273 lines
8.6 KiB
Python
273 lines
8.6 KiB
Python
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# 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 import mock
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from unittest.mock import MagicMock, Mock, patch
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import neo4j
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import pytest
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from neo4j_graphrag.embeddings import Embedder
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from neo4j_graphrag.experimental.components.data_loader import PdfLoader
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from neo4j_graphrag.experimental.components.types import (
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LexicalGraphConfig,
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)
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from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError
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from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
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from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
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from neo4j_graphrag.llm.base import LLMInterface
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@mock.patch(
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"neo4j_graphrag.experimental.components.kg_writer.get_version",
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return_value=((5, 23, 0), False, False),
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)
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@pytest.mark.asyncio
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async def test_knowledge_graph_builder_from_pdf_deprecated_kwarg(_: Mock) -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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with pytest.warns(DeprecationWarning, match="from_pdf"):
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kg_builder = SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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from_pdf=True,
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)
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file_path = "path/to/test.pdf"
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with patch.object(
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kg_builder.runner.pipeline,
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"run",
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return_value=PipelineResult(run_id="test_run", result=None),
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) as mock_run:
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await kg_builder.run_async(file_path=file_path)
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pipe_inputs = mock_run.call_args[1]["data"]
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assert "file_loader" in pipe_inputs
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@mock.patch(
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"neo4j_graphrag.experimental.components.kg_writer.get_version",
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return_value=((5, 23, 0), False, False),
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)
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@pytest.mark.asyncio
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async def test_knowledge_graph_builder_pdf_loader_deprecated_kwarg(_: Mock) -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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loader = PdfLoader()
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with pytest.warns(DeprecationWarning, match="pdf_loader"):
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kg_builder = SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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pdf_loader=loader,
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)
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file_path = "path/to/test.pdf"
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with patch.object(
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kg_builder.runner.pipeline,
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"run",
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return_value=PipelineResult(run_id="test_run", result=None),
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) as mock_run:
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await kg_builder.run_async(file_path=file_path)
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pipe_inputs = mock_run.call_args[1]["data"]
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assert "file_loader" in pipe_inputs
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@mock.patch(
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"neo4j_graphrag.experimental.components.kg_writer.get_version",
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return_value=((5, 23, 0), False, False),
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)
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@pytest.mark.asyncio
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async def test_knowledge_graph_builder_document_info_with_file(_: Mock) -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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kg_builder = SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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from_file=True,
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)
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file_path = "path/to/test.pdf"
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with patch.object(
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kg_builder.runner.pipeline,
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"run",
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return_value=PipelineResult(run_id="test_run", result=None),
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) as mock_run:
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await kg_builder.run_async(
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file_path=file_path, document_metadata={"source": "google drive"}
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)
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pipe_inputs = mock_run.call_args[1]["data"]
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assert "file_loader" in pipe_inputs
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assert pipe_inputs["file_loader"] == {
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"filepath": file_path,
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"metadata": {"source": "google drive"},
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}
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assert "extractor" not in pipe_inputs
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@mock.patch(
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"neo4j_graphrag.experimental.components.kg_writer.get_version",
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return_value=((5, 23, 0), False, False),
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)
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@pytest.mark.asyncio
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async def test_knowledge_graph_builder_document_info_with_text(_: Mock) -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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kg_builder = SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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from_file=False,
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)
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text_input = "May thy knife chip and shatter."
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with patch.object(
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kg_builder.runner.pipeline,
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"run",
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return_value=PipelineResult(run_id="test_run", result=None),
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) as mock_run:
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await kg_builder.run_async(
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text=text_input,
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file_path="my_document.txt",
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document_metadata={"source": "google drive"},
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)
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pipe_inputs = mock_run.call_args[1]["data"]
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assert "splitter" in pipe_inputs
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assert pipe_inputs["splitter"] == {"text": text_input}
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assert pipe_inputs["extractor"]["document_info"]["path"] == "my_document.txt"
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assert pipe_inputs["extractor"]["document_info"]["metadata"] == {
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"source": "google drive"
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}
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@mock.patch(
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"neo4j_graphrag.experimental.components.kg_writer.get_version",
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return_value=((5, 23, 0), False, False),
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)
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@pytest.mark.asyncio
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async def test_knowledge_graph_builder_with_entities_and_file(_: Mock) -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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entities = ["Document", "Section"]
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relations = ["CONTAINS"]
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potential_schema = [("Document", "CONTAINS", "Section")]
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kg_builder = SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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entities=entities,
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relations=relations,
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potential_schema=potential_schema,
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from_file=True,
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)
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file_path = "path/to/test.pdf"
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with patch.object(
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kg_builder.runner.pipeline,
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"run",
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return_value=PipelineResult(run_id="test_run", result=None),
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) as mock_run:
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await kg_builder.run_async(file_path=file_path)
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pipe_inputs = mock_run.call_args[1]["data"]
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assert pipe_inputs["schema"]["node_types"] == entities
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assert pipe_inputs["schema"]["relationship_types"] == relations
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assert pipe_inputs["schema"]["patterns"] == potential_schema
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def test_simple_kg_pipeline_on_error_invalid_value() -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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with pytest.raises(PipelineDefinitionError):
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SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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on_error="INVALID_VALUE",
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)
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def test_knowledge_graph_builder_pdf_loader_and_file_loader_conflict() -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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with pytest.raises(ValueError, match="pdf_loader"):
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SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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file_loader=PdfLoader(),
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pdf_loader=PdfLoader(),
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)
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@mock.patch(
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"neo4j_graphrag.experimental.components.kg_writer.get_version",
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return_value=((5, 23, 0), False, False),
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)
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@pytest.mark.asyncio
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async def test_knowledge_graph_builder_with_lexical_graph_config(_: Mock) -> None:
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llm = MagicMock(spec=LLMInterface)
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driver = MagicMock(spec=neo4j.Driver)
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embedder = MagicMock(spec=Embedder)
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chunk_node_label = "TestChunk"
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document_nodel_label = "TestDocument"
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lexical_graph_config = LexicalGraphConfig(
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chunk_node_label=chunk_node_label, document_node_label=document_nodel_label
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)
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kg_builder = SimpleKGPipeline(
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llm=llm,
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driver=driver,
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embedder=embedder,
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from_file=False,
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lexical_graph_config=lexical_graph_config,
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)
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text_input = "May thy knife chip and shatter."
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with patch.object(
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kg_builder.runner.pipeline,
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"run",
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return_value=PipelineResult(run_id="test_run", result=None),
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) as mock_run:
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await kg_builder.run_async(text=text_input)
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pipe_inputs = mock_run.call_args[1]["data"]
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assert "extractor" in pipe_inputs
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assert pipe_inputs["extractor"]["lexical_graph_config"] == lexical_graph_config
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assert pipe_inputs["extractor"]["document_info"] is not None
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assert pipe_inputs["extractor"]["document_info"]["path"] == "document.txt"
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