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feat: FAISS in OpenSearch: Support HNSW for cosine (#3217)

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* support cosine similiarity with faiss

* update docs

* update api docs

* fix tests

* Revert "update api docs"

This reverts commit 6138fdfefb3beaee2d55c5729cd4a2745ea6b143.

* fix api docs

* collapse test

* rename similairity to space_type mappings

* only normalize for faiss

* fix merge

* fix docs normalization

* get rid of List[np.array]

* update docs

* fix tests and tutorials

* fix mypy

* fix mypy

* fix mypy again

* again mypy

* blacken

* update tutorial  4 docs

* fix embeddingretriever

* fix faiss

* move dense specific logic to DenseRetriever

* fix mypy

* cosine tests for all documents stores

* fix pinecone

* add docstring

* docstring corrections

* update docs

* add integration test marker

* docstrings update

* update docs

* fix typo

* update docs

* fix MockDenseRetriever

* run integration tests for all documentstores

* fix test_update_embeddings_cosine_similarity

* fix faiss tests not running

* blacken

* make test_cosine_sanity_check integration test

* split PR

* update docs

* manually revert tutorial doc change

* Fix embedding type

* set integration marker correctly

* make BaseDocumentStore.normalize_embedding static

* format

* fix handling of opensearch_faiss param

* fix merge

* add DenseRetriever typing

* organize imports in conftest.py

* organize imports in conftest.py (2)

* fix DenseRetriever import

* add opensearch-tests-linux
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40
.github/workflows/tests.yml vendored
View File

@ -289,6 +289,44 @@ jobs:
channel: '#haystack'
if: failure() && github.repository_owner == 'deepset-ai' && github.ref == 'refs/heads/main'
opensearch-tests-linux:
needs:
- mypy
- pylint
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Setup Opensearch
run: |
docker run -d -p 9201:9200 -p 9600:9600 -e "discovery.type=single-node" opensearchproject/opensearch:1.3.5
# TODO Let's try to remove this one from the unit tests
- name: Install pdftotext
run: wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz && tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
- name: Setup Python
uses: ./.github/actions/python_cache/
- name: Install Haystack
run: pip install .
- name: Run tests
env:
TOKENIZERS_PARALLELISM: 'false'
run: |
pytest ${{ env.PYTEST_PARAMS }} -m "opensearch and not integration" test/document_stores/ --document_store_type=opensearch
- name: Dump docker logs on failure
if: failure()
uses: jwalton/gh-docker-logs@v1
- uses: act10ns/slack@v1
with:
status: ${{ job.status }}
channel: '#haystack'
if: failure() && github.repository_owner == 'deepset-ai' && github.ref == 'refs/heads/main'
faiss-tests-linux:
needs:
- mypy
@ -627,7 +665,7 @@ jobs:
- "pipelines"
- "modeling"
- "others"
- "document_stores/test_opensearch.py"
- "document_stores"
runs-on: ubuntu-latest

77
docs/_src/api/api/document_store.md
View File

@ -678,32 +678,32 @@ def write_documents(documents: Union[List[dict], List[Document]],
Indexes documents for later queries in Elasticsearch.
Behaviour if a document with the same ID already exists in ElasticSearch:
If a document with the same ID already exists in Elasticsearch:
a) (Default) Throw Elastic's standard error message for duplicate IDs.
b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
(This is only relevant if you pass your own ID when initializing a `Document`.
If don't set custom IDs for your Documents or just pass a list of dictionaries here,
they will automatically get UUIDs assigned. See the `Document` class for details)
If you don't set custom IDs for your Documents or just pass a list of dictionaries here,
they automatically get UUIDs assigned. See the `Document` class for details.)
**Arguments**:
- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
- `documents`: A list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"content": "<the-actual-text>"}.
Optionally: Include meta data via {"content": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
It can be used for filtering and is accessible in the responses of the Finder.
Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
should be changed to what you have set for self.content_field and self.name_field.
- `index`: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
You can use it for filtering and you can access it in the responses of the Finder.
Advanced: If you are using your own Elasticsearch mapping, change the key names in the dictionary
to what you have set for self.content_field and self.name_field.
- `index`: Elasticsearch index where the documents should be indexed. If you don't specify it, self.index is used.
- `batch_size`: Number of documents that are passed to Elasticsearch's bulk function at a time.
- `duplicate_documents`: Handle duplicates document based on parameter options.
Parameter options : ( 'skip','overwrite','fail')
skip: Ignore the duplicates documents
- `duplicate_documents`: Handle duplicate documents based on parameter options.
Parameter options: ( 'skip','overwrite','fail')
skip: Ignore the duplicate documents
overwrite: Update any existing documents with the same ID when adding documents.
fail: an error is raised if the document ID of the document being added already
fail: Raises an error if the document ID of the document being added already
exists.
- `headers`: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='})
Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information.
- `headers`: Custom HTTP headers to pass to Elasticsearch client (for example {'Authorization': 'Basic YWRtaW46cm9vdA=='})
For more information, see [HTTP/REST clients and security](https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html).
**Raises**:
@ -1742,6 +1742,55 @@ More info at https://www.elastic.co/guide/en/elasticsearch/reference/current/ana
- `knn_engine`: The engine you want to use for the nearest neighbor search by OpenSearch's KNN plug-in. Possible values: "nmslib" or "faiss". Defaults to "nmslib".
For more information, see [k-NN Index](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/).
<a id="opensearch.OpenSearchDocumentStore.write_documents"></a>
#### OpenSearchDocumentStore.write\_documents
```python
def write_documents(documents: Union[List[dict], List[Document]],
index: Optional[str] = None,
batch_size: int = 10_000,
duplicate_documents: Optional[str] = None,
headers: Optional[Dict[str, str]] = None)
```
Indexes documents for later queries in OpenSearch.
If a document with the same ID already exists in OpenSearch:
a) (Default) Throw Elastic's standard error message for duplicate IDs.
b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
(This is only relevant if you pass your own ID when initializing a `Document`.
If you don't set custom IDs for your Documents or just pass a list of dictionaries here,
they automatically get UUIDs assigned. See the `Document` class for details.)
**Arguments**:
- `documents`: A list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"content": "<the-actual-text>"}.
Optionally: Include meta data via {"content": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
You can use it for filtering and you can access it in the responses of the Finder.
Advanced: If you are using your own OpenSearch mapping, change the key names in the dictionary
to what you have set for self.content_field and self.name_field.
- `index`: OpenSearch index where the documents should be indexed. If you don't specify it, self.index is used.
- `batch_size`: Number of documents that are passed to OpenSearch's bulk function at a time.
- `duplicate_documents`: Handle duplicate documents based on parameter options.
Parameter options: ( 'skip','overwrite','fail')
skip: Ignore the duplicate documents
overwrite: Update any existing documents with the same ID when adding documents.
fail: Raises an error if the document ID of the document being added already
exists.
- `headers`: Custom HTTP headers to pass to OpenSearch client (for example {'Authorization': 'Basic YWRtaW46cm9vdA=='})
For more information, see [HTTP/REST clients and security](https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html).
**Raises**:
- `DuplicateDocumentError`: Exception trigger on duplicate document
**Returns**:
None
<a id="opensearch.OpenSearchDocumentStore.query_by_embedding"></a>
#### OpenSearchDocumentStore.query\_by\_embedding

7
haystack/document_stores/base.py
View File

@ -349,7 +349,8 @@ class BaseDocumentStore(BaseComponent):
) -> int:
pass
def normalize_embedding(self, emb: np.ndarray) -> None:
@staticmethod
def normalize_embedding(emb: np.ndarray) -> None:
"""
Performs L2 normalization of embeddings vector inplace. Input can be a single vector (1D array) or a matrix
(2D array).
@ -358,10 +359,10 @@ class BaseDocumentStore(BaseComponent):
# Single vec
if len(emb.shape) == 1:
self._normalize_embedding_1D(emb)
BaseDocumentStore._normalize_embedding_1D(emb)
# 2D matrix
else:
self._normalize_embedding_2D(emb)
BaseDocumentStore._normalize_embedding_2D(emb)
@staticmethod
@njit # (fastmath=True)

