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>

libs/community/langchain_community/vectorstores/elasticsearch.py

@@ -388,7 +388,6 @@ class ElasticsearchStore(VectorStore):
             from langchain_community.vectorstores import ElasticsearchStore
             from langchain_community.embeddings.openai import OpenAIEmbeddings
 
-            embeddings = OpenAIEmbeddings()
             vectorstore = ElasticsearchStore(
                 embedding=OpenAIEmbeddings(),
                 index_name="langchain-demo",
@@ -693,6 +692,25 @@ class ElasticsearchStore(VectorStore):
 
         return selected_docs
 
+    @staticmethod
+    def _identity_fn(score: float) -> float:
+        return score
+
+    def _select_relevance_score_fn(self) -> Callable[[float], float]:
+        """
+        The 'correct' relevance function
+        may differ depending on a few things, including:
+        - the distance / similarity metric used by the VectorStore
+        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
+        - embedding dimensionality
+        - etc.
+
+        Vectorstores should define their own selection based method of relevance.
+        """
+        # All scores from Elasticsearch are already normalized similarities:
+        # https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params
+        return self._identity_fn
+
     def similarity_search_with_score(
         self, query: str, k: int = 4, filter: Optional[List[dict]] = None, **kwargs: Any
     ) -> List[Tuple[Document, float]]:
@@ -706,6 +724,9 @@ class ElasticsearchStore(VectorStore):
         Returns:
             List of Documents most similar to the query and score for each
         """
+        if isinstance(self.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid:
+            raise ValueError("scores are currently not supported in hybrid mode")
+
         return self._search(query=query, k=k, filter=filter, **kwargs)
 
     def similarity_search_by_vector_with_relevance_scores(
@@ -725,6 +746,9 @@ class ElasticsearchStore(VectorStore):
         Returns:
             List of Documents most similar to the embedding and score for each
         """
+        if isinstance(self.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid:
+            raise ValueError("scores are currently not supported in hybrid mode")
+
         return self._search(query_vector=embedding, k=k, filter=filter, **kwargs)
 
     def _search(

libs/community/poetry.lock

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@@ -1182,7 +1181,6 @@ files = [
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@@ -1769,6 +1765,42 @@ files = [
 duckdb = ">=0.4.0"
 sqlalchemy = ">=1.3.22"
 
+[[package]]
+name = "elastic-transport"
+version = "8.12.0"
+description = "Transport classes and utilities shared among Python Elastic client libraries"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "elastic-transport-8.12.0.tar.gz", hash = "sha256:48839b942fcce199eece1558ecea6272e116c58da87ca8d495ef12eb61effaf7"},
+    {file = "elastic_transport-8.12.0-py3-none-any.whl", hash = "sha256:87d9dc9dee64a05235e7624ed7e6ab6e5ca16619aa7a6d22e853273b9f1cfbee"},
+]
+
+[package.dependencies]
+certifi = "*"
+urllib3 = ">=1.26.2,<3"
+
+[package.extras]
+develop = ["aiohttp", "furo", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "pytest-mock", "requests", "sphinx (>2)", "sphinx-autodoc-typehints", "trustme"]
+
+[[package]]
+name = "elasticsearch"
+version = "8.12.0"
+description = "Python client for Elasticsearch"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "elasticsearch-8.12.0-py3-none-any.whl", hash = "sha256:d394c5ce746bb8cb97827feae57759dae462bce34df221a6fdb6875c56476389"},
+    {file = "elasticsearch-8.12.0.tar.gz", hash = "sha256:58fd3876682f7529c33b9eeee701e71cfcc334bb45d725e315e22a0a5e2611fb"},
+]
+
+[package.dependencies]
+elastic-transport = ">=8,<9"
+
+[package.extras]
+async = ["aiohttp (>=3,<4)"]
+requests = ["requests (>=2.4.0,<3.0.0)"]
+
 [[package]]
 name = "entrypoints"
 version = "0.4"
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-    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
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@@ -7714,9 +7726,7 @@ python-versions = ">=3.7"
 files = [
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-    {file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:787af80107fb691934a01889ca8f82a44adedbf5ef3d6ad7d0f0b9ac557e0c34"},
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-    {file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0666031df46b9badba9bed00092a1ffa3aa063a5e68fa244acd9f08070e936d3"},
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@@ -7753,9 +7763,7 @@ files = [
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@@ -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"

libs/community/pyproject.toml

@@ -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]

libs/community/tests/integration_tests/vectorstores/test_elasticsearch.py

@@ -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:

libs/community/tests/unit_tests/vectorstores/test_elasticsearch.py

@@ -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)