From 3ebaa496e7de10a1b5c4fa096997497ab0136e03 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 2 Oct 2024 21:54:28 +0200 Subject: [PATCH 01/26] CRUD operations and tests for document store --- integrations/azure_ai_search/.gitignore | 163 ++++++++ integrations/azure_ai_search/CHANGELOG.md | 99 +++++ integrations/azure_ai_search/LICENSE | 201 ++++++++++ integrations/azure_ai_search/README.md | 32 ++ integrations/azure_ai_search/pydoc/config.yml | 32 ++ integrations/azure_ai_search/pyproject.toml | 158 ++++++++ .../retrievers/embedding_retriever.py | 105 +++++ .../azure_ai_search/__init__.py | 6 + .../azure_ai_search/document_store.py | 378 ++++++++++++++++++ .../document_stores/azure_ai_search/errors.py | 13 + .../azure_ai_search/tests/__init__.py | 3 + .../azure_ai_search/tests/conftest.py | 67 ++++ .../tests/test_document_store.py | 118 ++++++ 13 files changed, 1375 insertions(+) create mode 100644 integrations/azure_ai_search/.gitignore create mode 100644 integrations/azure_ai_search/CHANGELOG.md create mode 100644 integrations/azure_ai_search/LICENSE create mode 100644 integrations/azure_ai_search/README.md create mode 100644 integrations/azure_ai_search/pydoc/config.yml create mode 100644 integrations/azure_ai_search/pyproject.toml create mode 100644 integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py create mode 100644 integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py create mode 100644 integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py create mode 100644 integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py create mode 100644 integrations/azure_ai_search/tests/__init__.py create mode 100644 integrations/azure_ai_search/tests/conftest.py create mode 100644 integrations/azure_ai_search/tests/test_document_store.py diff --git a/integrations/azure_ai_search/.gitignore b/integrations/azure_ai_search/.gitignore new file mode 100644 index 000000000..d1c340c1f --- /dev/null +++ b/integrations/azure_ai_search/.gitignore @@ -0,0 +1,163 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# VS Code +.vscode diff --git a/integrations/azure_ai_search/CHANGELOG.md b/integrations/azure_ai_search/CHANGELOG.md new file mode 100644 index 000000000..dd1ddb86e --- /dev/null +++ b/integrations/azure_ai_search/CHANGELOG.md @@ -0,0 +1,99 @@ +# Changelog + +## [integrations/opensearch-v0.8.1] - 2024-07-15 + +### 🚀 Features + +- Add raise_on_failure param to OpenSearch retrievers (#852) +- Add filter_policy to opensearch integration (#822) + +### 🐛 Bug Fixes + +- `OpenSearch` - Fallback to default filter policy when deserializing retrievers without the init parameter (#895) + +### ⚙️ Miscellaneous Tasks + +- Update ruff invocation to include check parameter (#853) + +## [integrations/opensearch-v0.7.1] - 2024-06-27 + +### 🐛 Bug Fixes + +- Serialization for custom_query in OpenSearch retrievers (#851) +- Support legacy filters with OpenSearchDocumentStore (#850) + +## [integrations/opensearch-v0.7.0] - 2024-06-25 + +### 🚀 Features + +- Defer the database connection to when it's needed (#753) +- Improve `OpenSearchDocumentStore.__init__` arguments (#739) +- Return_embeddings flag for opensearch (#784) +- Add create_index option to OpenSearchDocumentStore (#840) +- Add custom_query param to OpenSearch retrievers (#841) + +### 🐛 Bug Fixes + +- Fix order of API docs (#447) + +This PR will also push the docs to Readme + +### 📚 Documentation + +- Update category slug (#442) +- Fixing opensearch docstrings (#521) +- Small consistency improvements (#536) +- Disable-class-def (#556) + +### ⚙️ Miscellaneous Tasks + +- Retry tests to reduce flakyness (#836) + +### Opensearch + +- Generate API docs (#324) + +## [integrations/opensearch-v0.2.0] - 2024-01-17 + +### 🐛 Bug Fixes + +- Fix links in docstrings (#188) + + + +### 🚜 Refactor + +- Use `hatch_vcs` to manage integrations versioning (#103) + +## [integrations/opensearch-v0.1.1] - 2023-12-05 + +### 🐛 Bug Fixes + +- Fix import and increase version (#77) + + + +## [integrations/opensearch-v0.1.0] - 2023-12-04 + +### 🐛 Bug Fixes + +- Fix license headers + + +## [integrations/opensearch-v0.0.2] - 2023-11-30 + +### 🚀 Features + +- Extend OpenSearch params support (#70) + +### Build + +- Bump OpenSearch integration version to 0.0.2 (#71) + +## [integrations/opensearch-v0.0.1] - 2023-11-30 + +### 🚀 Features + +- [OpenSearch] add document store, BM25Retriever and EmbeddingRetriever (#68) + + diff --git a/integrations/azure_ai_search/LICENSE b/integrations/azure_ai_search/LICENSE new file mode 100644 index 000000000..de4c7f39f --- /dev/null +++ b/integrations/azure_ai_search/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2023 deepset GmbH + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/integrations/azure_ai_search/README.md b/integrations/azure_ai_search/README.md new file mode 100644 index 000000000..40a2f8eaa --- /dev/null +++ b/integrations/azure_ai_search/README.md @@ -0,0 +1,32 @@ +[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/opensearch.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/opensearch.yml) + +[![PyPI - Version](https://img.shields.io/pypi/v/opensearch-haystack.svg)](https://pypi.org/project/opensearch-haystack) +[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/opensearch-haystack.svg)](https://pypi.org/project/opensearch-haystack) + +# OpenSearch Document Store + +Document Store for Haystack 2.x, supports OpenSearch. + +## Installation + +```console +pip install opensearch-haystack +``` + +## Testing + +To run tests first start a Docker container running OpenSearch. We provide a utility `docker-compose.yml` for that: + +```console +docker-compose up +``` + +Then run tests: + +```console +hatch run test +``` + +## License + +`opensearch-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. diff --git a/integrations/azure_ai_search/pydoc/config.yml b/integrations/azure_ai_search/pydoc/config.yml new file mode 100644 index 000000000..7b2e20d83 --- /dev/null +++ b/integrations/azure_ai_search/pydoc/config.yml @@ -0,0 +1,32 @@ +loaders: + - type: haystack_pydoc_tools.loaders.CustomPythonLoader + search_path: [../src] + modules: [ + "haystack_integrations.components.retrievers.opensearch.bm25_retriever", + "haystack_integrations.components.retrievers.opensearch.embedding_retriever", + "haystack_integrations.document_stores.opensearch.document_store", + "haystack_integrations.document_stores.opensearch.filters", + ] + ignore_when_discovered: ["__init__"] +processors: + - type: filter + expression: + documented_only: true + do_not_filter_modules: false + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: haystack_pydoc_tools.renderers.ReadmeIntegrationRenderer + excerpt: OpenSearch integration for Haystack + category_slug: integrations-api + title: OpenSearch + slug: integrations-opensearch + order: 180 + markdown: + descriptive_class_title: false + classdef_code_block: false + descriptive_module_title: true + add_method_class_prefix: true + add_member_class_prefix: false + filename: _readme_opensearch.md diff --git a/integrations/azure_ai_search/pyproject.toml b/integrations/azure_ai_search/pyproject.toml new file mode 100644 index 000000000..c7061cae4 --- /dev/null +++ b/integrations/azure_ai_search/pyproject.toml @@ -0,0 +1,158 @@ +[build-system] +requires = ["hatchling", "hatch-vcs"] +build-backend = "hatchling.build" + +[project] +name = "azure-ai-search-haystack" +dynamic = ["version"] +description = 'Haystack 2.x Document Store for Azure AI Search' +readme = "README.md" +requires-python = ">=3.8" +license = "Apache-2.0" +keywords = [] +authors = [{ name = "deepset", email = "info@deepset.ai" }] +classifiers = [ + "License :: OSI Approved :: Apache Software License", + "Development Status :: 4 - Beta", + "Programming Language :: Python", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", +] +dependencies = ["haystack-ai", "azure-search-documents>=11.5"] + +[project.urls] +Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch#readme" +Issues = "https://github.com/deepset-ai/haystack-core-integrations/issues" +Source = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch" + +[tool.hatch.build.targets.wheel] +packages = ["src/haystack_integrations"] + +[tool.hatch.version] +source = "vcs" +tag-pattern = 'integrations\/azure-ai-search-v(?P.*)' + +[tool.hatch.version.raw-options] +root = "../.." +git_describe_command = 'git describe --tags --match="integrations/azure-ai-search-v[0-9]*"' + +[tool.hatch.envs.default] +dependencies = [ + "coverage[toml]>=6.5", + "pytest", + "pytest-rerunfailures", + "pytest-xdist", + "haystack-pydoc-tools", +] +[tool.hatch.envs.default.scripts] +test = "pytest --reruns 0 --reruns-delay 30 -x {args:tests}" +test-cov = "coverage run -m pytest --reruns 3 --reruns-delay 30 -x {args:tests}" +cov-report = ["- coverage combine", "coverage report"] +cov = ["test-cov", "cov-report"] + +docs = ["pydoc-markdown pydoc/config.yml"] + +[[tool.hatch.envs.all.matrix]] +python = ["3.8", "3.9", "3.10", "3.11"] + +[tool.hatch.envs.lint] +detached = true +dependencies = ["black>=23.1.0", "mypy>=1.0.0", "ruff>=0.0.243"] +[tool.hatch.envs.lint.scripts] +typing = "mypy --install-types --non-interactive --explicit-package-bases {args:src/ tests}" +style = ["ruff check {args:.}", "black --check --diff {args:.}"] +fmt = ["black {args:.}", "ruff check --fix {args:.}", "style"] +all = ["style", "typing"] + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.black] +target-version = ["py38"] +line-length = 120 +skip-string-normalization = true + +[tool.ruff] +target-version = "py38" +line-length = 120 +select = [ + "A", + "ARG", + "B", + "C", + "DTZ", + "E", + "EM", + "F", + "FBT", + "I", + "ICN", + "ISC", + "N", + "PLC", + "PLE", + "PLR", + "PLW", + "Q", + "RUF", + "S", + "T", + "TID", + "UP", + "W", + "YTT", +] +ignore = [ + # Allow non-abstract empty methods in abstract base classes + "B027", + # Allow boolean positional values in function calls, like `dict.get(... True)` + "FBT003", + # Ignore checks for possible passwords + "S105", + "S106", + "S107", + # Ignore complexity + "C901", + "PLR0911", + "PLR0912", + "PLR0913", + "PLR0915", +] +unfixable = [ + # Don't touch unused imports + "F401", +] + +[tool.ruff.isort] +known-first-party = ["src"] + +[tool.ruff.flake8-tidy-imports] +ban-relative-imports = "parents" + +[tool.ruff.per-file-ignores] +# Tests can use magic values, assertions, and relative imports +"tests/**/*" = ["PLR2004", "S101", "TID252"] + +[tool.coverage.run] +source = ["haystack_integrations"] +branch = true +parallel = false + + +[tool.coverage.report] +omit = ["*/tests/*", "*/__init__.py"] +show_missing = true +exclude_lines = ["no cov", "if __name__ == .__main__.:", "if TYPE_CHECKING:"] + + +[tool.pytest.ini_options] +minversion = "6.0" +markers = ["unit: unit tests", "integration: integration tests"] + +[[tool.mypy.overrides]] +module = ["haystack.*", "haystack_integrations.*", "pytest.*", "azure-ai-search.*"] +ignore_missing_imports = true \ No newline at end of file diff --git a/integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py b/integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py new file mode 100644 index 000000000..bb16e8027 --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py @@ -0,0 +1,105 @@ +import logging +import os +from dataclasses import asdict +from typing import Any, Dict, List, Optional, Union + +from azure.search.documents.models import VectorizedQuery +from haystack import Document, component +from haystack.document_stores.types import FilterPolicy +from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore + +# from haystack.components.embedders import AzureOpenAIDocumentEmbedder, AzureOpenAITextEmbedder +# from .vectorizer import create_vectorizer, get_document_emebeddings, get_text_embeddings + +logger = logging.getLogger(__name__) + + +@component +class AzureAISearchEmbeddingRetriever: + """ + Retrieves documents from the AzureAISearchDocumentStore using a vector similarity metric. + + Must be connected to the AzureAISearchDocumentStore to run. + """ + + def __init__( + self, + *, + document_store: AzureAISearchDocumentStore, + filters: Optional[Dict[str, Any]] = None, + top_k: int = 10, + filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE, + ): + """ + Create the AzureAISearchEmbeddingRetriever component. + + :param document_store: An instance of AzureAISearchDocumentStore to use with the Retriever. + :param filters: Filters applied when fetching documents from the Document Store. + Filters are applied during the approximate kNN search to ensure the Retriever returns + `top_k` matching documents. + :param top_k: Maximum number of documents to return. + + """ + self.filters = filters or {} + self.top_k = top_k + self.document_store = document_store + self.filter_policy = ( + filter_policy if isinstance(filter_policy, FilterPolicy) else FilterPolicy.from_str(filter_policy) + ) + + if not isinstance(document_store, AzureAISearchDocumentStore): + message = "document_store must be an instance of AstraDocumentStore" + raise Exception(message) + + @component.output_types(documents=List[Document]) + def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None): + """Retrieve documents from the AzureAISearchDocumentStore. + + :param query_embedding: floats representing the query embedding + :param filters: Filters applied to the retrieved Documents. The way runtime filters are applied depends on + the `filter_policy` chosen at retriever initialization. See init method docstring for more + details. + :param top_k: the maximum number of documents to retrieve. + :returns: a dictionary with the following keys: + - `documents`: A list of documents retrieved from the AzureAISearchDocumentStore. + """ + # filters = apply_filter_policy(self.filter_policy, self.filters, filters) + top_k = top_k or self.top_k + + return {"documents": self._vector_search(query_embedding, top_k, filters=filters)} + + def _vector_search( + self, + query_embedding: List[float], + *, + top_k: int = 10, + fields: Optional[List[str]] = None, + ) -> List[Document]: + """ + Retrieves documents that are most similar to the query embedding using a vector similarity metric. + It uses the vector configuration of the document store. By default it uses the HNSW algorithm with cosine similarity. + + This method is not meant to be part of the public interface of + `AzureAISearchDocumentStore` nor called directly. + `AzureAISearchEmbeddingRetriever` uses this method directly and is the public interface for it. + + :param query_embedding: Embedding of the query. + :param filters: Filters applied to the retrieved Documents. Defaults to None. + Filters are applied during the approximate kNN search to ensure that top_k matching documents are returned. + :param top_k: Maximum number of Documents to return, defaults to 10 + + :raises ValueError: If `query_embedding` is an empty list + :returns: List of Document that are most similar to `query_embedding` + """ + + if not query_embedding: + msg = "query_embedding must be a non-empty list of floats" + raise ValueError(msg) + + # embedding = get_embeddings(input=query, model=embedding_model_name, dimensions=self._embedding_dimension) + + vector_query = VectorizedQuery(vector=query_embedding, k_nearest_neighbors=3, fields="embeddings") + + results = self.client.search(search_text=None, vector_queries=[vector_query], select=fields) + + return results diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py new file mode 100644 index 000000000..51fb2b911 --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py @@ -0,0 +1,6 @@ +# SPDX-FileCopyrightText: 2023-present deepset GmbH +# +# SPDX-License-Identifier: Apache-2.0 +from .document_store import DEFAULT_VECTOR_SEARCH, AzureAISearchDocumentStore + +__all__ = ["AzureAISearchDocumentStore", "DEFAULT_VECTOR_SEARCH"] diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py new file mode 100644 index 000000000..80f0d785d --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -0,0 +1,378 @@ +# SPDX-FileCopyrightText: 2023-present deepset GmbH +# +# SPDX-License-Identifier: Apache-2.0 +import logging +import os +from dataclasses import asdict +from typing import Any, Dict, List, Optional + +from azure.core.credentials import AzureKeyCredential +from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceNotFoundError +from azure.identity import DefaultAzureCredential +from azure.search.documents import SearchClient +from azure.search.documents.indexes import SearchIndexClient +from azure.search.documents.indexes.models import ( + HnswAlgorithmConfiguration, + HnswParameters, + SearchableField, + SearchField, + SearchFieldDataType, + SearchIndex, + SimpleField, + VectorSearch, + VectorSearchAlgorithmMetric, + VectorSearchProfile, +) +from haystack import default_from_dict, default_to_dict +from haystack.dataclasses import Document +from haystack.document_stores.errors import DuplicateDocumentError +from haystack.document_stores.types import DuplicatePolicy +from haystack.utils import Secret, deserialize_secrets_inplace + +from .errors import AzureAISearchDocumentStoreConfigError + +type_mapping = {str: "Edm.String", bool: "Edm.Boolean", int: "Edm.Int32", float: "Edm.Double"} + +MAX_UPLOAD_BATCH_SIZE = 1000 + +DEFAULT_VECTOR_SEARCH = VectorSearch( + profiles=[ + VectorSearchProfile(name="default-vector-config", algorithm_configuration_name="cosine-algorithm-config") + ], + algorithms=[ + HnswAlgorithmConfiguration( + name="cosine-algorithm-config", + parameters=HnswParameters( + metric=VectorSearchAlgorithmMetric.COSINE, + ), + ) + ], +) + +logger = logging.getLogger(__name__) +logging.getLogger("azure").setLevel(logging.ERROR) +logging.getLogger("azure.identity").setLevel(logging.DEBUG) + + +class AzureAISearchDocumentStore: + def __init__( + self, + *, + api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), + azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), + index_name: str = "default", + embedding_dimension: int = 768, # whats a better default value + metadata_fields: Optional[Dict[str, type]] = None, + vector_search_configuration: VectorSearch = None, + create_index: bool = True, + **kwargs, + ): + """ + A document store using [Azure AI Search](https://azure.microsoft.com/products/ai-services/ai-search/) + as the backend. + + :param azure_endpoint: The URL endpoint of an Azure AI Search service. + :param api_key: The API key to use for authentication. + :param index_name: Name of index in Azure AI Search, if it doesn't exist it will be created. + :param embedding_dimension: Dimension of the embeddings. + :param metadata_fields: A dictionary of metatada keys and their types to create + additional fields in index schema. As fields in Azure SearchIndex cannot be dynamic, it is necessary to specify the metadata fields in advance. + :param vector_search_configuration: Configuration option related to vector search. + Default configuration uses the HNSW algorithm with cosine similarity to handle vector searches. + + :param kwargs: Optional keyword parameters for Azure AI Search. + Some of the supported parameters: + - `api_version`: The Search API version to use for requests. + - `audience`: sets the Audience to use for authentication with Azure Active Directory (AAD). + The audience is not considered when using a shared key. If audience is not provided, the public cloud audience will be assumed. + + For more information on parameters, see the [official Azure AI Search documentation](https://learn.microsoft.com/en-us/azure/search/) + """ + + azure_endpoint = azure_endpoint or os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT") + if not azure_endpoint: + msg = "Please provide an Azure endpoint or set the environment variable AZURE_OPENAI_ENDPOINT." + raise ValueError(msg) + api_key = api_key or os.environ.get("AZURE_SEARCH_API_KEY") + + self._client = None + self._index_client = None + self._index_fields = None # stores all fields in the final schema of index + self._api_key = api_key + self._azure_endpoint = azure_endpoint + self._index_name = index_name + self._embedding_dimension = embedding_dimension + self._metadata_fields = metadata_fields + self._vector_search_configuration = vector_search_configuration or DEFAULT_VECTOR_SEARCH + self._create_index = create_index + self._kwargs = kwargs + + @property + def client(self) -> SearchClient: + + if isinstance(self._azure_endpoint, Secret): + self._azure_endpoint = self._azure_endpoint.resolve_value() + + if isinstance(self._api_key, Secret): + self._api_key = self._api_key.resolve_value() + credential = AzureKeyCredential(self._api_key) if self._api_key else DefaultAzureCredential() + try: + if not self._index_client: + self._index_client = SearchIndexClient(self._azure_endpoint, credential, **self._kwargs) + if not self.index_exists(self._index_name): + # Create a new index if it does not exist + logger.debug( + "The index '%s' does not exist. A new index will be created.", + self._index_name, + ) + self.create_index(self._index_name) + except (HttpResponseError, ClientAuthenticationError) as error: + msg = f"Failed to authenticate with Azure Search: {error}" + raise AzureAISearchDocumentStoreConfigError(msg) from error + + self._client = self._index_client.get_search_client(self._index_name) + return self._client + + def create_index(self, index_name: str, **kwargs) -> None: + """ + Creates a new search index. + :param index_name: Name of the index to create. If None, the index name from the constructor is used. + :param kwargs: Optional keyword parameters. + + """ + + # default fields to create index based on Haystack Document + default_fields = [ + SimpleField(name="id", type=SearchFieldDataType.String, key=True, filterable=True), + SearchableField(name="content", type=SearchFieldDataType.String), + SearchField( + name="embedding", + type=SearchFieldDataType.Collection(SearchFieldDataType.Single), + searchable=True, + vector_search_dimensions=self._embedding_dimension, + vector_search_profile_name="default-vector-config", + ), + ] + + if not index_name: + index_name = self._index_name + fields = default_fields + if self._metadata_fields: + fields.extend(self._create_metadata_index_fields(self._metadata_fields)) + + self._index_fields = fields + index = SearchIndex(name=index_name, fields=fields, vector_search=self._vector_search_configuration, **kwargs) + self._index_client.create_index(index) + + def to_dict(self) -> Dict[str, Any]: + # This is not the best solution to serialise this class but is the fastest to implement. + # Not all kwargs types can be serialised to text so this can fail. We must serialise each + # type explicitly to handle this properly. + """ + Serializes the component to a dictionary. + + :returns: + Dictionary with serialized data. + """ + return default_to_dict( + self, + azure_endpoint=self._azure_endpoint.to_dict() if self._azure_endpoint is not None else None, + api_key=self._api_key.to_dict() if self._api_key is not None else None, + index_name=self._index_name, + create_index=self._create_index, + embedding_dimension=self._embedding_dimension, + metadata_fields=self._metadata_fields, + vector_search_configuration=self._vector_search_configuration, + **self._kwargs, + ) + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> "AzureAISearchDocumentStore": + """ + Deserializes the component from a dictionary. + + :param data: + Dictionary to deserialize from. + + :returns: + Deserialized component. + """ + + deserialize_secrets_inplace(data["init_parameters"], keys=["api_key", "azure_endpoint"]) + return default_from_dict(cls, data) + + def count_documents(self, **kwargs: Any) -> int: + """ + Returns how many documents are present in the search index. + + :param kwargs: additional keyword parameters. + :returns: list of retrieved documents. + """ + return self.client.get_document_count(**kwargs) + + def write_documents(self, documents: List[Document], policy: DuplicatePolicy = DuplicatePolicy.FAIL) -> int: + """ + Writes the provided documents to search index. + + :param documents: documents to write to the index. + :return: the number of documents