5180c1e395
This update introduces checks for vector dimension compatibility before migrating legacy data in both PostgreSQL and Qdrant storage implementations. If a dimension mismatch is detected, the migration is skipped to prevent data loss, and a new empty table or collection is created for the new embedding model. Key changes include: - Added dimension checks in `PGVectorStorage` and `QdrantVectorDBStorage` classes. - Enhanced logging to inform users about dimension mismatches and the creation of new storage. - Updated E2E tests to validate the new behavior, ensuring legacy data is preserved and new structures are created correctly. Impact: - Prevents potential data corruption during migrations with mismatched dimensions. - Improves user experience by providing clear logging and maintaining legacy data integrity. Testing: - New tests confirm that the system behaves as expected when encountering dimension mismatches.
522 lines
17 KiB
Python
522 lines
17 KiB
Python
import pytest
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from unittest.mock import MagicMock, patch, AsyncMock
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import numpy as np
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from lightrag.utils import EmbeddingFunc
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from lightrag.kg.qdrant_impl import QdrantVectorDBStorage
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# Mock QdrantClient
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@pytest.fixture
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def mock_qdrant_client():
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with patch("lightrag.kg.qdrant_impl.QdrantClient") as mock_client_cls:
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client = mock_client_cls.return_value
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client.collection_exists.return_value = False
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client.count.return_value.count = 0
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# Mock payload schema and vector config for get_collection
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collection_info = MagicMock()
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collection_info.payload_schema = {}
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# Mock vector dimension to match mock_embedding_func (768d)
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collection_info.config.params.vectors.size = 768
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client.get_collection.return_value = collection_info
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yield client
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# Mock get_data_init_lock to avoid async lock issues in tests
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@pytest.fixture(autouse=True)
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def mock_data_init_lock():
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with patch("lightrag.kg.qdrant_impl.get_data_init_lock") as mock_lock:
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mock_lock_ctx = AsyncMock()
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mock_lock.return_value = mock_lock_ctx
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yield mock_lock
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# Mock Embedding function
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@pytest.fixture
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def mock_embedding_func():
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async def embed_func(texts, **kwargs):
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return np.array([[0.1] * 768 for _ in texts])
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func = EmbeddingFunc(embedding_dim=768, func=embed_func, model_name="test-model")
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return func
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@pytest.mark.asyncio
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async def test_qdrant_collection_naming(mock_qdrant_client, mock_embedding_func):
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"""Test if collection name is correctly generated with model suffix"""
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=mock_embedding_func,
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workspace="test_ws",
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)
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# Verify collection name contains model suffix
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expected_suffix = "test_model_768d"
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assert expected_suffix in storage.final_namespace
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assert storage.final_namespace == f"lightrag_vdb_chunks_{expected_suffix}"
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# Verify legacy namespace (should not include workspace, just the base collection name)
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assert storage.legacy_namespace == "lightrag_vdb_chunks"
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@pytest.mark.asyncio
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async def test_qdrant_migration_trigger(mock_qdrant_client, mock_embedding_func):
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"""Test if migration logic is triggered correctly"""
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=mock_embedding_func,
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workspace="test_ws",
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)
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# Setup mocks for migration scenario
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# 1. New collection does not exist
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mock_qdrant_client.collection_exists.side_effect = (
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lambda name: name == storage.legacy_namespace
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)
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# 2. Legacy collection exists and has data
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mock_qdrant_client.count.return_value.count = 100
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# 3. Mock scroll for data migration
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mock_point = MagicMock()
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mock_point.id = "old_id"
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mock_point.vector = [0.1] * 768
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mock_point.payload = {"content": "test"}
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# First call returns points, second call returns empty (end of scroll)
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mock_qdrant_client.scroll.side_effect = [([mock_point], "next_offset"), ([], None)]
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# Initialize storage (triggers migration)
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await storage.initialize()
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# Verify migration steps
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# 1. Legacy count checked
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mock_qdrant_client.count.assert_any_call(
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collection_name=storage.legacy_namespace, exact=True
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)
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# 2. New collection created
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mock_qdrant_client.create_collection.assert_called()
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# 3. Data scrolled from legacy
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assert mock_qdrant_client.scroll.call_count >= 1
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call_args = mock_qdrant_client.scroll.call_args_list[0]
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assert call_args.kwargs["collection_name"] == storage.legacy_namespace
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assert call_args.kwargs["limit"] == 500
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# 4. Data upserted to new
