mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-08-27 01:46:33 +00:00
8cd73a9d20
* added core install and functionality of pinecone doc store (init, upsert, query, delete) * implemented core functionality of Pinecone doc store * Update Documentation & Code Style * updated filtering to use Haystack filtering and reduced default batch_size * Update Documentation & Code Style * removed debugging code * updated Pinecone filtering to use filter_utils * removed uneeded methods and minor tweaks to current methods * fixed typing issues * Update Documentation & Code Style * Allow filters in al methods except get_embedding_count * Fix skipping document store tests * Update Documentation & Code Style * Fix handling of Milvus1 and Milvus2 in tests * Update Documentation & Code Style * Fix handling of Milvus1 and Milvus2 in tests * Update Documentation & Code Style * Remove SQL from tests requiring embeddings * Update Documentation & Code Style * Fix get_embedding_count of Milvus2 * Make sure to start Milvus2 tests with a new collection * Add pinecone to test suite * Update Documentation & Code Style * Fix typing * Update Documentation & Code Style * Add pinecone to docstores dependendcy * Add PineconeDocStore to API Documentation * Add missing comma * Update Documentation & Code Style * Adapt format of doc strings * Update Documentation & Code Style * Set API key as environment variable * Skip Pinecone tests in forks * Add sleep after deleting index * Add sleep after deleting index * Add sleep after creating index * Add check if index ready * Remove printing of index stats * Create new index for each pinecone test * Use RestAPI instead of Python API for describe_index_stats * Fix accessing describe_index_stats * Remove usages of describe_index_stats * Run pinecone tests separately * Update Documentation & Code Style * Add pdftotext to pinecone tests * Remove sleep from doc store fixture * Add describe_index_stats * Remove unused imports * Use pull_request_target trigger * Revert use pull_request_target trigger * Remove set_config * Add os to conftest * Integrate review comments * Set include_values to False * Remove quotation marks from pinecone.Index type * Update Documentation & Code Style * Update Documentation & Code Style * Fix number of args in error messages Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: bogdankostic <bogdankostic@web.de>
482 lines
20 KiB
Python
482 lines
20 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 pathlib import Path
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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 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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from .conftest import SAMPLES_PATH
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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", "milvus1"),
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("embedding", "elasticsearch"),
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("embedding", "faiss"),
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("embedding", "memory"),
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("embedding", "milvus1"),
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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) == 5
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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(
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"document_store", ["elasticsearch", "faiss", "memory", "milvus1", "milvus", "weaviate", "pinecone"], indirect=True
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)
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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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# always normalize vector as faiss returns normalized vectors and other document stores do not
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doc_1 = document_store.get_document_by_id("1").embedding
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doc_1 /= np.linalg.norm(doc_1)
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assert len(doc_1) == 768
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assert abs(doc_1[0] - (-0.0250)) < 0.001
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doc_2 = document_store.get_document_by_id("2").embedding
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doc_2 /= np.linalg.norm(doc_2)
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assert abs(doc_2[0] - (-0.0314)) < 0.001
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doc_3 = document_store.get_document_by_id("3").embedding
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doc_3 /= np.linalg.norm(doc_3)
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assert abs(doc_3[0] - (-0.0200)) < 0.001
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doc_4 = document_store.get_document_by_id("4").embedding
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doc_4 /= np.linalg.norm(doc_4)
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assert abs(doc_4[0] - (-0.0070)) < 0.001
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doc_5 = document_store.get_document_by_id("5").embedding
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doc_5 /= np.linalg.norm(doc_5)
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assert abs(doc_5[0] - (-0.0049)) < 0.001
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"document_store", ["elasticsearch", "faiss", "memory", "milvus1", "milvus", "weaviate", "pinecone"], indirect=True
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)
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@pytest.mark.parametrize("retriever", ["retribert"], indirect=True)
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@pytest.mark.embedding_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", index="haystack_test", 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.embedding_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(tmp_path, retriever, document_store):
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retriever.save(f"{tmp_path}/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(f"{tmp_path}/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.embedding_dim(512)
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def test_table_text_retriever_saving_and_loading(tmp_path, retriever, document_store):
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retriever.save(f"{tmp_path}/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(f"{tmp_path}/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.embedding_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_PATH / "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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@pytest.mark.elasticsearch
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def test_elasticsearch_highlight():
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client = Elasticsearch()
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client.indices.delete(index="haystack_hl_test", ignore=[404])
