Add language translation pipeline example documentation.

Author: The-Great-Aeochs

docs/.nav.yml

@@ -20,6 +20,8 @@ nav:
             - Intent Classification Pipeline Example:
                 - Prompt Registration: "register-and-refine/examples/intent-classification-pipeline-registration/prompt.md"
                 - Pipeline Registration: "register-and-refine/examples/intent-classification-pipeline-registration/pipeline.md"
+            - Language Translation Pipeline Example:
+                - Pipeline Registration: "register-and-refine/examples/language-translation-pipeline-registration/pipeline.md"
 
   - Evaluate and Approve:
       - Overview: "evaluate-and-approve/index.md"

docs/register-and-refine/examples/language-translation-pipeline-registration/english-to-french-pipeline-code-config.png

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+# Pipeline Registration Guide: English to French Translation
+
+This guide walks you through registering an **English to French Translation Pipeline** on the Corridor platform. This pipeline automatically detects English text and provides high-quality French translations using Gemini 2.0 Flash.
+
+**What This Pipeline Does:**
+
+- Detects if input text is in English
+- Translates English text to French with preserved tone and style
+- Returns error messages for non-English input
+- Tracks API usage costs
+
+If you are new to Pipelines, read [What are Pipelines?](../../inventory-management/pipelines/index.md) to understand how they work.
+
+
+## Prerequisites
+
+Before registering this pipeline, ensure you have:
+
+- ✅ **Registered Gemini 2.0 Flash Model** - Follow the [Model Registration Guide](../../model/) to register the model
+
+- ✅ **API Token Configured** - Ensure `GOOGLE_API_TOKEN` is set up in Platform Integrations
+
+**Quick Check:** Navigate to **GenAI Studio → Model Catalog** and verify `gemini_2_0_flash` is available.
+
+If you haven't completed these steps, please do so before proceeding.
+
+
+## Registration Steps
+
+### Step 1. Navigate to Pipeline Registry
+
+Go to **GenAI Studio → Pipeline Registry** and click the **Create** button.
+
+### Step 2. Fill in Basic Information
+
+![Pipeline Basic Information](english-to-french-pipeline-description.png)
+
+**Basic Information** fields help organize and identify your pipeline:
+
+- **Description:** Clear explanation of what the pipeline does and its workflow
+- **Usecase Type:** The primary use case category - select **Translation**
+- **Task Type:** Specific task the pipeline performs - select **Generative Responses**
+- **Impact of Generated Output:** Scope of the pipeline's usage - select **External Facing**
+- **Data Usage:** Whether the pipeline uses additional data sources - leave empty for this pipeline
+- **Group:** Category for organizing similar pipelines - select **Example Pipelines**
+- **Permissible Purpose:** Approved use cases and business scenarios for this pipeline
+
+**Example Description:**
+
+```
+English to French Translation Assistant - Translate English text to French using Gemini 2.0 Flash.
+
+Key Features:
+- Automatic English language detection
+- High-quality French translations using Gemini 2.0 Flash
+- Preserves tone, style, and cultural nuances
+- Cost tracking for API usage
+
+Usage:
+- Simple translation: "Hello, how are you?"
+- Any English text: "The weather is beautiful today."
+- Formal or informal: Automatically preserves the tone
+
+Note: This pipeline only translates FROM English TO French.
+```
+
+### Step 3. Configure Code Settings
+
+![Pipeline Code Configuration](english-to-french-pipeline-code-config.png)
+
+**Code Settings** define how your pipeline operates and which resources it uses.
+
+**Configuration Fields:**
+
+- **Alias:** `english_to_french_translation`: A Python variable name to reference this pipeline in code
+
+- **Input Type:** Select **Python Function** : This pipeline uses custom Python code for translation logic
+
+- **Agent Provider:** Select **Other** : We're not using a pre-built agent provider for this translation pipeline
+
+- **Pipeline Type:** Select **Chat Based Pipeline** :Enables conversational interface and message history.
+ 
+- **Context Type:** `dict[str, str]`
+  
+  - Data type for storing information across conversation turns
+  - For this pipeline, context stores translation metadata (costs, language detection)
+
+- **Interaction Type:** `TypedDict[{'role': str, 'content': str}]`
+  
+  - Format for conversation history messages
+  - Standard chat message format with role (user/assistant) and content
+
+ 
+💡 *Note: While this is a single-turn translation, Chat Based Pipeline allows for future enhancements like multi-turn conversations*
+
+
+### Step 4. Add Resources
+
+![Pipeline Resources](english-to-french-pipeline-resource.png)
+
+**Resources** are the pre-registered components your pipeline will use.
+
+Click **+ Create New** or search for existing resources to add:
