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https://github.com/open-webui/open-webui.git
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Merge pull request #15289 from Anush008/main
refactor: Updated Qdrant multi-tenancy implementation
This commit is contained in:
Tim Jaeryang Baek
GitHub
commit
a3add18fa9
3 changed files with 150 additions and 532 deletions
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@ -1840,10 +1840,10 @@ MILVUS_IVF_FLAT_NLIST = int(os.environ.get("MILVUS_IVF_FLAT_NLIST", "128"))
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QDRANT_URI = os.environ.get("QDRANT_URI", None)
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QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", None)
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QDRANT_ON_DISK = os.environ.get("QDRANT_ON_DISK", "false").lower() == "true"
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QDRANT_PREFER_GRPC = os.environ.get("QDRANT_PREFER_GRPC", "False").lower() == "true"
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QDRANT_PREFER_GRPC = os.environ.get("QDRANT_PREFER_GRPC", "false").lower() == "true"
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QDRANT_GRPC_PORT = int(os.environ.get("QDRANT_GRPC_PORT", "6334"))
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ENABLE_QDRANT_MULTITENANCY_MODE = (
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os.environ.get("ENABLE_QDRANT_MULTITENANCY_MODE", "false").lower() == "true"
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os.environ.get("ENABLE_QDRANT_MULTITENANCY_MODE", "true").lower() == "true"
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)
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QDRANT_COLLECTION_PREFIX = os.environ.get("QDRANT_COLLECTION_PREFIX", "open-webui")
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@ -1,5 +1,5 @@
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import logging
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from typing import Optional, Tuple
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from typing import Optional, Tuple, List, Dict, Any
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from urllib.parse import urlparse
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import grpc
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@ -24,11 +24,25 @@ from qdrant_client.http.models import PointStruct
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from qdrant_client.models import models
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NO_LIMIT = 999999999
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TENANT_ID_FIELD = "tenant_id"
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DEFAULT_DIMENSION = 384
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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def _tenant_filter(tenant_id: str) -> models.FieldCondition:
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return models.FieldCondition(
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key=TENANT_ID_FIELD, match=models.MatchValue(value=tenant_id)
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)
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def _metadata_filter(key: str, value: Any) -> models.FieldCondition:
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return models.FieldCondition(
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key=f"metadata.{key}", match=models.MatchValue(value=value)
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)
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class QdrantClient(VectorDBBase):
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def __init__(self):
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self.collection_prefix = QDRANT_COLLECTION_PREFIX
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@ -39,24 +53,26 @@ class QdrantClient(VectorDBBase):
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self.GRPC_PORT = QDRANT_GRPC_PORT
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if not self.QDRANT_URI:
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self.client = None
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return
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raise ValueError(
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"QDRANT_URI is not set. Please configure it in the environment variables."
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)
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# Unified handling for either scheme
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parsed = urlparse(self.QDRANT_URI)
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host = parsed.hostname or self.QDRANT_URI
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http_port = parsed.port or 6333 # default REST port
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if self.PREFER_GRPC:
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self.client = Qclient(
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self.client = (
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Qclient(
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host=host,
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port=http_port,
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grpc_port=self.GRPC_PORT,
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prefer_grpc=self.PREFER_GRPC,
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api_key=self.QDRANT_API_KEY,
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)
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else:
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self.client = Qclient(url=self.QDRANT_URI, api_key=self.QDRANT_API_KEY)
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if self.PREFER_GRPC
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else Qclient(url=self.QDRANT_URI, api_key=self.QDRANT_API_KEY)
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)
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# Main collection types for multi-tenancy
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self.MEMORY_COLLECTION = f"{self.collection_prefix}_memories"
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@ -66,23 +82,13 @@ class QdrantClient(VectorDBBase):
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self.HASH_BASED_COLLECTION = f"{self.collection_prefix}_hash-based"
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def _result_to_get_result(self, points) -> GetResult:
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ids = []
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documents = []
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metadatas = []
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ids, documents, metadatas = [], [], []
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for point in points:
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payload = point.payload
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ids.append(point.id)
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documents.append(payload["text"])
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metadatas.append(payload["metadata"])
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return GetResult(
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**{
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"ids": [ids],
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"documents": [documents],
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"metadatas": [metadatas],
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}
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)
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return GetResult(ids=[ids], documents=[documents], metadatas=[metadatas])
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def _get_collection_and_tenant_id(self, collection_name: str) -> Tuple[str, str]:
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"""
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@ -114,162 +120,47 @@ class QdrantClient(VectorDBBase):
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else:
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return self.KNOWLEDGE_COLLECTION, tenant_id
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def _extract_error_message(self, exception):
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"""
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Extract error message from either HTTP or gRPC exceptions
