mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-11-04 11:49:23 +00:00
aa6f768efa
* Fix duplicate question in Reader.eval() * Add duplicate question support in document store * Support duplicate questions in retriever eval * Update tutorial * Rename key_tuple * Change error message * Add warning when more than 6 labels * Allow for label grouping options * Add support for aggregating by label meta * Satisfy mypy * Fix duplicate question in Reader.eval() * Add duplicate question support in document store * Support duplicate questions in retriever eval * Update tutorial * Rename key_tuple * Change error message * Add warning when more than 6 labels * Allow for label grouping options * Add support for aggregating by label meta * Satisfy mypy * Make label field flexible, add docstrings * Satisfy mypy * Fix failing tests * Adjust docstring * Fix tutorial Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
634 lines
25 KiB
Python
634 lines
25 KiB
Python
import numpy as np
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import pytest
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from elasticsearch import Elasticsearch
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from conftest import get_document_store
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from haystack import Document, Label
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from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
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from haystack.document_store.faiss import FAISSDocumentStore
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@pytest.mark.elasticsearch
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def test_init_elastic_client():
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# defaults
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_ = ElasticsearchDocumentStore()
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# list of hosts + single port
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_ = ElasticsearchDocumentStore(host=["localhost", "127.0.0.1"], port=9200)
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# list of hosts + list of ports (wrong)
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with pytest.raises(Exception):
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_ = ElasticsearchDocumentStore(host=["localhost", "127.0.0.1"], port=[9200])
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# list of hosts + list
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_ = ElasticsearchDocumentStore(host=["localhost", "127.0.0.1"], port=[9200, 9200])
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# only api_key
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with pytest.raises(Exception):
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_ = ElasticsearchDocumentStore(host=["localhost"], port=[9200], api_key="test")
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# api_key + id
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_ = ElasticsearchDocumentStore(host=["localhost"], port=[9200], api_key="test", api_key_id="test")
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "sql", "milvus"], indirect=True)
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def test_write_with_duplicate_doc_ids(document_store):
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documents = [
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Document(
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text="Doc1",
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id_hash_keys=["key1"]
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),
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Document(
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text="Doc2",
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id_hash_keys=["key1"]
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)
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]
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document_store.write_documents(documents, duplicate_documents="skip")
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with pytest.raises(Exception):
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document_store.write_documents(documents, duplicate_documents="fail")
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@pytest.mark.elasticsearch
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def test_get_all_documents_without_filters(document_store_with_docs):
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documents = document_store_with_docs.get_all_documents()
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assert all(isinstance(d, Document) for d in documents)
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assert len(documents) == 3
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assert {d.meta["name"] for d in documents} == {"filename1", "filename2", "filename3"}
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assert {d.meta["meta_field"] for d in documents} == {"test1", "test2", "test3"}
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@pytest.mark.elasticsearch
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def test_get_all_document_filter_duplicate_text_value(document_store):
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documents = [
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Document(
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text="Doc1",
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meta={"f1": "0"},
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id_hash_keys=["Doc1", "1"]
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),
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Document(
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text="Doc1",
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meta={"f1": "1", "meta_id": "0"},
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id_hash_keys=["Doc1", "2"]
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),
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Document(
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text="Doc2",
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meta={"f3": "0"},
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id_hash_keys=["Doc2", "3"]
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)
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]
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document_store.write_documents(documents)
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documents = document_store.get_all_documents(filters={"f1": ["1"]})
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assert documents[0].text == "Doc1"
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assert len(documents) == 1
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assert {d.meta["meta_id"] for d in documents} == {"0"}
