refactor: Updated Qdrant multi-tenancy implementation

Signed-off-by: Anush008 <anushshetty90@gmail.com>

backend/open_webui/config.py

@@ -1794,10 +1794,10 @@ MILVUS_IVF_FLAT_NLIST = int(os.environ.get("MILVUS_IVF_FLAT_NLIST", "128"))
 QDRANT_URI = os.environ.get("QDRANT_URI", None)
 QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", None)
 QDRANT_ON_DISK = os.environ.get("QDRANT_ON_DISK", "false").lower() == "true"
-QDRANT_PREFER_GRPC = os.environ.get("QDRANT_PREFER_GRPC", "False").lower() == "true"
+QDRANT_PREFER_GRPC = os.environ.get("QDRANT_PREFER_GRPC", "false").lower() == "true"
 QDRANT_GRPC_PORT = int(os.environ.get("QDRANT_GRPC_PORT", "6334"))
 ENABLE_QDRANT_MULTITENANCY_MODE = (
-    os.environ.get("ENABLE_QDRANT_MULTITENANCY_MODE", "false").lower() == "true"
+    os.environ.get("ENABLE_QDRANT_MULTITENANCY_MODE", "true").lower() == "true"
 )
 
 # OpenSearch

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

@@ -23,6 +23,7 @@ from qdrant_client.http.models import PointStruct
 from qdrant_client.models import models
 
 NO_LIMIT = 999999999
+TENANT_ID_FIELD = "tenant_id"
 
 log = logging.getLogger(__name__)
 log.setLevel(SRC_LOG_LEVELS["RAG"])
@@ -113,96 +114,14 @@ class QdrantClient(VectorDBBase):
         else:
             return self.KNOWLEDGE_COLLECTION, tenant_id
 
-    def _extract_error_message(self, exception):
+    def _create_multi_tenant_collection(
-        """
+        self,
-        Extract error message from either HTTP or gRPC exceptions
+        mt_collection_name: str,
-
+        dimension: int = 384,
-        Returns:
-            tuple: (status_code, error_message)
-        """
-        # Check if it's an HTTP exception
-        if isinstance(exception, UnexpectedResponse):
-            try:
-                error_data = exception.structured()
-                error_msg = error_data.get("status", {}).get("error", "")
-                return exception.status_code, error_msg
-            except Exception as inner_e:
-                log.error(f"Failed to parse HTTP error: {inner_e}")
-                return exception.status_code, str(exception)
-
-        # Check if it's a gRPC exception
-        elif isinstance(exception, grpc.RpcError):
-            # Extract status code from gRPC error
-            status_code = None
-            if hasattr(exception, "code") and callable(exception.code):
-                status_code = exception.code().value[0]
-
-            # Extract error message
-            error_msg = str(exception)
-            if "details =" in error_msg:
-                # Parse the details line which contains the actual error message
-                try:
-                    details_line = [
-                        line.strip()
-                        for line in error_msg.split("\n")
-                        if "details =" in line
-                    ][0]
-                    error_msg = details_line.split("details =")[1].strip(' "')
-                except (IndexError, AttributeError):
-                    # Fall back to full message if parsing fails
-                    pass
-
-            return status_code, error_msg
-
-        # For any other type of exception
-        return None, str(exception)
-
-    def _is_collection_not_found_error(self, exception):
-        """
-        Check if the exception is due to collection not found, supporting both HTTP and gRPC
-        """
-        status_code, error_msg = self._extract_error_message(exception)
-
-        # HTTP error (404)
-        if (
-            status_code == 404
-            and "Collection" in error_msg
-            and "doesn't exist" in error_msg
-        ):
-            return True
-
-        # gRPC error (NOT_FOUND status)
-        if (
-            isinstance(exception, grpc.RpcError)
-            and exception.code() == grpc.StatusCode.NOT_FOUND
-        ):
-            return True
-
-        return False
-
-    def _is_dimension_mismatch_error(self, exception):
-        """
-        Check if the exception is due to dimension mismatch, supporting both HTTP and gRPC
-        """
-        status_code, error_msg = self._extract_error_message(exception)
-
-        # Common patterns in both HTTP and gRPC
-        return (
-            "Vector dimension error" in error_msg
-            or "dimensions mismatch" in error_msg
-            or "invalid vector size" in error_msg
-        )
-
-    def _create_multi_tenant_collection_if_not_exists(
-        self, mt_collection_name: str, dimension: int = 384
     ):
         """
-        Creates a collection with multi-tenancy configuration if it doesn't exist.
+        Creates a collection with multi-tenancy configuration and payload indexes for tenant_id and metadata fields.
-        Default dimension is set to 384 which corresponds to 'sentence-transformers/all-MiniLM-L6-v2'.
-        When creating collections dynamically (insert/upsert), the actual vector dimensions will be used.
         """
-        try:
-            # Try to create the collection directly - will fail if it already exists
         self.client.create_collection(
             collection_name=mt_collection_name,
             vectors_config=models.VectorParams(
@@ -210,45 +129,21 @@ class QdrantClient(VectorDBBase):
                 distance=models.Distance.COSINE,
                 on_disk=self.QDRANT_ON_DISK,
             ),
-                hnsw_config=models.HnswConfigDiff(
+        )
-                    payload_m=16,  # Enable per-tenant indexing
+        log.info(
-                    m=0,
+            f"Multi-tenant collection {mt_collection_name} created with dimension {dimension}!"
-                    on_disk=self.QDRANT_ON_DISK,
-                ),
         )
 
