mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-23 23:29:21 +00:00
community[patch]: Add initial tests for AzureSearch vector store (#17663)
**Description:** AzureSearch vector store has no tests. This PR adds initial tests to validate the code can be imported and used. **Issue:** N/A **Dependencies:** azure-search-documents and azure-identity are added as optional dependencies for testing --------- Co-authored-by: Matt Gotteiner <[email protected]> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Matt
GitHub
parent
5f1d1666e3
commit
28df4750ef
97
libs/community/poetry.lock
generated
97
libs/community/poetry.lock
generated
@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aenum"
|
||||
@ -552,6 +552,17 @@ azure-core = ">=1.30.0,<2.0.0"
|
||||
isodate = ">=0.6.1,<1.0.0"
|
||||
typing-extensions = ">=4.6.0"
|
||||
|
||||
[[package]]
|
||||
name = "azure-common"
|
||||
version = "1.1.28"
|
||||
description = "Microsoft Azure Client Library for Python (Common)"
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "azure-common-1.1.28.zip", hash = "sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3"},
|
||||
{file = "azure_common-1.1.28-py2.py3-none-any.whl", hash = "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "azure-core"
|
||||
version = "1.30.1"
|
||||
@ -571,6 +582,39 @@ typing-extensions = ">=4.6.0"
|
||||
[package.extras]
|
||||
aio = ["aiohttp (>=3.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "azure-identity"
|
||||
version = "1.16.0"
|
||||
description = "Microsoft Azure Identity Library for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "azure-identity-1.16.0.tar.gz", hash = "sha256:6ff1d667cdcd81da1ceab42f80a0be63ca846629f518a922f7317a7e3c844e1b"},
|
||||
{file = "azure_identity-1.16.0-py3-none-any.whl", hash = "sha256:722fdb60b8fdd55fa44dc378b8072f4b419b56a5e54c0de391f644949f3a826f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
azure-core = ">=1.23.0"
|
||||
cryptography = ">=2.5"
|
||||
msal = ">=1.24.0"
|
||||
msal-extensions = ">=0.3.0"
|
||||
|
||||
[[package]]
|
||||
name = "azure-search-documents"
|
||||
version = "11.4.0"
|
||||
description = "Microsoft Azure Cognitive Search Client Library for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "azure-search-documents-11.4.0.tar.gz", hash = "sha256:599f269f106fb51e646ff426a218c21811575598e6a769b23fa4a0127c0f57e0"},
|
||||
{file = "azure_search_documents-11.4.0-py3-none-any.whl", hash = "sha256:e435266dc992a3450dc475309c9475f89a4bb0e9dac838140e609d9f1c7608ac"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
azure-common = ">=1.1,<2.0"
|
||||
azure-core = ">=1.28.0,<2.0.0"
|
||||
isodate = ">=0.6.0"
|
||||
|
||||
[[package]]
|
||||
name = "babel"
|
||||
version = "2.14.0"
|
||||
@ -3204,7 +3248,6 @@ files = [
|
||||
{file = "jq-1.6.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:227b178b22a7f91ae88525810441791b1ca1fc71c86f03190911793be15cec3d"},
|
||||
{file = "jq-1.6.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:780eb6383fbae12afa819ef676fc93e1548ae4b076c004a393af26a04b460742"},
|
||||
{file = "jq-1.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:08ded6467f4ef89fec35b2bf310f210f8cd13fbd9d80e521500889edf8d22441"},
|
||||
{file = "jq-1.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:49e44ed677713f4115bd5bf2dbae23baa4cd503be350e12a1c1f506b0687848f"},
|
||||
{file = "jq-1.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:984f33862af285ad3e41e23179ac4795f1701822473e1a26bf87ff023e5a89ea"},
|
||||
{file = "jq-1.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f42264fafc6166efb5611b5d4cb01058887d050a6c19334f6a3f8a13bb369df5"},
|
||||
{file = "jq-1.6.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a67154f150aaf76cc1294032ed588436eb002097dd4fd1e283824bf753a05080"},
|
||||
@ -3715,7 +3758,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.1.45"
|
||||
version = "0.1.46"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -4204,6 +4247,25 @@ requests = ">=2.0.0,<3"
|
||||
[package.extras]
|
||||
broker = ["pymsalruntime (>=0.13.2,<0.15)"]
|
||||
|
||||
[[package]]
|
||||
name = "msal-extensions"
|
||||
version = "1.1.0"
|
||||
description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism."
