mirror of
https://github.com/deepset-ai/haystack.git
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5e36988b31
* Add support for tables in SQLDocumentStore, FAISSDocumentStore and MilvuDocumentStore * Add support for WeaviateDocumentStore * Make sure that embedded meta fields are strings + add embedding_dim to WeaviateDocStore in test config * Add latest docstring and tutorial changes * Represent tables in WeaviateDocumentStore as nested lists * Fix mypy Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
976 lines
57 KiB
Python
976 lines
57 KiB
Python
import subprocess
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import time
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from subprocess import run
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from sys import platform
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import numpy as np
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import psutil
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import pytest
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import requests
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from elasticsearch import Elasticsearch
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from haystack.nodes.answer_generator.transformers import Seq2SeqGenerator
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from haystack.document_stores.graphdb import GraphDBKnowledgeGraph
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from milvus import Milvus
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import weaviate
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from haystack.document_stores.weaviate import WeaviateDocumentStore
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from haystack.document_stores.milvus import MilvusDocumentStore
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from haystack.nodes.answer_generator.transformers import RAGenerator, RAGeneratorType
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from haystack.modeling.infer import Inferencer, QAInferencer
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from haystack.nodes.ranker import SentenceTransformersRanker
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from haystack.nodes.document_classifier.transformers import TransformersDocumentClassifier
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from haystack.nodes.retriever.sparse import ElasticsearchFilterOnlyRetriever, ElasticsearchRetriever, TfidfRetriever
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from haystack.nodes.retriever.dense import DensePassageRetriever, EmbeddingRetriever, TableTextRetriever
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from haystack.schema import Document
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from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
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from haystack.document_stores.faiss import FAISSDocumentStore
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from haystack.document_stores.memory import InMemoryDocumentStore
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from haystack.document_stores.sql import SQLDocumentStore
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from haystack.nodes.reader.farm import FARMReader
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from haystack.nodes.reader.transformers import TransformersReader
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from haystack.nodes.reader.table import TableReader
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from haystack.nodes.summarizer.transformers import TransformersSummarizer
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from haystack.nodes.translator import TransformersTranslator
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from haystack.nodes.question_generator import QuestionGenerator
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def pytest_addoption(parser):
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parser.addoption("--document_store_type", action="store", default="elasticsearch, faiss, memory, milvus, weaviate")
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def pytest_generate_tests(metafunc):
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# Get selected docstores from CLI arg
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document_store_type = metafunc.config.option.document_store_type
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selected_doc_stores = [item.strip() for item in document_store_type.split(",")]
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# parametrize document_store fixture if it's in the test function argument list
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# but does not have an explicit parametrize annotation e.g
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# @pytest.mark.parametrize("document_store", ["memory"], indirect=False)
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found_mark_parametrize_document_store = False
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for marker in metafunc.definition.iter_markers('parametrize'):
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if 'document_store' in marker.args[0] or 'document_store_with_docs' in marker.args[0] or 'document_store_type' in marker.args[0]:
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found_mark_parametrize_document_store = True
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break
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# for all others that don't have explicit parametrization, we add the ones from the CLI arg
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if 'document_store' in metafunc.fixturenames and not found_mark_parametrize_document_store:
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metafunc.parametrize("document_store", selected_doc_stores, indirect=True)
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def _sql_session_rollback(self, attr):
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"""
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Inject SQLDocumentStore at runtime to do a session rollback each time it is called. This allows to catch
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errors where an intended operation is still in a transaction, but not committed to the database.
