Updated docs

docs/docs/Tutorial/TranslatorOpenSource.md

@@ -0,0 +1,111 @@
+# `TranslatorOpenSource` Class Documentation
+
+The `TranslatorOpenSource` class is designed for language detection and translation using open-source large language models (LLMs) deployed on a custom endpoint. This class enables interaction with these models to detect the language of text and translate between languages.
+
+## Initialization
+
+```python
+from llmtranslate import TranslatorOpenSource
+
+# Create the translator object
+translator = TranslatorOpenSource(api_key="YOUR_API_KEY", llm_endpoint="YOUR_LLM_ENDPOINT", model="mistralai/Mistral-Nemo-Instruct-2407")
+```
+
+
+The constructor initializes the `TranslatorOpenSource` class by setting up the API key, endpoint, and large language model (LLM). It also configures the maximum text length limits depending on the model.
+
+### Parameters:
+
+- **`api_key`**:  
+   A string representing the API key that authenticates access to the LLM service. This key ensures that the service can securely connect to your model deployment.
+
+- **`llm_endpoint`**:  
+   A string representing the endpoint URL where the LLM model is deployed. This is usually a REST API endpoint provided by your deployment platform (e.g., Hugging Face or other open-source model deployment platforms).
+
+- **`model`**:  
+   A string representing the name of the model used for translation. In this case, the `mistralai/Mistral-Nemo-Instruct-2407` model is used. This model determines the capabilities and performance of the translation system.
+
+   - For models in `MINI_MODELS`:
+     - `max_length = 30`: Limits the maximum length of text chunks that can be translated in a single request.
+     - `max_length_mini_text_chunk = 20`: Limits the maximum length of mini text chunks for smaller translations.
+   - For larger models (those not in `MINI_MODELS`):
+     - `max_length = 100`: Allows larger text chunks for translation.
+     - `max_length_mini_text_chunk = 50`: Allows larger mini text chunks for more efficient translation handling.
+
+## Example Usage
+
+### Synchronous Language Detection
+
+```python
+from llmtranslate import TranslatorOpenSource
+
+# Create the translator object
+translator = TranslatorOpenSource(api_key="YOUR_API_KEY", llm_endpoint="YOUR_LLM_ENDPOINT", model="mistralai/Mistral-Nemo-Instruct-2407")
+
+# Detect the language of a given text
+detected_language = translator.get_text_language("Bonjour tout le monde")
+
+if detected_language is not None:
+    print(detected_language.ISO_639_1_code)  # Output: 'fr'
+    print(detected_language.ISO_639_2_code)  # Output: 'fra'
+    print(detected_language.ISO_639_3_code)  # Output: 'fra'
+    print(detected_language.language_name)   # Output: 'French'
+```
+
+### Asynchronous Language Detection
+
+```python
+import asyncio
+from llmtranslate import TranslatorOpenSource
+
+# Create the translator object
+translator = TranslatorOpenSource(api_key="YOUR_API_KEY", llm_endpoint="YOUR_LLM_ENDPOINT", model="mistralai/Mistral-Nemo-Instruct-2407")
+
+# Async function to detect language
+async def detect_language_async():
+    detected_language = await translator.async_get_text_language("Hola, ¿cómo estás?")
+    if detected_language is not None:
+        print(detected_language.ISO_639_1_code)  # Output: 'es'
+        print(detected_language.language_name)   # Output: 'Spanish'
+
+# Run the async function
+asyncio.run(detect_language_async())
+```
+
+### Synchronous Translation
+
+```python
+from llmtranslate import TranslatorOpenSource
+
+# Create the translator object
+translator = TranslatorOpenSource(api_key="YOUR_API_KEY", llm_endpoint="YOUR_LLM_ENDPOINT", model="mistralai/Mistral-Nemo-Instruct-2407")
+
+# Translate text from one language to another
+translated_text = translator.translate(
+    text="Hallo, wie geht's?", 
+    to_language="en"  # Target language in ISO 639-1 format
+)
+print(translated_text)  # Output: "Hello, how are you?"
+```
+
+### Asynchronous Translation
+
+```python
+import asyncio
+from llmtranslate import TranslatorOpenSource
+
+# Create the translator object
+translator = TranslatorOpenSource(api_key="YOUR_API_KEY", llm_endpoint="YOUR_LLM_ENDPOINT", model="mistralai/Mistral-Nemo-Instruct-2407")
+
+# Async function to translate text
+async def translate_text_async():
+    translated_text = await translator.async_translate_text(
+        text="Cześć, jak się masz?", 
+        to_language="en"  # Target language in ISO 639-1 format
+    )
+    print(translated_text)  # Output: "Hello, how are you?"
+
+# Run the async function
+asyncio.run(translate_text_async())
+```
+

docs/mkdocs.yml

@@ -17,6 +17,7 @@ nav:
     - Quick Start Guide - OpenAI: Tutorial/Quick Start Guide.md
     - Choosing OpenAI Model for Translation: Tutorial/Choosing OpenAI Model for Translation.md
     - TranslatorMistralCloud: Tutorial/TranslatorMistralCloud.md
+    - TranslatorOpenSource: Tutorial/TranslatorOpenSource.md
 
 
 markdown_extensions:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "llmtranslate"
-version = "0.4.4"
+version = "0.4.5"
 description = "A Python library for language detection and translation using OpenAI's GPT-4o."
 authors = ["Adam Pawelek <[email protected]>"]
 readme = "README.md"