47
haystack/document_stores/elasticsearch.py
View File

@ -433,30 +433,30 @@ class BaseElasticsearchDocumentStore(KeywordDocumentStore):
"""
Indexes documents for later queries in Elasticsearch.
Behaviour if a document with the same ID already exists in ElasticSearch:
If a document with the same ID already exists in Elasticsearch:
a) (Default) Throw Elastic's standard error message for duplicate IDs.
b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
(This is only relevant if you pass your own ID when initializing a `Document`.
If don't set custom IDs for your Documents or just pass a list of dictionaries here,
they will automatically get UUIDs assigned. See the `Document` class for details)
If you don't set custom IDs for your Documents or just pass a list of dictionaries here,
they automatically get UUIDs assigned. See the `Document` class for details.)
:param documents: a list of Python dictionaries or a list of Haystack Document objects.
:param documents: A list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"content": "<the-actual-text>"}.
Optionally: Include meta data via {"content": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
It can be used for filtering and is accessible in the responses of the Finder.
Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary
should be changed to what you have set for self.content_field and self.name_field.
:param index: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used.
You can use it for filtering and you can access it in the responses of the Finder.
Advanced: If you are using your own Elasticsearch mapping, change the key names in the dictionary
to what you have set for self.content_field and self.name_field.
:param index: Elasticsearch index where the documents should be indexed. If you don't specify it, self.index is used.
:param batch_size: Number of documents that are passed to Elasticsearch's bulk function at a time.
:param duplicate_documents: Handle duplicates document based on parameter options.
Parameter options : ( 'skip','overwrite','fail')
skip: Ignore the duplicates documents
:param duplicate_documents: Handle duplicate documents based on parameter options.
Parameter options: ( 'skip','overwrite','fail')
skip: Ignore the duplicate documents
overwrite: Update any existing documents with the same ID when adding documents.
fail: an error is raised if the document ID of the document being added already
fail: Raises an error if the document ID of the document being added already
exists.
:param headers: Custom HTTP headers to pass to elasticsearch client (e.g. {'Authorization': 'Basic YWRtaW46cm9vdA=='})
Check out https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html for more information.
:param headers: Custom HTTP headers to pass to Elasticsearch client (for example {'Authorization': 'Basic YWRtaW46cm9vdA=='})
For more information, see [HTTP/REST clients and security](https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html).
:raises DuplicateDocumentError: Exception trigger on duplicate document
:return: None
"""
@ -1483,10 +1483,7 @@ class BaseElasticsearchDocumentStore(KeywordDocumentStore):
with tqdm(total=document_count, position=0, unit=" Docs", desc="Updating embeddings") as progress_bar:
for result_batch in get_batches_from_generator(result, batch_size):
document_batch = [self._convert_es_hit_to_document(hit, return_embedding=False) for hit in result_batch]
embeddings = retriever.embed_documents(document_batch)
self._validate_embeddings_shape(
embeddings=embeddings, num_documents=len(document_batch), embedding_dim=self.embedding_dim
)
embeddings = self._embed_documents(document_batch, retriever)
doc_updates = []
for doc, emb in zip(document_batch, embeddings):
@ -1501,6 +1498,20 @@ class BaseElasticsearchDocumentStore(KeywordDocumentStore):
self._bulk(documents=doc_updates, request_timeout=300, refresh=self.refresh_type, headers=headers)
progress_bar.update(batch_size)
def _embed_documents(self, documents: List[Document], retriever: DenseRetriever) -> np.ndarray:
"""
Embed a list of documents using a Retriever.
:param documents: List of documents to embed.
:param retriever: Retriever to use for embedding.
:return: embeddings of documents.
"""
embeddings = retriever.embed_documents(documents)
self._validate_embeddings_shape(
embeddings=embeddings, num_documents=len(documents), embedding_dim=self.embedding_dim
)
return embeddings
def delete_all_documents(
self,
index: Optional[str] = None,

105
haystack/document_stores/opensearch.py
View File

@ -19,12 +19,22 @@ from haystack.schema import Document
from haystack.document_stores.base import get_batches_from_generator
from haystack.document_stores.filter_utils import LogicalFilterClause
from haystack.errors import DocumentStoreError
from haystack.nodes.retriever import DenseRetriever
from .elasticsearch import BaseElasticsearchDocumentStore, prepare_hosts
logger = logging.getLogger(__name__)
SIMILARITY_SPACE_TYPE_MAPPINGS = {
"nmslib": {"cosine": "cosinesimil", "dot_product": "innerproduct", "l2": "l2"},
"faiss": {"cosine": "innerproduct", "dot_product": "innerproduct", "l2": "l2"},
}
SPACE_TYPE_SIMILARITY_MAPPINGS = {
engine: {v: k for k, v in mapping.items()} for engine, mapping in SIMILARITY_SPACE_TYPE_MAPPINGS.items()
}
class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
def __init__(
self,
@ -171,15 +181,8 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
if knn_engine not in {"nmslib", "faiss"}:
raise ValueError(f"knn_engine must be either 'nmslib' or 'faiss' but was {knn_engine}")
if knn_engine == "faiss" and similarity not in {"dot_product", "l2"}:
raise ValueError(
f"knn_engine=`faiss` was set to similarity {similarity}. Currently, we only support 'dot_product' and 'l2' similarities. Set the similarity to one of the supported values."
)
self.knn_engine = knn_engine
self.embeddings_field_supports_similarity = False
self.similarity_to_space_type = {"cosine": "cosinesimil", "dot_product": "innerproduct", "l2": "l2"}
self.space_type_to_similarity = {v: k for k, v in self.similarity_to_space_type.items()}
super().__init__(
client=client,
index=index,
@ -262,6 +265,72 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
return client
def write_documents(
self,
documents: Union[List[dict], List[Document]],
index: Optional[str] = None,
batch_size: int = 10_000,
duplicate_documents: Optional[str] = None,
headers: Optional[Dict[str, str]] = None,
):
"""
Indexes documents for later queries in OpenSearch.
If a document with the same ID already exists in OpenSearch:
a) (Default) Throw Elastic's standard error message for duplicate IDs.
b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents.
(This is only relevant if you pass your own ID when initializing a `Document`.
If you don't set custom IDs for your Documents or just pass a list of dictionaries here,
they automatically get UUIDs assigned. See the `Document` class for details.)
:param documents: A list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"content": "<the-actual-text>"}.
Optionally: Include meta data via {"content": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
You can use it for filtering and you can access it in the responses of the Finder.
Advanced: If you are using your own OpenSearch mapping, change the key names in the dictionary
to what you have set for self.content_field and self.name_field.
:param index: OpenSearch index where the documents should be indexed. If you don't specify it, self.index is used.
:param batch_size: Number of documents that are passed to OpenSearch's bulk function at a time.
:param duplicate_documents: Handle duplicate documents based on parameter options.
Parameter options: ( 'skip','overwrite','fail')
skip: Ignore the duplicate documents
overwrite: Update any existing documents with the same ID when adding documents.
fail: Raises an error if the document ID of the document being added already
exists.
:param headers: Custom HTTP headers to pass to OpenSearch client (for example {'Authorization': 'Basic YWRtaW46cm9vdA=='})
For more information, see [HTTP/REST clients and security](https://www.elastic.co/guide/en/elasticsearch/reference/current/http-clients.html).
:raises DuplicateDocumentError: Exception trigger on duplicate document
:return: None
"""
if self.knn_engine == "faiss" and self.similarity == "cosine":
field_map = self._create_document_field_map()
documents = [Document.from_dict(d, field_map=field_map) if isinstance(d, dict) else d for d in documents]
embeddings_to_index = np.array([d.embedding for d in documents], dtype="float32")
self.normalize_embedding(embeddings_to_index)
for document, embedding in zip(documents, embeddings_to_index):
document.embedding = None if np.isnan(embedding).any() else embedding
super().write_documents(
documents=documents,
index=index,
batch_size=batch_size,
duplicate_documents=duplicate_documents,
headers=headers,
)
def _embed_documents(self, documents: List[Document], retriever: DenseRetriever) -> np.ndarray:
"""
Embed a list of documents using a Retriever.
:param documents: List of documents to embed.
:param retriever: Retriever to use for embedding.
:return: embeddings of documents.
"""
embeddings = super()._embed_documents(documents, retriever)
if self.knn_engine == "faiss" and self.similarity == "cosine":
self.normalize_embedding(embeddings)
return embeddings
def query_by_embedding(
self,
query_emb: np.ndarray,
@ -444,7 +513,9 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
]
# Check if desired index settings are equal to settings in existing index
embedding_field_similarity = self.space_type_to_similarity[embedding_field_space_type]
embedding_field_similarity = SPACE_TYPE_SIMILARITY_MAPPINGS[self.knn_engine][
embedding_field_space_type
]
if embedding_field_similarity != self.similarity:
self.embeddings_field_supports_similarity = False
logger.warning(
@ -515,7 +586,7 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
if self.embedding_field:
index_definition["settings"]["index"] = {"knn": True}
# global ef_search setting affects only nmslib, for faiss it is set in the field mapping
if self.index_type == "hnsw":
if self.knn_engine == "nmslib" and self.index_type == "hnsw":
index_definition["settings"]["index"]["knn.algo_param.ef_search"] = 20
index_definition["mappings"]["properties"][self.embedding_field] = self._get_embedding_field_mapping(
similarity=self.similarity
@ -533,7 +604,7 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
raise e
def _get_embedding_field_mapping(self, similarity: str):
space_type = self.similarity_to_space_type[similarity]
space_type = SIMILARITY_SPACE_TYPE_MAPPINGS[self.knn_engine][similarity]
method: dict = {"space_type": space_type, "name": "hnsw", "engine": self.knn_engine}
if self.index_type == "flat":
@ -589,6 +660,9 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
Generate Elasticsearch query for vector similarity.
"""
if self.embeddings_field_supports_similarity:
if self.knn_engine == "faiss" and self.similarity == "cosine":
self.normalize_embedding(query_emb)
query: dict = {
"bool": {"must": [{"knn": {self.embedding_field: {"vector": query_emb.tolist(), "k": top_k}}}]}
}
@ -603,7 +677,7 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
"params": {
"field": self.embedding_field,
"query_value": query_emb.tolist(),
"space_type": self.similarity_to_space_type[self.similarity],
"space_type": SIMILARITY_SPACE_TYPE_MAPPINGS["nmslib"][self.similarity],
},
},
}
@ -613,14 +687,17 @@ class OpenSearchDocumentStore(BaseElasticsearchDocumentStore):
def _get_raw_similarity_score(self, score):
# adjust scores according to https://opensearch.org/docs/latest/search-plugins/knn/approximate-knn
# and https://opensearch.org/docs/latest/search-plugins/knn/knn-score-script/
if self.similarity == "dot_product":
# space type is required as criterion as there is no consistent similarity-to-space-type mapping accross knn engines
space_type = SIMILARITY_SPACE_TYPE_MAPPINGS[self.knn_engine][self.similarity]
if space_type == "innerproduct":
if score > 1:
score = score - 1
else:
score = -(1 / score - 1)
elif self.similarity == "l2":
elif space_type == "l2":
score = 1 / score - 1
elif self.similarity == "cosine":
elif space_type == "cosinesimil":
if self.embeddings_field_supports_similarity:
score = -(1 / score - 2)
else:

112
test/conftest.py
View File

@ -23,34 +23,20 @@ import psutil
import pytest
import requests
from haystack import Answer
from haystack.nodes.base import BaseComponent
try:
from milvus import Milvus
milvus1 = True
except ImportError:
milvus1 = False
from pymilvus import utility
try:
from elasticsearch import Elasticsearch
from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
import weaviate
from haystack.document_stores.weaviate import WeaviateDocumentStore
from haystack.document_stores import MilvusDocumentStore, PineconeDocumentStore
from haystack.document_stores.graphdb import GraphDBKnowledgeGraph
from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.document_stores.sql import SQLDocumentStore
except (ImportError, ModuleNotFoundError) as ie:
from haystack.utils.import_utils import _optional_component_not_installed
_optional_component_not_installed("test", "test", ie)
from haystack.document_stores import BaseDocumentStore, DeepsetCloudDocumentStore, InMemoryDocumentStore
from haystack import Answer, BaseComponent
from haystack.document_stores import (
BaseDocumentStore,
DeepsetCloudDocumentStore,
InMemoryDocumentStore,
ElasticsearchDocumentStore,
WeaviateDocumentStore,
MilvusDocumentStore,
PineconeDocumentStore,
OpenSearchDocumentStore,
GraphDBKnowledgeGraph,
FAISSDocumentStore,
SQLDocumentStore,
)
from haystack.nodes import (
BaseReader,
BaseRetriever,
@ -59,28 +45,42 @@ from haystack.nodes import (
BaseSummarizer,
BaseTranslator,
DenseRetriever,
)
from haystack.nodes.answer_generator.transformers import Seq2SeqGenerator
from haystack.nodes.answer_generator.transformers import RAGenerator
from haystack.nodes.ranker import SentenceTransformersRanker
from haystack.nodes.document_classifier.transformers import TransformersDocumentClassifier
from haystack.nodes.retriever.sparse import FilterRetriever, BM25Retriever, TfidfRetriever
from haystack.nodes.retriever.dense import (
Seq2SeqGenerator,
RAGenerator,
SentenceTransformersRanker,
TransformersDocumentClassifier,
FilterRetriever,
BM25Retriever,
TfidfRetriever,
DensePassageRetriever,
EmbeddingRetriever,
MultihopEmbeddingRetriever,
TableTextRetriever,
FARMReader,
TransformersReader,
TableReader,
RCIReader,
TransformersSummarizer,
TransformersTranslator,
QuestionGenerator,
)
from haystack.nodes.reader.farm import FARMReader
from haystack.nodes.reader.transformers import TransformersReader
from haystack.nodes.reader.table import TableReader, RCIReader
from haystack.nodes.summarizer.transformers import TransformersSummarizer
from haystack.nodes.translator import TransformersTranslator
from haystack.nodes.question_generator import QuestionGenerator
from haystack.modeling.infer import Inferencer, QAInferencer
from haystack.schema import Document
from haystack.utils.import_utils import _optional_component_not_installed
try:
from elasticsearch import Elasticsearch
import weaviate
except (ImportError, ModuleNotFoundError) as ie:
_optional_component_not_installed("test", "test", ie)
try:
from milvus import Milvus
milvus1 = True
except ImportError:
milvus1 = False
from pymilvus import utility
from .mocks import pinecone as pinecone_mock
@ -152,6 +152,7 @@ def pytest_collection_modifyitems(config, items):
"pinecone": [pytest.mark.pinecone],
# FIXME GraphDB can't be treated as a regular docstore, it fails most of their tests
"graphdb": [pytest.mark.integration],
"opensearch": [pytest.mark.opensearch],
}
for item in items:
for name, markers in name_to_markers.items():
@ -1005,6 +1006,7 @@ def get_document_store(
similarity: str = "cosine",
recreate_index: bool = True,
): # cosine is default similarity as dot product is not supported by Weaviate
document_store: BaseDocumentStore
if document_store_type == "sql":
document_store = SQLDocumentStore(url=get_sql_url(tmp_path), index=index, isolation_level="AUTOCOMMIT")
@ -1078,6 +1080,30 @@ def get_document_store(
metadata_config={"indexed": META_FIELDS},
)
elif document_store_type == "opensearch_faiss":
document_store = OpenSearchDocumentStore(
index=index,
return_embedding=True,
embedding_dim=embedding_dim,
embedding_field=embedding_field,
similarity=similarity,
recreate_index=recreate_index,
port=9201,
knn_engine="faiss",
)
elif document_store_type == "opensearch":
document_store = OpenSearchDocumentStore(
index=index,
return_embedding=True,
embedding_dim=embedding_dim,
embedding_field=embedding_field,
similarity=similarity,
recreate_index=recreate_index,
port=9201,
knn_engine="nmslib",
)
else:
raise Exception(f"No document store fixture for '{document_store_type}'")

