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import logging
from typing import Optional, Tuple, List, Dict, Any
from urllib.parse import urlparse
import grpc
from open_webui.config import (
QDRANT_API_KEY,
QDRANT_GRPC_PORT,
QDRANT_ON_DISK,
QDRANT_PREFER_GRPC,
QDRANT_URI,
QDRANT_COLLECTION_PREFIX,
)
from open_webui.env import SRC_LOG_LEVELS
from open_webui.retrieval.vector.main import (
GetResult,
SearchResult,
VectorDBBase,
VectorItem,
)
from qdrant_client import QdrantClient as Qclient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models import PointStruct
from qdrant_client.models import models
NO_LIMIT = 999999999
TENANT_ID_FIELD = "tenant_id"
DEFAULT_DIMENSION = 384
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
def _tenant_filter(tenant_id: str) -> models.FieldCondition:
return models.FieldCondition(
key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
)
def _metadata_filter(key: str, value: Any) -> models.FieldCondition:
return models.FieldCondition(
key=f"metadata.{key}", match=models.MatchValue(value=value)
)
class QdrantClient(VectorDBBase):
def __init__(self):
self.collection_prefix = QDRANT_COLLECTION_PREFIX
self.QDRANT_URI = QDRANT_URI
self.QDRANT_API_KEY = QDRANT_API_KEY
self.QDRANT_ON_DISK = QDRANT_ON_DISK
self.PREFER_GRPC = QDRANT_PREFER_GRPC
self.GRPC_PORT = QDRANT_GRPC_PORT
if not self.QDRANT_URI:
raise ValueError(
"QDRANT_URI is not set. Please configure it in the environment variables."
)
# Unified handling for either scheme
parsed = urlparse(self.QDRANT_URI)
host = parsed.hostname or self.QDRANT_URI
http_port = parsed.port or 6333 # default REST port
self.client = (
Qclient(
host=host,
port=http_port,
grpc_port=self.GRPC_PORT,
prefer_grpc=self.PREFER_GRPC,
api_key=self.QDRANT_API_KEY,
)
if self.PREFER_GRPC
else Qclient(url=self.QDRANT_URI, api_key=self.QDRANT_API_KEY)
)
# Main collection types for multi-tenancy
self.MEMORY_COLLECTION = f"{self.collection_prefix}_memories"
self.KNOWLEDGE_COLLECTION = f"{self.collection_prefix}_knowledge"
self.FILE_COLLECTION = f"{self.collection_prefix}_files"
self.WEB_SEARCH_COLLECTION = f"{self.collection_prefix}_web-search"
self.HASH_BASED_COLLECTION = f"{self.collection_prefix}_hash-based"
def _result_to_get_result(self, points) -> GetResult:
ids, documents, metadatas = [], [], []
for point in points:
payload = point.payload
ids.append(point.id)
documents.append(payload["text"])
metadatas.append(payload["metadata"])
return GetResult(ids=[ids], documents=[documents], metadatas=[metadatas])
def _get_collection_and_tenant_id(self, collection_name: str) -> Tuple[str, str]:
"""
Maps the traditional collection name to multi-tenant collection and tenant ID.
Returns:
tuple: (collection_name, tenant_id)
"""
# Check for user memory collections
tenant_id = collection_name
if collection_name.startswith("user-memory-"):
return self.MEMORY_COLLECTION, tenant_id
# Check for file collections
elif collection_name.startswith("file-"):
return self.FILE_COLLECTION, tenant_id
# Check for web search collections
elif collection_name.startswith("web-search-"):
return self.WEB_SEARCH_COLLECTION, tenant_id
# Handle hash-based collections (YouTube and web URLs)
elif len(collection_name) == 63 and all(
c in "0123456789abcdef" for c in collection_name
):
return self.HASH_BASED_COLLECTION, tenant_id
else:
return self.KNOWLEDGE_COLLECTION, tenant_id
def _create_multi_tenant_collection(
self, mt_collection_name: str, dimension: int = DEFAULT_DIMENSION
):
"""
Creates a collection with multi-tenancy configuration and payload indexes for tenant_id and metadata fields.
"""
self.client.create_collection(
collection_name=mt_collection_name,
vectors_config=models.VectorParams(
size=dimension,
distance=models.Distance.COSINE,
on_disk=self.QDRANT_ON_DISK,
),
)
log.info(
f"Multi-tenant collection {mt_collection_name} created with dimension {dimension}!"