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Returns:
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tuple: (status_code, error_message)
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"""
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# Check if it's an HTTP exception
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if isinstance(exception, UnexpectedResponse):
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try:
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error_data = exception.structured()
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error_msg = error_data.get("status", {}).get("error", "")
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return exception.status_code, error_msg
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except Exception as inner_e:
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log.error(f"Failed to parse HTTP error: {inner_e}")
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return exception.status_code, str(exception)
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# Check if it's a gRPC exception
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elif isinstance(exception, grpc.RpcError):
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# Extract status code from gRPC error
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status_code = None
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if hasattr(exception, "code") and callable(exception.code):
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status_code = exception.code().value[0]
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# Extract error message
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error_msg = str(exception)
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if "details =" in error_msg:
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# Parse the details line which contains the actual error message
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try:
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details_line = [
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line.strip()
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for line in error_msg.split("\n")
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if "details =" in line
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][0]
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error_msg = details_line.split("details =")[1].strip(' "')
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except (IndexError, AttributeError):
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# Fall back to full message if parsing fails
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pass
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return status_code, error_msg
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# For any other type of exception
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return None, str(exception)
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def _is_collection_not_found_error(self, exception):
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"""
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Check if the exception is due to collection not found, supporting both HTTP and gRPC
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"""
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status_code, error_msg = self._extract_error_message(exception)
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# HTTP error (404)
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if (
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status_code == 404
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and "Collection" in error_msg
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and "doesn't exist" in error_msg
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):
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return True
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# gRPC error (NOT_FOUND status)
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if (
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isinstance(exception, grpc.RpcError)
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and exception.code() == grpc.StatusCode.NOT_FOUND
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):
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return True
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return False
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def _is_dimension_mismatch_error(self, exception):
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"""
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Check if the exception is due to dimension mismatch, supporting both HTTP and gRPC
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"""
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status_code, error_msg = self._extract_error_message(exception)
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# Common patterns in both HTTP and gRPC
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return (
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"Vector dimension error" in error_msg
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or "dimensions mismatch" in error_msg
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or "invalid vector size" in error_msg
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)
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def _create_multi_tenant_collection_if_not_exists(
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self, mt_collection_name: str, dimension: int = 384
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def _create_multi_tenant_collection(
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self, mt_collection_name: str, dimension: int = DEFAULT_DIMENSION
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):
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"""
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Creates a collection with multi-tenancy configuration if it doesn't exist.
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Default dimension is set to 384 which corresponds to 'sentence-transformers/all-MiniLM-L6-v2'.
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When creating collections dynamically (insert/upsert), the actual vector dimensions will be used.
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Creates a collection with multi-tenancy configuration and payload indexes for tenant_id and metadata fields.
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"""
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try:
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# Try to create the collection directly - will fail if it already exists
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self.client.create_collection(
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collection_name=mt_collection_name,
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vectors_config=models.VectorParams(
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size=dimension,
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distance=models.Distance.COSINE,
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on_disk=self.QDRANT_ON_DISK,
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),
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hnsw_config=models.HnswConfigDiff(
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payload_m=16, # Enable per-tenant indexing
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m=0,
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on_disk=self.QDRANT_ON_DISK,
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),
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)
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self.client.create_collection(
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collection_name=mt_collection_name,
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vectors_config=models.VectorParams(
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size=dimension,
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distance=models.Distance.COSINE,
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on_disk=self.QDRANT_ON_DISK,
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),
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)
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log.info(
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f"Multi-tenant collection {mt_collection_name} created with dimension {dimension}!"
