diff --git a/src/datacustomcode/templates/function/example/chunking_with_prediction/config.json b/src/datacustomcode/templates/function/example/chunking_with_prediction/config.json
new file mode 100644
index 0000000..2f911e9
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+++ b/src/datacustomcode/templates/function/example/chunking_with_prediction/config.json
@@ -0,0 +1,3 @@
+{
+    "entryPoint": "entrypoint.py"
+}
diff --git a/src/datacustomcode/templates/function/example/chunking_with_prediction/entrypoint.py b/src/datacustomcode/templates/function/example/chunking_with_prediction/entrypoint.py
new file mode 100644
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+++ b/src/datacustomcode/templates/function/example/chunking_with_prediction/entrypoint.py
@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025, Salesforce, Inc.
+# SPDX-License-Identifier: Apache-2
+
+"""
+Housing Sale Price Prediction with Einstein Regression
+
+This example uses Einstein regression model to predict housing sale prices
+based on property features like Year_Built__c.
+
+Model: YH_Regression_Python_Predicted_SalePrice_CM_12l_ATC937af934
+Type: Regression
+Input: Year_Built__c (numeric)
+Output: Predicted_SalePrice
+"""
+
+import logging
+from typing import (
+    Any,
+    Dict,
+    Optional,
+)
+
+from datacustomcode.einstein_predictions.types import (
+    PredictionColumBuilder,
+    PredictionRequestBuilder,
+    PredictionType,
+)
+from datacustomcode.function import Runtime
+from datacustomcode.function.feature_types.chunking import (
+    ChunkType,
+    SearchIndexChunkingV1Output,
+    SearchIndexChunkingV1Request,
+    SearchIndexChunkingV1Response,
+)
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+# Configuration
+PREDICTION_MODEL_NAME = "YH_Regression_Python_Predicted_SalePrice_CM_12l_ATC937af934"
+
+
+def predict_sale_price(
+    features: Dict[str, Any],
+    runtime: Runtime,
+) -> Optional[float]:
+    """Predict housing sale price using Einstein regression model.
+
+    Args:
+        features: Extracted housing features (numeric and string)
+        runtime: Runtime with prediction client
+
+    Returns:
+        Predicted sale price or None if prediction fails
+    """
+    try:
+        # Build prediction columns - handle both numeric and string values
+        prediction_columns = []
+
+        for column_name, value in features.items():
+            if isinstance(value, str):
+                # String values (e.g., Garage_Qual__c)
+                column = (
+                    PredictionColumBuilder()
+                    .set_column_name(column_name)
+                    .set_string_values([value])
+                    .build()
+                )
+            elif isinstance(value, (int, float)):
+                # Numeric values
+                column = (
+                    PredictionColumBuilder()
+                    .set_column_name(column_name)
+                    .set_double_values([float(value)])
+                    .build()
+                )
+            else:
+                # Skip unsupported types
+                logger.warning(
+                    f"Skipping field {column_name} with unsupported type {type(value)}"
+                )
+                continue
+
+            prediction_columns.append(column)
+
+        # Build regression prediction request
+        prediction_request = (
+            PredictionRequestBuilder()
+            .set_prediction_type(PredictionType.REGRESSION)
+            .set_model_api_name(PREDICTION_MODEL_NAME)
+            .set_prediction_columns(prediction_columns)
+            .build()
+        )
+
+        prediction_response = runtime.einstein_predictions.predict(prediction_request)
+
+        if not prediction_response.is_success:
+            logger.error(f"Prediction failed: {prediction_response.data}")
+            return None
+
+        # Parse regression response
+        if prediction_response.data is None:
+            logger.warning("Prediction response data is None")
+            return None
+
+        results = prediction_response.data.get("results", [])
+        if not results:
+            logger.warning("No results in prediction response")
+            return None
+
+        first_result = results[0]
+        prediction_type = first_result.get("type")
+
+        if prediction_type != "RegressionPredictionSuccess":
+            logger.error(f"Unexpected prediction type: {prediction_type}")
+            logger.error(f"Full result: {first_result}")
+            return None
+
+        prediction_data = first_result.get("prediction", {})
+        predicted_value = prediction_data.get("value")
+
+        if predicted_value is None:
+            logger.warning("No predicted value in response")
+            return None
+
+        logger.info(f"Predicted sale price: ${predicted_value:,.2f}")
+
+        # Log top contributors (which features influenced the price most)
+        top_contributors = prediction_data.get("topContributors", [])
+        if top_contributors:
+            logger.info(f"Top price contributors: {top_contributors}")
+
+        return float(predicted_value)
+
+    except Exception as e:
+        logger.error(f"Prediction failed with error: {e}", exc_info=True)
+        return None
+
+
+def enrich_property_with_price(
+    source_dmo_fields: Dict[str, Any],
+    runtime: Runtime,
+) -> Dict[str, str]:
+    """Enrich property data with predicted sale price.
