support llm gateway callout for script

README.md

@@ -305,6 +305,55 @@ Options:
 - `--function-invoke-opt TEXT`: Currently we support only `UnstructuredChunking` for functions.
 
 
+## Testing LLM Gateway
+
+You can use AI models configured in Salesforce to generate responses while transforming your data. Below is a sample code example:
+
+```
+from datacustomcode.client import Client, llm_gateway_generate_text_col
+
+
+def main():
+  client = Client()
+  df = client.read_dlo("Input__dll")
+  # llm_gateway_generate_text_col returns a struct
+  # {status, response, error_code, error_message} per row, so per-row
+  # failures don't abort the Spark job. Pick the field you want with [].
+  df_generated = df.withColumn(
+    "greeting__c",
+    llm_gateway_generate_text_col(
+        "In one sentence, greet {name} from {city}.",
+        {"name": col("name__c"), "city": col("homecity__c")},
+        model_id="sfdc_ai__DefaultGPT4Omni", # An AI model in your org
+    )["response"],
+  )
+
+  dlo_name = "Output_dll"
+  client.write_to_dlo(dlo_name, df_upper1, write_mode=WriteMode.APPEND)
+
+  greeting = client.llm_gateway_generate_text("In one sentence, generate a greeting message", "sfdc_ai__DefaultGPT52")
+
+if __name__ == "__main__":
+  main()
+```
+
+In order to test this code on your local machine before deploying it to Data Cloud, you must first set up an External Client App that allows access to the Agent API. Follow this guide to create the ECA https://developer.salesforce.com/docs/ai/agentforce/guide/agent-api-get-started.html#create-a-salesforce-app. You must use `http://localhost:1717/OauthRedirect` as the callback URL.
+
+Once the ECA is set up, log in to your org using this ECA
+```
+sf org login web \
+  --alias myorg \
+  --instance-url https://{MY_DOMAIN_URL} \
+  --client-id {CONSUMER_KEY} \
+  --scopes "sfap_api api"
+```
+
+then you can test your code using `myorg` alias
+```
+datacustomcode run ./payload/entrypoint.py --sf-cli-org myorg
+```
+
+
 ## Docker usage
 
 The SDK provides Docker-based development options that allow you to test your code in an environment that closely resembles Data Cloud's execution environment.

src/datacustomcode/__init__.py

@@ -17,8 +17,11 @@
     "AuthType",
     "Client",
     "Credentials",
+    "DefaultSparkLLMGateway",
     "PrintDataCloudWriter",
     "QueryAPIDataCloudReader",
+    "SparkLLMGateway",
+    "llm_gateway_generate_text_col",
 ]
 
 
@@ -44,4 +47,16 @@ def __getattr__(name: str):
         from datacustomcode.io.reader.query_api import QueryAPIDataCloudReader
 
         return QueryAPIDataCloudReader
+    elif name == "SparkLLMGateway":
+        from datacustomcode.llm_gateway import SparkLLMGateway
+
+        return SparkLLMGateway
+    elif name == "DefaultSparkLLMGateway":
+        from datacustomcode.llm_gateway import DefaultSparkLLMGateway
+
+        return DefaultSparkLLMGateway
+    elif name == "llm_gateway_generate_text_col":
+        from datacustomcode.client import llm_gateway_generate_text_col
+
+        return llm_gateway_generate_text_col
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/datacustomcode/client.py

@@ -18,24 +18,87 @@
 from typing import (
     TYPE_CHECKING,
     ClassVar,
+    Dict,
     Optional,
+    Union,
 )
 
 from datacustomcode.config import config
 from datacustomcode.file.path.default import DefaultFindFilePath
 from datacustomcode.io.reader.base import BaseDataCloudReader
+from datacustomcode.llm_gateway_config import spark_llm_gateway_config
 from datacustomcode.spark.default import DefaultSparkSessionProvider
 
 if TYPE_CHECKING:
     from pathlib import Path
 
-    from pyspark.sql import DataFrame as PySparkDataFrame
+    from pyspark.sql import Column, DataFrame as PySparkDataFrame
 
     from datacustomcode.io.reader.base import BaseDataCloudReader
     from datacustomcode.io.writer.base import BaseDataCloudWriter, WriteMode
+    from datacustomcode.llm_gateway.spark_base import SparkLLMGateway
     from datacustomcode.spark.base import BaseSparkSessionProvider
 
