update redisvl dependency

tests/litellm/caching/test_redis_semantic_cache.py

@@ -0,0 +1,130 @@
+import os
+import sys
+from unittest.mock import MagicMock, patch, AsyncMock
+
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../../..")
+)  # Adds the parent directory to the system path
+
+
+# Tests for RedisSemanticCache
+def test_redis_semantic_cache_initialization(monkeypatch):
+    # Mock the redisvl import
+    semantic_cache_mock = MagicMock()
+    with patch.dict("sys.modules", {
+        "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
+        "redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=MagicMock())
+    }):
+        from litellm.caching.redis_semantic_cache import RedisSemanticCache
+        
+        # Set environment variables
+        monkeypatch.setenv("REDIS_HOST", "localhost")
+        monkeypatch.setenv("REDIS_PORT", "6379")
+        monkeypatch.setenv("REDIS_PASSWORD", "test_password")
+        
+        # Initialize the cache with a similarity threshold
+        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
+        
+        # Verify the semantic cache was initialized with correct parameters
+        assert redis_semantic_cache.similarity_threshold == 0.8
+        
+        # Use pytest.approx for floating point comparison to handle precision issues
+        assert redis_semantic_cache.distance_threshold == pytest.approx(0.2, abs=1e-10)
+        assert redis_semantic_cache.embedding_model == "text-embedding-ada-002"
+        
+        # Test initialization with missing similarity_threshold
+        with pytest.raises(ValueError, match="similarity_threshold must be provided"):
+            RedisSemanticCache()
+
+
+def test_redis_semantic_cache_get_cache(monkeypatch):
+    # Mock the redisvl import and embedding function
+    semantic_cache_mock = MagicMock()
+    custom_vectorizer_mock = MagicMock()
+    
+    with patch.dict("sys.modules", {
+        "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
+        "redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=custom_vectorizer_mock)
+    }):
+        from litellm.caching.redis_semantic_cache import RedisSemanticCache
+        
+        # Set environment variables
+        monkeypatch.setenv("REDIS_HOST", "localhost")
+        monkeypatch.setenv("REDIS_PORT", "6379")
+        monkeypatch.setenv("REDIS_PASSWORD", "test_password")
+        
+        # Initialize cache
+        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
+        
+        # Mock the llmcache.check method to return a result
+        mock_result = [
+            {
+                "prompt": "What is the capital of France?",
+                "response": '{"content": "Paris is the capital of France."}',
+                "vector_distance": 0.1  # Distance of 0.1 means similarity of 0.9
+            }
+        ]
+        redis_semantic_cache.llmcache.check = MagicMock(return_value=mock_result)
+        
+        # Mock the embedding function
+        with patch("litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2, 0.3]}]}):
+            # Test get_cache with a message
+            result = redis_semantic_cache.get_cache(
+                key="test_key",
+                messages=[{"content": "What is the capital of France?"}]
+            )
+            
+            # Verify result is properly parsed
+            assert result == {"content": "Paris is the capital of France."}
+            
+            # Verify llmcache.check was called
+            redis_semantic_cache.llmcache.check.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_redis_semantic_cache_async_get_cache(monkeypatch):
+    # Mock the redisvl import
+    semantic_cache_mock = MagicMock()
+    custom_vectorizer_mock = MagicMock()
+    
+    with patch.dict("sys.modules", {
+        "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
+        "redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=custom_vectorizer_mock)
+    }):
+        from litellm.caching.redis_semantic_cache import RedisSemanticCache
+        
+        # Set environment variables
+        monkeypatch.setenv("REDIS_HOST", "localhost")
+        monkeypatch.setenv("REDIS_PORT", "6379")
+        monkeypatch.setenv("REDIS_PASSWORD", "test_password")
+        
+        # Initialize cache
+        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
+        
+        # Mock the async methods
+        mock_result = [
+            {
+                "prompt": "What is the capital of France?",
+                "response": '{"content": "Paris is the capital of France."}',
+                "vector_distance": 0.1  # Distance of 0.1 means similarity of 0.9
+            }
+        ]
+        
+        redis_semantic_cache.llmcache.acheck = AsyncMock(return_value=mock_result)
+        redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
+        
+        # Test async_get_cache with a message
+        result = await redis_semantic_cache.async_get_cache(
+            key="test_key",
+            messages=[{"content": "What is the capital of France?"}],
+            metadata={}
+        )
+        
+        # Verify result is properly parsed
+        assert result == {"content": "Paris is the capital of France."}
+        
+        # Verify methods were called
+        redis_semantic_cache._get_async_embedding.assert_called_once()
+        redis_semantic_cache.llmcache.acheck.assert_called_once()