Make lint

Author: ritaagarwala-sf

src/datacustomcode/cli.py

@@ -210,8 +210,8 @@ def deploy(
 ):
     from datacustomcode.deploy import (
         COMPUTE_TYPES,
-        CodeExtensionMetadata,
         USE_IN_FEATURE_MAPPING_FOR_CONNECT_API,
+        CodeExtensionMetadata,
         deploy_full,
         infer_use_in_feature,
     )
@@ -255,7 +255,9 @@ def deploy(
             raise click.Abort()
 
         # Map user-provided feature names to API names
-        mapped_feature = USE_IN_FEATURE_MAPPING_FOR_CONNECT_API.get(use_in_feature, use_in_feature)
+        mapped_feature = USE_IN_FEATURE_MAPPING_FOR_CONNECT_API.get(
+            use_in_feature, use_in_feature
+        )
         metadata.functionInvokeOptions = [mapped_feature]
 
     try:
@@ -286,10 +288,7 @@ def init(directory: str, code_type: str, use_in_feature: Optional[str]):
         update_config,
         write_sdk_config,
     )
-    from datacustomcode.template import (
-        copy_function_template,
-        copy_script_template,
-    )
+    from datacustomcode.template import copy_function_template, copy_script_template
 
     click.echo("Copying template to " + click.style(directory, fg="blue", bold=True))
     if code_type == "script":
@@ -397,5 +396,10 @@ def run(
     from datacustomcode.run import run_entrypoint
 
     run_entrypoint(
-        entrypoint, config_file, dependencies, profile, test_file=test_with, sf_cli_org=sf_cli_org
+        entrypoint,
+        config_file,
+        dependencies,
+        profile,
+        test_file=test_with,
+        sf_cli_org=sf_cli_org,
     )

src/datacustomcode/constants.py

@@ -42,4 +42,4 @@
 REQUEST_TYPE_TO_FEATURE = {
     "SearchIndexChunkingV1Request": "SearchIndexChunking",
     "SearchIndexChunkingV1Response": "SearchIndexChunking",
-}
+}

src/datacustomcode/deploy.py

@@ -35,10 +35,7 @@
 import requests
 
 from datacustomcode.cmd import cmd_output
-from datacustomcode.constants import (
-    REQUEST_TYPE_TO_FEATURE,
-    USE_IN_FEATURE_MAPPING_FOR_CONNECT_API,
-)
+from datacustomcode.constants import REQUEST_TYPE_TO_FEATURE
 from datacustomcode.scan import find_base_directory, get_package_type
 
 DATA_CUSTOM_CODE_PATH = "services/data/v63.0/ssot/data-custom-code"
@@ -85,7 +82,9 @@ def infer_use_in_feature(entrypoint_path: str) -> Union[str, None]:
     """
     from datacustomcode.function_utils import inspect_function_types_static
 
-    request_type_name, response_type_name = inspect_function_types_static(entrypoint_path)
+    request_type_name, response_type_name = inspect_function_types_static(
+        entrypoint_path
+    )
 
     if not request_type_name or not response_type_name:
         return None

src/datacustomcode/function/feature_types/chunking.py

@@ -22,7 +22,6 @@
     Any,
     Dict,
     List,
-    Literal,
 )
 
 from pydantic import BaseModel, Field
@@ -76,7 +75,10 @@ class SearchIndexChunkingV1Request(BaseModel):
 
 class SearchIndexChunkingV1Response(BaseModel):
     """Batch response for UDS chunking"""
+
     output: List[SearchIndexChunkOutput] = Field(
         default_factory=list, description="Flat list of chunks from all docs"
     )
-    status: SearchIndexStatusResponse = Field(..., description="Overall operation status")
+    status: SearchIndexStatusResponse = Field(
+        ..., description="Overall operation status"
+    )

src/datacustomcode/function_utils.py

@@ -19,9 +19,12 @@
 import importlib.util
 import inspect
 import json
-import sys
 import typing
-from typing import Any, Optional, Tuple
+from typing import (
+    Any,
+    Optional,
+    Tuple,
+)
 
 
 def load_function_module(entrypoint_path: str, module_name: str = "function_module"):
@@ -59,7 +62,7 @@ def get_function_callable(module):
         AttributeError: If module doesn't have a 'function' attribute
     """
     if not hasattr(module, "function"):
-        raise AttributeError(f"Module does not have a 'function' callable")
+        raise AttributeError("Module does not have a 'function' callable")
     return module.function
 
