mirror of
https://github.com/hwchase17/langchain.git
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community[patch]: ElasticsearchStore: add relevance function selector (#16378)
Implement similarity function selector for ElasticsearchStore. The scores coming back from Elasticsearch are already similarities (not distances) and they are already normalized (see [docs](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params)). Hence we leave the scores untouched and just forward them. This fixes #11539. However, in hybrid mode (when keyword search and vector search are involved) Elasticsearch currently returns no scores. This PR adds an error message around this fact. We need to think a bit more to come up with a solution for this case. This PR also corrects a small error in the Elasticsearch integration test. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Max Jakob
GitHub
parent
54f90fc6bc
commit
de209af533
@ -388,7 +388,6 @@ class ElasticsearchStore(VectorStore):
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from langchain_community.vectorstores import ElasticsearchStore
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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vectorstore = ElasticsearchStore(
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embedding=OpenAIEmbeddings(),
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index_name="langchain-demo",
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@ -693,6 +692,25 @@ class ElasticsearchStore(VectorStore):
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return selected_docs
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@staticmethod
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def _identity_fn(score: float) -> float:
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return score
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def _select_relevance_score_fn(self) -> Callable[[float], float]:
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"""
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The 'correct' relevance function
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may differ depending on a few things, including:
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- the distance / similarity metric used by the VectorStore
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- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
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- embedding dimensionality
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- etc.
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Vectorstores should define their own selection based method of relevance.
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"""
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# All scores from Elasticsearch are already normalized similarities:
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# https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params
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return self._identity_fn
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def similarity_search_with_score(
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self, query: str, k: int = 4, filter: Optional[List[dict]] = None, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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@ -706,6 +724,9 @@ class ElasticsearchStore(VectorStore):
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Returns:
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List of Documents most similar to the query and score for each
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"""
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if isinstance(self.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid:
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raise ValueError("scores are currently not supported in hybrid mode")
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return self._search(query=query, k=k, filter=filter, **kwargs)
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def similarity_search_by_vector_with_relevance_scores(
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@ -725,6 +746,9 @@ class ElasticsearchStore(VectorStore):
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Returns:
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List of Documents most similar to the embedding and score for each
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"""
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if isinstance(self.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid:
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raise ValueError("scores are currently not supported in hybrid mode")
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return self._search(query_vector=embedding, k=k, filter=filter, **kwargs)
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def _search(
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68
libs/community/poetry.lock
generated
68
libs/community/poetry.lock
generated
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@ -1191,7 +1189,6 @@ files = [
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@ -1200,7 +1197,6 @@ files = [
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@ -1769,6 +1765,42 @@ files = [
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duckdb = ">=0.4.0"
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sqlalchemy = ">=1.3.22"
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[[package]]
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name = "elastic-transport"
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version = "8.12.0"
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description = "Transport classes and utilities shared among Python Elastic client libraries"
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optional = true
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python-versions = ">=3.7"
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files = [
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{file = "elastic-transport-8.12.0.tar.gz", hash = "sha256:48839b942fcce199eece1558ecea6272e116c58da87ca8d495ef12eb61effaf7"},
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]
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[package.dependencies]