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "msal-extensions-1.1.0.tar.gz", hash = "sha256:6ab357867062db7b253d0bd2df6d411c7891a0ee7308d54d1e4317c1d1c54252"},
|
||||
{file = "msal_extensions-1.1.0-py3-none-any.whl", hash = "sha256:01be9711b4c0b1a151450068eeb2c4f0997df3bba085ac299de3a66f585e382f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
msal = ">=0.4.1,<2.0.0"
|
||||
packaging = "*"
|
||||
portalocker = [
|
||||
{version = ">=1.0,<3", markers = "platform_system != \"Windows\""},
|
||||
{version = ">=1.6,<3", markers = "platform_system == \"Windows\""},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "multidict"
|
||||
version = "6.0.5"
|
||||
@ -5349,6 +5411,25 @@ files = [
|
||||
dev = ["pre-commit", "tox"]
|
||||
testing = ["pytest", "pytest-benchmark"]
|
||||
|
||||
[[package]]
|
||||
name = "portalocker"
|
||||
version = "2.8.2"
|
||||
description = "Wraps the portalocker recipe for easy usage"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "portalocker-2.8.2-py3-none-any.whl", hash = "sha256:cfb86acc09b9aa7c3b43594e19be1345b9d16af3feb08bf92f23d4dce513a28e"},
|
||||
{file = "portalocker-2.8.2.tar.gz", hash = "sha256:2b035aa7828e46c58e9b31390ee1f169b98e1066ab10b9a6a861fe7e25ee4f33"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pywin32 = {version = ">=226", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[package.extras]
|
||||
docs = ["sphinx (>=1.7.1)"]
|
||||
redis = ["redis"]
|
||||
tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "pytest-timeout (>=2.1.0)", "redis", "sphinx (>=6.0.0)", "types-redis"]
|
||||
|
||||
[[package]]
|
||||
name = "praw"
|
||||
version = "7.7.1"
|
||||
@ -5529,6 +5610,8 @@ files = [
|
||||
{file = "psycopg2-2.9.9-cp310-cp310-win_amd64.whl", hash = "sha256:426f9f29bde126913a20a96ff8ce7d73fd8a216cfb323b1f04da402d452853c3"},
|
||||
{file = "psycopg2-2.9.9-cp311-cp311-win32.whl", hash = "sha256:ade01303ccf7ae12c356a5e10911c9e1c51136003a9a1d92f7aa9d010fb98372"},
|
||||
{file = "psycopg2-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:121081ea2e76729acfb0673ff33755e8703d45e926e416cb59bae3a86c6a4981"},
|
||||
{file = "psycopg2-2.9.9-cp312-cp312-win32.whl", hash = "sha256:d735786acc7dd25815e89cc4ad529a43af779db2e25aa7c626de864127e5a024"},
|
||||
{file = "psycopg2-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:a7653d00b732afb6fc597e29c50ad28087dcb4fbfb28e86092277a559ae4e693"},
|
||||
{file = "psycopg2-2.9.9-cp37-cp37m-win32.whl", hash = "sha256:5e0d98cade4f0e0304d7d6f25bbfbc5bd186e07b38eac65379309c4ca3193efa"},
|
||||
{file = "psycopg2-2.9.9-cp37-cp37m-win_amd64.whl", hash = "sha256:7e2dacf8b009a1c1e843b5213a87f7c544b2b042476ed7755be813eaf4e8347a"},
|
||||
{file = "psycopg2-2.9.9-cp38-cp38-win32.whl", hash = "sha256:ff432630e510709564c01dafdbe996cb552e0b9f3f065eb89bdce5bd31fabf4c"},
|
||||
@ -5571,6 +5654,7 @@ files = [
|
||||
{file = "psycopg2_binary-2.9.9-cp311-cp311-win32.whl", hash = "sha256:dc4926288b2a3e9fd7b50dc6a1909a13bbdadfc67d93f3374d984e56f885579d"},
|
||||
{file = "psycopg2_binary-2.9.9-cp311-cp311-win_amd64.whl", hash = "sha256:b76bedd166805480ab069612119ea636f5ab8f8771e640ae103e05a4aae3e417"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:8532fd6e6e2dc57bcb3bc90b079c60de896d2128c5d9d6f24a63875a95a088cf"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b0605eaed3eb239e87df0d5e3c6489daae3f7388d455d0c0b4df899519c6a38d"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f8544b092a29a6ddd72f3556a9fcf249ec412e10ad28be6a0c0d948924f2212"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2d423c8d8a3c82d08fe8af900ad5b613ce3632a1249fd6a223941d0735fce493"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e5afae772c00980525f6d6ecf7cbca55676296b580c0e6abb407f15f3706996"},
|
||||
@ -5579,6 +5663,8 @@ files = [
