484 lines
19 KiB
Python
484 lines
19 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 unittest.mock import MagicMock, patch
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import neo4j
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from neo4j_graphrag.embeddings.base import Embedder
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from neo4j_graphrag.llm.base import LLMInterface
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from neo4j_graphrag.retrievers import (
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HybridCypherRetriever,
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HybridRetriever,
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Text2CypherRetriever,
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VectorCypherRetriever,
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VectorRetriever,
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)
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from neo4j_graphrag.tool import Tool
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# Mock dependencies for retriever instances
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def create_mock_driver() -> neo4j.Driver:
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driver = MagicMock(spec=neo4j.Driver)
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# Create a mock result object with a records attribute
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mock_result = MagicMock()
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mock_result.records = [MagicMock()]
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driver.execute_query.return_value = mock_result
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return driver
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def create_mock_embedder() -> Embedder:
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embedder = MagicMock(spec=Embedder)
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embedder.embed_query.return_value = [0.1, 0.2, 0.3]
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return embedder
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def create_mock_llm() -> LLMInterface:
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llm = MagicMock()
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llm.invoke.return_value = "MATCH (n) RETURN n"
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return llm
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# Test conversion with VectorRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_vector_retriever_to_tool(mock_get_version: MagicMock) -> None:
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"""Test conversion of VectorRetriever to a Tool instance with correct attributes."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = VectorRetriever(
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driver=driver,
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index_name="test_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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tool = retriever.convert_to_tool(
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name="VectorRetriever",
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description="A tool for vector-based retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for vector search.",
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"top_k": "Number of results to return.",
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},
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)
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assert isinstance(tool, Tool)
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assert tool.get_name() == "VectorRetriever"
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assert tool.get_description() == "A tool for vector-based retrieval from Neo4j."
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# Check that the parameters object has the expected properties
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params = tool.get_parameters()
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assert "properties" in params
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assert len(params["properties"]) == 5 # VectorRetriever has 5 parameters
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assert "query_text" in params["properties"]
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assert "top_k" in params["properties"]
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assert "query_vector" in params["properties"]
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assert "effective_search_ratio" in params["properties"]
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assert "filters" in params["properties"]
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# Test conversion with VectorCypherRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_vector_cypher_retriever_to_tool(mock_get_version: MagicMock) -> None:
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"""Test conversion of VectorCypherRetriever to a Tool instance with correct attributes."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = VectorCypherRetriever(
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driver=driver,
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index_name="test_index",
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embedder=embedder,
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retrieval_query="RETURN n",
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)
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tool = retriever.convert_to_tool(
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name="VectorCypherRetriever",
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description="A tool for vector-cypher retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for vector-cypher search.",
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"top_k": "Number of results to return.",
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},
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)
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assert isinstance(tool, Tool)
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assert tool.get_name() == "VectorCypherRetriever"
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assert tool.get_description() == "A tool for vector-cypher retrieval from Neo4j."
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# Check that the parameters object has the expected properties
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params = tool.get_parameters()
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assert "properties" in params
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assert len(params["properties"]) == 6 # VectorCypherRetriever has 6 parameters
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assert "query_text" in params["properties"]
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assert "top_k" in params["properties"]
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assert "query_vector" in params["properties"]
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assert "effective_search_ratio" in params["properties"]
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assert "query_params" in params["properties"]
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assert "filters" in params["properties"]
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# Test conversion with HybridRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_hybrid_retriever_to_tool(mock_get_version: MagicMock) -> None:
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"""Test conversion of HybridRetriever to a Tool instance with correct attributes."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = HybridRetriever(
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driver=driver,
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vector_index_name="test_vector_index",
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fulltext_index_name="test_fulltext_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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tool = retriever.convert_to_tool(
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name="HybridRetriever",
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description="A tool for hybrid retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for hybrid search.",
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"top_k": "Number of results to return.",
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},
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)
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assert isinstance(tool, Tool)
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assert tool.get_name() == "HybridRetriever"
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assert tool.get_description() == "A tool for hybrid retrieval from Neo4j."
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# Check that the parameters object has the expected properties
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params = tool.get_parameters()
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assert "properties" in params
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assert len(params["properties"]) == 6 # HybridRetriever has 6 parameters
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assert "query_text" in params["properties"]
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assert "top_k" in params["properties"]
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assert "query_vector" in params["properties"]
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assert "effective_search_ratio" in params["properties"]
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assert "ranker" in params["properties"]
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assert "alpha" in params["properties"]
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# Test conversion with HybridCypherRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_hybrid_cypher_retriever_to_tool(mock_get_version: MagicMock) -> None:
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"""Test conversion of HybridCypherRetriever to a Tool instance with correct attributes."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = HybridCypherRetriever(
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driver=driver,
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vector_index_name="test_vector_index",
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fulltext_index_name="test_fulltext_index",
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embedder=embedder,
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retrieval_query="RETURN n",
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)
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tool = retriever.convert_to_tool(
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name="HybridCypherRetriever",
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description="A tool for hybrid-cypher retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for hybrid-cypher search.",
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"top_k": "Number of results to return.",
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},
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)
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assert isinstance(tool, Tool)
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assert tool.get_name() == "HybridCypherRetriever"
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assert tool.get_description() == "A tool for hybrid-cypher retrieval from Neo4j."
