102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
import pytest
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from datasets import Dataset
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from haiku.rag.store.engine import Store
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from haiku.rag.store.models.document import Document
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from haiku.rag.store.repositories.chunk import ChunkRepository
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from haiku.rag.store.repositories.document import DocumentRepository
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@pytest.mark.asyncio
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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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# Create a store and repositories
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store = Store(temp_db_path)
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doc_repo = DocumentRepository(store)
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chunk_repo = ChunkRepository(store)
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# Load first 20 documents with embeddings (reduced for faster testing)
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num_documents = 20
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documents = []
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for i in range(num_documents):
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doc_data = qa_corpus[i]
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document_text = doc_data["document_extracted"]
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# Create a Document instance
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document = Document(
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content=document_text,
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metadata={
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"source": "qa_corpus",
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"topic": doc_data.get("document_topic", ""),
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"document_id": doc_data.get("document_id", ""),
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"question": doc_data["question"],
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},
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)
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# Create the document with chunks and embeddings
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from haiku.rag.utils import text_to_docling_document
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docling_document = text_to_docling_document(document_text, name="test.md")
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created_document = await doc_repo._create_with_docling(
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document, docling_document
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)
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documents.append((created_document, doc_data))
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for i in range(3): # Test with first few documents
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target_document, doc_data = documents[i]
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question = doc_data["question"]
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# Test vector search
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vector_results = await chunk_repo.search(
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question, limit=5, 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 chunk_repo.search(question, limit=5, search_type="fts")
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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 chunk_repo.search(
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question, limit=5, 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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store.close()
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@pytest.mark.asyncio
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async def test_chunks_include_document_info(temp_db_path):
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"""Test that search results include document URI and metadata."""
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store = Store(temp_db_path)
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doc_repo = DocumentRepository(store)
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chunk_repo = ChunkRepository(store)
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# Create a document with URI and metadata
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document = 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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# Create the document with chunks
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from haiku.rag.utils import text_to_docling_document
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docling_document = text_to_docling_document(document.content, name="test.md")
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created_document = await doc_repo._create_with_docling(document, docling_document)
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# Search for chunks
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results = await chunk_repo.search("test document", limit=1, search_type="hybrid")
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assert len(results) > 0
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chunk, _ = results[0]
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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 == {"title": "Test Document", "author": "Test Author"}
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assert chunk.document_id == created_document.id
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store.close()
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