From db38e451331711d4afecdbcd6d3b2d985ae818a3 Mon Sep 17 00:00:00 2001 From: Mclamee Date: Fri, 20 Feb 2026 17:28:20 +0800 Subject: [PATCH] feat(vectordb): add Qdrant backend support Add Qdrant as an alternative open-source vector database backend alongside existing VikingDB/Volcengine backends. Key changes: - QdrantCollection: full VectorDBCollection implementation with support for dense/sparse hybrid search, scalar search with native order_by, full-text keyword search via payload indexes, multimodal search, aggregate data with pagination, and proper filter translation - QdrantProject: VectorDBProject implementation for collection lifecycle management with configurable distance metrics and vector dimensions - QdrantConfig: configuration model with URL, API key, gRPC, and timeout settings - Backend factory: register 'qdrant' backend type in viking_vector_index_backend.py - Auto-create TextIndex for text fields in create_index - Use FilterSelector for delete_all_data instead of drop/recreate - Paginate aggregate_data scroll to handle large collections - Track created indexes properly in has_index Dependencies: - qdrant-client >= 1.9.0 (optional extra: `pip install openviking[qdrant]`) Tests: - Comprehensive unit tests for QdrantCollection (752 lines) - Unit tests for QdrantProject (266 lines) --- .../storage/vectordb/collection/__init__.py | 10 + .../vectordb/collection/qdrant_collection.py | 1051 ++++++++++ .../storage/vectordb/project/__init__.py | 11 + .../vectordb/project/qdrant_project.py | 316 +++ .../storage/viking_vector_index_backend.py | 39 +- .../utils/config/vectordb_config.py | 38 +- pyproject.toml | 9 + tests/vectordb/test_qdrant_collection.py | 774 +++++++ tests/vectordb/test_qdrant_project.py | 266 +++ uv.lock | 1836 +++++++++-------- 10 files changed, 3528 insertions(+), 822 deletions(-) create mode 100644 openviking/storage/vectordb/collection/qdrant_collection.py create mode 100644 openviking/storage/vectordb/project/qdrant_project.py create mode 100644 tests/vectordb/test_qdrant_collection.py create mode 100644 tests/vectordb/test_qdrant_project.py diff --git a/openviking/storage/vectordb/collection/__init__.py b/openviking/storage/vectordb/collection/__init__.py index 94bee3e3..7e7f1e20 100644 --- a/openviking/storage/vectordb/collection/__init__.py +++ b/openviking/storage/vectordb/collection/__init__.py @@ -26,3 +26,13 @@ "LocalCollection", "get_or_create_local_collection", ] + +# Optional Qdrant support (requires qdrant-client) +try: + from openviking.storage.vectordb.collection.qdrant_collection import ( + QdrantCollection, + get_or_create_qdrant_collection, + ) + __all__.extend(["QdrantCollection", "get_or_create_qdrant_collection"]) +except ImportError: + pass # qdrant-client not installed diff --git a/openviking/storage/vectordb/collection/qdrant_collection.py b/openviking/storage/vectordb/collection/qdrant_collection.py new file mode 100644 index 00000000..9e57378e --- /dev/null +++ b/openviking/storage/vectordb/collection/qdrant_collection.py @@ -0,0 +1,1051 @@ +# Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd. +# SPDX-License-Identifier: Apache-2.0 +""" +Qdrant collection implementation for OpenViking. + +Provides ICollection interface implementation using Qdrant vector database, +with support for hybrid search (dense + sparse vectors) and RRF fusion. +""" + +import hashlib +import random +import uuid +from typing import Any, Dict, List, Optional + +from qdrant_client import QdrantClient +from qdrant_client.models import ( + Direction, + Distance, + FieldCondition, + Filter, + FilterSelector, + Fusion, + FusionQuery, + MatchAny, + MatchText, + MatchValue, + OrderBy, + PointStruct, + Prefetch, + Range, + SparseVector, + SparseVectorParams, + TextIndexParams, + TextIndexType, + VectorParams, +) + +from openviking.storage.vectordb.collection.collection import Collection, ICollection +from openviking.storage.vectordb.collection.result import ( + AggregateResult, + DataItem, + FetchDataInCollectionResult, + SearchItemResult, + SearchResult, + UpsertDataResult, +) +from openviking.storage.vectordb.index.index import IIndex +from openviking.utils import get_logger + +logger = get_logger(__name__) + + +# Distance metric mapping from OpenViking to Qdrant +DISTANCE_MAPPING = { + "cosine": Distance.COSINE, + "l2": Distance.EUCLID, + "ip": Distance.DOT, + "dot": Distance.DOT, + "euclid": Distance.EUCLID, +} + +# Unique namespace UUID for deterministic ID conversion (generated via uuid4) +OPENVIKING_NAMESPACE = uuid.UUID("f47ac10b-58cc-4372-a567-0e02b2c3d479") + + +def _stable_sparse_index(key: str) -> int: + """Convert a string sparse key to a stable integer index using MD5.""" + return int(hashlib.md5(key.encode()).hexdigest()[:8], 16) % (2**31) + + +def _stable_sparse_indices(sparse_vector: Dict[str, float]) -> List[int]: + """Convert sparse vector keys to stable integer indices.""" + return [int(k) if k.isdigit() else _stable_sparse_index(k) for k in sparse_vector.keys()] + + +def string_to_qdrant_id(string_id: str) -> str: + """Convert arbitrary string ID to a valid Qdrant UUID. + + Uses UUID5 (namespace-based) for deterministic conversion, + so the same string always maps to the same UUID. + + If the input is already a valid UUID, it's returned as-is. + """ + # Check if already a valid UUID + try: + uuid.UUID(string_id) + return string_id + except (ValueError, AttributeError): + pass + + # Convert string to deterministic UUID5 + return str(uuid.uuid5(OPENVIKING_NAMESPACE, string_id)) + + +def get_or_create_qdrant_collection( + client: QdrantClient, + collection_name: str, + meta_data: Optional[Dict[str, Any]] = None, + vector_dim: int = 0, + distance_metric: str = "cosine", + vectorizer_adapter: Optional[Any] = None, +) -> Collection: + """Create or retrieve a Qdrant Collection. + + Args: + client: QdrantClient instance + collection_name: Name of the collection + meta_data: Collection metadata configuration + vector_dim: Dimension of dense vectors + distance_metric: Distance metric for similarity search + vectorizer_adapter: Optional vectorizer for multimodal search + + Returns: + Collection: Collection instance wrapping QdrantCollection + """ + collection = QdrantCollection( + client=client, + collection_name=collection_name, + meta_data=meta_data or {}, + vector_dim=vector_dim, + distance_metric=distance_metric, + vectorizer_adapter=vectorizer_adapter, + ) + return Collection(collection) + + +class QdrantCollection(ICollection): + """ + Qdrant implementation of ICollection interface. + + Supports: + - Dense vector search + - Sparse vector search + - Hybrid search with RRF fusion + - Scalar filtering + - CRUD operations + """ + + # Reserved field names + DENSE_VECTOR_NAME = "dense" + SPARSE_VECTOR_NAME = "sparse" + + def __init__( + self, + client: QdrantClient, + collection_name: str, + meta_data: Optional[Dict[str, Any]] = None, + vector_dim: int = 0, + distance_metric: str = "cosine", + vectorizer_adapter: Optional[Any] = None, + ): + """Initialize Qdrant collection. + + Args: + client: QdrantClient instance + collection_name: Name of the collection + meta_data: Collection metadata (OpenViking schema format) + vector_dim: Dimension of dense vectors + distance_metric: Distance metric for similarity search + vectorizer_adapter: Optional vectorizer for multimodal search + """ + super().__init__() + self.client = client + self.collection_name = collection_name + self.meta_data = meta_data or {} + self.vector_dim = vector_dim + self.distance = DISTANCE_MAPPING.get(distance_metric.lower(), Distance.COSINE) + self.vectorizer_adapter = vectorizer_adapter + + # Extract vector dimension from meta_data if not provided + if self.vector_dim == 0 and self.meta_data.get("Fields"): + for field in self.meta_data["Fields"]: + if field.get("FieldType") == "vector": + self.vector_dim = field.get("Dim", 0) + break + + # Check if collection exists, create if not + self._ensure_collection() + self._created_indexes = {"default"} # Track created index names; "default" always exists + + def _ensure_collection(self): + """Ensure the collection exists in Qdrant.""" + try: + collections = self.client.get_collections().collections + exists = any(c.name == self.collection_name for c in collections) + + if not exists and self.vector_dim > 0: + logger.info( + f"Creating Qdrant collection '{self.collection_name}' " + f"with dim={self.vector_dim}, distance={self.distance}" + ) + self.client.create_collection( + collection_name=self.collection_name, + vectors_config={ + self.DENSE_VECTOR_NAME: VectorParams( + size=self.vector_dim, + distance=self.distance, + ) + }, + sparse_vectors_config={ + self.SPARSE_VECTOR_NAME: SparseVectorParams() + }, + ) + except Exception as e: + logger.error(f"Error ensuring collection: {e}") + raise + + def update(self, fields: Optional[Dict[str, Any]] = None, description: Optional[str] = None): + """Update collection metadata.""" + if fields: + self.meta_data.update(fields) + if description: + self.meta_data["Description"] = description + + def get_meta_data(self) -> Dict[str, Any]: + """Get collection metadata.""" + try: + info = self.client.get_collection(self.collection_name) + return { + **self.meta_data, + "CollectionName": self.collection_name, + "PointsCount": info.points_count, + # vectors_count may not exist in all Qdrant versions + "VectorsCount": getattr(info, 'vectors_count', info.points_count), + "Status": info.status.name if info.status else "unknown", + } + except Exception as e: + logger.error(f"Error getting collection metadata: {e}") + return self.meta_data + + def close(self): + """Close the collection (no-op for Qdrant, client manages connection).""" + pass + + def drop(self): + """Drop the collection.""" + try: + self.client.delete_collection(self.collection_name) + logger.info(f"Dropped Qdrant collection: {self.collection_name}") + except Exception as