 
+def _build_spark_llm_gateway() -> "SparkLLMGateway":
+    """Instantiate the SDK-configured :class:`SparkLLMGateway`.
+
+    Raises:
+        RuntimeError: If no ``spark_llm_gateway_config`` has been loaded.
+    """
+    cfg = spark_llm_gateway_config.spark_llm_gateway_config
+    if cfg is None:
+        raise RuntimeError(
+            "spark_llm_gateway_config is not configured. Add a "
+            "'spark_llm_gateway_config' section to config.yaml."
+        )
+    return cfg.to_object()
+
+
+def llm_gateway_generate_text_col(
+    template: str,
+    values: Union[Dict[str, "Column"], "Column"],
+    model_id: Optional[str] = None,
+) -> "Column":
+    """Build a Spark Column that runs the LLM Gateway per row.
+
+    The returned Column yields a struct ``{status, response, error_code,
+    error_message}`` for each row. Use ``[...]`` (or ``getField``) to pick the
+    field you want, e.g. ``llm_gateway_generate_text_col(...)["response"]``.
+    Per-row failures populate ``status`` / ``error_code`` / ``error_message``
+    so a single bad row does not abort the whole Spark job.
+
+    Example:
+
+        >>> result = llm_gateway_generate_text_col(
+        ...     "In one sentence, greet {name} from {city}.",
+        ...     {"name": col("name__c"), "city": col("homecity__c")},
+        ...     model_id="sfdc_ai__DefaultGPT4Omni",
+        ... )
+        >>> df.withColumn("greeting__c", result["response"])
+        >>> # …or keep the struct around and inspect failures:
+        >>> df.withColumn("llm", result).select(
+        ...     "llm.status", "llm.response", "llm.error_message"
+        ... )
+
+    Args:
+        template: The prompt template, with ``{field}`` placeholders matching
+            keys in ``values``. Substitution uses ``str.format``.
+        values: Either a mapping from placeholder name to Spark ``Column``, or
+            a single ``Column`` whose value is already a struct.
+        model_id: LLM model id. Defaults to ``sfdc_ai__DefaultGPT4Omni``.
+
+    Returns:
+        A Spark ``Column`` of ``StructType`` with fields ``status``,
+        ``response``, ``error_code``, and ``error_message`` (all nullable
+        strings). On success, ``status == "SUCCESS"`` and ``response`` holds
+        the generated text; on failure, ``status == "ERROR"`` and the
+        ``error_*`` fields carry diagnostic detail.
+    """
+    gateway = Client()._get_spark_llm_gateway()
+    return gateway.llm_gateway_generate_text_col(template, values, model_id=model_id)
+
+
 class DataCloudObjectType(Enum):
     DLO = "dlo"
     DMO = "dmo"
@@ -94,18 +157,21 @@ class Client:
         finder: Find a file path
         reader: A custom reader to use for reading Data Cloud objects.
         writer: A custom writer to use for writing Data Cloud objects.
+        spark_llm_gateway: Optional custom :class:`SparkLLMGateway`.
 
     Example:
     >>> client = Client()
     >>> file_path = client.find_file_path("data.csv")
     >>> dlo = client.read_dlo("my_dlo")
     >>> client.write_to_dmo("my_dmo", dlo)
+    >>> answer = client.llm_gateway_generate_text("Generate a greeting message")
     """
 
     _instance: ClassVar[Optional[Client]] = None
     _reader: BaseDataCloudReader
     _writer: BaseDataCloudWriter
     _file: DefaultFindFilePath
+    _spark_llm_gateway: Optional[SparkLLMGateway]
     _data_layer_history: dict[DataCloudObjectType, set[str]]
     _code_type: str
 
@@ -114,11 +180,13 @@ def __new__(
         reader: Optional[BaseDataCloudReader] = None,
         writer: Optional[BaseDataCloudWriter] = None,
         spark_provider: Optional[BaseSparkSessionProvider] = None,
+        spark_llm_gateway: Optional[SparkLLMGateway] = None,
         code_type: str = "script",
     ) -> Client:
 
         if cls._instance is None:
             cls._instance = super().__new__(cls)
+            cls._instance._spark_llm_gateway = spark_llm_gateway
             # Initialize Readers and Writers from config
             # and/or provided reader and writer
             if reader is None or writer is None:
@@ -225,6 +293,41 @@ def find_file_path(self, file_name: str) -> Path:
 
         return self._file.find_file_path(file_name)  # type: ignore[no-any-return]
 