+
+**LLMs / Models:** `gemini_2_0_flash` - The foundation model for generating translations
+
+**Prompts (Optional):** `english_to_french_translation` - The translation prompt for generating translations
+
+- Follow the [Prompt Registration Guide](../../intent-classification-pipeline-registration/prompt/) to create a reusable prompt
+
+**Other Resources** (Not required for this pipeline):
+
+- **RAGs:** For retrieving translation dictionaries or context
+- **Agents & Sub-Pipelines:** For complex multi-step translation workflows
+- **Helper Functions:** For pre/post-processing text
+
+### Step 5. Write Pipeline Scoring Logic
+
+![Pipeline Scoring Logic](english-to-french-pipeline-scoring-logic.png)
+
+**Pipeline Scoring Logic** orchestrates how resources work together to perform the translation.
+
+**Variables Available in the Pipeline:**
+
+- `user_message` - The English text to translate (type: String)
+- `history` - Previous conversation messages (type: list[TypedDict[{'role': str, 'content': str}]])
+- `context` - Information stored across turns (type: dict[str, str])
+
+**Complete Pipeline Code:**
+
+```python
+# Step 1: Generate strict translation prompt
+prompt = english_to_french_translation(user_message=user_message)
+
+# Step 2: Get translation from Gemini
+result = gemini_2_0_flash(
+    text=prompt,
+    temperature=0.3,
+    system_instruction='None'
+)
+
+translated_text = result["response"]
+
+# Step 3: Return result
+return {
+    "output": translated_text
+}
+```
+
+**What This Code Does:**
+
+- Use the registered Translation Prompt to convert the user's message to a French translation
+- Call the registered Gemini 2.0 Flash Model with the translation prompt to generate the translation:
+- Return the translation as the output of the pipeline
+
+### Step 6. Add Examples (Optional)
+
+![Pipeline Examples Section](english-to-french-pipeline-examples.png)
+
+Add test examples to validate pipeline behavior:
+
+| Input | Expected Output |
+|-------|----------------|
+| "Hello, how are you?" | "Bonjour, comment allez-vous ?" |
+| "The weather is beautiful today." | "Le temps est magnifique aujourd'hui." |
+| "Thank you very much!" | "Merci beaucoup !" |
+| "Hola, ¿cómo estás?" (Spanish) | "Error: Input text must be in English. Detected language: Spanish" |
+
+**Note:** Examples help with testing and documenting expected behavior. They also serve as regression tests when updating the pipeline.
+
+### Step 7. Save the Pipeline
+
+Click **Create** to register the pipeline.
+
+The pipeline is now:
+
+- ✅ Available in the Pipeline Registry
+- ✅ Ready for simulation and testing
+- ✅ Ready for use in downstream applications
+
+
+## Testing Your Pipeline
+
+After creating the pipeline, test it to verify translation quality and error handling
+
+### Quick Test (During Creation/Editing)
+
+![Test Code](english-to-french-pipeline-test-code.png)
+
+1. While creating or editing the pipeline, scroll to the **Code** section
+2. Click **Test Code** in the bottom right corner
+3. Enter test inputs to verify logic without saving
+
+**Sample Test Cases:**
+
+```python
+# Test Case 1: Simple greeting
+user_message = "Hello, how are you?"
+# Expected: "Bonjour, comment allez-vous ?"
+
+# Test Case 2: Non-English input (error handling)
+user_message = "Hola, ¿cómo estás?"
+# Expected: "Error: Input text must be in English. Detected language: Spanish"
+```
+
+### Interactive Test (After Saving)
+
+- Navigate to your saved pipeline
+- Click **Run** → **Chat Session** (top right corner)
+- Enter sample English messages to test the translation flow
+
+**Verify:**
+
+   - Translations are accurate and natural
+   - Tone and style are preserved (formal/informal)
+   - Non-English inputs return proper error messages
+   - Output format is clean (no extra explanations)
+
+**Chat Session Testing Tips:**
+
+- Test both formal and informal language
+- Try technical terms and idioms
+- Verify cultural nuances are preserved
+- Test edge cases (very short/long text, special characters)
+
+## Want to Improve/Extend Your Pipeline? Try These Ideas:
+
+- Auto-detect source language using NLP techiques and see how it performs compared to the current pipeline
+- Add translation confidence scores and quality of the translations using evaluation providers
+- Extend to support other languages
+
+## Conclusion: 
+
+You've successfully learned how to register an English to French Translation Pipeline that:
+
+- ✅ Detects English language automatically
+- ✅ Provides high-quality French translations
+- ✅ Handles non-English input gracefully
+- ✅ Maintains clean, production-ready code with reusable translation prompt and Gemini 2.0 Flash model
+
+## Related Documentation
+
+- [Model Registration Guide](../../model/) - Register foundation models like Gemini 2.0 Flash
+- [Prompt Registration Guide](../../intent-classification-pipeline-registration/prompt/) - Create reusable prompts