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@pytest.mark.elasticsearch
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def test_get_all_documents_with_correct_filters(document_store_with_docs):
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documents = document_store_with_docs.get_all_documents(filters={"meta_field": ["test2"]})
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assert len(documents) == 1
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assert documents[0].meta["name"] == "filename2"
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documents = document_store_with_docs.get_all_documents(filters={"meta_field": ["test1", "test3"]})
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assert len(documents) == 2
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assert {d.meta["name"] for d in documents} == {"filename1", "filename3"}
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assert {d.meta["meta_field"] for d in documents} == {"test1", "test3"}
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@pytest.mark.parametrize("document_store_with_docs", ["sql"], indirect=True)
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def test_get_all_documents_with_correct_filters_legacy_sqlite(document_store_with_docs):
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document_store_with_docs.use_windowed_query = False
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documents = document_store_with_docs.get_all_documents(filters={"meta_field": ["test2"]})
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assert len(documents) == 1
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assert documents[0].meta["name"] == "filename2"
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documents = document_store_with_docs.get_all_documents(filters={"meta_field": ["test1", "test3"]})
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assert len(documents) == 2
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assert {d.meta["name"] for d in documents} == {"filename1", "filename3"}
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assert {d.meta["meta_field"] for d in documents} == {"test1", "test3"}
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@pytest.mark.elasticsearch
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def test_get_all_documents_with_incorrect_filter_name(document_store_with_docs):
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documents = document_store_with_docs.get_all_documents(filters={"incorrect_meta_field": ["test2"]})
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assert len(documents) == 0
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@pytest.mark.elasticsearch
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def test_get_all_documents_with_incorrect_filter_value(document_store_with_docs):
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documents = document_store_with_docs.get_all_documents(filters={"meta_field": ["incorrect_value"]})
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assert len(documents) == 0
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@pytest.mark.elasticsearch
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def test_get_documents_by_id(document_store_with_docs):
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documents = document_store_with_docs.get_all_documents()
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doc = document_store_with_docs.get_document_by_id(documents[0].id)
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assert doc.id == documents[0].id
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assert doc.text == documents[0].text
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@pytest.mark.elasticsearch
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def test_get_document_count(document_store):
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documents = [
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{"text": "text1", "id": "1", "meta_field_for_count": "a"},
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{"text": "text2", "id": "2", "meta_field_for_count": "b"},
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{"text": "text3", "id": "3", "meta_field_for_count": "b"},
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{"text": "text4", "id": "4", "meta_field_for_count": "b"},
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]
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document_store.write_documents(documents)
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assert document_store.get_document_count() == 4
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assert document_store.get_document_count(filters={"meta_field_for_count": ["a"]}) == 1
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assert document_store.get_document_count(filters={"meta_field_for_count": ["b"]}) == 3
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@pytest.mark.elasticsearch
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def test_get_all_documents_generator(document_store):
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documents = [
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{"text": "text1", "id": "1", "meta_field_for_count": "a"},
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{"text": "text2", "id": "2", "meta_field_for_count": "b"},
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{"text": "text3", "id": "3", "meta_field_for_count": "b"},
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{"text": "text4", "id": "4", "meta_field_for_count": "b"},
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{"text": "text5", "id": "5", "meta_field_for_count": "b"},
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]
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document_store.write_documents(documents)
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assert len(list(document_store.get_all_documents_generator(batch_size=2))) == 5
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("document_store", ["elasticsearch", "sql", "faiss", "milvus"], indirect=True)
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@pytest.mark.parametrize("update_existing_documents", [True, False])
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def test_update_existing_documents(document_store, update_existing_documents):
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original_docs = [
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{"text": "text1_orig", "id": "1", "meta_field_for_count": "a"},
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]
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updated_docs = [
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{"text": "text1_new", "id": "1", "meta_field_for_count": "a"},
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]