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"""
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method = object.__getattribute__(self, attr)
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if callable(method):
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try:
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self.session.rollback()
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except AttributeError:
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pass
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return method
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SQLDocumentStore.__getattribute__ = _sql_session_rollback
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def pytest_collection_modifyitems(config,items):
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for item in items:
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# add pytest markers for tests that are not explicitly marked but include some keywords
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# in the test name (e.g. test_elasticsearch_client would get the "elasticsearch" marker)
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if "generator" in item.nodeid:
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item.add_marker(pytest.mark.generator)
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elif "summarizer" in item.nodeid:
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item.add_marker(pytest.mark.summarizer)
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elif "tika" in item.nodeid:
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item.add_marker(pytest.mark.tika)
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elif "elasticsearch" in item.nodeid:
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item.add_marker(pytest.mark.elasticsearch)
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elif "graphdb" in item.nodeid:
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item.add_marker(pytest.mark.graphdb)
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elif "pipeline" in item.nodeid:
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item.add_marker(pytest.mark.pipeline)
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elif "slow" in item.nodeid:
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item.add_marker(pytest.mark.slow)
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elif "weaviate" in item.nodeid:
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item.add_marker(pytest.mark.weaviate)
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# if the cli argument "--document_store_type" is used, we want to skip all tests that have markers of other docstores
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# Example: pytest -v test_document_store.py --document_store_type="memory" => skip all tests marked with "elasticsearch"
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document_store_types_to_run = config.getoption("--document_store_type")
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keywords = []
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for i in item.keywords:
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if "-" in i:
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keywords.extend(i.split("-"))
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else:
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keywords.append(i)
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for cur_doc_store in ["elasticsearch", "faiss", "sql", "memory", "milvus", "weaviate"]:
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if cur_doc_store in keywords and cur_doc_store not in document_store_types_to_run:
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skip_docstore = pytest.mark.skip(
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reason=f'{cur_doc_store} is disabled. Enable via pytest --document_store_type="{cur_doc_store}"')
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item.add_marker(skip_docstore)
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@pytest.fixture(scope="session")
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def elasticsearch_fixture():
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# test if a ES cluster is already running. If not, download and start an ES instance locally.
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try:
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client = Elasticsearch(hosts=[{"host": "localhost", "port": "9200"}])
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client.info()
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except:
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print("Starting Elasticsearch ...")
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status = subprocess.run(
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['docker rm haystack_test_elastic'],
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shell=True
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)
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status = subprocess.run(
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['docker run -d --name haystack_test_elastic -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2'],
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shell=True
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)
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if status.returncode:
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raise Exception(
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"Failed to launch Elasticsearch. Please check docker container logs.")
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time.sleep(30)
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@pytest.fixture(scope="session")
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def milvus_fixture():
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# test if a Milvus server is already running. If not, start Milvus docker container locally.
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# Make sure you have given > 6GB memory to docker engine
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try:
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milvus_server = Milvus(uri="tcp://localhost:19530", timeout=5, wait_timeout=5)
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milvus_server.server_status(timeout=5)
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except:
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print("Starting Milvus ...")
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status = subprocess.run(['docker run -d --name milvus_cpu_0.10.5 -p 19530:19530 -p 19121:19121 '
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'milvusdb/milvus:0.10.5-cpu-d010621-4eda95'], shell=True)
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time.sleep(40)
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@pytest.fixture(scope="session")
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def weaviate_fixture():
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# test if a Weaviate server is already running. If not, start Weaviate docker container locally.
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# Make sure you have given > 6GB memory to docker engine
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try:
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weaviate_server = weaviate.Client(url='http://localhost:8080', timeout_config=(5, 15))
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weaviate_server.is_ready()
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except:
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print("Starting Weaviate servers ...")
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status = subprocess.run(
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['docker rm haystack_test_weaviate'],
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shell=True
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)
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status = subprocess.run(
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['docker run -d --name haystack_test_weaviate -p 8080:8080 semitechnologies/weaviate:1.7.2'],
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shell=True
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)
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if status.returncode:
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raise Exception(
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"Failed to launch Weaviate. Please check docker container logs.")
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time.sleep(60)
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@pytest.fixture(scope="session")
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def graphdb_fixture():
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# test if a GraphDB instance is already running. If not, download and start a GraphDB instance locally.
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try:
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kg = GraphDBKnowledgeGraph()
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# fail if not running GraphDB
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kg.delete_index()
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except:
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print("Starting GraphDB ...")
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status = subprocess.run(
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['docker rm haystack_test_graphdb'],
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shell=True
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)
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status = subprocess.run(
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['docker run -d -p 7200:7200 --name haystack_test_graphdb docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'],
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shell=True
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)
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if status.returncode:
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raise Exception(
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"Failed to launch GraphDB. Please check docker container logs.")
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time.sleep(30)
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@pytest.fixture(scope="session")
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def tika_fixture():
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try:
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tika_url = "http://localhost:9998/tika"
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ping = requests.get(tika_url)
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if ping.status_code != 200:
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raise Exception(
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"Unable to connect Tika. Please check tika endpoint {0}.".format(tika_url))
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except:
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print("Starting Tika ...")
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status = subprocess.run(
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['docker run -d --name tika -p 9998:9998 apache/tika:1.24.1'],
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shell=True
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)
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if status.returncode:
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raise Exception(
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"Failed to launch Tika. Please check docker container logs.")
