chore: import upstream snapshot with attribution
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# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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@@ -0,0 +1,205 @@
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# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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"""Tests for dynamic community selection with type handling."""
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from unittest.mock import MagicMock
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from graphrag.data_model.community import Community
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from graphrag.data_model.community_report import CommunityReport
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from graphrag.query.context_builder.dynamic_community_selection import (
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DynamicCommunitySelection,
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)
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def create_mock_tokenizer() -> MagicMock:
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"""Create a mock tokenizer."""
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tokenizer = MagicMock()
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tokenizer.encode.return_value = [1, 2, 3]
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return tokenizer
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def create_mock_model() -> MagicMock:
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"""Create a mock chat model."""
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return MagicMock()
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def test_dynamic_community_selection_handles_int_children():
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"""Test that DynamicCommunitySelection correctly handles children IDs as integers.
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This tests the fix for issue #2004 where children IDs could be integers
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while self.reports keys are strings, causing child communities to be skipped.
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"""
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# Create communities with integer children (simulating the bug scenario)
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# Note: Even though the type annotation says list[str], actual data may have ints
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communities = [
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Community(
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id="comm-0",
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short_id="0",
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title="Root Community",
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level="0",
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parent="",
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children=[1, 2], # type: ignore[list-item] # Integer children - testing bug fix
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),
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Community(
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id="comm-1",
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short_id="1",
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title="Child Community 1",
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level="1",
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parent="0",
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children=[],
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),
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Community(
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id="comm-2",
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short_id="2",
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title="Child Community 2",
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level="1",
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parent="0",
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children=[],
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),
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]
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# Create community reports with string community_id
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reports = [
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CommunityReport(
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id="report-0",
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short_id="0",
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title="Report 0",
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community_id="0",
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summary="Root community summary",
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full_content="Root community full content",
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rank=1.0,
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),
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CommunityReport(
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id="report-1",
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short_id="1",
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title="Report 1",
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community_id="1",
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summary="Child 1 summary",
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full_content="Child 1 full content",
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rank=1.0,
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),
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CommunityReport(
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id="report-2",
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short_id="2",
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title="Report 2",
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community_id="2",
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summary="Child 2 summary",
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full_content="Child 2 full content",
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rank=1.0,
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),
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]
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model = create_mock_model()
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tokenizer = create_mock_tokenizer()
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selector = DynamicCommunitySelection(
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community_reports=reports,
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communities=communities,
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model=model,
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tokenizer=tokenizer,
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threshold=1,
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keep_parent=False,
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max_level=2,
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)
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# Verify that reports are keyed by string
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assert "0" in selector.reports
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assert "1" in selector.reports
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assert "2" in selector.reports
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# Verify that communities are keyed by string short_id
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assert "0" in selector.communities
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assert "1" in selector.communities
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assert "2" in selector.communities
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# Verify that the children are properly accessible
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# Before the fix, int children would fail the `in self.reports` check
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root_community = selector.communities["0"]
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for child in root_community.children:
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child_id = str(child)
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# This should now work with the fix
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assert child_id in selector.reports, (
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f"Child {child} (as '{child_id}') should be found in reports"
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)
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def test_dynamic_community_selection_handles_str_children():
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"""Test that DynamicCommunitySelection works correctly with string children IDs."""
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communities = [
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Community(
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id="comm-0",
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short_id="0",
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title="Root Community",
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level="0",
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parent="",
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children=["1", "2"], # String children - expected type
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),
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Community(
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id="comm-1",
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short_id="1",
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title="Child Community 1",
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level="1",
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parent="0",
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children=[],
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),
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Community(
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id="comm-2",
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short_id="2",
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title="Child Community 2",
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level="1",
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parent="0",
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children=[],
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),
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]
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reports = [
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CommunityReport(
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id="report-0",
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short_id="0",
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title="Report 0",
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community_id="0",
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summary="Root community summary",
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full_content="Root community full content",
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rank=1.0,
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),
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CommunityReport(
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id="report-1",
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short_id="1",
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title="Report 1",
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community_id="1",
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summary="Child 1 summary",
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full_content="Child 1 full content",
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rank=1.0,
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),
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CommunityReport(
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id="report-2",
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short_id="2",
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title="Report 2",
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community_id="2",
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summary="Child 2 summary",
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full_content="Child 2 full content",
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rank=1.0,
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),
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]
