535 lines
19 KiB
Python
535 lines
19 KiB
Python
import asyncio
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import configparser
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import hashlib
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import os
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import uuid
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from dataclasses import dataclass
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from typing import Any, List, final
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import numpy as np
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import pipmaster as pm
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from ..base import BaseVectorStorage
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from ..kg.shared_storage import get_data_init_lock, get_storage_lock
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from ..utils import compute_mdhash_id, logger
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if not pm.is_installed("qdrant-client"):
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pm.install("qdrant-client")
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from qdrant_client import QdrantClient, models # type: ignore
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DEFAULT_WORKSPACE = "_"
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WORKSPACE_ID_FIELD = "workspace_id"
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ENTITY_PREFIX = "ent-"
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CREATED_AT_FIELD = "created_at"
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ID_FIELD = "id"
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config = configparser.ConfigParser()
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config.read("config.ini", "utf-8")
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def compute_mdhash_id_for_qdrant(
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content: str, prefix: str = "", style: str = "simple"
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) -> str:
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"""
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Generate a UUID based on the content and support multiple formats.
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:param content: The content used to generate the UUID.
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:param style: The format of the UUID, optional values are "simple", "hyphenated", "urn".
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:return: A UUID that meets the requirements of Qdrant.
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"""
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if not content:
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raise ValueError("Content must not be empty.")
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# Use the hash value of the content to create a UUID.
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hashed_content = hashlib.sha256((prefix + content).encode("utf-8")).digest()
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generated_uuid = uuid.UUID(bytes=hashed_content[:16], version=4)
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# Return the UUID according to the specified format.
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if style == "simple":
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return generated_uuid.hex
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elif style == "hyphenated":
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return str(generated_uuid)
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elif style == "urn":
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return f"urn:uuid:{generated_uuid}"
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else:
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raise ValueError("Invalid style. Choose from 'simple', 'hyphenated', or 'urn'.")
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def workspace_filter_condition(workspace: str) -> models.FieldCondition:
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"""
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Create a workspace filter condition for Qdrant queries.
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"""
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return models.FieldCondition(
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key=WORKSPACE_ID_FIELD, match=models.MatchValue(value=workspace)
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)
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@final
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@dataclass
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class QdrantVectorDBStorage(BaseVectorStorage):
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def __init__(
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self, namespace, global_config, embedding_func, workspace=None, meta_fields=None
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):
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super().__init__(
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namespace=namespace,
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workspace=workspace or "",
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global_config=global_config,
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embedding_func=embedding_func,
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meta_fields=meta_fields or set(),
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)
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self.__post_init__()
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@staticmethod
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def setup_collection(client: QdrantClient, collection_name: str, **kwargs):
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exists = client.collection_exists(collection_name)
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if not exists:
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client.create_collection(collection_name, **kwargs)
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client.create_payload_index(
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collection_name=collection_name,
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field_name=WORKSPACE_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, # Optimize storage structure for tenant co-location
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),
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)
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def __post_init__(self):
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# Check for QDRANT_WORKSPACE environment variable first (higher priority)
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# This allows administrators to force a specific workspace for all Qdrant storage instances
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qdrant_workspace = os.environ.get("QDRANT_WORKSPACE")
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if qdrant_workspace and qdrant_workspace.strip():
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# Use environment variable value, overriding the passed workspace parameter
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effective_workspace = qdrant_workspace.strip()
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logger.info(
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f"Using QDRANT_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')"
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)
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else:
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# Use the workspace parameter passed during initialization
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effective_workspace = self.workspace
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if effective_workspace:
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logger.debug(
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f"Using passed workspace parameter: '{effective_workspace}'"
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)
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self.effective_workspace = effective_workspace or DEFAULT_WORKSPACE
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# Use a shared collection with payload-based partitioning (Qdrant's recommended approach)
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# Ref: https://qdrant.tech/documentation/guides/multiple-partitions/
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self.final_namespace = self.namespace
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logger.debug(
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f"Using shared collection '{self.final_namespace}' with workspace '{self.effective_workspace}' for payload-based partitioning"
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)
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kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
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cosine_threshold = kwargs.get("cosine_better_than_threshold")
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if cosine_threshold is None:
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raise ValueError(
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"cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
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)
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self.cosine_better_than_threshold = cosine_threshold
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# Initialize client as None - will be created in initialize() method
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self._client = None
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self._max_batch_size = self.global_config["embedding_batch_num"]
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self._initialized = False
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async def initialize(self):
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"""Initialize Qdrant collection"""
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async with get_data_init_lock():
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if self._initialized:
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return
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try:
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# Create QdrantClient if not already created
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if self._client is None:
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self._client = QdrantClient(
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url=os.environ.get(
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"QDRANT_URL", config.get("qdrant", "uri", fallback=None)
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),
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api_key=os.environ.get(
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"QDRANT_API_KEY",
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config.get("qdrant", "apikey", fallback=None),
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),
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)
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logger.debug(
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f"[{self.workspace}] QdrantClient created successfully"
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)
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# Setup collection (create if not exists and configure indexes)
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QdrantVectorDBStorage.setup_collection(
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self._client,
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self.final_namespace,
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vectors_config=models.VectorParams(
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size=self.embedding_func.embedding_dim,
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distance=models.Distance.COSINE,
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),
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)
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self._initialized = True
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logger.info(