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time.sleep(30)
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@pytest.fixture(scope="session")
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def xpdf_fixture():
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verify_installation = run(["pdftotext"], shell=True)
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if verify_installation.returncode == 127:
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if platform.startswith("linux"):
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platform_id = "linux"
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sudo_prefix = "sudo"
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elif platform.startswith("darwin"):
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platform_id = "mac"
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# For Mac, generally sudo need password in interactive console.
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# But most of the cases current user already have permission to copy to /user/local/bin.
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# Hence removing sudo requirement for Mac.
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sudo_prefix = ""
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else:
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raise Exception(
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"""Currently auto installation of pdftotext is not supported on {0} platform """.format(platform)
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)
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commands = """ wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-{0}-4.03.tar.gz &&
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tar -xvf xpdf-tools-{0}-4.03.tar.gz &&
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{1} cp xpdf-tools-{0}-4.03/bin64/pdftotext /usr/local/bin""".format(platform_id, sudo_prefix)
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run([commands], shell=True)
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verify_installation = run(["pdftotext -v"], shell=True)
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if verify_installation.returncode == 127:
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raise Exception(
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"""pdftotext is not installed. It is part of xpdf or poppler-utils software suite.
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You can download for your OS from here: https://www.xpdfreader.com/download.html."""
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)
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@pytest.fixture(scope="module")
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def rag_generator():
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return RAGenerator(
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model_name_or_path="facebook/rag-token-nq",
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generator_type=RAGeneratorType.TOKEN,
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max_length=20
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)
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@pytest.fixture(scope="module")
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def question_generator():
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return QuestionGenerator(model_name_or_path="valhalla/t5-small-e2e-qg")
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@pytest.fixture(scope="module")
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def eli5_generator():
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return Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5", max_length=20)
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@pytest.fixture(scope="module")
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def summarizer():
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return TransformersSummarizer(
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model_name_or_path="google/pegasus-xsum",
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use_gpu=-1
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)
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@pytest.fixture(scope="module")
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def en_to_de_translator():
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return TransformersTranslator(
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model_name_or_path="Helsinki-NLP/opus-mt-en-de",
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)
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@pytest.fixture(scope="module")
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def de_to_en_translator():
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return TransformersTranslator(
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model_name_or_path="Helsinki-NLP/opus-mt-de-en",
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)
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@pytest.fixture(scope="module")
