50 lines
1.8 KiB
Python
50 lines
1.8 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):
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"""Test that documents can be found by searching with their associated questions."""
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# Create an in-memory store and repositories
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store = Store(":memory:")
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doc_repo = DocumentRepository(store)
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chunk_repo = ChunkRepository(store)
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num_documents = 10
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# Load first 10 documents with embeddings (reduced for faster testing)
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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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created_document = await doc_repo.create(document)
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documents.append((created_document, doc_data))
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for i in range(num_documents): # 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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# Search for chunks using the question
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search_results = await chunk_repo.search_chunks(question, limit=5)
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# Check if target document is in results
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target_document_ids = {chunk.document_id for chunk in search_results}
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assert target_document.id in target_document_ids
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store.close()
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