fix: support async redis caching

litellm/tests/test_caching.py

@@ -266,8 +266,9 @@ async def test_embedding_caching_azure_individual_items():
     """
     Tests caching for individual items in an embedding list
 
-    Assert if the same embeddingresponse object is returned for the duplicate item in 2 embedding list calls
-
+    - Cache an item
+    - call aembedding(..) with the item + 1 unique item
+    - compare to a 2nd aembedding (...) with 2 unique items
     ```
     embedding_1 = ["hey how's it going", "I'm doing well"]
     embedding_val_1 = embedding(...)
@@ -280,31 +281,98 @@ async def test_embedding_caching_azure_individual_items():
     """
     litellm.cache = Cache()
     common_msg = f"hey how's it going {uuid.uuid4()}"
-    embedding_1 = [common_msg, "I'm doing well"]
-    embedding_2 = [common_msg, "I'm fine"]
+    common_msg_2 = f"hey how's it going {uuid.uuid4()}"
+    embedding_2 = [
+        common_msg,
+        f"I'm fine {uuid.uuid4()}",
+        common_msg,
+        common_msg,
+        common_msg,
+    ] * 20
+    embedding_2 = [
+        common_msg,
+        f"I'm fine {uuid.uuid4()}",
+        common_msg,
+        common_msg,
+        common_msg,
+    ] * 20
+    embedding_3 = [
+        common_msg_2,
+        common_msg_2,
+        common_msg_2,
+        common_msg_2,
+        f"I'm fine {uuid.uuid4()}",
+    ] * 20  # make sure azure doesn't return cached 'i'm fine' responses
 
     embedding_val_1 = await aembedding(
         model="azure/azure-embedding-model", input=embedding_1, caching=True
     )
 
+    second_response_start_time = time.time()
     embedding_val_2 = await aembedding(
         model="azure/azure-embedding-model", input=embedding_2, caching=True
     )
-    print(f"embedding_val_2: {embedding_val_2}")
-    if (
-        embedding_val_2["data"][0]["embedding"]
-        != embedding_val_1["data"][0]["embedding"]
-    ):
-        print(f"embedding1: {embedding_val_1}")
-        print(f"embedding2: {embedding_val_2}")
-        pytest.fail("Error occurred: Embedding caching failed")
-    if (
-        embedding_val_2["data"][1]["embedding"]
-        == embedding_val_1["data"][1]["embedding"]
-    ):
-        print(f"embedding1: {embedding_val_1}")
-        print(f"embedding2: {embedding_val_2}")
-        pytest.fail("Error occurred: Embedding caching failed")
+    if embedding_val_2 is not None:
+        second_response_end_time = time.time()
+        second_response_time = second_response_end_time - second_response_start_time
+
+    third_response_start_time = time.time()
+    embedding_val_3 = await aembedding(
+        model="azure/azure-embedding-model", input=embedding_3, cache={"no-cache": True}
+    )
+    if embedding_val_3 is not None:
+        third_response_end_time = time.time()
+        third_response_time = third_response_end_time - third_response_start_time
+
+    print(f"second_response_time: {second_response_time}")
+    print(f"third_response_time: {third_response_time}")
+
+    assert (
+        second_response_time < third_response_time - 0.5
+    )  # make sure it's actually faster
+    raise Exception(f"it works {second_response_time} < {third_response_time}")
+
+
+@pytest.mark.asyncio
+async def test_redis_cache_basic():
+    """
+    Init redis client
+    - write to client
+    - read from client
+    """
+    litellm.set_verbose = False
+
+    random_number = random.randint(
+        1, 100000
+    )  # add a random number to ensure it's always adding / reading from cache
+    messages = [
+        {"role": "user", "content": f"write a one sentence poem about: {random_number}"}
+    ]
+    litellm.cache = Cache(
+        type="redis",
+        host=os.environ["REDIS_HOST"],
+        port=os.environ["REDIS_PORT"],
+        password=os.environ["REDIS_PASSWORD"],
+    )
+    response1 = completion(
+        model="gpt-3.5-turbo",
+        messages=messages,
+    )
+
+    cache_key = litellm.cache.get_cache_key(
+        model="gpt-3.5-turbo",
+        messages=messages,
+    )
+    print(f"cache_key: {cache_key}")
+    litellm.cache.add_cache(result=response1, cache_key=cache_key)
+    print(f"cache key pre async get: {cache_key}")
+    stored_val = await litellm.cache.async_get_cache(
+        model="gpt-3.5-turbo",
+        messages=messages,
+    )
+    print(f"stored_val: {stored_val}")
+    assert stored_val["id"] == response1.id
+    raise Exception("it worked!")
 
 
 def test_redis_cache_completion():