added to index. + """ + + if len(documents) > 0: + if not isinstance(documents[0], Document): + msg = "param 'documents' must contain a list of objects of type Document" + raise ValueError(msg) + + def _convert_input_document(documents: Document): + document_dict = asdict(documents) + if not isinstance(document_dict["id"], str): + msg = f"Document id {document_dict['id']} is not a string, " + raise Exception(msg) + index_document = self._default_index_mapping(document_dict) + return index_document + + documents_to_write = [] + for doc in documents: + try: + self.client.get_document(doc.id) + if policy == DuplicatePolicy.SKIP: + logger.info(f"Document with ID {doc.id} already exists. Skipping.") + continue + elif policy == DuplicatePolicy.FAIL: + msg = f"Document with ID {doc.id} already exists." + raise DuplicateDocumentError(msg) + elif policy == DuplicatePolicy.OVERWRITE: + logger.info(f"Document with ID {doc.id} already exists. Overwriting.") + documents_to_write.append(_convert_input_document(doc)) + except ResourceNotFoundError: + # Document does not exist, safe to add + documents_to_write.append(_convert_input_document(doc)) + + if documents_to_write != []: + self.client.upload_documents(documents_to_write) + return len(documents_to_write) + + def delete_documents(self, document_ids: List[str]) -> None: + """ + Deletes all documents with a matching document_ids from the search index. + + :param document_ids: ids of the documents to be deleted. + """ + if self.count_documents == 0: + return + documents = self._get_raw_documents_by_id(document_ids) + if documents: + self.client.delete_documents(documents) + + def _get_raw_documents_by_id(self, document_ids: List[str]): + """ + Retrieves all Azure documents with a matching document_ids from the document store. + + :param document_ids: ids of the documents to be retrieved. + :returns: list of retrieved Azure documents. + """ + azure_documents = [] + for doc_id in document_ids: + try: + document = self.client.get_document(doc_id) + azure_documents.append(document) + except ResourceNotFoundError: + logger.warning(f"Document with ID {doc_id} not found.") + return azure_documents + + def get_documents_by_id(self, document_ids: List[str]) -> List[Document]: + return self._convert_search_result_to_documents(self._get_raw_documents_by_id(document_ids)) + + def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Document]: + + # TODO: Implement this method to filter documents based on metadata fields + # For now the implementation is similar to search_documents + """ + Calls the Azure AI Search client's search method and handles pagination. + :param search_text: The text to search for. If not supplied, all documents will be retrieved. + :returns: A list of Documents that match the given filters. + """ + + search_text = "*" # default to search all documents + azure_docs = [] + if filters: + # Handle filtering by 'id' first + document_ids = filters.get("id") + if document_ids: + azure_docs = self._get_raw_documents_by_id(document_ids) + return self._convert_search_result_to_documents(azure_docs) + + # Handle filtering by 'content' + search_text = filters.get("content", "*") + + # Perform search with pagination + result = self.client.search(search_text=search_text, top=self.count_documents()) + azure_docs = list(result) + return self._convert_search_result_to_documents(azure_docs) + + def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) -> List[Document]: + + documents = [] + for azure_doc in azure_docs: + + embedding = azure_doc.get("embedding") + if embedding == self._dummy_vector: + embedding = None + + # Filter out meta fields + meta = { + key: value + for key, value in azure_doc.items() + if key not in ["id", "content", "embedding"] and not key.startswith("@") + } + + # Create the document with meta only if it's non-empty + doc = Document( + id=azure_doc["id"], content=azure_doc["content"], embedding=embedding, meta=meta if meta else {} + ) + + documents.append(doc) + return documents + + def index_exists(self, index_name: Optional[str]) -> bool: + if self._index_client and index_name: + return index_name in self._index_client.list_index_names() + + def _default_index_mapping(self, document: Dict[str, Any]) -> Dict[str, Any]: + """Map the document keys to fields of search index""" + + keys_to_remove = ["dataframe", "blob", "sparse_embedding", "score"] + index_document = {k: v for k, v in document.items() if k not in keys_to_remove} + + metadata = index_document.pop("meta", None) + for key, value in metadata.items(): + index_document[key] = value + if index_document["embedding"] is None: + self._dummy_vector = [-10.0] * self._embedding_dimension + index_document["embedding"] = self._dummy_vector + + return index_document + + def _create_metadata_index_fields(self, metadata: Dict[str, Any]) -> List[SimpleField]: + """Create a list of index fields for storing metadata values.""" + + index_fields = [] + metadata_field_mapping = self._map_metadata_field_types(metadata) + + for key, field_type in metadata_field_mapping.items(): + index_fields.append(SimpleField(name=key, type=field_type, filterable=True)) + + return index_fields + + def _map_metadata_field_types(self, metadata: Dict[str, type]) -> Dict[str, str]: + """Map metadata field types to Azure Search field types.""" + metadata_field_mapping = {} + + for key, value_type in metadata.items(): + field_type = type_mapping.get(value_type) + if not field_type: + error_message = f"Unsupported field type for key '{key}': {value_type}" + raise ValueError(error_message) + metadata_field_mapping[key] = field_type + + return metadata_field_mapping diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py new file mode 100644 index 000000000..b2756050a --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py @@ -0,0 +1,13 @@ +from haystack.document_stores.errors import DocumentStoreError + + +class AzureAISearchDocumentStoreError(DocumentStoreError): + """Parent class for all AzureAISearchDocumentStore exceptions.""" + + pass + + +class AzureAISearchDocumentStoreConfigError(AzureAISearchDocumentStoreError): + """Raised when a configuration is not valid for a AzureAISearchDocumentStore.""" + + pass diff --git a/integrations/azure_ai_search/tests/__init__.py b/integrations/azure_ai_search/tests/__init__.py new file mode 100644 index 000000000..e873bc332 --- /dev/null +++ b/integrations/azure_ai_search/tests/__init__.py @@ -0,0 +1,3 @@ +# SPDX-FileCopyrightText: 2023-present deepset GmbH +# +# SPDX-License-Identifier: Apache-2.0 diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py new file mode 100644 index 000000000..189d04e57 --- /dev/null +++ b/integrations/azure_ai_search/tests/conftest.py @@ -0,0 +1,67 @@ +import os +import time + +import pytest +from azure.core.credentials import AzureKeyCredential +from azure.core.exceptions import ResourceNotFoundError +from azure.search.documents.indexes import SearchIndexClient +from haystack.document_stores.types import DuplicatePolicy + +from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore + +# This is the approximate time in seconds it takes for the documents to be available +SLEEP_TIME_IN_SECONDS = 5 + + +@pytest.fixture() +def sleep_time(): + return SLEEP_TIME_IN_SECONDS + + +@pytest.fixture +def document_store(request): + """ + This is the most basic requirement for the child class: provide + an instance of this document store so the base class can use it. + """ + index_name = "haystack_test_integration" + metadata_fields = getattr(request, "param", {}).get("metadata_fields", None) + + azure_endpoint = os.environ["AZURE_SEARCH_SERVICE_ENDPOINT"] + api_key = os.environ["AZURE_SEARCH_API_KEY"] + + client = SearchIndexClient(azure_endpoint, AzureKeyCredential(api_key)) + if index_name in client.list_index_names(): + client.delete_index(index_name) + + store = AzureAISearchDocumentStore( + api_key=api_key, + azure_endpoint=azure_endpoint, + index_name=index_name, + create_index=True, + embedding_dimension=15, + metadata_fields=metadata_fields, + ) + + # Override some methods to wait for the documents to be available + original_write_documents = store.write_documents + + def write_documents_and_wait(documents, policy=DuplicatePolicy.NONE): + written_docs = original_write_documents(documents, policy) + time.sleep(SLEEP_TIME_IN_SECONDS) + return written_docs + + original_delete_documents = store.delete_documents + + def delete_documents_and_wait(filters): + original_delete_documents(filters) + time.sleep(SLEEP_TIME_IN_SECONDS) + + store.write_documents = write_documents_and_wait + store.delete_documents = delete_documents_and_wait + + yield store + try: + client.delete_index(index_name) + except ResourceNotFoundError: + pass diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py new file mode 100644 index 000000000..c7fae3f18 --- /dev/null +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -0,0 +1,118 @@ +# SPDX-FileCopyrightText: 2023-present deepset GmbH +# +# SPDX-License-Identifier: Apache-2.0 +import os +from unittest.mock import patch +import pytest +from haystack.dataclasses.document import Document +from haystack.testing.document_store import ( + CountDocumentsTest, + DeleteDocumentsTest, + WriteDocumentsTest, +) +from haystack.utils.auth import EnvVarSecret, Secret + +from haystack_integrations.document_stores.azure_ai_search import DEFAULT_VECTOR_SEARCH, AzureAISearchDocumentStore + + +@patch("haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore") +def test_to_dict(monkeypatch): + monkeypatch.setenv("AZURE_SEARCH_API_KEY", "test-api-key") + monkeypatch.setenv("AZURE_SEARCH_SERVICE_ENDPOINT", "test-endpoint") + document_store = AzureAISearchDocumentStore() + res = document_store.to_dict() + assert res == { + "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", + "init_parameters": { + "azure_endpoint": {"env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], "strict": False, "type": "env_var"}, + "api_key": {"env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False, "type": "env_var"}, + "index_name": "default", + "embedding_dimension": 768, + "metadata_fields": None, + "create_index": True, + "vector_search_configuration": DEFAULT_VECTOR_SEARCH, + }, + } + + +@patch("haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore") +def test_from_dict(monkeypatch): + monkeypatch.setenv("AZURE_SEARCH_API_KEY", "test-api-key") + monkeypatch.setenv("AZURE_SEARCH_SERVICE_ENDPOINT", "test-endpoint") + + data = { + "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", + "init_parameters": { + "azure_endpoint": {"env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], "strict": False, "type": "env_var"}, + "api_key": {"env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False, "type": "env_var"}, + "embedding_dimension": 768, + "index_name": "default", + "metadata_fields": None, + "create_index": False, + "vector_search_configuration": DEFAULT_VECTOR_SEARCH, + }, + } + document_store = AzureAISearchDocumentStore.from_dict(data) + assert isinstance(document_store._api_key, EnvVarSecret) + assert isinstance(document_store._azure_endpoint, EnvVarSecret) + assert document_store._index_name == "default" + assert document_store._embedding_dimension == 768 + assert document_store._metadata_fields is None + assert document_store._create_index is False + assert document_store._vector_search_configuration == DEFAULT_VECTOR_SEARCH + + +@patch("haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore") +def test_init_is_lazy(_mock_azure_search_client): + AzureAISearchDocumentStore(azure_endppoint=Secret.from_token("test_endpoint")) + _mock_azure_search_client.assert_not_called() + + +@patch("haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore") +def test_init(_mock_azure_search_client): + + document_store = AzureAISearchDocumentStore( + api_key=Secret.from_token("fake-api-key"), + azure_endpoint=Secret.from_token("fake_endpoint"), + index_name="my_index", + create_index=False, + embedding_dimension=15, + metadata_fields={"Title": str, "Pages": int}, + ) + + assert document_store._index_name == "my_index" + assert document_store._create_index is False + assert document_store._embedding_dimension == 15 + assert document_store._metadata_fields == {"Title": str, "Pages": int} + assert document_store._vector_search_configuration == DEFAULT_VECTOR_SEARCH + + +@pytest.mark.skipif( + not os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT", None) and not os.environ.get("AZURE_SEARCH_API_KEY", None), + reason="Missing AZURE_SEARCH_SERVICE_ENDPOINT or AZURE_SEARCH_API_KEY.", +) +class TestDocumentStore(CountDocumentsTest, WriteDocumentsTest, DeleteDocumentsTest): + + def test_write_documents(self, document_store: AzureAISearchDocumentStore): + docs = [Document(id="1")] + assert document_store.write_documents(docs) == 1 + + # Parametrize the test with metadata fields + @pytest.mark.parametrize( + "document_store", + [ + {"metadata_fields": {"author": str, "publication_year": int, "rating": float}}, + ], + indirect=True, + ) + def test_write_documents_with_meta(self, document_store: AzureAISearchDocumentStore): + docs = [ + Document( + id="1", + meta={"author": "Tom", "publication_year": 2021, "rating": 4.5}, + content="This is a test document.", + ) + ] + document_store.write_documents(docs) + doc = document_store.get_documents_by_id(["1"]) + assert doc[0] == docs[0] From e5227a6996c5b6a86ac0029c9706daba89c1cc22 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Mon, 14 Oct 2024 15:45:39 +0200 Subject: [PATCH 02/26] Add embedding retriever and tests --- integrations/azure_ai_search/pydoc/config.yml | 16 +- integrations/azure_ai_search/pyproject.toml | 4 +- .../retrievers/embedding_retriever.py | 105 ------------- .../retrievers/azure_ai_search/__init__.py | 3 + .../azure_ai_search/embedding_retriever.py | 126 ++++++++++++++++ .../azure_ai_search/document_store.py | 52 ++++++- .../tests/test_document_store.py | 1 + .../tests/test_embedding_retriever.py | 140 ++++++++++++++++++ 8 files changed, 324 insertions(+), 123 deletions(-) delete mode 100644 integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py create mode 100644 integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/__init__.py create mode 100644 integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py create mode 100644 integrations/azure_ai_search/tests/test_embedding_retriever.py diff --git a/integrations/azure_ai_search/pydoc/config.yml b/integrations/azure_ai_search/pydoc/config.yml index 7b2e20d83..6c7aa6e13 100644 --- a/integrations/azure_ai_search/pydoc/config.yml +++ b/integrations/azure_ai_search/pydoc/config.yml @@ -2,10 +2,10 @@ loaders: - type: haystack_pydoc_tools.loaders.CustomPythonLoader search_path: [../src] modules: [ - "haystack_integrations.components.retrievers.opensearch.bm25_retriever", - "haystack_integrations.components.retrievers.opensearch.embedding_retriever", - "haystack_integrations.document_stores.opensearch.document_store", - "haystack_integrations.document_stores.opensearch.filters", + "haystack_integrations.components.retrievers.azure_ai_search.bm25_retriever", + "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever", + "haystack_integrations.document_stores.azure_ai_search.document_store", + "haystack_integrations.document_stores.azure_ai_search.filters", ] ignore_when_discovered: ["__init__"] processors: @@ -18,10 +18,10 @@ processors: - type: crossref renderer: type: haystack_pydoc_tools.renderers.ReadmeIntegrationRenderer - excerpt: OpenSearch integration for Haystack + excerpt: Azure AI Search integration for Haystack category_slug: integrations-api - title: OpenSearch - slug: integrations-opensearch + title: Azure AI Search + slug: integrations-azure_ai_search order: 180 markdown: descriptive_class_title: false @@ -29,4 +29,4 @@ renderer: descriptive_module_title: true add_method_class_prefix: true add_member_class_prefix: false - filename: _readme_opensearch.md + filename: _readme_azure_ai_search.md diff --git a/integrations/azure_ai_search/pyproject.toml b/integrations/azure_ai_search/pyproject.toml index c7061cae4..3ce17f2ee 100644 --- a/integrations/azure_ai_search/pyproject.toml +++ b/integrations/azure_ai_search/pyproject.toml @@ -22,7 +22,7 @@ classifiers = [ "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ] -dependencies = ["haystack-ai", "azure-search-documents>=11.5"] +dependencies = ["haystack-ai", "azure-search-documents>=11.5", "azure-identity"] [project.urls] Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch#readme" @@ -154,5 +154,5 @@ minversion = "6.0" markers = ["unit: unit tests", "integration: integration tests"] [[tool.mypy.overrides]] -module = ["haystack.*", "haystack_integrations.*", "pytest.*", "azure-ai-search.*"] +module = ["haystack.*", "haystack_integrations.*", "pytest.*", "azure-ai-search.*", "azure.identity.*"] ignore_missing_imports = true \ No newline at end of file diff --git a/integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py b/integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py deleted file mode 100644 index bb16e8027..000000000 --- a/integrations/azure_ai_search/src/haystack_integrations/components/azure_ai_search/retrievers/embedding_retriever.py +++ /dev/null @@ -1,105 +0,0 @@ -import logging -import os -from dataclasses import asdict -from typing import Any, Dict, List, Optional, Union - -from azure.search.documents.models import VectorizedQuery -from haystack import Document, component -from haystack.document_stores.types import FilterPolicy -from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore - -# from haystack.components.embedders import AzureOpenAIDocumentEmbedder, AzureOpenAITextEmbedder -# from .vectorizer import create_vectorizer, get_document_emebeddings, get_text_embeddings - -logger = logging.getLogger(__name__) - - -@component -class AzureAISearchEmbeddingRetriever: - """ - Retrieves documents from the AzureAISearchDocumentStore using a vector similarity metric. - - Must be connected to the AzureAISearchDocumentStore to run. - """ - - def __init__( - self, - *, - document_store: AzureAISearchDocumentStore, - filters: Optional[Dict[str, Any]] = None, - top_k: int = 10, - filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE, - ): - """ - Create the AzureAISearchEmbeddingRetriever component. - - :param document_store: An instance of AzureAISearchDocumentStore to use with the Retriever. - :param filters: Filters applied when fetching documents from the Document Store. - Filters are applied during the approximate kNN search to ensure the Retriever returns - `top_k` matching documents. - :param top_k: Maximum number of documents to return. - - """ - self.filters = filters or {} - self.top_k = top_k - self.document_store = document_store - self.filter_policy = ( - filter_policy if isinstance(filter_policy, FilterPolicy) else FilterPolicy.from_str(filter_policy) - ) - - if not isinstance(document_store, AzureAISearchDocumentStore): - message = "document_store must be an instance of AstraDocumentStore" - raise Exception(message) - - @component.output_types(documents=List[Document]) - def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None): - """Retrieve documents from the AzureAISearchDocumentStore. - - :param query_embedding: floats representing the query embedding - :param filters: Filters applied to the retrieved Documents. The way runtime filters are applied depends on - the `filter_policy` chosen at retriever initialization. See init method docstring for more - details. - :param top_k: the maximum number of documents to retrieve. - :returns: a dictionary with the following keys: - - `documents`: A list of documents retrieved from the AzureAISearchDocumentStore. - """ - # filters = apply_filter_policy(self.filter_policy, self.filters, filters) - top_k = top_k or self.top_k - - return {"documents": self._vector_search(query_embedding, top_k, filters=filters)} - - def _vector_search( - self, - query_embedding: List[float], - *, - top_k: int = 10, - fields: Optional[List[str]] = None, - ) -> List[Document]: - """ - Retrieves documents that are most similar to the query embedding using a vector similarity metric. - It uses the vector configuration of the document store. By default it uses the HNSW algorithm with cosine similarity. - - This method is not meant to be part of the public interface of - `AzureAISearchDocumentStore` nor called directly. - `AzureAISearchEmbeddingRetriever` uses this method directly and is the public interface for it. - - :param query_embedding: Embedding of the query. - :param filters: Filters applied to the retrieved Documents. Defaults to None. - Filters are applied during the approximate kNN search to ensure that top_k matching documents are returned. - :param top_k: Maximum number of Documents to return, defaults to 10 - - :raises ValueError: If `query_embedding` is an empty list - :returns: List of Document that are most similar to `query_embedding` - """ - - if not query_embedding: - msg = "query_embedding must be a non-empty list of floats" - raise ValueError(msg) - - # embedding = get_embeddings(input=query, model=embedding_model_name, dimensions=self._embedding_dimension) - - vector_query = VectorizedQuery(vector=query_embedding, k_nearest_neighbors=3, fields="embeddings") - - results = self.client.search(search_text=None, vector_queries=[vector_query], select=fields) - - return results diff --git a/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/__init__.py b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/__init__.py new file mode 100644 index 000000000..eb75ffa6c --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/__init__.py @@ -0,0 +1,3 @@ +from .embedding_retriever import AzureAISearchEmbeddingRetriever + +__all__ = ["AzureAISearchEmbeddingRetriever"] diff --git a/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py new file mode 100644 index 000000000..9c8b668c4 --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py @@ -0,0 +1,126 @@ +import logging +from typing import Any, Dict, List, Optional, Union + +from haystack import Document, component, default_from_dict, default_to_dict +from haystack.document_stores.types import FilterPolicy +from haystack.document_stores.types.filter_policy import apply_filter_policy + +from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore + +logger = logging.getLogger(__name__) + + +@component +class AzureAISearchEmbeddingRetriever: + """ + Retrieves documents from the AzureAISearchDocumentStore using a vector similarity metric. + Must be connected to the AzureAISearchDocumentStore to run. + + """ + + def __init__( + self, + *, + document_store: AzureAISearchDocumentStore, + filters: Optional[Dict[str, Any]] = None, + top_k: int = 10, + filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE, + raise_on_failure: bool = True, + ): + """ + Create the AzureAISearchEmbeddingRetriever component. + + :param document_store: An instance of AzureAISearchDocumentStore to use with the Retriever. + :param filters: Filters applied when fetching documents from the Document Store. + Filters are applied during the approximate kNN search to ensure the Retriever returns + `top_k` matching documents. + :param top_k: Maximum number of documents to return. + :filter_policy: Policy to determine how filters are applied. Possible options: + + """ + self._filters = filters or {} + self._top_k = top_k + self._document_store = document_store + self._filter_policy = ( + filter_policy if isinstance(filter_policy, FilterPolicy) else FilterPolicy.from_str(filter_policy) + ) + self._raise_on_failure = raise_on_failure + + if not isinstance(document_store, AzureAISearchDocumentStore): + message = "document_store must be an instance of AzureAISearchDocumentStore" + raise Exception(message) + + def to_dict(self) -> Dict[str, Any]: + """ + Serializes the component to a dictionary. + + :returns: + Dictionary with serialized data. + """ + return default_to_dict( + self, + filters=self._filters, + top_k=self._top_k, + document_store=self._document_store.to_dict(), + filter_policy=self._filter_policy.value, + ) + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> "AzureAISearchEmbeddingRetriever": + """ + Deserializes the component from a dictionary. + + :param data: + Dictionary to deserialize from. + + :returns: + Deserialized component. + """ + data["init_parameters"]["document_store"] = AzureAISearchDocumentStore.from_dict( + data["init_parameters"]["document_store"] + ) + + # Pipelines serialized with old versions of the component might not + # have the filter_policy field. + if "filter_policy" in data["init_parameters"]: + data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(data["init_parameters"]["filter_policy"]) + return default_from_dict(cls, data) + + @component.output_types(documents=List[Document]) + def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None): + """Retrieve documents from the AzureAISearchDocumentStore. + + :param query_embedding: floats representing the query embedding + :param filters: Filters applied to the retrieved Documents. The way runtime filters are applied depends on + the `filter_policy` chosen at retriever initialization. See init method docstring for more + details. + :param top_k: the maximum number of documents to retrieve. + :returns: a dictionary with the following keys: + - `documents`: A list of documents retrieved from the AzureAISearchDocumentStore. + """ + filters = apply_filter_policy(self._filter_policy, self._filters, filters) + top_k = top_k or self._top_k + if filters is None: + filters = self._filters + if top_k is None: + top_k = self._top_k + + docs: List[Document] = [] + + try: + docs = self._document_store._embedding_retrieval( + query_embedding=query_embedding, + filters=filters, + top_k=top_k, + ) + except Exception as e: + if self._raise_on_failure: + raise e + else: + logger.warning( + "An error during embedding retrieval occurred and will be ignored by returning empty results: %s", + str(e), + exc_info=True, + ) + + return {"documents": docs} diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 80f0d785d..8722eaa43 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -23,6 +23,7 @@ VectorSearchAlgorithmMetric, VectorSearchProfile, ) +from azure.search.documents.models import VectorizedQuery from haystack import default_from_dict, default_to_dict from haystack.dataclasses import Document from haystack.document_stores.errors import DuplicateDocumentError @@ -61,7 +62,7 @@ def __init__( api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), index_name: str = "default", - embedding_dimension: int = 768, # whats a better default value + embedding_dimension: int = 768, metadata_fields: Optional[Dict[str, type]] = None, vector_search_configuration: VectorSearch = None, create_index: bool = True, @@ -102,6 +103,7 @@ def __init__( self._azure_endpoint = azure_endpoint self._index_name = index_name self._embedding_dimension = embedding_dimension + self._dummy_vector = [-10.0] * self._embedding_dimension self._metadata_fields = metadata_fields self._vector_search_configuration = vector_search_configuration or DEFAULT_VECTOR_SEARCH self._create_index = create_index @@ -149,6 +151,7 @@ def create_index(self, index_name: str, **kwargs) -> None: name="embedding", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, + hidden=False, vector_search_dimensions=self._embedding_dimension, vector_search_profile_name="default-vector-config", ), @@ -218,19 +221,20 @@ def write_documents(self, documents: List[Document], policy: DuplicatePolicy = D :return: the number of documents added to index. """ - if len(documents) > 0: - if not isinstance(documents[0], Document): - msg = "param 'documents' must contain a list of objects of type Document" - raise ValueError(msg) - def _convert_input_document(documents: Document): document_dict = asdict(documents) if not isinstance(document_dict["id"], str): msg = f"Document id {document_dict['id']} is not a string, " raise Exception(msg) index_document = self._default_index_mapping(document_dict) + return index_document + if len(documents) > 0: + if not isinstance(documents[0], Document): + msg = "param 'documents' must contain a list of objects of type Document" + raise ValueError(msg) + documents_to_write = [] for doc in documents: try: @@ -343,12 +347,10 @@ def _default_index_mapping(self, document: Dict[str, Any]) -> Dict[str, Any]: keys_to_remove = ["dataframe", "blob", "sparse_embedding", "score"] index_document = {k: v for k, v in document.items() if k not in keys_to_remove} - metadata = index_document.pop("meta", None) for key, value in metadata.items(): index_document[key] = value if index_document["embedding"] is None: - self._dummy_vector = [-10.0] * self._embedding_dimension index_document["embedding"] = self._dummy_vector return index_document @@ -376,3 +378,37 @@ def _map_metadata_field_types(self, metadata: Dict[str, type]) -> Dict[str, str] metadata_field_mapping[key] = field_type return metadata_field_mapping + + def _embedding_retrieval( + self, + query_embedding: List[float], + *, + top_k: int = 10, + fields: Optional[List[str]] = None, + filters: Optional[Dict[str, Any]] = None, # TODO will be used in the future + ) -> List[Document]: + """ + Retrieves documents that are most similar to the query embedding using a vector similarity metric. + It uses the vector configuration of the document store. By default it uses the HNSW algorithm with cosine similarity. + + This method is not meant to be part of the public interface of + `AzureAISearchDocumentStore` nor called directly. + `AzureAISearchEmbeddingRetriever` uses this method directly and is the public interface for it. + + :param query_embedding: Embedding of the query. + :param filters: Filters applied to the retrieved Documents. Defaults to None. + Filters are applied during the approximate kNN search to ensure that top_k matching documents are returned. + :param top_k: Maximum number of Documents to return, defaults to 10 + + :raises ValueError: If `query_embedding` is an empty list + :returns: List of Document that are most similar to `query_embedding` + """ + + if not query_embedding: + msg = "query_embedding must be a non-empty list of floats" + raise ValueError(msg) + + vector_query = VectorizedQuery(vector=query_embedding, k_nearest_neighbors=3, fields="embedding") + result = self.client.search(search_text=None, vector_queries=[vector_query], select=fields, top=top_k) + azure_docs = list(result) + return self._convert_search_result_to_documents(azure_docs) diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index c7fae3f18..ffb529673 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -3,6 +3,7 @@ # SPDX-License-Identifier: Apache-2.0 import os from unittest.mock import patch + import pytest from haystack.dataclasses.document import Document from haystack.testing.document_store import ( diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py new file mode 100644 index 000000000..6fb0d673e --- /dev/null +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -0,0 +1,140 @@ +# SPDX-FileCopyrightText: 2023-present deepset GmbH +# +# SPDX-License-Identifier: Apache-2.0 +import os +from typing import List +from unittest.mock import Mock + +import pytest +from azure.core.exceptions import HttpResponseError +from haystack.dataclasses import Document +from haystack.document_stores.types import FilterPolicy +from numpy.random import rand + +from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever +from haystack_integrations.document_stores.azure_ai_search import DEFAULT_VECTOR_SEARCH, AzureAISearchDocumentStore + + +def test_init_default(): + mock_store = Mock(spec=AzureAISearchDocumentStore) + retriever = AzureAISearchEmbeddingRetriever(document_store=mock_store) + assert retriever._document_store == mock_store + assert retriever._filters == {} + assert retriever._top_k == 10 + assert retriever._filter_policy == FilterPolicy.REPLACE + + retriever = AzureAISearchEmbeddingRetriever(document_store=mock_store, filter_policy="replace") + assert retriever._filter_policy == FilterPolicy.REPLACE + + with pytest.raises(ValueError): + AzureAISearchEmbeddingRetriever(document_store=mock_store, filter_policy="unknown") + + +def test_to_dict(): + document_store = AzureAISearchDocumentStore(hosts="some fake host") + retriever = AzureAISearchEmbeddingRetriever(document_store=document_store) + res = retriever.to_dict() + type_s = "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever.AzureAISearchEmbeddingRetriever" + assert res == { + "type": type_s, + "init_parameters": { + "filters": {}, + "top_k": 10, + "document_store": { + "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", + "init_parameters": { + "azure_endpoint": { + "type": "env_var", + "env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], + "strict": False, + }, + "api_key": {"type": "env_var", "env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False}, + "index_name": "default", + "create_index": True, + "embedding_dimension": 768, + "metadata_fields": None, + "vector_search_configuration": DEFAULT_VECTOR_SEARCH, + "hosts": "some fake host", + }, + }, + "filter_policy": "replace", + }, + } + + +def test_from_dict(): + type_s = "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever.AzureAISearchEmbeddingRetriever" + data = { + "type": type_s, + "init_parameters": { + "filters": {}, + "top_k": 10, + "document_store": { + "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", + "init_parameters": { + "azure_endpoint": { + "type": "env_var", + "env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], + "strict": False, + }, + "api_key": {"type": "env_var", "env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False}, + "index_name": "default", + "create_index": True, + "embedding_dimension": 768, + "metadata_fields": None, + "vector_search_configuration": DEFAULT_VECTOR_SEARCH, + "hosts": "some fake host", + }, + }, + "filter_policy": "replace", + }, + } + retriever = AzureAISearchEmbeddingRetriever.from_dict(data) + assert isinstance(retriever._document_store, AzureAISearchDocumentStore) + assert retriever._filters == {} + assert retriever._top_k == 10 + assert retriever._filter_policy == FilterPolicy.REPLACE + + +@pytest.mark.skipif( + not os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT", None) and not os.environ.get("AZURE_SEARCH_API_KEY", None), + reason="Missing AZURE_SEARCH_SERVICE_ENDPOINT or AZURE_SEARCH_API_KEY.", +) +@pytest.mark.integration +class TestRetriever: + + def test_run(self, document_store: AzureAISearchDocumentStore): + docs = [Document(id="1")] + document_store.write_documents(docs) + retriever = AzureAISearchEmbeddingRetriever(document_store=document_store) + res = retriever.run(query_embedding=[0.1] * 15) + assert res["documents"] == docs + + def test_embedding_retrieval(self, document_store: AzureAISearchDocumentStore): + query_embedding = [0.1] * 15 + most_similar_embedding = [0.8] * 15 + second_best_embedding = [0.8] * 7 + [0.1] * 3 + [0.2] * 5 + another_embedding = rand(15).tolist() + + docs = [ + Document(content="This is first document", embedding=most_similar_embedding), + Document(content="This is second document", embedding=second_best_embedding), + Document(content="This is thrid document", embedding=another_embedding), + ] + + document_store.write_documents(docs) + retriever = AzureAISearchEmbeddingRetriever(document_store=document_store) + results = retriever.run(query_embedding=query_embedding) + results = document_store._embedding_retrieval(query_embedding=query_embedding, top_k=1) + assert len(results) == 1 + assert results[0].content == "This is first document" + + def test_empty_query_embedding(self, document_store: AzureAISearchDocumentStore): + query_embedding: List[float] = [] + with pytest.raises(ValueError): + document_store._embedding_retrieval(query_embedding=query_embedding) + + def test_query_embedding_wrong_dimension(self, document_store: AzureAISearchDocumentStore): + query_embedding = [0.1] * 4 + with pytest.raises(HttpResponseError): + document_store._embedding_retrieval(query_embedding=query_embedding) From 875d0753b21b868d6bf6cc7f881fb9f7619c8d64 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 16 Oct 2024 14:39:25 +0200 Subject: [PATCH 03/26] Add comparison filters logic --- .../azure_ai_search/document_store.py | 33 +++-- .../document_stores/azure_ai_search/errors.py | 7 ++ .../azure_ai_search/filters.py | 117 ++++++++++++++++++ .../tests/test_embedding_retriever.py | 2 +- 4 files changed, 147 insertions(+), 12 deletions(-) create mode 100644 integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 8722eaa43..b0ddb5a79 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -31,6 +31,7 @@ from haystack.utils import Secret, deserialize_secrets_inplace from .errors import AzureAISearchDocumentStoreConfigError +from .filters import normalize_filters type_mapping = {str: "Edm.String", bool: "Edm.Boolean", int: "Edm.Int32", float: "Edm.Double"} @@ -299,20 +300,30 @@ def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Doc search_text = "*" # default to search all documents azure_docs = [] - if filters: + if not filters: + result = self.client.search(search_text=search_text, top=self.count_documents()) + + elif filters: # Handle filtering by 'id' first - document_ids = filters.get("id") - if document_ids: - azure_docs = self._get_raw_documents_by_id(document_ids) - return self._convert_search_result_to_documents(azure_docs) + if "id" in filters: + document_ids = filters.get("id") + if document_ids: + azure_docs = self._get_raw_documents_by_id(document_ids) + return self._convert_search_result_to_documents(azure_docs) # Handle filtering by 'content' - search_text = filters.get("content", "*") + if "content" in filters: + search_text = filters.get("content") + + else : + normalized_filters = normalize_filters(filters) + print ("Normalized filters: ", normalized_filters) + result = self.client.search(filter=normalized_filters) + print ("Result: ", result) # Perform search with pagination - result = self.client.search(search_text=search_text, top=self.count_documents()) - azure_docs = list(result) - return self._convert_search_result_to_documents(azure_docs) + #azure_docs = list(result) + return self._convert_search_result_to_documents(result) def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) -> List[Document]: @@ -408,7 +419,7 @@ def _embedding_retrieval( msg = "query_embedding must be a non-empty list of floats" raise ValueError(msg) - vector_query = VectorizedQuery(vector=query_embedding, k_nearest_neighbors=3, fields="embedding") - result = self.client.search(search_text=None, vector_queries=[vector_query], select=fields, top=top_k) + vector_query = VectorizedQuery(vector=query_embedding, k_nearest_neighbors=top_k, fields="embedding") + result = self.client.search(search_text=None, vector_queries=[vector_query], select=fields, filter=filters) azure_docs = list(result) return self._convert_search_result_to_documents(azure_docs) diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py index b2756050a..ad4ba7098 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py @@ -11,3 +11,10 @@ class AzureAISearchDocumentStoreConfigError(AzureAISearchDocumentStoreError): """Raised when a configuration is not valid for a AzureAISearchDocumentStore.""" pass + + + +class AzureAISearchDocumentStoreFilterError(DocumentStoreError): + """Raised when filter is not valid for AzureAISearchDocumentStore.""" + + pass \ No newline at end of file diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py new file mode 100644 index 000000000..e4d61bc57 --- /dev/null +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -0,0 +1,117 @@ +from collections import defaultdict +from dataclasses import dataclass +from typing import Any, Dict, List +from haystack.utils import raise_on_invalid_filter_syntax +from .errors import AzureAISearchDocumentStoreFilterError + + +LOGICAL_OPERATORS = { + "AND": "and", + "OR": "or", + "NOT": "not" +} + + +def normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]: + """ + Converts Haystack filters in Azure AI Search compatible filters. + """ + if not isinstance(filters, dict): + msg = "Filters must be a dictionary" + raise AzureAISearchDocumentStoreFilterError(msg) + + if "field" in filters: + return _parse_comparison_condition(filters) # return a string + return _parse_logical_condition(filters) + + +def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]: + if "operator" not in condition: + msg = f"'operator' key missing in {condition}" + raise AzureAISearchDocumentStoreFilterError(msg) + if "conditions" not in condition: + msg = f"'conditions' key missing in {condition}" + raise AzureAISearchDocumentStoreFilterError(msg) + #raise_on_invalid_filter_syntax(condition) + operator = condition["operator"] + if operator not in LOGICAL_OPERATORS: + msg = f"Unknown operator {operator}" + raise AzureAISearchDocumentStoreFilterError(msg) + conditions = [_parse_comparison_condition(c) for c in condition["conditions"]] + final_filter = "" + for c in conditions[:-1]: + final_filter += f"({c}) {LOGICAL_OPERATORS[operator]} " + + return final_filter + conditions[-1] + + +def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: + if "field" not in condition: + msg = f"'field' key missing in {condition}" + raise AzureAISearchDocumentStoreFilterError(msg) + field: str = "" + # remove the "meta." prefix from the field name + if condition["field"].startswith("meta."): + field = condition["field"][5:] + else: + field = condition["field"] + + if "operator" not in condition: + msg = f"'operator' key missing in {condition}" + raise AzureAISearchDocumentStoreFilterError(msg) + if "value" not in condition: + msg = f"'value' key missing in {condition}" + raise AzureAISearchDocumentStoreFilterError(msg) + operator: str = condition["operator"] + value: Any = condition["value"] + + if operator not in COMPARISON_OPERATORS: + msg = f"Unknown operator {operator}. Valid operators are: {list(COMPARISON_OPERATORS.keys())}" + raise AzureAISearchDocumentStoreFilterError(msg) + return COMPARISON_OPERATORS[operator](field, value) + +def _eq(field: str, value: Any) -> str: + #if value is None: + #return f"not {field} eq null" + print ("Check in eq") + print (f"{field} eq '{value}'") + return f"{field} eq '{value}'" + +def _ne(field: str, value: Any) -> str: + #if value is None: + #return f"{field} eq null" + return f"not ({field} eq '{value}')" + +def _gt(field: str, value: Any) -> str: + return f"{field} gt {value}" + +def _ge(field: str, value: Any) -> str: + return f"{field} ge {value}" + +def _lt(field: str, value: Any) -> str: + return f"{field} lt {value}" + +def _le(field: str, value: Any) -> str: + return f"{field} le {value}" + +def _in(field: str, value: Any) -> str: + if not isinstance(value, list): + msg = f"Value must be a list when using 'in' comparators" + raise AzureAISearchDocumentStoreFilterError(msg) + elif any([not isinstance(v, str) for v in value]): + msg = f"Azure AI Search only supports string values for 'in' comparators" + raise AzureAISearchDocumentStoreFilterError(msg) + values = ", ".join([str(v) for v in value]) + return f"search.in({field},'{values}')" + + +COMPARISON_OPERATORS = { + "==": _eq, + "!=": _ne, + ">": _gt, + ">=": _ge, + "<": _lt, + "<=": _le, + "in": _in, +# "not in": "$nin", +} \ No newline at end of file diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py index 6fb0d673e..78130dbf6 100644 --- a/integrations/azure_ai_search/tests/test_embedding_retriever.py +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -7,7 +7,7 @@ import pytest from azure.core.exceptions import HttpResponseError -from haystack.dataclasses import Document +from haystack.dataclasses import Document, Pipeline from haystack.document_stores.types import FilterPolicy from numpy.random import rand From a0a45df791e0a0a908fde845d9d9cb11084cc8e2 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Thu, 17 Oct 2024 14:08:16 +0200 Subject: [PATCH 04/26] Fix errors --- .../azure_ai_search/document_store.py | 88 +++++++++---------- .../document_stores/azure_ai_search/errors.py | 3 +- .../azure_ai_search/filters.py | 51 ++++++----- 3 files changed, 71 insertions(+), 71 deletions(-) diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index b0ddb5a79..95f7bc160 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -269,64 +269,42 @@ def delete_documents(self, document_ids: List[str]) -> None: if documents: self.client.delete_documents(documents) - def _get_raw_documents_by_id(self, document_ids: List[str]): - """ - Retrieves all Azure documents with a matching document_ids from the document store. - - :param document_ids: ids of the documents to be retrieved. - :returns: list of retrieved Azure documents. - """ - azure_documents = [] - for doc_id in document_ids: - try: - document = self.client.get_document(doc_id) - azure_documents.append(document) - except ResourceNotFoundError: - logger.warning(f"Document with ID {doc_id} not found.") - return azure_documents - def get_documents_by_id(self, document_ids: List[str]) -> List[Document]: return self._convert_search_result_to_documents(self._get_raw_documents_by_id(document_ids)) - def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Document]: + def search_documents(self, search_text: Optional[str] = "*", top_k: Optional[int] = 10) -> List[Document]: + """ + Returns all documents that match the provided search_text. + If search_text is None, returns all documents. + :param search_text: the text to search for in the Document list. + :param top_k: Maximum number of documents to return. + :returns: A list of Documents that match the given search_text. + """ + result = self.client.search(search_text=search_text, top=top_k) + return self._convert_search_result_to_documents(list(result)) - # TODO: Implement this method to filter documents based on metadata fields - # For now the implementation is similar to search_documents + def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Document]: """ - Calls the Azure AI Search client's search method and handles pagination. - :param search_text: The text to search for. If not supplied, all documents will be retrieved. + Returns the documents that match the provided filters. + Filters should be given as a dictionary supporting filtering by metadata. For details on + filters, see the [metadata filtering documentation](https://docs.haystack.deepset.ai/docs/metadata-filtering). + + :param filters: the filters to apply to the document list. :returns: A list of Documents that match the given filters. """ - search_text = "*" # default to search all documents - azure_docs = [] - if not filters: - result = self.client.search(search_text=search_text, top=self.count_documents()) - - elif filters: - # Handle filtering by 'id' first - if "id" in filters: - document_ids = filters.get("id") - if document_ids: - azure_docs = self._get_raw_documents_by_id(document_ids) - return self._convert_search_result_to_documents(azure_docs) - - # Handle filtering by 'content' - if "content" in filters: - search_text = filters.get("content") - - else : - normalized_filters = normalize_filters(filters) - print ("Normalized filters: ", normalized_filters) - - result = self.client.search(filter=normalized_filters) - print ("Result: ", result) - # Perform search with pagination - #azure_docs = list(result) + if filters: + normalized_filters = normalize_filters(filters) + print("Normalized filters: ", normalized_filters) + + result = self.client.search(filter=normalized_filters) + print("Result: ", result) return self._convert_search_result_to_documents(result) def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) -> List[Document]: - + """ + Converts Azure search results to Haystack Documents. + """ documents = [] for azure_doc in azure_docs: @@ -353,6 +331,22 @@ def index_exists(self, index_name: Optional[str]) -> bool: if self._index_client and index_name: return index_name in self._index_client.list_index_names() + def _get_raw_documents_by_id(self, document_ids: List[str]): + """ + Retrieves all Azure documents with a matching document_ids from the document store. + + :param document_ids: ids of the