chore: final cleanup

backend/open_webui/retrieval/vector/dbs/s3vector.py

@@ -11,19 +11,36 @@ log.setLevel(SRC_LOG_LEVELS["RAG"])
 class S3VectorClient(VectorDBBase):
     """
     AWS S3 Vector integration for Open WebUI Knowledge.
-    Assumes AWS credentials are available via environment variables or IAM roles.
     """
+    
     def __init__(self):
         self.bucket_name = S3_VECTOR_BUCKET_NAME
         self.region = S3_VECTOR_REGION
-        self.client = boto3.client("s3vectors", region_name=self.region)
+        
+        # Simple validation - log warnings instead of raising exceptions
+        if not self.bucket_name:
+            log.warning("S3_VECTOR_BUCKET_NAME not set - S3Vector will not work")
+        if not self.region:
+            log.warning("S3_VECTOR_REGION not set - S3Vector will not work")
+            
+        if self.bucket_name and self.region:
+            try:
+                self.client = boto3.client("s3vectors", region_name=self.region)
+                log.info(f"S3Vector client initialized for bucket '{self.bucket_name}' in region '{self.region}'")
+            except Exception as e:
+                log.error(f"Failed to initialize S3Vector client: {e}")
+                self.client = None
+        else:
+            self.client = None
 
-    def _create_index(self, index_name: str, dimension: int, data_type: str = "float32", distance_metric: str = "cosine"):
+    def _create_index(self, index_name: str, dimension: int, data_type: str = "float32", distance_metric: str = "cosine") -> None:
         """
         Create a new index in the S3 vector bucket for the given collection if it does not exist.
         """
         if self.has_collection(index_name):
+            log.debug(f"Index '{index_name}' already exists, skipping creation")
             return
+            
         try:
             self.client.create_index(
                 vectorBucketName=self.bucket_name,
@@ -35,12 +52,11 @@ class S3VectorClient(VectorDBBase):
             log.info(f"Created S3 index: {index_name} (dim={dimension}, type={data_type}, metric={distance_metric})")
         except Exception as e:
             log.error(f"Error creating S3 index '{index_name}': {e}")
+            raise
 
     def _filter_metadata(self, metadata: Dict[str, Any], item_id: str) -> Dict[str, Any]:
         """
         Filter vector metadata keys to comply with S3 Vector API limit of 10 keys maximum.
-        If AWS S3 Vector feature starts supporting more than 10 keys, this should be adjusted, and preferably removed.
-        Limitation is documented here: https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-limitations.html
         """
         if not isinstance(metadata, dict) or len(metadata) <= 10:
             return metadata
@@ -82,6 +98,7 @@ class S3VectorClient(VectorDBBase):
         """
         Check if a vector index (collection) exists in the S3 vector bucket.
         """
+            
         try:
             response = self.client.list_indexes(vectorBucketName=self.bucket_name)
             indexes = response.get("indexes", [])
@@ -89,10 +106,12 @@ class S3VectorClient(VectorDBBase):
         except Exception as e:
             log.error(f"Error listing indexes: {e}")
             return False
+            
     def delete_collection(self, collection_name: str) -> None:
         """
         Delete an entire S3 Vector index/collection.
         """
+            
         if not self.has_collection(collection_name):
             log.warning(f"Collection '{collection_name}' does not exist, nothing to delete")
             return
@@ -108,11 +127,9 @@ class S3VectorClient(VectorDBBase):
             log.error(f"Error deleting collection '{collection_name}': {e}")
             raise
 
-    def insert(self, collection_name: str, items: List[Dict[str, Any]]) -> None:
+    def insert(self, collection_name: str, items: List[VectorItem]) -> None:
         """
         Insert vector items into the S3 Vector index. Create index if it does not exist.
-        
-        Supports both knowledge collection indexes and file-specific indexes (file-{file_id}).
         """
         if not items:
             log.warning("No items to insert")
@@ -143,10 +160,7 @@ class S3VectorClient(VectorDBBase):
                 metadata = item.get("metadata", {}).copy()
                 
                 # Add the text field to metadata so it's available for retrieval
-                if "text" in item:
-                    metadata["text"] = item["text"]
-                else:
-                    log.warning(f"No 'text' field found in item with ID: {item.get('id')}")
+                metadata["text"] = item["text"]
                 
                 # Filter metadata to comply with S3 Vector API limit of 10 keys
                 metadata = self._filter_metadata(metadata, item["id"])
@@ -169,7 +183,7 @@ class S3VectorClient(VectorDBBase):
             log.error(f"Error inserting vectors: {e}")
             raise
 