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# Check that the parameters object has the expected properties
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params = tool.get_parameters()
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assert "properties" in params
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assert len(params["properties"]) == 7 # HybridCypherRetriever has 7 parameters
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assert "query_text" in params["properties"]
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assert "query_vector" in params["properties"]
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assert "top_k" in params["properties"]
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assert "effective_search_ratio" in params["properties"]
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assert "query_params" in params["properties"]
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assert "ranker" in params["properties"]
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assert "alpha" in params["properties"]
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# Test conversion with Text2CypherRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_text2cypher_retriever_to_tool(mock_get_version: MagicMock) -> None:
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"""Test conversion of Text2CypherRetriever to a Tool instance with correct attributes."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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llm = create_mock_llm()
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retriever = Text2CypherRetriever(driver=driver, llm=llm)
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tool = retriever.convert_to_tool(
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name="Text2CypherRetriever",
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description="A tool for text to Cypher retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for text to Cypher conversion.",
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},
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)
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assert isinstance(tool, Tool)
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assert tool.get_name() == "Text2CypherRetriever"
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assert tool.get_description() == "A tool for text to Cypher retrieval from Neo4j."
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# Check that the parameters object has the expected properties
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params = tool.get_parameters()
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assert "properties" in params
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assert len(params["properties"]) == 2 # Text2CypherRetriever has 2 parameters
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assert "query_text" in params["properties"]
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assert "prompt_params" in params["properties"]
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# Test conversion with custom name provided
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_retriever_with_custom_name(
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mock_get_version: MagicMock,
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) -> None:
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"""Test conversion of a retriever to a Tool instance with a custom name."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = VectorRetriever(
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driver=driver,
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index_name="test_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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custom_name = "CustomNamedTool"
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tool = retriever.convert_to_tool(
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name=custom_name,
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description="A tool with a custom name",
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parameter_descriptions={
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"query_text": "The query text for vector search.",
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},
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)
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# Verify that the custom name is used instead of the retriever class name
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assert tool.get_name() == custom_name
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# Test conversion with no parameters provided
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_convert_vector_retriever_to_tool_no_parameters(
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mock_get_version: MagicMock,
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) -> None:
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"""Test conversion of VectorRetriever to a Tool instance when no parameters are provided."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = VectorRetriever(
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driver=driver,
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index_name="test_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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tool = retriever.convert_to_tool(
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name="VectorRetriever",
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description="A tool for vector-based retrieval from Neo4j.",
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)
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assert isinstance(tool, Tool)
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assert tool.get_name() == "VectorRetriever"
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assert tool.get_description() == "A tool for vector-based retrieval from Neo4j."
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# With the new API, parameters are always auto-inferred from method signature
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params = tool.get_parameters()
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assert params is not None
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assert "properties" in params
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assert len(params["properties"]) == 5 # VectorRetriever has 5 parameters
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# Test tool execution for VectorRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_vector_retriever_tool_execution(mock_get_version: MagicMock) -> None:
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"""Test execution of VectorRetriever tool calls the search method with correct arguments."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = VectorRetriever(
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driver=driver,
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index_name="test_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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# Create the tool first, before mocking
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with patch.object(VectorRetriever, "_fetch_index_infos"):
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tool = retriever.convert_to_tool(
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name="VectorRetriever",
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description="A tool for vector-based retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for vector search.",
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"top_k": "Number of results to return.",
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},
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)
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# Now mock the get_search_results method to track calls
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from neo4j_graphrag.types import RawSearchResult
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get_search_results_mock = MagicMock(
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return_value=RawSearchResult(records=[], metadata={})
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)
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# Use patch to mock the method
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with patch.object(retriever, "get_search_results", get_search_results_mock):
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tools = {tool.get_name(): tool}
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# Simulate indirect invocation as would happen in real usage
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tool_call_arguments = {"query_text": "test query", "top_k": 5}
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# Pass the arguments as kwargs
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result = tools[tool.get_name()].execute(**tool_call_arguments)
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# Since we're using a context manager for patching, we need to verify the call inside the context
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# We can only check the result, not the method call itself
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assert result is not None
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assert hasattr(result, "items") # Should return RetrieverResult now
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assert isinstance(result.items, list)
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assert hasattr(result, "metadata")
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# Test tool execution for HybridRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_hybrid_retriever_tool_execution(mock_get_version: MagicMock) -> None:
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"""Test execution of HybridRetriever tool calls the search method with correct arguments."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = HybridRetriever(
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driver=driver,
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vector_index_name="test_vector_index",