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certifi = "*"
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urllib3 = ">=1.26.2,<3"
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[package.extras]
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develop = ["aiohttp", "furo", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "pytest-mock", "requests", "sphinx (>2)", "sphinx-autodoc-typehints", "trustme"]
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[[package]]
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name = "elasticsearch"
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version = "8.12.0"
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description = "Python client for Elasticsearch"
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optional = true
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python-versions = ">=3.7"
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files = [
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{file = "elasticsearch-8.12.0-py3-none-any.whl", hash = "sha256:d394c5ce746bb8cb97827feae57759dae462bce34df221a6fdb6875c56476389"},
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{file = "elasticsearch-8.12.0.tar.gz", hash = "sha256:58fd3876682f7529c33b9eeee701e71cfcc334bb45d725e315e22a0a5e2611fb"},
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]
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[package.dependencies]
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elastic-transport = ">=8,<9"
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[package.extras]
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async = ["aiohttp (>=3,<4)"]
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requests = ["requests (>=2.4.0,<3.0.0)"]
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[[package]]
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name = "entrypoints"
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version = "0.4"
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|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
|
||||
@ -6734,7 +6748,6 @@ files = [
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
|
||||
@ -6742,7 +6755,6 @@ files = [
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
|
||||
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
|
||||
@ -7714,9 +7726,7 @@ python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:638c2c0b6b4661a4fd264f6fb804eccd392745c5887f9317feb64bb7cb03b3ea"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e3b5036aa326dc2df50cba3c958e29b291a80f604b1afa4c8ce73e78e1c9f01d"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:787af80107fb691934a01889ca8f82a44adedbf5ef3d6ad7d0f0b9ac557e0c34"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c14eba45983d2f48f7546bb32b47937ee2cafae353646295f0e99f35b14286ab"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0666031df46b9badba9bed00092a1ffa3aa063a5e68fa244acd9f08070e936d3"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:89a01238fcb9a8af118eaad3ffcc5dedaacbd429dc6fdc43fe430d3a941ff965"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-win32.whl", hash = "sha256:cabafc7837b6cec61c0e1e5c6d14ef250b675fa9c3060ed8a7e38653bd732ff8"},
|
||||
{file = "SQLAlchemy-2.0.23-cp310-cp310-win_amd64.whl", hash = "sha256:87a3d6b53c39cd173990de2f5f4b83431d534a74f0e2f88bd16eabb5667e65c6"},
|
||||
@ -7753,9 +7763,7 @@ files = [
|
||||
{file = "SQLAlchemy-2.0.23-cp38-cp38-win_amd64.whl", hash = "sha256:964971b52daab357d2c0875825e36584d58f536e920f2968df8d581054eada4b"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:616fe7bcff0a05098f64b4478b78ec2dfa03225c23734d83d6c169eb41a93e55"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0e680527245895aba86afbd5bef6c316831c02aa988d1aad83c47ffe92655e74"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9585b646ffb048c0250acc7dad92536591ffe35dba624bb8fd9b471e25212a35"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4895a63e2c271ffc7a81ea424b94060f7b3b03b4ea0cd58ab5bb676ed02f4221"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:cc1d21576f958c42d9aec68eba5c1a7d715e5fc07825a629015fe8e3b0657fb0"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:967c0b71156f793e6662dd839da54f884631755275ed71f1539c95bbada9aaab"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-win32.whl", hash = "sha256:0a8c6aa506893e25a04233bc721c6b6cf844bafd7250535abb56cb6cc1368884"},
|
||||
{file = "SQLAlchemy-2.0.23-cp39-cp39-win_amd64.whl", hash = "sha256:f3420d00d2cb42432c1d0e44540ae83185ccbbc67a6054dcc8ab5387add6620b"},
|
||||
@ -9167,9 +9175,9 @@ 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", "cohere", "dashvector", "databricks-vectorsearch", "datasets", "dgml-utils", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hologres-vector", "html2text", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict", "zhipuai"]
|
||||
extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cohere", "dashvector", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hologres-vector", "html2text", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict", "zhipuai"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "3b7748a2cbf7b875397483cfdee5e695f447880dffc6c90c67d92de9cc04a7bb"
|
||||
content-hash = "e512944a95e344bcf8b15066e289798c53fb39299f6e0190bf69371f43e6f63a"
|
||||
|
||||
@ -87,6 +87,7 @@ datasets = {version = "^2.15.0", optional = true}
|
||||
azure-ai-documentintelligence = {version = "^1.0.0b1", optional = true}
|
||||
oracle-ads = {version = "^2.9.1", optional = true}
|
||||
zhipuai = {version = "^1.0.7", optional = true}
|
||||
elasticsearch = {version = "^8.12.0", optional = true}
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
@ -249,6 +250,7 @@ extended_testing = [
|
||||
"azure-ai-documentintelligence",
|
||||
"oracle-ads",
|
||||
"zhipuai",
|
||||
"elasticsearch",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
|
||||
@ -157,7 +157,7 @@ class TestElasticsearch:
|
||||
output = docsearch.similarity_search("foo", k=1, custom_query=assert_query)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
async def test_similarity_search_without_metadat_async(
|
||||
async def test_similarity_search_without_metadata_async(
|
||||
self, elasticsearch_connection: dict, index_name: str
|
||||
) -> None:
|
||||
"""Test end to end construction and search without metadata."""