documents to be retrieved. + :returns: list of retrieved Azure documents. + """ + azure_documents = [] + for doc_id in document_ids: + try: + document = self.client.get_document(doc_id) + azure_documents.append(document) + except ResourceNotFoundError: + logger.warning(f"Document with ID {doc_id} not found.") + return azure_documents + def _default_index_mapping(self, document: Dict[str, Any]) -> Dict[str, Any]: """Map the document keys to fields of search index""" diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py index ad4ba7098..ec7fc2c8e 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py @@ -13,8 +13,7 @@ class AzureAISearchDocumentStoreConfigError(AzureAISearchDocumentStoreError): pass - class AzureAISearchDocumentStoreFilterError(DocumentStoreError): """Raised when filter is not valid for AzureAISearchDocumentStore.""" - pass \ No newline at end of file + pass diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py index e4d61bc57..4732aed36 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -1,15 +1,12 @@ from collections import defaultdict from dataclasses import dataclass from typing import Any, Dict, List + from haystack.utils import raise_on_invalid_filter_syntax + from .errors import AzureAISearchDocumentStoreFilterError - -LOGICAL_OPERATORS = { - "AND": "and", - "OR": "or", - "NOT": "not" -} +LOGICAL_OPERATORS = {"AND": "and", "OR": "or", "NOT": "not"} def normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]: @@ -21,7 +18,7 @@ def normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]: raise AzureAISearchDocumentStoreFilterError(msg) if "field" in filters: - return _parse_comparison_condition(filters) # return a string + return _parse_comparison_condition(filters) # return a string return _parse_logical_condition(filters) @@ -32,17 +29,21 @@ def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]: if "conditions" not in condition: msg = f"'conditions' key missing in {condition}" raise AzureAISearchDocumentStoreFilterError(msg) - #raise_on_invalid_filter_syntax(condition) + operator = condition["operator"] if operator not in LOGICAL_OPERATORS: msg = f"Unknown operator {operator}" raise AzureAISearchDocumentStoreFilterError(msg) conditions = [_parse_comparison_condition(c) for c in condition["conditions"]] + + final_filter = f" {LOGICAL_OPERATORS[operator]} ".join([f"({c})" for c in conditions]) + return final_filter + final_filter = "" for c in conditions[:-1]: final_filter += f"({c}) {LOGICAL_OPERATORS[operator]} " - - return final_filter + conditions[-1] + + return final_filter + "(" + conditions[-1] + ")" def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: @@ -70,36 +71,42 @@ def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: raise AzureAISearchDocumentStoreFilterError(msg) return COMPARISON_OPERATORS[operator](field, value) + def _eq(field: str, value: Any) -> str: - #if value is None: - #return f"not {field} eq null" - print ("Check in eq") - print (f"{field} eq '{value}'") - return f"{field} eq '{value}'" + if isinstance(value, str): + return f"{field} eq '{value}'" + return f"{field} eq {value}" + def _ne(field: str, value: Any) -> str: - #if value is None: - #return f"{field} eq null" - return f"not ({field} eq '{value}')" + + if isinstance(value, str): + return f"not ({field} eq '{value}')" + return f"not ({field} eq {value})" + def _gt(field: str, value: Any) -> str: return f"{field} gt {value}" + def _ge(field: str, value: Any) -> str: return f"{field} ge {value}" + def _lt(field: str, value: Any) -> str: return f"{field} lt {value}" + def _le(field: str, value: Any) -> str: return f"{field} le {value}" + def _in(field: str, value: Any) -> str: if not isinstance(value, list): - msg = f"Value must be a list when using 'in' comparators" + msg = "Value must be a list when using 'in' comparators" raise AzureAISearchDocumentStoreFilterError(msg) elif any([not isinstance(v, str) for v in value]): - msg = f"Azure AI Search only supports string values for 'in' comparators" + msg = "Azure AI Search only supports string values for 'in' comparators" raise AzureAISearchDocumentStoreFilterError(msg) values = ", ".join([str(v) for v in value]) return f"search.in({field},'{values}')" @@ -113,5 +120,5 @@ def _in(field: str, value: Any) -> str: "<": _lt, "<=": _le, "in": _in, -# "not in": "$nin", -} \ No newline at end of file + # "not in": "$nin", +} From 8b4b59f093a5cd47d96ee3831ed084aff17b58ab Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 18 Oct 2024 11:54:23 +0200 Subject: [PATCH 05/26] Fix filter tests --- .../azure_ai_search/document_store.py | 7 +- .../azure_ai_search/filters.py | 6 +- .../azure_ai_search/tests/conftest.py | 10 +-- .../tests/test_document_store.py | 84 +++++++++++++++++++ .../tests/test_embedding_retriever.py | 2 +- 5 files changed, 99 insertions(+), 10 deletions(-) diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 95f7bc160..b684af520 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -292,14 +292,15 @@ def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Doc :param filters: the filters to apply to the document list. :returns: A list of Documents that match the given filters. """ - if filters: normalized_filters = normalize_filters(filters) print("Normalized filters: ", normalized_filters) result = self.client.search(filter=normalized_filters) print("Result: ", result) - return self._convert_search_result_to_documents(result) + return self._convert_search_result_to_documents(result) + else: + return self.search_documents() def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) -> List[Document]: """ @@ -316,7 +317,7 @@ def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) meta = { key: value for key, value in azure_doc.items() - if key not in ["id", "content", "embedding"] and not key.startswith("@") + if key not in ["id", "content", "embedding"] and not key.startswith("@") and value is not None } # Create the document with meta only if it's non-empty diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py index 4732aed36..ed70186e7 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -35,7 +35,7 @@ def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]: msg = f"Unknown operator {operator}" raise AzureAISearchDocumentStoreFilterError(msg) conditions = [_parse_comparison_condition(c) for c in condition["conditions"]] - + final_filter = f" {LOGICAL_OPERATORS[operator]} ".join([f"({c})" for c in conditions]) return final_filter @@ -65,6 +65,8 @@ def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: raise AzureAISearchDocumentStoreFilterError(msg) operator: str = condition["operator"] value: Any = condition["value"] + if value is None: + value = "null" if operator not in COMPARISON_OPERATORS: msg = f"Unknown operator {operator}. Valid operators are: {list(COMPARISON_OPERATORS.keys())}" @@ -73,6 +75,8 @@ def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: def _eq(field: str, value: Any) -> str: + if value == "null": + return f"{field} eq {value}" if isinstance(value, str): return f"{field} eq '{value}'" return f"{field} eq {value}" diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 189d04e57..559fefddb 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -39,7 +39,7 @@ def document_store(request): azure_endpoint=azure_endpoint, index_name=index_name, create_index=True, - embedding_dimension=15, + embedding_dimension=768, metadata_fields=metadata_fields, ) @@ -61,7 +61,7 @@ def delete_documents_and_wait(filters): store.delete_documents = delete_documents_and_wait yield store - try: - client.delete_index(index_name) - except ResourceNotFoundError: - pass + #try: + #client.delete_index(index_name) + #except ResourceNotFoundError: + #pass diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index ffb529673..e89dbff28 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -3,6 +3,8 @@ # SPDX-License-Identifier: Apache-2.0 import os from unittest.mock import patch +from typing import List +import random import pytest from haystack.dataclasses.document import Document @@ -10,6 +12,7 @@ CountDocumentsTest, DeleteDocumentsTest, WriteDocumentsTest, + FilterDocumentsTest ) from haystack.utils.auth import EnvVarSecret, Secret @@ -117,3 +120,84 @@ def test_write_documents_with_meta(self, document_store: AzureAISearchDocumentSt document_store.write_documents(docs) doc = document_store.get_documents_by_id(["1"]) assert doc[0] == docs[0] + +def _random_embeddings(n): + return [random.random() for _ in range(n)] +TEST_EMBEDDING_1 = _random_embeddings(768) +TEST_EMBEDDING_2 = _random_embeddings(768) + +@pytest.mark.skipif( + not os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT", None) and not os.environ.get("AZURE_SEARCH_API_KEY", None), + reason="Missing AZURE_SEARCH_SERVICE_ENDPOINT or AZURE_SEARCH_API_KEY.", +) +@pytest.mark.parametrize( + "document_store", + [ + {"metadata_fields": {"name": str, "page": str, "chapter": str, "number": int, "date": str}}, + ], + indirect=True, + ) +class TestFilters(FilterDocumentsTest): + + @pytest.fixture + def filterable_docs(self) -> List[Document]: + """Fixture that returns a list of Documents that can be used to test filtering.""" + documents = [] + for i in range(3): + documents.append( + Document( + content=f"A Foo Document {i}", + meta={ + "name": f"name_{i}", + "page": "100", + "chapter": "intro", + "number": 2, + "date": "1969-07-21T20:17:40", + }, + embedding=_random_embeddings(768), + ) + ) + documents.append( + Document( + content=f"A Bar Document {i}", + meta={ + "name": f"name_{i}", + "page": "123", + "chapter": "abstract", + "number": -2, + "date": "1972-12-11T19:54:58", + }, + embedding=_random_embeddings(768), + ) + ) + documents.append( + Document( + content=f"A Foobar Document {i}", + meta={ + "name": f"name_{i}", + "page": "90", + "chapter": "conclusion", + "number": -10, + "date": "1989-11-09T17:53:00", + }, + embedding=_random_embeddings(768), + ) + ) + + documents.append( + Document(content=f"Doc {i} with zeros emb", meta={"name": "zeros_doc"}, embedding=TEST_EMBEDDING_1) + ) + documents.append( + Document(content=f"Doc {i} with ones emb", meta={"name": "ones_doc"}, embedding=TEST_EMBEDDING_2) + ) + return documents + + def test_comparison_equal_with_dataframe(self, document_store, filterable_docs): + pass + + def test_comparison_equal_with_none(self, document_store, filterable_docs): + """Test filter_documents() with == comparator and None""" + document_store.write_documents(filterable_docs) + result = document_store.filter_documents(filters={"field": "meta.number", "operator": "==", "value": None}) + print (result) + self.assert_documents_are_equal(result, [d for d in filterable_docs if d.meta.get("number") is None]) diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py index 78130dbf6..6fb0d673e 100644 --- a/integrations/azure_ai_search/tests/test_embedding_retriever.py +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -7,7 +7,7 @@ import pytest from azure.core.exceptions import HttpResponseError -from haystack.dataclasses import Document, Pipeline +from haystack.dataclasses import Document from haystack.document_stores.types import FilterPolicy from numpy.random import rand From 42161ef74cdb1e625f08b29321c47a36e3ff9142 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 18 Oct 2024 18:56:24 +0200 Subject: [PATCH 06/26] Add filters with tests --- .../azure_ai_search/document_store.py | 9 +- .../document_stores/azure_ai_search/errors.py | 3 +- .../azure_ai_search/filters.py | 38 +++- .../azure_ai_search/tests/conftest.py | 9 +- .../tests/test_document_store.py | 179 ++++++++++++++++-- 5 files changed, 206 insertions(+), 32 deletions(-) diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index b684af520..1c784caed 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -4,6 +4,7 @@ import logging import os from dataclasses import asdict +from datetime import datetime from typing import Any, Dict, List, Optional from azure.core.credentials import AzureKeyCredential @@ -33,7 +34,13 @@ from .errors import AzureAISearchDocumentStoreConfigError from .filters import normalize_filters -type_mapping = {str: "Edm.String", bool: "Edm.Boolean", int: "Edm.Int32", float: "Edm.Double"} +type_mapping = { + str: "Edm.String", + bool: "Edm.Boolean", + int: "Edm.Int32", + float: "Edm.Double", + datetime: "Edm.DateTimeOffset", +} MAX_UPLOAD_BATCH_SIZE = 1000 diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py index ec7fc2c8e..0fbc80696 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/errors.py @@ -1,4 +1,5 @@ from haystack.document_stores.errors import DocumentStoreError +from haystack.errors import FilterError class AzureAISearchDocumentStoreError(DocumentStoreError): @@ -13,7 +14,7 @@ class AzureAISearchDocumentStoreConfigError(AzureAISearchDocumentStoreError): pass -class AzureAISearchDocumentStoreFilterError(DocumentStoreError): +class AzureAISearchDocumentStoreFilterError(FilterError): """Raised when filter is not valid for AzureAISearchDocumentStore.""" pass diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py index ed70186e7..4fb4c1f6a 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -1,12 +1,15 @@ from collections import defaultdict from dataclasses import dataclass +from datetime import datetime from typing import Any, Dict, List +from dateutil import parser from haystack.utils import raise_on_invalid_filter_syntax from .errors import AzureAISearchDocumentStoreFilterError LOGICAL_OPERATORS = {"AND": "and", "OR": "or", "NOT": "not"} +numeric_types = [int, float] def normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]: @@ -29,7 +32,7 @@ def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]: if "conditions" not in condition: msg = f"'conditions' key missing in {condition}" raise AzureAISearchDocumentStoreFilterError(msg) - + operator = condition["operator"] if operator not in LOGICAL_OPERATORS: msg = f"Unknown operator {operator}" @@ -75,33 +78,36 @@ def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: def _eq(field: str, value: Any) -> str: - if value == "null": - return f"{field} eq {value}" - if isinstance(value, str): + if isinstance(value, str) and value != "null": return f"{field} eq '{value}'" return f"{field} eq {value}" def _ne(field: str, value: Any) -> str: - - if isinstance(value, str): + if isinstance(value, str) and value != "null": return f"not ({field} eq '{value}')" return f"not ({field} eq {value})" def _gt(field: str, value: Any) -> str: + _validate_type(value, "gt") + print(f"{field} gt {value}") return f"{field} gt {value}" def _ge(field: str, value: Any) -> str: + _validate_type(value, "ge") return f"{field} ge {value}" def _lt(field: str, value: Any) -> str: + # If value is a string, check if it's a valid ISO 8601 datetime string + _validate_type(value, "lt") return f"{field} lt {value}" def _le(field: str, value: Any) -> str: + _validate_type(value, "le") return f"{field} le {value}" @@ -116,6 +122,26 @@ def _in(field: str, value: Any) -> str: return f"search.in({field},'{values}')" +def _validate_type(value: Any, operator: str) -> None: + """Validates that the value is either a number, datetime, or a valid ISO 8601 date string.""" + msg = f"Invalid value type for '{operator}' comparator. Supported types are: int, float, or ISO 8601 string." + + if isinstance(value, str): + # Attempt to parse the string as an ISO 8601 datetime + try: + parser.isoparse(value) + except ValueError: + raise AzureAISearchDocumentStoreFilterError(msg) + elif type(value) not in numeric_types: + raise AzureAISearchDocumentStoreFilterError(msg) + + +def _comparison_operator(field: str, value: Any, operator: str) -> str: + """Generic function for comparison operators ('gt', 'ge', 'lt', 'le').""" + _validate_type(value, operator) + return f"{field} {operator} {value}" + + COMPARISON_OPERATORS = { "==": _eq, "!=": _ne, diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 559fefddb..5bf0ef23c 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -61,7 +61,8 @@ def delete_documents_and_wait(filters): store.delete_documents = delete_documents_and_wait yield store - #try: - #client.delete_index(index_name) - #except ResourceNotFoundError: - #pass + try: + client.delete_index(index_name) + print("deleting index") + except ResourceNotFoundError: + pass diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index e89dbff28..bd4eb95f2 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -2,17 +2,19 @@ # # SPDX-License-Identifier: Apache-2.0 import os -from unittest.mock import patch -from typing import List import random +from datetime import datetime +from typing import List +from unittest.mock import patch import pytest from haystack.dataclasses.document import Document +from haystack.errors import FilterError from haystack.testing.document_store import ( CountDocumentsTest, DeleteDocumentsTest, + FilterDocumentsTest, WriteDocumentsTest, - FilterDocumentsTest ) from haystack.utils.auth import EnvVarSecret, Secret @@ -121,24 +123,28 @@ def test_write_documents_with_meta(self, document_store: AzureAISearchDocumentSt doc = document_store.get_documents_by_id(["1"]) assert doc[0] == docs[0] + def _random_embeddings(n): - return [random.random() for _ in range(n)] + return [round(random.random(), 7) for _ in range(n)] + + TEST_EMBEDDING_1 = _random_embeddings(768) TEST_EMBEDDING_2 = _random_embeddings(768) + @pytest.mark.skipif( not os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT", None) and not os.environ.get("AZURE_SEARCH_API_KEY", None), reason="Missing AZURE_SEARCH_SERVICE_ENDPOINT or AZURE_SEARCH_API_KEY.", ) @pytest.mark.parametrize( - "document_store", - [ - {"metadata_fields": {"name": str, "page": str, "chapter": str, "number": int, "date": str}}, - ], - indirect=True, - ) + "document_store", + [ + {"metadata_fields": {"name": str, "page": str, "chapter": str, "number": int, "date": datetime}}, + ], + indirect=True, +) class TestFilters(FilterDocumentsTest): - + @pytest.fixture def filterable_docs(self) -> List[Document]: """Fixture that returns a list of Documents that can be used to test filtering.""" @@ -152,7 +158,7 @@ def filterable_docs(self) -> List[Document]: "page": "100", "chapter": "intro", "number": 2, - "date": "1969-07-21T20:17:40", + "date": "1969-07-21T20:17:40Z", }, embedding=_random_embeddings(768), ) @@ -165,7 +171,7 @@ def filterable_docs(self) -> List[Document]: "page": "123", "chapter": "abstract", "number": -2, - "date": "1972-12-11T19:54:58", + "date": "1972-12-11T19:54:58Z", }, embedding=_random_embeddings(768), ) @@ -178,12 +184,12 @@ def filterable_docs(self) -> List[Document]: "page": "90", "chapter": "conclusion", "number": -10, - "date": "1989-11-09T17:53:00", + "date": "1989-11-09T17:53:00Z", }, embedding=_random_embeddings(768), ) ) - + documents.append( Document(content=f"Doc {i} with zeros emb", meta={"name": "zeros_doc"}, embedding=TEST_EMBEDDING_1) ) @@ -192,12 +198,145 @@ def filterable_docs(self) -> List[Document]: ) return documents + # Overriding this method to compare the documents with the same order + def assert_documents_are_equal(self, received: List[Document], expected: List[Document]): + """ + Assert that two lists of Documents are equal. + + This is used in every test, if a Document Store implementation has a different behaviour + it should override this method. This can happen for example when the Document Store sets + a score to returned Documents. Since we can't know what the score will be, we can't compare + the Documents reliably. + """ + sorted_recieved = sorted(received, key=lambda doc: doc.id) + sorted_expected = sorted(expected, key=lambda doc: doc.id) + assert sorted_recieved == sorted_expected + def test_comparison_equal_with_dataframe(self, document_store, filterable_docs): pass - def test_comparison_equal_with_none(self, document_store, filterable_docs): - """Test filter_documents() with == comparator and None""" + def test_comparison_not_equal_with_dataframe(self, document_store, filterable_docs): + pass + + def test_comparison_greater_than_with_dataframe(self, document_store, filterable_docs): + pass + + def test_comparison_less_than_with_dataframe(self, document_store, filterable_docs): + pass + + def test_comparison_greater_than_equal_with_dataframe(self, document_store, filterable_docs): + pass + + def test_comparison_less_than_equal_with_dataframe(self, document_store, filterable_docs): + pass + + def test_comparison_greater_than_with_iso_date(self, document_store, filterable_docs): + """Test filter_documents() with > comparator and datetime""" + document_store.write_documents(filterable_docs) + result = document_store.filter_documents( + {"field": "meta.date", "operator": ">", "value": "1972-12-11T19:54:58Z"} + ) + self.assert_documents_are_equal( + result, + [ + d + for d in filterable_docs + if d.meta.get("date") is not None + and datetime.fromisoformat(d.meta["date"]) > datetime.fromisoformat("1972-12-11T19:54:58Z") + ], + ) + + def test_comparison_greater_than_equal_with_iso_date(self, document_store, filterable_docs): + """Test filter_documents() with >= comparator and datetime""" + document_store.write_documents(filterable_docs) + result = document_store.filter_documents( + {"field": "meta.date", "operator": ">=", "value": "1969-07-21T20:17:40Z"} + ) + self.assert_documents_are_equal( + result, + [ + d + for d in filterable_docs + if d.meta.get("date") is not None + and datetime.fromisoformat(d.meta["date"]) >= datetime.fromisoformat("1969-07-21T20:17:40Z") + ], + ) + + def test_comparison_less_than_with_iso_date(self, document_store, filterable_docs): + """Test filter_documents() with < comparator and datetime""" + document_store.write_documents(filterable_docs) + result = document_store.filter_documents( + {"field": "meta.date", "operator": "<", "value": "1969-07-21T20:17:40Z"} + ) + self.assert_documents_are_equal( + result, + [ + d + for d in filterable_docs + if d.meta.get("date") is not None + and datetime.fromisoformat(d.meta["date"]) < datetime.fromisoformat("1969-07-21T20:17:40Z") + ], + ) + + def test_comparison_less_than_equal_with_iso_date(self, document_store, filterable_docs): + """Test filter_documents() with <= comparator and datetime""" + document_store.write_documents(filterable_docs) + result = document_store.filter_documents( + {"field": "meta.date", "operator": "<=", "value": "1969-07-21T20:17:40Z"} + ) + self.assert_documents_are_equal( + result, + [ + d + for d in filterable_docs + if d.meta.get("date") is not None + and datetime.fromisoformat(d.meta["date"]) <= datetime.fromisoformat("1969-07-21T20:17:40Z") + ], + ) + + def test_comparison_greater_than_with_none(self, document_store, filterable_docs): + """Test filter_documents() with > comparator and None""" + document_store.write_documents(filterable_docs) + with pytest.raises(FilterError): + document_store.filter_documents(filters={"field": "meta.number", "operator": ">", "value": None}) + + def test_comparison_greater_than_equal_with_none(self, document_store, filterable_docs): + """Test filter_documents() with >= comparator and None""" + document_store.write_documents(filterable_docs) + with pytest.raises(FilterError): + document_store.filter_documents(filters={"field": "meta.number", "operator": ">=", "value": None}) + + def test_comparison_less_than_with_none(self, document_store, filterable_docs): + """Test filter_documents() with < comparator and None""" + document_store.write_documents(filterable_docs) + with pytest.raises(FilterError): + document_store.filter_documents(filters={"field": "meta.number", "operator": "<", "value": None}) + + def test_comparison_less_than_equal_with_none(self, document_store, filterable_docs): + """Test filter_documents() with <= comparator and None""" document_store.write_documents(filterable_docs) - result = document_store.filter_documents(filters={"field": "meta.number", "operator": "==", "value": None}) - print (result) - self.assert_documents_are_equal(result, [d for d in filterable_docs if d.meta.get("number") is None]) + with pytest.raises(FilterError): + document_store.filter_documents(filters={"field": "meta.number", "operator": "<=", "value": None}) + + # Override this as Azure AI Search does not support in operator for integer fields + def test_comparison_in(self, document_store, filterable_docs): + """Test filter_documents() with 'in' comparator""" + document_store.write_documents(filterable_docs) + result = document_store.filter_documents({"field": "meta.page", "operator": "in", "value": ["100", "123"]}) + assert len(result) + expected = [d for d in filterable_docs if d.meta.get("page") is not None and d.meta["page"] in ["100", "123"]] + self.assert_documents_are_equal(result, expected) + + # Implementation needs to be fixed for NOT operator + def test_not_operator(self, document_store, filterable_docs): + pass + + # not supported + def test_comparison_not_in(self, document_store, filterable_docs): + pass + + def test_comparison_not_in_with_with_non_list(self, document_store, filterable_docs): + pass + + def test_comparison_not_in_with_with_non_list_iterable(self, document_store, filterable_docs): + pass From f07943e5693234ecfee58e7f336cebb211214c52 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 23 Oct 2024 11:35:15 +0200 Subject: [PATCH 07/26] Prepare files for review --- integrations/azure_ai_search/README.md | 24 +--- integrations/azure_ai_search/pyproject.toml | 20 +-- .../azure_ai_search/embedding_retriever.py | 29 ++-- .../azure_ai_search/__init__.py | 3 +- .../azure_ai_search/document_store.py | 41 +++--- .../azure_ai_search/filters.py | 134 ++++++------------ .../azure_ai_search/tests/conftest.py | 3 +- .../tests/test_document_store.py | 14 +- .../tests/test_embedding_retriever.py | 22 ++- 9 files changed, 120 insertions(+), 170 deletions(-) diff --git a/integrations/azure_ai_search/README.md b/integrations/azure_ai_search/README.md index 40a2f8eaa..c597fc2b4 100644 --- a/integrations/azure_ai_search/README.md +++ b/integrations/azure_ai_search/README.md @@ -1,32 +1,20 @@ -[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/opensearch.