|
|
|
|
# Mapping the content and title field as "text" perform search on these both fields.
|
|
document_store = ElasticsearchDocumentStore(
|
|
index="haystack_hl_test",
|
|
content_field="title",
|
|
custom_mapping={"mappings": {"properties": {"content": {"type": "text"}, "title": {"type": "text"}}}},
|
|
)
|
|
documents = [
|
|
{
|
|
"title": "Green tea components",
|
|
"meta": {
|
|
"content": "The green tea plant contains a range of healthy compounds that make it into the final drink"
|
|
},
|
|
"id": "1",
|
|
},
|
|
{
|
|
"title": "Green tea catechin",
|
|
"meta": {"content": "Green tea contains a catechin called epigallocatechin-3-gallate (EGCG)."},
|
|
"id": "2",
|
|
},
|
|
{
|
|
"title": "Minerals in Green tea",
|
|
"meta": {"content": "Green tea also has small amounts of minerals that can benefit your health."},
|
|
"id": "3",
|
|
},
|
|
{
|
|
"title": "Green tea Benefits",
|
|
"meta": {"content": "Green tea does more than just keep you alert, it may also help boost brain function."},
|
|
"id": "4",
|
|
},
|
|
]
|
|
document_store.write_documents(documents)
|
|
|
|
# Enabled highlighting on "title"&"content" field only using custom query
|
|
retriever_1 = ElasticsearchRetriever(
|
|
document_store=document_store,
|
|
custom_query="""{
|
|
"size": 20,
|
|
"query": {
|
|
"bool": {
|
|
"should": [
|
|
{
|
|
"multi_match": {
|
|
"query": ${query},
|
|
"fields": [
|
|
"content^3",
|
|
"title^5"
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
},
|
|
"highlight": {
|
|
"pre_tags": [
|
|
"**"
|
|
],
|
|
"post_tags": [
|
|
"**"
|
|
],
|
|
"number_of_fragments": 3,
|
|
"fragment_size": 5,
|
|
"fields": {
|
|
"content": {},
|
|
"title": {}
|
|
}
|
|
}
|
|
}""",
|
|
)
|
|
results = retriever_1.retrieve(query="is green tea healthy")
|
|
|
|
assert len(results[0].meta["highlighted"]) == 2
|
|
assert results[0].meta["highlighted"]["title"] == ["**Green**", "**tea** components"]
|
|
assert results[0].meta["highlighted"]["content"] == ["The **green**", "**tea** plant", "range of **healthy**"]
|
|
|
|
# Enabled highlighting on "title" field only using custom query
|
|
retriever_2 = ElasticsearchRetriever(
|
|
document_store=document_store,
|
|
custom_query="""{
|
|
"size": 20,
|
|
"query": {
|
|
"bool": {
|
|
"should": [
|
|
{
|
|
"multi_match": {
|
|
"query": ${query},
|
|
"fields": [
|
|
"content^3",
|
|
"title^5"
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
},
|
|
"highlight": {
|
|
"pre_tags": [
|
|
"**"
|
|
],
|
|
"post_tags": [
|
|
"**"
|
|
],
|
|
"number_of_fragments": 3,
|
|
"fragment_size": 5,
|
|
"fields": {
|
|
"title": {}
|
|
}
|
|
}
|
|
}""",
|
|
)
|
|
results = retriever_2.retrieve(query="is green tea healthy")
|
|
|
|
assert len(results[0].meta["highlighted"]) == 1
|
|
assert results[0].meta["highlighted"]["title"] == ["**Green**", "**tea** components"]
|