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)
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# Create tenant ID payload index
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self.client.create_payload_index(
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collection_name=mt_collection_name,
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field_name=TENANT_ID_FIELD,
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field_schema=models.KeywordIndexParams(
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type=models.KeywordIndexType.KEYWORD,
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is_tenant=True,
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on_disk=self.QDRANT_ON_DISK,
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),
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)
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for field in ("metadata.hash", "metadata.file_id"):
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self.client.create_payload_index(
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collection_name=mt_collection_name,
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field_name="tenant_id",
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field_name=field,
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field_schema=models.KeywordIndexParams(
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type=models.KeywordIndexType.KEYWORD,
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is_tenant=True,
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on_disk=self.QDRANT_ON_DISK,
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),
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wait=True,
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)
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# Create payload indexes for efficient filtering on metadata.hash and metadata.file_id
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self.client.create_payload_index(
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collection_name=mt_collection_name,
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field_name="metadata.hash",
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field_schema=models.KeywordIndexParams(
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type=models.KeywordIndexType.KEYWORD,
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is_tenant=False,
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on_disk=self.QDRANT_ON_DISK,
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),
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)
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self.client.create_payload_index(
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collection_name=mt_collection_name,
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field_name="metadata.file_id",
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field_schema=models.KeywordIndexParams(
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type=models.KeywordIndexType.KEYWORD,
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is_tenant=False,
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on_disk=self.QDRANT_ON_DISK,
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),
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)
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log.info(
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f"Multi-tenant collection {mt_collection_name} created with dimension {dimension}!"
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)
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except (UnexpectedResponse, grpc.RpcError) as e:
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# Check for the specific error indicating collection already exists
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status_code, error_msg = self._extract_error_message(e)
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# HTTP status code 409 or gRPC ALREADY_EXISTS
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if (isinstance(e, UnexpectedResponse) and status_code == 409) or (
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isinstance(e, grpc.RpcError)
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and e.code() == grpc.StatusCode.ALREADY_EXISTS
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):
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if "already exists" in error_msg:
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log.debug(f"Collection {mt_collection_name} already exists")
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return
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# If it's not an already exists error, re-raise
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raise e
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except Exception as e:
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raise e
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def _create_points(self, items: list[VectorItem], tenant_id: str):
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def _create_points(
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self, items: List[VectorItem], tenant_id: str
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) -> List[PointStruct]:
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"""
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Create point structs from vector items with tenant ID.
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"""
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@ -280,56 +171,42 @@ class QdrantClient(VectorDBBase):
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payload={
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"text": item["text"],
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"metadata": item["metadata"],
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"tenant_id": tenant_id,
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TENANT_ID_FIELD: tenant_id,
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},
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)
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for item in items
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]
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def _ensure_collection(
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self, mt_collection_name: str, dimension: int = DEFAULT_DIMENSION
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):
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"""
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Ensure the collection exists and payload indexes are created for tenant_id and metadata fields.
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"""
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if not self.client.collection_exists(collection_name=mt_collection_name):
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self._create_multi_tenant_collection(mt_collection_name, dimension)
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def has_collection(self, collection_name: str) -> bool:
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"""
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Check if a logical collection exists by checking for any points with the tenant ID.
