mirror of
https://github.com/deepset-ai/haystack.git
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2e9f3c1512
* 1. Prevent update_embeddings function in FAISSDocumentStore to set faiss_index as None when document store does not have any docs. 2. cleaning up tests by adding fixture for retriever. * TfidfRetriever need document store with documents during initialization as it call fit() function in constructor so fixing it by checking self.paragraphs of None * Fix naming of retriever's fixture (embedded to embedding and tfid to tfidf)
217 lines
8.9 KiB
Python
217 lines
8.9 KiB
Python
import os
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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 pytest
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import requests
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from elasticsearch import Elasticsearch
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from haystack.retriever.sparse import ElasticsearchFilterOnlyRetriever, ElasticsearchRetriever, TfidfRetriever
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from haystack.retriever.dense import DensePassageRetriever, EmbeddingRetriever
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from haystack import Document
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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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from haystack.document_store.memory import InMemoryDocumentStore
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from haystack.document_store.sql import SQLDocumentStore
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from haystack.reader.farm import FARMReader
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from haystack.reader.transformers import TransformersReader
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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:7.9.1'],
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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 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 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(tika_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}-4.02.tar.gz &&
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tar -xvf xpdf-tools-{0}-4.02.tar.gz &&
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{1} cp xpdf-tools-{0}-4.02/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(params=["elasticsearch", "faiss", "memory", "sql"])
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def document_store(request, test_docs_xs, elasticsearch_fixture):
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return get_document_store(request.param)
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@pytest.fixture()
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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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{"text": "My name is Carla and I live in Berlin", "meta": {"meta_field": "test1", "name": "filename1"}},
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# meta_field at the top level for backward compatibility
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{"text": "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(text="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(params=["farm", "transformers"])
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def reader(request):
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if request.param == "farm":
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return FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad",
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use_gpu=False, top_k_per_sample=5, num_processes=0)
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if request.param == "transformers":
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return TransformersReader(model="distilbert-base-uncased-distilled-squad",
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tokenizer="distilbert-base-uncased",
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use_gpu=-1)
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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"])
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def no_answer_reader(request):
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if request.param == "farm":
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return FARMReader(model_name_or_path="deepset/roberta-base-squad2",
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use_gpu=False, top_k_per_sample=5, no_ans_boost=0, num_processes=0)
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if request.param == "transformers":
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return TransformersReader(model="deepset/roberta-base-squad2",
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tokenizer="deepset/roberta-base-squad2",
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use_gpu=-1, top_k_per_candidate=5)
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@pytest.fixture()
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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(question="Who lives in Berlin?", documents=docs, top_k=5)
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return prediction
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@pytest.fixture()
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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(question="What is the meaning of life?", documents=docs, top_k=5)
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return prediction
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@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql"])
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def document_store_with_docs(request, test_docs_xs, elasticsearch_fixture):
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document_store = get_document_store(request.param)
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document_store.write_documents(test_docs_xs)
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return document_store
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@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql"])
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def document_store(request, test_docs_xs, elasticsearch_fixture):
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return get_document_store(request.param)
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@pytest.fixture(params=["es_filter_only", "elsticsearch", "dpr", "embedding", "tfidf"])
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def retriever(request, document_store):
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return get_retriever(request.param, document_store)
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@pytest.fixture(params=["es_filter_only", "elsticsearch", "dpr", "embedding", "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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def get_document_store(document_store_type):
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if document_store_type == "sql":
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if os.path.exists("haystack_test.db"):
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os.remove("haystack_test.db")
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document_store = SQLDocumentStore(url="sqlite:///haystack_test.db")
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elif document_store_type == "memory":
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document_store = InMemoryDocumentStore()
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elif document_store_type == "elasticsearch":
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# make sure we start from a fresh index
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client = Elasticsearch()
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client.indices.delete(index='haystack_test*', ignore=[404])
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document_store = ElasticsearchDocumentStore(index="haystack_test")
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elif document_store_type == "faiss":
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if os.path.exists("haystack_test_faiss.db"):
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os.remove("haystack_test_faiss.db")
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document_store = FAISSDocumentStore(sql_url="sqlite:///haystack_test_faiss.db")
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else:
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raise Exception(f"No document store fixture for '{document_store_type}'")
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return document_store
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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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remove_sep_tok_from_untitled_passages=True)
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elif retriever_type == "tfidf":
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return TfidfRetriever(document_store=document_store)
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elif retriever_type == "embedding":
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retriever = EmbeddingRetriever(document_store=document_store,
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embedding_model="deepset/sentence_bert",
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use_gpu=False)
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elif retriever_type == "elsticsearch":
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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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else:
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raise Exception(f"No retriever fixture for '{retriever_type}'")
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return retriever
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