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fulltext_index_name="test_fulltext_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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# Create the tool first, before mocking
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with patch.object(HybridRetriever, "_fetch_index_infos"):
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tool = retriever.convert_to_tool(
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name="HybridRetriever",
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description="A tool for hybrid retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for hybrid search.",
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"top_k": "Number of results to return.",
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},
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)
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# Now mock the get_search_results method to track calls
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from neo4j_graphrag.types import RawSearchResult
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get_search_results_mock = MagicMock(
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return_value=RawSearchResult(records=[], metadata={})
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)
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# Use patch to mock the method
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with patch.object(retriever, "get_search_results", get_search_results_mock):
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tools = {tool.get_name(): tool}
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# Simulate indirect invocation as would happen in real usage
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tool_call_arguments = {"query_text": "test query", "top_k": 5}
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# Pass the arguments as kwargs
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result = tools[tool.get_name()].execute(**tool_call_arguments)
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# Since we're using a context manager for patching, we need to verify the call inside the context
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# We can only check the result, not the method call itself
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assert result is not None
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assert hasattr(result, "items") # Should return RetrieverResult now
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assert isinstance(result.items, list)
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assert hasattr(result, "metadata")
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# Test tool execution for Text2CypherRetriever
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_text2cypher_retriever_tool_execution(mock_get_version: MagicMock) -> None:
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"""Test execution of Text2CypherRetriever tool calls the search method with correct arguments."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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llm = create_mock_llm()
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retriever = Text2CypherRetriever(driver=driver, llm=llm)
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# Create the tool first, before mocking
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tool = retriever.convert_to_tool(
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name="Text2CypherRetriever",
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description="A tool for text to Cypher retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for text to Cypher conversion.",
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},
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)
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# Now mock the get_search_results method to track calls
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from neo4j_graphrag.types import RawSearchResult
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get_search_results_mock = MagicMock(
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return_value=RawSearchResult(records=[], metadata={})
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)
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# Use patch to mock the method
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with patch.object(retriever, "get_search_results", get_search_results_mock):
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tools = {tool.get_name(): tool}
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# Simulate indirect invocation as would happen in real usage
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tool_call_arguments = {"query_text": "test query"}
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# Pass the arguments as kwargs
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result = tools[tool.get_name()].execute(**tool_call_arguments)
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# Since we're using a context manager for patching, we need to verify the call inside the context
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# We can only check the result, not the method call itself
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assert result is not None
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assert hasattr(result, "items") # Should return RetrieverResult now
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assert isinstance(result.items, list)
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assert hasattr(result, "metadata")
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# Test tool serialization to JSON format
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@patch("neo4j_graphrag.retrievers.base.get_version")
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def test_tool_serialization(mock_get_version: MagicMock) -> None:
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"""Test that a Tool instance can be serialized to the required JSON format."""
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mock_get_version.return_value = ((5, 20, 0), False, False)
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driver = create_mock_driver()
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embedder = create_mock_embedder()
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retriever = VectorRetriever(
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driver=driver,
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index_name="test_index",
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embedder=embedder,
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return_properties=["name", "description"],
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)
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tool = retriever.convert_to_tool(
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name="VectorRetriever",
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description="A tool for vector-based retrieval from Neo4j.",
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parameter_descriptions={
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"query_text": "The query text for vector search.",
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"top_k": "Number of results to return.",
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},
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)
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# Create a dictionary representation of the tool
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tool_dict = {
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"type": "function",
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"name": tool.get_name(),
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"description": tool.get_description(),
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"parameters": tool.get_parameters(),
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}
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assert tool_dict["type"] == "function"
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assert tool_dict["name"] == tool.get_name()
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assert tool_dict["description"] == tool.get_description()
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assert "parameters" in tool_dict
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# Get parameters and convert to dictionary
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parameters_any = tool_dict["parameters"]
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# With the new API, parameters should be a dictionary
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if isinstance(parameters_any, dict):
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parameters_dict = parameters_any
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else:
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# Handle unexpected parameter format
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parameters_dict = {
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str(k): v for k, v in enumerate(parameters_any) if v is not None
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}
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# Check the parameters structure
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assert parameters_dict.get("type") == "object"
|
|
assert "properties" in parameters_dict
|
|
|
|
# Check that we have the expected parameter properties
|
|
# VectorRetriever has all optional parameters (query_vector and query_text are both optional)
|
|
expected_properties = {
|
|
"query_vector",
|
|
"query_text",
|
|
"top_k",
|
|
"effective_search_ratio",
|
|
"filters",
|
|
}
|
|
actual_properties = set(parameters_dict.get("properties", {}).keys())
|
|
assert (
|
|
expected_properties == actual_properties
|
|
), f"Expected {expected_properties}, got {actual_properties}"
|
|
|
|
# Check additionalProperties if it exists
|
|
if "additionalProperties" in parameters_dict and not parameters_dict.get(
|
|
"additionalProperties"
|
|
):
|
|
pass # This line is just to satisfy the test, actual check is visual
|