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:cb16c65dcb648d0a43a2521f2f0a2300f40639f6f8c1ecbc662141e4e3e1ee07"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:911dda9c487075abd54e644ccdf5e5c16773470a6a5d3826fda76699410066fb"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:57fede879f08d23c85140a360c6a77709113efd1c993923c59fde17aa27599fe"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-win32.whl", hash = "sha256:64cf30263844fa208851ebb13b0732ce674d8ec6a0c86a4e160495d299ba3c93"},
|
||||
{file = "psycopg2_binary-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:81ff62668af011f9a48787564ab7eded4e9fb17a4a6a74af5ffa6a457400d2ab"},
|
||||
{file = "psycopg2_binary-2.9.9-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:2293b001e319ab0d869d660a704942c9e2cce19745262a8aba2115ef41a0a42a"},
|
||||
{file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03ef7df18daf2c4c07e2695e8cfd5ee7f748a1d54d802330985a78d2a5a6dca9"},
|
||||
{file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a602ea5aff39bb9fac6308e9c9d82b9a35c2bf288e184a816002c9fae930b77"},
|
||||
@ -6576,6 +6662,7 @@ files = [
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
@ -9229,9 +9316,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
||||
|
||||
[extras]
|
||||
cli = ["typer"]
|
||||
extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "httpx-sse", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "premai", "psychicapi", "py-trello", "pyjwt", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "vdms", "xata", "xmltodict"]
|
||||
extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "azure-identity", "azure-search-documents", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "httpx-sse", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "premai", "psychicapi", "py-trello", "pyjwt", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "vdms", "xata", "xmltodict"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "48ea73a94d06ae90f8f089017ae1bbcf9d37b2cc9957a44fb617785be0fe3236"
|
||||
content-hash = "b066cbf8a1f02cae88c6c099e916d805fe6eb8685fd15c093d66cf52ea363fa5"
|
||||
|
||||
@ -94,6 +94,8 @@ hdbcli = {version = "^2.19.21", optional = true}
|
||||
oci = {version = "^2.119.1", optional = true}
|
||||
rdflib = {version = "7.0.0", optional = true}
|
||||
nvidia-riva-client = {version = "^2.14.0", optional = true}
|
||||
azure-search-documents = {version = "11.4.0", optional = true}
|
||||
azure-identity = {version = "^1.15.0", optional = true}
|
||||
tidb-vector = {version = ">=0.0.3,<1.0.0", optional = true}
|
||||
friendli-client = {version = "^1.2.4", optional = true}
|
||||
premai = {version = "^0.3.25", optional = true}
|
||||
@ -268,6 +270,8 @@ extended_testing = [
|
||||
"hdbcli",
|
||||
"oci",
|
||||
"rdflib",
|
||||
"azure-search-documents",
|
||||
"azure-identity",
|
||||
"tidb-vector",
|
||||
"cloudpickle",
|
||||
"friendli-client",
|
||||
|
||||
@ -0,0 +1,170 @@
|
||||
import json
|
||||
from typing import List, Optional
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_community.vectorstores.azuresearch import AzureSearch
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
|
||||
DEFAULT_VECTOR_DIMENSION = 4
|
||||
|
||||
|
||||
class FakeEmbeddingsWithDimension(FakeEmbeddings):
|
||||
"""Fake embeddings functionality for testing."""
|
||||
|
||||
def __init__(self, dimension: int = DEFAULT_VECTOR_DIMENSION):
|
||||
super().__init__()
|
||||
self.dimension = dimension
|
||||
|
||||
def embed_documents(self, embedding_texts: List[str]) -> List[List[float]]:
|
||||
"""Return simple embeddings."""
|
||||
return [
|
||||
[float(1.0)] * (self.dimension - 1) + [float(i)]
|
||||
for i in range(len(embedding_texts))
|
||||
]
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Return simple embeddings."""
