haystack/test/document_stores/test_weaviate.py
Sara Zan ff4303c51b
[CI refactoring] Categorize tests into folders (#2554)
* Categorize tests into folders

* Fix linux_ci.yml and an import

* Wrong path
2022-05-17 09:55:53 +01:00

108 lines
3.8 KiB
Python

import numpy as np
import pytest
from haystack.schema import Document
from ..conftest import get_document_store
import uuid
embedding_dim = 768
def get_uuid():
return str(uuid.uuid4())
DOCUMENTS = [
{"content": "text1", "id": "not a correct uuid", "key": "a"},
{"content": "text2", "id": get_uuid(), "key": "b", "embedding": np.random.rand(embedding_dim).astype(np.float32)},
{"content": "text3", "id": get_uuid(), "key": "b", "embedding": np.random.rand(embedding_dim).astype(np.float32)},
{"content": "text4", "id": get_uuid(), "key": "b", "embedding": np.random.rand(embedding_dim).astype(np.float32)},
{"content": "text5", "id": get_uuid(), "key": "b", "embedding": np.random.rand(embedding_dim).astype(np.float32)},
]
DOCUMENTS_XS = [
# current "dict" format for a document
{
"content": "My name is Carla and I live in Berlin",
"id": get_uuid(),
"meta": {"metafield": "test1", "name": "filename1"},
"embedding": np.random.rand(embedding_dim).astype(np.float32),
},
# meta_field at the top level for backward compatibility
{
"content": "My name is Paul and I live in New York",
"id": get_uuid(),
"metafield": "test2",
"name": "filename2",
"embedding": np.random.rand(embedding_dim).astype(np.float32),
},
# Document object for a doc
Document(
content="My name is Christelle and I live in Paris",
id=get_uuid(),
meta={"metafield": "test3", "name": "filename3"},
embedding=np.random.rand(embedding_dim).astype(np.float32),
),
]
@pytest.fixture(params=["weaviate"])
def document_store_with_docs(request, tmp_path):
document_store = get_document_store(request.param, tmp_path=tmp_path)
document_store.write_documents(DOCUMENTS_XS)
yield document_store
document_store.delete_index(document_store.index)
@pytest.fixture(params=["weaviate"])
def document_store(request, tmp_path):
document_store = get_document_store(request.param, tmp_path=tmp_path)
yield document_store
document_store.delete_index(document_store.index)
@pytest.mark.weaviate
@pytest.mark.parametrize("document_store", ["weaviate"], indirect=True)
@pytest.mark.parametrize("batch_size", [2])
def test_weaviate_write_docs(document_store, batch_size):
# Write in small batches
for i in range(0, len(DOCUMENTS), batch_size):
document_store.write_documents(DOCUMENTS[i : i + batch_size])
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
documents_indexed = document_store.get_all_documents(batch_size=batch_size)
assert len(documents_indexed) == len(DOCUMENTS)
@pytest.mark.weaviate
@pytest.mark.parametrize("document_store_with_docs", ["weaviate"], indirect=True)
def test_query_by_embedding(document_store_with_docs):
docs = document_store_with_docs.query_by_embedding(np.random.rand(embedding_dim).astype(np.float32))
assert len(docs) == 3
docs = document_store_with_docs.query_by_embedding(np.random.rand(embedding_dim).astype(np.float32), top_k=1)
assert len(docs) == 1
docs = document_store_with_docs.query_by_embedding(
np.random.rand(embedding_dim).astype(np.float32), filters={"name": ["filename2"]}
)
assert len(docs) == 1
@pytest.mark.weaviate
@pytest.mark.parametrize("document_store_with_docs", ["weaviate"], indirect=True)
def test_query(document_store_with_docs):
query_text = "My name is Carla and I live in Berlin"
with pytest.raises(Exception):
docs = document_store_with_docs.query(query_text)
docs = document_store_with_docs.query(filters={"name": ["filename2"]})
assert len(docs) == 1
docs = document_store_with_docs.query(filters={"content": [query_text.lower()]})
assert len(docs) == 1
docs = document_store_with_docs.query(filters={"content": ["live"]})
assert len(docs) == 3