-            # Create tenant ID payload index
         self.client.create_payload_index(
             collection_name=mt_collection_name,
-                field_name="tenant_id",
+            field_name=TENANT_ID_FIELD,
             field_schema=models.KeywordIndexParams(
                 type=models.KeywordIndexType.KEYWORD,
                 is_tenant=True,
                 on_disk=self.QDRANT_ON_DISK,
             ),
-                wait=True,
         )
 
-            log.info(
-                f"Multi-tenant collection {mt_collection_name} created with dimension {dimension}!"
-            )
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            # Check for the specific error indicating collection already exists
-            status_code, error_msg = self._extract_error_message(e)
-
-            # HTTP status code 409 or gRPC ALREADY_EXISTS
-            if (isinstance(e, UnexpectedResponse) and status_code == 409) or (
-                isinstance(e, grpc.RpcError)
-                and e.code() == grpc.StatusCode.ALREADY_EXISTS
-            ):
-                if "already exists" in error_msg:
-                    log.debug(f"Collection {mt_collection_name} already exists")
-                    return
-            # If it's not an already exists error, re-raise
-            raise e
-        except Exception as e:
-            raise e
-
     def _create_points(self, items: list[VectorItem], tenant_id: str):
         """
         Create point structs from vector items with tenant ID.
@@ -260,50 +155,41 @@ class QdrantClient(VectorDBBase):
                 payload={
                     "text": item["text"],
                     "metadata": item["metadata"],
-                    "tenant_id": tenant_id,
+                    TENANT_ID_FIELD: tenant_id,
                 },
             )
             for item in items
         ]
 
+    def _ensure_collection(
+        self,
+        mt_collection_name: str,
+        dimension: int = 384,
+    ):
+        """
+        Ensure the collection exists and payload indexes are created for tenant_id and metadata fields.
+        """
+        if self.client.collection_exists(collection_name=mt_collection_name):
+            return
+        self._create_multi_tenant_collection(mt_collection_name, dimension)
+
     def has_collection(self, collection_name: str) -> bool:
         """
         Check if a logical collection exists by checking for any points with the tenant ID.
         """
         if not self.client:
             return False
-
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
-
+        if not self.client.collection_exists(collection_name=mt_collection):
-        # Create tenant filter
+            return False
         tenant_filter = models.FieldCondition(
-            key="tenant_id", match=models.MatchValue(value=tenant_id)
+            key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
         )
-
+        count_result = self.client.count(
-        try:
-            # Try directly querying - most of the time collection should exist
-            response = self.client.query_points(
             collection_name=mt_collection,
-                query_filter=models.Filter(must=[tenant_filter]),
+            count_filter=models.Filter(must=[tenant_filter]),
-                limit=1,
         )
-
+        return count_result.count > 0
-            # Collection exists with this tenant ID if there are points
-            return len(response.points) > 0
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            if self._is_collection_not_found_error(e):
-                log.debug(f"Collection {mt_collection} doesn't exist")
-                return False
-            else:
-                # For other API errors, log and return False
-                _, error_msg = self._extract_error_message(e)
-                log.warning(f"Unexpected Qdrant error: {error_msg}")
-                return False
-        except Exception as e:
-            # For any other errors, log and return False
-            log.debug(f"Error checking collection {mt_collection}: {e}")
-            return False
 
     def delete(
         self,
@@ -317,17 +203,16 @@ class QdrantClient(VectorDBBase):
         if not self.client:
             return None
 
