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community[patch]: support momento vector index filter expressions (#14978)

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**Description**

For the Momento Vector Index (MVI) vector store implementation, pass
through `filter_expression` kwarg to the MVI client, if specified. This
change will enable the MVI self query implementation in a future PR.

Also fixes some integration tests.
This commit is contained in:
Michael Landis 2023-12-20 19:11:43 -08:00 committed by GitHub
parent 300c1cbf92
commit 1c934fff0e
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 32 additions and 3 deletions
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14
libs/community/langchain_community/vectorstores/momento_vector_index.py
View File

@ -306,8 +306,13 @@ class MomentoVectorIndex(VectorStore):
if "top_k" in kwargs: if "top_k" in kwargs:
k = kwargs["k"] k = kwargs["k"]
filter_expression = kwargs.get("filter_expression", None)
response = self._client.search( response = self._client.search(
self.index_name, embedding, top_k=k, metadata_fields=ALL_METADATA self.index_name,
embedding,
top_k=k,
metadata_fields=ALL_METADATA,
filter_expression=filter_expression,
) )
if not isinstance(response, Search.Success): if not isinstance(response, Search.Success):
@ -366,8 +371,13 @@ class MomentoVectorIndex(VectorStore):
from momento.requests.vector_index import ALL_METADATA from momento.requests.vector_index import ALL_METADATA
from momento.responses.vector_index import SearchAndFetchVectors from momento.responses.vector_index import SearchAndFetchVectors
filter_expression = kwargs.get("filter_expression", None)
response = self._client.search_and_fetch_vectors( response = self._client.search_and_fetch_vectors(
self.index_name, embedding, top_k=fetch_k, metadata_fields=ALL_METADATA self.index_name,
embedding,
top_k=fetch_k,
metadata_fields=ALL_METADATA,
filter_expression=filter_expression,
) )
if isinstance(response, SearchAndFetchVectors.Success): if isinstance(response, SearchAndFetchVectors.Success):

21
libs/community/tests/integration_tests/vectorstores/test_momento_vector_index.py
View File

@ -1,10 +1,12 @@
import os
import time import time
import uuid import uuid
from typing import Iterator, List from typing import Generator, Iterator, List
import pytest import pytest
from langchain_core.documents import Document from langchain_core.documents import Document
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MomentoVectorIndex from langchain_community.vectorstores import MomentoVectorIndex
@ -24,6 +26,23 @@ def wait() -> None:
time.sleep(1) time.sleep(1)
@pytest.fixture(scope="module")
def embedding_openai() -> OpenAIEmbeddings:
if not os.environ.get("OPENAI_API_KEY"):
raise ValueError("OPENAI_API_KEY is not set")
return OpenAIEmbeddings()
@pytest.fixture(scope="function")
def texts() -> Generator[List[str], None, None]:
# Load the documents from a file located in the fixtures directory
documents = TextLoader(
os.path.join(os.path.dirname(__file__), "fixtures", "sharks.txt")
).load()
yield [doc.page_content for doc in documents]
@pytest.fixture(scope="function") @pytest.fixture(scope="function")
def vector_store( def vector_store(
embedding_openai: OpenAIEmbeddings, random_index_name: str embedding_openai: OpenAIEmbeddings, random_index_name: str