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/opensearch.yml) +[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/azure_ai_search.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/azure_ai_search.yml) -[![PyPI - Version](https://img.shields.io/pypi/v/opensearch-haystack.svg)](https://pypi.org/project/opensearch-haystack) -[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/opensearch-haystack.svg)](https://pypi.org/project/opensearch-haystack) +[![PyPI - Version](https://img.shields.io/pypi/v/azure-ai-search-haystack.svg)](https://pypi.org/project/azure-ai-search-haystack) +[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/azure-ai-search-haystack.svg)](https://pypi.org/project/azure-ai-search-haystack) # OpenSearch Document Store -Document Store for Haystack 2.x, supports OpenSearch. +Document Store for Haystack 2.x, supports Azure AI Search. ## Installation ```console -pip install opensearch-haystack +pip install azure-ai-search-haystack ``` ## Testing -To run tests first start a Docker container running OpenSearch. We provide a utility `docker-compose.yml` for that: - -```console -docker-compose up -``` - -Then run tests: - -```console -hatch run test -``` - ## License -`opensearch-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. +`azure-ai-search-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. diff --git a/integrations/azure_ai_search/pyproject.toml b/integrations/azure_ai_search/pyproject.toml index 3ce17f2ee..70dbf535f 100644 --- a/integrations/azure_ai_search/pyproject.toml +++ b/integrations/azure_ai_search/pyproject.toml @@ -22,12 +22,12 @@ classifiers = [ "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ] -dependencies = ["haystack-ai", "azure-search-documents>=11.5", "azure-identity"] +dependencies = ["haystack-ai>=2.0", "azure-search-documents>=11.5", "azure-identity"] [project.urls] -Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch#readme" +Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_ai_search#readme" Issues = "https://github.com/deepset-ai/haystack-core-integrations/issues" -Source = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch" +Source = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_ai_search" [tool.hatch.build.targets.wheel] packages = ["src/haystack_integrations"] @@ -64,7 +64,7 @@ detached = true dependencies = ["black>=23.1.0", "mypy>=1.0.0", "ruff>=0.0.243"] [tool.hatch.envs.lint.scripts] typing = "mypy --install-types --non-interactive --explicit-package-bases {args:src/ tests}" -style = ["ruff check {args:.}", "black --check --diff {args:.}"] +style = ["ruff check {args:src/}", "black --check --diff {args:.}"] fmt = ["black {args:.}", "ruff check --fix {args:.}", "style"] all = ["style", "typing"] @@ -79,6 +79,8 @@ skip-string-normalization = true [tool.ruff] target-version = "py38" line-length = 120 + +[tool.ruff.lint] select = [ "A", "ARG", @@ -127,15 +129,15 @@ unfixable = [ "F401", ] -[tool.ruff.isort] +[tool.ruff.lint.isort] known-first-party = ["src"] -[tool.ruff.flake8-tidy-imports] +[tool.ruff.lint.flake8-tidy-imports] ban-relative-imports = "parents" -[tool.ruff.per-file-ignores] +[tool.ruff.lint.per-file-ignores] # Tests can use magic values, assertions, and relative imports -"tests/**/*" = ["PLR2004", "S101", "TID252"] +"tests/**/*" = ["PLR2004", "S101", "TID252", "S311"] [tool.coverage.run] source = ["haystack_integrations"] @@ -154,5 +156,5 @@ minversion = "6.0" markers = ["unit: unit tests", "integration: integration tests"] [[tool.mypy.overrides]] -module = ["haystack.*", "haystack_integrations.*", "pytest.*", "azure-ai-search.*", "azure.identity.*"] +module = ["haystack.*", "haystack_integrations.*", "pytest.*", "azure.identity.*", "mypy.*", "azure.core.*", "azure.search.documents.*"] ignore_missing_imports = true \ No newline at end of file diff --git a/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py index 9c8b668c4..fe23718c8 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py +++ b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py @@ -5,7 +5,7 @@ from haystack.document_stores.types import FilterPolicy from haystack.document_stores.types.filter_policy import apply_filter_policy -from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore +from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore, normalize_filters logger = logging.getLogger(__name__) @@ -98,29 +98,28 @@ def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = :returns: a dictionary with the following keys: - `documents`: A list of documents retrieved from the AzureAISearchDocumentStore. """ - filters = apply_filter_policy(self._filter_policy, self._filters, filters) - top_k = top_k or self._top_k - if filters is None: - filters = self._filters - if top_k is None: - top_k = self._top_k - docs: List[Document] = [] + top_k = top_k or self._top_k + if filters is not None: + applied_filters = apply_filter_policy(self._filter_policy, self._filters, filters) + normalized_filters = normalize_filters(applied_filters) + else: + normalized_filters = "" try: docs = self._document_store._embedding_retrieval( query_embedding=query_embedding, - filters=filters, + filters=normalized_filters, top_k=top_k, ) except Exception as e: if self._raise_on_failure: raise e - else: - logger.warning( - "An error during embedding retrieval occurred and will be ignored by returning empty results: %s", - str(e), - exc_info=True, - ) + logger.warning( + "An error occurred during embedding retrieval and will be ignored, returning empty results: %s", + str(e), + exc_info=True, + ) + docs = [] return {"documents": docs} diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py index 51fb2b911..635878a38 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py @@ -2,5 +2,6 @@ # # SPDX-License-Identifier: Apache-2.0 from .document_store import DEFAULT_VECTOR_SEARCH, AzureAISearchDocumentStore +from .filters import normalize_filters -__all__ = ["AzureAISearchDocumentStore", "DEFAULT_VECTOR_SEARCH"] +__all__ = ["AzureAISearchDocumentStore", "DEFAULT_VECTOR_SEARCH", "normalize_filters"] diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 1c784caed..26e5c0cd6 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -67,8 +67,8 @@ class AzureAISearchDocumentStore: def __init__( self, *, - api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), - azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), + api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), # noqa: B008 + azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), # noqa: B008 index_name: str = "default", embedding_dimension: int = 768, metadata_fields: Optional[Dict[str, type]] = None, @@ -85,15 +85,17 @@ def __init__( :param index_name: Name of index in Azure AI Search, if it doesn't exist it will be created. :param embedding_dimension: Dimension of the embeddings. :param metadata_fields: A dictionary of metatada keys and their types to create - additional fields in index schema. As fields in Azure SearchIndex cannot be dynamic, it is necessary to specify the metadata fields in advance. + additional fields in index schema. As fields in Azure SearchIndex cannot be dynamic, + it is necessary to specify the metadata fields in advance. :param vector_search_configuration: Configuration option related to vector search. - Default configuration uses the HNSW algorithm with cosine similarity to handle vector searches. + Default configuration uses the HNSW algorithm with cosine similarity to handle vector searches. :param kwargs: Optional keyword parameters for Azure AI Search. - Some of the supported parameters: - - `api_version`: The Search API version to use for requests. - - `audience`: sets the Audience to use for authentication with Azure Active Directory (AAD). - The audience is not considered when using a shared key. If audience is not provided, the public cloud audience will be assumed. + Some of the supported parameters: + - `api_version`: The Search API version to use for requests. + - `audience`: sets the Audience to use for authentication with Azure Active Directory (AAD). + The audience is not considered when using a shared key. If audience is not provided, + the public cloud audience will be assumed. For more information on parameters, see the [official Azure AI Search documentation](https://learn.microsoft.com/en-us/azure/search/) """ @@ -136,11 +138,10 @@ def client(self) -> SearchClient: self._index_name, ) self.create_index(self._index_name) + self._client = self._index_client.get_search_client(self._index_name) except (HttpResponseError, ClientAuthenticationError) as error: msg = f"Failed to authenticate with Azure Search: {error}" raise AzureAISearchDocumentStoreConfigError(msg) from error - - self._client = self._index_client.get_search_client(self._index_name) return self._client def create_index(self, index_name: str, **kwargs) -> None: @@ -148,7 +149,6 @@ def create_index(self, index_name: str, **kwargs) -> None: Creates a new search index. :param index_name: Name of the index to create. If None, the index name from the constructor is used. :param kwargs: Optional keyword parameters. - """ # default fields to create index based on Haystack Document @@ -301,10 +301,7 @@ def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Doc """ if filters: normalized_filters = normalize_filters(filters) - print("Normalized filters: ", normalized_filters) - result = self.client.search(filter=normalized_filters) - print("Result: ", result) return self._convert_search_result_to_documents(result) else: return self.search_documents() @@ -336,8 +333,18 @@ def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) return documents def index_exists(self, index_name: Optional[str]) -> bool: + """ + Check if the index exists in the Azure AI Search service. + + :param index_name: The name of the index to check. + :returns bool: whether the index exists. + """ + if self._index_client and index_name: return index_name in self._index_client.list_index_names() + else: + msg = "Index name is required to check if the index exists." + raise ValueError(msg) def _get_raw_documents_by_id(self, document_ids: List[str]): """ @@ -381,6 +388,7 @@ def _create_metadata_index_fields(self, metadata: Dict[str, Any]) -> List[Simple def _map_metadata_field_types(self, metadata: Dict[str, type]) -> Dict[str, str]: """Map metadata field types to Azure Search field types.""" + metadata_field_mapping = {} for key, value_type in metadata.items(): @@ -398,11 +406,12 @@ def _embedding_retrieval( *, top_k: int = 10, fields: Optional[List[str]] = None, - filters: Optional[Dict[str, Any]] = None, # TODO will be used in the future + filters: Optional[Dict[str, Any]] = None, ) -> List[Document]: """ Retrieves documents that are most similar to the query embedding using a vector similarity metric. - It uses the vector configuration of the document store. By default it uses the HNSW algorithm with cosine similarity. + It uses the vector configuration of the document store. By default it uses the HNSW algorithm + with cosine similarity. This method is not meant to be part of the public interface of `AzureAISearchDocumentStore` nor called directly. diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py index 4fb4c1f6a..925d19810 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -1,36 +1,31 @@ -from collections import defaultdict -from dataclasses import dataclass -from datetime import datetime -from typing import Any, Dict, List +from typing import Any, Dict from dateutil import parser -from haystack.utils import raise_on_invalid_filter_syntax from .errors import AzureAISearchDocumentStoreFilterError LOGICAL_OPERATORS = {"AND": "and", "OR": "or", "NOT": "not"} -numeric_types = [int, float] -def normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]: +def normalize_filters(filters: Dict[str, Any]) -> str: """ Converts Haystack filters in Azure AI Search compatible filters. """ if not isinstance(filters, dict): - msg = "Filters must be a dictionary" + msg = """Filters must be a dictionary. + See https://docs.haystack.deepset.ai/docs/metadata-filtering for details on filters syntax.""" raise AzureAISearchDocumentStoreFilterError(msg) if "field" in filters: - return _parse_comparison_condition(filters) # return a string + return _parse_comparison_condition(filters) return _parse_logical_condition(filters) -def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]: - if "operator" not in condition: - msg = f"'operator' key missing in {condition}" - raise AzureAISearchDocumentStoreFilterError(msg) - if "conditions" not in condition: - msg = f"'conditions' key missing in {condition}" +def _parse_logical_condition(condition: Dict[str, Any]) -> str: + missing_keys = [key for key in ("operator", "value") if key not in condition] + if missing_keys: + msg = f"""Missing key(s) {missing_keys} in {condition}. + See https://docs.haystack.deepset.ai/docs/metadata-filtering for details on filters syntax.""" raise AzureAISearchDocumentStoreFilterError(msg) operator = condition["operator"] @@ -39,116 +34,71 @@ def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]: raise AzureAISearchDocumentStoreFilterError(msg) conditions = [_parse_comparison_condition(c) for c in condition["conditions"]] - final_filter = f" {LOGICAL_OPERATORS[operator]} ".join([f"({c})" for c in conditions]) - return final_filter - - final_filter = "" - for c in conditions[:-1]: - final_filter += f"({c}) {LOGICAL_OPERATORS[operator]} " - - return final_filter + "(" + conditions[-1] + ")" + if operator == "NOT": + return f"not ({' and '.join([f'({c})' for c in conditions])})" + else: + return f" {LOGICAL_OPERATORS[operator]} ".join([f"({c})" for c in conditions]) -def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]: - if "field" not in condition: - msg = f"'field' key missing in {condition}" +def _parse_comparison_condition(condition: Dict[str, Any]) -> str: + missing_keys = [key for key in ("field", "operator", "value") if key not in condition] + if missing_keys: + msg = f"""Missing key(s) {missing_keys} in {condition}. + See https://docs.haystack.deepset.ai/docs/metadata-filtering for details on filters syntax.""" raise AzureAISearchDocumentStoreFilterError(msg) - field: str = "" - # remove the "meta." prefix from the field name - if condition["field"].startswith("meta."): - field = condition["field"][5:] - else: - field = condition["field"] - if "operator" not in condition: - msg = f"'operator' key missing in {condition}" - raise AzureAISearchDocumentStoreFilterError(msg) - if "value" not in condition: - msg = f"'value' key missing in {condition}" - raise AzureAISearchDocumentStoreFilterError(msg) - operator: str = condition["operator"] - value: Any = condition["value"] - if value is None: - value = "null" + # Remove the "meta." prefix from the field name if present + field = condition["field"][5:] if condition["field"].startswith("meta.") else condition["field"] + operator = condition["operator"] + value = "null" if condition["value"] is None else condition["value"] if operator not in COMPARISON_OPERATORS: msg = f"Unknown operator {operator}. Valid operators are: {list(COMPARISON_OPERATORS.keys())}" raise AzureAISearchDocumentStoreFilterError(msg) + return COMPARISON_OPERATORS[operator](field, value) def _eq(field: str, value: Any) -> str: - if isinstance(value, str) and value != "null": - return f"{field} eq '{value}'" - return f"{field} eq {value}" + return f"{field} eq '{value}'" if isinstance(value, str) and value != "null" else f"{field} eq {value}" def _ne(field: str, value: Any) -> str: - if isinstance(value, str) and value != "null": - return f"not ({field} eq '{value}')" - return f"not ({field} eq {value})" - - -def _gt(field: str, value: Any) -> str: - _validate_type(value, "gt") - print(f"{field} gt {value}") - return f"{field} gt {value}" - - -def _ge(field: str, value: Any) -> str: - _validate_type(value, "ge") - return f"{field} ge {value}" - - -def _lt(field: str, value: Any) -> str: - # If value is a string, check if it's a valid ISO 8601 datetime string - _validate_type(value, "lt") - return f"{field} lt {value}" - - -def _le(field: str, value: Any) -> str: - _validate_type(value, "le") - return f"{field} le {value}" + return f"not ({field} eq '{value}')" if isinstance(value, str) and value != "null" else f"not ({field} eq {value})" def _in(field: str, value: Any) -> str: - if not isinstance(value, list): - msg = "Value must be a list when using 'in' comparators" - raise AzureAISearchDocumentStoreFilterError(msg) - elif any([not isinstance(v, str) for v in value]): - msg = "Azure AI Search only supports string values for 'in' comparators" + if not isinstance(value, list) or any(not isinstance(v, str) for v in value): + msg = "Azure AI Search only supports a list of strings for 'in' comparators" raise AzureAISearchDocumentStoreFilterError(msg) - values = ", ".join([str(v) for v in value]) + values = ", ".join(map(str, value)) return f"search.in({field},'{values}')" +def _comparison_operator(field: str, value: Any, operator: str) -> str: + _validate_type(value, operator) + return f"{field} {operator} {value}" + + def _validate_type(value: Any, operator: str) -> None: - """Validates that the value is either a number, datetime, or a valid ISO 8601 date string.""" + """Validates that the value is either an integer, float, or ISO 8601 string.""" msg = f"Invalid value type for '{operator}' comparator. Supported types are: int, float, or ISO 8601 string." if isinstance(value, str): - # Attempt to parse the string as an ISO 8601 datetime try: parser.isoparse(value) - except ValueError: - raise AzureAISearchDocumentStoreFilterError(msg) - elif type(value) not in numeric_types: + except ValueError as e: + raise AzureAISearchDocumentStoreFilterError(msg) from e + elif not isinstance(value, (int, float)): raise AzureAISearchDocumentStoreFilterError(msg) -def _comparison_operator(field: str, value: Any, operator: str) -> str: - """Generic function for comparison operators ('gt', 'ge', 'lt', 'le').""" - _validate_type(value, operator) - return f"{field} {operator} {value}" - - COMPARISON_OPERATORS = { "==": _eq, "!=": _ne, - ">": _gt, - ">=": _ge, - "<": _lt, - "<=": _le, "in": _in, - # "not in": "$nin", + ">": lambda f, v: _comparison_operator(f, v, "gt"), + ">=": lambda f, v: _comparison_operator(f, v, "ge"), + "<": lambda f, v: _comparison_operator(f, v, "lt"), + "<=": lambda f, v: _comparison_operator(f, v, "le"), } diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 5bf0ef23c..cb4dd1bb5 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -9,7 +9,7 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore -# This is the approximate time in seconds it takes for the documents to be available +# This is the approximate time in seconds it takes for the documents to be available in Azure Search index SLEEP_TIME_IN_SECONDS = 5 @@ -63,6 +63,5 @@ def delete_documents_and_wait(filters): yield store try: client.delete_index(index_name) - print("deleting index") except ResourceNotFoundError: pass diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index bd4eb95f2..a069ce0c0 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -125,7 +125,7 @@ def test_write_documents_with_meta(self, document_store: AzureAISearchDocumentSt def _random_embeddings(n): - return [round(random.random(), 7) for _ in range(n)] + return [round(random.random(), 7) for _ in range(n)] # nosec: S311 TEST_EMBEDDING_1 = _random_embeddings(768) @@ -327,10 +327,6 @@ def test_comparison_in(self, document_store, filterable_docs): expected = [d for d in filterable_docs if d.meta.get("page") is not None and d.meta["page"] in ["100", "123"]] self.assert_documents_are_equal(result, expected) - # Implementation needs to be fixed for NOT operator - def test_not_operator(self, document_store, filterable_docs): - pass - # not supported def test_comparison_not_in(self, document_store, filterable_docs): pass @@ -340,3 +336,11 @@ def test_comparison_not_in_with_with_non_list(self, document_store, filterable_d def test_comparison_not_in_with_with_non_list_iterable(self, document_store, filterable_docs): pass + + def test_missing_condition_operator_key(self, document_store, filterable_docs): + """Test filter_documents() with missing operator key""" + document_store.write_documents(filterable_docs) + with pytest.raises(FilterError): + document_store.filter_documents( + filters={"conditions": [{"field": "meta.name", "operator": "eq", "value": "test"}]} + ) diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py index 6fb0d673e..4b0c92b99 100644 --- a/integrations/azure_ai_search/tests/test_embedding_retriever.py +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -9,7 +9,7 @@ from azure.core.exceptions import HttpResponseError from haystack.dataclasses import Document from haystack.document_stores.types import FilterPolicy -from numpy.random import rand +from numpy.random import rand # type: ignore from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever from haystack_integrations.document_stores.azure_ai_search import DEFAULT_VECTOR_SEARCH, AzureAISearchDocumentStore @@ -34,14 +34,13 @@ def test_to_dict(): document_store = AzureAISearchDocumentStore(hosts="some fake host") retriever = AzureAISearchEmbeddingRetriever(document_store=document_store) res = retriever.to_dict() - type_s = "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever.AzureAISearchEmbeddingRetriever" assert res == { - "type": type_s, + "type": "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever.AzureAISearchEmbeddingRetriever", # noqa: E501 "init_parameters": { "filters": {}, "top_k": 10, "document_store": { - "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", + "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", # noqa: E501 "init_parameters": { "azure_endpoint": { "type": "env_var", @@ -63,14 +62,13 @@ def test_to_dict(): def test_from_dict(): - type_s = "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever.AzureAISearchEmbeddingRetriever" data = { - "type": type_s, + "type": "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever.AzureAISearchEmbeddingRetriever", # noqa: E501 "init_parameters": { "filters": {}, "top_k": 10, "document_store": { - "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", + "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", # noqa: E501 "init_parameters": { "azure_endpoint": { "type": "env_var", @@ -107,14 +105,14 @@ def test_run(self, document_store: AzureAISearchDocumentStore): docs = [Document(id="1")] document_store.write_documents(docs) retriever = AzureAISearchEmbeddingRetriever(document_store=document_store) - res = retriever.run(query_embedding=[0.1] * 15) + res = retriever.run(query_embedding=[0.1] * 768) assert res["documents"] == docs def test_embedding_retrieval(self, document_store: AzureAISearchDocumentStore): - query_embedding = [0.1] * 15 - most_similar_embedding = [0.8] * 15 - second_best_embedding = [0.8] * 7 + [0.1] * 3 + [0.2] * 5 - another_embedding = rand(15).tolist() + query_embedding = [0.1] * 768 + most_similar_embedding = [0.8] * 768 + second_best_embedding = [0.8] * 200 + [0.1] * 300 + [0.2] * 268 + another_embedding = rand(768).tolist() docs = [ Document(content="This is first document", embedding=most_similar_embedding), From f383d46bd7b8c0dd0fdb7adc829cad3443ff176f Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 23 Oct 2024 11:37:24 +0200 Subject: [PATCH 08/26] Delete integrations/azure_ai_search/CHANGELOG.md --- integrations/azure_ai_search/CHANGELOG.md | 99 ----------------------- 1 file changed, 99 deletions(-) delete mode 100644 integrations/azure_ai_search/CHANGELOG.md diff --git a/integrations/azure_ai_search/CHANGELOG.md b/integrations/azure_ai_search/CHANGELOG.md deleted file mode 100644 index dd1ddb86e..000000000 --- a/integrations/azure_ai_search/CHANGELOG.md +++ /dev/null @@ -1,99 +0,0 @@ -# Changelog - -## [integrations/opensearch-v0.8.1] - 2024-07-15 - -### 🚀 Features - -- Add raise_on_failure param to OpenSearch retrievers (#852) -- Add filter_policy to opensearch integration (#822) - -### 🐛 Bug Fixes - -- `OpenSearch` - Fallback to default filter policy when deserializing retrievers without the init parameter (#895) - -### ⚙️ Miscellaneous Tasks - -- Update ruff invocation to include check parameter (#853) - -## [integrations/opensearch-v0.7.1] - 2024-06-27 - -### 🐛 Bug Fixes - -- Serialization for custom_query in OpenSearch retrievers (#851) -- Support legacy filters with OpenSearchDocumentStore (#850) - -## [integrations/opensearch-v0.7.0] - 2024-06-25 - -### 🚀 Features - -- Defer the database connection to when it's needed (#753) -- Improve `OpenSearchDocumentStore.