-    def upsert(self, collection_name: str, items: List[Dict[str, Any]]) -> None:
+    def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
         """
         Insert or update vector items in the S3 Vector index. Create index if it does not exist.
         """
@@ -202,8 +216,7 @@ class S3VectorClient(VectorDBBase):
                 # Prepare metadata, ensuring the text field is preserved
                 metadata = item.get("metadata", {}).copy()
                 # Add the text field to metadata so it's available for retrieval
-                if "text" in item:
-                    metadata["text"] = item["text"]
+                metadata["text"] = item["text"]
                 
                 # Filter metadata to comply with S3 Vector API limit of 10 keys
                 metadata = self._filter_metadata(metadata, item["id"])
@@ -230,17 +243,8 @@ class S3VectorClient(VectorDBBase):
     def search(self, collection_name: str, vectors: List[List[Union[float, int]]], limit: int) -> Optional[SearchResult]:
         """
         Search for similar vectors in a collection using multiple query vectors.
-        
-        Uses S3 Vector's query_vectors API to perform similarity search.
-        
-        Args:
-            collection_name: Name of the collection to search in
-            vectors: List of query vectors to search with
-            limit: Maximum number of results to return per query
-            
-        Returns:
-            SearchResult containing IDs, documents, metadatas, and distances
         """
+            
         if not self.has_collection(collection_name):
             log.warning(f"Collection '{collection_name}' does not exist")
             return None
@@ -343,18 +347,8 @@ class S3VectorClient(VectorDBBase):
     def query(self, collection_name: str, filter: Dict, limit: Optional[int] = None) -> Optional[GetResult]:
         """
         Query vectors from a collection using metadata filter.
-        
-        For S3 Vector, this uses the list_vectors API with metadata filters.
-        Note: S3 Vector supports metadata filtering, but the exact filter syntax may vary.
-        
-        Args:
-            collection_name: Name of the collection to query
-            filter: Dictionary containing metadata filter conditions
-            limit: Maximum number of results to return (optional)
-            
-        Returns:
-            GetResult containing IDs, documents, and metadatas
         """
+            
         if not self.has_collection(collection_name):
             log.warning(f"Collection '{collection_name}' does not exist")
             return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
@@ -423,10 +417,8 @@ class S3VectorClient(VectorDBBase):
     def get(self, collection_name: str) -> Optional[GetResult]:
         """
         Retrieve all vectors from a collection.
-        
-        Uses S3 Vector's list_vectors API to get all vectors with their data and metadata.
-        Handles pagination automatically to retrieve all vectors.
         """
+            
         if not self.has_collection(collection_name):
             log.warning(f"Collection '{collection_name}' does not exist")
             return GetResult(ids=[[]], documents=[[]], metadatas=[[]])
@@ -519,10 +511,8 @@ class S3VectorClient(VectorDBBase):
     def delete(self, collection_name: str, ids: Optional[List[str]] = None, filter: Optional[Dict] = None) -> None:
         """
         Delete vectors by ID or filter from a collection.
-        
-        For S3 Vector, we support deletion by IDs. Filter-based deletion requires querying first.
-        For knowledge collections, also handles cleanup of related file-specific collections.
         """
+            
         if not self.has_collection(collection_name):
             log.warning(f"Collection '{collection_name}' does not exist, nothing to delete")
             return
@@ -578,9 +568,9 @@ class S3VectorClient(VectorDBBase):
 
     def reset(self) -> None:
         """
-        Reset/clear all vector data. For S3 Vector, this would mean deleting all indexes.
-        Use with caution as this is destructive.
+        Reset/clear all vector data. For S3 Vector, this deletes all indexes.
         """
+            
         try:
             log.warning("Reset called - this will delete all vector indexes in the S3 bucket")
             
@@ -616,14 +606,6 @@ class S3VectorClient(VectorDBBase):
     def _matches_filter(self, metadata: Dict[str, Any], filter: Dict[str, Any]) -> bool:
         """
         Check if metadata matches the given filter conditions.
-        Supports basic equality matching and simple logical operations.
-        
-        Args:
-            metadata: The metadata to check
-            filter: The filter conditions to match against
-            
-        Returns:
-            True if metadata matches all filter conditions, False otherwise
         """
         if not isinstance(metadata, dict) or not isinstance(filter, dict):
             return False