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mock_qdrant_client.upsert.assert_called()
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# 5. Payload index created
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mock_qdrant_client.create_payload_index.assert_called()
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@pytest.mark.asyncio
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async def test_qdrant_no_migration_needed(mock_qdrant_client, mock_embedding_func):
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"""Test scenario where new collection already exists"""
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=mock_embedding_func,
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workspace="test_ws",
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)
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# New collection exists and Legacy exists (warning case)
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# or New collection exists and Legacy does not exist (normal case)
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# Mocking case where both exist to test logic flow but without migration
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# Logic in code:
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# Case 1: Both exist -> Warning only
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# Case 2: Only new exists -> Ensure index
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# Let's test Case 2: Only new collection exists
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mock_qdrant_client.collection_exists.side_effect = (
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lambda name: name == storage.final_namespace
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)
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# Initialize
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await storage.initialize()
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# Should check index but NOT migrate
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# In Qdrant implementation, Case 2 calls get_collection
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mock_qdrant_client.get_collection.assert_called_with(storage.final_namespace)
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mock_qdrant_client.scroll.assert_not_called()
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# ============================================================================
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# Tests for scenarios described in design document (Lines 606-649)
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# ============================================================================
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@pytest.mark.asyncio
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async def test_scenario_1_new_workspace_creation(
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mock_qdrant_client, mock_embedding_func
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):
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"""
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场景1：新建workspace
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预期：直接创建lightrag_vdb_chunks_text_embedding_3_large_3072d
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"""
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# Use a large embedding model
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large_model_func = EmbeddingFunc(
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embedding_dim=3072,
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func=mock_embedding_func.func,
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model_name="text-embedding-3-large",
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)
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=large_model_func,
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workspace="test_new",
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)
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# Case 3: Neither legacy nor new collection exists
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mock_qdrant_client.collection_exists.return_value = False
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# Initialize storage
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await storage.initialize()
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# Verify: Should create new collection with model suffix
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expected_collection = "lightrag_vdb_chunks_text_embedding_3_large_3072d"
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assert storage.final_namespace == expected_collection
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# Verify create_collection was called with correct name
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create_calls = [
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call for call in mock_qdrant_client.create_collection.call_args_list
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]
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assert len(create_calls) > 0
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assert (
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create_calls[0][0][0] == expected_collection
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or create_calls[0].kwargs.get("collection_name") == expected_collection
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)
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# Verify no migration was attempted
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mock_qdrant_client.scroll.assert_not_called()
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print(
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f"✅ Scenario 1: New workspace created with collection '{expected_collection}'"
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)
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@pytest.mark.asyncio
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async def test_scenario_2_legacy_upgrade_migration(
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mock_qdrant_client, mock_embedding_func
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):
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"""
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场景2：从旧版本升级
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已存在lightrag_vdb_chunks（无后缀）
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预期：自动迁移数据到lightrag_vdb_chunks_text_embedding_ada_002_1536d
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"""
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# Use ada-002 model
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ada_func = EmbeddingFunc(
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embedding_dim=1536,
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func=mock_embedding_func.func,
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model_name="text-embedding-ada-002",
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)
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=ada_func,
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workspace="test_legacy",
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)
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legacy_collection = storage.legacy_namespace
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new_collection = storage.final_namespace
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# Case 4: Only legacy collection exists
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mock_qdrant_client.collection_exists.side_effect = (
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lambda name: name == legacy_collection
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)
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# Mock legacy collection info with 1536d vectors
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legacy_collection_info = MagicMock()
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legacy_collection_info.payload_schema = {}
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legacy_collection_info.config.params.vectors.size = 1536
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mock_qdrant_client.get_collection.return_value = legacy_collection_info
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# Mock legacy data
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mock_qdrant_client.count.return_value.count = 150
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# Mock scroll results (simulate migration in batches)
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mock_points = []
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for i in range(10):
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point = MagicMock()
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point.id = f"legacy-{i}"
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point.vector = [0.1] * 1536
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point.payload = {"content": f"Legacy document {i}", "id": f"doc-{i}"}
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mock_points.append(point)
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# First batch returns points, second batch returns empty
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mock_qdrant_client.scroll.side_effect = [(mock_points, "offset1"), ([], None)]
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# Initialize (triggers migration)
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await storage.initialize()
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# Verify: New collection should be created
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expected_new_collection = "lightrag_vdb_chunks_text_embedding_ada_002_1536d"
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assert storage.final_namespace == expected_new_collection
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# Verify migration steps
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# 1. Check legacy count
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mock_qdrant_client.count.assert_any_call(
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collection_name=legacy_collection, exact=True
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)
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# 2. Create new collection
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mock_qdrant_client.create_collection.assert_called()
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# 3. Scroll legacy data
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scroll_calls = [call for call in mock_qdrant_client.scroll.call_args_list]
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assert len(scroll_calls) >= 1
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assert scroll_calls[0].kwargs["collection_name"] == legacy_collection
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# 4. Upsert to new collection
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upsert_calls = [call for call in mock_qdrant_client.upsert.call_args_list]
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assert len(upsert_calls) >= 1
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assert upsert_calls[0].kwargs["collection_name"] == new_collection
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# 5. Verify legacy collection was automatically deleted after successful migration
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# This prevents Case 1 warnings on next startup
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delete_calls = [
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call for call in mock_qdrant_client.delete_collection.call_args_list
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]
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assert (
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len(delete_calls) >= 1
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), "Legacy collection should be deleted after successful migration"
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# Check if legacy_collection was passed to delete_collection
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deleted_collection = (
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delete_calls[0][0][0]
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if delete_calls[0][0]
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else delete_calls[0].kwargs.get("collection_name")
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)
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assert (
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deleted_collection == legacy_collection
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), f"Expected to delete '{legacy_collection}', but deleted '{deleted_collection}'"
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print(
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f"✅ Scenario 2: Legacy data migrated from '{legacy_collection}' to '{expected_new_collection}' and legacy collection deleted"
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)
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@pytest.mark.asyncio
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async def test_scenario_3_multi_model_coexistence(mock_qdrant_client):
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"""
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场景3：多模型并存
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预期：两个独立的collection，互不干扰
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"""
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# Model A: bge-small with 768d
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async def embed_func_a(texts, **kwargs):
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return np.array([[0.1] * 768 for _ in texts])
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model_a_func = EmbeddingFunc(
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embedding_dim=768, func=embed_func_a, model_name="bge-small"
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)
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# Model B: bge-large with 1024d
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async def embed_func_b(texts, **kwargs):
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return np.array([[0.2] * 1024 for _ in texts])
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model_b_func = EmbeddingFunc(
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embedding_dim=1024, func=embed_func_b, model_name="bge-large"
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)
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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# Create storage for workspace A with model A
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storage_a = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=model_a_func,
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workspace="workspace_a",
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)
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# Create storage for workspace B with model B
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storage_b = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=model_b_func,
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workspace="workspace_b",
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)
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# Verify: Collection names are different
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assert storage_a.final_namespace != storage_b.final_namespace
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# Verify: Model A collection
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expected_collection_a = "lightrag_vdb_chunks_bge_small_768d"
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assert storage_a.final_namespace == expected_collection_a
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# Verify: Model B collection
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expected_collection_b = "lightrag_vdb_chunks_bge_large_1024d"
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assert storage_b.final_namespace == expected_collection_b
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# Verify: Different embedding dimensions are preserved
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assert storage_a.embedding_func.embedding_dim == 768
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assert storage_b.embedding_func.embedding_dim == 1024
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print("✅ Scenario 3: Multi-model coexistence verified")
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print(f" - Workspace A: {expected_collection_a} (768d)")
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print(f" - Workspace B: {expected_collection_b} (1024d)")
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print(" - Collections are independent")
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@pytest.mark.asyncio
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async def test_case1_empty_legacy_auto_cleanup(mock_qdrant_client, mock_embedding_func):
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"""
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Case 1a: 新旧collection都存在，且旧库为空
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预期：自动删除旧库
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"""
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=mock_embedding_func,
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workspace="test_ws",
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)
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legacy_collection = storage.legacy_namespace
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new_collection = storage.final_namespace
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# Mock: Both collections exist
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mock_qdrant_client.collection_exists.side_effect = lambda name: name in [
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legacy_collection,
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new_collection,
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]
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# Mock: Legacy collection is empty (0 records)
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def count_mock(collection_name, exact=True):
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mock_result = MagicMock()
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if collection_name == legacy_collection:
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mock_result.count = 0 # Empty legacy collection
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else:
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mock_result.count = 100 # New collection has data
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return mock_result
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mock_qdrant_client.count.side_effect = count_mock
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# Mock get_collection for Case 2 check
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collection_info = MagicMock()
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collection_info.payload_schema = {"workspace_id": True}
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mock_qdrant_client.get_collection.return_value = collection_info
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# Initialize storage
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await storage.initialize()
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# Verify: Empty legacy collection should be automatically cleaned up
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# Empty collections are safe to delete without data loss risk
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delete_calls = [
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call for call in mock_qdrant_client.delete_collection.call_args_list
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]
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assert len(delete_calls) >= 1, "Empty legacy collection should be auto-deleted"
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deleted_collection = (
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delete_calls[0][0][0]
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if delete_calls[0][0]
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else delete_calls[0].kwargs.get("collection_name")
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)
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assert (
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deleted_collection == legacy_collection
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), f"Expected to delete '{legacy_collection}', but deleted '{deleted_collection}'"
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print(
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f"✅ Case 1a: Empty legacy collection '{legacy_collection}' auto-deleted successfully"
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)
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@pytest.mark.asyncio
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async def test_case1_nonempty_legacy_warning(mock_qdrant_client, mock_embedding_func):
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"""
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Case 1b: 新旧collection都存在，且旧库有数据
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预期：警告但不删除
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"""
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config = {
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"embedding_batch_num": 10,
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"vector_db_storage_cls_kwargs": {"cosine_better_than_threshold": 0.8},
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}
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storage = QdrantVectorDBStorage(
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namespace="chunks",
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global_config=config,
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embedding_func=mock_embedding_func,
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workspace="test_ws",
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)
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legacy_collection = storage.legacy_namespace
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new_collection = storage.final_namespace
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# Mock: Both collections exist
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mock_qdrant_client.collection_exists.side_effect = lambda name: name in [
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legacy_collection,
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new_collection,
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]
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# Mock: Legacy collection has data (50 records)
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def count_mock(collection_name, exact=True):
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mock_result = MagicMock()
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if collection_name == legacy_collection:
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mock_result.count = 50 # Legacy has data
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else:
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mock_result.count = 100 # New collection has data
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return mock_result
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mock_qdrant_client.count.side_effect = count_mock
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# Mock get_collection for Case 2 check
|
||
collection_info = MagicMock()
|
||
collection_info.payload_schema = {"workspace_id": True}
|
||
mock_qdrant_client.get_collection.return_value = collection_info
|
||
|
||
# Initialize storage
|
||
await storage.initialize()
|
||
|
||
# Verify: Legacy collection with data should be preserved
|
||
# We never auto-delete collections that contain data to prevent accidental data loss
|
||
delete_calls = [
|
||
call for call in mock_qdrant_client.delete_collection.call_args_list
|
||
]
|
||
# Check if legacy collection was deleted (it should not be)
|
||
legacy_deleted = any(
|
||
(call[0][0] if call[0] else call.kwargs.get("collection_name"))
|
||
== legacy_collection
|
||
for call in delete_calls
|
||
)
|
||
assert not legacy_deleted, "Legacy collection with data should NOT be auto-deleted"
|
||
|
||
print(
|
||
f"✅ Case 1b: Legacy collection '{legacy_collection}' with data preserved (warning only)"
|
||
)
|