__init__` arguments (#739) -- Return_embeddings flag for opensearch (#784) -- Add create_index option to OpenSearchDocumentStore (#840) -- Add custom_query param to OpenSearch retrievers (#841) - -### 🐛 Bug Fixes - -- Fix order of API docs (#447) - -This PR will also push the docs to Readme - -### 📚 Documentation - -- Update category slug (#442) -- Fixing opensearch docstrings (#521) -- Small consistency improvements (#536) -- Disable-class-def (#556) - -### ⚙️ Miscellaneous Tasks - -- Retry tests to reduce flakyness (#836) - -### Opensearch - -- Generate API docs (#324) - -## [integrations/opensearch-v0.2.0] - 2024-01-17 - -### 🐛 Bug Fixes - -- Fix links in docstrings (#188) - - - -### 🚜 Refactor - -- Use `hatch_vcs` to manage integrations versioning (#103) - -## [integrations/opensearch-v0.1.1] - 2023-12-05 - -### 🐛 Bug Fixes - -- Fix import and increase version (#77) - - - -## [integrations/opensearch-v0.1.0] - 2023-12-04 - -### 🐛 Bug Fixes - -- Fix license headers - - -## [integrations/opensearch-v0.0.2] - 2023-11-30 - -### 🚀 Features - -- Extend OpenSearch params support (#70) - -### Build - -- Bump OpenSearch integration version to 0.0.2 (#71) - -## [integrations/opensearch-v0.0.1] - 2023-11-30 - -### 🚀 Features - -- [OpenSearch] add document store, BM25Retriever and EmbeddingRetriever (#68) - - From 4bf9b5b9d914e9183ffa0d06c752ad1ca53e396a Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 23 Oct 2024 11:38:19 +0200 Subject: [PATCH 09/26] Delete integrations/azure_ai_search/README.md --- integrations/azure_ai_search/README.md | 20 -------------------- 1 file changed, 20 deletions(-) delete mode 100644 integrations/azure_ai_search/README.md diff --git a/integrations/azure_ai_search/README.md b/integrations/azure_ai_search/README.md deleted file mode 100644 index c597fc2b4..000000000 --- a/integrations/azure_ai_search/README.md +++ /dev/null @@ -1,20 +0,0 @@ -[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/azure_ai_search.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/azure_ai_search.yml) - -[![PyPI - Version](https://img.shields.io/pypi/v/azure-ai-search-haystack.svg)](https://pypi.org/project/azure-ai-search-haystack) -[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/azure-ai-search-haystack.svg)](https://pypi.org/project/azure-ai-search-haystack) - -# OpenSearch Document Store - -Document Store for Haystack 2.x, supports Azure AI Search. - -## Installation - -```console -pip install azure-ai-search-haystack -``` - -## Testing - -## License - -`azure-ai-search-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. From 4a5e718e3538f419ba1fce9ad57d765ada0ddbe3 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 23 Oct 2024 15:05:37 +0200 Subject: [PATCH 10/26] Add github workflow --- .github/workflows/azure_ai_search.yml | 71 +++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 .github/workflows/azure_ai_search.yml diff --git a/.github/workflows/azure_ai_search.yml b/.github/workflows/azure_ai_search.yml new file mode 100644 index 000000000..294bb4c64 --- /dev/null +++ b/.github/workflows/azure_ai_search.yml @@ -0,0 +1,71 @@ +# This workflow comes from https://github.com/ofek/hatch-mypyc +# https://github.com/ofek/hatch-mypyc/blob/5a198c0ba8660494d02716cfc9d79ce4adfb1442/.github/workflows/test.yml +name: Test / azure_ai_search + +on: + schedule: + - cron: "0 0 * * *" + pull_request: + paths: + - "integrations/azure_ai_search/**" + - ".github/workflows/azure_ai_search.yml" + +concurrency: + group: azure_ai_search-${{ github.head_ref }} + cancel-in-progress: true + +env: + PYTHONUNBUFFERED: "1" + FORCE_COLOR: "1" + AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }} + AZURE_SEARCH_SERVICE_ENDPOINT: ${{ secrets.AZURE_SEARCH_SERVICE_ENDPOINT }} + +defaults: + run: + working-directory: integrations/azure_ai_search + +jobs: + run: + name: Python ${{ matrix.python-version }} on ${{ startsWith(matrix.os, 'macos-') && 'macOS' || startsWith(matrix.os, 'windows-') && 'Windows' || 'Linux' }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest] + python-version: ["3.8", "3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install Hatch + run: pip install --upgrade hatch + + - name: Lint + if: matrix.python-version == '3.9' + run: hatch run lint:all + + - name: Generate docs + if: matrix.python-version == '3.9' && runner.os == 'Linux' + run: hatch run docs + + - name: Run tests + run: hatch run cov-retry + + - name: Nightly - run unit tests with Haystack main branch + if: github.event_name == 'schedule' + run: | + hatch run pip install git+https://github.com/deepset-ai/haystack.git + hatch run cov-retry -m "not integration" + + - name: Send event to Datadog for nightly failures + if: failure() && github.event_name == 'schedule' + uses: ./.github/actions/send_failure + with: + title: | + Core integrations nightly tests failure: ${{ github.workflow }} + api-key: ${{ secrets.CORE_DATADOG_API_KEY }} From 49687ca29b38320b0e76e8a30fad2fd69cca7f42 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 23 Oct 2024 15:33:54 +0200 Subject: [PATCH 11/26] Add README file --- integrations/azure_ai_search/README.md | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 integrations/azure_ai_search/README.md diff --git a/integrations/azure_ai_search/README.md b/integrations/azure_ai_search/README.md new file mode 100644 index 000000000..915a23b63 --- /dev/null +++ b/integrations/azure_ai_search/README.md @@ -0,0 +1,26 @@ +# Azure AI Search Document Store for Haystack + +[![PyPI - Version](https://img.shields.io/pypi/v/azure-ai-search-haystack.svg)](https://pypi.org/project/azure-ai-search-haystack) +[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/azure-ai-search-haystack.svg)](https://pypi.org/project/azure-ai-search-haystack) + +----- + +**Table of Contents** + +- [Azure AI Search Document Store for Haystack](#azure-ai-search-document-store-for-haystack) + - [Installation](#installation) + - [Examples](#examples) + - [License](#license) + +## Installation + +```console +pip install azure-ai-search-haystack +``` + +## Examples +You can find a code example showing how to use the Document Store and the Retriever in the documentation or in [this Colab](https://colab.research.google.com/drive/1YpDetI8BRbObPDEVdfqUcwhEX9UUXP-m?usp=sharing). + +## License + +`azure-ai-search-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. From 162811a932da686edaf1fa19eeae0d622f6941c2 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 23 Oct 2024 15:43:32 +0200 Subject: [PATCH 12/26] Fix config files --- integrations/azure_ai_search/pyproject.toml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/integrations/azure_ai_search/pyproject.toml b/integrations/azure_ai_search/pyproject.toml index 70dbf535f..08c44c3c9 100644 --- a/integrations/azure_ai_search/pyproject.toml +++ b/integrations/azure_ai_search/pyproject.toml @@ -48,12 +48,14 @@ dependencies = [ "pytest-xdist", "haystack-pydoc-tools", ] + [tool.hatch.envs.default.scripts] -test = "pytest --reruns 0 --reruns-delay 30 -x {args:tests}" -test-cov = "coverage run -m pytest --reruns 3 --reruns-delay 30 -x {args:tests}" +test = "pytest {args:tests}" +test-cov = "coverage run -m pytest {args:tests}" +test-cov-retry = "test-cov --reruns 3 --reruns-delay 30 -x" cov-report = ["- coverage combine", "coverage report"] cov = ["test-cov", "cov-report"] - +cov-retry = ["test-cov-retry", "cov-report"] docs = ["pydoc-markdown pydoc/config.yml"] [[tool.hatch.envs.all.matrix]] From 990be6a8feb7ef5bb1226a1eb43b5290d7035f04 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Thu, 24 Oct 2024 11:32:15 +0200 Subject: [PATCH 13/26] Fix linting errors --- integrations/azure_ai_search/pydoc/config.yml | 1 - .../azure_ai_search/document_store.py | 25 +++++++++++++------ .../azure_ai_search/filters.py | 2 +- .../tests/test_document_store.py | 8 ++++-- 4 files changed, 24 insertions(+), 12 deletions(-) diff --git a/integrations/azure_ai_search/pydoc/config.yml b/integrations/azure_ai_search/pydoc/config.yml index 6c7aa6e13..ec411af60 100644 --- a/integrations/azure_ai_search/pydoc/config.yml +++ b/integrations/azure_ai_search/pydoc/config.yml @@ -2,7 +2,6 @@ loaders: - type: haystack_pydoc_tools.loaders.CustomPythonLoader search_path: [../src] modules: [ - "haystack_integrations.components.retrievers.azure_ai_search.bm25_retriever", "haystack_integrations.components.retrievers.azure_ai_search.embedding_retriever", "haystack_integrations.document_stores.azure_ai_search.document_store", "haystack_integrations.document_stores.azure_ai_search.filters", diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 26e5c0cd6..411ae6288 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -108,7 +108,7 @@ def __init__( self._client = None self._index_client = None - self._index_fields = None # stores all fields in the final schema of index + self._index_fields = [] # type: List[Any] # stores all fields in the final schema of index self._api_key = api_key self._azure_endpoint = azure_endpoint self._index_name = index_name @@ -138,10 +138,17 @@ def client(self) -> SearchClient: self._index_name, ) self.create_index(self._index_name) - self._client = self._index_client.get_search_client(self._index_name) except (HttpResponseError, ClientAuthenticationError) as error: msg = f"Failed to authenticate with Azure Search: {error}" raise AzureAISearchDocumentStoreConfigError(msg) from error + + # Get the search client, if index client is initialized + if self._index_client: + self._client = self._index_client.get_search_client(self._index_name) + else: + msg = "Search Index Client is not initialized." + raise AzureAISearchDocumentStoreConfigError(msg) + return self._client def create_index(self, index_name: str, **kwargs) -> None: @@ -151,7 +158,7 @@ def create_index(self, index_name: str, **kwargs) -> None: :param kwargs: Optional keyword parameters. """ - # default fields to create index based on Haystack Document + # default fields to create index based on Haystack Document (id, content, embedding) default_fields = [ SimpleField(name="id", type=SearchFieldDataType.String, key=True, filterable=True), SearchableField(name="content", type=SearchFieldDataType.String), @@ -167,13 +174,15 @@ def create_index(self, index_name: str, **kwargs) -> None: if not index_name: index_name = self._index_name - fields = default_fields if self._metadata_fields: - fields.extend(self._create_metadata_index_fields(self._metadata_fields)) + default_fields.extend(self._create_metadata_index_fields(self._metadata_fields)) - self._index_fields = fields - index = SearchIndex(name=index_name, fields=fields, vector_search=self._vector_search_configuration, **kwargs) - self._index_client.create_index(index) + self._index_fields = default_fields + index = SearchIndex( + name=index_name, fields=default_fields, vector_search=self._vector_search_configuration, **kwargs + ) + if self._index_client: + self._index_client.create_index(index) def to_dict(self) -> Dict[str, Any]: # This is not the best solution to serialise this class but is the fastest to implement. diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py index 925d19810..525e36be3 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -22,7 +22,7 @@ def normalize_filters(filters: Dict[str, Any]) -> str: def _parse_logical_condition(condition: Dict[str, Any]) -> str: - missing_keys = [key for key in ("operator", "value") if key not in condition] + missing_keys = [key for key in ("operator", "conditions") if key not in condition] if missing_keys: msg = f"""Missing key(s) {missing_keys} in {condition}. See https://docs.haystack.deepset.ai/docs/metadata-filtering for details on filters syntax.""" diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index a069ce0c0..ebffe6b83 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -145,6 +145,7 @@ def _random_embeddings(n): ) class TestFilters(FilterDocumentsTest): + # Overriding to change "date" to compatible ISO format and remove incompatible fields (dataframes) for search index @pytest.fixture def filterable_docs(self) -> List[Document]: """Fixture that returns a list of Documents that can be used to test filtering.""" @@ -198,7 +199,7 @@ def filterable_docs(self) -> List[Document]: ) return documents - # Overriding this method to compare the documents with the same order + # Overriding to compare the documents with the same order def assert_documents_are_equal(self, received: List[Document], expected: List[Document]): """ Assert that two lists of Documents are equal. @@ -212,6 +213,7 @@ def assert_documents_are_equal(self, received: List[Document], expected: List[Do sorted_expected = sorted(expected, key=lambda doc: doc.id) assert sorted_recieved == sorted_expected + # Dataframes are not supported in serach index def test_comparison_equal_with_dataframe(self, document_store, filterable_docs): pass @@ -230,6 +232,7 @@ def test_comparison_greater_than_equal_with_dataframe(self, document_store, filt def test_comparison_less_than_equal_with_dataframe(self, document_store, filterable_docs): pass + # Azure search index supports UTC datetime in ISO format def test_comparison_greater_than_with_iso_date(self, document_store, filterable_docs): """Test filter_documents() with > comparator and datetime""" document_store.write_documents(filterable_docs) @@ -294,6 +297,7 @@ def test_comparison_less_than_equal_with_iso_date(self, document_store, filterab ], ) + # Override as comparison operators with None/null raise errors def test_comparison_greater_than_with_none(self, document_store, filterable_docs): """Test filter_documents() with > comparator and None""" document_store.write_documents(filterable_docs) @@ -318,7 +322,7 @@ def test_comparison_less_than_equal_with_none(self, document_store, filterable_d with pytest.raises(FilterError): document_store.filter_documents(filters={"field": "meta.number", "operator": "<=", "value": None}) - # Override this as Azure AI Search does not support in operator for integer fields + # Override as Azure AI Search supports 'in' operator only for strings def test_comparison_in(self, document_store, filterable_docs): """Test filter_documents() with 'in' comparator""" document_store.write_documents(filterable_docs) From 9f35b57d71505981540149faee5a3110f43dab6d Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 25 Oct 2024 11:51:06 +0200 Subject: [PATCH 14/26] Add examples --- .../azure_ai_search/example/document_store.py | 37 +++++++++++++++++++ .../example/embedding_retrieval.py | 37 +++++++++++++++++++ integrations/azure_ai_search/pyproject.toml | 6 ++- 3 files changed, 78 insertions(+), 2 deletions(-) create mode 100644 integrations/azure_ai_search/example/document_store.py create mode 100644 integrations/azure_ai_search/example/embedding_retrieval.py diff --git a/integrations/azure_ai_search/example/document_store.py b/integrations/azure_ai_search/example/document_store.py new file mode 100644 index 000000000..ac490aa5e --- /dev/null +++ b/integrations/azure_ai_search/example/document_store.py @@ -0,0 +1,37 @@ +from haystack import Document +from haystack.document_stores.types import DuplicatePolicy + +from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore + +document_store = AzureAISearchDocumentStore( + metadata_fields={"version": float, "label": str}, + index_name="document-store-example", +) + +documents = [ + Document( + content="Use pip to install a basic version of Haystack's latest release: pip install farm-haystack.", + meta={"version": 1.15, "label": "first"}, + ), + Document( + content="Use pip to install a Haystack's latest release: pip install farm-haystack[inference].", + meta={"version": 1.22, "label": "second"}, + ), + Document( + content="Use pip to install only the Haystack 2.0 code: pip install haystack-ai.", + meta={"version": 2.0, "label": "third"}, + ), +] +document_store.write_documents(documents, policy=DuplicatePolicy.SKIP) + +filters = { + "operator": "AND", + "conditions": [ + {"field": "meta.version", "operator": ">", "value": 1.21}, + {"field": "meta.label", "operator": "in", "value": ["first", "third"]}, + ], +} + +results = document_store.filter_documents(filters) +for doc in results: + print(doc) diff --git a/integrations/azure_ai_search/example/embedding_retrieval.py b/integrations/azure_ai_search/example/embedding_retrieval.py new file mode 100644 index 000000000..f026d77b8 --- /dev/null +++ b/integrations/azure_ai_search/example/embedding_retrieval.py @@ -0,0 +1,37 @@ +from haystack import Document, Pipeline +from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder +from haystack.document_stores.types import DuplicatePolicy + +from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever +from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore + +document_store = AzureAISearchDocumentStore() + +model = "sentence-transformers/all-mpnet-base-v2" + +documents = [ + Document(content="There are over 7,000 languages spoken around the world today."), + Document( + content="""Elephants have been observed to behave in a way that indicates a + high level of self-awareness, such as recognizing themselves in mirrors.""" + ), + Document( + content="""In certain parts of the world, like the Maldives, Puerto Rico, and + San Diego, you can witness the phenomenon of bioluminescent waves.""" + ), +] + +document_embedder = SentenceTransformersDocumentEmbedder(model=model) +document_embedder.warm_up() +documents_with_embeddings = document_embedder.run(documents) +document_store.write_documents(documents_with_embeddings.get("documents"), policy=DuplicatePolicy.SKIP) +query_pipeline = Pipeline() +query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder(model=model)) +query_pipeline.add_component("retriever", AzureAISearchEmbeddingRetriever(document_store=document_store)) +query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding") + +query = "How many languages are there?" + +result = query_pipeline.run({"text_embedder": {"text": query}}) + +print(result["retriever"]["documents"][0]) diff --git a/integrations/azure_ai_search/pyproject.toml b/integrations/azure_ai_search/pyproject.toml index 08c44c3c9..c90ebfc5d 100644 --- a/integrations/azure_ai_search/pyproject.toml +++ b/integrations/azure_ai_search/pyproject.toml @@ -7,7 +7,7 @@ name = "azure-ai-search-haystack" dynamic = ["version"] description = 'Haystack 2.x Document Store for Azure AI Search' readme = "README.md" -requires-python = ">=3.8" +requires-python = ">=3.8,<3.13" license = "Apache-2.0" keywords = [] authors = [{ name = "deepset", email = "info@deepset.ai" }] @@ -22,7 +22,7 @@ classifiers = [ "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ] -dependencies = ["haystack-ai>=2.0", "azure-search-documents>=11.5", "azure-identity"] +dependencies = ["haystack-ai", "azure-search-documents>=11.5", "azure-identity", "torch>=1.11.0"] [project.urls] Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_ai_search#readme" @@ -130,6 +130,7 @@ unfixable = [ # Don't touch unused imports "F401", ] +exclude = ["example"] [tool.ruff.lint.isort] known-first-party = ["src"] @@ -140,6 +141,7 @@ ban-relative-imports = "parents" [tool.ruff.lint.per-file-ignores] # Tests can use magic values, assertions, and relative imports "tests/**/*" = ["PLR2004", "S101", "TID252", "S311"] +"example/**/*" = ["T201"] [tool.coverage.run] source = ["haystack_integrations"] From 48b507f5bbe2575b7102d0f04b74d0630ed35c81 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 25 Oct 2024 12:01:23 +0200 Subject: [PATCH 15/26] Fix conftest --- integrations/azure_ai_search/tests/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index cb4dd1bb5..1a187a79f 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -10,7 +10,7 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore # This is the approximate time in seconds it takes for the documents to be available in Azure Search index -SLEEP_TIME_IN_SECONDS = 5 +SLEEP_TIME_IN_SECONDS = 10 @pytest.fixture() From bc347b98ec854f5f52dcd23fe77d6ec820a99dc8 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 25 Oct 2024 12:38:34 +0200 Subject: [PATCH 16/26] Fix the conftest --- integrations/azure_ai_search/tests/conftest.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 1a187a79f..2101ced3b 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -10,7 +10,7 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore # This is the approximate time in seconds it takes for the documents to be available in Azure Search index -SLEEP_TIME_IN_SECONDS = 10 +SLEEP_TIME_IN_SECONDS = 5 @pytest.fixture() @@ -46,7 +46,7 @@ def document_store(request): # Override some methods to wait for the documents to be available original_write_documents = store.write_documents - def write_documents_and_wait(documents, policy=DuplicatePolicy.NONE): + def write_documents_and_wait(documents, policy=DuplicatePolicy.OVERWRITE): written_docs = original_write_documents(documents, policy) time.sleep(SLEEP_TIME_IN_SECONDS) return written_docs From 485960b0a9ec132efdeddad662a109be13ae1362 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 25 Oct 2024 13:27:25 +0200 Subject: [PATCH 17/26] Fix iso-date format --- .../azure_ai_search/tests/test_document_store.