langchain/tests/integration_tests/vectorstores/test_redis.py

"""Test Redis functionality."""
from typing import List

import pytest

from langchain.docstore.document import Document
from langchain.vectorstores.redis import Redis
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

TEST_INDEX_NAME = "test"
TEST_REDIS_URL = "redis://localhost:6379"
TEST_SINGLE_RESULT = [Document(page_content="foo")]
TEST_SINGLE_WITH_METADATA_RESULT = [Document(page_content="foo", metadata={"a": "b"})]
TEST_RESULT = [Document(page_content="foo"), Document(page_content="foo")]
COSINE_SCORE = pytest.approx(0.05, abs=0.002)
IP_SCORE = -8.0
EUCLIDEAN_SCORE = 1.0


def drop(index_name: str) -> bool:
    return Redis.drop_index(
        index_name=index_name, delete_documents=True, redis_url=TEST_REDIS_URL
    )


@pytest.fixture
def texts() -> List[str]:
    return ["foo", "bar", "baz"]


def test_redis(texts: List[str]) -> None:
    """Test end to end construction and search."""
    docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
    output = docsearch.similarity_search("foo", k=1)
    assert output == TEST_SINGLE_RESULT
    assert drop(docsearch.index_name)


def test_redis_new_vector(texts: List[str]) -> None:
    """Test adding a new document"""
    docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
    docsearch.add_texts(["foo"])
    output = docsearch.similarity_search("foo", k=2)
    assert output == TEST_RESULT
    assert drop(docsearch.index_name)


def test_redis_from_existing(texts: List[str]) -> None:
    """Test adding a new document"""
    Redis.from_texts(
        texts, FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
    )
    # Test creating from an existing
    docsearch2 = Redis.from_existing_index(
        FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
    )
    output = docsearch2.similarity_search("foo", k=1)
    assert output == TEST_SINGLE_RESULT


def test_redis_from_texts_return_keys(texts: List[str]) -> None:
    """Test from_texts_return_keys constructor."""
    docsearch, keys = Redis.from_texts_return_keys(
        texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL
    )
    output = docsearch.similarity_search("foo", k=1)
    assert output == TEST_SINGLE_RESULT
    assert len(keys) == len(texts)
    assert drop(docsearch.index_name)


def test_redis_from_documents(texts: List[str]) -> None:
    """Test from_documents constructor."""
    docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
    docsearch = Redis.from_documents(docs, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
    output = docsearch.similarity_search("foo", k=1)
    assert output == TEST_SINGLE_WITH_METADATA_RESULT
    assert drop(docsearch.index_name)


def test_redis_add_texts_to_existing() -> None:
    """Test adding a new document"""
    # Test creating from an existing
    docsearch = Redis.from_existing_index(
        FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
    )
    docsearch.add_texts(["foo"])
    output = docsearch.similarity_search("foo", k=2)
    assert output == TEST_RESULT
    assert drop(TEST_INDEX_NAME)


def test_cosine(texts: List[str]) -> None:
    """Test cosine distance."""
    docsearch = Redis.from_texts(
        texts,
        FakeEmbeddings(),
        redis_url=TEST_REDIS_URL,
        distance_metric="COSINE",
    )
    output = docsearch.similarity_search_with_score("far", k=2)
    _, score = output[1]
    assert score == COSINE_SCORE
    assert drop(docsearch.index_name)


def test_l2(texts: List[str]) -> None:
    """Test Flat L2 distance."""
    docsearch = Redis.from_texts(
        texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, distance_metric="L2"
    )
    output = docsearch.similarity_search_with_score("far", k=2)
    _, score = output[1]
    assert score == EUCLIDEAN_SCORE
    assert drop(docsearch.index_name)


def test_ip(texts: List[str]) -> None:
    """Test inner product distance."""
    docsearch = Redis.from_texts(
        texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, distance_metric="IP"
    )
    output = docsearch.similarity_search_with_score("far", k=2)
    _, score = output[1]
    assert score == IP_SCORE
    assert drop(docsearch.index_name)