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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? to understand how they work.

Prerequisites

Before registering this pipeline, ensure you have:

  • ✅ Registered Gemini 2.0 Flash Model - Follow the Model Registration Guide 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

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

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

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

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

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:

# 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

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

  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:

# 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