)
self.client.create_payload_index(
collection_name=mt_collection_name,
field_name=TENANT_ID_FIELD,
field_schema=models.KeywordIndexParams(
type=models.KeywordIndexType.KEYWORD,
is_tenant=True,
on_disk=self.QDRANT_ON_DISK,
),
)
for field in ("metadata.hash", "metadata.file_id"):
self.client.create_payload_index(
collection_name=mt_collection_name,
field_name=field,
field_schema=models.KeywordIndexParams(
type=models.KeywordIndexType.KEYWORD,
on_disk=self.QDRANT_ON_DISK,
),
)
def _create_points(
self, items: List[VectorItem], tenant_id: str
) -> List[PointStruct]:
"""
Create point structs from vector items with tenant ID.
"""
return [
PointStruct(
id=item["id"],
vector=item["vector"],
payload={
"text": item["text"],
"metadata": item["metadata"],
TENANT_ID_FIELD: tenant_id,
},
)
for item in items
]
def _ensure_collection(
self, mt_collection_name: str, dimension: int = DEFAULT_DIMENSION
):
"""
Ensure the collection exists and payload indexes are created for tenant_id and metadata fields.
"""
if not self.client.collection_exists(collection_name=mt_collection_name):
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
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
if not self.client.collection_exists(collection_name=mt_collection):
return False
tenant_filter = _tenant_filter(tenant_id)
count_result = self.client.count(
collection_name=mt_collection,
count_filter=models.Filter(must=[tenant_filter]),
)
return count_result.count > 0
def delete(
self,
collection_name: str,
ids: Optional[List[str]] = None,
filter: Optional[Dict[str, Any]] = None,
):
"""
Delete vectors by ID or filter from a collection with tenant isolation.
"""
if not self.client:
return None
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
must_conditions = [_tenant_filter(tenant_id)]
should_conditions = []
if ids:
should_conditions = [_metadata_filter("id", id_value) for id_value in ids]
elif filter:
must_conditions += [_metadata_filter(k, v) for k, v in filter.items()]
return self.client.delete(
collection_name=mt_collection,
points_selector=models.FilterSelector(
filter=models.Filter(must=must_conditions, should=should_conditions)
),
)
def search(
self, collection_name: str, vectors: List[List[float | int]], limit: int
) -> Optional[SearchResult]:
"""
Search for the nearest neighbor items based on the vectors with tenant isolation.
"""
if not self.client or not vectors:
return None
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
tenant_filter = _tenant_filter(tenant_id)
query_response = self.client.query_points(
collection_name=mt_collection,
query=vectors[0],
limit=limit,
query_filter=models.Filter(must=[tenant_filter]),
)
get_result = self._result_to_get_result(query_response.points)
return SearchResult(
ids=get_result.ids,
documents=get_result.documents,
metadatas=get_result.metadatas,
distances=[[(point.score + 1.0) / 2.0 for point in query_response.points]],
)
def query(
self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None
):
"""
Query points with filters and tenant isolation.
"""
if not self.client:
return None
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
if limit is None:
limit = NO_LIMIT
tenant_filter = _tenant_filter(tenant_id)
field_conditions = [_metadata_filter(k, v) for k, v in filter.items()]
combined_filter = models.Filter(must=[tenant_filter, *field_conditions])
points = self.client.query_points(
collection_name=mt_collection,
query_filter=combined_filter,
limit=limit,
)
return self._result_to_get_result(points.points)
def get(self, collection_name: str) -> Optional[GetResult]:
"""
Get all items in a collection with tenant isolation.
"""
if not self.client:
return None
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
tenant_filter = _tenant_filter(tenant_id)
points = self.client.query_points(
collection_name=mt_collection,
query_filter=models.Filter(must=[tenant_filter]),
limit=NO_LIMIT,
)
return self._result_to_get_result(points.points)
def upsert(self, collection_name: str, items: List[VectorItem]):
"""
Upsert items with tenant ID.
"""
if not self.client or not items:
return None
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
dimension = len(items[0]["vector"])
self._ensure_collection(mt_collection, dimension)
points = self._create_points(items, tenant_id)
self.client.upload_points(mt_collection, points)
return None
def insert(self, collection_name: str, items: List[VectorItem]):
"""
Insert items with tenant ID.
"""
return self.upsert(collection_name, items)
def reset(self):
"""
Reset the database by deleting all collections.
"""
if not self.client:
return None
for collection in self.client.get_collections().collections:
if collection.name.startswith(self.collection_prefix):
self.client.delete_collection(collection_name=collection.name)
def delete_collection(self, collection_name: str):
"""
Delete a collection.
"""
if not self.client:
return None
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
self.client.delete(
collection_name=mt_collection,
points_selector=models.FilterSelector(
filter=models.Filter(must=[_tenant_filter(tenant_id)])
),
)
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