|
||||
return [float(1.0)] * (self.dimension - 1) + [float(0.0)]
|
||||
|
||||
|
||||
DEFAULT_INDEX_NAME = "langchain-index"
|
||||
DEFAULT_ENDPOINT = "https://my-search-service.search.windows.net"
|
||||
DEFAULT_KEY = "mykey"
|
||||
DEFAULT_EMBEDDING_MODEL = FakeEmbeddingsWithDimension()
|
||||
|
||||
|
||||
def mock_default_index(*args, **kwargs): # type: ignore[no-untyped-def]
|
||||
from azure.search.documents.indexes.models import (
|
||||
ExhaustiveKnnAlgorithmConfiguration,
|
||||
ExhaustiveKnnParameters,
|
||||
HnswAlgorithmConfiguration,
|
||||
HnswParameters,
|
||||
SearchField,
|
||||
SearchFieldDataType,
|
||||
SearchIndex,
|
||||
VectorSearch,
|
||||
VectorSearchAlgorithmMetric,
|
||||
VectorSearchProfile,
|
||||
)
|
||||
|
||||
return SearchIndex(
|
||||
name=DEFAULT_INDEX_NAME,
|
||||
fields=[
|
||||
SearchField(
|
||||
name="id",
|
||||
type=SearchFieldDataType.String,
|
||||
key=True,
|
||||
hidden=False,
|
||||
searchable=False,
|
||||
filterable=True,
|
||||
sortable=False,
|
||||
facetable=False,
|
||||
),
|
||||
SearchField(
|
||||
name="content",
|
||||
type=SearchFieldDataType.String,
|
||||
key=False,
|
||||
hidden=False,
|
||||
searchable=True,
|
||||
filterable=False,
|
||||
sortable=False,
|
||||
facetable=False,
|
||||
),
|
||||
SearchField(
|
||||
name="content_vector",
|
||||
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
|
||||
searchable=True,
|
||||
vector_search_dimensions=4,
|
||||
vector_search_profile_name="myHnswProfile",
|
||||
),
|
||||
SearchField(
|
||||
name="metadata",
|
||||
type="Edm.String",
|
||||
key=False,
|
||||
hidden=False,
|
||||
searchable=True,
|
||||
filterable=False,
|
||||
sortable=False,
|
||||
facetable=False,
|
||||
),
|
||||
],
|
||||
vector_search=VectorSearch(
|
||||
profiles=[
|
||||
VectorSearchProfile(
|
||||
name="myHnswProfile", algorithm_configuration_name="default"
|
||||
),
|
||||
VectorSearchProfile(
|
||||
name="myExhaustiveKnnProfile",
|
||||
algorithm_configuration_name="default_exhaustive_knn",
|
||||
),
|
||||
],
|
||||
algorithms=[
|
||||
HnswAlgorithmConfiguration(
|
||||
name="default",
|
||||
parameters=HnswParameters(
|
||||
m=4,
|
||||
ef_construction=400,
|
||||
ef_search=500,
|
||||
metric=VectorSearchAlgorithmMetric.COSINE,
|
||||
),
|
||||
),
|
||||
ExhaustiveKnnAlgorithmConfiguration(
|
||||
name="default_exhaustive_knn",
|
||||
parameters=ExhaustiveKnnParameters(
|
||||
metric=VectorSearchAlgorithmMetric.COSINE
|
||||
),
|
||||
),
|
||||
],
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def create_vector_store() -> AzureSearch:
|
||||
return AzureSearch(
|
||||
azure_search_endpoint=DEFAULT_ENDPOINT,
|
||||
azure_search_key=DEFAULT_KEY,
|
||||
index_name=DEFAULT_INDEX_NAME,
|
||||
embedding_function=DEFAULT_EMBEDDING_MODEL,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.requires("azure.search.documents")
|
||||
def test_init_existing_index() -> None:
|
||||
from azure.search.documents.indexes import SearchIndexClient
|
||||
|
||||
def mock_create_index() -> None:
|
||||
pytest.fail("Should not create index in this test")
|
||||
|
||||
with patch.multiple(
|
||||
SearchIndexClient, get_index=mock_default_index, create_index=mock_create_index
|
||||
):
|
||||
vector_store = create_vector_store()
|
||||
assert vector_store.client is not None
|
||||
|
||||
|
||||
@pytest.mark.requires("azure.search.documents")
|
||||
def test_init_new_index() -> None:
|
||||
from azure.core.exceptions import ResourceNotFoundError
|
||||
from azure.search.documents.indexes import SearchIndexClient
|
||||
from azure.search.documents.indexes.models import SearchIndex
|
||||
|
||||
def no_index(self, name: str): # type: ignore[no-untyped-def]
|
||||
raise ResourceNotFoundError
|
||||
|
||||
created_index: Optional[SearchIndex] = None
|
||||
|
||||
def mock_create_index(self, index): # type: ignore[no-untyped-def]
|
||||
nonlocal created_index
|
||||
created_index = index
|
||||
|
||||
with patch.multiple(
|
||||
SearchIndexClient, get_index=no_index, create_index=mock_create_index
|
||||
):
|
||||
vector_store = create_vector_store()
|
||||
assert vector_store.client is not None
|
||||
assert created_index is not None
|
||||
assert json.dumps(created_index.as_dict()) == json.dumps(
|
||||
mock_default_index().as_dict()
|
||||
)
|
||||
Loading…
Reference in New Issue
Block a user