e: + logger.error(f"Error dropping collection: {e}") + raise + + # ========================================================================= + # Index Operations (Qdrant manages indexes automatically) + # ========================================================================= + + def create_index(self, index_name: str, meta_data: Dict[str, Any]) -> IIndex: + """Create index — auto-creates payload indexes and text indexes for text-like fields.""" + scalar_fields = meta_data.get("ScalarIndex", []) + # Fields that should get full-text index (for keyword search support) + text_field_names = {"content", "abstract", "text", "title", "description", "name"} + + for field in scalar_fields: + try: + if field.lower() in text_field_names: + # Create full-text index for text-like fields + self.client.create_payload_index( + collection_name=self.collection_name, + field_name=field, + field_schema=TextIndexParams( + type=TextIndexType.TEXT, + lowercase=True, + ), + ) + logger.info(f"Created text index for field: {field}") + else: + self.client.create_payload_index( + collection_name=self.collection_name, + field_name=field, + field_schema="keyword", + ) + except Exception as e: + logger.warning(f"Error creating payload index for '{field}': {e}") + self._created_indexes.add(index_name) + return None # Qdrant doesn't expose index objects + + def has_index(self, index_name: str) -> bool: + """Check if index has been created for this collection.""" + return index_name in self._created_indexes + + def get_index(self, index_name: str) -> Optional[IIndex]: + """Get index (returns None, Qdrant manages indexes internally).""" + return None + + def update_index( + self, + index_name: str, + scalar_index: Optional[Dict[str, Any]] = None, + description: Optional[str] = None, + ): + """Update index (no-op for Qdrant).""" + pass + + def get_index_meta_data(self, index_name: str) -> Dict[str, Any]: + """Get index metadata.""" + return {"IndexName": index_name, "backend": "qdrant"} + + def list_indexes(self) -> List[str]: + """List all created indexes.""" + return list(self._created_indexes) + + def drop_index(self, index_name: str): + """Remove index from tracking.""" + self._created_indexes.discard(index_name) + + # ========================================================================= + # Search Operations + # ========================================================================= + + @staticmethod + def _payload_selector(output_fields: Optional[List[str]] = None): + """Convert output_fields to Qdrant with_payload parameter.""" + if output_fields: + # Always include _original_id for ID mapping + fields = list(output_fields) + if QdrantCollection.ORIGINAL_ID_FIELD not in fields: + fields.append(QdrantCollection.ORIGINAL_ID_FIELD) + return fields + return True + + def search_by_vector( + self, + index_name: str, + dense_vector: Optional[List[float]] = None, + limit: int = 10, + offset: int = 0, + filters: Optional[Dict[str, Any]] = None, + sparse_vector: Optional[Dict[str, float]] = None, + output_fields: Optional[List[str]] = None, + ) -> SearchResult: + """Perform vector similarity search with optional hybrid mode. + + Supports: + - Dense vector only + - Sparse vector only + - Hybrid (dense + sparse) with RRF fusion + """ + try: + qdrant_filter = self._convert_filter(filters) if filters else None + with_payload = self._payload_selector(output_fields) + + # Hybrid search: both dense and sparse vectors + if dense_vector and sparse_vector: + return self._hybrid_search( + dense_vector=dense_vector, + sparse_vector=sparse_vector, + limit=limit, + offset=offset, + qdrant_filter=qdrant_filter, + with_payload=with_payload, + ) + + # Dense vector only search + if dense_vector: + results = self.client.query_points( + collection_name=self.collection_name, + query=dense_vector, + using=self.DENSE_VECTOR_NAME, + limit=limit, + offset=offset, + query_filter=qdrant_filter, + with_payload=with_payload, + ) + return self._convert_query_results(results) + + # Sparse vector only search + if sparse_vector: + indices = _stable_sparse_indices(sparse_vector) + values = list(sparse_vector.values()) + + results = self.client.query_points( + collection_name=self.collection_name, + query=SparseVector(indices=indices, values=values), + using=self.SPARSE_VECTOR_NAME, + limit=limit, + offset=offset, + query_filter=qdrant_filter, + with_payload=with_payload, + ) + return self._convert_query_results(results) + + # No vector provided, return empty + return SearchResult(data=[]) + + except Exception as e: + logger.error(f"Error in search_by_vector: {e}") + return SearchResult(data=[]) + + def _hybrid_search( + self, + dense_vector: List[float], + sparse_vector: Dict[str, float], + limit: int, + offset: int, + qdrant_filter: Optional[Filter], + with_payload=True, + ) -> SearchResult: + """Perform hybrid search with RRF fusion.""" + try: + # Convert sparse vector + indices = _stable_sparse_indices(sparse_vector) + values = list(sparse_vector.values()) + + # Prefetch more results for better fusion + prefetch_limit = max(limit * 3, 20) + + results = self.client.query_points( + collection_name=self.collection_name, + prefetch=[ + Prefetch( + query=dense_vector, + using=self.DENSE_VECTOR_NAME, + limit=prefetch_limit, + filter=qdrant_filter, + ), + Prefetch( + query=SparseVector(indices=indices, values=values), + using=self.SPARSE_VECTOR_NAME, + limit=prefetch_limit, + filter=qdrant_filter, + ), + ], + query=FusionQuery(fusion=Fusion.RRF), + limit=limit, + offset=offset, + with_payload=with_payload, + ) + + return self._convert_query_results(results) + + except Exception as e: + logger.error(f"Error in hybrid search: {e}") + # Fallback to dense-only search + results = self.client.query_points( + collection_name=self.collection_name, + query=dense_vector, + using=self.DENSE_VECTOR_NAME, + limit=limit, + offset=offset, + query_filter=qdrant_filter, + with_payload=with_payload, + ) + return self._convert_query_results(results) + + def search_by_keywords( + self, + index_name: str, + keywords: Optional[List[str]] = None, + query: Optional[str] = None, + limit: int = 10, + offset: int = 0, + filters: Optional[Dict[str, Any]] = None, + output_fields: Optional[List[str]] = None, + ) -> SearchResult: + """Search by keywords using Qdrant full-text index. + + Requires a TextIndex on the target field (e.g., "abstract", "content"). + Uses MatchText filter for keyword matching. + """ + try: + # Build search text from keywords or query + search_text = query or "" + if keywords: + search_text = " ".join(keywords) + + if not search_text: + return SearchResult(data=[]) + + # Determine which text fields have full-text indexes + text_fields = self._get_text_indexed_fields() + if not text_fields: + # Fallback: try common field names + text_fields = ["abstract", "content", "text"] + + # Build full-text filter conditions + text_conditions = [] + for field_name in text_fields: + text_conditions.append( + FieldCondition( + key=field_name, + match=MatchText(text=search_text), + ) + ) + + # Combine text conditions: if multiple fields, use "should" (OR) + if len(text_conditions) == 1: + text_filter = Filter(must=text_conditions) + else: + text_filter = Filter(should=text_conditions) + + # Merge with user-provided filters + base_filter = self._convert_filter(filters) if filters else None + if base_filter: + final_filter = Filter(must=[text_filter, base_filter]) + else: + final_filter = text_filter + + # Scroll with filter + results, _ = self.client.scroll( + collection_name=self.collection_name, + limit=limit, + scroll_filter=final_filter, + with_payload=True, + ) + + return self._convert_scroll_results(results) + + except Exception as e: + logger.error(f"Error in search_by_keywords: {e}") + return SearchResult(data=[]) + + def search_by_id( + self, + index_name: str, + id: Any, + limit: int = 10, + offset: int = 0, + filters: Optional[Dict[str, Any]] = None, + output_fields: Optional[List[str]] = None, + ) -> SearchResult: + """Search for similar items using an existing document's ID.""" + try: + original_id = str(id) + qdrant_id = string_to_qdrant_id(original_id) + + # Fetch the point to get its vector + points = self.client.retrieve( + collection_name=self.collection_name, + ids=[qdrant_id], + with_vectors=True, + ) + + if not points: + return SearchResult(data=[]) + + point = points[0] + dense_vector = None + + # Extract dense vector + if hasattr(point, 'vector') and point.vector: + if isinstance(point.vector, dict): + dense_vector = point.vector.get(self.DENSE_VECTOR_NAME) + else: + dense_vector = point.vector + + if dense_vector: + result = self.search_by_vector( + index_name=index_name, + dense_vector=dense_vector, + limit=limit + 1, # +1 to account for self-exclusion + offset=offset, + filters=filters, + output_fields=output_fields, + ) + # Remove the query document from results + result.data = [item for item in result.data if item.id != original_id][:limit] + return result + + return SearchResult(data=[]) + + except Exception as e: + logger.error(f"Error in search_by_id: {e}") + return SearchResult(data=[]) + + def search_by_multimodal( + self, + index_name: str, + text: Optional[str] = None, + image: Optional[Any] = None, + video: Optional[Any] = None, + limit: int = 10, + offset: int = 0, + filters: Optional[Dict[str, Any]] = None, + output_fields: Optional[List[str]] = None, + ) -> SearchResult: + """Multimodal search via vectorizer delegation (same pattern as LocalCollection).""" + if not self.vectorizer_adapter: + raise ValueError("vectorizer is not initialized") + + if not text and not image and not video: + raise ValueError("At least one of text, image, or video must be provided") + + dense_vector, sparse_vector = self.vectorizer_adapter.vectorize_one( + text=text, image=image, video=video + ) + return self.search_by_vector( + index_name, dense_vector, limit, offset, filters, sparse_vector, output_fields + ) + + def search_by_random( + self, + index_name: str, + limit: int = 10, + offset: int = 0, + filters: Optional[Dict[str, Any]] = None, + output_fields: Optional[List[str]] = None, + ) -> SearchResult: + """Retrieve random documents using a random vector query.""" + if self.vector_dim > 0: + random_vector = [random.uniform(-1, 1) for _ in range(self.vector_dim)] + return self.search_by_vector( + index_name, random_vector, limit, offset, filters, None, output_fields + ) + + # Fallback to scroll if vector dimension unknown + try: + qdrant_filter = self._convert_filter(filters) if filters else None + with_payload = self._payload_selector(output_fields) + + results, _ = self.client.scroll( + collection_name=self.collection_name, + limit=limit, + offset=offset, + scroll_filter=qdrant_filter, + with_payload=with_payload, + ) + + return self._convert_scroll_results(results) + + except Exception as e: + logger.error(f"Error in search_by_random: {e}") + return SearchResult(data=[]) + + def search_by_scalar( + self, + index_name: str, + field: str, + order: Optional[str] = "desc", + limit: int = 10, + offset: int = 0, + filters: Optional[Dict[str, Any]] = None, + output_fields: Optional[List[str]] = None, + ) -> SearchResult: + """Retrieve documents sorted by a scalar field using native order_by.""" + try: + qdrant_filter = self._convert_filter(filters) if filters else None + direction = Direction.DESC if order == "desc" else Direction.ASC + + results, _ = self.client.scroll( + collection_name=self.collection_name, + limit=limit, + scroll_filter=qdrant_filter, + with_payload=True, + order_by=OrderBy(key=field, direction=direction), + ) + + data = [] + for point in results: + payload = point.payload or {} + original_id = payload.pop(self.ORIGINAL_ID_FIELD, None) or str(point.id) + field_value = payload.get(field, 0) + payload["id"] = original_id + data.append(SearchItemResult( + id=original_id, + fields=payload, + score=float(field_value) if isinstance(field_value, (int, float)) else 0.0, + )) + + return SearchResult(data=data) + + except Exception as e: + logger.error(f"Error in search_by_scalar: {e}") + return SearchResult(data=[]) + + # ========================================================================= + # CRUD Operations + # ========================================================================= + + # Reserved payload field for storing original string ID + ORIGINAL_ID_FIELD = "_original_id" + + def upsert_data(self, data_list: List[Dict[str, Any]], ttl: int = 0) -> UpsertDataResult: + """Insert or update data points.""" + try: + points = [] + ids = [] + + for original_data in data_list: + # Make a copy to avoid modifying original + data = dict(original_data) + + # Get or generate original ID + original_id = data.get("id") + if not original_id: + original_id = str(uuid.uuid4()) + + # Convert to valid Qdrant UUID + qdrant_id = string_to_qdrant_id(original_id) + + # Return original IDs to caller + ids.append(original_id) + + # Extract vectors + vectors = {} + dense_vector = data.pop("vector", None) or data.pop("dense_vector", None) + sparse_vector = data.pop("sparse_vector", None) + + if dense_vector: + vectors[self.DENSE_VECTOR_NAME] = dense_vector + + if sparse_vector: + if isinstance(sparse_vector, dict): + indices = _stable_sparse_indices(sparse_vector) + values = list(sparse_vector.values()) + vectors[self.SPARSE_VECTOR_NAME] = SparseVector( + indices=indices, values=values + ) + elif isinstance(sparse_vector, SparseVector): + vectors[self.SPARSE_VECTOR_NAME] = sparse_vector + + # Remaining fields become payload, store original ID for retrieval + payload = {k: v for k, v in data.items() if k != "id" and v is not None} + payload[self.ORIGINAL_ID_FIELD] = original_id + + # Skip records without vectors - Qdrant requires valid vectors + if not vectors: + logger.warning(f"Skipping record {original_id}: no vector data") + continue + + # Create point with converted UUID + point = PointStruct( + id=qdrant_id, + vector=vectors, + payload=payload, + ) + points.append(point) + + # Upsert to Qdrant (skip if no valid points) + if points: + self.client.upsert( + collection_name=self.collection_name, + points=points, + ) + else: + logger.warning("No valid points to upsert - all records skipped due to missing vectors") + + return UpsertDataResult(ids=ids) + + except Exception as e: + logger.error(f"Error upserting data: {e}") + raise + + def fetch_data(self, primary_keys: List[Any]) -> FetchDataInCollectionResult: + """Fetch data by primary keys.""" + try: + # Convert string IDs to Qdrant UUIDs + id_mapping = {string_to_qdrant_id(str(pk)): str(pk) for pk in primary_keys} + qdrant_ids = list(id_mapping.keys()) + + points = self.client.retrieve( + collection_name=self.collection_name, + ids=qdrant_ids, + with_payload=True, + ) + + items = [] + found_original_ids = set() + + for point in points: + payload = point.payload or {} + # Get original ID from payload or mapping + original_id = payload.pop(self.ORIGINAL_ID_FIELD, None) or id_mapping.get(str(point.id), str(point.id)) + payload["id"] = original_id + items.append(DataItem(id=original_id, fields=payload)) + found_original_ids.add(original_id) + + # Find IDs that don't exist (using original IDs) + ids_not_exist = [str(pk) for pk in primary_keys if str(pk) not in found_original_ids] + + return FetchDataInCollectionResult(items=items, ids_not_exist=ids_not_exist) + + except Exception as e: + logger.error(f"Error fetching data: {e}") + return FetchDataInCollectionResult(items=[], ids_not_exist=[str(pk) for pk in primary_keys]) + + def delete_data(self, primary_keys: List[Any]): + """Delete data by primary keys.""" + try: + # Convert string IDs to Qdrant UUIDs + qdrant_ids = [string_to_qdrant_id(str(pk)) for pk in primary_keys] + + self.client.delete( + collection_name=self.collection_name, + points_selector=qdrant_ids, + ) + except Exception as e: + logger.error(f"Error deleting data: {e}") + raise + + def delete_all_data(self): + """Delete all data in the collection without dropping it.""" + try: + self.client.delete( + collection_name=self.collection_name, + points_selector=FilterSelector( + filter=Filter() # Empty filter matches all points + ), + ) + logger.info(f"Deleted all data from collection: {self.collection_name}") + except Exception as e: + logger.error(f"Error deleting all data: {e}") + raise + + def aggregate_data( + self, + index_name: str, + op: str = "count", + field: Optional[str] = None, + filters: Optional[Dict[str, Any]] = None, + cond: Optional[Dict[str, Any]] = None, + ) -> AggregateResult: + """Aggregate data (limited support - mainly count).""" + try: + qdrant_filter = self._convert_filter(filters) if filters else None + + if op == "count": + if field: + # Group by field — paginated scroll to cover all records + counts = {} + offset = None + while True: + results, next_offset = self.client.scroll( + collection_name=self.collection_name, + limit=1000, + offset=offset, + scroll_filter=qdrant_filter, + with_payload=True, + ) + if not results: + break + + for point in results: + payload = point.payload or {} + value = str(payload.get(field, "_null_")) + counts[value] = counts.get(value, 0) + 1 + + if next_offset is None: + break + offset = next_offset + + # Apply conditions if provided + if cond: + filtered_counts = {} + for k, v in counts.items(): + if "gt" in cond and v <= cond["gt"]: + continue + if "lt" in cond and v >= cond["lt"]: + continue + if "gte" in cond and v < cond["gte"]: + continue + if "lte" in cond and v > cond["lte"]: + continue + filtered_counts[k] = v + counts = filtered_counts + + return AggregateResult(agg=counts, op=op, field=field) + else: + # Total count — use native exact count + count = self.client.count( + collection_name=self.collection_name, + count_filter=qdrant_filter, + exact=True, + ) + return AggregateResult(agg={"_total": count.count}, op=op, field=None) + + return AggregateResult(agg={}, op=op, field=field) + + except Exception as e: + logger.error(f"Error in aggregate_data: {e}") + return AggregateResult(agg={"_total": 0}, op=op, field=field) + + # ========================================================================= + # Helper Methods + # ========================================================================= + + def _get_text_indexed_fields(self) -> List[str]: + """Get field names that have full-text indexes.""" + try: + info = self.client.get_collection(self.collection_name) + text_fields = [] + if info.payload_schema: + for field_name, schema in info.payload_schema.items(): + # Check if field has text index params + if hasattr(schema, 'params') and schema.params: + params = schema.params + # TextIndexParams has tokenizer attribute + if hasattr(params, 'tokenizer') and params.tokenizer is not None: + text_fields.append(field_name) + # Check data_type for text + if hasattr(schema, 'data_type') and schema.data_type: + if "text" in str(schema.data_type).lower(): + if field_name not in text_fields: + text_fields.append(field_name) + return text_fields + except Exception: + return [] + + def _convert_filter(self, filter_dict: Dict[str, Any]) -> Optional[Filter]: + """Convert OpenViking filter format to Qdrant Filter.""" + if not filter_dict: + return None + + # If already a Qdrant Filter, return as-is + if isinstance(filter_dict, Filter): + return filter_dict + + try: + # OpenViking filter format: {"op": "must", "field": "name", "conds": ["value"]} + # or nested: {"op": "and", "conds": [...]