> For the complete documentation index, see [llms.txt](https://myshell-ai.gitbook.io/neuralautomata/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://myshell-ai.gitbook.io/neuralautomata/modules.md).

# Modules

We have several pre-defined Modules and the corresponding configs

### LLMFunctionModule

```python
            module_type='LLMFunctionModule',
            config=dict(
                type='LLMFunctionConfig',
                function_name="generate_example_sentence",
                function_description='${f"Propose an example sentence containing the given word in {language}"}',
                function_parameters=[
                    dict(
                        name="example_sentence",
                        type="str",
                        description="The example_sentence containing the given word."
                    ),
                    dict(
                        name="translated_example_sentence",
                        type="str",
                        description="The translated example_sentence containing the given word."
                    ),
                ],
                system_prompt='''${f"You are teaching {language} class for {native_language} students. To help them better know how to use these {language} words. You will make example sentences to demonstrate how to use it and then translate the sentence into {native_language} to help students understand the meanings of the sentence. Sentences should preferably be around 20 words."}''',
                user_prompt='''${f"{added_words[word_idx]}. Remember, try you best to let student understand how to use the word through this sentence and then you will translate this sentence into {native_language}"}''',
            ),
```

### MSTTSFunctionModule

```python
        "generate_tts_for_example_sentence_state": TaskState(
            name="generate_tts_for_example_sentence_state",
            next_state="whether_generate_word_speech",
            module_type='MSTTSFunctionModule',
            config=dict(
                name='sen_tts_audio_path',
                input='''${f"{materials_dict.get(words_list[word_idx], {}).get('example_sentence', example_sentence)}"}''',
                language="${f'{language.lower()}'}",
                save_path='${f"demos/duolingo/data/app/curriculum/{language}/audio/sent_{words_list[word_idx]}.mp3"}',
            ),
            outputs={
                "sen_tts_audio_path": ValueType(
                    type='str',
                    value="${sen_tts_audio_path}",
                ),
                "single_meterial": ValueType(
                    type="dict",
                    value="${{**single_meterial, 'sen_tts_audio_path': sen_tts_audio_path}}"
                ),
            },            
        ),
```

### ShuffleFunctionModule

```python
        "shuffle_words": TaskState(
            name="shuffle_words",
            module_type='ShuffleFunctionModule',
            config=dict(
                type='ShuffleFunctionConfig',
                elements='${[words[quiz_idx], wrong_words[0], wrong_words[1], wrong_words[2]]}',
            ),
```

### MSTTSPronounceAssessModule

```python
pronounce_state = TaskState(
    name="pronounce_state",
    module_type="MSTTSPronounceAssessModule",
    inputs={
        "text": Textbox(
            label="text",
        ),
        "audio_path": Textbox(
            label="audio_path",
        )
    },
    config=dict(
        name="assess_result",
        language="japanese",
        text="${text}",
        audio_path="${audio_path}",
    ),
    outputs={
        "result": ValueType(
            type="dict",
            value="${assess_result}"
        )
    }
)
```

### ProdiaImagenModule

```python
state = TaskState(
    name="prodia imagen",
    module_type='ProdiaImagenModule',
    inputs={
        "Prodia_Description": Textbox(),
        "Prodia_ModelName": ValueType(
            type='str',
            value='absolutereality'
        )
    },
    config=dict(
        type='ProdiaImagenConfig',
        prodia_model_name='${Prodia_ModelName}',
        description='${Prodia_Description}',
        enhanced_prompt="",
        negative_prompt="(Character not centered:1.3), badhands, bad anatomy, extra hands, extra fingers,signature, artist name, upper body, (worst quality, lowquality:1.4),(blush:1.2), (jpeg artifacts:1.4), bokeh,blurry, monochrome, dusty sunbeams, trembling.motion lines, motion blur, emphasis lines, text, title,logo, nude, nsfw"
   ),
    outputs={
        "image_path": Textbox(
            value="${image_path_0}"
        )
    },
    title="Prodia",
)
```

### JsonRWFunctionModule

```python
        "login_state": TaskState(
            name="login_state",
            module_type='JsonRWFunctionModule',
            config=dict(
                filename=f"{demo_root}/data/app/user_info.json",
                mode="read",
                var_type="dict",
                var_name="user_info",
            ),
            outputs={
                "user_info": ValueType(
                    type="dict",
                    value="${user_info}"
                )
            },
            next_state="check_user_info",
        ),
```


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