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document_store.write_documents(original_docs)
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assert document_store.get_document_count() == 1
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if update_existing_documents:
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document_store.write_documents(updated_docs, duplicate_documents="overwrite")
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else:
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with pytest.raises(Exception):
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document_store.write_documents(updated_docs, duplicate_documents="fail")
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stored_docs = document_store.get_all_documents()
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assert len(stored_docs) == 1
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if update_existing_documents:
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assert stored_docs[0].text == updated_docs[0]["text"]
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else:
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assert stored_docs[0].text == original_docs[0]["text"]
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@pytest.mark.elasticsearch
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def test_write_document_meta(document_store):
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documents = [
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{"text": "dict_without_meta", "id": "1"},
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{"text": "dict_with_meta", "meta_field": "test2", "name": "filename2", "id": "2"},
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Document(text="document_object_without_meta", id="3"),
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Document(text="document_object_with_meta", meta={"meta_field": "test4", "name": "filename3"}, id="4"),
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]
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document_store.write_documents(documents)
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documents_in_store = document_store.get_all_documents()
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assert len(documents_in_store) == 4
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assert not document_store.get_document_by_id("1").meta
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assert document_store.get_document_by_id("2").meta["meta_field"] == "test2"
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assert not document_store.get_document_by_id("3").meta
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assert document_store.get_document_by_id("4").meta["meta_field"] == "test4"
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@pytest.mark.elasticsearch
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def test_write_document_index(document_store):
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documents = [
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{"text": "text1", "id": "1"},
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{"text": "text2", "id": "2"},
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]
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document_store.write_documents([documents[0]], index="haystack_test_1")
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assert len(document_store.get_all_documents(index="haystack_test_1")) == 1
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document_store.write_documents([documents[1]], index="haystack_test_2")
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assert len(document_store.get_all_documents(index="haystack_test_2")) == 1
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assert len(document_store.get_all_documents(index="haystack_test_1")) == 1
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assert len(document_store.get_all_documents()) == 0
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "milvus"], indirect=True)
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def test_document_with_embeddings(document_store):
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documents = [
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{"text": "text1", "id": "1", "embedding": np.random.rand(768).astype(np.float32)},
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{"text": "text2", "id": "2", "embedding": np.random.rand(768).astype(np.float64)},
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{"text": "text3", "id": "3", "embedding": np.random.rand(768).astype(np.float32).tolist()},
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{"text": "text4", "id": "4", "embedding": np.random.rand(768).astype(np.float32)},
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]
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document_store.write_documents(documents, index="haystack_test_1")
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assert len(document_store.get_all_documents(index="haystack_test_1")) == 4
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documents_without_embedding = document_store.get_all_documents(index="haystack_test_1", return_embedding=False)
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assert documents_without_embedding[0].embedding is None
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documents_with_embedding = document_store.get_all_documents(index="haystack_test_1", return_embedding=True)
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assert isinstance(documents_with_embedding[0].embedding, (list, np.ndarray))
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@pytest.mark.parametrize("retriever", ["dpr", "embedding"], indirect=True)
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@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "milvus"], indirect=True)
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def test_update_embeddings(document_store, retriever):
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documents = []
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for i in range(6):
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documents.append({"text": f"text_{i}", "id": str(i), "meta_field": f"value_{i}"})
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documents.append({"text": "text_0", "id": "6", "meta_field": "value_0"})
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document_store.write_documents(documents, index="haystack_test_1")
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document_store.update_embeddings(retriever, index="haystack_test_1", batch_size=3)
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documents = document_store.get_all_documents(index="haystack_test_1", return_embedding=True)