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"""
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if not self.client:
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return False
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# Map to multi-tenant collection and tenant ID
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mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
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# Create tenant filter
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tenant_filter = models.FieldCondition(
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key="tenant_id", match=models.MatchValue(value=tenant_id)
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)
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try:
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# Try directly querying - most of the time collection should exist
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response = self.client.query_points(
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collection_name=mt_collection,
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query_filter=models.Filter(must=[tenant_filter]),
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limit=1,
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)
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# Collection exists with this tenant ID if there are points
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return len(response.points) > 0
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except (UnexpectedResponse, grpc.RpcError) as e:
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if self._is_collection_not_found_error(e):
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log.debug(f"Collection {mt_collection} doesn't exist")
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return False
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else:
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# For other API errors, log and return False
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_, error_msg = self._extract_error_message(e)
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log.warning(f"Unexpected Qdrant error: {error_msg}")
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return False
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except Exception as e:
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# For any other errors, log and return False
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log.debug(f"Error checking collection {mt_collection}: {e}")
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if not self.client.collection_exists(collection_name=mt_collection):
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return False
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tenant_filter = _tenant_filter(tenant_id)
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count_result = self.client.count(
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collection_name=mt_collection,
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count_filter=models.Filter(must=[tenant_filter]),
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)
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return count_result.count > 0
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def delete(
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self,
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collection_name: str,
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ids: Optional[list[str]] = None,
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filter: Optional[dict] = None,
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ids: Optional[List[str]] = None,
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filter: Optional[Dict[str, Any]] = None,
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):
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"""
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Delete vectors by ID or filter from a collection with tenant isolation.
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|
@ -337,189 +214,76 @@ class QdrantClient(VectorDBBase):
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if not self.client:
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return None
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# Map to multi-tenant collection and tenant ID
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mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
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if not self.client.collection_exists(collection_name=mt_collection):
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log.debug(f"Collection {mt_collection} doesn't exist, nothing to delete")
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return None
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# Create tenant filter
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tenant_filter = models.FieldCondition(
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key="tenant_id", match=models.MatchValue(value=tenant_id)
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must_conditions = [_tenant_filter(tenant_id)]
|
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should_conditions = []
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if ids:
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should_conditions = [_metadata_filter("id", id_value) for id_value in ids]
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elif filter:
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must_conditions += [_metadata_filter(k, v) for k, v in filter.items()]
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|
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return self.client.delete(
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collection_name=mt_collection,
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points_selector=models.FilterSelector(
|
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filter=models.Filter(must=must_conditions, should=should_conditions)
|
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),
|
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)
|
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|
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must_conditions = [tenant_filter]
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should_conditions = []
|
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|
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if ids:
|
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for id_value in ids:
|
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should_conditions.append(
|
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models.FieldCondition(
|
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key="metadata.id",
|
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match=models.MatchValue(value=id_value),
|
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),
|
||||
)
|
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elif filter:
|
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for key, value in filter.items():
|
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must_conditions.append(
|
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models.FieldCondition(
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key=f"metadata.{key}",
|
||||
match=models.MatchValue(value=value),
|
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),
|
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)
|
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|
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try:
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# Try to delete directly - most of the time collection should exist
|
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update_result = self.client.delete(
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collection_name=mt_collection,
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points_selector=models.FilterSelector(
|
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filter=models.Filter(must=must_conditions, should=should_conditions)
|
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),
|
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)
|
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|
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return update_result
|
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except (UnexpectedResponse, grpc.RpcError) as e:
|
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if self._is_collection_not_found_error(e):
|
||||
log.debug(
|
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f"Collection {mt_collection} doesn't exist, nothing to delete"
|
||||
)
|
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return None
|
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else:
|
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# For other API errors, log and re-raise
|
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_, error_msg = self._extract_error_message(e)
|
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log.warning(f"Unexpected Qdrant error: {error_msg}")
|
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raise
|
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except Exception as e:
|
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# For non-Qdrant exceptions, re-raise
|
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raise
|
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|
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def search(
|
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self, collection_name: str, vectors: list[list[float | int]], limit: int
|
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self, collection_name: str, vectors: List[List[float | int]], limit: int
|
||||
) -> Optional[SearchResult]:
|
||||
"""
|
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Search for the nearest neighbor items based on the vectors with tenant isolation.