} + + op = filter_dict.get("op", "must") + field = filter_dict.get("field") + conds = filter_dict.get("conds", []) + + if field: + # Simple field filter + conditions = [] + + if isinstance(conds, dict): + # Range filter: {"gt": 10, "lt": 100} + range_params = {} + for key in ("gt", "gte", "lt", "lte"): + if key in conds: + range_params[key] = conds[key] + + if range_params: + conditions.append(FieldCondition( + key=field, + range=Range(**range_params), + )) + elif isinstance(conds, list): + # Filter out any non-scalar values (like Filter objects) + scalar_conds = [c for c in conds if isinstance(c, (str, int, float, bool))] + if len(scalar_conds) == 1: + conditions.append(FieldCondition( + key=field, + match=MatchValue(value=scalar_conds[0]), + )) + elif len(scalar_conds) > 1: + conditions.append(FieldCondition( + key=field, + match=MatchAny(any=scalar_conds), + )) + elif isinstance(conds, (str, int, float, bool)): + conditions.append(FieldCondition( + key=field, + match=MatchValue(value=conds), + )) + + if op == "must": + return Filter(must=conditions) + elif op == "should": + return Filter(should=conditions) + elif op == "must_not": + return Filter(must_not=conditions) + + elif op in ["and", "or"]: + # Nested conditions - collect all FieldConditions from nested filters + all_must = [] + all_should = [] + all_must_not = [] + for cond in conds: + if isinstance(cond, Filter): + if cond.must: + all_must.extend(cond.must) + if cond.should: + all_should.extend(cond.should) + if cond.must_not: + all_must_not.extend(cond.must_not) + continue + + nested = self._convert_filter(cond) + if nested: + if nested.must: + all_must.extend(nested.must) + if nested.should: + all_should.extend(nested.should) + if nested.must_not: + if op == "and": + all_must_not.extend(nested.must_not) + else: + logger.warning( + "must_not inside 'or' filter is not fully supported, " + "conditions will be applied at top level" + ) + all_must_not.extend(nested.must_not) + + all_conditions = all_must or all_should + if all_conditions or all_must_not: + if op == "and": + return Filter( + must=all_conditions if all_conditions else None, + must_not=all_must_not if all_must_not else None, + ) + else: + return Filter( + should=all_conditions if all_conditions else None, + must_not=all_must_not if all_must_not else None, + ) + + return None + + except Exception as e: + logger.warning(f"Error converting filter: {e}") + return None + + def _convert_search_results(self, results) -> SearchResult: + """Convert Qdrant search results to OpenViking SearchResult.""" + data = [] + for point in results: + payload = point.payload or {} + # Use original ID from payload if available + original_id = payload.pop(self.ORIGINAL_ID_FIELD, None) or str(point.id) + payload["id"] = original_id + data.append(SearchItemResult( + id=original_id, + fields=payload, + score=point.score, + )) + return SearchResult(data=data) + + def _convert_query_results(self, results) -> SearchResult: + """Convert Qdrant query_points results to OpenViking SearchResult.""" + data = [] + for point in results.points: + payload = point.payload or {} + # Use original ID from payload if available + original_id = payload.pop(self.ORIGINAL_ID_FIELD, None) or str(point.id) + payload["id"] = original_id + data.append(SearchItemResult( + id=original_id, + fields=payload, + score=point.score if hasattr(point, 'score') else 0.0, + )) + return SearchResult(data=data) + + def _convert_scroll_results(self, results) -> SearchResult: + """Convert Qdrant scroll results to OpenViking SearchResult.""" + data = [] + for point in results: + payload = point.payload or {} + # Use original ID from payload if available + original_id = payload.pop(self.ORIGINAL_ID_FIELD, None) or str(point.id) + payload["id"] = original_id + data.append(SearchItemResult( + id=original_id, + fields=payload, + score=0.0, # Scroll doesn't have scores + )) + return SearchResult(data=data) diff --git a/openviking/storage/vectordb/project/__init__.py b/openviking/storage/vectordb/project/__init__.py index 819b3465..f21ec117 100644 --- a/openviking/storage/vectordb/project/__init__.py +++ b/openviking/storage/vectordb/project/__init__.py @@ -1,2 +1,13 @@ # Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd. # SPDX-License-Identifier: Apache-2.0 + +# Optional Qdrant support (requires qdrant-client) +try: + from openviking.storage.vectordb.project.qdrant_project import ( + QdrantProject, + get_or_create_qdrant_project, + ) + + __all__ = ["QdrantProject", "get_or_create_qdrant_project"] +except ImportError: + __all__ = [] diff --git a/openviking/storage/vectordb/project/qdrant_project.py b/openviking/storage/vectordb/project/qdrant_project.py new file mode 100644 index 00000000..c0c03ed2 --- /dev/null +++ b/openviking/storage/vectordb/project/qdrant_project.py @@ -0,0 +1,316 @@ +# Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd. +# SPDX-License-Identifier: Apache-2.0 +""" +Qdrant project implementation for OpenViking. + +Provides project-level management for Qdrant collections with support for +hybrid search (dense + sparse vectors) and RRF fusion. +""" + +from typing import Any, Dict, Optional + +from qdrant_client import QdrantClient + +from openviking.storage.vectordb.collection.collection import Collection +from openviking.storage.vectordb.collection.qdrant_collection import ( + get_or_create_qdrant_collection, +) +from openviking.storage.vectordb.project.project import IProject, Project +from openviking.storage.vectordb.utils.dict_utils import ThreadSafeDictManager +from openviking.utils import get_logger + +logger = get_logger(__name__) + + +def get_or_create_qdrant_project( + url: str = "http://localhost:6333", + api_key: Optional[str] = None, + grpc_port: Optional[int] = None, + prefer_grpc: bool = False, + timeout: Optional[float] = None, + project_name: str = "default", + vector_dim: int = 0, + distance_metric: str = "cosine", +) -> Project: + """Get or create a Qdrant project. + + Args: + url: Qdrant server URL + api_key: API key for authentication + grpc_port: gRPC port for faster operations + prefer_grpc: Whether to prefer gRPC over HTTP + timeout: Connection timeout in seconds + project_name: Name of the project (used as prefix for collections) + vector_dim: Default vector dimension for collections + distance_metric: Default distance metric for similarity search + + Returns: + Project instance wrapping QdrantProject + """ + project = QdrantProject( + url=url, + api_key=api_key, + grpc_port=grpc_port, + prefer_grpc=prefer_grpc, + timeout=timeout, + project_name=project_name, + vector_dim=vector_dim, + distance_metric=distance_metric, + ) + return Project(project) + + +class QdrantProject(IProject): + """Qdrant project implementation. + + Manages multiple Qdrant collections under a project namespace. + Supports automatic collection discovery and creation. + """ + + def __init__( + self, + url: str = "http://localhost:6333", + api_key: Optional[str] = None, + grpc_port: Optional[int] = None, + prefer_grpc: bool = False, + timeout: Optional[float] = None, + project_name: str = "default", + vector_dim: int = 0, + distance_metric: str = "cosine", + ): + """Initialize Qdrant project. + + Args: + url: Qdrant server URL + api_key: API key for authentication + grpc_port: gRPC port for faster operations + prefer_grpc: Whether to prefer gRPC over HTTP + timeout: Connection timeout in seconds + project_name: Name of the project + vector_dim: Default vector dimension + distance_metric: Default distance metric + """ + super().__init__(project_name) + + self.url = url + self.api_key = api_key + self.vector_dim = vector_dim + self.distance_metric = distance_metric + + # Initialize Qdrant client + client_kwargs = {"url": url} + if api_key: + client_kwargs["api_key"] = api_key + if grpc_port: + client_kwargs["grpc_port"] = grpc_port + if prefer_grpc: + client_kwargs["prefer_grpc"] = prefer_grpc + if timeout: + client_kwargs["timeout"] = timeout + + self.client = QdrantClient(**client_kwargs) + + # Collection cache + self.collections = ThreadSafeDictManager[Collection]() + + # Load existing collections + self._load_existing_collections() + + logger.info(f"Qdrant project initialized: {url}, project={project_name}") + + def _load_existing_collections(self): + """Load existing collections from Qdrant server.""" + try: + collections_info = self.client.get_collections() + + for collection_info in collections_info.collections: + collection_name = collection_info.name + + # Skip if not belonging to this project (if using prefixes) + # For now, we load all collections + + try: + # Get collection details to determine vector dimension + info = self.client.get_collection(collection_name) + + # Extract vector dimension from config + vector_dim = self.vector_dim + if info.config and info.config.params: + vectors_config = info.config.params.vectors + if isinstance(vectors_config, dict): + # Named vectors + if "dense" in vectors_config: + vector_dim = vectors_config["dense"].size + elif hasattr(vectors_config, "size"): + vector_dim = vectors_config.size + + # Create collection wrapper + collection = get_or_create_qdrant_collection( + client=self.client, + collection_name=collection_name, + vector_dim=vector_dim, + distance_metric=self.distance_metric, + ) + self.collections.set(collection_name, collection) + + logger.info(f"Loaded Qdrant collection: {collection_name}") + + except Exception as e: + logger.warning(f"Failed to load collection {collection_name}: {e}") + continue + + except Exception as e: + logger.error(f"Failed to load existing collections: {e}") + + def close(self): + """Close the project and release resources.""" + try: + # Close all collections + for name in self.collections.list_names(): + try: + collection = self.collections.get(name) + if collection: + collection.close() + except Exception as e: + logger.warning(f"Error closing collection {name}: {e}") + + self.collections.clear() + + # Close Qdrant client + if hasattr(self.client, 'close'): + self.client.close() + + logger.info("Qdrant project closed") + + except Exception as e: + logger.error(f"Error closing Qdrant project: {e}") + + def has_collection(self, collection_name: str) -> bool: + """Check if a collection exists.""" + # Check local cache first + if