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f"[{self.workspace}] Qdrant collection '{self.namespace}' initialized successfully"
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)
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Failed to initialize Qdrant collection '{self.namespace}': {e}"
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)
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raise
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async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
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logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}")
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if not data:
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return
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import time
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current_time = int(time.time())
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list_data = [
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{
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ID_FIELD: k,
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WORKSPACE_ID_FIELD: self.effective_workspace,
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CREATED_AT_FIELD: current_time,
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**{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields},
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}
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for k, v in data.items()
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]
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contents = [v["content"] for v in data.values()]
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batches = [
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contents[i : i + self._max_batch_size]
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for i in range(0, len(contents), self._max_batch_size)
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]
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embedding_tasks = [self.embedding_func(batch) for batch in batches]
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embeddings_list = await asyncio.gather(*embedding_tasks)
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embeddings = np.concatenate(embeddings_list)
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list_points = []
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for i, d in enumerate(list_data):
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list_points.append(
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models.PointStruct(
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id=compute_mdhash_id_for_qdrant(
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d[ID_FIELD], prefix=self.effective_workspace
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),
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vector=embeddings[i],
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payload=d,
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)
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)
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results = self._client.upsert(
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collection_name=self.final_namespace, points=list_points, wait=True
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)
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return results
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async def query(
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self, query: str, top_k: int, query_embedding: list[float] = None
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) -> list[dict[str, Any]]:
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if query_embedding is not None:
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embedding = query_embedding
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else:
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embedding_result = await self.embedding_func(
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[query], _priority=5
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) # higher priority for query
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embedding = embedding_result[0]
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results = self._client.query_points(
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collection_name=self.final_namespace,
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query=embedding,
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limit=top_k,
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with_payload=True,
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score_threshold=self.cosine_better_than_threshold,
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query_filter=models.Filter(
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must=[workspace_filter_condition(self.effective_workspace)]
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),
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).points
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return [
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{
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**dp.payload,
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"distance": dp.score,
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CREATED_AT_FIELD: dp.payload.get(CREATED_AT_FIELD),
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}
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for dp in results
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]
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async def index_done_callback(self) -> None:
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# Qdrant handles persistence automatically
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pass
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async def delete(self, ids: List[str]) -> None:
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"""Delete vectors with specified IDs
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Args:
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ids: List of vector IDs to be deleted
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"""
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try:
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if not ids:
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return
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# Convert regular ids to Qdrant compatible ids
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qdrant_ids = [
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compute_mdhash_id_for_qdrant(id, prefix=self.effective_workspace)
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for id in ids
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]
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# Delete points from the collection with workspace filtering
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self._client.delete(
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collection_name=self.final_namespace,
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points_selector=models.PointIdsList(points=qdrant_ids),
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wait=True,
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)
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logger.debug(
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f"[{self.workspace}] Successfully deleted {len(ids)} vectors from {self.namespace}"
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)
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {e}"
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)
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async def delete_entity(self, entity_name: str) -> None:
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"""Delete an entity by name
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Args:
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entity_name: Name of the entity to delete
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"""
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try:
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# Generate the entity ID using the same function as used for storage
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entity_id = compute_mdhash_id(entity_name, prefix=ENTITY_PREFIX)
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qdrant_entity_id = compute_mdhash_id_for_qdrant(
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entity_id, prefix=self.effective_workspace
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)
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# Delete the entity point by its Qdrant ID directly
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self._client.delete(
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collection_name=self.final_namespace,
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points_selector=models.PointIdsList(points=[qdrant_entity_id]),
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wait=True,
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)
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logger.debug(
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f"[{self.workspace}] Successfully deleted entity {entity_name}"
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)
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except Exception as e:
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logger.error(f"[{self.workspace}] Error deleting entity {entity_name}: {e}")
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async def delete_entity_relation(self, entity_name: str) -> None:
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"""Delete all relations associated with an entity
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Args:
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entity_name: Name of the entity whose relations should be deleted
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"""
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try:
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# Find relations where the entity is either source or target, with workspace filtering
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results = self._client.scroll(
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collection_name=self.final_namespace,
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scroll_filter=models.Filter(
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must=[workspace_filter_condition(self.effective_workspace)],
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should=[
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models.FieldCondition(
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key="src_id", match=models.MatchValue(value=entity_name)
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),
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models.FieldCondition(
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key="tgt_id", match=models.MatchValue(value=entity_name)
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),
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],
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),
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with_payload=True,
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limit=1000, # Adjust as needed for your use case
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)
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# Extract points that need to be deleted
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relation_points = results[0]
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ids_to_delete = [point.id for point in relation_points]
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if ids_to_delete:
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# Delete the relations with workspace filtering
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assert isinstance(self._client, QdrantClient)
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self._client.delete(
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collection_name=self.final_namespace,
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points_selector=models.PointIdsList(points=ids_to_delete),
