82fd10e0ee
Convert all LanceDB operations from sync calls wrapped in async functions to the native async API (connect_async, AsyncConnection, AsyncTable, AsyncQuery). Database I/O no longer blocks the event loop. - Store and HaikuRAG use async context managers (async with). Store initialization is deferred to __aenter__; direct construction without async with is no longer supported. - Index creation uses config objects (FTS, BTree, IvfPq) instead of string-based index_type parameter. - Upgrade callbacks are async. - HaikuRAG tracks background vacuum tasks and awaits them in __aexit__ and before destructive rebuild operations to avoid races with concurrent table mutations. - temp_db_path fixture uses pytest's tmp_path for reliable async cleanup.
294 lines
11 KiB
Python
294 lines
11 KiB
Python
import pytest
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from datasets import Dataset
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import Config
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from haiku.rag.store.models import SearchResult
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@pytest.mark.vcr()
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async def test_search_qa_corpus(qa_corpus: Dataset, temp_db_path):
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"""Test that documents can be found by searching with their associated questions."""
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async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
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# Load unique documents (limited to 10)
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seen_documents = set()
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documents = []
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for doc_data in qa_corpus:
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if len(seen_documents) >= 10:
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break
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document_text = doc_data["document_extracted"]
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document_id = doc_data.get("document_id", "")
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if document_id in seen_documents:
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continue
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seen_documents.add(document_id)
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# Create the document with chunks and embeddings
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created_document = await client.create_document(content=document_text)
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documents.append((created_document, doc_data))
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# Test with first few unique documents
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for target_document, doc_data in documents:
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question = doc_data["question"]
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# Test vector search (limit=10 to accommodate different embedding models)
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vector_results = await client.chunk_repository.search(
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question, limit=10, search_type="vector"
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)
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target_document_ids = {chunk.document_id for chunk, _ in vector_results}
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assert target_document.id in target_document_ids
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# Test FTS search
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fts_results = await client.chunk_repository.search(
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question, limit=10, search_type="fts"
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)
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target_document_ids = {chunk.document_id for chunk, _ in fts_results}
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assert target_document.id in target_document_ids
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# Test hybrid search
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hybrid_results = await client.chunk_repository.search(
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question, limit=10, search_type="hybrid"
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)
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target_document_ids = {chunk.document_id for chunk, _ in hybrid_results}
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assert target_document.id in target_document_ids
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@pytest.mark.vcr()
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async def test_search_chunk_includes_document_provenance(temp_db_path):
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"""Test that raw chunk search results include document URI, metadata, and ID."""
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async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
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# Create a document with URI and metadata but no title
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created_document = await client.create_document(
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content="This is a test document with some content for searching.",
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uri="https://example.com/test.html",
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metadata={"title": "Test Document", "author": "Test Author"},
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)
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# Search for chunks
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results = await client.chunk_repository.search(
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"test document", limit=1, search_type="hybrid"
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)
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assert len(results) > 0
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chunk, score = results[0]
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# Test that score is valid
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assert isinstance(score, int | float), (
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f"Score should be numeric, got {type(score)}"
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)
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assert score >= 0, f"Score should be non-negative, got {score}"
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# Verify the chunk includes document information
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assert chunk.document_uri == "https://example.com/test.html"
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assert chunk.document_meta == {
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"title": "Test Document",
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"author": "Test Author",
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}
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assert chunk.document_id == created_document.id
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assert chunk.document_title is None
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@pytest.mark.vcr()
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async def test_search_score_types(temp_db_path):
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"""Test that different search types return appropriate score ranges."""
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async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
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# Create multiple documents with different content
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documents_content = [
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"Machine learning algorithms are powerful tools for data analysis and pattern recognition.",
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"Deep learning neural networks can process complex datasets and identify hidden patterns.",
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"Natural language processing enables computers to understand and generate human text.",
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"Computer vision systems can interpret and analyze visual information from images.",
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]
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for content in documents_content:
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await client.create_document(content=content)
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query = "machine learning"
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# Test vector search scores (should be converted from distances)
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vector_results = await client.chunk_repository.search(
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query, limit=3, search_type="vector"
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)
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assert len(vector_results) > 0
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vector_scores = [score for _, score in vector_results]
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# Test FTS search scores (should be native LanceDB FTS scores)
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fts_results = await client.chunk_repository.search(
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query, limit=3, search_type="fts"
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)
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assert len(fts_results) > 0
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fts_scores = [score for _, score in fts_results]
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# Test hybrid search scores (should be native LanceDB relevance scores)
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hybrid_results = await client.chunk_repository.search(
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query, limit=3, search_type="hybrid"
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)
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assert len(hybrid_results) > 0
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hybrid_scores = [score for _, score in hybrid_results]
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# All scores should be numeric and non-negative
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for scores, search_type in [
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(vector_scores, "vector"),
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(fts_scores, "fts"),
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(hybrid_scores, "hybrid"),
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]:
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for score in scores:
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assert isinstance(score, int | float), (
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f"{search_type} score should be numeric"
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)
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assert score >= 0, f"{search_type} score should be non-negative"
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# Vector scores should typically be small (0-1 range due to distance conversion)
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assert all(0 <= score <= 1 for score in vector_scores), (
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"Vector scores should be in 0-1 range"
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)
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# Scores should be sorted in descending order (most relevant first)
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for scores, search_type in [
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(vector_scores, "vector"),
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(fts_scores, "fts"),
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(hybrid_scores, "hybrid"),
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]:
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for i in range(len(scores) - 1):
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assert scores[i] >= scores[i + 1], (
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f"{search_type} results should be sorted by score descending"
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)
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@pytest.mark.vcr()
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async def test_search_returns_search_result(temp_db_path):
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"""Test that client.search() returns SearchResult with provenance info."""
