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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>
348 lines
16 KiB
Python
348 lines
16 KiB
Python
import time
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import numpy as np
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import pandas as pd
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import pytest
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from elasticsearch import Elasticsearch
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from haystack.document_stores import WeaviateDocumentStore
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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.milvus import MilvusDocumentStore
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from haystack.nodes.retriever.dense import DensePassageRetriever, TableTextRetriever
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from haystack.nodes.retriever.sparse import ElasticsearchRetriever, ElasticsearchFilterOnlyRetriever, TfidfRetriever
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from transformers import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast
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@pytest.fixture()
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def docs():
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documents = [
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Document(
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content="""Aaron Aaron ( or ; ""Ahärôn"") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother's spokesman (""prophet"") to the Pharaoh. Part of the Law (Torah) that Moses received from""",
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meta={"name": "0"},
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id="1",
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),
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Document(
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content="""Democratic Republic of the Congo to the south. Angola's capital, Luanda, lies on the Atlantic coast in the northwest of the country. Angola, although located in a tropical zone, has a climate that is not characterized for this region, due to the confluence of three factors: As a result, Angola's climate is characterized by two seasons: rainfall from October to April and drought, known as ""Cacimbo"", from May to August, drier, as the name implies, and with lower temperatures. On the other hand, while the coastline has high rainfall rates, decreasing from North to South and from to , with""",
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id="2",
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),
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Document(
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content="""Schopenhauer, describing him as an ultimately shallow thinker: ""Schopenhauer has quite a crude mind ... where real depth starts, his comes to an end."" His friend Bertrand Russell had a low opinion on the philosopher, and attacked him in his famous ""History of Western Philosophy"" for hypocritically praising asceticism yet not acting upon it. On the opposite isle of Russell on the foundations of mathematics, the Dutch mathematician L. E. J. Brouwer incorporated the ideas of Kant and Schopenhauer in intuitionism, where mathematics is considered a purely mental activity, instead of an analytic activity wherein objective properties of reality are""",
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meta={"name": "1"},
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id="3",
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),
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Document(
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content="""The Dothraki vocabulary was created by David J. Peterson well in advance of the adaptation. HBO hired the Language Creatio""",
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meta={"name": "2"},
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id="4",
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),
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Document(
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content="""The title of the episode refers to the Great Sept of Baelor, the main religious building in King's Landing, where the episode's pivotal scene takes place. In the world created by George R. R. Martin""",
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meta={},
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id="5",
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),
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]
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return documents
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#TODO check if we this works with only "memory" arg
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@pytest.mark.parametrize(
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"retriever_with_docs,document_store_with_docs",
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[
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("dpr", "elasticsearch"),
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("dpr", "faiss"),
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("dpr", "memory"),
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("dpr", "milvus"),
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("embedding", "elasticsearch"),
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("embedding", "faiss"),
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("embedding", "memory"),
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("embedding", "milvus"),
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("elasticsearch", "elasticsearch"),
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("es_filter_only", "elasticsearch"),
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("tfidf", "memory"),
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],
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indirect=True,
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)
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def test_retrieval(retriever_with_docs, document_store_with_docs):
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if not isinstance(retriever_with_docs, (ElasticsearchRetriever, ElasticsearchFilterOnlyRetriever, TfidfRetriever)):
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document_store_with_docs.update_embeddings(retriever_with_docs)
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# test without filters
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res = retriever_with_docs.retrieve(query="Who lives in Berlin?")
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assert res[0].content == "My name is Carla and I live in Berlin"
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assert len(res) == 3
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assert res[0].meta["name"] == "filename1"
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# test with filters
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if not isinstance(document_store_with_docs, (FAISSDocumentStore, MilvusDocumentStore)) and not isinstance(
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retriever_with_docs, TfidfRetriever
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):
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# single filter
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result = retriever_with_docs.retrieve(query="godzilla", filters={"name": ["filename3"]}, top_k=5)
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assert len(result) == 1
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assert type(result[0]) == Document
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assert result[0].content == "My name is Christelle and I live in Paris"
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assert result[0].meta["name"] == "filename3"
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# multiple filters
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result = retriever_with_docs.retrieve(
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query="godzilla", filters={"name": ["filename2"], "meta_field": ["test2", "test3"]}, top_k=5
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)
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assert len(result) == 1
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assert type(result[0]) == Document
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assert result[0].meta["name"] == "filename2"
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result = retriever_with_docs.retrieve(
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query="godzilla", filters={"name": ["filename1"], "meta_field": ["test2", "test3"]}, top_k=5
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)
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assert len(result) == 0
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@pytest.mark.elasticsearch
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def test_elasticsearch_custom_query():
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client = Elasticsearch()
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client.indices.delete(index="haystack_test_custom", ignore=[404])
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document_store = ElasticsearchDocumentStore(
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index="haystack_test_custom", content_field="custom_text_field", embedding_field="custom_embedding_field"
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)
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documents = [
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{"content": "test_1", "meta": {"year": "2019"}},
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{"content": "test_2", "meta": {"year": "2020"}},
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{"content": "test_3", "meta": {"year": "2021"}},
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{"content": "test_4", "meta": {"year": "2021"}},
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{"content": "test_5", "meta": {"year": "2021"}},
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]
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document_store.write_documents(documents)
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# test custom "terms" query
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retriever = ElasticsearchRetriever(
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document_store=document_store,