165
test/document_stores/test_document_store.py
View File

@ -1,6 +1,7 @@
from copy import deepcopy
import logging
import math
import sys
from typing import List
from uuid import uuid4
import numpy as np
@ -16,19 +17,27 @@ from elasticsearch.exceptions import RequestError
from ..conftest import (
deepset_cloud_fixture,
get_document_store,
ensure_ids_are_correct_uuids,
MOCK_DC,
DC_API_ENDPOINT,
DC_API_KEY,
DC_TEST_INDEX,
SAMPLES_PATH,
)
from haystack.document_stores import WeaviateDocumentStore, DeepsetCloudDocumentStore, InMemoryDocumentStore
from haystack.document_stores import (
WeaviateDocumentStore,
DeepsetCloudDocumentStore,
InMemoryDocumentStore,
MilvusDocumentStore,
FAISSDocumentStore,
ElasticsearchDocumentStore,
OpenSearchDocumentStore,
)
from haystack.document_stores.base import BaseDocumentStore
from haystack.document_stores.es_converter import elasticsearch_index_to_document_store
from haystack.errors import DuplicateDocumentError
from haystack.schema import Document, Label, Answer, Span
from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.nodes import EmbeddingRetriever, PreProcessor
from haystack.pipelines import DocumentSearchPipeline
from haystack.utils import DeepsetCloudError
@ -2108,3 +2117,151 @@ def test_elasticsearch_brownfield_support(document_store_with_docs):
assert all("numeric_field" in doc.meta for doc in transferred_documents)
# Check if number of transferred_documents is equal to number of unique words.
assert len(transferred_documents) == len(set(" ".join(original_content).split()))
@pytest.mark.parametrize(
"document_store",
["faiss", "milvus1", "milvus", "weaviate", "opensearch_faiss", "opensearch", "elasticsearch", "memory"],
indirect=True,
)
def test_cosine_similarity(document_store: BaseDocumentStore):
# below we will write documents to the store and then query it to see if vectors were normalized or not
ensure_ids_are_correct_uuids(docs=DOCUMENTS, document_store=document_store)
document_store.write_documents(documents=DOCUMENTS)
query = np.random.rand(768).astype(np.float32)
query_results = document_store.query_by_embedding(
query_emb=query, top_k=len(DOCUMENTS), return_embedding=True, scale_score=False
)
# check if search with cosine similarity returns the correct number of results
assert len(query_results) == len(DOCUMENTS)
original_embeddings = {doc["content"]: doc["embedding"] for doc in DOCUMENTS}
for doc in query_results:
result_emb = doc.embedding
original_emb = original_embeddings[doc.content]
expected_emb = original_emb
# embeddings of document stores which only support dot product out of the box must be normalized
if (
isinstance(document_store, (FAISSDocumentStore, MilvusDocumentStore, WeaviateDocumentStore))
or type(document_store).name == "Milvus1DocumentStore"
or isinstance(document_store, OpenSearchDocumentStore)
and document_store.knn_engine == "faiss"
):
expected_emb = original_emb / np.linalg.norm(original_emb)
# check if the stored embedding was normalized or not
np.testing.assert_allclose(
expected_emb, result_emb, rtol=0.2, atol=5e-07
) # high tolerance necessary for Milvus 2
# check if the score is plausible for cosine similarity
cosine_score = np.dot(result_emb, query) / (np.linalg.norm(result_emb) * np.linalg.norm(query))
assert cosine_score == pytest.approx(doc.score, 0.01)
@pytest.mark.parametrize(
"document_store",
["faiss", "milvus1", "milvus", "weaviate", "opensearch_faiss", "opensearch", "elasticsearch", "memory"],
indirect=True,
)
def test_update_embeddings_cosine_similarity(document_store: BaseDocumentStore):
# below we will write documents to the store and then query it to see if vectors were normalized
ensure_ids_are_correct_uuids(docs=DOCUMENTS, document_store=document_store)
# clear embeddings
docs = deepcopy(DOCUMENTS)
for doc in docs:
doc.pop("embedding")
document_store.write_documents(documents=docs)
original_embeddings = {}
# now check if vectors are normalized when updating embeddings
class MockRetriever:
def embed_documents(self, docs):
embeddings = []
for doc in docs:
embedding = np.random.rand(768).astype(np.float32)
original_embeddings[doc.content] = embedding
embeddings.append(embedding)
return np.stack(embeddings)
retriever = MockRetriever()
document_store.update_embeddings(retriever=retriever)
query = np.random.rand(768).astype(np.float32)
query_results = document_store.query_by_embedding(
query_emb=query, top_k=len(DOCUMENTS), return_embedding=True, scale_score=False
)
# check if search with cosine similarity returns the correct number of results
assert len(query_results) == len(DOCUMENTS)
for doc in query_results:
result_emb = doc.embedding
original_emb = original_embeddings[doc.content]
expected_emb = original_emb
# embeddings of document stores which only support dot product out of the box must be normalized
if (
isinstance(document_store, (FAISSDocumentStore, MilvusDocumentStore, WeaviateDocumentStore))
or type(document_store).name == "Milvus1DocumentStore"
or isinstance(document_store, OpenSearchDocumentStore)
and document_store.knn_engine == "faiss"
):
expected_emb = original_emb / np.linalg.norm(original_emb)
# check if the stored embedding was normalized or not
np.testing.assert_allclose(
expected_emb, result_emb, rtol=0.2, atol=5e-07
) # high tolerance necessary for Milvus 2
# check if the score is plausible for cosine similarity
cosine_score = np.dot(result_emb, query) / (np.linalg.norm(result_emb) * np.linalg.norm(query))
assert cosine_score == pytest.approx(doc.score, 0.01)
@pytest.mark.parametrize(
"document_store_small",
["faiss", "milvus1", "milvus", "weaviate", "memory", "elasticsearch", "opensearch", "opensearch_faiss"],
indirect=True,
)
def test_cosine_sanity_check(document_store_small):
VEC_1 = np.array([0.1, 0.2, 0.3], dtype="float32")
VEC_2 = np.array([0.4, 0.5, 0.6], dtype="float32")
# This is the cosine similarity of VEC_1 and VEC_2 calculated using sklearn.metrics.pairwise.cosine_similarity
# The score is normalized to yield a value between 0 and 1.
KNOWN_COSINE = 0.9746317
KNOWN_SCALED_COSINE = (KNOWN_COSINE + 1) / 2
docs = [{"name": "vec_1", "text": "vec_1", "content": "vec_1", "embedding": VEC_1}]
ensure_ids_are_correct_uuids(docs=docs, document_store=document_store_small)
document_store_small.write_documents(documents=docs)
query_results = document_store_small.query_by_embedding(
query_emb=VEC_2, top_k=1, return_embedding=True, scale_score=True
)
# check if faiss returns the same cosine similarity. Manual testing with faiss yielded 0.9746318
assert math.isclose(query_results[0].score, KNOWN_SCALED_COSINE, abs_tol=0.0002)
query_results = document_store_small.query_by_embedding(
query_emb=VEC_2, top_k=1, return_embedding=True, scale_score=False
)
# check if faiss returns the same cosine similarity. Manual testing with faiss yielded 0.9746318
assert math.isclose(query_results[0].score, KNOWN_COSINE, abs_tol=0.0002)
def test_normalize_embeddings_diff_shapes():
VEC_1 = np.array([0.1, 0.2, 0.3], dtype="float32")
BaseDocumentStore.normalize_embedding(VEC_1)
assert np.linalg.norm(VEC_1) - 1 < 0.01
VEC_1 = np.array([0.1, 0.2, 0.3], dtype="float32").reshape(1, -1)
BaseDocumentStore.normalize_embedding(VEC_1)
assert np.linalg.norm(VEC_1) - 1 < 0.01

