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https://github.com/open-webui/open-webui.git
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a66206f44f
The prefix string for qdrant collection is now configurable, which means the same qdrant cluster can be used to host more open webui instances and to be able to separate the collections between the different owui instances.
223 lines
8 KiB
Python
223 lines
8 KiB
Python
from typing import Optional
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import logging
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from urllib.parse import urlparse
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from qdrant_client import QdrantClient as Qclient
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from qdrant_client.http.models import PointStruct
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from qdrant_client.models import models
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from open_webui.retrieval.vector.main import (
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VectorDBBase,
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VectorItem,
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SearchResult,
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GetResult,
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)
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from open_webui.config import (
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QDRANT_URI,
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QDRANT_API_KEY,
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QDRANT_ON_DISK,
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QDRANT_GRPC_PORT,
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QDRANT_PREFER_GRPC,
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QDRANT_COLLECTION_PREFIX,
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)
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from open_webui.env import SRC_LOG_LEVELS
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NO_LIMIT = 999999999
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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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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self.QDRANT_URI = QDRANT_URI
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self.QDRANT_API_KEY = QDRANT_API_KEY
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self.QDRANT_ON_DISK = QDRANT_ON_DISK
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self.PREFER_GRPC = QDRANT_PREFER_GRPC
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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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# 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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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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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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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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def _create_collection(self, collection_name: str, dimension: int):
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collection_name_with_prefix = f"{self.collection_prefix}_{collection_name}"
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self.client.create_collection(
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collection_name=collection_name_with_prefix,
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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(f"collection {collection_name_with_prefix} successfully created!")
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def _create_collection_if_not_exists(self, collection_name, dimension):
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if not self.has_collection(collection_name=collection_name):
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self._create_collection(
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collection_name=collection_name, dimension=dimension
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)
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def _create_points(self, items: list[VectorItem]):
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return [
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PointStruct(
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id=item["id"],
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vector=item["vector"],
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payload={"text": item["text"], "metadata": item["metadata"]},
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)
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for item in items
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]
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def has_collection(self, collection_name: str) -> bool:
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return self.client.collection_exists(
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f"{self.collection_prefix}_{collection_name}"
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)
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def delete_collection(self, collection_name: str):
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return self.client.delete_collection(
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collection_name=f"{self.collection_prefix}_{collection_name}"
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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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) -> Optional[SearchResult]:
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# Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
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if limit is None:
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limit = NO_LIMIT # otherwise qdrant would set limit to 10!
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query_response = self.client.query_points(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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query=vectors[0],
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limit=limit,
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)
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get_result = self._result_to_get_result(query_response.points)
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return SearchResult(
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ids=get_result.ids,
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documents=get_result.documents,
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metadatas=get_result.metadatas,
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# qdrant distance is [-1, 1], normalize to [0, 1]
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distances=[[(point.score + 1.0) / 2.0 for point in query_response.points]],
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)
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def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
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# Construct the filter string for querying
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if not self.has_collection(collection_name):
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return None
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try:
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if limit is None:
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limit = NO_LIMIT # otherwise qdrant would set limit to 10!
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field_conditions = []
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for key, value in filter.items():
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field_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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points = self.client.query_points(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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query_filter=models.Filter(should=field_conditions),
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limit=limit,
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)
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return self._result_to_get_result(points.points)
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except Exception as e:
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log.exception(f"Error querying a collection '{collection_name}': {e}")
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return None
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def get(self, collection_name: str) -> Optional[GetResult]:
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# Get all the items in the collection.
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points = self.client.query_points(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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limit=NO_LIMIT, # otherwise qdrant would set limit to 10!
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)
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return self._result_to_get_result(points.points)
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def insert(self, collection_name: str, items: list[VectorItem]):
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# Insert the items into the collection, if the collection does not exist, it will be created.
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self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
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points = self._create_points(items)
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self.client.upload_points(f"{self.collection_prefix}_{collection_name}", points)
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def upsert(self, collection_name: str, items: list[VectorItem]):
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# Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
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self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
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points = self._create_points(items)
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return self.client.upsert(f"{self.collection_prefix}_{collection_name}", points)
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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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):
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# Delete the items from the collection based on the ids.
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field_conditions = []
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if ids:
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for id_value in ids:
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field_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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),
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elif filter:
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for key, value in filter.items():
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field_conditions.append(
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models.FieldCondition(
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key=f"metadata.{key}",
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match=models.MatchValue(value=value),
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),
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),
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return self.client.delete(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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points_selector=models.FilterSelector(
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filter=models.Filter(must=field_conditions)
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),
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)
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def reset(self):
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# Resets the database. This will delete all collections and item entries.
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collection_names = self.client.get_collections().collections
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for collection_name in collection_names:
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if collection_name.name.startswith(self.collection_prefix):
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self.client.delete_collection(collection_name=collection_name.name)
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