|
||||
"""
|
||||
if not self.client:
|
||||
if not self.client or not vectors:
|
||||
return None
|
||||
|
||||
# Map to multi-tenant collection and tenant ID
|
||||
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
|
||||
|
||||
# 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)
|
||||
)
|
||||
|
||||
# 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]
|
||||
else:
|
||||
vectors = [
|
||||
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,
|
||||
)
|
||||
query_response = self.client.query_points(
|
||||
collection_name=mt_collection,
|
||||
query=vectors[0],
|
||||
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}")
|
||||
if not self.client.collection_exists(collection_name=mt_collection):
|
||||
log.debug(f"Collection {mt_collection} doesn't exist, search returns None")
|
||||
return None
|
||||
|
||||
def query(self, collection_name: str, filter: dict, limit: Optional[int] = 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
|
||||
|
||||
# Map to multi-tenant collection and tenant ID
|
||||
mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
|
||||
|
||||
# Set default limit if not provided
|
||||
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
|
||||
|
||||
# Create tenant filter
|
||||
tenant_filter = models.FieldCondition(
|
||||
key="tenant_id", match=models.MatchValue(value=tenant_id)
|
||||
)
|
||||
|
||||
# Create metadata filters
|
||||
field_conditions = []
|
||||
for key, value in filter.items():
|
||||
field_conditions.append(
|
||||
models.FieldCondition(
|
||||
key=f"metadata.{key}", match=models.MatchValue(value=value)
|
||||
)
|
||||
)
|
||||
|
||||
# Combine tenant filter with metadata filters
|
||||
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])
|
||||
|
||||
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
|
||||
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]:
|
||||
"""
|
||||
|
|
@ -527,169 +291,36 @@ 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)
|
||||
|
||||
# Create tenant filter
|
||||
tenant_filter = models.FieldCondition(
|
||||
key="tenant_id", 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]),
|
||||
limit=NO_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, 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}")
|
||||
if not self.client.collection_exists(collection_name=mt_collection):
|
||||
log.debug(f"Collection {mt_collection} doesn't exist, get returns None")
|
||||
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
|
||||
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]):
|
||||
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
|
||||
|
||||
# Create points with tenant ID
|
||||
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
|
||||
|
||||
# Handle the operation with error retry
|
||||
return self._handle_operation_with_error_retry(
|
||||
"upsert", mt_collection, points, dimension
|
||||
)
|
||||
def insert(self, collection_name: str, items: List[VectorItem]):
|
||||
"""
|
||||
Insert items with tenant ID.
|
||||
"""
|
||||
return self.upsert(collection_name, items)
|
||||
|
||||
def reset(self):
|
||||
"""
|
||||
|
|
@ -697,11 +328,9 @@ class QdrantClient(VectorDBBase):
|
|||
"""
|
||||
if not self.client:
|
||||
return None
|
||||
|
||||
collection_names = self.client.get_collections().collections
|
||||
for collection_name in collection_names:
|
||||
if collection_name.name.startswith(self.collection_prefix):
|
||||
self.client.delete_collection(collection_name=collection_name.name)
|
||||
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):
|
||||
"""
|
||||
|
|
@ -709,24 +338,13 @@ 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)
|
||||
|
||||
tenant_filter = models.FieldCondition(
|
||||
key="tenant_id", match=models.MatchValue(value=tenant_id)
|
||||
)
|
||||
|
||||
field_conditions = [tenant_filter]
|
||||
|
||||
update_result = self.client.delete(
|
||||
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=field_conditions)
|
||||
filter=models.Filter(must=[_tenant_filter(tenant_id)])
|
||||
),
|
||||
)
|
||||
|
||||
if self.client.get_collection(mt_collection).points_count == 0:
|
||||
self.client.delete_collection(mt_collection)
|
||||
|
||||
return update_result
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ langchain-community==0.3.26
|
|||
fake-useragent==2.1.0
|
||||
chromadb==0.6.3
|
||||
pymilvus==2.5.0
|
||||
qdrant-client~=1.12.0
|
||||
qdrant-client==1.14.3
|
||||
opensearch-py==2.8.0
|
||||
playwright==1.49.1 # Caution: version must match docker-compose.playwright.yaml
|
||||
elasticsearch==9.0.1
|
||||
|
|
|
|||
Loading…
Reference in a new issue