Update redis integration tests (#4937)

tests/integration_tests/vectorstores/test_redis.py

@@ -1,4 +1,6 @@
 """Test Redis functionality."""
+from typing import List
+
 import pytest
 
 from langchain.docstore.document import Document
@@ -8,6 +10,7 @@
 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
@@ -20,28 +23,30 @@ def drop(index_name: str) -> bool:
     )
 
 
-def test_redis() -> None:
+@pytest.fixture
+def texts() -> List[str]:
+    return ["foo", "bar", "baz"]
+
+
+def test_redis(texts: List[str]) -> None:
     """Test end to end construction and search."""
-    texts = ["foo", "bar", "baz"]
     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() -> None:
+def test_redis_new_vector(texts: List[str]) -> None:
     """Test adding a new document"""
-    texts = ["foo", "bar", "baz"]
     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() -> None:
+def test_redis_from_existing(texts: List[str]) -> None:
     """Test adding a new document"""
-    texts = ["foo", "bar", "baz"]
     Redis.from_texts(
         texts, FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
     )
@@ -53,6 +58,26 @@ def test_redis_from_existing() -> None:
     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
@@ -65,9 +90,8 @@ def test_redis_add_texts_to_existing() -> None:
     assert drop(TEST_INDEX_NAME)
 
 
-def test_cosine() -> None:
+def test_cosine(texts: List[str]) -> None:
     """Test cosine distance."""
-    texts = ["foo", "bar", "baz"]
     docsearch = Redis.from_texts(
         texts,
         FakeEmbeddings(),
@@ -80,9 +104,8 @@ def test_cosine() -> None:
     assert drop(docsearch.index_name)
 
 
-def test_l2() -> None:
+def test_l2(texts: List[str]) -> None:
     """Test Flat L2 distance."""
-    texts = ["foo", "bar", "baz"]
     docsearch = Redis.from_texts(
         texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, distance_metric="L2"
     )
@@ -92,9 +115,8 @@ def test_l2() -> None:
     assert drop(docsearch.index_name)
 
 
-def test_ip() -> None:
+def test_ip(texts: List[str]) -> None:
     """Test inner product distance."""
-    texts = ["foo", "bar", "baz"]
     docsearch = Redis.from_texts(
         texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, distance_metric="IP"
     )