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index ebffe6b83..754a8c0d0 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -3,7 +3,7 @@ # SPDX-License-Identifier: Apache-2.0 import os import random -from datetime import datetime +from datetime import datetime, timezone from typing import List from unittest.mock import patch @@ -245,7 +245,8 @@ def test_comparison_greater_than_with_iso_date(self, document_store, filterable_ d for d in filterable_docs if d.meta.get("date") is not None - and datetime.fromisoformat(d.meta["date"]) > datetime.fromisoformat("1972-12-11T19:54:58Z") + and datetime.strptime(d.meta["date"], "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) + > datetime.strptime("1972-12-11T19:54:58Z", "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) ], ) @@ -261,7 +262,8 @@ def test_comparison_greater_than_equal_with_iso_date(self, document_store, filte d for d in filterable_docs if d.meta.get("date") is not None - and datetime.fromisoformat(d.meta["date"]) >= datetime.fromisoformat("1969-07-21T20:17:40Z") + and datetime.strptime(d.meta["date"], "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) + >= datetime.strptime("1969-07-21T20:17:40Z", "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) ], ) @@ -277,7 +279,8 @@ def test_comparison_less_than_with_iso_date(self, document_store, filterable_doc d for d in filterable_docs if d.meta.get("date") is not None - and datetime.fromisoformat(d.meta["date"]) < datetime.fromisoformat("1969-07-21T20:17:40Z") + and datetime.strptime(d.meta["date"], "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) + < datetime.strptime("1969-07-21T20:17:40Z", "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) ], ) @@ -293,7 +296,8 @@ def test_comparison_less_than_equal_with_iso_date(self, document_store, filterab d for d in filterable_docs if d.meta.get("date") is not None - and datetime.fromisoformat(d.meta["date"]) <= datetime.fromisoformat("1969-07-21T20:17:40Z") + and datetime.strptime(d.meta["date"], "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) + <= datetime.strptime("1969-07-21T20:17:40Z", "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) ], ) From 89ff7aa45900861d1ccc8dfde8c3a3cf1a7b944f Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 25 Oct 2024 14:59:32 +0200 Subject: [PATCH 18/26] Add instructions to examples --- integrations/azure_ai_search/example/document_store.py | 8 ++++++++ .../azure_ai_search/example/embedding_retrieval.py | 9 +++++++++ integrations/azure_ai_search/tests/conftest.py | 2 +- 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/integrations/azure_ai_search/example/document_store.py b/integrations/azure_ai_search/example/document_store.py index ac490aa5e..b3a87c64a 100644 --- a/integrations/azure_ai_search/example/document_store.py +++ b/integrations/azure_ai_search/example/document_store.py @@ -3,6 +3,14 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore +""" +This example demonstrates how to use the AzureAISearchDocumentStore to write and filter documents. +To run this example, you'll need an Azure Search service endpoint and API key, which can either be +set as environment variables (AZURE_SEARCH_SERVICE_ENDPOINT and AZURE_SEARCH_API_KEY) or +provided directly to AzureAISearchDocumentStore(as params "api_key", "azure_endpoint"). +Otherwise you can use DefaultAzureCredential to authenticate with Azure services. +See more details at https://learn.microsoft.com/en-us/azure/search/keyless-connections?tabs=python%2Cazure-cli +""" document_store = AzureAISearchDocumentStore( metadata_fields={"version": float, "label": str}, index_name="document-store-example", diff --git a/integrations/azure_ai_search/example/embedding_retrieval.py b/integrations/azure_ai_search/example/embedding_retrieval.py index f026d77b8..20904f5f7 100644 --- a/integrations/azure_ai_search/example/embedding_retrieval.py +++ b/integrations/azure_ai_search/example/embedding_retrieval.py @@ -5,6 +5,15 @@ from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore +""" +This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents based on a query. +To run this example, you'll need an Azure Search service endpoint and API key, which can either be +set as environment variables (AZURE_SEARCH_SERVICE_ENDPOINT and AZURE_SEARCH_API_KEY) or +provided directly to AzureAISearchDocumentStore(as params "api_key", "azure_endpoint"). +Otherwise you can use DefaultAzureCredential to authenticate with Azure services. +See more details at https://learn.microsoft.com/en-us/azure/search/keyless-connections?tabs=python%2Cazure-cli +""" + document_store = AzureAISearchDocumentStore() model = "sentence-transformers/all-mpnet-base-v2" diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 2101ced3b..2427d2550 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -10,7 +10,7 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore # This is the approximate time in seconds it takes for the documents to be available in Azure Search index -SLEEP_TIME_IN_SECONDS = 5 +SLEEP_TIME_IN_SECONDS = 10 @pytest.fixture() From d84ced9f8a62e6898ee1f4cae2d458961b1d0d56 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Fri, 25 Oct 2024 15:20:04 +0200 Subject: [PATCH 19/26] Config matrix for CI tests --- .github/workflows/azure_ai_search.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/azure_ai_search.yml b/.github/workflows/azure_ai_search.yml index 294bb4c64..f6d544865 100644 --- a/.github/workflows/azure_ai_search.yml +++ b/.github/workflows/azure_ai_search.yml @@ -30,6 +30,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: fail-fast: false + max-parallel: 1 matrix: os: [ubuntu-latest] python-version: ["3.8", "3.9", "3.10", "3.11"] From c42c1f43ba8fdab074dfb81d8a557b9e55c44961 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Mon, 28 Oct 2024 11:03:46 +0100 Subject: [PATCH 20/26] Update the example based on review --- .github/workflows/azure_ai_search.yml | 1 - .../example/embedding_retrieval.py | 19 +++++++++++++++---- .../azure_ai_search/tests/conftest.py | 6 ++++-- 3 files changed, 19 insertions(+), 7 deletions(-) diff --git a/.github/workflows/azure_ai_search.yml b/.github/workflows/azure_ai_search.yml index f6d544865..294bb4c64 100644 --- a/.github/workflows/azure_ai_search.yml +++ b/.github/workflows/azure_ai_search.yml @@ -30,7 +30,6 @@ jobs: runs-on: ${{ matrix.os }} strategy: fail-fast: false - max-parallel: 1 matrix: os: [ubuntu-latest] python-version: ["3.8", "3.9", "3.10", "3.11"] diff --git a/integrations/azure_ai_search/example/embedding_retrieval.py b/integrations/azure_ai_search/example/embedding_retrieval.py index 20904f5f7..e323c33e3 100644 --- a/integrations/azure_ai_search/example/embedding_retrieval.py +++ b/integrations/azure_ai_search/example/embedding_retrieval.py @@ -1,12 +1,13 @@ from haystack import Document, Pipeline from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder +from haystack.components.writers import DocumentWriter from haystack.document_stores.types import DuplicatePolicy from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore """ -This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents based on a query. +This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents using embeddings based on a query. To run this example, you'll need an Azure Search service endpoint and API key, which can either be set as environment variables (AZURE_SEARCH_SERVICE_ENDPOINT and AZURE_SEARCH_API_KEY) or provided directly to AzureAISearchDocumentStore(as params "api_key", "azure_endpoint"). @@ -14,7 +15,7 @@ See more details at https://learn.microsoft.com/en-us/azure/search/keyless-connections?tabs=python%2Cazure-cli """ -document_store = AzureAISearchDocumentStore() +document_store = AzureAISearchDocumentStore(index_name="retrieval-example") model = "sentence-transformers/all-mpnet-base-v2" @@ -32,8 +33,18 @@ document_embedder = SentenceTransformersDocumentEmbedder(model=model) document_embedder.warm_up() -documents_with_embeddings = document_embedder.run(documents) -document_store.write_documents(documents_with_embeddings.get("documents"), policy=DuplicatePolicy.SKIP) + +# Indexing Pipeline +indexing_pipeline = Pipeline() +indexing_pipeline.add_component(instance=document_embedder, name="doc_embedder") +indexing_pipeline.add_component( + instance=DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP), name="doc_writer" +) +indexing_pipeline.connect("doc_embedder", "doc_writer") + +indexing_pipeline.run({"doc_embedder": {"documents": documents}}) + +# Query Pipeline query_pipeline = Pipeline() query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder(model=model)) query_pipeline.add_component("retriever", AzureAISearchEmbeddingRetriever(document_store=document_store)) diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 2427d2550..48549d244 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -1,5 +1,6 @@ import os import time +import uuid import pytest from azure.core.credentials import AzureKeyCredential @@ -10,7 +11,7 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore # This is the approximate time in seconds it takes for the documents to be available in Azure Search index -SLEEP_TIME_IN_SECONDS = 10 +SLEEP_TIME_IN_SECONDS = 5 @pytest.fixture() @@ -24,7 +25,8 @@ def document_store(request): This is the most basic requirement for the child class: provide an instance of this document store so the base class can use it. """ - index_name = "haystack_test_integration" + index_name = f"haystack_test_{uuid.uuid4().hex}" + print (index_name) metadata_fields = getattr(request, "param", {}).get("metadata_fields", None) azure_endpoint = os.environ["AZURE_SEARCH_SERVICE_ENDPOINT"] From 54570e397d21d0e6fe5d20fc1356f2cd69cff339 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Mon, 28 Oct 2024 11:06:01 +0100 Subject: [PATCH 21/26] Fix linting --- integrations/azure_ai_search/example/embedding_retrieval.py | 5 +++-- integrations/azure_ai_search/tests/conftest.py | 1 - 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/integrations/azure_ai_search/example/embedding_retrieval.py b/integrations/azure_ai_search/example/embedding_retrieval.py index e323c33e3..088b08653 100644 --- a/integrations/azure_ai_search/example/embedding_retrieval.py +++ b/integrations/azure_ai_search/example/embedding_retrieval.py @@ -7,8 +7,9 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore """ -This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents using embeddings based on a query. -To run this example, you'll need an Azure Search service endpoint and API key, which can either be +This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents +using embeddings based on a query. To run this example, you'll need an Azure Search service endpoint +and API key, which can either be set as environment variables (AZURE_SEARCH_SERVICE_ENDPOINT and AZURE_SEARCH_API_KEY) or provided directly to AzureAISearchDocumentStore(as params "api_key", "azure_endpoint"). Otherwise you can use DefaultAzureCredential to authenticate with Azure services. diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 48549d244..3017c79c2 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -26,7 +26,6 @@ def document_store(request): an instance of this document store so the base class can use it. """ index_name = f"haystack_test_{uuid.uuid4().hex}" - print (index_name) metadata_fields = getattr(request, "param", {}).get("metadata_fields", None) azure_endpoint = os.environ["AZURE_SEARCH_SERVICE_ENDPOINT"] From b9563baf55b974e100abb217e28d61044dc5e627 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Mon, 28 Oct 2024 15:05:41 +0100 Subject: [PATCH 22/26] Fix ser/deserialization --- .../azure_ai_search/document_store.py | 20 +++++++++++-------- .../tests/test_document_store.py | 13 +++++++++++- .../tests/test_embedding_retriever.py | 13 +++++++++++- 3 files changed, 36 insertions(+), 10 deletions(-) diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 411ae6288..625112fb0 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -70,7 +70,7 @@ def __init__( api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), # noqa: B008 azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), # noqa: B008 index_name: str = "default", - embedding_dimension: int = 768, + embedding_dimension: Optional[int] = 768, metadata_fields: Optional[Dict[str, type]] = None, vector_search_configuration: VectorSearch = None, create_index: bool = True, @@ -104,6 +104,7 @@ def __init__( if not azure_endpoint: msg = "Please provide an Azure endpoint or set the environment variable AZURE_OPENAI_ENDPOINT." raise ValueError(msg) + api_key = api_key or os.environ.get("AZURE_SEARCH_API_KEY") self._client = None @@ -122,15 +123,16 @@ def __init__( @property def client(self) -> SearchClient: - if isinstance(self._azure_endpoint, Secret): - self._azure_endpoint = self._azure_endpoint.resolve_value() + # resolve secrets for authentication + resolved_endpoint = ( + self._azure_endpoint.resolve_value() if isinstance(self._azure_endpoint, Secret) else self._azure_endpoint + ) + resolved_key = self._api_key.resolve_value() if isinstance(self._api_key, Secret) else self._api_key - if isinstance(self._api_key, Secret): - self._api_key = self._api_key.resolve_value() - credential = AzureKeyCredential(self._api_key) if self._api_key else DefaultAzureCredential() + credential = AzureKeyCredential(resolved_key) if resolved_key else DefaultAzureCredential() try: if not self._index_client: - self._index_client = SearchIndexClient(self._azure_endpoint, credential, **self._kwargs) + self._index_client = SearchIndexClient(resolved_endpoint, credential, **self._kwargs) if not self.index_exists(self._index_name): # Create a new index if it does not exist logger.debug( @@ -202,7 +204,7 @@ def to_dict(self) -> Dict[str, Any]: create_index=self._create_index, embedding_dimension=self._embedding_dimension, metadata_fields=self._metadata_fields, - vector_search_configuration=self._vector_search_configuration, + vector_search_configuration=self._vector_search_configuration.as_dict(), **self._kwargs, ) @@ -219,6 +221,8 @@ def from_dict(cls, data: Dict[str, Any]) -> "AzureAISearchDocumentStore": """ deserialize_secrets_inplace(data["init_parameters"], keys=["api_key", "azure_endpoint"]) + if (vector_search_configuration := data["init_parameters"].get("vector_search_configuration")) is not None: + data["init_parameters"]["vector_search_configuration"] = VectorSearch.from_dict(vector_search_configuration) return default_from_dict(cls, data) def count_documents(self, **kwargs: Any) -> int: diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index 754a8c0d0..50733a8a4 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -36,7 +36,18 @@ def test_to_dict(monkeypatch): "embedding_dimension": 768, "metadata_fields": None, "create_index": True, - "vector_search_configuration": DEFAULT_VECTOR_SEARCH, + "vector_search_configuration": { + "profiles": [ + {"name": "default-vector-config", "algorithm_configuration_name": "cosine-algorithm-config"} + ], + "algorithms": [ + { + "name": "cosine-algorithm-config", + "kind": "hnsw", + "parameters": {"m": 4, "ef_construction": 400, "ef_search": 500, "metric": "cosine"}, + } + ], + }, }, } diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py index 4b0c92b99..af4b21478 100644 --- a/integrations/azure_ai_search/tests/test_embedding_retriever.py +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -52,7 +52,18 @@ def test_to_dict(): "create_index": True, "embedding_dimension": 768, "metadata_fields": None, - "vector_search_configuration": DEFAULT_VECTOR_SEARCH, + "vector_search_configuration": { + "profiles": [ + {"name": "default-vector-config", "algorithm_configuration_name": "cosine-algorithm-config"} + ], + "algorithms": [ + { + "name": "cosine-algorithm-config", + "kind": "hnsw", + "parameters": {"m": 4, "ef_construction": 400, "ef_search": 500, "metric": "cosine"}, + } + ], + }, "hosts": "some fake host", }, }, From 23d1e2298ab620b421e063ec359f71ea6723b365 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Tue, 29 Oct 2024 13:54:12 +0100 Subject: [PATCH 23/26] Add a check for index schema --- .../azure_ai_search/example/document_store.py | 3 +- .../azure_ai_search/document_store.py | 39 ++++++++++--------- 2 files changed, 21 insertions(+), 21 deletions(-) diff --git a/integrations/azure_ai_search/example/document_store.py b/integrations/azure_ai_search/example/document_store.py index b3a87c64a..dfd3c8186 100644 --- a/integrations/azure_ai_search/example/document_store.py +++ b/integrations/azure_ai_search/example/document_store.py @@ -41,5 +41,4 @@ } results = document_store.filter_documents(filters) -for doc in results: - print(doc) +print(results) diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 625112fb0..777efe20d 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -70,7 +70,7 @@ def __init__( api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), # noqa: B008 azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), # noqa: B008 index_name: str = "default", - embedding_dimension: Optional[int] = 768, + embedding_dimension: int = 768, metadata_fields: Optional[Dict[str, type]] = None, vector_search_configuration: VectorSearch = None, create_index: bool = True, @@ -100,12 +100,12 @@ def __init__( For more information on parameters, see the [official Azure AI Search documentation](https://learn.microsoft.com/en-us/azure/search/) """ - azure_endpoint = azure_endpoint or os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT") + azure_endpoint = azure_endpoint or os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT") or None if not azure_endpoint: msg = "Please provide an Azure endpoint or set the environment variable AZURE_OPENAI_ENDPOINT." raise ValueError(msg) - api_key = api_key or os.environ.get("AZURE_SEARCH_API_KEY") + api_key = api_key or os.environ.get("AZURE_SEARCH_API_KEY") or None self._client = None self._index_client = None @@ -144,8 +144,10 @@ def client(self) -> SearchClient: msg = f"Failed to authenticate with Azure Search: {error}" raise AzureAISearchDocumentStoreConfigError(msg) from error - # Get the search client, if index client is initialized - if self._index_client: + if self._index_client: # type: ignore # self._index_client is not None (verified in the run method) + # Get the search client, if index client is initialized + index_fields = self._index_client.get_index(self._index_name).fields + self._index_fields = [field.name for field in index_fields] self._client = self._index_client.get_search_client(self._index_name) else: msg = "Search Index Client is not initialized." @@ -178,8 +180,6 @@ def create_index(self, index_name: str, **kwargs) -> None: index_name = self._index_name if self._metadata_fields: default_fields.extend(self._create_metadata_index_fields(self._metadata_fields)) - - self._index_fields = default_fields index = SearchIndex( name=index_name, fields=default_fields, vector_search=self._vector_search_configuration, **kwargs ) @@ -247,7 +247,7 @@ def _convert_input_document(documents: Document): if not isinstance(document_dict["id"], str): msg = f"Document id {document_dict['id']} is not a string, " raise Exception(msg) - index_document = self._default_index_mapping(document_dict) + index_document = self._convert_haystack_documents_to_azure(document_dict) return index_document @@ -324,17 +324,17 @@ def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) Converts Azure search results to Haystack Documents. """ documents = [] - for azure_doc in azure_docs: + for azure_doc in azure_docs: embedding = azure_doc.get("embedding") if embedding == self._dummy_vector: embedding = None - # Filter out meta fields + # Anything besides default fields (id, content, and embedding) is considered metadata meta = { key: value for key, value in azure_doc.items() - if key not in ["id", "content", "embedding"] and not key.startswith("@") and value is not None + if key not in ["id", "content", "embedding"] and key in self._index_fields and value is not None } # Create the document with meta only if it's non-empty @@ -375,14 +375,11 @@ def _get_raw_documents_by_id(self, document_ids: List[str]): logger.warning(f"Document with ID {doc_id} not found.") return azure_documents - def _default_index_mapping(self, document: Dict[str, Any]) -> Dict[str, Any]: + def _convert_haystack_documents_to_azure(self, document: Dict[str, Any]) -> Dict[str, Any]: """Map the document keys to fields of search index""" - keys_to_remove = ["dataframe", "blob", "sparse_embedding", "score"] - index_document = {k: v for k, v in document.items() if k not in keys_to_remove} - metadata = index_document.pop("meta", None) - for key, value in metadata.items(): - index_document[key] = value + # Because Azure Search does not allow dynamic fields, we only include fields that are part of the schema + index_document = {k: v for k, v in {**document, **document.get("meta", {})}.items() if k in self._index_fields} if index_document["embedding"] is None: index_document["embedding"] = self._dummy_vector @@ -405,11 +402,15 @@ def _map_metadata_field_types(self, metadata: Dict[str, type]) -> Dict[str, str] metadata_field_mapping = {} for key, value_type in metadata.items(): + + # Azure Search index only allows field names starting with letters + field_name = next((key[i:] for i, char in enumerate(key) if char.isalpha()), key) + field_type = type_mapping.get(value_type) if not field_type: - error_message = f"Unsupported field type for key '{key}': {value_type}" + error_message = f"Unsupported field type for key '{field_name}': {value_type}" raise ValueError(error_message) - metadata_field_mapping[key] = field_type + metadata_field_mapping[field_name] = field_type return metadata_field_mapping From 3afc3b9a1c20b688155649cf3e437d887b8bfbe2 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Thu, 31 Oct 2024 17:53:07 +0100 Subject: [PATCH 24/26] Updated code based on PR comments --- integrations/azure_ai_search/.gitignore | 163 ------------------ integrations/azure_ai_search/pyproject.toml | 3 +- .../azure_ai_search/embedding_retriever.py | 11 +- .../azure_ai_search/document_store.py | 31 ++-- .../tests/test_document_store.py | 48 +++--- .../tests/test_embedding_retriever.py | 2 - 6 files changed, 39 insertions(+), 219 deletions(-) delete mode 100644 integrations/azure_ai_search/.gitignore diff --git a/integrations/azure_ai_search/.gitignore b/integrations/azure_ai_search/.gitignore deleted file mode 100644 index d1c340c1f..000000000 --- a/integrations/azure_ai_search/.gitignore +++ /dev/null @@ -1,163 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - -# VS Code -.vscode diff --git a/integrations/azure_ai_search/pyproject.toml b/integrations/azure_ai_search/pyproject.toml index c90ebfc5d..49ca623e7 100644 --- a/integrations/azure_ai_search/pyproject.toml +++ b/integrations/azure_ai_search/pyproject.toml @@ -15,14 +15,13 @@ classifiers = [ "License :: OSI Approved :: Apache Software License", "Development Status :: 4 - Beta", "Programming Language :: Python", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ] -dependencies = ["haystack-ai", "azure-search-documents>=11.5", "azure-identity", "torch>=1.11.0"] +dependencies = ["haystack-ai", "azure-search-documents>=11.5", "azure-identity"] [project.urls] Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_ai_search#readme" diff --git a/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py index fe23718c8..ab649f874 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py +++ b/integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py @@ -25,7 +25,6 @@ def __init__( filters: Optional[Dict[str, Any]] = None, top_k: int = 10, filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE, - raise_on_failure: bool = True, ): """ Create the AzureAISearchEmbeddingRetriever component. @@ -44,7 +43,6 @@ def __init__( self._filter_policy = ( filter_policy if isinstance(filter_policy, FilterPolicy) else FilterPolicy.from_str(filter_policy) ) - self._raise_on_failure = raise_on_failure if not isinstance(document_store, AzureAISearchDocumentStore): message = "document_store must be an instance of AzureAISearchDocumentStore" @@ -113,13 +111,6 @@ def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = top_k=top_k, ) except Exception as e: - if self._raise_on_failure: - raise e - logger.warning( - "An error occurred during embedding retrieval and will be ignored, returning empty results: %s", - str(e), - exc_info=True, - ) - docs = [] + raise e return {"documents": docs} diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 777efe20d..7b07c81a8 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -73,7 +73,6 @@ def __init__( embedding_dimension: int = 768, metadata_fields: Optional[Dict[str, type]] = None, vector_search_configuration: VectorSearch = None, - create_index: bool = True, **kwargs, ): """ @@ -117,7 +116,6 @@ def __init__( self._dummy_vector = [-10.0] * self._embedding_dimension self._metadata_fields = metadata_fields self._vector_search_configuration = vector_search_configuration or DEFAULT_VECTOR_SEARCH - self._create_index = create_index self._kwargs = kwargs @property @@ -133,13 +131,13 @@ def client(self) -> SearchClient: try: if not self._index_client: self._index_client = SearchIndexClient(resolved_endpoint, credential, **self._kwargs) - if not self.index_exists(self._index_name): + if not self._index_exists(self._index_name): # Create a new index if it does not exist logger.debug( "The index '%s' does not exist. A new index will be created.", self._index_name, ) - self.create_index(self._index_name) + self._create_index(self._index_name) except (HttpResponseError, ClientAuthenticationError) as error: msg = f"Failed to authenticate with Azure Search: {error}" raise AzureAISearchDocumentStoreConfigError(msg) from error @@ -155,7 +153,7 @@ def client(self) -> SearchClient: return self._client - def create_index(self, index_name: str, **kwargs) -> None: + def _create_index(self, index_name: str, **kwargs) -> None: """ Creates a new search index. :param index_name: Name of the index to create. If None, the index name from the constructor is used. @@ -201,7 +199,6 @@ def to_dict(self) -> Dict[str, Any]: azure_endpoint=self._azure_endpoint.to_dict() if self._azure_endpoint is not None else None, api_key=self._api_key.to_dict() if self._api_key is not None else None, index_name=self._index_name, - create_index=self._create_index, embedding_dimension=self._embedding_dimension, metadata_fields=self._metadata_fields, vector_search_configuration=self._vector_search_configuration.as_dict(), @@ -225,14 +222,13 @@ def from_dict(cls, data: Dict[str, Any]) -> "AzureAISearchDocumentStore": data["init_parameters"]["vector_search_configuration"] = VectorSearch.from_dict(vector_search_configuration) return default_from_dict(cls, data) - def count_documents(self, **kwargs: Any) -> int: + def count_documents(self) -> int: """ Returns how many documents are present in the search index. - :param kwargs: additional keyword parameters. :returns: list of retrieved documents. """ - return self.client.get_document_count(**kwargs) + return self.client.get_document_count() def write_documents(self, documents: List[Document], policy: DuplicatePolicy = DuplicatePolicy.FAIL) -> int: """ @@ -292,7 +288,7 @@ def delete_documents(self, document_ids: List[str]) -> None: def get_documents_by_id(self, document_ids: List[str]) -> List[Document]: return self._convert_search_result_to_documents(self._get_raw_documents_by_id(document_ids)) - def search_documents(self, search_text: Optional[str] = "*", top_k: Optional[int] = 10) -> List[Document]: + def search_documents(self, search_text: str = "*", top_k: int = 10) -> List[Document]: """ Returns all documents that match the provided search_text. If search_text is None, returns all documents. @@ -345,7 +341,7 @@ def _convert_search_result_to_documents(self, azure_docs: List[Dict[str, Any]]) documents.append(doc) return documents - def index_exists(self, index_name: Optional[str]) -> bool: + def _index_exists(self, index_name: Optional[str]) -> bool: """ Check if the index exists in the Azure AI Search service. @@ -403,14 +399,19 @@ def _map_metadata_field_types(self, metadata: Dict[str, type]) -> Dict[str, str] for key, value_type in metadata.items(): - # Azure Search index only allows field names starting with letters - field_name = next((key[i:] for i, char in enumerate(key) if char.isalpha()), key) + if not key[0].isalpha(): + msg = ( + f"Azure Search index only allows field names starting with letters. " + f"Invalid key: {key} will be dropped." + ) + logger.warning(msg) + continue field_type = type_mapping.get(value_type) if not field_type: - error_message = f"Unsupported field type for key '{field_name}': {value_type}" + error_message = f"Unsupported field type for key '{key}': {value_type}" raise ValueError(error_message) - metadata_field_mapping[field_name] = field_type + metadata_field_mapping[key] = field_type return metadata_field_mapping diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index 50733a8a4..8e1aaec80 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -35,7 +35,6 @@ def test_to_dict(monkeypatch): "index_name": "default", "embedding_dimension": 768, "metadata_fields": None, - "create_index": True, "vector_search_configuration": { "profiles": [ {"name": "default-vector-config", "algorithm_configuration_name": "cosine-algorithm-config"} @@ -65,7 +64,6 @@ def test_from_dict(monkeypatch): "embedding_dimension": 768, "index_name": "default", "metadata_fields": None, - "create_index": False, "vector_search_configuration": DEFAULT_VECTOR_SEARCH, }, } @@ -75,7 +73,6 @@ def test_from_dict(monkeypatch): assert document_store._index_name == "default" assert document_store._embedding_dimension == 768 assert document_store._metadata_fields is None - assert document_store._create_index is False assert document_store._vector_search_configuration == DEFAULT_VECTOR_SEARCH @@ -92,13 +89,11 @@ def test_init(_mock_azure_search_client): api_key=Secret.from_token("fake-api-key"), azure_endpoint=Secret.from_token("fake_endpoint"), index_name="my_index", - create_index=False, embedding_dimension=15, metadata_fields={"Title": str, "Pages": int}, ) assert document_store._index_name == "my_index" - assert document_store._create_index is False assert document_store._embedding_dimension == 15 assert document_store._metadata_fields == {"Title": str, "Pages": int} assert document_store._vector_search_configuration == DEFAULT_VECTOR_SEARCH @@ -156,7 +151,8 @@ def _random_embeddings(n): ) class TestFilters(FilterDocumentsTest): - # Overriding to change "date" to compatible ISO format and remove incompatible fields (dataframes) for search index + # Overriding to change "date" to compatible ISO 8601 format + # and remove incompatible fields (dataframes) for Azure search index @pytest.fixture def filterable_docs(self) -> List[Document]: """Fixture that returns a list of Documents that can be used to test filtering.""" @@ -224,26 +220,25 @@ def assert_documents_are_equal(self, received: List[Document], expected: List[Do sorted_expected = sorted(expected, key=lambda doc: doc.id) assert sorted_recieved == sorted_expected - # Dataframes are not supported in serach index - def test_comparison_equal_with_dataframe(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support dataframes") + def test_comparison_equal_with_dataframe(self, document_store, filterable_docs): ... - def test_comparison_not_equal_with_dataframe(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support dataframes") + def test_comparison_not_equal_with_dataframe(self, document_store, filterable_docs): ... - def test_comparison_greater_than_with_dataframe(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support dataframes") + def test_comparison_greater_than_with_dataframe(self, document_store, filterable_docs): ... - def test_comparison_less_than_with_dataframe(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support dataframes") + def test_comparison_less_than_with_dataframe(self, document_store, filterable_docs): ... - def test_comparison_greater_than_equal_with_dataframe(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support dataframes") + def test_comparison_greater_than_equal_with_dataframe(self, document_store, filterable_docs): ... - def test_comparison_less_than_equal_with_dataframe(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support dataframes") + def test_comparison_less_than_equal_with_dataframe(self, document_store, filterable_docs): ... - # Azure search index supports UTC datetime in ISO format + # Azure search index supports UTC datetime in ISO 8601 format def test_comparison_greater_than_with_iso_date(self, document_store, filterable_docs): """Test filter_documents() with > comparator and datetime""" document_store.write_documents(filterable_docs) @@ -346,15 +341,14 @@ def test_comparison_in(self, document_store, filterable_docs): expected = [d for d in filterable_docs if d.meta.get("page") is not None and d.meta["page"] in ["100", "123"]] self.assert_documents_are_equal(result, expected) - # not supported - def test_comparison_not_in(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support not in operator") + def test_comparison_not_in(self, document_store, filterable_docs): ... - def test_comparison_not_in_with_with_non_list(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support not in operator") + def test_comparison_not_in_with_with_non_list(self, document_store, filterable_docs): ... - def test_comparison_not_in_with_with_non_list_iterable(self, document_store, filterable_docs): - pass + @pytest.mark.skip(reason="Azure AI search index does not support not in operator") + def test_comparison_not_in_with_with_non_list_iterable(self, document_store, filterable_docs): ... def test_missing_condition_operator_key(self, document_store, filterable_docs): """Test filter_documents() with missing operator key""" diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py index af4b21478..83db9c058 100644 --- a/integrations/azure_ai_search/tests/test_embedding_retriever.py +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -49,7 +49,6 @@ def test_to_dict(): }, "api_key": {"type": "env_var", "env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False}, "index_name": "default", - "create_index": True, "embedding_dimension": 768, "metadata_fields": None, "vector_search_configuration": { @@ -88,7 +87,6 @@ def test_from_dict(): }, "api_key": {"type": "env_var", "env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False}, "index_name": "default", - "create_index": True, "embedding_dimension": 768, "metadata_fields": None, "vector_search_configuration": DEFAULT_VECTOR_SEARCH, From d4793e87140f61a89564cf02c76dc57be152728c Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 6 Nov 2024 16:06:07 +0100 Subject: [PATCH 25/26] Fixes and nested filters logic --- .github/workflows/azure_ai_search.yml | 4 +- .../azure_ai_search/example/document_store.py | 16 +++--- .../azure_ai_search/document_store.py | 39 +++++-------- .../azure_ai_search/filters.py | 12 +++- .../tests/test_document_store.py | 57 ++++++++++++++++++- .../tests/test_embedding_retriever.py | 8 +-- 6 files changed, 91 insertions(+), 45 deletions(-) diff --git a/.github/workflows/azure_ai_search.yml b/.github/workflows/azure_ai_search.yml index 294bb4c64..050265dfa 100644 --- a/.github/workflows/azure_ai_search.yml +++ b/.github/workflows/azure_ai_search.yml @@ -31,8 +31,8 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + os: [ubuntu-latest, windows-latest, macos-latest] + python-version: ["3.9", "3.10", "3.11"] steps: - uses: actions/checkout@v4 diff --git a/integrations/azure_ai_search/example/document_store.py b/integrations/azure_ai_search/example/document_store.py index dfd3c8186..779f28935 100644 --- a/integrations/azure_ai_search/example/document_store.py +++ b/integrations/azure_ai_search/example/document_store.py @@ -18,16 +18,16 @@ documents = [ Document( - content="Use pip to install a basic version of Haystack's latest release: pip install farm-haystack.", - meta={"version": 1.15, "label": "first"}, + content="This is an introduction to using Python for data analysis.", + meta={"version": 1.0, "label": "chapter_one"}, ), Document( - content="Use pip to install a Haystack's latest release: pip install farm-haystack[inference].", - meta={"version": 1.22, "label": "second"}, + content="Learn how to use Python libraries for machine learning.", + meta={"version": 1.5, "label": "chapter_two"}, ), Document( - content="Use pip to install only the Haystack 2.0 code: pip install haystack-ai.", - meta={"version": 2.0, "label": "third"}, + content="Advanced Python techniques for data visualization.", + meta={"version": 2.0, "label": "chapter_three"}, ), ] document_store.write_documents(documents, policy=DuplicatePolicy.SKIP) @@ -35,8 +35,8 @@ filters = { "operator": "AND", "conditions": [ - {"field": "meta.version", "operator": ">", "value": 1.21}, - {"field": "meta.label", "operator": "in", "value": ["first", "third"]}, + {"field": "meta.version", "operator": ">", "value": 1.2}, + {"field": "meta.label", "operator": "in", "value": ["chapter_one", "chapter_three"]}, ], } diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py index 7b07c81a8..0b59b6e37 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py @@ -27,7 +27,6 @@ from azure.search.documents.models import VectorizedQuery from haystack import default_from_dict, default_to_dict from haystack.dataclasses import Document -from haystack.document_stores.errors import DuplicateDocumentError from haystack.document_stores.types import DuplicatePolicy from haystack.utils import Secret, deserialize_secrets_inplace @@ -42,8 +41,6 @@ datetime: "Edm.DateTimeOffset", } -MAX_UPLOAD_BATCH_SIZE = 1000 - DEFAULT_VECTOR_SEARCH = VectorSearch( profiles=[ VectorSearchProfile(name="default-vector-config", algorithm_configuration_name="cosine-algorithm-config") @@ -68,7 +65,7 @@ def __init__( self, *, api_key: Secret = Secret.from_env_var("AZURE_SEARCH_API_KEY", strict=False), # noqa: B008 - azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=False), # noqa: B008 + azure_endpoint: Secret = Secret.from_env_var("AZURE_SEARCH_SERVICE_ENDPOINT", strict=True), # noqa: B008 index_name: str = "default", embedding_dimension: int = 768, metadata_fields: Optional[Dict[str, type]] = None, @@ -83,9 +80,10 @@ def __init__( :param api_key: The API key to use for authentication. :param index_name: Name of index in Azure AI Search, if it doesn't exist it will be created. :param embedding_dimension: Dimension of the embeddings. - :param metadata_fields: A dictionary of metatada keys and their types to create + :param metadata_fields: A dictionary of metadata keys and their types to create additional fields in index schema. As fields in Azure SearchIndex cannot be dynamic, it is necessary to specify the metadata fields in advance. + (e.g. metadata_fields = {"author": str, "date": datetime}) :param vector_search_configuration: Configuration option related to vector search. Default configuration uses the HNSW algorithm with cosine similarity to handle vector searches. @@ -142,7 +140,7 @@ def client(self) -> SearchClient: msg = f"Failed to authenticate with Azure Search: {error}" raise AzureAISearchDocumentStoreConfigError(msg) from error - if self._index_client: # type: ignore # self._index_client is not None (verified in the run method) + if self._index_client: # Get the search client, if index client is initialized index_fields = self._index_client.get_index(self._index_name).fields self._index_fields = [field.name for field in index_fields] @@ -230,7 +228,7 @@ def count_documents(self) -> int: """ return self.client.get_document_count() - def write_documents(self, documents: List[Document], policy: DuplicatePolicy = DuplicatePolicy.FAIL) -> int: + def write_documents(self, documents: List[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE) -> int: """ Writes the provided documents to search index. @@ -252,25 +250,16 @@ def _convert_input_document(documents: Document): msg = "param 'documents' must contain a list of objects of type Document" raise ValueError(msg) - documents_to_write = [] - for doc in documents: - try: - self.client.get_document(doc.id) - if policy == DuplicatePolicy.SKIP: - logger.info(f"Document with ID {doc.id} already exists. Skipping.") - continue - elif policy == DuplicatePolicy.FAIL: - msg = f"Document with ID {doc.id} already exists." - raise DuplicateDocumentError(msg) - elif policy == DuplicatePolicy.OVERWRITE: - logger.info(f"Document with ID {doc.id} already exists. Overwriting.") - documents_to_write.append(_convert_input_document(doc)) - except ResourceNotFoundError: - # Document does not exist, safe to add - documents_to_write.append(_convert_input_document(doc)) + if policy not in [DuplicatePolicy.NONE, DuplicatePolicy.OVERWRITE]: + logger.warning( + f"AzureAISearchDocumentStore only supports `DuplicatePolicy.OVERWRITE`" + f"but got {policy}. Overwriting duplicates is enabled by default." + ) + client = self.client + documents_to_write = [(_convert_input_document(doc)) for doc in documents] if documents_to_write != []: - self.client.upload_documents(documents_to_write) + client.upload_documents(documents_to_write) return len(documents_to_write) def delete_documents(self, document_ids: List[str]) -> None: @@ -279,7 +268,7 @@ def delete_documents(self, document_ids: List[str]) -> None: :param document_ids: ids of the documents to be deleted. """ - if self.count_documents == 0: + if self.count_documents() == 0: return documents = self._get_raw_documents_by_id(document_ids) if documents: diff --git a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py index 525e36be3..650e3f8be 100644 --- a/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py +++ b/integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/filters.py @@ -32,8 +32,16 @@ def _parse_logical_condition(condition: Dict[str, Any]) -> str: if operator not in LOGICAL_OPERATORS: msg = f"Unknown operator {operator}" raise AzureAISearchDocumentStoreFilterError(msg) - conditions = [_parse_comparison_condition(c) for c in condition["conditions"]] - + conditions = [] + for c in condition["conditions"]: + # Recursively parse if the condition itself is a logical condition + if isinstance(c, dict) and "operator" in c and c["operator"] in LOGICAL_OPERATORS: + conditions.append(_parse_logical_condition(c)) + else: + # Otherwise, parse it as a comparison condition + conditions.append(_parse_comparison_condition(c)) + + # Format the result based on the operator if operator == "NOT": return f"not ({' and '.join([f'({c})' for c in conditions])})" else: diff --git a/integrations/azure_ai_search/tests/test_document_store.py b/integrations/azure_ai_search/tests/test_document_store.py index 8e1aaec80..1bcd967c6 100644 --- a/integrations/azure_ai_search/tests/test_document_store.py +++ b/integrations/azure_ai_search/tests/test_document_store.py @@ -30,7 +30,7 @@ def test_to_dict(monkeypatch): assert res == { "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", "init_parameters": { - "azure_endpoint": {"env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], "strict": False, "type": "env_var"}, + "azure_endpoint": {"env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], "strict": True, "type": "env_var"}, "api_key": {"env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False, "type": "env_var"}, "index_name": "default", "embedding_dimension": 768, @@ -59,7 +59,7 @@ def test_from_dict(monkeypatch): data = { "type": "haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore", "init_parameters": { - "azure_endpoint": {"env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], "strict": False, "type": "env_var"}, + "azure_endpoint": {"env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], "strict": True, "type": "env_var"}, "api_key": {"env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False, "type": "env_var"}, "embedding_dimension": 768, "index_name": "default", @@ -78,7 +78,7 @@ def test_from_dict(monkeypatch): @patch("haystack_integrations.document_stores.azure_ai_search.document_store.AzureAISearchDocumentStore") def test_init_is_lazy(_mock_azure_search_client): - AzureAISearchDocumentStore(azure_endppoint=Secret.from_token("test_endpoint")) + AzureAISearchDocumentStore(azure_endpoint=Secret.from_token("test_endpoint")) _mock_azure_search_client.assert_not_called() @@ -129,6 +129,12 @@ def test_write_documents_with_meta(self, document_store: AzureAISearchDocumentSt doc = document_store.get_documents_by_id(["1"]) assert doc[0] == docs[0] + @pytest.mark.skip(reason="Azure AI search index overwrites duplicate documents by default") + def test_write_documents_duplicate_fail(self, document_store: AzureAISearchDocumentStore): ... + + @pytest.mark.skip(reason="Azure AI search index overwrites duplicate documents by default") + def test_write_documents_duplicate_skip(self, document_store: AzureAISearchDocumentStore): ... + def _random_embeddings(n): return [round(random.random(), 7) for _ in range(n)] # nosec: S311 @@ -357,3 +363,48 @@ def test_missing_condition_operator_key(self, document_store, filterable_docs): document_store.filter_documents( filters={"conditions": [{"field": "meta.name", "operator": "eq", "value": "test"}]} ) + + def test_nested_logical_filters(self, document_store, filterable_docs): + document_store.write_documents(filterable_docs) + filters = { + "operator": "OR", + "conditions": [ + {"field": "meta.name", "operator": "==", "value": "name_0"}, + { + "operator": "AND", + "conditions": [ + {"field": "meta.number", "operator": "!=", "value": 0}, + {"field": "meta.page", "operator": "==", "value": "123"}, + ], + }, + { + "operator": "AND", + "conditions": [ + {"field": "meta.chapter", "operator": "==", "value": "conclusion"}, + {"field": "meta.page", "operator": "==", "value": "90"}, + ], + }, + ], + } + result = document_store.filter_documents(filters=filters) + self.assert_documents_are_equal( + result, + [ + doc + for doc in filterable_docs + if ( + # Ensure all required fields are present in doc.meta + ("name" in doc.meta and doc.meta.get("name") == "name_0") + or ( + all(key in doc.meta for key in ["number", "page"]) + and doc.meta.get("number") != 0 + and doc.meta.get("page") == "123" + ) + or ( + all(key in doc.meta for key in ["page", "chapter"]) + and doc.meta.get("chapter") == "conclusion" + and doc.meta.get("page") == "90" + ) + ) + ], + ) diff --git a/integrations/azure_ai_search/tests/test_embedding_retriever.py b/integrations/azure_ai_search/tests/test_embedding_retriever.py index 83db9c058..d4615ec44 100644 --- a/integrations/azure_ai_search/tests/test_embedding_retriever.py +++ b/integrations/azure_ai_search/tests/test_embedding_retriever.py @@ -45,7 +45,7 @@ def test_to_dict(): "azure_endpoint": { "type": "env_var", "env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], - "strict": False, + "strict": True, }, "api_key": {"type": "env_var", "env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False}, "index_name": "default", @@ -83,7 +83,7 @@ def test_from_dict(): "azure_endpoint": { "type": "env_var", "env_vars": ["AZURE_SEARCH_SERVICE_ENDPOINT"], - "strict": False, + "strict": True, }, "api_key": {"type": "env_var", "env_vars": ["AZURE_SEARCH_API_KEY"], "strict": False}, "index_name": "default", @@ -132,9 +132,7 @@ def test_embedding_retrieval(self, document_store: AzureAISearchDocumentStore): document_store.write_documents(docs) retriever = AzureAISearchEmbeddingRetriever(document_store=document_store) results = retriever.run(query_embedding=query_embedding) - results = document_store._embedding_retrieval(query_embedding=query_embedding, top_k=1) - assert len(results) == 1 - assert results[0].content == "This is first document" + assert results["documents"][0].content == "This is first document" def test_empty_query_embedding(self, document_store: AzureAISearchDocumentStore): query_embedding: List[float] = [] From f94944e4d5f3d2ec4495933e601a3f7a023cb82e Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Wed, 6 Nov 2024 16:45:39 +0100 Subject: [PATCH 26/26] Reducing parallel tests for CI --- .github/workflows/azure_ai_search.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/azure_ai_search.yml b/.github/workflows/azure_ai_search.yml index 050265dfa..1c10edc91 100644 --- a/.github/workflows/azure_ai_search.yml +++ b/.github/workflows/azure_ai_search.yml @@ -30,8 +30,9 @@ jobs: runs-on: ${{ matrix.os }} strategy: fail-fast: false + max-parallel: 3 matrix: - os: [ubuntu-latest, windows-latest, macos-latest] + os: [ubuntu-latest, windows-latest] python-version: ["3.9", "3.10", "3.11"] steps: