docs: Add translation documentation

- Added detailed explanations of the translationn method
- Included examples of how translation services are configured and utilized within the codebase.

docs/source/index.rst

@@ -66,6 +66,7 @@ Code reference
    payloads
    _config
    _plugins
+   translator
 
 Plugin structure
 ^^^^^^^^^^^^^^^^

docs/source/translator.rst

@@ -0,0 +1,165 @@
+The `translator.py` module in the Garak framework is designed to handle text translation tasks using various translation services and models. 
+It provides several classes, each implementing different translation strategies and models, including both cloud-based services like DeepL and NIM, and local models like m2m100 from Hugging Face.
+
+garak.translator
+=============
+
+.. automodule:: garak.translator
+   :members:
+   :undoc-members:
+   :show-inheritance:   
+
+Multilingual support
+====================
+
+This feature adds multilingual probes and detector keywords and triggers.
+You can check the model vulnerability for multilingual languages.
+
+* limitation:
+  - This function only supports for `bcp47` code is "en".
+  - Huggingface detector only supports English. You need to bring the target language NLI model for the detector.
+  - Some detectors only support English, such as the `snowball` detector.
+  - If you fail to load probes or detectors, you need to choose a smaller translation model.
+
+pre-requirements
+----------------
+
+.. code-block:: bash
+
+    pip install nvidia-riva-client==2.16.0  pyenchant==3.2.2
+
+Support translation service
+---------------------------
+
+- Huggingface
+  - This code uses the following translation models:
+    - `Helsinki-NLP/opus-mt-en-{lang} <https://huggingface.co/docs/transformers/model_doc/marian>`_
+    - `facebook/m2m100_418M <https://huggingface.co/facebook/m2m100_418M>`_
+    - `facebook/m2m100_1.2B <https://huggingface.co/facebook/m2m100_1.2B>`_
+- `DeepL <https://www.deepl.com/docs-api>`_
+- `NIM <https://build.nvidia.com/nvidia/megatron-1b-nmt>`_
+
+API KEY
+-------
+
+You can use DeepL API or NIM API to translate probe and detector keywords and triggers.
+
+You need an API key for the preferred service.
+- `DeepL <https://www.deepl.com/en/pro-api>`_
+- `NIM <https://build.nvidia.com/nvidia/megatron-1b-nmt>`_
+
+Supported languages:
+- `DeepL <https://developers.deepl.com/docs/resources/supported-languages>`_
+- `NIM <https://build.nvidia.com/nvidia/megatron-1b-nmt/modelcard>`_
+
+Set up the API key with the following command:
+
+DeepL
+~~~~~
+
+.. code-block:: bash
+
+    export DEEPL_API_KEY=xxxx
+
+NIM
+~~~
+
+.. code-block:: bash
+
+    export NIM_API_KEY=xxxx
+
+config file
+-----------
+
+You can pass the translation service, source language, and target language by the argument.
+
+- translation_service: "nim" or "deepl", "local"
+- lang_spec: "ja", "ja,fr" etc. (you can set multiple language codes)
+
+* Note: The `Helsinki-NLP/opus-mt-en-{lang}` case uses different language formats. The language codes used to name models are inconsistent. Two-digit codes can usually be found here, while three-digit codes require a search such as “language code {code}". More details can be found `here <https://github.com/Helsinki-NLP/OPUS-MT-train/tree/master/models>`_.
+
+You can also configure this via a config file:
+
+.. code-block:: yaml
+
+    run:
+      translation_service: {you choose translation service "nim" or "deepl", "local"}
+      lang_spec: {you choose language code}
+
+Examples for multilingual
+-------------------------
+
+DeepL
+~~~~~
+
+To use the translation option for garak, run the following command:
+
+.. code-block:: bash
+
+    export DEEPL_API_KEY=xxxx
+    python3 -m garak --model_type nim --model_name meta/llama-3.1-8b-instruct --probes encoding --translation_service deepl --lang_spec ja
+
+If you save the config file as "garak/configs/simple_translate_config_deepl.yaml", use this command:
+
+.. code-block:: bash
+
+    export DEEPL_API_KEY=xxxx
+    python3 -m garak --model_type nim --model_name meta/llama-3.1-8b-instruct --probes encoding --config garak/configs/simple_translate_config_deepl.yaml
+
+Example config file:
+
+.. code-block:: yaml
+
+    run:
+      translation_service: "deepl"
+      lang_spec: "ja"
+
+NIM
+~~~
+
+For NIM, run the following command:
+
+.. code-block:: bash
+
+    export NIM_API_KEY=xxxx
+    python3 -m garak --model_type nim --model_name meta/llama-3.1-8b-instruct --probes encoding --translation_service nim --lang_spec ja
+
+If you save the config file as "garak/configs/simple_translate_config_nim.yaml", use this command:
+
+.. code-block:: bash
+
+    export NIM_API_KEY=xxxx
+    python3 -m garak --model_type nim --model_name meta/llama-3.1-8b-instruct --probes encoding --config garak/configs/simple_translate_config_nim.yaml
+
+Example config file:
+
+.. code-block:: yaml
+
+    run:
+      translation_service: "nim"
+      lang_spec: "ja"
+
+Local
+~~~~~
+
+For local translation, use the following command:
+
+.. code-block:: bash
+
+    python3 -m garak --model_type nim --model_name meta/llama-3.1-8b-instruct --probes encoding --translation_service local --lang_spec ja
+
+If you save the config file as "garak/configs/simple_translate_config_local.yaml", use this command:
+
+.. code-block:: bash
+
+    python3 -m garak --model_type nim --model_name meta/llama-3.1-8b-instruct --probes encoding --config garak/configs/simple_translate_config_local.yaml
+
+Example config file:
+
+.. code-block:: yaml
+
+    run:
+      translation_service: local
+      local_model_name: "facebook/m2m100_418M"
+      local_tokenizer_name: "facebook/m2m100_418M"
+      lang_spec: "ja"