285
test/document_stores/test_faiss.py Normal file
View File

@ -0,0 +1,285 @@
import sys
import yaml
import faiss
import pytest
import numpy as np
from haystack.schema import Document
from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.pipelines import Pipeline
from haystack.nodes.retriever.dense import EmbeddingRetriever
from ..conftest import MockDenseRetriever
DOCUMENTS = [
{
"meta": {"name": "name_1", "year": "2020", "month": "01"},
"content": "text_1",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_2", "year": "2020", "month": "02"},
"content": "text_2",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_3", "year": "2020", "month": "03"},
"content": "text_3",
"embedding": np.random.rand(768).astype(np.float64),
},
{
"meta": {"name": "name_4", "year": "2021", "month": "01"},
"content": "text_4",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_5", "year": "2021", "month": "02"},
"content": "text_5",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_6", "year": "2021", "month": "03"},
"content": "text_6",
"embedding": np.random.rand(768).astype(np.float64),
},
]
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_index_save_and_load(tmp_path, sql_url):
document_store = FAISSDocumentStore(
sql_url=sql_url,
index="haystack_test",
progress_bar=False, # Just to check if the init parameters are kept
isolation_level="AUTOCOMMIT",
)
document_store.write_documents(DOCUMENTS)
# test saving the index
document_store.save(tmp_path / "haystack_test_faiss")
# clear existing faiss_index
document_store.faiss_indexes[document_store.index].reset()
# test faiss index is cleared
assert document_store.faiss_indexes[document_store.index].ntotal == 0
# test loading the index
new_document_store = FAISSDocumentStore.load(tmp_path / "haystack_test_faiss")
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
# test saving and loading the loaded faiss index
new_document_store.save(tmp_path / "haystack_test_faiss")
reloaded_document_store = FAISSDocumentStore.load(tmp_path / "haystack_test_faiss")
# check faiss index is restored
assert reloaded_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(reloaded_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not reloaded_document_store.progress_bar
# test loading the index via init
new_document_store = FAISSDocumentStore(faiss_index_path=tmp_path / "haystack_test_faiss")
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_index_save_and_load_custom_path(tmp_path, sql_url):
document_store = FAISSDocumentStore(
sql_url=sql_url,
index="haystack_test",
progress_bar=False, # Just to check if the init parameters are kept
isolation_level="AUTOCOMMIT",
)
document_store.write_documents(DOCUMENTS)
# test saving the index
document_store.save(index_path=tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json")
# clear existing faiss_index
document_store.faiss_indexes[document_store.index].reset()
# test faiss index is cleared
assert document_store.faiss_indexes[document_store.index].ntotal == 0
# test loading the index
new_document_store = FAISSDocumentStore.load(
index_path=tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json"
)
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
# test saving and loading the loaded faiss index
new_document_store.save(tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json")
reloaded_document_store = FAISSDocumentStore.load(
tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json"
)
# check faiss index is restored
assert reloaded_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(reloaded_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not reloaded_document_store.progress_bar
# test loading the index via init
new_document_store = FAISSDocumentStore(
faiss_index_path=tmp_path / "haystack_test_faiss", faiss_config_path=tmp_path / "custom_path.json"
)
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_index_mutual_exclusive_args(tmp_path):
with pytest.raises(ValueError):
FAISSDocumentStore(
sql_url=f"sqlite:////{tmp_path/'haystack_test.db'}",
faiss_index_path=f"{tmp_path/'haystack_test'}",
isolation_level="AUTOCOMMIT",
)
with pytest.raises(ValueError):
FAISSDocumentStore(
f"sqlite:////{tmp_path/'haystack_test.db'}",
faiss_index_path=f"{tmp_path/'haystack_test'}",
isolation_level="AUTOCOMMIT",
)
@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
@pytest.mark.parametrize("index_buffer_size", [10_000, 2])
@pytest.mark.parametrize("batch_size", [2])
def test_faiss_write_docs(document_store, index_buffer_size, batch_size):
document_store.index_buffer_size = index_buffer_size
# Write in small batches
for i in range(0, len(DOCUMENTS), batch_size):
document_store.write_documents(DOCUMENTS[i : i + batch_size])
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
# test if correct vectors are associated with docs
for i, doc in enumerate(documents_indexed):
# we currently don't get the embeddings back when we call document_store.get_all_documents()
original_doc = [d for d in DOCUMENTS if d["content"] == doc.content][0]
stored_emb = document_store.faiss_indexes[document_store.index].reconstruct(int(doc.meta["vector_id"]))
# compare original input vec with stored one (ignore extra dim added by hnsw)
# original input vec is normalized as faiss only stores normalized vectors
assert np.allclose(original_doc["embedding"] / np.linalg.norm(original_doc["embedding"]), stored_emb, rtol=0.01)
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
@pytest.mark.parametrize("index_factory", ["Flat", "HNSW", "IVF1,Flat"])
def test_faiss_retrieving(index_factory, tmp_path):
document_store = FAISSDocumentStore(
sql_url=f"sqlite:////{tmp_path/'test_faiss_retrieving.db'}",
faiss_index_factory_str=index_factory,
isolation_level="AUTOCOMMIT",
)
document_store.delete_all_documents(index="document")
if "ivf" in index_factory.lower():
document_store.train_index(DOCUMENTS)
document_store.write_documents(DOCUMENTS)
retriever = EmbeddingRetriever(
document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False
)
result = retriever.retrieve(query="How to test this?")
assert len(result) == len(DOCUMENTS)
assert type(result[0]) == Document
# Cleanup
document_store.faiss_indexes[document_store.index].reset()
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_passing_index_from_outside(tmp_path):
d = 768
nlist = 2
quantizer = faiss.IndexFlatIP(d)
index = "haystack_test_1"
faiss_index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_INNER_PRODUCT)
faiss_index.set_direct_map_type(faiss.DirectMap.Hashtable)
faiss_index.nprobe = 2
document_store = FAISSDocumentStore(
sql_url=f"sqlite:////{tmp_path/'haystack_test_faiss.db'}",
faiss_index=faiss_index,
index=index,
isolation_level="AUTOCOMMIT",
)
document_store.delete_documents()
# as it is a IVF index we need to train it before adding docs
document_store.train_index(DOCUMENTS)
document_store.write_documents(documents=DOCUMENTS)
documents_indexed = document_store.get_all_documents()
# test if vectors ids are associated with docs
for doc in documents_indexed:
assert 0 <= int(doc.meta["vector_id"]) <= 7
@pytest.mark.integration
def test_pipeline_with_existing_faiss_docstore(tmp_path):
document_store: FAISSDocumentStore = FAISSDocumentStore(
sql_url=f'sqlite:///{(tmp_path / "faiss_document_store.db").absolute()}'
)
retriever = MockDenseRetriever(document_store=document_store)
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, update_existing_embeddings=True)
document_store.save(tmp_path / "existing_faiss_document_store")
query_config = f"""
version: ignore
components:
- name: DPRRetriever
type: MockDenseRetriever
params:
document_store: ExistingFAISSDocumentStore
- name: ExistingFAISSDocumentStore
type: FAISSDocumentStore
params:
faiss_index_path: '{tmp_path / "existing_faiss_document_store"}'
pipelines:
- name: query_pipeline
nodes:
- name: DPRRetriever
inputs: [Query]
"""
pipeline = Pipeline.load_from_config(yaml.safe_load(query_config))
existing_document_store = pipeline.get_document_store()
faiss_index = existing_document_store.faiss_indexes["document"]
assert faiss_index.ntotal == len(DOCUMENTS)