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def test_docs_xs():
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return [
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# current "dict" format for a document
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{"content": "My name is Carla and I live in Berlin", "meta": {"meta_field": "test1", "name": "filename1"}},
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# metafield at the top level for backward compatibility
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{"content": "My name is Paul and I live in New York", "meta_field": "test2", "name": "filename2"},
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# Document object for a doc
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Document(content="My name is Christelle and I live in Paris", meta={"meta_field": "test3", "name": "filename3"})
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]
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@pytest.fixture(scope="module")
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def reader_without_normalized_scores():
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return FARMReader(
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model_name_or_path="distilbert-base-uncased-distilled-squad",
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use_gpu=False,
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top_k_per_sample=5,
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num_processes=0,
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use_confidence_scores=False
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)
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@pytest.fixture(params=["farm", "transformers"], scope="module")
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def reader(request):
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if request.param == "farm":
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return FARMReader(
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model_name_or_path="distilbert-base-uncased-distilled-squad",
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use_gpu=False,
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top_k_per_sample=5,
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num_processes=0
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)
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if request.param == "transformers":
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return TransformersReader(
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model_name_or_path="distilbert-base-uncased-distilled-squad",
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tokenizer="distilbert-base-uncased",
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use_gpu=-1
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)
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@pytest.fixture(scope="module")
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def table_reader():
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return TableReader(model_name_or_path="google/tapas-base-finetuned-wtq")
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@pytest.fixture(scope="module")
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def ranker():
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return SentenceTransformersRanker(
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model_name_or_path="cross-encoder/ms-marco-MiniLM-L-12-v2",
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)
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@pytest.fixture(scope="module")
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def document_classifier():
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return TransformersDocumentClassifier(
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model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion",
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use_gpu=False
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)
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@pytest.fixture(scope="module")
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def zero_shot_document_classifier():
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return TransformersDocumentClassifier(
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model_name_or_path="cross-encoder/nli-distilroberta-base",
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use_gpu=False,
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task="zero-shot-classification",
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labels=["negative", "positive"]
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)
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@pytest.fixture(scope="module")
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def batched_document_classifier():
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return TransformersDocumentClassifier(
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model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion",
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use_gpu=False,