 
@@ -108,7 +111,9 @@ def get_function_signature_types(
     return request_type, response_type, request_type_name, response_type_name
 
 
-def inspect_function_types_static(entrypoint_path: str) -> Tuple[Optional[str], Optional[str]]:
+def inspect_function_types_static(
+    entrypoint_path: str,
+) -> Tuple[Optional[str], Optional[str]]:
     """Inspect function types using static AST parsing (no imports).
 
     This parses the Python file without executing it, so it doesn't
@@ -121,7 +126,7 @@ def inspect_function_types_static(entrypoint_path: str) -> Tuple[Optional[str],
         Tuple of (request_type_name, response_type_name)
     """
     try:
-        with open(entrypoint_path, 'r') as f:
+        with open(entrypoint_path, "r") as f:
             tree = ast.parse(f.read(), filename=entrypoint_path)
 
         # Find the 'function' definition
@@ -132,7 +137,9 @@ def inspect_function_types_static(entrypoint_path: str) -> Tuple[Optional[str],
                 if node.args.args and len(node.args.args) > 0:
                     first_param = node.args.args[0]
                     if first_param.annotation:
-                        request_type_name = _get_type_name_from_ast(first_param.annotation)
+                        request_type_name = _get_type_name_from_ast(
+                            first_param.annotation
+                        )
 
                 # Get response type (return annotation)
                 response_type_name = None
@@ -175,7 +182,7 @@ def _import_pydantic_model(entrypoint_path: str, type_name: str) -> Optional[Any
         The Pydantic model class, or None if not found
     """
     try:
-        with open(entrypoint_path, 'r') as f:
+        with open(entrypoint_path, "r") as f:
             tree = ast.parse(f.read(), filename=entrypoint_path)
 
         # Find where this type is imported from
@@ -344,12 +351,12 @@ def generate_test_json(entrypoint_path: str, output_path: str) -> None:
 
     # Check if it's a Pydantic model
     if not hasattr(request_type, "model_fields"):
-        raise ValueError(f"Request parameter type must be a Pydantic model")
+        raise ValueError("Request parameter type must be a Pydantic model")
 
     # Generate sample data for ALL fields (use defaults where available)
     sample_data = _generate_model_sample_data(request_type)
     sample_instance = request_type(**sample_data)
 
     # Write to file
     with open(output_path, "w") as f:
-        json.dump(sample_instance.model_dump(), f, indent=2)
+        json.dump(sample_instance.model_dump(), f, indent=2)

src/datacustomcode/run.py

@@ -177,7 +177,9 @@ def run_function_with_test(entrypoint: str, test_file: str) -> None:
     try:
         request = request_type(**test_data)
     except Exception as e:
-        raise ValueError(f"Failed to parse test data as {request_type.__name__}: {e}") from e
+        raise ValueError(
+            f"Failed to parse test data as {request_type.__name__}: {e}"
+        ) from e
 
     # Import Runtime
     from datacustomcode.function import Runtime

src/datacustomcode/template.py

@@ -69,10 +69,12 @@ def copy_function_template(target_dir: str, use_in_feature: str) -> None:
             destination = os.path.join(target_dir, item)
 
             if os.path.isdir(source):
-                logger.debug(f"Copying feature-specific directory {source} to {destination}...")
+                logger.debug(
+                    f"Copying feature-specific directory {source} to {destination}..."
+                )
                 shutil.copytree(source, destination, dirs_exist_ok=True)
             else:
-                logger.debug(f"Copying feature-specific file {source} to {destination}...")
+                logger.debug(
+                    f"Copying feature-specific file {source} to {destination}..."
+                )
                 shutil.copy2(source, destination)
-
-

src/datacustomcode/templates/function/chunking/requirements.txt

@@ -1,2 +1,2 @@
 # Packages required for the chunking function
-langchain-text-splitters>=0.3.0
+langchain-text-splitters>=0.3.0