+
+    Args:
+        source_dmo_fields: Property features from source DMO
+        runtime: Runtime for predictions
+
+    Returns:
+        Citations dictionary with predicted price
+    """
+    citations = {}
+
+    # Copy original fields to citations
+    if source_dmo_fields:
+        for key, value in source_dmo_fields.items():
+            citations[key] = str(value)
+
+    # Get price prediction - pass source_dmo_fields directly as features
+    predicted_price = predict_sale_price(source_dmo_fields, runtime)
+
+    if predicted_price is not None:
+        citations["predicted_sale_price"] = f"${predicted_price:,.2f}"
+        citations["predicted_sale_price_raw"] = str(predicted_price)
+        citations["prediction_status"] = "success"
+    else:
+        citations["predicted_sale_price"] = "N/A"
+        citations["prediction_status"] = "failed"
+
+    return citations
+
+
+def function(
+    request: SearchIndexChunkingV1Request, runtime: Runtime
+) -> SearchIndexChunkingV1Response:
+    """Housing price prediction using Einstein regression.
+
+    Predicts sale prices for properties based on Year_Built__c feature
+    and adds predictions to citations for real estate data enrichment.
+
+    Input format:
+    {
+      "input": [
+        {
+          "text": "Beautiful 3BR house built in 1990",
+          "metadata": {
+            "source_dmo_fields": {
+              "Year_Built__c": 1990,
+            }
+          }
+        }
+      ]
+    }
+
+    Output format:
+    {
+      "output": [
+        {
+          "text": "Beautiful 3BR house built in 1990",
+          "seq_no": 1,
+          "citations": {
+            "Year_Built__c": "1990",
+            "predicted_sale_price": "$350,000.00",
+            "predicted_sale_price_raw": "350000.0",
+            "prediction_status": "success"
+          }
+        }
+      ]
+    }
+
+    Args:
+        request: Input properties to enrich
+        runtime: Runtime with prediction API access
+
+    Returns:
+        Properties enriched with predicted sale prices
+    """
+
+    enriched_properties = []
+    seq_no = 1
+
+    for doc_idx, doc in enumerate(request.input):
+        text = doc.text
+        metadata = doc.metadata
+
+        source_dmo_fields = {}
+        if metadata and metadata.source_dmo_fields:
+            source_dmo_fields = dict(metadata.source_dmo_fields)
+
+        # Enrich with price prediction - pass source_dmo_fields directly
+        citations = enrich_property_with_price(source_dmo_fields, runtime)
+
+        # Create output
+        property_output = SearchIndexChunkingV1Output(
+            chunk_type=ChunkType.TEXT,
+            text=text.strip(),
+            seq_no=seq_no,
+            citations=citations,
+        )
+        enriched_properties.append(property_output)
+
+        seq_no += 1
+
+    return SearchIndexChunkingV1Response(output=enriched_properties)
diff --git a/src/datacustomcode/templates/function/example/chunking_with_prediction/tests/test.json b/src/datacustomcode/templates/function/example/chunking_with_prediction/tests/test.json
new file mode 100644
index 0000000..9968b39
--- /dev/null
+++ b/src/datacustomcode/templates/function/example/chunking_with_prediction/tests/test.json
@@ -0,0 +1,40 @@
+{
+  "input": [
+    {
+      "text": "Luxury 5-bedroom house with 5000 sq ft living area, built in 2023",
+      "metadata": {
+        "type": "text",
+        "source_dmo_fields": {
+          "First_Flr_SF__c": 2600,
+          "Full_Bath__c": 4,
+          "Garage_Cars__c": 4,
+          "Garage_Qual__c": "good",
+          "Gr_Liv_Area__c": 5000,
+          "Lot_Area__c": 3000,
+          "Overall_Cond__c": 10,
+          "Second_Flr_SF__c": 2400,
+          "Total_Bsmt_SF__c": 0,
+          "Year_Built__c": 2023
+        }
+      }
+    },
+    {
+      "text": "Spacious 4-bedroom family home with 3500 sq ft living space, built in 2020",
+      "metadata": {
+        "type": "text",
+        "source_dmo_fields": {
+          "First_Flr_SF__c": 2000,
+          "Full_Bath__c": 3,
+          "Garage_Cars__c": 3,
+          "Garage_Qual__c": "excellent",
+          "Gr_Liv_Area__c": 3500,
+          "Lot_Area__c": 8000,
+          "Overall_Cond__c": 9,
+          "Second_Flr_SF__c": 1500,
+          "Total_Bsmt_SF__c": 1000,
+          "Year_Built__c": 2020
+        }
+      }
+    }
+  ]
+}
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