self.collections.has(collection_name): + return True + + # Check Qdrant server + try: + collections_info = self.client.get_collections() + for info in collections_info.collections: + if info.name == collection_name: + return True + return False + except Exception as e: + logger.error(f"Error checking collection existence: {e}") + return False + + def get_collection(self, collection_name: str) -> Optional[Collection]: + """Retrieve a collection by name.""" + # Check cache first + collection = self.collections.get(collection_name) + if collection: + return collection + + # Try to load from Qdrant + if self.has_collection(collection_name): + try: + info = self.client.get_collection(collection_name) + + # Extract vector dimension + vector_dim = self.vector_dim + if info.config and info.config.params: + vectors_config = info.config.params.vectors + if isinstance(vectors_config, dict) and "dense" in vectors_config: + vector_dim = vectors_config["dense"].size + elif hasattr(vectors_config, "size"): + vector_dim = vectors_config.size + + collection = get_or_create_qdrant_collection( + client=self.client, + collection_name=collection_name, + vector_dim=vector_dim, + distance_metric=self.distance_metric, + ) + self.collections.set(collection_name, collection) + return collection + + except Exception as e: + logger.error(f"Error loading collection {collection_name}: {e}") + return None + + return None + + def get_collections(self) -> Dict[str, Collection]: + """Get all collections in the project.""" + return self.collections.get_all() + + def list_collections(self) -> list: + """List all collection names.""" + try: + collections_info = self.client.get_collections() + return [info.name for info in collections_info.collections] + except Exception as e: + logger.error(f"Error listing collections: {e}") + return self.collections.list_names() + + def create_collection( + self, + collection_name: str, + collection_meta: Dict[str, Any], + ) -> Collection: + """Create a new collection. + + Args: + collection_name: Name for the new collection + collection_meta: Collection metadata including Fields definition + + Returns: + The newly created Collection instance + """ + try: + # Check if already exists + if self.has_collection(collection_name): + logger.warning(f"Collection {collection_name} already exists, returning existing") + return self.get_collection(collection_name) + + # Extract vector dimension from metadata + vector_dim = self.vector_dim + for field in collection_meta.get("Fields", []): + if field.get("FieldType") == "vector": + vector_dim = field.get("Dim", self.vector_dim) + break + + # Create collection + collection = get_or_create_qdrant_collection( + client=self.client, + collection_name=collection_name, + meta_data=collection_meta, + vector_dim=vector_dim, + distance_metric=self.distance_metric, + ) + + # Cache it + self.collections.set(collection_name, collection) + + logger.info(f"Created Qdrant collection: {collection_name} (dim={vector_dim})") + return collection + + except Exception as e: + logger.error(f"Error creating collection {collection_name}: {e}") + raise + + def drop_collection(self, collection_name: str): + """Delete a collection.""" + try: + # Remove from cache + collection = self.collections.get(collection_name) + if collection: + collection.close() + self.collections.remove(collection_name) + + # Delete from Qdrant + self.client.delete_collection(collection_name) + + logger.info(f"Dropped Qdrant collection: {collection_name}") + + except Exception as e: + logger.error(f"Error dropping collection {collection_name}: {e}") + raise diff --git a/openviking/storage/viking_vector_index_backend.py b/openviking/storage/viking_vector_index_backend.py index bcfbdf1c..66072d48 100644 --- a/openviking/storage/viking_vector_index_backend.py +++ b/openviking/storage/viking_vector_index_backend.py @@ -152,8 +152,29 @@ def __init__( ) self.project = get_or_create_local_project(path=str(project_path)) logger.info(f"VikingDB backend initialized with local storage: {project_path}") + elif config.backend == "qdrant": + # Qdrant mode + if not config.qdrant or not config.qdrant.url: + raise ValueError("Qdrant backend requires URL configuration") + + self._mode = config.backend + from openviking.storage.vectordb.project.qdrant_project import ( + get_or_create_qdrant_project, + ) + + self.project = get_or_create_qdrant_project( + url=config.qdrant.url, + api_key=config.qdrant.api_key, + grpc_port=config.qdrant.grpc_port, + prefer_grpc=config.qdrant.prefer_grpc, + timeout=config.qdrant.timeout, + project_name=self.DEFAULT_PROJECT_NAME, + vector_dim=self.vector_dim, + distance_metric=self.distance_metric, + ) + logger.info(f"VikingDB backend initialized with Qdrant: {config.qdrant.url}") else: - raise ValueError(f"Unsupported VikingDB backend type: {config.type}") + raise ValueError(f"Unsupported VikingDB backend type: {config.backend}") self._collection_configs: Dict[str, Dict[str, Any]] = {} # Cache meta_data at collection level to avoid repeated remote calls @@ -373,10 +394,18 @@ async def insert(self, collection: str, data: Dict[str, Any]) -> str: fields = self._get_meta_data(collection, coll).get("Fields", []) fields_dict = {item["FieldName"]: item for item in fields} - new_data = {} - for k in data: - if k in fields_dict and data[k] is not None: - new_data[k] = data[k] + + # If no Fields metadata, pass all data through (for Qdrant compatibility) + if not fields_dict: + new_data = {k: v for k, v in data.items() if v is not None} + else: + new_data = {} + # Always include vector fields even if not in schema Fields + vector_fields = {"vector", "sparse_vector", "dense_vector"} + for k in data: + if data[k] is not None: + if k in fields_dict or k in vector_fields: + new_data[k] = data[k] try: coll.upsert_data([new_data]) diff --git a/openviking_cli/utils/config/vectordb_config.py b/openviking_cli/utils/config/vectordb_config.py index 4052e0c4..257394e3 100644 --- a/openviking_cli/utils/config/vectordb_config.py +++ b/openviking_cli/utils/config/vectordb_config.py @@ -32,6 +32,29 @@ class VikingDBConfig(BaseModel): model_config = {"extra": "forbid"} +class QdrantConfig(BaseModel): + """Configuration for Qdrant vector database.""" + + url: Optional[str] = Field( + default="http://localhost:6333", + description="Qdrant server URL (e.g., 'http://localhost:6333')", + ) + api_key: Optional[str] = Field( + default=None, description="Qdrant API key for authentication (optional)" + ) + grpc_port: Optional[int] = Field( + default=None, description="Qdrant gRPC port for faster operations (optional)" + ) + prefer_grpc: bool = Field( + default=False, description="Whether to prefer gRPC over HTTP" + ) + timeout: Optional[float] = Field( + default=None, description="Connection timeout in seconds" + ) + + model_config = {"extra": "forbid"} + + class VectorDBBackendConfig(BaseModel): """ Configuration for VectorDB backend. @@ -42,7 +65,7 @@ class VectorDBBackendConfig(BaseModel): backend: str = Field( default="local", - description="VectorDB backend type: 'local' (file-based), 'http' (remote service), or 'volcengine' (VikingDB)", + description="VectorDB backend type: 'local' (file-based), 'http' (remote service), 'volcengine' (VikingDB), or 'qdrant' (Qdrant)", ) name: Optional[str] = Field(default=COLLECTION_NAME, description="Collection name for VectorDB") @@ -90,14 +113,19 @@ class VectorDBBackendConfig(BaseModel): description="VikingDB private deployment configuration for 'vikingdb' type", ) + qdrant: Optional[QdrantConfig] = Field( + default_factory=lambda: QdrantConfig(), + description="Qdrant configuration for 'qdrant' type", + ) + model_config = {"extra": "forbid"} @model_validator(mode="after") def validate_config(self): """Validate configuration completeness and consistency""" - if self.backend not in ["local", "http", "volcengine", "vikingdb"]: + if self.backend not in ["local", "http", "volcengine", "vikingdb", "qdrant"]: raise ValueError( - f"Invalid VectorDB backend: '{self.backend}'. Must be one of: 'local', 'http', 'volcengine', 'vikingdb'" + f"Invalid VectorDB backend: '{self.backend}'. Must be one of: 'local', 'http', 'volcengine', 'vikingdb', 'qdrant'" ) if self.backend == "local": @@ -117,4 +145,8 @@ def validate_config(self): if not self.vikingdb or not self.vikingdb.host: raise ValueError("VectorDB vikingdb backend requires 'host' to be set") + elif self.backend == "qdrant": + if not self.qdrant or not self.qdrant.url: + raise ValueError("VectorDB qdrant backend requires 'url' to be set in qdrant config") + return self diff --git a/pyproject.toml b/pyproject.toml index 0924e749..1e661997 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,6 +76,9 @@ doc = [ "sphinx-rtd-theme>=1.3.0", "myst-parser>=2.0.0", ] +qdrant = [ + "qdrant-client>=1.9.0", +] [project.urls] Homepage = "https://github.com/volcengine/openviking" @@ -152,3 +155,9 @@ quote-style = "double" indent-style = "space" skip-magic-trailing-comma = false line-ending = "auto" + +[dependency-groups] +dev = [ + "pytest>=8.4.2", + "pytest-asyncio>=1.2.0", +] diff --git a/tests/vectordb/test_qdrant_collection.py b/tests/vectordb/test_qdrant_collection.py new file mode 100644 index 00000000..ea682e5d --- /dev/null +++ b/tests/vectordb/test_qdrant_collection.py @@ -0,0 +1,774 @@ +# Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd. +# SPDX-License-Identifier: Apache-2.0 +""" +Tests for Qdrant collection implementation. + +Prerequisites: + - Qdrant server running on localhost:6333 + - qdrant-client installed: pip install qdrant-client + +Run tests: + pytest tests/vectordb/test_qdrant_collection.py -v + pytest tests/vectordb/test_qdrant_collection.py -v -k "basic" +""" + +import unittest +import uuid + +import pytest + +# Skip all tests if qdrant-client is not installed +pytest.importorskip("qdrant_client") + +from qdrant_client import QdrantClient + +from