606
test/document_stores/test_faiss_and_milvus.py
View File

@ -1,606 +0,0 @@
import os
import sys
import math
from pathlib import Path
import yaml
import faiss
import pytest
import numpy as np
from haystack.schema import Document
from haystack.pipelines import DocumentSearchPipeline
from haystack.document_stores.base import BaseDocumentStore
from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.pipelines import Pipeline
from haystack.nodes.retriever.dense import EmbeddingRetriever
from ..conftest import ensure_ids_are_correct_uuids, SAMPLES_PATH, MockDenseRetriever
DOCUMENTS = [
{
"meta": {"name": "name_1", "year": "2020", "month": "01"},
"content": "text_1",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_2", "year": "2020", "month": "02"},
"content": "text_2",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_3", "year": "2020", "month": "03"},
"content": "text_3",
"embedding": np.random.rand(768).astype(np.float64),
},
{
"meta": {"name": "name_4", "year": "2021", "month": "01"},
"content": "text_4",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_5", "year": "2021", "month": "02"},
"content": "text_5",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_6", "year": "2021", "month": "03"},
"content": "text_6",
"embedding": np.random.rand(768).astype(np.float64),
},
]
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_index_save_and_load(tmp_path, sql_url):
document_store = FAISSDocumentStore(
sql_url=sql_url,
index="haystack_test",
progress_bar=False, # Just to check if the init parameters are kept
isolation_level="AUTOCOMMIT",
)
document_store.write_documents(DOCUMENTS)
# test saving the index
document_store.save(tmp_path / "haystack_test_faiss")
# clear existing faiss_index
document_store.faiss_indexes[document_store.index].reset()
# test faiss index is cleared
assert document_store.faiss_indexes[document_store.index].ntotal == 0
# test loading the index
new_document_store = FAISSDocumentStore.load(tmp_path / "haystack_test_faiss")
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
# test saving and loading the loaded faiss index
new_document_store.save(tmp_path / "haystack_test_faiss")
reloaded_document_store = FAISSDocumentStore.load(tmp_path / "haystack_test_faiss")
# check faiss index is restored
assert reloaded_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(reloaded_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not reloaded_document_store.progress_bar
# test loading the index via init
new_document_store = FAISSDocumentStore(faiss_index_path=tmp_path / "haystack_test_faiss")
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_index_save_and_load_custom_path(tmp_path, sql_url):
document_store = FAISSDocumentStore(
sql_url=sql_url,
index="haystack_test",
progress_bar=False, # Just to check if the init parameters are kept
isolation_level="AUTOCOMMIT",
)
document_store.write_documents(DOCUMENTS)
# test saving the index
document_store.save(index_path=tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json")
# clear existing faiss_index
document_store.faiss_indexes[document_store.index].reset()
# test faiss index is cleared
assert document_store.faiss_indexes[document_store.index].ntotal == 0
# test loading the index
new_document_store = FAISSDocumentStore.load(
index_path=tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json"
)
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
# test saving and loading the loaded faiss index
new_document_store.save(tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json")
reloaded_document_store = FAISSDocumentStore.load(
tmp_path / "haystack_test_faiss", config_path=tmp_path / "custom_path.json"
)
# check faiss index is restored
assert reloaded_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(reloaded_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not reloaded_document_store.progress_bar
# test loading the index via init
new_document_store = FAISSDocumentStore(
faiss_index_path=tmp_path / "haystack_test_faiss", faiss_config_path=tmp_path / "custom_path.json"
)
# check faiss index is restored
assert new_document_store.faiss_indexes[document_store.index].ntotal == len(DOCUMENTS)
# check if documents are restored
assert len(new_document_store.get_all_documents()) == len(DOCUMENTS)
# Check if the init parameters are kept
assert not new_document_store.progress_bar
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_index_mutual_exclusive_args(tmp_path):
with pytest.raises(ValueError):
FAISSDocumentStore(
sql_url=f"sqlite:////{tmp_path/'haystack_test.db'}",
faiss_index_path=f"{tmp_path/'haystack_test'}",
isolation_level="AUTOCOMMIT",
)
with pytest.raises(ValueError):
FAISSDocumentStore(
f"sqlite:////{tmp_path/'haystack_test.db'}",
faiss_index_path=f"{tmp_path/'haystack_test'}",
isolation_level="AUTOCOMMIT",
)
@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
@pytest.mark.parametrize("index_buffer_size", [10_000, 2])
@pytest.mark.parametrize("batch_size", [2])
def test_faiss_write_docs(document_store, index_buffer_size, batch_size):
document_store.index_buffer_size = index_buffer_size
# Write in small batches
for i in range(0, len(DOCUMENTS), batch_size):
document_store.write_documents(DOCUMENTS[i : i + batch_size])
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
# test if correct vectors are associated with docs
for i, doc in enumerate(documents_indexed):
# we currently don't get the embeddings back when we call document_store.get_all_documents()
original_doc = [d for d in DOCUMENTS if d["content"] == doc.content][0]
stored_emb = document_store.faiss_indexes[document_store.index].reconstruct(int(doc.meta["vector_id"]))
# compare original input vec with stored one (ignore extra dim added by hnsw)
# original input vec is normalized as faiss only stores normalized vectors
assert np.allclose(original_doc["embedding"] / np.linalg.norm(original_doc["embedding"]), stored_emb, rtol=0.01)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
@pytest.mark.parametrize("batch_size", [4, 6])
def test_update_docs(document_store, retriever, batch_size):
# initial write
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=batch_size)
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
# test if correct vectors are associated with docs
for doc in documents_indexed:
original_doc = [d for d in DOCUMENTS if d["content"] == doc.content][0]
updated_embedding = retriever.embed_documents([Document.from_dict(original_doc)])
stored_doc = document_store.get_all_documents(filters={"name": [doc.meta["name"]]})[0]
# compare original input vec with stored one (ignore extra dim added by hnsw)
# original input vec is normalized as faiss only stores normalized vectors
a = updated_embedding / np.linalg.norm(updated_embedding)
assert np.allclose(a[0], stored_doc.embedding, rtol=0.2) # high tolerance necessary for Milvus 2
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["milvus1", "milvus", "faiss"], indirect=True)
def test_update_existing_docs(document_store, retriever):
document_store.duplicate_documents = "overwrite"
old_document = Document(content="text_1")
# initial write
document_store.write_documents([old_document])
document_store.update_embeddings(retriever=retriever)
old_documents_indexed = document_store.get_all_documents(return_embedding=True)
assert len(old_documents_indexed) == 1
# Update document data
new_document = Document(content="text_2")
new_document.id = old_document.id
document_store.write_documents([new_document])
document_store.update_embeddings(retriever=retriever)
new_documents_indexed = document_store.get_all_documents(return_embedding=True)
assert len(new_documents_indexed) == 1
assert old_documents_indexed[0].id == new_documents_indexed[0].id
assert old_documents_indexed[0].content == "text_1"
assert new_documents_indexed[0].content == "text_2"
print(type(old_documents_indexed[0].embedding))
print(type(new_documents_indexed[0].embedding))
assert not np.allclose(old_documents_indexed[0].embedding, new_documents_indexed[0].embedding, rtol=0.01)
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_update_with_empty_store(document_store, retriever):
# Call update with empty doc store
document_store.update_embeddings(retriever=retriever)
# initial write
document_store.write_documents(DOCUMENTS)
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
@pytest.mark.parametrize("index_factory", ["Flat", "HNSW", "IVF1,Flat"])
def test_faiss_retrieving(index_factory, tmp_path):
document_store = FAISSDocumentStore(
sql_url=f"sqlite:////{tmp_path/'test_faiss_retrieving.db'}",
faiss_index_factory_str=index_factory,
isolation_level="AUTOCOMMIT",
)
document_store.delete_all_documents(index="document")
if "ivf" in index_factory.lower():
document_store.train_index(DOCUMENTS)
document_store.write_documents(DOCUMENTS)
retriever = EmbeddingRetriever(
document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False
)
result = retriever.retrieve(query="How to test this?")
assert len(result) == len(DOCUMENTS)
assert type(result[0]) == Document
# Cleanup
document_store.faiss_indexes[document_store.index].reset()
@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_finding(document_store, retriever):
document_store.write_documents(DOCUMENTS)
pipe = DocumentSearchPipeline(retriever=retriever)
prediction = pipe.run(query="How to test this?", params={"Retriever": {"top_k": 1}})
assert len(prediction.get("documents", [])) == 1
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_with_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
document_store.delete_documents(filters={"name": ["name_1", "name_2", "name_3", "name_4"]})
documents = document_store.get_all_documents()
assert len(documents) == 2