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batch_size=16
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)
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@pytest.fixture(scope="module")
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def indexing_document_classifier():
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return TransformersDocumentClassifier(
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model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion",
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use_gpu=False,
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batch_size=16,
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classification_field="class_field"
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)
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# TODO Fix bug in test_no_answer_output when using
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# @pytest.fixture(params=["farm", "transformers"])
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@pytest.fixture(params=["farm"], scope="module")
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def no_answer_reader(request):
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if request.param == "farm":
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return FARMReader(
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model_name_or_path="deepset/roberta-base-squad2",
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use_gpu=False,
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top_k_per_sample=5,
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no_ans_boost=0,
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return_no_answer=True,
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num_processes=0
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)
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if request.param == "transformers":
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return TransformersReader(
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model_name_or_path="deepset/roberta-base-squad2",
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tokenizer="deepset/roberta-base-squad2",
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use_gpu=-1,
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top_k_per_candidate=5
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)
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@pytest.fixture(scope="module")
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def prediction(reader, test_docs_xs):
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docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
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prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=5)
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return prediction
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@pytest.fixture(scope="module")
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def no_answer_prediction(no_answer_reader, test_docs_xs):
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docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
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prediction = no_answer_reader.predict(query="What is the meaning of life?", documents=docs, top_k=5)
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return prediction
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@pytest.fixture(params=["es_filter_only", "elasticsearch", "dpr", "embedding", "tfidf", "table_text_retriever"])
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def retriever(request, document_store):
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return get_retriever(request.param, document_store)
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|
|
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# @pytest.fixture(params=["es_filter_only", "elasticsearch", "dpr", "embedding", "tfidf"])
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@pytest.fixture(params=["tfidf"])
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def retriever_with_docs(request, document_store_with_docs):
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return get_retriever(request.param, document_store_with_docs)
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|
|
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def get_retriever(retriever_type, document_store):
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if retriever_type == "dpr":
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retriever = DensePassageRetriever(document_store=document_store,
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query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
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passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
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use_gpu=False, embed_title=True)
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elif retriever_type == "tfidf":
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retriever = TfidfRetriever(document_store=document_store)
