Update pinecone.py

backend/open_webui/retrieval/vector/dbs/pinecone.py

@@ -6,6 +6,7 @@ from pinecone import Pinecone, ServerlessSpec
 # Add gRPC support for better performance (Pinecone best practice)
 try:
     from pinecone.grpc import PineconeGRPC
+
     GRPC_AVAILABLE = True
 except ImportError:
     GRPC_AVAILABLE = False
@@ -60,7 +61,7 @@ class PineconeClient(VectorDBBase):
             self.client = PineconeGRPC(
                 api_key=self.api_key,
                 pool_threads=20,  # Improved connection pool size
-                timeout=30        # Reasonable timeout for operations
+                timeout=30,  # Reasonable timeout for operations
             )
             self.using_grpc = True
             log.info("Using Pinecone gRPC client for optimal performance")
@@ -69,7 +70,7 @@ class PineconeClient(VectorDBBase):
             self.client = Pinecone(
                 api_key=self.api_key,
                 pool_threads=20,  # Improved connection pool size
-                timeout=30        # Reasonable timeout for operations
+                timeout=30,  # Reasonable timeout for operations
             )
             self.using_grpc = False
             log.info("Using Pinecone HTTP client (gRPC not available)")
@@ -133,18 +134,34 @@ class PineconeClient(VectorDBBase):
             except Exception as e:
                 error_str = str(e).lower()
                 # Check if it's a retryable error (rate limits, network issues, timeouts)
-                is_retryable = any(keyword in error_str for keyword in [
-                    'rate limit', 'quota', 'timeout', 'network', 'connection', 
-                    'unavailable', 'internal error', '429', '500', '502', '503', '504'
-                ])
-                
+                is_retryable = any(
+                    keyword in error_str
+                    for keyword in [
+                        "rate limit",
+                        "quota",
+                        "timeout",
+                        "network",
+                        "connection",
+                        "unavailable",
+                        "internal error",
+                        "429",
+                        "500",
+                        "502",
+                        "503",
+                        "504",
+                    ]
+                )
+
                 if not is_retryable or attempt == max_retries - 1:
                     # Don't retry for non-retryable errors or on final attempt
                     raise
-                
+
                 # Exponential backoff with jitter
-                delay = (2 ** attempt) + random.uniform(0, 1)
-                log.warning(f"Pinecone operation failed (attempt {attempt + 1}/{max_retries}), retrying in {delay:.2f}s: {e}")
+                delay = (2**attempt) + random.uniform(0, 1)
+                log.warning(
+                    f"Pinecone operation failed (attempt {attempt + 1}/{max_retries}), "
+                    f"retrying in {delay:.2f}s: {e}"
+                )
                 time.sleep(delay)
 
     def _create_points(
@@ -273,7 +290,8 @@ class PineconeClient(VectorDBBase):
         elapsed = time.time() - start_time
         log.debug(f"Insert of {len(points)} vectors took {elapsed:.2f} seconds")
         log.info(
-            f"Successfully inserted {len(points)} vectors in parallel batches into '{collection_name_with_prefix}'"
+            f"Successfully inserted {len(points)} vectors in parallel batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -304,7 +322,8 @@ class PineconeClient(VectorDBBase):
         elapsed = time.time() - start_time
         log.debug(f"Upsert of {len(points)} vectors took {elapsed:.2f} seconds")
         log.info(
-            f"Successfully upserted {len(points)} vectors in parallel batches into '{collection_name_with_prefix}'"
+            f"Successfully upserted {len(points)} vectors in parallel batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     async def insert_async(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -335,7 +354,8 @@ class PineconeClient(VectorDBBase):
                 log.error(f"Error in async insert batch: {result}")
                 raise result
         log.info(
-            f"Successfully async inserted {len(points)} vectors in batches into '{collection_name_with_prefix}'"
+            f"Successfully async inserted {len(points)} vectors in batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     async def upsert_async(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -366,7 +386,8 @@ class PineconeClient(VectorDBBase):
                 log.error(f"Error in async upsert batch: {result}")
                 raise result
         log.info(
-            f"Successfully async upserted {len(points)} vectors in batches into '{collection_name_with_prefix}'"
+            f"Successfully async upserted {len(points)} vectors in batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     def search(
@@ -507,10 +528,12 @@ class PineconeClient(VectorDBBase):
                     # This is a limitation of Pinecone - be careful with ID uniqueness
                     self.index.delete(ids=batch_ids)
                     log.debug(
-                        f"Deleted batch of {len(batch_ids)} vectors by ID from '{collection_name_with_prefix}'"
+                        f"Deleted batch of {len(batch_ids)} vectors by ID "
+                        f"from '{collection_name_with_prefix}'"
                     )
                 log.info(
-                    f"Successfully deleted {len(ids)} vectors by ID from '{collection_name_with_prefix}'"
+                    f"Successfully deleted {len(ids)} vectors by ID "
+                    f"from '{collection_name_with_prefix}'"
                 )
 
             elif filter: