878 lines
30 KiB
Python
878 lines
30 KiB
Python
# Copyright (c) "Neo4j"
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# Neo4j Sweden AB [https://neo4j.com]
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# #
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# #
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# https://www.apache.org/licenses/LICENSE-2.0
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# #
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from unittest.mock import MagicMock, patch
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import neo4j
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import pytest
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from neo4j.exceptions import CypherSyntaxError
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from neo4j_graphrag.exceptions import (
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EmbeddingRequiredError,
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RetrieverInitializationError,
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SearchValidationError,
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)
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from neo4j_graphrag.neo4j_queries import get_search_query
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from neo4j_graphrag.retrievers import VectorCypherRetriever, VectorRetriever
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from neo4j_graphrag.types import (
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RetrieverResult,
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RetrieverResultItem,
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SearchType,
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)
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def test_vector_retriever_initialization(driver: MagicMock) -> None:
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with patch("neo4j_graphrag.retrievers.base.get_version") as mock_get_version:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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VectorRetriever(driver=driver, index_name="my-index")
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mock_get_version.assert_called_once()
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_vector_retriever_invalid_index_name(
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mock_get_version: MagicMock, driver: MagicMock
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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with pytest.raises(RetrieverInitializationError) as exc_info:
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VectorRetriever(driver=driver, index_name=42) # type: ignore
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assert "index_name" in str(exc_info.value)
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assert "Input should be a valid string" in str(exc_info.value)
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_vector_retriever_invalid_database_name(
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mock_get_version: MagicMock, driver: MagicMock
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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with pytest.raises(RetrieverInitializationError) as exc_info:
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VectorRetriever(
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driver=driver,
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index_name="my-index",
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neo4j_database=42, # type: ignore
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)
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assert "neo4j_database" in str(exc_info.value)
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assert "Input should be a valid string" in str(exc_info.value)
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_vector_cypher_retriever_invalid_retrieval_query(
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mock_get_version: MagicMock, driver: MagicMock
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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with pytest.raises(RetrieverInitializationError) as exc_info:
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VectorCypherRetriever(driver=driver, index_name="my-index", retrieval_query=42) # type: ignore
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assert "retrieval_query" in str(exc_info.value)
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assert "Input should be a valid string" in str(exc_info.value)
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_vector_cypher_retriever_invalid_database_name(
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mock_get_version: MagicMock, driver: MagicMock
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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retrieval_query = """
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RETURN node.id AS node_id, node.text AS text, score
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"""
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with pytest.raises(RetrieverInitializationError) as exc_info:
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VectorCypherRetriever(
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driver=driver,
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index_name="my-index",
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retrieval_query=retrieval_query,
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neo4j_database=42, # type: ignore
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)
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assert "neo4j_database" in str(exc_info.value)
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assert "Input should be a valid string" in str(exc_info.value)
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def test_vector_cypher_retriever_initialization(driver: MagicMock) -> None:
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with patch("neo4j_graphrag.retrievers.base.get_version") as mock_get_version:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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VectorCypherRetriever(driver=driver, index_name="my-index", retrieval_query="")
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mock_get_version.assert_called_once()
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@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
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@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_similarity_search_vector_happy_path(
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mock_get_version: MagicMock,
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_fetch_index_infos: MagicMock,
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_mock_supports_search: MagicMock,
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driver: MagicMock,
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neo4j_record: MagicMock,
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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index_name = "my-index"
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dimensions = 1536
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query_vector = [1.0 for _ in range(dimensions)]
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top_k = 5
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effective_search_ratio = 2
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database = "neo4j"
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retriever = VectorRetriever(driver, index_name, neo4j_database=database)
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expected_records = [neo4j.Record({"node": {"text": "dummy-node"}, "score": 1.0})]
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retriever.driver.execute_query.return_value = [ # type: ignore
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expected_records,
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None,
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None,
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]
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search_query, _ = get_search_query(SearchType.VECTOR)
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records = retriever.search(
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query_vector=query_vector,
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top_k=top_k,
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effective_search_ratio=effective_search_ratio,
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)
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retriever.driver.execute_query.assert_called_once_with( # type: ignore
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search_query,
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{
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"vector_index_name": index_name,
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"top_k": top_k,
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"effective_search_ratio": effective_search_ratio,
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"query_vector": query_vector,
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},
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database_=database,
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routing_=neo4j.RoutingControl.READ,
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)
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assert records == RetrieverResult(
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items=[
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RetrieverResultItem(
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content="{'text': 'dummy-node'}",
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metadata={"score": 1.0, "nodeLabels": None, "id": None},
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),
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],
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metadata={"__retriever": "VectorRetriever", "query_vector": query_vector},
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)
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@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
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@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_similarity_search_text_happy_path(
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mock_get_version: MagicMock,
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_fetch_index_infos: MagicMock,
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_mock_supports_search: MagicMock,
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driver: MagicMock,
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embedder: MagicMock,
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neo4j_record: MagicMock,
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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embed_query_vector = [1.0 for _ in range(1536)]
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embedder.embed_query.return_value = embed_query_vector
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index_name = "my-index"
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query_text = "may thy knife chip and shatter"
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top_k = 5
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effective_search_ratio = 2
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retriever = VectorRetriever(driver, index_name, embedder)
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driver.execute_query.return_value = [
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[neo4j_record],
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None,
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None,
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]
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search_query, _ = get_search_query(SearchType.VECTOR)
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records = retriever.search(
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query_text=query_text,
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top_k=top_k,
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effective_search_ratio=effective_search_ratio,
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)
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embedder.embed_query.assert_called_once_with(query_text)
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driver.execute_query.assert_called_once_with(
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search_query,
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{
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"vector_index_name": index_name,
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"top_k": top_k,
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"effective_search_ratio": effective_search_ratio,
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"query_vector": embed_query_vector,
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},
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database_=None,
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routing_=neo4j.RoutingControl.READ,
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)
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assert records == RetrieverResult(
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items=[
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RetrieverResultItem(
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content="dummy-node",
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metadata={"score": 1.0, "nodeLabels": None, "id": None},
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),
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],
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metadata={"__retriever": "VectorRetriever", "query_vector": embed_query_vector},
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)
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@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
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@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_similarity_search_text_return_properties(
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mock_get_version: MagicMock,
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_fetch_index_infos: MagicMock,
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_mock_supports_search: MagicMock,
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driver: MagicMock,
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embedder: MagicMock,
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neo4j_record: MagicMock,
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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embed_query_vector = [1.0 for _ in range(3)]
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embedder.embed_query.return_value = embed_query_vector
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index_name = "my-index"
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query_text = "may thy knife chip and shatter"
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top_k = 5
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effective_search_ratio = 2
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return_properties = ["node-property-1", "node-property-2"]
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retriever = VectorRetriever(
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driver, index_name, embedder, return_properties=return_properties
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)
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driver.execute_query.return_value = [
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[
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neo4j_record,
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],
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None,
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None,
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]
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search_query, _ = get_search_query(
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search_type=SearchType.VECTOR, return_properties=return_properties
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)
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records = retriever.search(
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query_text=query_text,
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top_k=top_k,
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effective_search_ratio=effective_search_ratio,
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)
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embedder.embed_query.assert_called_once_with(query_text)
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driver.execute_query.assert_called_once_with(
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search_query.rstrip(),
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{
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"vector_index_name": index_name,
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"top_k": top_k,
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"effective_search_ratio": effective_search_ratio,
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"query_vector": embed_query_vector,
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},
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database_=None,
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routing_=neo4j.RoutingControl.READ,
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)
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assert records == RetrieverResult(
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items=[
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RetrieverResultItem(
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content="dummy-node",
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metadata={"score": 1.0, "nodeLabels": None, "id": None},
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),
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],
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metadata={"__retriever": "VectorRetriever", "query_vector": embed_query_vector},
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)
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def test_vector_retriever_search_missing_embedder_for_text(
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vector_retriever: VectorRetriever,
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) -> None:
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query_text = "may thy knife chip and shatter"
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top_k = 5
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with pytest.raises(
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EmbeddingRequiredError, match="Embedding method required for text query"
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):
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vector_retriever.search(query_text=query_text, top_k=top_k)
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def test_vector_retriever_search_both_text_and_vector(
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vector_retriever: VectorRetriever,
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) -> None:
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query_text = "may thy knife chip and shatter"
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query_vector = [1.1, 2.2, 3.3]
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top_k = 5
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with pytest.raises(
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SearchValidationError,
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match="You must provide exactly one of query_vector or query_text.",
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):
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vector_retriever.search(
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query_text=query_text,
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query_vector=query_vector,
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top_k=top_k,
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)
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@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
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@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_vector_retriever_with_result_format_function(
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mock_get_version: MagicMock,
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_fetch_index_infos: MagicMock,
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_mock_supports_search: MagicMock,
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driver: MagicMock,
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embedder: MagicMock,
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neo4j_record: MagicMock,
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result_formatter: MagicMock,
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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embed_query_vector = [1.0 for _ in range(1536)]
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embedder.embed_query.return_value = embed_query_vector
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index_name = "my-index"
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retriever = VectorRetriever(
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driver,
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index_name,
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embedder=embedder,
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result_formatter=result_formatter,
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)
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query_text = "may thy knife chip and shatter"
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top_k = 5
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driver.execute_query.return_value = [
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[neo4j_record],
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None,
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None,
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]
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records = retriever.search(
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query_text=query_text,
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top_k=top_k,
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)
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assert records == RetrieverResult(
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items=[
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RetrieverResultItem(
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content="dummy-node", metadata={"score": 1.0, "node_id": 123}
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),
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],
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metadata={"__retriever": "VectorRetriever", "query_vector": embed_query_vector},
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)
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def test_vector_cypher_retriever_search_missing_embedder_for_text(
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vector_cypher_retriever: VectorCypherRetriever,
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) -> None:
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query_text = "may thy knife chip and shatter"
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top_k = 5
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with pytest.raises(
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EmbeddingRequiredError, match="Embedding method required for text query"
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):
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vector_cypher_retriever.search(query_text=query_text, top_k=top_k)
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def test_vector_cypher_retriever_search_both_text_and_vector(
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vector_cypher_retriever: VectorCypherRetriever,
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) -> None:
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query_text = "may thy knife chip and shatter"
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query_vector = [1.1, 2.2, 3.3]
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top_k = 5
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with pytest.raises(
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SearchValidationError,
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match="You must provide exactly one of query_vector or query_text.",
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):
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vector_cypher_retriever.search(
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query_text=query_text,
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query_vector=query_vector,
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top_k=top_k,
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)
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@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
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@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_retrieval_query_happy_path(
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mock_get_version: MagicMock,
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_fetch_index_infos: MagicMock,
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_mock_supports_search: MagicMock,
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driver: MagicMock,
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embedder: MagicMock,
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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embed_query_vector = [1.0 for _ in range(1536)]
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embedder.embed_query.return_value = embed_query_vector
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index_name = "my-index"
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retrieval_query = """
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RETURN node.id AS node_id, node.text AS text, score
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"""
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database = "neo4j"
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retriever = VectorCypherRetriever(
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driver,
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index_name,
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retrieval_query,
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embedder=embedder,
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neo4j_database=database,
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)
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query_text = "may thy knife chip and shatter"
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top_k = 5
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effective_search_ratio = 2
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record = neo4j.Record({"node_id": 123, "text": "dummy-text", "score": 1.0})
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driver.execute_query.return_value = [
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[record],
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None,
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None,
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]
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search_query, _ = get_search_query(
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SearchType.VECTOR, retrieval_query=retrieval_query
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)
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records = retriever.search(
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query_text=query_text,
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top_k=top_k,
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effective_search_ratio=effective_search_ratio,
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)
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embedder.embed_query.assert_called_once_with(query_text)
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driver.execute_query.assert_called_once_with(
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search_query,
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{
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"vector_index_name": index_name,
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"top_k": top_k,
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"effective_search_ratio": effective_search_ratio,
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"query_vector": embed_query_vector,
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},
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database_=database,
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routing_=neo4j.RoutingControl.READ,
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)
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assert records == RetrieverResult(
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items=[
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RetrieverResultItem(
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content="<Record node_id=123 text='dummy-text' score=1.0>",
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metadata=None,
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),
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],
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metadata={
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"__retriever": "VectorCypherRetriever",
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"query_vector": embed_query_vector,
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},
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)
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@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
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@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_retrieval_query_with_result_format_function(
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mock_get_version: MagicMock,
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_fetch_index_infos: MagicMock,
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_mock_supports_search: MagicMock,
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driver: MagicMock,
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embedder: MagicMock,
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neo4j_record: MagicMock,
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result_formatter: MagicMock,
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) -> None:
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mock_get_version.return_value = ((5, 23, 0), False, False)
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embed_query_vector = [1.0 for _ in range(1536)]
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embedder.embed_query.return_value = embed_query_vector
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index_name = "my-index"
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retrieval_query = """
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RETURN node.id AS node_id, node.text AS text, score
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"""
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retriever = VectorCypherRetriever(
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driver,
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index_name,
|
|
retrieval_query,
|
|
embedder=embedder,
|
|
result_formatter=result_formatter,
|
|
)
|
|
query_text = "may thy knife chip and shatter"
|
|
top_k = 5
|
|
effective_search_ratio = 2
|
|
driver.execute_query.return_value = [
|
|
[neo4j_record],
|
|
None,
|
|
None,
|
|
]
|
|
search_query, _ = get_search_query(
|
|
SearchType.VECTOR, retrieval_query=retrieval_query
|
|
)
|
|
|
|
records = retriever.search(
|
|
query_text=query_text,
|
|
top_k=top_k,
|
|
effective_search_ratio=effective_search_ratio,
|
|
)
|
|
|
|
embedder.embed_query.assert_called_once_with(query_text)
|
|
driver.execute_query.assert_called_once_with(
|
|
search_query,
|
|
{
|
|
"vector_index_name": index_name,
|
|
"top_k": top_k,
|
|
"effective_search_ratio": effective_search_ratio,
|
|
"query_vector": embed_query_vector,
|
|
},
|
|
database_=None,
|
|
routing_=neo4j.RoutingControl.READ,
|
|
)
|
|
assert records == RetrieverResult(
|
|
items=[
|
|
RetrieverResultItem(
|
|
content="dummy-node", metadata={"score": 1.0, "node_id": 123}
|
|
),
|
|
],
|
|
metadata={
|
|
"__retriever": "VectorCypherRetriever",
|
|
"query_vector": embed_query_vector,
|
|
},
|
|
)
|
|
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
|
|
@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_retrieval_query_with_params(
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
embedder: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((5, 23, 0), False, False)
|
|
embed_query_vector = [1.0 for _ in range(1536)]
|
|
embedder.embed_query.return_value = embed_query_vector
|
|
index_name = "my-index"
|
|
retrieval_query = """
|
|
RETURN node.id AS node_id, node.text AS text, score, {test: $param} AS metadata
|
|
"""
|
|
query_params = {
|
|
"param": "dummy-param",
|
|
}
|
|
retriever = VectorCypherRetriever(
|
|
driver,
|
|
index_name,
|
|
retrieval_query,
|
|
embedder=embedder,
|
|
)
|
|
query_text = "may thy knife chip and shatter"
|
|
top_k = 5
|
|
effective_search_ratio = 2
|
|
driver.execute_query.return_value = [
|
|
[neo4j.Record({"node_id": 123, "text": "dummy-text", "score": 1.0})],
|
|
None,
|
|
None,
|
|
]
|
|
search_query, _ = get_search_query(
|
|
SearchType.VECTOR, retrieval_query=retrieval_query
|
|
)
|
|
|
|
records = retriever.search(
|
|
query_text=query_text,
|
|
top_k=top_k,
|
|
effective_search_ratio=effective_search_ratio,
|
|
query_params=query_params,
|
|
)
|
|
|
|
embedder.embed_query.assert_called_once_with(query_text)
|
|
|
|
driver.execute_query.assert_called_once_with(
|
|
search_query,
|
|
{
|
|
"vector_index_name": index_name,
|
|
"top_k": top_k,
|
|
"effective_search_ratio": effective_search_ratio,
|
|
"query_vector": embed_query_vector,
|
|
"param": "dummy-param",
|
|
},
|
|
database_=None,
|
|
routing_=neo4j.RoutingControl.READ,
|
|
)
|
|
|
|
assert records == RetrieverResult(
|
|
items=[
|
|
RetrieverResultItem(
|
|
content="<Record node_id=123 text='dummy-text' score=1.0>",
|
|
metadata=None,
|
|
),
|
|
],
|
|
metadata={
|
|
"__retriever": "VectorCypherRetriever",
|
|
"query_vector": embed_query_vector,
|
|
},
|
|
)
|
|
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=False)
|
|
@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_retrieval_query_cypher_error(
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
embedder: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((5, 23, 0), False, False)
|
|
embed_query_vector = [1.0 for _ in range(1536)]
|
|
embedder.embed_query.return_value = embed_query_vector
|
|
index_name = "my-index"
|
|
retrieval_query = """
|
|
this is not a cypher query
|
|
"""
|
|
retriever = VectorCypherRetriever(
|
|
driver, index_name, retrieval_query, embedder=embedder
|
|
)
|
|
query_text = "may thy knife chip and shatter"
|
|
top_k = 5
|
|
driver.execute_query.side_effect = CypherSyntaxError
|
|
|
|
with pytest.raises(CypherSyntaxError):
|
|
retriever.search(
|
|
query_text=query_text,
|
|
top_k=top_k,
|
|
)
|
|
|
|
|
|
# --- SEARCH clause routing tests ---
|
|
|
|
|
|
class TestVectorRetrieverSearchClausePath:
|
|
"""Tests for VectorRetriever routing to SEARCH clause on Neo4j 2026.01+."""
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_uses_search_clause_no_filters(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retriever = VectorRetriever(driver=driver, index_name="my-index")
|
|
retriever._node_label = "Document"
|
|
retriever._embedding_node_property = "embedding"
|
|
|
|
query_vector = [1.0, 2.0, 3.0]
|
|
driver.execute_query.return_value = [[], None, None]
|
|
|
|
retriever.search(query_vector=query_vector, top_k=5)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" in executed_query
|
|
assert "SCORE AS score" in executed_query
|
|
assert "db.index.vector.queryNodes" not in executed_query
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_uses_search_clause_with_compatible_filters(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retriever = VectorRetriever(driver=driver, index_name="my-index")
|
|
retriever._node_label = "Document"
|
|
retriever._embedding_node_property = "embedding"
|
|
retriever._embedding_dimension = 3
|
|
retriever._filterable_properties = ["year"]
|
|
|
|
query_vector = [1.0, 2.0, 3.0]
|
|
driver.execute_query.return_value = [[], None, None]
|
|
|
|
retriever.search(
|
|
query_vector=query_vector,
|
|
top_k=5,
|
|
filters={"year": {"$gte": 2020}},
|
|
)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" in executed_query
|
|
assert "WHERE" in executed_query
|
|
assert "db.index.vector.queryNodes" not in executed_query
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_falls_back_with_incompatible_filters(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
"""$or filters are incompatible, should fall back to procedure path."""
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retriever = VectorRetriever(driver=driver, index_name="my-index")
|
|
retriever._node_label = "Document"
|
|
retriever._embedding_node_property = "embedding"
|
|
retriever._embedding_dimension = 3
|
|
|
|
query_vector = [1.0, 2.0, 3.0]
|
|
driver.execute_query.return_value = [[], None, None]
|
|
|
|
retriever.search(
|
|
query_vector=query_vector,
|
|
top_k=5,
|
|
filters={"$or": [{"a": 1}, {"b": 2}]},
|
|
)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
# Should fall back to brute-force (exact KNN) path
|
|
assert "SEARCH node IN (VECTOR INDEX" not in executed_query
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_falls_back_when_no_node_label(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
"""Without _node_label, SEARCH clause cannot be used."""
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retriever = VectorRetriever(driver=driver, index_name="my-index")
|
|
retriever._node_label = None
|
|
|
|
query_vector = [1.0, 2.0, 3.0]
|
|
driver.execute_query.return_value = [[], None, None]
|
|
|
|
retriever.search(query_vector=query_vector, top_k=5)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" not in executed_query
|
|
assert "db.index.vector.queryNodes" in executed_query
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_search_clause_with_return_properties(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retriever = VectorRetriever(
|
|
driver=driver,
|
|
index_name="my-index",
|
|
return_properties=["name", "text"],
|
|
)
|
|
retriever._node_label = "Document"
|
|
retriever._embedding_node_property = "embedding"
|
|
|
|
driver.execute_query.return_value = [[], None, None]
|
|
retriever.search(query_vector=[1.0, 2.0], top_k=3)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" in executed_query
|
|
assert ".name" in executed_query
|
|
assert ".text" in executed_query
|
|
|
|
|
|
class TestVectorCypherRetrieverSearchClausePath:
|
|
"""Tests for VectorCypherRetriever routing to SEARCH clause."""
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_uses_search_clause_with_retrieval_query(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
embedder: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
embed_query_vector = [1.0, 2.0, 3.0]
|
|
embedder.embed_query.return_value = embed_query_vector
|
|
retrieval_query = "RETURN node.id AS node_id, node.text AS text, score"
|
|
|
|
retriever = VectorCypherRetriever(
|
|
driver=driver,
|
|
index_name="my-index",
|
|
retrieval_query=retrieval_query,
|
|
embedder=embedder,
|
|
)
|
|
retriever._node_label = "Document"
|
|
|
|
driver.execute_query.return_value = [[], None, None]
|
|
retriever.search(query_text="test query", top_k=5)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" in executed_query
|
|
assert retrieval_query in executed_query
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_falls_back_when_no_node_label(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retrieval_query = "RETURN node.id AS node_id, score"
|
|
|
|
retriever = VectorCypherRetriever(
|
|
driver=driver,
|
|
index_name="my-index",
|
|
retrieval_query=retrieval_query,
|
|
)
|
|
retriever._node_label = None
|
|
|
|
driver.execute_query.return_value = [[], None, None]
|
|
retriever.search(query_vector=[1.0, 2.0, 3.0], top_k=5)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" not in executed_query
|
|
|
|
@patch("neo4j_graphrag.retrievers.vector.supports_search_clause", return_value=True)
|
|
@patch("neo4j_graphrag.retrievers.VectorCypherRetriever._fetch_index_infos")
|
|
@patch("neo4j_graphrag.retrievers.base.get_version")
|
|
def test_search_clause_with_compatible_filters(
|
|
self,
|
|
mock_get_version: MagicMock,
|
|
_fetch_index_infos: MagicMock,
|
|
_mock_supports_search: MagicMock,
|
|
driver: MagicMock,
|
|
) -> None:
|
|
mock_get_version.return_value = ((2026, 1, 0), False, True)
|
|
retrieval_query = "RETURN node.id AS node_id, score"
|
|
|
|
retriever = VectorCypherRetriever(
|
|
driver=driver,
|
|
index_name="my-index",
|
|
retrieval_query=retrieval_query,
|
|
)
|
|
retriever._node_label = "Document"
|
|
retriever._embedding_dimension = 3
|
|
retriever._filterable_properties = ["status"]
|
|
|
|
driver.execute_query.return_value = [[], None, None]
|
|
retriever.search(
|
|
query_vector=[1.0, 2.0, 3.0],
|
|
top_k=5,
|
|
filters={"status": "active"},
|
|
)
|
|
|
|
call_args = driver.execute_query.call_args
|
|
executed_query = call_args[0][0]
|
|
assert "SEARCH node IN (VECTOR INDEX" in executed_query
|
|
assert "WHERE" in executed_query
|