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async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
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await client.create_document(
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content="Machine learning models can classify images with high accuracy.",
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uri="https://example.com/ml.html",
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title="ML Guide",
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)
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results = await client.search("machine learning", limit=3)
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assert len(results) > 0
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result = results[0]
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assert isinstance(result, SearchResult)
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assert result.content
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assert result.score > 0
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assert result.document_uri == "https://example.com/ml.html"
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assert result.document_title == "ML Guide"
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assert result.chunk_id is not None
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assert result.document_id is not None
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# page_numbers and headings come from chunk metadata
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assert isinstance(result.page_numbers, list)
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assert isinstance(result.labels, list)
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assert len(result.labels) > 0
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@pytest.mark.vcr()
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async def test_search_graceful_degradation(temp_db_path):
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"""Test search works when docling data is unavailable."""
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from haiku.rag.store.models import Chunk
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async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
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# Import document with custom chunks (no docling document)
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custom_chunks = [
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Chunk(content="Custom chunk without docling metadata", metadata={}),
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]
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docling_doc = await client.convert("Document with custom chunks")
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await client.import_document(
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docling_document=docling_doc,
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chunks=custom_chunks,
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uri="https://example.com/custom.html",
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)
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results = await client.search("custom chunk", limit=3)
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assert len(results) > 0
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result = results[0]
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assert isinstance(result, SearchResult)
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assert result.content
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# Metadata defaults should still work
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assert result.page_numbers == []
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assert result.labels == []
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@pytest.mark.vcr()
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async def test_search_result_format_includes_metadata(temp_db_path):
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"""Test that formatted search results include document metadata."""
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async with HaikuRAG(temp_db_path, create=True) as client:
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await client.create_document(
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content="Important information about machine learning algorithms.",
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title="ML Guide",
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uri="https://example.com/ml-guide",
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)
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results = await client.search("machine learning", limit=1)
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assert len(results) > 0
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# Format with rank (the way agents use it)
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formatted = results[0].format_for_agent(rank=1, total=1)
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# Should include chunk ID and rank
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assert "[" in formatted and "]" in formatted
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assert "[rank 1 of 1]" in formatted
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# Should include document title in Source
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assert "ML Guide" in formatted
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assert "Source:" in formatted
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# Should include content
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assert "Content:" in formatted
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assert "machine learning" in formatted.lower()
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def test_search_result_primary_label_prioritizes_structural_types():
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"""Test _get_primary_label prioritizes structural labels correctly."""
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# Table should be prioritized
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result = SearchResult(
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content="test",
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score=0.5,
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chunk_id="c1",
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document_id="d1",
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labels=["paragraph", "table", "text"],
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)
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assert result._get_primary_label() == "table"
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# Code should be prioritized over paragraph
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result = SearchResult(
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content="test",
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score=0.5,
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chunk_id="c2",
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document_id="d2",
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labels=["paragraph", "code"],
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)
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assert result._get_primary_label() == "code"
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# list_item should be prioritized
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result = SearchResult(
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content="test",
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score=0.5,
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chunk_id="c3",
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document_id="d3",
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labels=["text", "list_item"],
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)
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assert result._get_primary_label() == "list_item"
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# Returns first label when no priority match
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result = SearchResult(
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content="test",
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score=0.5,
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chunk_id="c4",
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document_id="d4",
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labels=["paragraph", "text"],
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)
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assert result._get_primary_label() == "paragraph"
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# Returns None for empty labels
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result = SearchResult(
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content="test",
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score=0.5,
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chunk_id="c5",
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document_id="d5",
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labels=[],
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)
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assert result._get_primary_label() is None
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