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custom_query="""
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{
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"size": 10,
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"query": {
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"bool": {
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"should": [{
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"multi_match": {"query": ${query}, "type": "most_fields", "fields": ["content"]}}],
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"filter": [{"terms": {"year": ${years}}}]}}}""",
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)
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results = retriever.retrieve(query="test", filters={"years": ["2020", "2021"]})
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assert len(results) == 4
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# test custom "term" query
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retriever = ElasticsearchRetriever(
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document_store=document_store,
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custom_query="""
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{
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"size": 10,
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"query": {
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"bool": {
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"should": [{
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"multi_match": {"query": ${query}, "type": "most_fields", "fields": ["content"]}}],
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"filter": [{"term": {"year": ${years}}}]}}}""",
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)
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results = retriever.retrieve(query="test", filters={"years": "2021"})
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assert len(results) == 3
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
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def test_dpr_embedding(document_store, retriever, docs):
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document_store.return_embedding = True
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document_store.write_documents(docs)
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document_store.update_embeddings(retriever=retriever)
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time.sleep(1)
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doc_1 = document_store.get_document_by_id("1")
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assert len(doc_1.embedding) == 768
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assert abs(doc_1.embedding[0] - (-0.3063)) < 0.001
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doc_2 = document_store.get_document_by_id("2")
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assert abs(doc_2.embedding[0] - (-0.3914)) < 0.001
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doc_3 = document_store.get_document_by_id("3")
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assert abs(doc_3.embedding[0] - (-0.2470)) < 0.001
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doc_4 = document_store.get_document_by_id("4")
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assert abs(doc_4.embedding[0] - (-0.0802)) < 0.001
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doc_5 = document_store.get_document_by_id("5")
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assert abs(doc_5.embedding[0] - (-0.0551)) < 0.001
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever", ["retribert"], indirect=True)
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@pytest.mark.vector_dim(128)
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def test_retribert_embedding(document_store, retriever, docs):
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if isinstance(document_store, WeaviateDocumentStore):
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# Weaviate sets the embedding dimension to 768 as soon as it is initialized.
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# We need 128 here and therefore initialize a new WeaviateDocumentStore.
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document_store = WeaviateDocumentStore(
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weaviate_url="http://localhost:8080",
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index="haystack_test",
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embedding_dim=128
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)
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document_store.weaviate_client.schema.delete_all()
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document_store._create_schema_and_index_if_not_exist()
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document_store.return_embedding = True
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document_store.write_documents(docs)
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document_store.update_embeddings(retriever=retriever)
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time.sleep(1)
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assert len(document_store.get_document_by_id("1").embedding) == 128
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assert abs(document_store.get_document_by_id("1").embedding[0]) < 0.6
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assert abs(document_store.get_document_by_id("2").embedding[0]) < 0.03
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assert abs(document_store.get_document_by_id("3").embedding[0]) < 0.095
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assert abs(document_store.get_document_by_id("4").embedding[0]) < 0.3
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assert abs(document_store.get_document_by_id("5").embedding[0]) < 0.32
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever", ["table_text_retriever"], indirect=True)
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@pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
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@pytest.mark.vector_dim(512)
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def test_table_text_retriever_embedding(document_store, retriever, docs):
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document_store.return_embedding = True
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document_store.write_documents(docs)
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table_data = {
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"Mountain": ["Mount Everest", "K2", "Kangchenjunga", "Lhotse", "Makalu"],
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"Height": ["8848m", "8,611 m", "8 586m", "8 516 m", "8,485m"]
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}
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table = pd.DataFrame(table_data)
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table_doc = Document(content=table, content_type="table", id="6")
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document_store.write_documents([table_doc])
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document_store.update_embeddings(retriever=retriever)
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time.sleep(1)
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doc_1 = document_store.get_document_by_id("1")
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assert len(doc_1.embedding) == 512
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assert abs(doc_1.embedding[0] - (0.0593)) < 0.001
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doc_2 = document_store.get_document_by_id("2")
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assert abs(doc_2.embedding[0] - (0.9031)) < 0.001
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doc_3 = document_store.get_document_by_id("3")
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assert abs(doc_3.embedding[0] - (0.1366)) < 0.001
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doc_4 = document_store.get_document_by_id("4")
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assert abs(doc_4.embedding[0] - (0.0575)) < 0.001
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doc_5 = document_store.get_document_by_id("5")
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assert abs(doc_5.embedding[0] - (0.1486)) < 0.001
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doc_6 = document_store.get_document_by_id("6")
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assert len(doc_6.embedding) == 512
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assert abs(doc_6.embedding[0] - (0.2745)) < 0.001
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@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
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@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
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def test_dpr_saving_and_loading(retriever, document_store):
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retriever.save("test_dpr_save")
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def sum_params(model):
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s = []
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for p in model.parameters():
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n = p.cpu().data.numpy()
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s.append(np.sum(n))
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return sum(s)
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original_sum_query = sum_params(retriever.query_encoder)
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original_sum_passage = sum_params(retriever.passage_encoder)
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del retriever
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loaded_retriever = DensePassageRetriever.load("test_dpr_save", document_store)
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loaded_sum_query = sum_params(loaded_retriever.query_encoder)
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loaded_sum_passage = sum_params(loaded_retriever.passage_encoder)
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assert abs(original_sum_query - loaded_sum_query) < 0.1
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assert abs(original_sum_passage - loaded_sum_passage) < 0.1
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# comparison of weights (RAM intense!)