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model = create_mock_model()
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tokenizer = create_mock_tokenizer()
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selector = DynamicCommunitySelection(
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community_reports=reports,
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communities=communities,
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model=model,
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tokenizer=tokenizer,
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threshold=1,
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keep_parent=False,
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max_level=2,
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)
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# Verify that children can be found in reports
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root_community = selector.communities["0"]
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for child in root_community.children:
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child_id = str(child)
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assert child_id in selector.reports, (
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f"Child {child} (as '{child_id}') should be found in reports"
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)
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@@ -0,0 +1,166 @@
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# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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from typing import Any
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from graphrag.data_model.entity import Entity
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from graphrag.query.context_builder.entity_extraction import (
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EntityVectorStoreKey,
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map_query_to_entities,
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)
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from graphrag_llm.config import LLMProviderType, ModelConfig
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from graphrag_llm.embedding import create_embedding
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from graphrag_vectors import (
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TextEmbedder,
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VectorStore,
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VectorStoreDocument,
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VectorStoreSearchResult,
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)
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embedding_model = create_embedding(
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ModelConfig(
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type=LLMProviderType.MockLLM,
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model_provider="openai",
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model="text-embedding-3-small",
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mock_responses=[1.0, 1.0, 1.0],
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)
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)
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class MockVectorStore(VectorStore):
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def __init__(self, documents: list[VectorStoreDocument]) -> None:
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super().__init__(index_name="mock")
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self.documents = documents
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def connect(self, **kwargs: Any) -> None:
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raise NotImplementedError
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def create_index(self) -> None:
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raise NotImplementedError
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def load_documents(self, documents: list[VectorStoreDocument]) -> None:
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raise NotImplementedError
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def insert(self, document: VectorStoreDocument) -> None:
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raise NotImplementedError
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def similarity_search_by_vector(
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self,
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query_embedding: list[float],
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k: int = 10,
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select: list[str] | None = None,
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filters: Any = None,
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include_vectors: bool = True,
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) -> list[VectorStoreSearchResult]:
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return [
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VectorStoreSearchResult(document=document, score=1)
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for document in self.documents[:k]
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]
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def similarity_search_by_text(
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self,
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text: str,
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text_embedder: TextEmbedder,
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k: int = 10,
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select: list[str] | None = None,
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filters: Any = None,
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include_vectors: bool = True,
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) -> list[VectorStoreSearchResult]:
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return sorted(
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[
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VectorStoreSearchResult(
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document=document,
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score=abs(len(text) - len(str(document.id) or "")),
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)
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for document in self.documents
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],
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key=lambda x: x.score,
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)[:k]
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def search_by_id(
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self, id: str, select: list[str] | None = None, include_vectors: bool = True
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) -> VectorStoreDocument:
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result = self.documents[0]
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result.id = id
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return result
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def count(self) -> int:
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return len(self.documents)
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def remove(self, ids: list[str]) -> None:
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raise NotImplementedError
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def update(self, document: VectorStoreDocument) -> None:
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raise NotImplementedError
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def test_map_query_to_entities():
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entities = [
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Entity(
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id="2da37c7a-50a8-44d4-aa2c-fd401e19976c",
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short_id="sid1",
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title="t1",
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rank=2,
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),
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Entity(
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id="c4f93564-4507-4ee4-b102-98add401a965",
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short_id="sid2",
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title="t22",
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rank=4,
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),
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Entity(
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id="7c6f2bc9-47c9-4453-93a3-d2e174a02cd9",
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short_id="sid3",
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title="t333",
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rank=1,
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),
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Entity(
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id="8fd6d72a-8e9d-4183-8a97-c38bcc971c83",
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short_id="sid4",
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title="t4444",
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rank=3,
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),
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]
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assert map_query_to_entities(
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query="t22",
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text_embedding_vectorstore=MockVectorStore([
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VectorStoreDocument(id=entity.title, vector=None) for entity in entities
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]),
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text_embedder=embedding_model,
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all_entities_dict={entity.id: entity for entity in entities},
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embedding_vectorstore_key=EntityVectorStoreKey.TITLE,
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k=1,
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oversample_scaler=1,
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) == [
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Entity(
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id="c4f93564-4507-4ee4-b102-98add401a965",
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short_id="sid2",
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title="t22",
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rank=4,
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)
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]
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assert map_query_to_entities(
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query="",
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text_embedding_vectorstore=MockVectorStore([
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VectorStoreDocument(id=entity.id, vector=None) for entity in entities
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]),
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text_embedder=embedding_model,
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all_entities_dict={entity.id: entity for entity in entities},
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embedding_vectorstore_key=EntityVectorStoreKey.TITLE,
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k=2,
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) == [
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Entity(
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id="c4f93564-4507-4ee4-b102-98add401a965",
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short_id="sid2",
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title="t22",
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rank=4,
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),
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Entity(
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id="8fd6d72a-8e9d-4183-8a97-c38bcc971c83",
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short_id="sid4",
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title="t4444",
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rank=3,
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),
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]
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