translate the given english sentence to the target language'

6linktranslate(eng_sentence: str, target_lang: str) -> 'Translation': str by llm;

7link

8linkwith entry {

9link    print(translate("Hello World", "Language spoken in Somalia"));

10link}

You've successfully created a working example using the Jaclang and MTLLM.

Feel free to adapt and expand upon this example to suit your specific use case while exploring the extensive capabilities of MTLLM.