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# for p1, p2 in zip(retriever.query_encoder.parameters(), loaded_retriever.query_encoder.parameters()):
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# assert (p1.data.ne(p2.data).sum() == 0)
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#
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# for p1, p2 in zip(retriever.passage_encoder.parameters(), loaded_retriever.passage_encoder.parameters()):
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# assert (p1.data.ne(p2.data).sum() == 0)
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# attributes
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assert loaded_retriever.processor.embed_title == True
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assert loaded_retriever.batch_size == 16
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assert loaded_retriever.processor.max_seq_len_passage == 256
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assert loaded_retriever.processor.max_seq_len_query == 64
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# Tokenizer
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assert isinstance(loaded_retriever.passage_tokenizer, DPRContextEncoderTokenizerFast)
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assert isinstance(loaded_retriever.query_tokenizer, DPRQuestionEncoderTokenizerFast)
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assert loaded_retriever.passage_tokenizer.do_lower_case == True
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assert loaded_retriever.query_tokenizer.do_lower_case == True
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assert loaded_retriever.passage_tokenizer.vocab_size == 30522
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assert loaded_retriever.query_tokenizer.vocab_size == 30522
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assert loaded_retriever.passage_tokenizer.model_max_length == 512
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assert loaded_retriever.query_tokenizer.model_max_length == 512
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@pytest.mark.parametrize("retriever", ["table_text_retriever"], indirect=True)
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@pytest.mark.vector_dim(512)
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def test_table_text_retriever_saving_and_loading(retriever, document_store):
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retriever.save("test_table_text_retriever_save")
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def sum_params(model):
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s = []
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for p in model.parameters():
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n = p.cpu().data.numpy()
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s.append(np.sum(n))
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return sum(s)
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original_sum_query = sum_params(retriever.query_encoder)
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original_sum_passage = sum_params(retriever.passage_encoder)
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original_sum_table = sum_params(retriever.table_encoder)
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del retriever
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loaded_retriever = TableTextRetriever.load("test_table_text_retriever_save", document_store)
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loaded_sum_query = sum_params(loaded_retriever.query_encoder)
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loaded_sum_passage = sum_params(loaded_retriever.passage_encoder)
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loaded_sum_table = sum_params(loaded_retriever.table_encoder)
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assert abs(original_sum_query - loaded_sum_query) < 0.1
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assert abs(original_sum_passage - loaded_sum_passage) < 0.1
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assert abs(original_sum_table - loaded_sum_table) < 0.01
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# attributes
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assert loaded_retriever.processor.embed_meta_fields == ["name", "section_title", "caption"]
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assert loaded_retriever.batch_size == 16
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assert loaded_retriever.processor.max_seq_len_passage == 256
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assert loaded_retriever.processor.max_seq_len_table == 256
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assert loaded_retriever.processor.max_seq_len_query == 64
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# Tokenizer
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assert isinstance(loaded_retriever.passage_tokenizer, DPRContextEncoderTokenizerFast)
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assert isinstance(loaded_retriever.table_tokenizer, DPRContextEncoderTokenizerFast)
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assert isinstance(loaded_retriever.query_tokenizer, DPRQuestionEncoderTokenizerFast)
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assert loaded_retriever.passage_tokenizer.do_lower_case == True
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assert loaded_retriever.table_tokenizer.do_lower_case == True
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assert loaded_retriever.query_tokenizer.do_lower_case == True
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assert loaded_retriever.passage_tokenizer.vocab_size == 30522
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assert loaded_retriever.table_tokenizer.vocab_size == 30522
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assert loaded_retriever.query_tokenizer.vocab_size == 30522
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assert loaded_retriever.passage_tokenizer.model_max_length == 512
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assert loaded_retriever.table_tokenizer.model_max_length == 512
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assert loaded_retriever.query_tokenizer.model_max_length == 512
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@pytest.mark.vector_dim(128)
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def test_table_text_retriever_training(document_store):
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retriever = TableTextRetriever(
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document_store=document_store,
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query_embedding_model="prajjwal1/bert-tiny",
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passage_embedding_model="prajjwal1/bert-tiny",
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table_embedding_model="prajjwal1/bert-tiny",
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use_gpu=False
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)
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retriever.train(
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data_dir="samples/mmr",
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train_filename="sample.json",
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n_epochs=1,
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n_gpu=0,
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save_dir="test_table_text_retriever_train"
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)
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# Load trained model
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retriever = TableTextRetriever.load(load_dir="test_table_text_retriever_train", document_store=document_store)
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