mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
a6664be79c
Co-authored-by: Fangrui Liu <fangruil@moqi.ai> Co-authored-by: 刘 方瑞 <fangrui.liu@outlook.com> Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>
109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
"""Test MyScale functionality."""
|
|
import pytest
|
|
|
|
from langchain.docstore.document import Document
|
|
from langchain.vectorstores import MyScale, MyScaleSettings
|
|
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
|
|
|
|
|
def test_myscale() -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
config = MyScaleSettings()
|
|
config.table = "test_myscale"
|
|
docsearch = MyScale.from_texts(texts, FakeEmbeddings(), config=config)
|
|
output = docsearch.similarity_search("foo", k=1)
|
|
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
|
|
docsearch.drop()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_myscale_async() -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
config = MyScaleSettings()
|
|
config.table = "test_myscale_async"
|
|
docsearch = MyScale.from_texts(
|
|
texts=texts, embedding=FakeEmbeddings(), config=config
|
|
)
|
|
output = await docsearch.asimilarity_search("foo", k=1)
|
|
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
|
|
docsearch.drop()
|
|
|
|
|
|
def test_myscale_with_metadatas() -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
|
config = MyScaleSettings()
|
|
config.table = "test_myscale_with_metadatas"
|
|
docsearch = MyScale.from_texts(
|
|
texts=texts,
|
|
embedding=FakeEmbeddings(),
|
|
config=config,
|
|
metadatas=metadatas,
|
|
)
|
|
output = docsearch.similarity_search("foo", k=1)
|
|
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
|
docsearch.drop()
|
|
|
|
|
|
def test_myscale_with_metadatas_with_relevance_scores() -> None:
|
|
"""Test end to end construction and scored search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
|
config = MyScaleSettings()
|
|
config.table = "test_myscale_with_metadatas_with_relevance_scores"
|
|
docsearch = MyScale.from_texts(
|
|
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
|
|
)
|
|
output = docsearch.similarity_search_with_relevance_scores("foo", k=1)
|
|
assert output[0][0] == Document(page_content="foo", metadata={"page": "0"})
|
|
docsearch.drop()
|
|
|
|
|
|
def test_myscale_search_filter() -> None:
|
|
"""Test end to end construction and search with metadata filtering."""
|
|
texts = ["far", "bar", "baz"]
|
|
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
|
|
config = MyScaleSettings()
|
|
config.table = "test_myscale_search_filter"
|
|
docsearch = MyScale.from_texts(
|
|
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
|
|
)
|
|
output = docsearch.similarity_search(
|
|
"far", k=1, where_str=f"{docsearch.metadata_column}.first_letter='f'"
|
|
)
|
|
assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
|
|
output = docsearch.similarity_search(
|
|
"bar", k=1, where_str=f"{docsearch.metadata_column}.first_letter='b'"
|
|
)
|
|
assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
|
|
docsearch.drop()
|
|
|
|
|
|
def test_myscale_with_persistence() -> None:
|
|
"""Test end to end construction and search, with persistence."""
|
|
config = MyScaleSettings()
|
|
config.table = "test_myscale_with_persistence"
|
|
texts = [
|
|
"foo",
|
|
"bar",
|
|
"baz",
|
|
]
|
|
docsearch = MyScale.from_texts(
|
|
texts=texts, embedding=FakeEmbeddings(), config=config
|
|
)
|
|
|
|
output = docsearch.similarity_search("foo", k=1)
|
|
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
|
|
|
|
# Get a new VectorStore with same config
|
|
# it will reuse the table spontaneously
|
|
# unless you drop it
|
|
docsearch = MyScale(embedding=FakeEmbeddings(), config=config)
|
|
output = docsearch.similarity_search("foo", k=1)
|
|
|
|
# Clean up
|
|
docsearch.drop()
|