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
+        if not self.client.collection_exists(collection_name=mt_collection):
+            log.debug(f"Collection {mt_collection} doesn't exist, nothing to delete")
+            return None
 
-        # Create tenant filter
         tenant_filter = models.FieldCondition(
-            key="tenant_id", match=models.MatchValue(value=tenant_id)
+            key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
         )
-
         must_conditions = [tenant_filter]
         should_conditions = []
-
         if ids:
             for id_value in ids:
                 should_conditions.append(
@@ -346,7 +231,6 @@ class QdrantClient(VectorDBBase):
                 )
 
         try:
-            # Try to delete directly - most of the time collection should exist
             update_result = self.client.delete(
                 collection_name=mt_collection,
                 points_selector=models.FilterSelector(
@@ -355,20 +239,9 @@ class QdrantClient(VectorDBBase):
             )
 
             return update_result
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            if self._is_collection_not_found_error(e):
-                log.debug(
-                    f"Collection {mt_collection} doesn't exist, nothing to delete"
-                )
-                return None
-            else:
-                # For other API errors, log and re-raise
-                _, error_msg = self._extract_error_message(e)
-                log.warning(f"Unexpected Qdrant error: {error_msg}")
-                raise
         except Exception as e:
-            # For non-Qdrant exceptions, re-raise
+            log.warning(f"Error deleting from collection {mt_collection}: {e}")
-            raise
+            return None
 
     def search(
         self, collection_name: str, vectors: list[list[float | int]], limit: int
@@ -378,26 +251,19 @@ class QdrantClient(VectorDBBase):
         """
         if not self.client:
             return None
-
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
+        if not self.client.collection_exists(collection_name=mt_collection):
+            log.debug(f"Collection {mt_collection} doesn't exist, search returns None")
+            return None
 
-        # Get the vector dimension from the query vector
         dimension = len(vectors[0]) if vectors and len(vectors) > 0 else None
-
         try:
-            # Try the search operation directly - most of the time collection should exist
-
-            # Create tenant filter
             tenant_filter = models.FieldCondition(
-                key="tenant_id", match=models.MatchValue(value=tenant_id)
+                key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
             )
-
-            # Ensure vector dimensions match the collection
             collection_dim = self.client.get_collection(
                 mt_collection
             ).config.params.vectors.size
-
             if collection_dim != dimension:
                 if collection_dim < dimension:
                     vectors = [vector[:collection_dim] for vector in vectors]
@@ -406,8 +272,6 @@ class QdrantClient(VectorDBBase):
                         vector + [0] * (collection_dim - dimension)
                         for vector in vectors
                     ]
-
-            # Search with tenant filter
             prefetch_query = models.Prefetch(
                 filter=models.Filter(must=[tenant_filter]),
                 limit=NO_LIMIT,
@@ -418,30 +282,16 @@ class QdrantClient(VectorDBBase):
                 prefetch=prefetch_query,
                 limit=limit,
             )
-
             get_result = self._result_to_get_result(query_response.points)
             return SearchResult(
                 ids=get_result.ids,
                 documents=get_result.documents,
                 metadatas=get_result.metadatas,
-                # qdrant distance is [-1, 1], normalize to [0, 1]
                 distances=[
                     [(point.score + 1.0) / 2.0 for point in query_response.points]
                 ],
             )
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            if self._is_collection_not_found_error(e):
-                log.debug(
-                    f"Collection {mt_collection} doesn't exist, search returns None"
-                )
-                return None
-            else:
-                # For other API errors, log and re-raise
-                _, error_msg = self._extract_error_message(e)
-                log.warning(f"Unexpected Qdrant error during search: {error_msg}")
-                raise
         except Exception as e:
-            # For non-Qdrant exceptions, log and return None
             log.exception(f"Error searching collection '{collection_name}': {e}")
             return None
 