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retriever.fit()
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elif retriever_type == "embedding":
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retriever = EmbeddingRetriever(
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document_store=document_store,
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embedding_model="deepset/sentence_bert",
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use_gpu=False
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)
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elif retriever_type == "retribert":
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retriever = EmbeddingRetriever(document_store=document_store,
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embedding_model="yjernite/retribert-base-uncased",
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model_format="retribert",
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use_gpu=False)
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elif retriever_type == "elasticsearch":
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retriever = ElasticsearchRetriever(document_store=document_store)
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elif retriever_type == "es_filter_only":
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retriever = ElasticsearchFilterOnlyRetriever(document_store=document_store)
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elif retriever_type == "table_text_retriever":
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retriever = TableTextRetriever(document_store=document_store,
|
|
query_embedding_model="deepset/bert-small-mm_retrieval-question_encoder",
|
|
passage_embedding_model="deepset/bert-small-mm_retrieval-passage_encoder",
|
|
table_embedding_model="deepset/bert-small-mm_retrieval-table_encoder",
|
|
use_gpu=False)
|
|
else:
|
|
raise Exception(f"No retriever fixture for '{retriever_type}'")
|
|
|
|
return retriever
|
|
|
|
|
|
@pytest.fixture(params=["elasticsearch", "faiss", "memory", "milvus", "weaviate"])
|
|
def document_store_with_docs(request, test_docs_xs):
|
|
document_store = get_document_store(request.param)
|
|
document_store.write_documents(test_docs_xs)
|
|
yield document_store
|
|
document_store.delete_documents()
|
|
|
|
|
|
@pytest.fixture
|
|
def document_store(request, test_docs_xs):
|
|
vector_dim = request.node.get_closest_marker("vector_dim", pytest.mark.vector_dim(768))
|
|
document_store = get_document_store(request.param, vector_dim.args[0])
|
|
yield document_store
|
|
document_store.delete_documents()
|
|
|
|
@pytest.fixture(params=["faiss", "milvus", "weaviate"])
|
|
def document_store_cosine(request, test_docs_xs):
|
|
vector_dim = request.node.get_closest_marker("vector_dim", pytest.mark.vector_dim(768))
|
|
document_store = get_document_store(request.param, vector_dim.args[0], similarity="cosine")
|
|
yield document_store
|
|
document_store.delete_documents()
|
|
|
|
@pytest.fixture(params=["elasticsearch", "faiss", "memory", "milvus", "weaviate"])
|
|
def document_store_cosine_small(request, test_docs_xs):
|
|
vector_dim = request.node.get_closest_marker("vector_dim", pytest.mark.vector_dim(3))
|
|
document_store = get_document_store(request.param, vector_dim.args[0], similarity="cosine")
|
|
yield document_store
|
|
document_store.delete_documents()
|
|
|
|
def get_document_store(document_store_type, embedding_dim=768, embedding_field="embedding", index="haystack_test", similarity:str="dot_product"):
|
|
if document_store_type == "sql":
|
|
document_store = SQLDocumentStore(url="sqlite://", index=index)
|
|
elif document_store_type == "memory":
|
|
document_store = InMemoryDocumentStore(
|
|
return_embedding=True, embedding_dim=embedding_dim, embedding_field=embedding_field, index=index, similarity=similarity
|
|
)
|
|
elif document_store_type == "elasticsearch":
|
|
# make sure we start from a fresh index
|
|
client = Elasticsearch()
|
|
client.indices.delete(index=index+'*', ignore=[404])
|
|
document_store = ElasticsearchDocumentStore(
|
|
index=index, return_embedding=True, embedding_dim=embedding_dim, embedding_field=embedding_field, similarity=similarity
|
|
)
|
|
elif document_store_type == "faiss":
|
|
document_store = FAISSDocumentStore(
|
|
vector_dim=embedding_dim,
|
|
sql_url="sqlite://",
|
|
return_embedding=True,
|
|
embedding_field=embedding_field,
|
|
index=index,
|
|
similarity=similarity
|
|
)
|
|
elif document_store_type == "milvus":
|
|
document_store = MilvusDocumentStore(
|
|
vector_dim=embedding_dim,
|
|
sql_url="sqlite://",
|
|
return_embedding=True,
|
|
embedding_field=embedding_field,
|
|
index=index,
|
|
similarity=similarity
|
|
)
|
|
_, collections = document_store.milvus_server.list_collections()
|
|
for collection in collections:
|
|
if collection.startswith(index):
|
|
document_store.milvus_server.drop_collection(collection)
|
|
elif document_store_type == "weaviate":
|
|
document_store = WeaviateDocumentStore(
|
|
weaviate_url="http://localhost:8080",
|
|
index=index,
|
|
similarity=similarity,
|
|
embedding_dim=embedding_dim,
|
|
)
|
|