openviking.storage.vectordb.collection.qdrant_collection import ( + QdrantCollection, + get_or_create_qdrant_collection, + string_to_qdrant_id, +) + + +def is_qdrant_available(): + """Check if Qdrant server is available.""" + try: + client = QdrantClient(url="http://localhost:6333", timeout=5) + client.get_collections() + return True + except Exception: + return False + + +# Skip all tests if Qdrant server is not available +pytestmark = pytest.mark.skipif( + not is_qdrant_available(), + reason="Qdrant server not available on localhost:6333" +) + + +class TestStringToQdrantId(unittest.TestCase): + """Test ID conversion helper function.""" + + def test_valid_uuid_unchanged(self): + """Valid UUIDs should be returned as-is.""" + test_uuid = str(uuid.uuid4()) + result = string_to_qdrant_id(test_uuid) + self.assertEqual(result, test_uuid) + + def test_string_to_uuid_deterministic(self): + """Same string should always produce same UUID.""" + result1 = string_to_qdrant_id("doc_123") + result2 = string_to_qdrant_id("doc_123") + self.assertEqual(result1, result2) + + def test_different_strings_different_uuids(self): + """Different strings should produce different UUIDs.""" + result1 = string_to_qdrant_id("doc_1") + result2 = string_to_qdrant_id("doc_2") + self.assertNotEqual(result1, result2) + + def test_result_is_valid_uuid(self): + """Converted result should be a valid UUID string.""" + result = string_to_qdrant_id("any_string_id") + # Should not raise + uuid.UUID(result) + + +class TestQdrantCollectionBasic(unittest.TestCase): + """Basic CRUD operations for Qdrant collection.""" + + @classmethod + def setUpClass(cls): + """Set up test client.""" + cls.client = QdrantClient(url="http://localhost:6333") + + def setUp(self): + """Create a fresh collection for each test.""" + self.collection_name = f"test_basic_{uuid.uuid4().hex[:8]}" + self.vector_dim = 4 + self.collection = QdrantCollection( + client=self.client, + collection_name=self.collection_name, + vector_dim=self.vector_dim, + distance_metric="cosine", + ) + + def tearDown(self): + """Clean up collection after test.""" + try: + self.client.delete_collection(self.collection_name) + except Exception: + pass + + def test_collection_created(self): + """Collection should be created on initialization.""" + collections = self.client.get_collections().collections + names = [c.name for c in collections] + self.assertIn(self.collection_name, names) + + def test_upsert_and_fetch(self): + """Should insert and retrieve data correctly.""" + test_data = [ + {"id": "doc_1", "vector": [1.0, 0.0, 0.0, 0.0], "content": "test1"}, + {"id": "doc_2", "vector": [0.0, 1.0, 0.0, 0.0], "content": "test2"}, + ] + + # Insert + result = self.collection.upsert_data(test_data) + self.assertEqual(len(result.ids), 2) + self.assertIn("doc_1", result.ids) + self.assertIn("doc_2", result.ids) + + # Fetch + fetch_result = self.collection.fetch_data(["doc_1", "doc_2"]) + self.assertEqual(len(fetch_result.items), 2) + self.assertEqual(len(fetch_result.ids_not_exist), 0) + + # Verify content + items_by_id = {item.id: item for item in fetch_result.items} + self.assertEqual(items_by_id["doc_1"].fields["content"], "test1") + self.assertEqual(items_by_id["doc_2"].fields["content"], "test2") + + def test_fetch_missing_ids(self): + """Should report missing IDs correctly.""" + test_data = [{"id": "existing", "vector": [1.0, 0.0, 0.0, 0.0]}] + self.collection.upsert_data(test_data) + + fetch_result = self.collection.fetch_data(["existing", "missing"]) + self.assertEqual(len(fetch_result.items), 1) + self.assertIn("missing", fetch_result.ids_not_exist) + + def test_delete_data(self): + """Should delete data correctly.""" + test_data = [ + {"id": "to_delete", "vector": [1.0, 0.0, 0.0, 0.0]}, + {"id": "to_keep", "vector": [0.0, 1.0, 0.0, 0.0]}, + ] + self.collection.upsert_data(test_data) + + # Delete one + self.collection.delete_data(["to_delete"]) + + # Verify + fetch_result = self.collection.fetch_data(["to_delete", "to_keep"]) + self.assertEqual(len(fetch_result.items), 1) + self.assertEqual(fetch_result.items[0].id, "to_keep") + self.assertIn("to_delete", fetch_result.ids_not_exist) + + def test_fetch_by_uri_integration(self): + """Backend fetch_by_uri should return the record, not raise.""" + import asyncio + + from openviking.storage.viking_vector_index_backend import VikingVectorIndexBackend + from openviking_cli.utils.config.vectordb_config import QdrantConfig, VectorDBBackendConfig + + config = VectorDBBackendConfig( + backend="qdrant", + qdrant=QdrantConfig(url="http://localhost:6333"), + dimension=4, + ) + backend = VikingVectorIndexBackend(config=config) + coll_name = f"test_uri_{uuid.uuid4().hex[:8]}" + + async def run(): + await backend.create_collection(coll_name, { + "CollectionName": coll_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + {"FieldName": "uri", "FieldType": "string"}, + ], + }) + await backend.insert(coll_name, { + "id": "doc1", "vector": [1, 0, 0, 0], "uri": "/test/path" + }) + import time + time.sleep(0.5) + result = await backend.fetch_by_uri(coll_name, "/test/path") + await backend.drop_collection(coll_name) + return result + + result = asyncio.run(run()) + self.assertIsNotNone(result) + self.assertEqual(result["uri"], "/test/path") + + def test_delete_all_data_preserves_collection(self): + """delete_all_data should clear data but keep collection intact.""" + import time + self.collection.upsert_data([ + {"id": "a", "vector": [1, 0, 0, 0]}, + {"id": "b", "vector": [0, 1, 0, 0]}, + ]) + time.sleep(0.5) + # Verify data exists + count = self.client.count(self.collection_name).count + self.assertGreaterEqual(count, 2) + + # Delete all + self.collection.delete_all_data() + time.sleep(0.5) + + # Collection should still exist + collections = [c.name for c in self.client.get_collections().collections] + self.assertIn(self.collection_name, collections) + + # Data should be gone + count = self.client.count(self.collection_name).count + self.assertEqual(count, 0) + + # Should be able to insert again + self.collection.upsert_data([ + {"id": "c", "vector": [0, 0, 1, 0]}, + ]) + time.sleep(0.5) + count = self.client.count(self.collection_name).count + self.assertEqual(count, 1) + + def test_upsert_update_existing(self): + """Upsert should update existing documents.""" + # Insert + self.collection.upsert_data([ + {"id": "doc_1", "vector": [1.0, 0.0, 0.0, 0.0], "content": "original"} + ]) + + # Update + self.collection.upsert_data([ + {"id": "doc_1", "vector": [1.0, 0.0, 0.0, 0.0], "content": "updated"} + ]) + + # Verify + fetch_result = self.collection.fetch_data(["doc_1"]) + self.assertEqual(fetch_result.items[0].fields["content"], "updated") + + +class TestQdrantCollectionSearch(unittest.TestCase): + """Vector search operations for Qdrant collection.""" + + @classmethod + def setUpClass(cls): + """Set up test client and collection with data.""" + cls.client = QdrantClient(url="http://localhost:6333") + cls.collection_name = f"test_search_{uuid.uuid4().hex[:8]}" + cls.vector_dim = 4 + + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=cls.vector_dim, + distance_metric="cosine", + ) + + # Insert test data with distinct vectors + cls.test_data = [ + {"id": "north", "vector": [1.0, 0.0, 0.0, 0.0], "direction": "north"}, + {"id": "east", "vector": [0.0, 1.0, 0.0, 0.0], "direction": "east"}, + {"id": "south", "vector": [-1.0, 0.0, 0.0, 0.0], "direction": "south"}, + {"id": "west", "vector": [0.0, -1.0, 0.0, 0.0], "direction": "west"}, + {"id": "up", "vector": [0.0, 0.0, 1.0, 0.0], "direction": "up"}, + ] + cls.collection.upsert_data(cls.test_data) + + @classmethod + def tearDownClass(cls): + """Clean up collection.""" + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_dense_vector_search(self): + """Should find similar vectors.""" + # Search for vector similar to "north" + query_vector = [0.9, 0.1, 0.0, 0.0] + results = self.collection.search_by_vector( + index_name="default", + dense_vector=query_vector, + limit=3, + ) + + self.assertGreater(len(results.data), 0) + # "north" should be the top result + self.assertEqual(results.data[0].id, "north") + + def test_search_with_limit(self): + """Should respect limit parameter.""" + query_vector = [1.0, 0.0, 0.0, 0.0] + results = self.collection.search_by_vector( + index_name="default", + dense_vector=query_vector, + limit=2, + ) + self.assertEqual(len(results.data), 2) + + def test_search_returns_scores(self): + """Search results should include similarity scores.""" + query_vector = [1.0, 0.0, 0.0, 0.0] + results = self.collection.search_by_vector( + index_name="default", + dense_vector=query_vector, + limit=1, + ) + self.assertIsNotNone(results.data[0].score) + # Exact match should have high score (close to 1.0 for cosine) + self.assertGreater(results.data[0].score, 0.9) + + def test_search_by_random(self): + """Should return documents via scroll.""" + results = self.collection.search_by_random( + index_name="default", + limit=3, + ) + self.assertEqual(len(results.data), 3) + + def test_search_by_id_excludes_self(self): + """search_by_id should return similar items excluding the query document.""" + results = self.collection.search_by_id( + index_name="default", + id="north", + limit=3, + ) + # Should not contain the query document itself + result_ids = [item.id for item in results.data] + self.assertNotIn("north", result_ids) + # Should still return up to limit results + self.assertGreater(len(results.data), 0) + self.assertLessEqual(len(results.data), 3) + + def test_search_by_id_nonexistent(self): + """search_by_id with nonexistent ID should return empty.""" + results = self.collection.search_by_id( + index_name="default", + id="nonexistent_id_xyz", + limit=3, + ) + self.assertEqual(len(results.data), 0) + + def test_multimodal_without_vectorizer_raises(self): + """Should raise ValueError when no vectorizer is configured.""" + with