|
||||
@ -400,7 +400,7 @@ class TestElasticsearch:
|
||||
"script": {
|
||||
"source": """
|
||||
double value = dotProduct(params.query_vector, 'vector');
|
||||
return sigmoid(1, Math.E, -value);
|
||||
return sigmoid(1, Math.E, -value);
|
||||
""",
|
||||
"params": {
|
||||
"query_vector": [
|
||||
@ -777,6 +777,44 @@ class TestElasticsearch:
|
||||
)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
|
||||
|
||||
def test_elasticsearch_with_relevance_threshold(
|
||||
self, elasticsearch_connection: dict, index_name: str
|
||||
) -> None:
|
||||
"""Test to make sure the relevance threshold is respected."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
embeddings = FakeEmbeddings()
|
||||
|
||||
docsearch = ElasticsearchStore.from_texts(
|
||||
index_name=index_name,
|
||||
texts=texts,
|
||||
embedding=embeddings,
|
||||
metadatas=metadatas,
|
||||
**elasticsearch_connection,
|
||||
)
|
||||
|
||||
# Find a good threshold for testing
|
||||
query_string = "foo"
|
||||
embedded_query = embeddings.embed_query(query_string)
|
||||
top3 = docsearch.similarity_search_by_vector_with_relevance_scores(
|
||||
embedding=embedded_query, k=3
|
||||
)
|
||||
similarity_of_second_ranked = top3[1][1]
|
||||
assert len(top3) == 3
|
||||
|
||||
# Test threshold
|
||||
retriever = docsearch.as_retriever(
|
||||
search_type="similarity_score_threshold",
|
||||
search_kwargs={"score_threshold": similarity_of_second_ranked},
|
||||
)
|
||||
output = retriever.get_relevant_documents(query=query_string)
|
||||
|
||||
assert output == [
|
||||
top3[0][0],
|
||||
top3[1][0],
|
||||
# third ranked is out
|
||||
]
|
||||
|
||||
def test_elasticsearch_delete_ids(
|
||||
self, elasticsearch_connection: dict, index_name: str
|
||||
) -> None:
|
||||
|
||||
@ -0,0 +1,32 @@
|
||||
"""Test Elasticsearch functionality."""
|
||||
import pytest
|
||||
|
||||
from langchain_community.vectorstores.elasticsearch import (
|
||||
ApproxRetrievalStrategy,
|
||||
ElasticsearchStore,
|
||||
)
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
|
||||
|
||||
@pytest.mark.requires("elasticsearch")
|
||||
def test_elasticsearch_hybrid_scores_guard() -> None:
|
||||
"""Ensure an error is raised when search with score in hybrid mode
|
||||
because in this case Elasticsearch does not return any score.
|
||||
"""
|
||||
from elasticsearch import Elasticsearch
|
||||
|
||||
query_string = "foo"
|
||||
embeddings = FakeEmbeddings()
|
||||
|
||||
store = ElasticsearchStore(
|
||||
index_name="dummy_index",
|
||||
es_connection=Elasticsearch(hosts=["http://dummy-host:9200"]),
|
||||
embedding=embeddings,
|
||||
strategy=ApproxRetrievalStrategy(hybrid=True),
|
||||
)
|
||||
with pytest.raises(ValueError):
|
||||
store.similarity_search_with_score(query_string)
|
||||
|
||||
embedded_query = embeddings.embed_query(query_string)
|
||||
with pytest.raises(ValueError):
|
||||
store.similarity_search_by_vector_with_relevance_scores(embedded_query)
|
||||
Loading…
Reference in New Issue
Block a user