document_store.weaviate_client.schema.delete_all()
|
|
document_store._create_schema_and_index_if_not_exist()
|
|
else:
|
|
raise Exception(f"No document store fixture for '{document_store_type}'")
|
|
|
|
return document_store
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def adaptive_model_qa(num_processes):
|
|
"""
|
|
PyTest Fixture for a Question Answering Inferencer based on PyTorch.
|
|
"""
|
|
try:
|
|
model = Inferencer.load(
|
|
"deepset/bert-base-cased-squad2",
|
|
task_type="question_answering",
|
|
batch_size=16,
|
|
num_processes=num_processes,
|
|
gpu=False,
|
|
)
|
|
yield model
|
|
finally:
|
|
if num_processes != 0:
|
|
# close the pool
|
|
# we pass join=True to wait for all sub processes to close
|
|
# this is because below we want to test if all sub-processes
|
|
# have exited
|
|
model.close_multiprocessing_pool(join=True)
|
|
|
|
# check if all workers (sub processes) are closed
|
|
current_process = psutil.Process()
|
|
children = current_process.children()
|
|
assert len(children) == 0
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def bert_base_squad2(request):
|
|
model = QAInferencer.load(
|
|
"deepset/minilm-uncased-squad2",
|
|
task_type="question_answering",
|
|
batch_size=4,
|
|
num_processes=0,
|
|
multithreading_rust=False,
|
|
use_fast=True # TODO parametrize this to test slow as well
|
|
)
|
|
return model
|
|
|
|
|
|
|
|
DOCS_WITH_EMBEDDINGS = [
|
|
Document(
|
|
content="""The capital of Germany is the city state of Berlin.""",
|
|
embedding=np.array([2.22920075e-01, 1.07770450e-02, 3.35382462e-01, -7.27265477e-02,
|
|
-1.98119566e-01, -5.64537346e-02, 6.09261453e-01, 2.87229061e-01,
|
|
-7.73971230e-02, -2.23876238e-01, -5.47461927e-01, -1.08676875e+00,
|
|
2.95721531e-01, 7.53905892e-01, -3.36153835e-01, 1.94666490e-01,
|
|
2.92297024e-02, 6.56022906e-01, 2.67616689e-01, -3.81376356e-01,
|
|
-2.98582464e-01, -1.89207539e-01, 6.07246757e-01, 1.67709842e-01,
|
|
2.75577039e-01, -9.33986664e-01, 4.31648612e-01, -1.00929722e-01,
|
|
-4.82133955e-01, 7.30958655e-02, -4.85000134e-01, -1.17192902e-01,
|
|
-2.78178096e-01, 6.61195964e-02, 4.15457308e-01, 3.25128995e-02,
|
|
2.66546309e-01, 1.30013347e-01, 3.52349013e-01, -6.64731681e-01,
|
|
-6.83372736e-01, -3.16153020e-01, 3.67267191e-01, -4.05127078e-01,
|
|
-8.20419341e-02, -1.00207639e+00, -2.10523933e-01, 9.38237131e-01,
|
|
-2.96095699e-01, -1.82708800e-01, -9.05334055e-01, 2.68770158e-01,
|
|
3.29131901e-01, 9.00070250e-01, 4.34159547e-01, -5.65743327e-01,
|
|
-7.94787586e-01, -9.83037204e-02, -1.01550505e-01, 1.17718965e-01,
|
|
2.48768821e-01, 2.64568210e-01, -1.21708572e-01, 3.54779810e-01,
|
|
7.25113750e-01, 4.65293467e-01, -4.09185141e-02, -8.67474079e-03,
|
|
-2.21501254e-02, -6.34054065e-01, -9.91622388e-01, -2.93476105e-01,
|
|
-3.77548009e-01, -3.20685089e-01, 7.97941908e-02, -4.51179177e-01,
|
|
1.61721796e-01, 2.01941788e-01, 2.18551666e-01, 8.89380276e-02,
|
|
9.31400955e-02, -1.68867663e-01, 1.93741471e-01, 9.80174169e-03,
|
|
3.96567971e-01, 3.25188875e-01, -3.59817073e-02, -3.05083066e-01,
|
|
-2.91377038e-01, -2.59871781e-01, -2.33116597e-02, 8.39208305e-01,
|
|
1.92560270e-01, 1.10900663e-01, -6.46018386e-02, -7.44270265e-01,
|
|
1.63968995e-01, 1.13590971e-01, 2.35207364e-01, 1.82242617e-01,
|
|
-1.76687598e-01, 1.26516908e-01, 3.98482740e-01, 2.40804136e-01,
|
|
4.77896258e-02, -6.28400743e-01, -4.66124773e-01, 3.31229940e-02,
|
|
-2.50761136e-02, 4.35739040e-01, -2.45411038e-01, -2.20042571e-01,
|
|
8.92485529e-02, -4.65541370e-02, -1.38036475e-01, 5.56459785e-01,
|
|
5.61165631e-01, -9.59392071e-01, -2.86836233e-02, 2.67911255e-01,
|
|
4.45386730e-02, 8.50977540e-01, -6.11386299e-02, -1.98372751e-01,
|
|
4.68791090e-02, -5.06277978e-01, 1.34303793e-01, 7.62167692e-01,
|
|
-2.64607519e-01, 4.18876261e-02, 2.89180636e-01, 5.62154353e-01,
|
|
2.11251423e-01, 3.10281783e-01, 7.21961856e-02, -5.72963893e-01,
|
|
3.83405089e-01, 1.92931354e-01, -4.10556868e-02, 6.54039383e-02,
|
|
-7.69101679e-01, -3.99726629e-01, 4.27413732e-04, -8.61558840e-02,
|
|
6.74372464e-02, -6.33262277e-01, 1.29509404e-01, -7.98301876e-01,
|
|
-1.86452359e-01, -3.74487117e-02, 6.27469346e-02, 5.53238690e-01,
|
|
-1.18287519e-01, 2.22255215e-01, -3.88892442e-01, -5.07142544e-02,
|
|
1.17656231e+00, 1.06320940e-01, 4.87917721e-01, 1.30945101e-01,
|
|
5.19872069e-01, -5.09424150e-01, 7.24166155e-01, 2.54679173e-02,
|
|
4.71467018e-01, 2.11418241e-01, 7.24739254e-01, 8.81170094e-01,
|
|
5.08289784e-03, -2.56663375e-02, -7.88815022e-01, -3.99944574e-01,
|
|
4.35373336e-01, -1.85048744e-01, -3.40764970e-03, -3.34966034e-02,
|
|
-5.41758597e-01, 1.26321182e-01, 2.60807693e-01, 6.73399121e-03,
|
|
-2.97145188e-01, 8.47041607e-01, -9.33591202e-02, 5.28455973e-01,
|
|
-3.99243206e-01, 1.12693250e-01, -1.43983305e-01, 1.67462572e-01,
|
|
-5.15321195e-02, 4.32413101e-01, 4.54831392e-01, -4.17369545e-01,
|
|
2.24987328e-01, -7.20562488e-02, 1.14134535e-01, 4.92308468e-01,
|
|
-8.98665905e-01, 5.71145713e-01, -4.19293523e-01, 3.95240694e-01,
|
|
1.14924744e-01, -6.05691969e-01, 5.72251439e-01, -2.09341362e-01,
|
|
-2.33997151e-01, 1.25237420e-01, 1.88679054e-01, -1.65349171e-01,
|
|
1.09286271e-01, 3.67127098e-02, -8.38237703e-02, -7.06058443e-01,
|
|
2.02231467e-01, -2.55237550e-01, 1.09513953e-01, -1.31659687e-01,
|
|
-6.15252674e-01, -3.03829938e-01, 1.37894958e-01, -4.24786448e-01,
|
|
4.53196496e-01, -1.98051147e-02, -4.47584987e-01, 2.15226576e-01,
|
|
1.43030539e-01, 1.77718982e-01, -7.88647681e-02, 5.66962123e-01,
|
|
-4.94479597e-01, -2.12739050e-01, -6.91644847e-03, 3.48478556e-02,
|
|
-1.28705889e-01, -3.83449569e-02, 5.88934198e-02, -6.58130586e-01,
|
|
-5.36214471e-01, 2.55122989e-01, 8.75554740e-01, -4.75414962e-01,
|
|
9.52955902e-01, 1.35785684e-01, 1.23120442e-01, 5.05717754e-01,
|
|
1.48803160e-01, 4.04200435e-01, 2.76372820e-01, -2.47299239e-01,
|
|
-8.07143569e-01, 4.83604342e-01, -1.85374618e-01, -1.95024580e-01,
|
|
-1.25921816e-01, 7.70030096e-02, 1.42136604e-01, -3.08087885e-01,
|
|
1.59089297e-01, 3.25954586e-01, 1.59884766e-01, 6.17780030e-01,
|
|
2.01809742e-02, -3.50671440e-01, -6.72550499e-01, 4.13817167e-03,
|
|
-2.71146059e-01, 7.78259337e-02, -2.49091178e-01, -3.05263430e-01,
|
|
2.25365594e-01, 3.72759551e-01, 3.85581732e-01, -1.09396994e-01,
|
|
-3.47314715e-01, -1.12919524e-01, 1.29915655e-01, 8.06079060e-03,
|
|
3.92060950e-02, 1.97335854e-02, -6.53263927e-01, -1.24800075e-02,
|
|
-1.32480651e-01, 7.22689390e-01, -2.42579862e-01, -1.25441504e+00,
|
|
-1.32172257e-02, -1.10918665e+00, 8.78458321e-01, -3.46063733e-01,
|
|
-3.95923197e-01, 7.50630498e-01, -6.05472811e-02, 3.24168801e-03,
|
|
-1.87530309e-01, 1.40227765e-01, -2.82838583e-01, 1.33317798e-01,
|
|
1.89625695e-01, -8.86574462e-02, 2.12340951e-01, -2.99456716e-01,
|
|
-1.22753668e+00, 3.14115554e-01, -6.14668578e-02, 5.75566962e-02,
|
|
4.74531233e-01, -4.22935635e-01, -4.82785627e-02, 1.42783090e-01,
|
|
-5.56570292e+00, -3.73210102e-01, -3.54320705e-01, -4.24514055e-01,
|
|
-2.42527917e-01, 6.06708586e-01, 1.82795480e-01, 4.26353738e-02,
|
|
-3.05563331e-01, -5.30404389e-01, 8.72441947e-01, 1.09079361e-01,
|
|
2.24435870e-02, 6.74964964e-01, 2.53065675e-01, 3.88592303e-01,
|
|
1.19709507e-01, -3.20065737e-01, -3.31855297e-01, -2.44884327e-01,
|
|
-3.52100194e-01, -2.81845808e-01, 8.55575204e-01, 1.62181407e-01,
|
|
7.85247564e-01, -2.96381339e-02, -2.88497955e-01, 6.53568059e-02,
|
|
-7.08795369e-01, -8.04900110e-01, 4.42439765e-02, -4.29058880e-01,
|
|
3.26467693e-01, -1.53244287e-01, 3.99080157e-01, 4.89609912e-02,
|
|
4.12760764e-01, 3.94182920e-01, 1.94052160e-01, -4.93394583e-01,
|
|
-2.29200646e-02, 4.95266408e-01, -6.72604218e-02, 1.91921741e-02,
|
|
5.96645474e-01, -2.27546245e-01, -1.71159700e-01, -5.57102934e-02,
|
|
-8.21766138e-01, 5.46978891e-01, 7.12940097e-02, 2.39723727e-01,
|
|
3.99156392e-01, -4.60546672e-01, -3.68922651e-01, 4.64805663e-01,
|
|
9.51609537e-02, 4.60486412e-01, -4.56739873e-01, 1.10822640e-01,
|
|
-2.06528842e-01, 1.95380542e-02, -1.97392479e-01, -1.14359230e-01,
|
|
-2.01808512e-02, -1.26127511e-01, -7.95332611e-01, 6.66711032e-01,
|
|
2.22024679e-01, 7.37181231e-02, -2.25423813e-01, -8.22625279e-01,
|
|
2.40563720e-01, -6.11137688e-01, -6.11412823e-01, -1.45952356e+00,
|
|
-1.37649477e-04, -5.29529095e-01, 1.47666246e-01, 3.54295582e-01,
|
|
-6.83852911e-01, 1.97373703e-01, -1.56224251e-01, -1.39315836e-02,
|
|
1.52347326e-01, -1.78363323e-01, -2.45118767e-01, 4.62190807e-02,
|
|
6.59810960e-01, -5.49332023e-01, -3.59251350e-01, -8.36586714e-01,
|
|
4.37820464e-01, -7.46994615e-01, -6.13222957e-01, 5.13272882e-01,
|
|
-8.53794441e-02, -5.95119596e-01, 6.63125142e-02, -2.91639060e-01,
|
|
3.56542349e-01, -2.10935414e-01, -6.86178625e-01, -4.68558609e-01,
|
|
2.96867311e-01, 1.22527696e-01, 3.36856037e-01, 6.65121257e-01,
|
|
8.23574543e-01, -4.33361620e-01, -4.60013747e-01, 9.83969793e-02,
|
|
-2.06140336e-02, -9.26007748e-01, 4.21539873e-01, -1.04092360e+00,
|
|
-8.31346154e-01, 7.40615308e-01, 2.03596890e-01, -3.54458541e-01,
|
|
-1.00714087e-01, 6.06378078e-01, -1.25727594e-01, -7.54246935e-02,
|
|
-4.75891769e-01, -8.33747163e-03, -7.37221539e-01, 8.11691880e-02,
|
|
-4.76147123e-02, 1.16448052e-01, 6.71404898e-02, 8.09732974e-02,
|
|
4.06575024e-01, -6.49466589e-02, 6.71445608e-01, 1.64475188e-01,
|
|
2.04286948e-01, -3.85309786e-01, -1.56549439e-01, -7.14797437e-01,
|
|
2.02202156e-01, 5.64372897e-01, 3.26015085e-01, 1.32548913e-01,
|
|
7.73424655e-02, -3.40584129e-01, -1.97809264e-02, 2.23556265e-01,
|
|
-4.01336968e-01, -6.39470071e-02, 8.09292793e-01, 6.28170550e-01,
|
|
-6.66837394e-03, -1.85917675e-01, -8.26690435e-01, 2.90101707e-01,
|
|
3.71746987e-01, 3.63382846e-01, 3.30177546e-01, -1.59507245e-01,
|
|
4.05810446e-01, -6.38260722e-01, -4.45926607e-01, 3.86468828e-01,
|
|
5.87992489e-01, -1.67837348e-02, -8.56421649e-01, -1.46168426e-01,
|
|
1.47883758e-01, -3.02581072e-01, 3.04086179e-01, -3.63289267e-01,
|
|
-1.37931064e-01, 2.16311902e-01, -2.87937909e-01, 2.17867494e-01,
|
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),
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Document(
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content="""Berlin is the capital and largest city of Germany by both area and population.""",
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