Fix mypy and lint error

litellm/llms/gemini/vector_stores/transformation.py

@@ -209,7 +209,6 @@ class GeminiVectorStoreConfig(BaseVectorStoreConfig):
                 
                 # Process each grounding chunk
                 for chunk in grounding_chunks:
-                    web_chunk = chunk.get("web")
                     retrieved_context = chunk.get("retrievedContext")
                     
                     if retrieved_context:
@@ -318,7 +317,6 @@ class GeminiVectorStoreConfig(BaseVectorStoreConfig):
             store_name = response_data.get("name", "")
             display_name = response_data.get("displayName", "")
             create_time = response_data.get("createTime", "")
-            update_time = response_data.get("updateTime", "")
 
             # Convert ISO timestamp to Unix timestamp
             import datetime

litellm/rag/ingestion/base_ingestion.py

@@ -14,7 +14,7 @@ from __future__ import annotations
 
 import base64
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
 
 import httpx
 
@@ -49,11 +49,14 @@ class BaseRAGIngestion(ABC):
 
         # Extract configs from options
         self.ocr_config = ingest_options.get("ocr")
-        self.chunking_strategy: Dict[str, Any] = ingest_options.get(
-            "chunking_strategy", {"type": "auto"}
+        self.chunking_strategy: Dict[str, Any] = cast(
+            Dict[str, Any],
+            ingest_options.get("chunking_strategy") or {"type": "auto"},
         )
         self.embedding_config = ingest_options.get("embedding")
-        self.vector_store_config: Dict[str, Any] = ingest_options.get("vector_store") or {}
+        self.vector_store_config: Dict[str, Any] = cast(
+            Dict[str, Any], ingest_options.get("vector_store") or {}
+        )
         self.ingest_name = ingest_options.get("name")
 
     @property

litellm/rag/ingestion/gemini_ingestion.py

@@ -7,8 +7,6 @@ so this implementation skips the embedding step and directly uploads files.
 
 from __future__ import annotations
 
-import base64
-import json
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
 
 import httpx