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wait=True,
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)
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logger.debug(
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f"[{self.workspace}] Deleted {len(ids_to_delete)} relations for {entity_name}"
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)
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else:
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logger.debug(
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f"[{self.workspace}] No relations found for entity {entity_name}"
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)
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Error deleting relations for {entity_name}: {e}"
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)
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async def get_by_id(self, id: str) -> dict[str, Any] | None:
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"""Get vector data by its ID
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Args:
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id: The unique identifier of the vector
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Returns:
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The vector data if found, or None if not found
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"""
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try:
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# Convert to Qdrant compatible ID
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qdrant_id = compute_mdhash_id_for_qdrant(
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id, prefix=self.effective_workspace
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)
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# Retrieve the point by ID with workspace filtering
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result = self._client.retrieve(
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collection_name=self.final_namespace,
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ids=[qdrant_id],
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with_payload=True,
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)
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if not result:
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return None
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payload = result[0].payload
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if CREATED_AT_FIELD not in payload:
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payload[CREATED_AT_FIELD] = None
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return payload
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Error retrieving vector data for ID {id}: {e}"
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)
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return None
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async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
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"""Get multiple vector data by their IDs
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Args:
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ids: List of unique identifiers
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Returns:
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List of vector data objects that were found
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"""
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if not ids:
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return []
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try:
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# Convert to Qdrant compatible IDs
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qdrant_ids = [
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compute_mdhash_id_for_qdrant(id, prefix=self.effective_workspace)
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for id in ids
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]
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# Retrieve the points by IDs
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results = self._client.retrieve(
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collection_name=self.final_namespace,
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ids=qdrant_ids,
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with_payload=True,
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)
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# Ensure each result contains created_at field and preserve caller ordering
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payload_by_original_id: dict[str, dict[str, Any]] = {}
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payload_by_qdrant_id: dict[str, dict[str, Any]] = {}
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for point in results:
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payload = dict(point.payload or {})
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if CREATED_AT_FIELD not in payload:
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payload[CREATED_AT_FIELD] = None
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qdrant_point_id = str(point.id) if point.id is not None else ""
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if qdrant_point_id:
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payload_by_qdrant_id[qdrant_point_id] = payload
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original_id = payload.get(ID_FIELD)
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if original_id is not None:
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payload_by_original_id[str(original_id)] = payload
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ordered_payloads: list[dict[str, Any] | None] = []
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for requested_id, qdrant_id in zip(ids, qdrant_ids):
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payload = payload_by_original_id.get(str(requested_id))
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if payload is None:
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payload = payload_by_qdrant_id.get(str(qdrant_id))
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ordered_payloads.append(payload)
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return ordered_payloads
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Error retrieving vector data for IDs {ids}: {e}"
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)
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return []
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async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]:
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"""Get vectors by their IDs, returning only ID and vector data for efficiency
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Args:
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ids: List of unique identifiers
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Returns:
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Dictionary mapping IDs to their vector embeddings
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Format: {id: [vector_values], ...}
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"""
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if not ids:
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return {}
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try:
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# Convert to Qdrant compatible IDs
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qdrant_ids = [
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compute_mdhash_id_for_qdrant(id, prefix=self.effective_workspace)
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for id in ids
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]
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# Retrieve the points by IDs with vectors
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results = self._client.retrieve(
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collection_name=self.final_namespace,
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ids=qdrant_ids,
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with_vectors=True, # Important: request vectors
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with_payload=True,
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)
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vectors_dict = {}
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for point in results:
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if point and point.vector is not None and point.payload:
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# Get original ID from payload
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original_id = point.payload.get(ID_FIELD)
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if original_id:
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# Convert numpy array to list if needed
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vector_data = point.vector
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if isinstance(vector_data, np.ndarray):
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vector_data = vector_data.tolist()
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vectors_dict[original_id] = vector_data
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return vectors_dict
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Error retrieving vectors by IDs from {self.namespace}: {e}"
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)
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return {}
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async def drop(self) -> dict[str, str]:
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"""Drop all vector data from storage and clean up resources
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This method will delete all data for the current workspace from the Qdrant collection.
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Returns:
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dict[str, str]: Operation status and message
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- On success: {"status": "success", "message": "data dropped"}
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- On failure: {"status": "error", "message": "<error details>"}
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"""
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async with get_storage_lock():
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try:
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# Delete all points for the current workspace
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self._client.delete(
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collection_name=self.final_namespace,
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points_selector=models.FilterSelector(
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filter=models.Filter(
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must=[workspace_filter_condition(self.effective_workspace)]
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)
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),
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wait=True,
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)
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logger.info(
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f"[{self.workspace}] Process {os.getpid()} dropped workspace data from Qdrant collection {self.namespace}"
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)
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return {"status": "success", "message": "data dropped"}
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except Exception as e:
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logger.error(
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f"[{self.workspace}] Error dropping workspace data from Qdrant collection {self.namespace}: {e}"
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)
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return {"status": "error", "message": str(e)}
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