parameterized distance metrics; lint; format; tests (#4375)

# Parameterize Redis vectorstore index

Redis vectorstore allows for three different distance metrics: `L2`
(flat L2), `COSINE`, and `IP` (inner product). Currently, the
`Redis._create_index` method hard codes the distance metric to COSINE.

I've parameterized this as an argument in the `Redis.from_texts` method
-- pretty simple.

Fixes #4368 

## Before submitting

I've added an integration test showing indexes can be instantiated with
all three values in the `REDIS_DISTANCE_METRICS` literal. An example
notebook seemed overkill here. Normal API documentation would be more
appropriate, but no standards are in place for that yet.

## Who can review?

Not sure who's responsible for the vectorstore module... Maybe @eyurtsev
/ @hwchase17 / @agola11 ?

langchain/vectorstores/redis.py

@@ -11,6 +11,7 @@ from typing import (
     Dict,
     Iterable,
     List,
+    Literal,
     Mapping,
     Optional,
     Tuple,
@@ -39,6 +40,9 @@ REDIS_REQUIRED_MODULES = [
     {"name": "searchlight", "ver": 20400},
 ]
 
+# distance mmetrics
+REDIS_DISTANCE_METRICS = Literal["COSINE", "IP", "L2"]
+
 
 def _check_redis_module_exist(client: RedisType, required_modules: List[dict]) -> None:
     """Check if the correct Redis modules are installed."""
@@ -142,7 +146,9 @@ class Redis(VectorStore):
         self.vector_key = vector_key
         self.relevance_score_fn = relevance_score_fn
 
-    def _create_index(self, dim: int = 1536) -> None:
+    def _create_index(
+        self, dim: int = 1536, distance_metric: REDIS_DISTANCE_METRICS = "COSINE"
+    ) -> None:
         try:
             from redis.commands.search.field import TextField, VectorField
             from redis.commands.search.indexDefinition import IndexDefinition, IndexType
@@ -154,10 +160,7 @@ class Redis(VectorStore):
 
         # Check if index exists
         if not _check_index_exists(self.client, self.index_name):
-            # Constants
+            # Define schema
-            distance_metric = (
-                "COSINE"  # distance metric for the vectors (ex. COSINE, IP, L2)
-            )
             schema = (
                 TextField(name=self.content_key),
                 TextField(name=self.metadata_key),
@@ -364,6 +367,7 @@ class Redis(VectorStore):
         content_key: str = "content",
         metadata_key: str = "metadata",
         vector_key: str = "content_vector",
+        distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
         **kwargs: Any,
     ) -> Redis:
         """Create a Redis vectorstore from raw documents.
@@ -407,7 +411,7 @@ class Redis(VectorStore):
         embeddings = embedding.embed_documents(texts)
 
         # Create the search index
-        instance._create_index(dim=len(embeddings[0]))
+        instance._create_index(dim=len(embeddings[0]), distance_metric=distance_metric)
 
         # Add data to Redis
         instance.add_texts(texts, metadatas, embeddings)

tests/integration_tests/vectorstores/test_redis.py

@@ -1,4 +1,6 @@
 """Test Redis functionality."""
+import pytest
+
 from langchain.docstore.document import Document
 from langchain.vectorstores.redis import Redis
 from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@@ -7,6 +9,9 @@ TEST_INDEX_NAME = "test"
 TEST_REDIS_URL = "redis://localhost:6379"
 TEST_SINGLE_RESULT = [Document(page_content="foo")]
 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:
@@ -58,3 +63,42 @@ def test_redis_add_texts_to_existing() -> None:
     output = docsearch.similarity_search("foo", k=2)
     assert output == TEST_RESULT
     assert drop(TEST_INDEX_NAME)
+
+
+def test_cosine() -> None:
+    """Test cosine distance."""
+    texts = ["foo", "bar", "baz"]
+    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() -> None:
+    """Test Flat L2 distance."""
+    texts = ["foo", "bar", "baz"]
+    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() -> None:
+    """Test inner product distance."""
+    texts = ["foo", "bar", "baz"]
+    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)