self.assertRaises(ValueError) as ctx: + self.collection.search_by_multimodal( + index_name="default", text="test query", + ) + self.assertIn("vectorizer", str(ctx.exception).lower()) + + +class TestQdrantCollectionHybridSearch(unittest.TestCase): + """Hybrid search (dense + sparse) operations.""" + + @classmethod + def setUpClass(cls): + """Set up test client and collection with hybrid data.""" + cls.client = QdrantClient(url="http://localhost:6333") + cls.collection_name = f"test_hybrid_{uuid.uuid4().hex[:8]}" + cls.vector_dim = 4 + + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=cls.vector_dim, + distance_metric="cosine", + ) + + # Insert test data with both dense and sparse vectors + cls.test_data = [ + { + "id": "hybrid_1", + "vector": [1.0, 0.0, 0.0, 0.0], + "sparse_vector": {"0": 1.0, "1": 0.5}, + "content": "first document", + }, + { + "id": "hybrid_2", + "vector": [0.0, 1.0, 0.0, 0.0], + "sparse_vector": {"2": 1.0, "3": 0.5}, + "content": "second document", + }, + { + "id": "hybrid_3", + "vector": [0.5, 0.5, 0.0, 0.0], + "sparse_vector": {"0": 0.5, "2": 0.5}, + "content": "third document", + }, + ] + cls.collection.upsert_data(cls.test_data) + + @classmethod + def tearDownClass(cls): + """Clean up collection.""" + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_hybrid_search_with_rrf(self): + """Hybrid search should combine dense and sparse results.""" + results = self.collection.search_by_vector( + index_name="default", + dense_vector=[1.0, 0.0, 0.0, 0.0], + sparse_vector={"0": 1.0, "1": 0.5}, + limit=3, + ) + + self.assertEqual(len(results.data), 3) + # hybrid_1 should be top result (matches both dense and sparse) + self.assertEqual(results.data[0].id, "hybrid_1") + + def test_sparse_only_search(self): + """Should support sparse-only search.""" + results = self.collection.search_by_vector( + index_name="default", + sparse_vector={"2": 1.0, "3": 0.5}, + limit=2, + ) + + self.assertGreater(len(results.data), 0) + # hybrid_2 should be top result (best sparse match) + self.assertEqual(results.data[0].id, "hybrid_2") + + +class TestQdrantCollectionFilters(unittest.TestCase): + """Filter operations for Qdrant collection.""" + + @classmethod + def setUpClass(cls): + """Set up test client and collection with filterable data.""" + cls.client = QdrantClient(url="http://localhost:6333") + cls.collection_name = f"test_filters_{uuid.uuid4().hex[:8]}" + cls.vector_dim = 4 + + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=cls.vector_dim, + distance_metric="cosine", + ) + + # Insert test data with various field types + cls.test_data = [ + {"id": "doc_1", "vector": [1.0, 0, 0, 0], "category": "A", "score": 10}, + {"id": "doc_2", "vector": [1.0, 0, 0, 0], "category": "B", "score": 20}, + {"id": "doc_3", "vector": [1.0, 0, 0, 0], "category": "A", "score": 30}, + {"id": "doc_4", "vector": [1.0, 0, 0, 0], "category": "C", "score": 40}, + ] + cls.collection.upsert_data(cls.test_data) + + @classmethod + def tearDownClass(cls): + """Clean up collection.""" + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_filter_by_single_value(self): + """Should filter by exact match.""" + results = self.collection.search_by_random( + index_name="default", + limit=10, + filters={"op": "must", "field": "category", "conds": ["A"]}, + ) + + self.assertEqual(len(results.data), 2) + for item in results.data: + self.assertEqual(item.fields["category"], "A") + + def test_filter_by_multiple_values(self): + """Should filter by multiple values (OR).""" + results = self.collection.search_by_random( + index_name="default", + limit=10, + filters={"op": "must", "field": "category", "conds": ["A", "B"]}, + ) + + self.assertEqual(len(results.data), 3) + for item in results.data: + self.assertIn(item.fields["category"], ["A", "B"]) + + +class TestQdrantCollectionAggregate(unittest.TestCase): + """Aggregation operations for Qdrant collection.""" + + @classmethod + def setUpClass(cls): + """Set up test client and collection.""" + cls.client = QdrantClient(url="http://localhost:6333") + cls.collection_name = f"test_agg_{uuid.uuid4().hex[:8]}" + cls.vector_dim = 4 + + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=cls.vector_dim, + distance_metric="cosine", + ) + + # Insert test data + cls.test_data = [ + {"id": f"doc_{i}", "vector": [1.0, 0, 0, 0], "category": f"cat_{i % 3}"} + for i in range(10) + ] + cls.collection.upsert_data(cls.test_data) + + @classmethod + def tearDownClass(cls): + """Clean up collection.""" + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_count_total(self): + """Should count total documents.""" + result = self.collection.aggregate_data( + index_name="default", + op="count", + ) + self.assertEqual(result.agg.get("_total"), 10) + + def test_count_by_field(self): + """Should count by field value using paginated scroll.""" + result = self.collection.aggregate_data( + index_name="default", + op="count", + field="category", + ) + # Should have 3 categories: cat_0, cat_1, cat_2 + self.assertEqual(len(result.agg), 3) + # Total across all groups should equal 10 + self.assertEqual(sum(result.agg.values()), 10) + + +class TestQdrantCollectionFactory(unittest.TestCase): + """Test factory function.""" + + @classmethod + def setUpClass(cls): + """Set up test client.""" + cls.client = QdrantClient(url="http://localhost:6333") + + def setUp(self): + """Generate unique collection name.""" + self.collection_name = f"test_factory_{uuid.uuid4().hex[:8]}" + + def tearDown(self): + """Clean up collection.""" + try: + self.client.delete_collection(self.collection_name) + except Exception: + pass + + def test_get_or_create_collection(self): + """Factory should create wrapped collection.""" + collection = get_or_create_qdrant_collection( + client=self.client, + collection_name=self.collection_name, + vector_dim=4, + distance_metric="cosine", + ) + + # Should be wrapped Collection, not raw QdrantCollection + from openviking.storage.vectordb.collection.collection import Collection + self.assertIsInstance(collection, Collection) + + # Should be functional + collection.upsert_data([{"id": "test", "vector": [1, 0, 0, 0]}]) + result = collection.fetch_data(["test"]) + self.assertEqual(len(result.items), 1) + + +class TestQdrantCollectionScalarSearch(unittest.TestCase): + """Scalar sort operations using Qdrant native order_by.""" + + @classmethod + def setUpClass(cls): + try: + cls.client = QdrantClient(url="http://localhost:6333") + cls.client.get_collections() + except Exception: + raise unittest.SkipTest("Qdrant server not available") + cls.collection_name = f"test_scalar_{uuid.uuid4().hex[:8]}" + cls.vector_dim = 4 + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=cls.vector_dim, + distance_metric="cosine", + ) + # Create payload index for sorting (required by Qdrant order_by) + from qdrant_client.models import PayloadSchemaType + cls.client.create_payload_index( + collection_name=cls.collection_name, + field_name="timestamp", + field_schema=PayloadSchemaType.INTEGER, + ) + import time + time.sleep(0.3) + cls.test_data = [ + {"id": f"doc_{i}", "vector": [1.0, 0, 0, 0], "timestamp": i * 10, "category": f"cat_{i % 2}"} + for i in range(10) + ] + cls.collection.upsert_data(cls.test_data) + time.sleep(0.5) + + @classmethod + def tearDownClass(cls): + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_scalar_sort_desc(self): + """Should return results sorted by field descending.""" + results = self.collection.search_by_scalar( + index_name="default", field="timestamp", order="desc", limit=3, + ) + self.assertEqual(len(results.data), 3) + timestamps = [item.fields.get("timestamp") for item in results.data] + self.assertEqual(timestamps, sorted(timestamps, reverse=True)) + + def test_scalar_sort_asc(self): + """Should return results sorted by field ascending.""" + results = self.collection.search_by_scalar( + index_name="default", field="timestamp", order="asc", limit=3, + ) + self.assertEqual(len(results.data), 3) + timestamps = [item.fields.get("timestamp") for item in results.data] + self.assertEqual(timestamps, sorted(timestamps)) + + def test_scalar_sort_with_filter(self): + """Should apply filter and then sort.""" + results = self.collection.search_by_scalar( + index_name="default", field="timestamp", order="desc", limit=10, + filters={"op": "must", "field": "category", "conds": ["cat_0"]}, + ) + for item in results.data: + self.assertEqual(item.fields["category"], "cat_0") + + +class TestQdrantCollectionKeywordSearch(unittest.TestCase): + """Keyword search using Qdrant full-text index.""" + + @classmethod + def setUpClass(cls): + try: + cls.client = QdrantClient(url="http://localhost:6333") + cls.client.get_collections() + except Exception: + raise unittest.SkipTest("Qdrant server not available") + cls.collection_name = f"test_kw_{uuid.uuid4().hex[:8]}" + cls.vector_dim = 4 + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=cls.vector_dim, + distance_metric="cosine", + ) + # Create full-text index on "abstract" field (matches OpenViking schema) + from qdrant_client.models import TextIndexParams, TextIndexType, TokenizerType + cls.client.create_payload_index( + collection_name=cls.collection_name, + field_name="abstract", + field_schema=TextIndexParams( + type=TextIndexType.TEXT, + tokenizer=TokenizerType.WORD, + min_token_len=2, + lowercase=True, + ), + ) + import time + time.sleep(0.3) + cls.test_data = [ + {"id": "doc1", "vector": [1, 0, 0, 0], "abstract": "Python programming language tutorial"}, + {"id": "doc2", "vector": [0, 1, 0, 0], "abstract": "Java programming framework guide"}, + {"id": "doc3", "vector": [0, 0, 1, 0], "abstract": "Machine learning with Python models"}, + {"id": "doc4", "vector": [0, 0, 0, 1], "abstract": "Database systems architecture design"}, + ] + cls.collection.upsert_data(cls.test_data) + time.sleep(0.5) + + @classmethod + def