[autofix.ci] apply automated fixes

src/backend/base/langflow/components/knowledge_bases/ingestion.py

@@ -98,17 +98,9 @@ class NewKnowledgeBaseInput:
                                 display_name="Choose Embedding",
                                 info="Select the embedding model to use for this knowledge base.",
                                 required=True,
-                                options=OPENAI_EMBEDDING_MODEL_NAMES
-                                + HUGGINGFACE_MODEL_NAMES
-                                + COHERE_MODEL_NAMES,
-                                options_metadata=[
-                                    {"icon": "OpenAI"}
-                                    for _ in OPENAI_EMBEDDING_MODEL_NAMES
-                                ]
-                                + [
-                                    {"icon": "HuggingFace"}
-                                    for _ in HUGGINGFACE_MODEL_NAMES
-                                ]
+                                options=OPENAI_EMBEDDING_MODEL_NAMES + HUGGINGFACE_MODEL_NAMES + COHERE_MODEL_NAMES,
+                                options_metadata=[{"icon": "OpenAI"} for _ in OPENAI_EMBEDDING_MODEL_NAMES]
+                                + [{"icon": "HuggingFace"} for _ in HUGGINGFACE_MODEL_NAMES]
                                 + [{"icon": "Cohere"} for _ in COHERE_MODEL_NAMES],
                             ),
                             "03_api_key": SecretStrInput(
@@ -208,9 +200,7 @@ class NewKnowledgeBaseInput:
     ]
 
     # ------ Outputs -------------------------------------------------------
-    outputs = [
-        Output(display_name="Results", name="dataframe_output", method="build_kb_info")
-    ]
+    outputs = [Output(display_name="Results", name="dataframe_output", method="build_kb_info")]
 
     # ------ Internal helpers ---------------------------------------------
     def _get_kb_root(self) -> Path:
@@ -301,9 +291,7 @@ def _build_embedding_metadata(self, embedding_model, api_key) -> dict[str, Any]:
         if api_key_to_save:
             settings_service = get_settings_service()
             try:
-                encrypted_api_key = encrypt_api_key(
-                    api_key_to_save, settings_service=settings_service
-                )
+                encrypted_api_key = encrypt_api_key(api_key_to_save, settings_service=settings_service)
             except (TypeError, ValueError) as e:
                 self.log(f"Could not encrypt API key: {e}")
                 logger.error(f"Could not encrypt API key: {e}")
@@ -317,9 +305,7 @@ def _build_embedding_metadata(self, embedding_model, api_key) -> dict[str, Any]:
             "created_at": datetime.now(timezone.utc).isoformat(),
         }
 
-    def _save_embedding_metadata(
-        self, kb_path: Path, embedding_model: str, api_key: str
-    ) -> None:
+    def _save_embedding_metadata(self, kb_path: Path, embedding_model: str, api_key: str) -> None:
         """Save embedding model metadata."""
         embedding_metadata = self._build_embedding_metadata(embedding_model, api_key)
         metadata_path = kb_path / "embedding_metadata.json"
@@ -344,9 +330,7 @@ def _save_kb_files(
         except (OSError, TypeError, ValueError) as e:
             self.log(f"Error saving KB files: {e}")
 
-    def _build_column_metadata(
-        self, config_list: list[dict[str, Any]], df_source: pd.DataFrame
-    ) -> dict[str, Any]:
+    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:
         """Build detailed column metadata."""
         metadata: dict[str, Any] = {
             "total_columns": len(df_source.columns),
@@ -358,12 +342,8 @@ def _build_column_metadata(
 
         for config in config_list:
             col_name = config.get("column_name")
-            vectorize = (
-                config.get("vectorize") == "True" or config.get("vectorize") is True
-            )
-            identifier = (
-                config.get("identifier") == "True" or config.get("identifier") is True
-            )
+            vectorize = config.get("vectorize") == "True" or config.get("vectorize") is True
+            identifier = config.get("identifier") == "True" or config.get("identifier") is True
 
             # Add to columns list
             metadata["columns"].append(
@@ -402,9 +382,7 @@ async def _create_vector_store(
             embedding_function = self._build_embeddings(embedding_model, api_key)
 
             # Convert DataFrame to Data objects (following Local DB pattern)
-            data_objects = await self._convert_df_to_data_objects(
-                df_source, config_list
-            )
+            data_objects = await self._convert_df_to_data_objects(df_source, config_list)
 
             # Create vector store
             chroma = Chroma(
@@ -422,9 +400,7 @@ async def _create_vector_store(
             # Add documents to vector store
             if documents:
                 chroma.add_documents(documents)
-                self.log(
-                    f"Added {len(documents)} documents to vector store '{self.knowledge_base}'"
-                )
+                self.log(f"Added {len(documents)} documents to vector store '{self.knowledge_base}'")
 
         except (OSError, ValueError, RuntimeError) as e:
             self.log(f"Error creating vector store: {e}")
@@ -448,24 +424,16 @@ async def _convert_df_to_data_objects(
         all_docs = chroma.get()
 
         # Extract all _id values from metadata
-        id_list = [
-            metadata.get("_id")
-            for metadata in all_docs["metadatas"]
-            if metadata.get("_id")
-        ]
+        id_list = [metadata.get("_id") for metadata in all_docs["metadatas"] if metadata.get("_id")]
 