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assert len(documents) == 7
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for doc in documents:
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assert type(doc.embedding) is np.ndarray
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documents = document_store.get_all_documents(
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index="haystack_test_1",
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filters={"meta_field": ["value_0"]},
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return_embedding=True,
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)
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assert len(documents) == 2
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for doc in documents:
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assert doc.meta["meta_field"] == "value_0"
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np.testing.assert_array_almost_equal(documents[0].embedding, documents[1].embedding, decimal=4)
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documents = document_store.get_all_documents(
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index="haystack_test_1",
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filters={"meta_field": ["value_0", "value_5"]},
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return_embedding=True,
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)
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np.testing.assert_raises(
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AssertionError,
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np.testing.assert_array_equal,
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documents[0].embedding,
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documents[1].embedding
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)
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doc = {"text": "text_7", "id": "7", "meta_field": "value_7",
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"embedding": retriever.embed_queries(texts=["a random string"])[0]}
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document_store.write_documents([doc], index="haystack_test_1")
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documents = []
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for i in range(8, 11):
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documents.append({"text": f"text_{i}", "id": str(i), "meta_field": f"value_{i}"})
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document_store.write_documents(documents, index="haystack_test_1")
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doc_before_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
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embedding_before_update = doc_before_update.embedding
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# test updating only documents without embeddings
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document_store.update_embeddings(retriever, index="haystack_test_1", batch_size=3, update_existing_embeddings=False)
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doc_after_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
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embedding_after_update = doc_after_update.embedding
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np.testing.assert_array_equal(embedding_before_update, embedding_after_update)
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# test updating with filters
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if isinstance(document_store, FAISSDocumentStore):
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with pytest.raises(Exception):
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document_store.update_embeddings(
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retriever, index="haystack_test_1", update_existing_embeddings=True, filters={"meta_field": ["value"]}
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)
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else:
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document_store.update_embeddings(
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retriever, index="haystack_test_1", batch_size=3, filters={"meta_field": ["value_0", "value_1"]}
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)
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doc_after_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
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embedding_after_update = doc_after_update.embedding
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np.testing.assert_array_equal(embedding_before_update, embedding_after_update)
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# test update all embeddings
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document_store.update_embeddings(retriever, index="haystack_test_1", batch_size=3, update_existing_embeddings=True)
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assert document_store.get_embedding_count(index="haystack_test_1") == 11
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doc_after_update = document_store.get_all_documents(index="haystack_test_1", filters={"meta_field": ["value_7"]})[0]
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embedding_after_update = doc_after_update.embedding
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np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, embedding_before_update, embedding_after_update)
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# test update embeddings for newly added docs
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documents = []
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for i in range(12, 15):
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documents.append({"text": f"text_{i}", "id": str(i), "meta_field": f"value_{i}"})
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document_store.write_documents(documents, index="haystack_test_1")
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document_store.update_embeddings(retriever, index="haystack_test_1", batch_size=3, update_existing_embeddings=False)
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assert document_store.get_embedding_count(index="haystack_test_1") == 14
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@pytest.mark.elasticsearch
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def test_delete_all_documents(document_store_with_docs):
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assert len(document_store_with_docs.get_all_documents()) == 3
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document_store_with_docs.delete_all_documents()
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documents = document_store_with_docs.get_all_documents()
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assert len(documents) == 0
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@pytest.mark.elasticsearch
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def test_delete_documents(document_store_with_docs):
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assert len(document_store_with_docs.get_all_documents()) == 3
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document_store_with_docs.delete_documents()
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documents = document_store_with_docs.get_all_documents()
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assert len(documents) == 0
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
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def test_delete_documents_with_filters(document_store_with_docs):
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document_store_with_docs.delete_documents(filters={"meta_field": ["test1", "test2"]})
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documents = document_store_with_docs.get_all_documents()
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assert len(documents) == 1
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assert documents[0].meta["meta_field"] == "test3"
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@pytest.mark.elasticsearch
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def test_labels(document_store):
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label = Label(
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question="question",
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answer="answer",
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is_correct_answer=True,
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is_correct_document=True,
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document_id="123",
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offset_start_in_doc=12,
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no_answer=False,
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origin="gold_label",
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)
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document_store.write_labels([label], index="haystack_test_label")
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labels = document_store.get_all_labels(index="haystack_test_label")
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assert len(labels) == 1
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labels = document_store.get_all_labels()
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assert len(labels) == 0
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@pytest.mark.elasticsearch
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def test_multilabel(document_store):
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labels =[
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Label(
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question="question",
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answer="answer1",
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is_correct_answer=True,
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is_correct_document=True,
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document_id="123",
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offset_start_in_doc=12,
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no_answer=False,
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origin="gold_label",
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),
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# different answer in same doc
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Label(
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question="question",
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answer="answer2",
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is_correct_answer=True,
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is_correct_document=True,
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document_id="123",
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offset_start_in_doc=42,
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no_answer=False,
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origin="gold_label",
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),
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# answer in different doc
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Label(
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question="question",
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answer="answer3",
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is_correct_answer=True,
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is_correct_document=True,
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document_id="321",
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offset_start_in_doc=7,
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no_answer=False,
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origin="gold_label",
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),
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# 'no answer', should be excluded from MultiLabel
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Label(
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question="question",
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answer="",
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is_correct_answer=True,
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is_correct_document=True,
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document_id="777",
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offset_start_in_doc=0,
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no_answer=True,
|
|
origin="gold_label",
|
|
),
|
|
# is_correct_answer=False, should be excluded from MultiLabel
|
|
Label(
|
|
question="question",
|
|
answer="answer5",
|
|
is_correct_answer=False,
|
|
is_correct_document=True,
|
|
document_id="123",
|
|
offset_start_in_doc=99,
|
|
no_answer=True,
|
|
origin="gold_label",
|
|
),
|
|
]
|
|
document_store.write_labels(labels, index="haystack_test_multilabel")
|
|
multi_labels_open = document_store.get_all_labels_aggregated(index="haystack_test_multilabel", open_domain=True)
|
|
multi_labels = document_store.get_all_labels_aggregated(index="haystack_test_multilabel", open_domain=False)
|
|
labels = document_store.get_all_labels(index="haystack_test_multilabel")
|
|
|
|
assert len(multi_labels_open) == 1
|
|
assert len(multi_labels) == 3
|
|
assert len(labels) == 5
|
|
|
|
assert len(multi_labels[0].multiple_answers) == 2
|
|
assert len(multi_labels[0].multiple_answers) \
|
|
== len(multi_labels[0].multiple_document_ids) \
|
|
== len(multi_labels[0].multiple_offset_start_in_docs)
|
|
|
|
multi_labels = document_store.get_all_labels_aggregated()
|
|
assert len(multi_labels) == 0
|
|
|
|
# clean up
|
|
document_store.delete_documents(index="haystack_test_multilabel")
|
|
|
|
|
|
@pytest.mark.elasticsearch
|
|
def test_multilabel_no_answer(document_store):
|
|
labels = [
|
|
Label(
|
|
question="question",
|
|
answer="",
|
|
is_correct_answer=True,
|
|
is_correct_document=True,
|
|
document_id="777",
|
|
offset_start_in_doc=0,
|
|
no_answer=True,
|
|
origin="gold_label",
|
|
),
|
|
# no answer in different doc
|
|
Label(
|
|
question="question",
|
|
answer="",
|
|
is_correct_answer=True,
|
|
is_correct_document=True,
|
|
document_id="123",
|
|
offset_start_in_doc=0,
|
|
no_answer=True,
|
|
origin="gold_label",
|
|
),
|
|
# no answer in same doc, should be excluded
|
|
Label(
|
|
question="question",
|
|
answer="",
|
|
is_correct_answer=True,
|
|
is_correct_document=True,
|
|
document_id="777",
|
|
offset_start_in_doc=0,
|
|
no_answer=True,
|
|
origin="gold_label",
|
|
),
|
|
# no answer with is_correct_answer=False, should be excluded
|
|
Label(
|
|
question="question",
|
|
answer="",
|
|
is_correct_answer=False,
|
|
is_correct_document=True,
|
|
document_id="321",
|
|
offset_start_in_doc=0,
|
|
no_answer=True,
|
|
origin="gold_label",
|
|
),
|
|
]
|
|
|
|
document_store.write_labels(labels, index="haystack_test_multilabel_no_answer")
|
|
multi_labels = document_store.get_all_labels_aggregated(index="haystack_test_multilabel_no_answer")
|
|
labels = document_store.get_all_labels(index="haystack_test_multilabel_no_answer")
|
|
|
|
assert len(multi_labels) == 1
|
|
assert len(labels) == 4
|
|
|
|
assert len(multi_labels[0].multiple_document_ids) == 2
|
|
assert len(multi_labels[0].multiple_answers) \
|
|
== len(multi_labels[0].multiple_document_ids) \
|
|
== len(multi_labels[0].multiple_offset_start_in_docs)
|
|
|
|
# clean up
|
|
document_store.delete_documents(index="haystack_test_multilabel_no_answer")
|
|
|
|
|
|
@pytest.mark.elasticsearch
|
|
@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "sql"], indirect=True)
|
|
def test_update_meta(document_store):
|
|
documents = [
|
|
Document(
|
|
text="Doc1",
|
|
meta={"meta_key_1": "1", "meta_key_2": "1"}
|
|
),
|
|
Document(
|
|
text="Doc2",
|
|
meta={"meta_key_1": "2", "meta_key_2": "2"}
|
|
),
|
|
Document(
|
|
text="Doc3",
|
|
meta={"meta_key_1": "3", "meta_key_2": "3"}
|
|
)
|
|
]
|
|
document_store.write_documents(documents)
|
|
document_2 = document_store.get_all_documents(filters={"meta_key_2": ["2"]})[0]
|
|
document_store.update_document_meta(document_2.id, meta={"meta_key_1": "99", "meta_key_2": "2"})
|
|
updated_document = document_store.get_document_by_id(document_2.id)
|
|
assert len(updated_document.meta.keys()) == 2
|
|
assert updated_document.meta["meta_key_1"] == "99"
|
|
assert updated_document.meta["meta_key_2"] == "2"
|
|
|
|
|
|
@pytest.mark.elasticsearch
|
|
@pytest.mark.parametrize("document_store_type", ["elasticsearch", "memory"])
|
|
def test_custom_embedding_field(document_store_type):
|
|
document_store = get_document_store(
|
|
document_store_type=document_store_type, embedding_field="custom_embedding_field"
|
|
)
|
|
doc_to_write = {"text": "test", "custom_embedding_field": np.random.rand(768).astype(np.float32)}
|
|
document_store.write_documents([doc_to_write])
|
|
documents = document_store.get_all_documents(return_embedding=True)
|
|
assert len(documents) == 1
|
|
assert documents[0].text == "test"
|
|
np.testing.assert_array_equal(doc_to_write["custom_embedding_field"], documents[0].embedding)
|
|
|
|
|
|
@pytest.mark.elasticsearch
|
|
@pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
|
|
def test_get_meta_values_by_key(document_store):
|
|
documents = [
|
|
Document(
|
|
text="Doc1",
|
|
meta={"meta_key_1": "1", "meta_key_2": "11"}
|
|
),
|
|
Document(
|
|
text="Doc2",
|
|
meta={"meta_key_1": "2", "meta_key_2": "22"}
|
|
),
|
|
Document(
|
|
text="Doc3",
|
|
meta={"meta_key_1": "3", "meta_key_2": "33"}
|
|
)
|
|
]
|
|
document_store.write_documents(documents)
|
|
|
|
# test without filters or query
|
|
result = document_store.get_metadata_values_by_key(key="meta_key_1")
|
|
for bucket in result:
|
|
assert bucket["value"] in ["1", "2", "3"]
|
|
assert bucket["count"] == 1
|
|
|
|
# test with filters but no query
|
|
result = document_store.get_metadata_values_by_key(key="meta_key_1", filters={"meta_key_2": ["11", "22"]})
|
|
for bucket in result:
|
|
assert bucket["value"] in ["1", "2"]
|
|
assert bucket["count"] == 1
|
|
|
|
# test with filters & query
|
|
result = document_store.get_metadata_values_by_key(key="meta_key_1", query="Doc1")
|
|
for bucket in result:
|
|
assert bucket["value"] in ["1"]
|
|
assert bucket["count"] == 1
|
|
|
|
|
|
@pytest.mark.elasticsearch
|
|
def test_elasticsearch_custom_fields(elasticsearch_fixture):
|
|
client = Elasticsearch()
|
|
client.indices.delete(index='haystack_test_custom', ignore=[404])
|
|
document_store = ElasticsearchDocumentStore(index="haystack_test_custom", text_field="custom_text_field",
|
|
embedding_field="custom_embedding_field")
|
|
|
|
doc_to_write = {"custom_text_field": "test", "custom_embedding_field": np.random.rand(768).astype(np.float32)}
|
|
document_store.write_documents([doc_to_write])
|
|
documents = document_store.get_all_documents(return_embedding=True)
|
|
assert len(documents) == 1
|
|
assert documents[0].text == "test"
|
|
np.testing.assert_array_equal(doc_to_write["custom_embedding_field"], documents[0].embedding)
|
|
|
|
|
|
@pytest.mark.elasticsearch
|
|
def test_get_document_count_only_documents_without_embedding_arg():
|
|
documents = [
|
|
{"text": "text1", "id": "1", "embedding": np.random.rand(768).astype(np.float32), "meta_field_for_count": "a"},
|
|
{"text": "text2", "id": "2", "embedding": np.random.rand(768).astype(np.float64), "meta_field_for_count": "b"},
|
|
{"text": "text3", "id": "3", "embedding": np.random.rand(768).astype(np.float32).tolist()},
|
|
{"text": "text4", "id": "4", "meta_field_for_count": "b"},
|
|
{"text": "text5", "id": "5", "meta_field_for_count": "b"},
|
|
{"text": "text6", "id": "6", "meta_field_for_count": "c"},
|
|
{"text": "text7", "id": "7", "embedding": np.random.rand(768).astype(np.float64), "meta_field_for_count": "c"},
|
|
]
|
|
|
|
_index: str = "haystack_test_count"
|
|
document_store = ElasticsearchDocumentStore(index=_index)
|
|
document_store.delete_documents(index=_index)
|
|
|
|
document_store.write_documents(documents)
|
|
|
|
assert document_store.get_document_count() == 7
|
|
assert document_store.get_document_count(only_documents_without_embedding=True) == 3
|
|
assert document_store.get_document_count(only_documents_without_embedding=True,
|
|
filters={"meta_field_for_count": ["c"]}) == 1
|
|
assert document_store.get_document_count(only_documents_without_embedding=True,
|
|
filters={"meta_field_for_count": ["b"]}) == 2
|