+    def llm_gateway_generate_text(
+        self,
+        prompt: str,
+        model_id: Optional[str] = None,
+    ) -> str:
+        """Issue a one-shot LLM Gateway call. This is the scalar counterpart to
+        :func:`llm_gateway_generate_text_col`: it runs **once**  — not per row.
+        Use the column helper method instead when you want to fan a prompt out across
+        every row of a DataFrame.
+
+        Example:
+
+            >>> response = Client().llm_gateway_generate_text(
+            ...     "Generate a greeting message"
+            ... )
+
+        Args:
+            prompt: The literal prompt to send. Plain text — no
+                ``{field}`` substitution is performed on this string.
+            model_id: LLM model id to target. Defaults to
+                ``sfdc_ai__DefaultGPT4Omni`` when ``None``.
+
+        Returns:
+            The generated text as a plain Python ``str``; empty when the
+            gateway response carries no generated text.
+        """
+        return self._get_spark_llm_gateway().llm_gateway_generate_text(
+            prompt, model_id=model_id
+        )
+
+    def _get_spark_llm_gateway(self) -> SparkLLMGateway:
+        if self._spark_llm_gateway is None:
+            self._spark_llm_gateway = _build_spark_llm_gateway()
+        return self._spark_llm_gateway
+
     def _validate_data_layer_history_does_not_contain(
         self, data_cloud_object_type: DataCloudObjectType
     ) -> None:

src/datacustomcode/config.yaml

@@ -28,3 +28,6 @@ llm_gateway_config:
   type_config_name: DefaultLLMGateway
   options:
     credentials_profile: default
+
+spark_llm_gateway_config:
+  type_config_name: DefaultSparkLLMGateway

src/datacustomcode/einstein_platform_config.py

@@ -15,20 +15,23 @@
 
 from typing import (
     ClassVar,
+    Generic,
     Optional,
     Type,
-    cast,
+    TypeVar,
 )
 
 from datacustomcode.common_config import BaseObjectConfig
 
+_T = TypeVar("_T")
 
-class CredentialsObjectConfig(BaseObjectConfig):
+
+class CredentialsObjectConfig(BaseObjectConfig, Generic[_T]):
     type_to_create: ClassVar[Type]
     credentials_profile: Optional[str] = None
     sf_cli_org: Optional[str] = None
 
-    def to_object(self):
+    def to_object(self) -> _T:
         """Create an object instance, automatically including credentials in options"""
 
         options = self.options.copy()
@@ -38,4 +41,5 @@ def to_object(self):
             options["sf_cli_org"] = self.sf_cli_org
 
         type_ = self.type_to_create.subclass_from_config_name(self.type_config_name)
-        return cast(type_, type_(**options))
+        instance: _T = type_(**options)
+        return instance

src/datacustomcode/einstein_predictions_config.py

@@ -28,7 +28,7 @@
 _E = TypeVar("_E", bound=EinsteinPredictions)
 
 
-class EinsteinPredictionsObjectConfig(CredentialsObjectConfig, Generic[_E]):
+class EinsteinPredictionsObjectConfig(CredentialsObjectConfig[_E], Generic[_E]):
     type_to_create: ClassVar[Type[EinsteinPredictions]] = EinsteinPredictions  # type: ignore[type-abstract]
 
 

src/datacustomcode/function/feature_types/chunking.py

@@ -16,6 +16,7 @@
 """
 Pydantic models for Search Index Chunking V1
 """
+
 from enum import Enum
 from typing import (
     Dict,

src/datacustomcode/llm_gateway/__init__.py

@@ -15,8 +15,14 @@
 
 from datacustomcode.llm_gateway.base import LLMGateway
 from datacustomcode.llm_gateway.default import DefaultLLMGateway
+from datacustomcode.llm_gateway.errors import LLMGatewayCallError
+from datacustomcode.llm_gateway.spark_base import SparkLLMGateway
+from datacustomcode.llm_gateway.spark_default import DefaultSparkLLMGateway
 
 __all__ = [
     "DefaultLLMGateway",
+    "DefaultSparkLLMGateway",
     "LLMGateway",
+    "LLMGatewayCallError",
+    "SparkLLMGateway",
 ]

src/datacustomcode/proxy/base.py → src/datacustomcode/llm_gateway/errors.py

@@ -12,13 +12,25 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""Exceptions raised by LLM Gateway implementations."""
+
 from __future__ import annotations
 
-from abc import ABC
+from typing import Optional
 
-from datacustomcode.mixin import UserExtendableNamedConfigMixin
 
+class LLMGatewayCallError(RuntimeError):
+    """Raised when an LLM Gateway call returns an error."""
 
-class BaseProxyAccessLayer(ABC, UserExtendableNamedConfigMixin):
-    def __init__(self):
-        pass
+    def __init__(
+        self,
+        message: str,
+        *,
+        status: Optional[object] = None,
+        error_code: Optional[str] = None,
+        error_message: Optional[str] = None,
+    ) -> None:
+        super().__init__(message)
+        self.status = status
+        self.error_code = error_code
+        self.error_message = error_message