         # Get column roles
         content_cols = []
         identifier_cols = []
 
         for config in config_list:
             col_name = config.get("column_name")
-            vectorize = (
-                config.get("vectorize") == "True" or config.get("vectorize") is True
-            )
-            identifier = (
-                config.get("identifier") == "True" or config.get("identifier") is True
-            )
+            vectorize = config.get("vectorize") == "True" or config.get("vectorize") is True
+            identifier = config.get("identifier") == "True" or config.get("identifier") is True
 
             if vectorize:
                 content_cols.append(col_name)
@@ -475,11 +443,7 @@ async def _convert_df_to_data_objects(
         # Convert each row to a Data object
         for _, row in df_source.iterrows():
             # Build content text from identifier columns using list comprehension
-            identifier_parts = [
-                str(row[col])
-                for col in content_cols
-                if col in row and pd.notna(row[col])
-            ]
+            identifier_parts = [str(row[col]) for col in content_cols if col in row and pd.notna(row[col])]
 
             # Join all parts into a single string
             page_content = " ".join(identifier_parts)
@@ -491,11 +455,7 @@ async def _convert_df_to_data_objects(
 
             # Add identifier columns if they exist
             if identifier_cols:
-                identifier_parts = [
-                    str(row[col])
-                    for col in identifier_cols
-                    if col in row and pd.notna(row[col])
-                ]
+                identifier_parts = [str(row[col]) for col in identifier_cols if col in row and pd.notna(row[col])]
                 page_content = " ".join(identifier_parts)
 
             # Add metadata columns as simple key-value pairs
@@ -520,9 +480,7 @@ async def _convert_df_to_data_objects(
 
         return data_objects
 
-    def is_valid_collection_name(
-        self, name, min_length: int = 3, max_length: int = 63
-    ) -> bool:
+    def is_valid_collection_name(self, name, min_length: int = 3, max_length: int = 63) -> bool:
         """Validates collection name against conditions 1-3.
 
         1. Contains 3-63 characters
@@ -578,9 +536,7 @@ async def _kb_path(self) -> Path | None:
     async def build_kb_info(self) -> Data:
         """Main ingestion routine → returns a dict with KB metadata."""
         try:
-            input_value = (
-                self.input_df[0] if isinstance(self.input_df, list) else self.input_df
-            )
+            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df
             df_source: DataFrame = convert_to_dataframe(input_value)
 
             # Validate column configuration (using Structured Output patterns)
@@ -602,9 +558,7 @@ async def build_kb_info(self) -> Data:
                 try:
                     api_key = decrypt_api_key(metadata["api_key"], settings_service)
                 except (InvalidToken, TypeError, ValueError) as e:
-                    logger.error(
-                        f"Could not decrypt API key. Please provide it manually. Error: {e}"
-                    )
+                    logger.error(f"Could not decrypt API key. Please provide it manually. Error: {e}")
 
             # Check if a custom API key was provided, update metadata if so
             if self.api_key:
@@ -616,9 +570,7 @@ async def build_kb_info(self) -> Data:
                 )
 
             # Create vector store following Local DB component pattern
-            await self._create_vector_store(
-                df_source, config_list, embedding_model=embedding_model, api_key=api_key
-            )
+            await self._create_vector_store(df_source, config_list, embedding_model=embedding_model, api_key=api_key)
 
             # Save KB files (using File Component storage patterns)
             self._save_kb_files(kb_path, config_list)
@@ -635,9 +587,7 @@ async def build_kb_info(self) -> Data:
             }
 
             # Set status message
-            self.status = (
-                f"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks."
-            )
+            self.status = f"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks."
 
             return Data(data=meta)
 
@@ -685,9 +635,7 @@ async def update_build_config(
             if isinstance(field_value, dict) and "01_new_kb_name" in field_value:
                 # Validate the knowledge base name - Make sure it follows these rules:
                 if not self.is_valid_collection_name(field_value["01_new_kb_name"]):
-                    msg = (
-                        f"Invalid knowledge base name: {field_value['01_new_kb_name']}"
-                    )
+                    msg = f"Invalid knowledge base name: {field_value['01_new_kb_name']}"
                     raise ValueError(msg)
 
                 api_key = field_value.get("03_api_key", None)
@@ -701,9 +649,7 @@ async def update_build_config(
                     raise ValueError(msg)
 
                 # We need to test the API Key one time against the embedding model
-                embed_model = self._build_embeddings(
-                    embedding_model=field_value["02_embedding_model"], api_key=api_key
-                )
+                embed_model = self._build_embeddings(embedding_model=field_value["02_embedding_model"], api_key=api_key)
 
                 # Try to generate a dummy embedding to validate the API key without blocking the event loop
                 try:
@@ -719,9 +665,7 @@ async def update_build_config(
                     raise ValueError(msg) from e
 
                 # Create the new knowledge base directory
-                kb_path = (
-                    KNOWLEDGE_BASES_ROOT_PATH / kb_user / field_value["01_new_kb_name"]
-                )
+                kb_path = KNOWLEDGE_BASES_ROOT_PATH / kb_user / field_value["01_new_kb_name"]
                 kb_path.mkdir(parents=True, exist_ok=True)
 
                 # Save the embedding metadata
@@ -739,10 +683,7 @@ async def update_build_config(
             )
 
             # If the selected knowledge base is not available, reset it
-            if (
-                build_config["knowledge_base"]["value"]
-                not in build_config["knowledge_base"]["options"]
-            ):
+            if build_config["knowledge_base"]["value"] not in build_config["knowledge_base"]["options"]:
                 build_config["knowledge_base"]["value"] = None
 
         return build_config