Added registration guide for model

docs/.nav.yml

@@ -15,7 +15,10 @@ nav:
       - Lineage Tracking: "register-and-refine/lineage-tracking.md"
       - Prompt Optimization: "register-and-refine/prompt-optimization.md"
       - Corridor Sync: "register-and-refine/corridor_sync.md"
-
+      - Asset Registration Examples: 
+            - Model Registration: "register-and-refine/example/model.md"
+            - Prompt Registration: "register-and-refine/example/prompt.md"
+
   - Evaluate and Approve:
       - Overview: "evaluate-and-approve/index.md"
       - Simulation: "evaluate-and-approve/simulation.md"

docs/register-and-refine/example/model.md

@@ -0,0 +1,155 @@
+# Model Registration: Gemini 2.0 Flash
+
+This guide covers registering the Gemini 2.0 Flash model on the platform. 
+
+**Gemini 2.0 Flash** is Google's language model for classification and structured output tasks.
+
+---
+
+## Registration Steps
+
+### Step 1. Navigate to Model Catalog
+
+Go to **GenAI Studio → Model Catalog** and click the **Create** button.
+
+### Step 2. Fill in Basic Information
+
+![alt text](image-21.png)
+
+**Basic Information** fields help organize and identify your model:
+
+- **Name:** Human-readable identifier for the model (e.g., "Gemini 2.0 Flash")
+- **Description:** Brief explanation of the model's purpose and capabilities
+- **Group:** Category for organizing similar models together (e.g., "Foundation LLMs")
+- **Permissible Purpose:** Approved use cases and business scenarios for this model
+- **Ownership Type:** License type - Proprietary, Open Source, or Internal
+- **Model Type:** Classification of the model (e.g., "LLM" for language models)
+
+### Step 3. Configure Inferencing Logic 
+
+#### Choose Input Type
+
+**Input Type:** You have two options:
+
+- **API Based** - Use this when working with models through API providers (OpenAI, Anthropic, Google Vertex AI, etc.)
+
+- **Python Function** - Use this for custom Python implementations or local models
+
+For this guide, we'll use **API Based**.
+
+#### Select Model Provider
+
+**Model Provider:** Select `Google Vertex AI` from the dropdown
+
+Once you select a provider, additional fields will appear to configure how the model is called:
+
+![alt text](image-22.png)
+
+- **Alias:** Variable name to reference this model in pipeline code (e.g., `gemini_2_0_flash`)
+- **Output Type:** Data type returned by the model (e.g., `dict[str, str]`)
+- **Input Type:** Choose between API-based (for external providers) or Python Function (for custom code)
+- **Model Provider:** Select the API provider hosting the model (Google Vertex AI)
+- **Model:** Specific model version from the provider's catalog (Gemini 2.0 Flash)
+
+#### Define Arguments
+
+The inputs to the model - messages, system instruction, temperature, etc.
+
+Click **+ Add Argument** to add each argument:
+
+| Alias | Type | Is Optional | Default Value |
+|-------|------|-------------|---------------|
+| `text` | String | ☐ | - |
+| `temperature` | Numerical | ☑ | 0 |
+| `system_instruction` | String | ☑ | None |
+
+**Argument Descriptions:**
+
+- `text`: The input prompt to send to the model
+
+- `temperature`: Controls randomness (0 = deterministic, 1 = creative)
+
+- `system_instruction`: Optional system-level instructions for the model
+
+You can add additional arguments based on your model's requirements.
+
+#### Write Scoring Logic
+
+![alt text](image-23.png)
+
+Provide logic to initialize and score the model:
+
+```python
+import os
+from google import genai
+from google.genai import types
+
+client = genai.Client(api_key=os.getenv("GOOGLE_API_TOKEN"))
+
+config = types.GenerateContentConfig(
+    temperature=temperature, 
+    seed=2025, 
+    system_instruction=system_instruction
+)
+
+response = client.models.generate_content(
+    model="gemini-2.0-flash", 
+    contents=text, 
+    config=config
+)
+
+return {
+    "response": response.text,
+}
+```
+
+**What This Code Does:**
+
+- Authenticates using the `GOOGLE_API_TOKEN` environment variable (configured in Platform Integrations)
+- Sets up generation config with temperature and system instruction
+- Calls the Gemini 2.0 Flash model with the input text
+- Returns the generated response
+
+### Step 4. Save the Model
+
+Add any notes or additional information in the **Additional Information** section, then click **Create** to complete registration.
+
+
+### Step 5. Quick Example Run 
+
+Click **Test Code** to run a sample query.
+
+![alt text](image-19.png)
+
+Use the platform's test interface to verify:
+
+- Verify API authentication is working
+- Test with sample inputs before using in production
+- Debug any configuration issues
+- Validate the output format matches expectations
+
+## Usage in Pipelines
+
+Once registered, the model appears in your Resources library and can be selected for any downstream usages.
+
+**Reference in pipeline code:**
+```python
+# Call the registered model
+response = gemini_2_0_flash(
+    text=user_prompt,
+    temperature=0.7,
+    system_instruction="You are a helpful assistant."
+)
+
+# Access the response
+output_text = response["response"]
+```
+
+---
+
+## Related Documentation
+
+- [Prompt Registration Guide](../prompt/) - Create reusable prompts
+- [Google Gemini API Docs](https://ai.google.dev/gemini-api/docs) - Official Google documentation
+
+---