tearDownClass(cls): + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_keyword_search_single(self): + """Should find documents matching keyword.""" + results = self.collection.search_by_keywords( + index_name="default", keywords=["Python"], limit=10, + ) + self.assertGreater(len(results.data), 0) + for item in results.data: + self.assertIn("python", item.fields.get("abstract", "").lower()) + + def test_keyword_search_query_string(self): + """Should find documents matching query string.""" + results = self.collection.search_by_keywords( + index_name="default", query="programming", limit=10, + ) + self.assertGreater(len(results.data), 0) + for item in results.data: + self.assertIn("programming", item.fields.get("abstract", "").lower()) + + def test_keyword_search_no_match(self): + """Should return empty for non-matching keywords.""" + results = self.collection.search_by_keywords( + index_name="default", keywords=["nonexistent_xyz_abc"], limit=10, + ) + self.assertEqual(len(results.data), 0) + + +class TestQdrantCollectionIndex(unittest.TestCase): + """Index management operations.""" + + @classmethod + def setUpClass(cls): + try: + cls.client = QdrantClient(url="http://localhost:6333") + cls.client.get_collections() + except Exception: + raise unittest.SkipTest("Qdrant server not available") + cls.collection_name = f"test_idx_{uuid.uuid4().hex[:8]}" + cls.collection = QdrantCollection( + client=cls.client, + collection_name=cls.collection_name, + vector_dim=4, + distance_metric="cosine", + ) + + @classmethod + def tearDownClass(cls): + try: + cls.client.delete_collection(cls.collection_name) + except Exception: + pass + + def test_has_index_default(self): + """Default index should exist after collection creation.""" + self.assertTrue(self.collection.has_index("default")) + + def test_has_index_nonexistent(self): + """Non-existent index should return False.""" + self.assertFalse(self.collection.has_index("nonexistent_index_xyz")) + + def test_create_and_has_index(self): + """Created index should be tracked.""" + self.collection.create_index("test_index", {"ScalarIndex": []}) + self.assertTrue(self.collection.has_index("test_index")) + + def test_list_indexes_includes_default(self): + """list_indexes should include 'default'.""" + indexes = self.collection.list_indexes() + self.assertIn("default", indexes) + + def test_drop_index(self): + """Dropped index should no longer be tracked.""" + self.collection.create_index("temp_index", {"ScalarIndex": []}) + self.assertTrue(self.collection.has_index("temp_index")) + self.collection.drop_index("temp_index") + self.assertFalse(self.collection.has_index("temp_index")) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/vectordb/test_qdrant_project.py b/tests/vectordb/test_qdrant_project.py new file mode 100644 index 00000000..7456bd1c --- /dev/null +++ b/tests/vectordb/test_qdrant_project.py @@ -0,0 +1,266 @@ +# Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd. +# SPDX-License-Identifier: Apache-2.0 +""" +Tests for Qdrant project implementation. + +Prerequisites: + - Qdrant server running on localhost:6333 + - qdrant-client installed: pip install qdrant-client + +Run tests: + pytest tests/vectordb/test_qdrant_project.py -v +""" + +import unittest +import uuid + +import pytest + +# Skip all tests if qdrant-client is not installed +pytest.importorskip("qdrant_client") + +from qdrant_client import QdrantClient + +from openviking.storage.vectordb.project.qdrant_project import ( + QdrantProject, + get_or_create_qdrant_project, +) + + +def is_qdrant_available(): + """Check if Qdrant server is available.""" + try: + client = QdrantClient(url="http://localhost:6333", timeout=5) + client.get_collections() + return True + except Exception: + return False + + +# Skip all tests if Qdrant server is not available +pytestmark = pytest.mark.skipif( + not is_qdrant_available(), + reason="Qdrant server not available on localhost:6333" +) + + +class TestQdrantProjectBasic(unittest.TestCase): + """Basic project operations.""" + + def setUp(self): + """Set up test project.""" + self.project_name = f"test_project_{uuid.uuid4().hex[:8]}" + self.project = QdrantProject( + url="http://localhost:6333", + project_name=self.project_name, + vector_dim=4, + distance_metric="cosine", + ) + self.created_collections = [] + + def tearDown(self): + """Clean up created collections.""" + for name in self.created_collections: + try: + self.project.drop_collection(name) + except Exception: + pass + self.project.close() + + def test_list_collections(self): + """Should list existing collections.""" + collections = self.project.list_collections() + self.assertIsInstance(collections, list) + + def test_create_collection(self): + """Should create a new collection.""" + collection_name = f"test_create_{uuid.uuid4().hex[:8]}" + self.created_collections.append(collection_name) + + schema = { + "CollectionName": collection_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + ], + } + + collection = self.project.create_collection(collection_name, schema) + self.assertIsNotNone(collection) + + # Verify collection exists + collections = self.project.list_collections() + self.assertIn(collection_name, collections) + + def test_get_collection(self): + """Should retrieve existing collection.""" + collection_name = f"test_get_{uuid.uuid4().hex[:8]}" + self.created_collections.append(collection_name) + + # Create first + schema = { + "CollectionName": collection_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + ], + } + self.project.create_collection(collection_name, schema) + + # Get it back + collection = self.project.get_collection(collection_name) + self.assertIsNotNone(collection) + + def test_drop_collection(self): + """Should drop collection.""" + collection_name = f"test_drop_{uuid.uuid4().hex[:8]}" + + # Create + schema = { + "CollectionName": collection_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + ], + } + self.project.create_collection(collection_name, schema) + + # Verify exists + self.assertIn(collection_name, self.project.list_collections()) + + # Drop + self.project.drop_collection(collection_name) + + # Verify gone + self.assertNotIn(collection_name, self.project.list_collections()) + + def test_has_collection(self): + """Should check collection existence.""" + collection_name = f"test_has_{uuid.uuid4().hex[:8]}" + self.created_collections.append(collection_name) + + # Should not exist initially + self.assertFalse(self.project.has_collection(collection_name)) + + # Create + schema = { + "CollectionName": collection_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + ], + } + self.project.create_collection(collection_name, schema) + + # Should exist now + self.assertTrue(self.project.has_collection(collection_name)) + + +class TestQdrantProjectFactory(unittest.TestCase): + """Test factory function.""" + + def setUp(self): + """Set up test data.""" + self.created_collections = [] + + def tearDown(self): + """Clean up.""" + client = QdrantClient(url="http://localhost:6333") + for name in self.created_collections: + try: + client.delete_collection(name) + except Exception: + pass + + def test_get_or_create_project(self): + """Factory should create wrapped project.""" + project = get_or_create_qdrant_project( + url="http://localhost:6333", + project_name="test_factory", + vector_dim=4, + distance_metric="cosine", + ) + + # Should be wrapped Project + from openviking.storage.vectordb.project.project import Project + self.assertIsInstance(project, Project) + + # Should be functional + collection_name = f"test_factory_{uuid.uuid4().hex[:8]}" + self.created_collections.append(collection_name) + + schema = { + "CollectionName": collection_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + ], + } + collection = project.create_collection(collection_name, schema) + self.assertIsNotNone(collection) + + project.close() + + +class TestQdrantProjectCollectionOperations(unittest.TestCase): + """Test collection operations through project.""" + + def setUp(self): + """Set up test project and collection.""" + self.project_name = f"test_ops_{uuid.uuid4().hex[:8]}" + self.project = QdrantProject( + url="http://localhost:6333", + project_name=self.project_name, + vector_dim=4, + distance_metric="cosine", + ) + + self.collection_name = f"test_col_{uuid.uuid4().hex[:8]}" + schema = { + "CollectionName": self.collection_name, + "Fields": [ + {"FieldName": "id", "FieldType": "string", "IsPrimaryKey": True}, + {"FieldName": "vector", "FieldType": "vector", "Dim": 4}, + {"FieldName": "content", "FieldType": "string"}, + ], + } + self.collection = self.project.create_collection(self.collection_name, schema) + + def tearDown(self): + """Clean up.""" + try: + self.project.drop_collection(self.collection_name) + except Exception: + pass + self.project.close() + + def test_collection_upsert_through_project(self): + """Collection created through project should be functional.""" + test_data = [ + {"id": "doc_1", "vector": [1.0, 0.0, 0.0, 0.0], "content": "test1"}, + {"id": "doc_2", "vector": [0.0, 1.0, 0.0, 0.0], "content": "test2"}, + ] + + result = self.collection.upsert_data(test_data) + self.assertEqual(len(result.ids), 2) + + def test_collection_search_through_project(self): + """Search should work on collection created through project.""" + test_data = [ + {"id": "north", "vector": [1.0, 0.0, 0.0, 0.0], "content": "north"}, + {"id": "south", "vector": [-1.0, 0.0, 0.0, 0.0], "content": "south"}, + ] + self.collection.upsert_data(test_data) + + results = self.collection.search_by_vector( + index_name="default", + dense_vector=[0.9, 0.1, 0.0, 0.0], + limit=1, + ) + + self.assertEqual(len(results.data), 1) + self.assertEqual(results.data[0].id, "north") + + +if __name__ == "__main__": + unittest.main() diff --git a/uv.lock b/uv.lock index 678904d5..2d7ab1b3 100644 --- a/uv.lock +++ b/uv.lock @@ 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