@@ -451,20 +301,16 @@ class QdrantClient(VectorDBBase):
         """
         if not self.client:
             return None
-
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
+        if not self.client.collection_exists(collection_name=mt_collection):
+            log.debug(f"Collection {mt_collection} doesn't exist, query returns None")
+            return None
 
-        # Set default limit if not provided
         if limit is None:
             limit = NO_LIMIT
-
-        # Create tenant filter
         tenant_filter = models.FieldCondition(
-            key="tenant_id", match=models.MatchValue(value=tenant_id)
+            key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
         )
-
-        # Create metadata filters
         field_conditions = []
         for key, value in filter.items():
             field_conditions.append(
@@ -472,32 +318,15 @@ class QdrantClient(VectorDBBase):
                     key=f"metadata.{key}", match=models.MatchValue(value=value)
                 )
             )
-
-        # Combine tenant filter with metadata filters
         combined_filter = models.Filter(must=[tenant_filter, *field_conditions])
-
         try:
-            # Try the query directly - most of the time collection should exist
             points = self.client.query_points(
                 collection_name=mt_collection,
                 query_filter=combined_filter,
                 limit=limit,
             )
-
             return self._result_to_get_result(points.points)
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            if self._is_collection_not_found_error(e):
-                log.debug(
-                    f"Collection {mt_collection} doesn't exist, query returns None"
-                )
-                return None
-            else:
-                # For other API errors, log and re-raise
-                _, error_msg = self._extract_error_message(e)
-                log.warning(f"Unexpected Qdrant error during query: {error_msg}")
-                raise
         except Exception as e:
-            # For non-Qdrant exceptions, log and re-raise
             log.exception(f"Error querying collection '{collection_name}': {e}")
             return None
 
@@ -507,17 +336,15 @@ class QdrantClient(VectorDBBase):
         """
         if not self.client:
             return None
-
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
+        if not self.client.collection_exists(collection_name=mt_collection):
+            log.debug(f"Collection {mt_collection} doesn't exist, get returns None")
+            return None
 
-        # Create tenant filter
         tenant_filter = models.FieldCondition(
-            key="tenant_id", match=models.MatchValue(value=tenant_id)
+            key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
         )
-
         try:
-            # Try to get points directly - most of the time collection should exist
             points = self.client.query_points(
                 collection_name=mt_collection,
                 query_filter=models.Filter(must=[tenant_filter]),
@@ -525,151 +352,28 @@ class QdrantClient(VectorDBBase):
             )
 
             return self._result_to_get_result(points.points)
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            if self._is_collection_not_found_error(e):
-                log.debug(f"Collection {mt_collection} doesn't exist, get returns None")
-                return None
-            else:
-                # For other API errors, log and re-raise
-                _, error_msg = self._extract_error_message(e)
-                log.warning(f"Unexpected Qdrant error during get: {error_msg}")
-                raise
         except Exception as e:
-            # For non-Qdrant exceptions, log and return None
             log.exception(f"Error getting collection '{collection_name}': {e}")
             return None
 
