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127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
"""Test Redis functionality."""
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from typing import List
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import pytest
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from langchain.docstore.document import Document
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from langchain.vectorstores.redis import Redis
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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TEST_INDEX_NAME = "test"
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TEST_REDIS_URL = "redis://localhost:6379"
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TEST_SINGLE_RESULT = [Document(page_content="foo")]
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TEST_SINGLE_WITH_METADATA_RESULT = [Document(page_content="foo", metadata={"a": "b"})]
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TEST_RESULT = [Document(page_content="foo"), Document(page_content="foo")]
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COSINE_SCORE = pytest.approx(0.05, abs=0.002)
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IP_SCORE = -8.0
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EUCLIDEAN_SCORE = 1.0
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def drop(index_name: str) -> bool:
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return Redis.drop_index(
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index_name=index_name, delete_documents=True, redis_url=TEST_REDIS_URL
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)
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@pytest.fixture
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def texts() -> List[str]:
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return ["foo", "bar", "baz"]
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def test_redis(texts: List[str]) -> None:
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"""Test end to end construction and search."""
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docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
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output = docsearch.similarity_search("foo", k=1)
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assert output == TEST_SINGLE_RESULT
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assert drop(docsearch.index_name)
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def test_redis_new_vector(texts: List[str]) -> None:
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"""Test adding a new document"""
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docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
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docsearch.add_texts(["foo"])
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output = docsearch.similarity_search("foo", k=2)
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assert output == TEST_RESULT
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assert drop(docsearch.index_name)
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def test_redis_from_existing(texts: List[str]) -> None:
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"""Test adding a new document"""
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Redis.from_texts(
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texts, FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
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)
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# Test creating from an existing
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docsearch2 = Redis.from_existing_index(
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FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
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)
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output = docsearch2.similarity_search("foo", k=1)
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assert output == TEST_SINGLE_RESULT
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def test_redis_from_texts_return_keys(texts: List[str]) -> None:
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"""Test from_texts_return_keys constructor."""
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docsearch, keys = Redis.from_texts_return_keys(
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texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == TEST_SINGLE_RESULT
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assert len(keys) == len(texts)
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assert drop(docsearch.index_name)
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def test_redis_from_documents(texts: List[str]) -> None:
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"""Test from_documents constructor."""
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docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
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docsearch = Redis.from_documents(docs, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
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output = docsearch.similarity_search("foo", k=1)
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assert output == TEST_SINGLE_WITH_METADATA_RESULT
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assert drop(docsearch.index_name)
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def test_redis_add_texts_to_existing() -> None:
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"""Test adding a new document"""
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# Test creating from an existing
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docsearch = Redis.from_existing_index(
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FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
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)
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docsearch.add_texts(["foo"])
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output = docsearch.similarity_search("foo", k=2)
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assert output == TEST_RESULT
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assert drop(TEST_INDEX_NAME)
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def test_cosine(texts: List[str]) -> None:
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"""Test cosine distance."""
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docsearch = Redis.from_texts(
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texts,
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FakeEmbeddings(),
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redis_url=TEST_REDIS_URL,
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distance_metric="COSINE",
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)
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output = docsearch.similarity_search_with_score("far", k=2)
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_, score = output[1]
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assert score == COSINE_SCORE
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assert drop(docsearch.index_name)
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def test_l2(texts: List[str]) -> None:
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"""Test Flat L2 distance."""
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docsearch = Redis.from_texts(
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texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, distance_metric="L2"
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)
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output = docsearch.similarity_search_with_score("far", k=2)
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_, score = output[1]
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assert score == EUCLIDEAN_SCORE
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assert drop(docsearch.index_name)
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def test_ip(texts: List[str]) -> None:
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"""Test inner product distance."""
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docsearch = Redis.from_texts(
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texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, distance_metric="IP"
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)
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output = docsearch.similarity_search_with_score("far", k=2)
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_, score = output[1]
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assert score == IP_SCORE
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assert drop(docsearch.index_name)
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