assert document_store.get_embedding_count() == 2
assert {doc.meta["name"] for doc in documents} == {"name_5", "name_6"}
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_with_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
document_store.delete_documents(filters={"year": ["2020"]})
documents = document_store.get_all_documents()
assert len(documents) == 3
assert document_store.get_embedding_count() == 3
assert all("2021" == doc.meta["year"] for doc in documents)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_with_many_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
document_store.delete_documents(filters={"month": ["01"], "year": ["2020"]})
documents = document_store.get_all_documents()
assert len(documents) == 5
assert document_store.get_embedding_count() == 5
assert "name_1" not in {doc.meta["name"] for doc in documents}
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_by_id(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
doc_ids = [doc.id for doc in document_store.get_all_documents()]
ids_to_delete = doc_ids[0:3]
document_store.delete_documents(ids=ids_to_delete)
documents = document_store.get_all_documents()
assert len(documents) == len(doc_ids) - len(ids_to_delete)
assert document_store.get_embedding_count() == len(doc_ids) - len(ids_to_delete)
remaining_ids = [doc.id for doc in documents]
assert all(doc_id not in remaining_ids for doc_id in ids_to_delete)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_by_id_with_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
ids_to_delete = [doc.id for doc in document_store.get_all_documents(filters={"name": ["name_1", "name_2"]})]
ids_not_to_delete = [
doc.id for doc in document_store.get_all_documents(filters={"name": ["name_3", "name_4", "name_5", "name_6"]})
]
document_store.delete_documents(ids=ids_to_delete, filters={"name": ["name_1", "name_2", "name_3", "name_4"]})
documents = document_store.get_all_documents()
assert len(documents) == len(DOCUMENTS) - len(ids_to_delete)
assert document_store.get_embedding_count() == len(DOCUMENTS) - len(ids_to_delete)
assert all(doc.meta["name"] != "name_1" for doc in documents)
assert all(doc.meta["name"] != "name_2" for doc in documents)
all_ids_left = [doc.id for doc in documents]
assert all(doc_id in all_ids_left for doc_id in ids_not_to_delete)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_get_docs_with_filters_one_value(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
documents = document_store.get_all_documents(filters={"year": ["2020"]})
assert len(documents) == 3
assert all("2020" == doc.meta["year"] for doc in documents)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_get_docs_with_filters_many_values(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
documents = document_store.get_all_documents(filters={"name": ["name_5", "name_6"]})
assert len(documents) == 2
assert {doc.meta["name"] for doc in documents} == {"name_5", "name_6"}
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_get_docs_with_many_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
documents = document_store.get_all_documents(filters={"month": ["01"], "year": ["2020"]})
assert len(documents) == 1
assert "name_1" == documents[0].meta["name"]
assert "01" == documents[0].meta["month"]
assert "2020" == documents[0].meta["year"]
@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_pipeline(document_store, retriever):
documents = [
{"name": "name_1", "content": "text_1", "embedding": np.random.rand(768).astype(np.float32)},
{"name": "name_2", "content": "text_2", "embedding": np.random.rand(768).astype(np.float32)},
{"name": "name_3", "content": "text_3", "embedding": np.random.rand(768).astype(np.float64)},
{"name": "name_4", "content": "text_4", "embedding": np.random.rand(768).astype(np.float32)},
]
document_store.write_documents(documents)
pipeline = Pipeline()
pipeline.add_node(component=retriever, name="FAISS", inputs=["Query"])
output = pipeline.run(query="How to test this?", params={"FAISS": {"top_k": 3}})
assert len(output["documents"]) == 3
@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Test with tmp_path not working on windows runner")
def test_faiss_passing_index_from_outside(tmp_path):
d = 768
nlist = 2
quantizer = faiss.IndexFlatIP(d)
index = "haystack_test_1"
faiss_index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_INNER_PRODUCT)
faiss_index.set_direct_map_type(faiss.DirectMap.Hashtable)
faiss_index.nprobe = 2
document_store = FAISSDocumentStore(
sql_url=f"sqlite:////{tmp_path/'haystack_test_faiss.db'}",
faiss_index=faiss_index,
index=index,
isolation_level="AUTOCOMMIT",
)
document_store.delete_documents()
# as it is a IVF index we need to train it before adding docs
document_store.train_index(DOCUMENTS)
document_store.write_documents(documents=DOCUMENTS)
documents_indexed = document_store.get_all_documents()
# test if vectors ids are associated with docs
for doc in documents_indexed:
assert 0 <= int(doc.meta["vector_id"]) <= 7
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus", "weaviate"], indirect=True)
def test_cosine_similarity(document_store):
# below we will write documents to the store and then query it to see if vectors were normalized
ensure_ids_are_correct_uuids(docs=DOCUMENTS, document_store=document_store)
document_store.write_documents(documents=DOCUMENTS)
# note that the same query will be used later when querying after updating the embeddings
query = np.random.rand(768).astype(np.float32)
query_results = document_store.query_by_embedding(query_emb=query, top_k=len(DOCUMENTS), return_embedding=True)
# check if search with cosine similarity returns the correct number of results
assert len(query_results) == len(DOCUMENTS)
indexed_docs = {}
for doc in DOCUMENTS:
indexed_docs[doc["content"]] = doc["embedding"]
indexed_docs[doc["content"]] /= np.linalg.norm(doc["embedding"])
for doc in query_results:
result_emb = doc.embedding
original_emb = indexed_docs[doc.content].astype("float32")
# check if the stored embedding was normalized
np.testing.assert_allclose(
original_emb, result_emb, rtol=0.2, atol=5e-07
) # high tolerance necessary for Milvus 2
# check if the score is plausible for cosine similarity
assert 0 <= doc.score <= 1.0
# now check if vectors are normalized when updating embeddings
class MockRetriever:
def embed_documents(self, docs):
return np.random.rand(len(docs), 768).astype(np.float32)
retriever = MockRetriever()
document_store.update_embeddings(retriever=retriever)
query_results = document_store.query_by_embedding(query_emb=query, top_k=len(DOCUMENTS), return_embedding=True)
for doc in query_results:
original_emb = np.array([indexed_docs[doc.content]], dtype="float32")
document_store.normalize_embedding(original_emb[0])
# check if the original embedding has changed after updating the embeddings
assert not np.allclose(original_emb[0], doc.embedding, rtol=0.01)
@pytest.mark.parametrize("document_store_dot_product_small", ["faiss", "milvus1", "milvus"], indirect=True)
def test_normalize_embeddings_diff_shapes(document_store_dot_product_small):
VEC_1 = np.array([0.1, 0.2, 0.3], dtype="float32")
document_store_dot_product_small.normalize_embedding(VEC_1)
assert np.linalg.norm(VEC_1) - 1 < 0.01
VEC_1 = np.array([0.1, 0.2, 0.3], dtype="float32").reshape(1, -1)
document_store_dot_product_small.normalize_embedding(VEC_1)
assert np.linalg.norm(VEC_1) - 1 < 0.01
@pytest.mark.parametrize("document_store_small", ["faiss", "milvus1", "milvus", "weaviate"], indirect=True)
def test_cosine_sanity_check(document_store_small):
VEC_1 = np.array([0.1, 0.2, 0.3], dtype="float32")
VEC_2 = np.array([0.4, 0.5, 0.6], dtype="float32")
# This is the cosine similarity of VEC_1 and VEC_2 calculated using sklearn.metrics.pairwise.cosine_similarity
# The score is normalized to yield a value between 0 and 1.
KNOWN_COSINE = 0.9746317
KNOWN_SCALED_COSINE = (KNOWN_COSINE + 1) / 2
docs = [{"name": "vec_1", "text": "vec_1", "content": "vec_1", "embedding": VEC_1}]
ensure_ids_are_correct_uuids(docs=docs, document_store=document_store_small)
document_store_small.write_documents(documents=docs)
query_results = document_store_small.query_by_embedding(
query_emb=VEC_2, top_k=1, return_embedding=True, scale_score=True
)
# check if faiss returns the same cosine similarity. Manual testing with faiss yielded 0.9746318
assert math.isclose(query_results[0].score, KNOWN_SCALED_COSINE, abs_tol=0.00002)
query_results = document_store_small.query_by_embedding(
query_emb=VEC_2, top_k=1, return_embedding=True, scale_score=False
)
# check if faiss returns the same cosine similarity. Manual testing with faiss yielded 0.9746318
assert math.isclose(query_results[0].score, KNOWN_COSINE, abs_tol=0.00002)
@pytest.mark.integration
def test_pipeline_with_existing_faiss_docstore(tmp_path):
document_store: FAISSDocumentStore = FAISSDocumentStore(
sql_url=f'sqlite:///{(tmp_path / "faiss_document_store.db").absolute()}'
)
retriever = MockDenseRetriever(document_store=document_store)
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, update_existing_embeddings=True)
document_store.save(tmp_path / "existing_faiss_document_store")
query_config = f"""
version: ignore
components:
- name: DPRRetriever
type: MockDenseRetriever
params:
document_store: ExistingFAISSDocumentStore
- name: ExistingFAISSDocumentStore
type: FAISSDocumentStore
params:
faiss_index_path: '{tmp_path / "existing_faiss_document_store"}'
pipelines:
- name: query_pipeline
nodes:
- name: DPRRetriever
inputs: [Query]
"""
pipeline = Pipeline.load_from_config(yaml.safe_load(query_config))
existing_document_store = pipeline.get_document_store()
faiss_index = existing_document_store.faiss_indexes["document"]
assert faiss_index.ntotal == len(DOCUMENTS)