-    def _handle_operation_with_error_retry(
-        self, operation_name, mt_collection, points, dimension
-    ):
-        """
-        Private helper to handle common error cases for insert and upsert operations.
-
-        Args:
-            operation_name: 'insert' or 'upsert'
-            mt_collection: The multi-tenant collection name
-            points: The vector points to insert/upsert
-            dimension: The dimension of the vectors
-
-        Returns:
-            The operation result (for upsert) or None (for insert)
-        """
-        try:
-            if operation_name == "insert":
-                self.client.upload_points(mt_collection, points)
-                return None
-            else:  # upsert
-                return self.client.upsert(mt_collection, points)
-        except (UnexpectedResponse, grpc.RpcError) as e:
-            # Handle collection not found
-            if self._is_collection_not_found_error(e):
-                log.info(
-                    f"Collection {mt_collection} doesn't exist. Creating it with dimension {dimension}."
-                )
-                # Create collection with correct dimensions from our vectors
-                self._create_multi_tenant_collection_if_not_exists(
-                    mt_collection_name=mt_collection, dimension=dimension
-                )
-                # Try operation again - no need for dimension adjustment since we just created with correct dimensions
-                if operation_name == "insert":
-                    self.client.upload_points(mt_collection, points)
-                    return None
-                else:  # upsert
-                    return self.client.upsert(mt_collection, points)
-
-            # Handle dimension mismatch
-            elif self._is_dimension_mismatch_error(e):
-                # For dimension errors, the collection must exist, so get its configuration
-                mt_collection_info = self.client.get_collection(mt_collection)
-                existing_size = mt_collection_info.config.params.vectors.size
-
-                log.info(
-                    f"Dimension mismatch: Collection {mt_collection} expects {existing_size}, got {dimension}"
-                )
-
-                if existing_size < dimension:
-                    # Truncate vectors to fit
-                    log.info(
-                        f"Truncating vectors from {dimension} to {existing_size} dimensions"
-                    )
-                    points = [
-                        PointStruct(
-                            id=point.id,
-                            vector=point.vector[:existing_size],
-                            payload=point.payload,
-                        )
-                        for point in points
-                    ]
-                elif existing_size > dimension:
-                    # Pad vectors with zeros
-                    log.info(
-                        f"Padding vectors from {dimension} to {existing_size} dimensions with zeros"
-                    )
-                    points = [
-                        PointStruct(
-                            id=point.id,
-                            vector=point.vector
-                            + [0] * (existing_size - len(point.vector)),
-                            payload=point.payload,
-                        )
-                        for point in points
-                    ]
-                # Try operation again with adjusted dimensions
-                if operation_name == "insert":
-                    self.client.upload_points(mt_collection, points)
-                    return None
-                else:  # upsert
-                    return self.client.upsert(mt_collection, points)
-            else:
-                # Not a known error we can handle, log and re-raise
-                _, error_msg = self._extract_error_message(e)
-                log.warning(f"Unhandled Qdrant error: {error_msg}")
-                raise
-        except Exception as e:
-            # For non-Qdrant exceptions, re-raise
-            raise
-
-    def insert(self, collection_name: str, items: list[VectorItem]):
-        """
-        Insert items with tenant ID.
-        """
-        if not self.client or not items:
-            return None
-
-        # Map to multi-tenant collection and tenant ID
-        mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
-
-        # Get dimensions from the actual vectors
-        dimension = len(items[0]["vector"]) if items else None
-
-        # Create points with tenant ID
-        points = self._create_points(items, tenant_id)
-
-        # Handle the operation with error retry
-        return self._handle_operation_with_error_retry(
-            "insert", mt_collection, points, dimension
-        )
-
     def upsert(self, collection_name: str, items: list[VectorItem]):
         """
         Upsert items with tenant ID.
         """
         if not self.client or not items:
             return None
-
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
-
-        # Get dimensions from the actual vectors
         dimension = len(items[0]["vector"]) if items else None
-
+        self._ensure_collection(mt_collection, dimension)
-        # Create points with tenant ID
         points = self._create_points(items, tenant_id)
+        self.client.upload_points(mt_collection, points)
+        return None
 
-        # Handle the operation with error retry
+    def insert(self, collection_name: str, items: list[VectorItem]):
-        return self._handle_operation_with_error_retry(
+        """
-            "upsert", mt_collection, points, dimension
+        Insert items with tenant ID.
-        )
+        """
+        return self.upsert(collection_name, items)
 
     def reset(self):
         """
@@ -689,24 +393,21 @@ class QdrantClient(VectorDBBase):
         """
         if not self.client:
             return None
-
-        # Map to multi-tenant collection and tenant ID
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
+        if not self.client.collection_exists(collection_name=mt_collection):
+            log.debug(f"Collection {mt_collection} doesn't exist, nothing to delete")
+            return None
 
-        tenant_filter = models.FieldCondition(
+        self.client.delete(
-            key="tenant_id", match=models.MatchValue(value=tenant_id)
-        )
-
-        field_conditions = [tenant_filter]
-
-        update_result = self.client.delete(
             collection_name=mt_collection,
             points_selector=models.FilterSelector(
-                filter=models.Filter(must=field_conditions)
+                filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key=TENANT_ID_FIELD,
+                            match=models.MatchValue(value=tenant_id),
+                        )
+                    ]
+                )
             ),
         )
-
-        if self.client.get_collection(mt_collection).points_count == 0:
-            self.client.delete_collection(mt_collection)
-
-        return update_result