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ddec239fef
* nit * k * add steps * main util functions * functioning fully * quick nit * k * typing fix * k * address comments
92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
from collections.abc import Generator
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from unittest.mock import Mock
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from unittest.mock import patch
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import pytest
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from onyx.configs.constants import DocumentSource
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from onyx.connectors.models import Document
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from onyx.connectors.models import Section
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from onyx.indexing.embedder import DefaultIndexingEmbedder
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from onyx.indexing.models import ChunkEmbedding
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from onyx.indexing.models import DocAwareChunk
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from onyx.indexing.models import IndexChunk
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from shared_configs.enums import EmbeddingProvider
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from shared_configs.enums import EmbedTextType
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@pytest.fixture
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def mock_embedding_model() -> Generator[Mock, None, None]:
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with patch("onyx.indexing.embedder.EmbeddingModel") as mock:
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yield mock
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def test_default_indexing_embedder_embed_chunks(mock_embedding_model: Mock) -> None:
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# Setup
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embedder = DefaultIndexingEmbedder(
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model_name="test-model",
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normalize=True,
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query_prefix=None,
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passage_prefix=None,
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provider_type=EmbeddingProvider.OPENAI,
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)
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# Mock the encode method of the embedding model
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mock_embedding_model.return_value.encode.side_effect = [
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], # Main chunk embeddings
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[[7.0, 8.0, 9.0]], # Title embedding
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]
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# Create test input
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source_doc = Document(
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id="test_doc",
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source=DocumentSource.WEB,
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semantic_identifier="Test Document",
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metadata={"tags": ["tag1", "tag2"]},
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doc_updated_at=None,
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sections=[
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Section(text="This is a short section.", link="link1"),
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],
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)
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chunks: list[DocAwareChunk] = [
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DocAwareChunk(
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chunk_id=1,
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blurb="This is a short section.",
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content="Test chunk",
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source_links={0: "link1"},
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section_continuation=False,
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source_document=source_doc,
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title_prefix="Title: ",
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metadata_suffix_semantic="",
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metadata_suffix_keyword="",
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mini_chunk_texts=None,
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large_chunk_reference_ids=[],
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large_chunk_id=None,
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)
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]
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# Execute
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result: list[IndexChunk] = embedder.embed_chunks(chunks)
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# Assert
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assert len(result) == 1
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assert isinstance(result[0], IndexChunk)
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assert result[0].content == "Test chunk"
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assert result[0].embeddings == ChunkEmbedding(
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full_embedding=[1.0, 2.0, 3.0],
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mini_chunk_embeddings=[],
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)
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assert result[0].title_embedding == [7.0, 8.0, 9.0]
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# Verify the embedding model was called correctly
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mock_embedding_model.return_value.encode.assert_any_call(
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texts=["Title: Test chunk"],
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text_type=EmbedTextType.PASSAGE,
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large_chunks_present=False,
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)
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# title only embedding call
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mock_embedding_model.return_value.encode.assert_any_call(
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["Test Document"],
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text_type=EmbedTextType.PASSAGE,
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)
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