276
test/document_stores/test_sql_based.py Normal file
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@ -0,0 +1,276 @@
import math
import pytest
import numpy as np
from haystack.schema import Document
from haystack.pipelines import DocumentSearchPipeline
from haystack.pipelines import Pipeline
DOCUMENTS = [
{
"meta": {"name": "name_1", "year": "2020", "month": "01"},
"content": "text_1",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_2", "year": "2020", "month": "02"},
"content": "text_2",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_3", "year": "2020", "month": "03"},
"content": "text_3",
"embedding": np.random.rand(768).astype(np.float64),
},
{
"meta": {"name": "name_4", "year": "2021", "month": "01"},
"content": "text_4",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_5", "year": "2021", "month": "02"},
"content": "text_5",
"embedding": np.random.rand(768).astype(np.float32),
},
{
"meta": {"name": "name_6", "year": "2021", "month": "03"},
"content": "text_6",
"embedding": np.random.rand(768).astype(np.float64),
},
]
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
@pytest.mark.parametrize("batch_size", [4, 6])
def test_update_docs(document_store, retriever, batch_size):
# initial write
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=batch_size)
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
# test if correct vectors are associated with docs
for doc in documents_indexed:
original_doc = [d for d in DOCUMENTS if d["content"] == doc.content][0]
updated_embedding = retriever.embed_documents([Document.from_dict(original_doc)])
stored_doc = document_store.get_all_documents(filters={"name": [doc.meta["name"]]})[0]
# compare original input vec with stored one (ignore extra dim added by hnsw)
# original input vec is normalized as faiss only stores normalized vectors
a = updated_embedding / np.linalg.norm(updated_embedding)
assert np.allclose(a[0], stored_doc.embedding, rtol=0.2) # high tolerance necessary for Milvus 2
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["milvus1", "milvus", "faiss"], indirect=True)
def test_update_existing_docs(document_store, retriever):
document_store.duplicate_documents = "overwrite"
old_document = Document(content="text_1")
# initial write
document_store.write_documents([old_document])
document_store.update_embeddings(retriever=retriever)
old_documents_indexed = document_store.get_all_documents(return_embedding=True)
assert len(old_documents_indexed) == 1
# Update document data
new_document = Document(content="text_2")
new_document.id = old_document.id
document_store.write_documents([new_document])
document_store.update_embeddings(retriever=retriever)
new_documents_indexed = document_store.get_all_documents(return_embedding=True)
assert len(new_documents_indexed) == 1
assert old_documents_indexed[0].id == new_documents_indexed[0].id
assert old_documents_indexed[0].content == "text_1"
assert new_documents_indexed[0].content == "text_2"
print(type(old_documents_indexed[0].embedding))
print(type(new_documents_indexed[0].embedding))
assert not np.allclose(old_documents_indexed[0].embedding, new_documents_indexed[0].embedding, rtol=0.01)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_update_with_empty_store(document_store, retriever):
# Call update with empty doc store
document_store.update_embeddings(retriever=retriever)
# initial write
document_store.write_documents(DOCUMENTS)
documents_indexed = document_store.get_all_documents()
assert len(documents_indexed) == len(DOCUMENTS)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_finding(document_store, retriever):
document_store.write_documents(DOCUMENTS)
pipe = DocumentSearchPipeline(retriever=retriever)
prediction = pipe.run(query="How to test this?", params={"Retriever": {"top_k": 1}})
assert len(prediction.get("documents", [])) == 1
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_with_filters_multivalue(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
document_store.delete_documents(filters={"name": ["name_1", "name_2", "name_3", "name_4"]})
documents = document_store.get_all_documents()
assert len(documents) == 2
assert document_store.get_embedding_count() == 2
assert {doc.meta["name"] for doc in documents} == {"name_5", "name_6"}
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_with_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
document_store.delete_documents(filters={"year": ["2020"]})
documents = document_store.get_all_documents()
assert len(documents) == 3
assert document_store.get_embedding_count() == 3
assert all("2021" == doc.meta["year"] for doc in documents)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_with_many_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
document_store.delete_documents(filters={"month": ["01"], "year": ["2020"]})
documents = document_store.get_all_documents()
assert len(documents) == 5
assert document_store.get_embedding_count() == 5
assert "name_1" not in {doc.meta["name"] for doc in documents}
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_by_id(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
doc_ids = [doc.id for doc in document_store.get_all_documents()]
ids_to_delete = doc_ids[0:3]
document_store.delete_documents(ids=ids_to_delete)
documents = document_store.get_all_documents()
assert len(documents) == len(doc_ids) - len(ids_to_delete)
assert document_store.get_embedding_count() == len(doc_ids) - len(ids_to_delete)
remaining_ids = [doc.id for doc in documents]
assert all(doc_id not in remaining_ids for doc_id in ids_to_delete)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_delete_docs_by_id_with_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
ids_to_delete = [doc.id for doc in document_store.get_all_documents(filters={"name": ["name_1", "name_2"]})]
ids_not_to_delete = [
doc.id for doc in document_store.get_all_documents(filters={"name": ["name_3", "name_4", "name_5", "name_6"]})
]
document_store.delete_documents(ids=ids_to_delete, filters={"name": ["name_1", "name_2", "name_3", "name_4"]})
documents = document_store.get_all_documents()
assert len(documents) == len(DOCUMENTS) - len(ids_to_delete)
assert document_store.get_embedding_count() == len(DOCUMENTS) - len(ids_to_delete)
assert all(doc.meta["name"] != "name_1" for doc in documents)
assert all(doc.meta["name"] != "name_2" for doc in documents)
all_ids_left = [doc.id for doc in documents]
assert all(doc_id in all_ids_left for doc_id in ids_not_to_delete)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_get_docs_with_filters_one_value(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
documents = document_store.get_all_documents(filters={"year": ["2020"]})
assert len(documents) == 3
assert all("2020" == doc.meta["year"] for doc in documents)
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_get_docs_with_filters_many_values(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
documents = document_store.get_all_documents(filters={"name": ["name_5", "name_6"]})
assert len(documents) == 2
assert {doc.meta["name"] for doc in documents} == {"name_5", "name_6"}
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_get_docs_with_many_filters(document_store, retriever):
document_store.write_documents(DOCUMENTS)
document_store.update_embeddings(retriever=retriever, batch_size=4)
assert document_store.get_embedding_count() == 6
documents = document_store.get_all_documents(filters={"month": ["01"], "year": ["2020"]})
assert len(documents) == 1
assert "name_1" == documents[0].meta["name"]
assert "01" == documents[0].meta["month"]
assert "2020" == documents[0].meta["year"]
@pytest.mark.integration
@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
@pytest.mark.parametrize("document_store", ["faiss", "milvus1", "milvus"], indirect=True)
def test_pipeline(document_store, retriever):
documents = [
{"name": "name_1", "content": "text_1", "embedding": np.random.rand(768).astype(np.float32)},
{"name": "name_2", "content": "text_2", "embedding": np.random.rand(768).astype(np.float32)},
{"name": "name_3", "content": "text_3", "embedding": np.random.rand(768).astype(np.float64)},
{"name": "name_4", "content": "text_4", "embedding": np.random.rand(768).astype(np.float32)},
]
document_store.write_documents(documents)
pipeline = Pipeline()
pipeline.add_node(component=retriever, name="FAISS", inputs=["Query"])
output = pipeline.run(query="How to test this?", params={"FAISS": {"top_k": 3}})
assert len(output["documents"]) == 3