File size: 6,806 Bytes
0ae3f27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | from unittest.mock import MagicMock, patch
from mem0.memory.main import Memory
def test_memory_configuration_without_env_vars():
"""Test Memory configuration with mock config instead of environment variables"""
# Mock configuration without relying on environment variables
mock_config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4",
"temperature": 0.1,
"max_tokens": 1500,
},
},
"vector_store": {
"provider": "chroma",
"config": {
"collection_name": "test_collection",
"path": "./test_db",
},
},
"embedder": {
"provider": "openai",
"config": {
"model": "text-embedding-ada-002",
},
},
}
# Test messages similar to the main.py file
test_messages = [
{"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
{
"role": "assistant",
"content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions.",
},
]
# Mock the Memory class methods to avoid actual API calls
with patch.object(Memory, "__init__", return_value=None):
with patch.object(Memory, "from_config") as mock_from_config:
with patch.object(Memory, "add") as mock_add:
with patch.object(Memory, "get_all") as mock_get_all:
# Configure mocks
mock_memory_instance = MagicMock()
mock_from_config.return_value = mock_memory_instance
mock_add.return_value = {
"results": [
{"id": "1", "text": "Alex is a vegetarian"},
{"id": "2", "text": "Alex is allergic to nuts"},
]
}
mock_get_all.return_value = [
{"id": "1", "text": "Alex is a vegetarian", "metadata": {"category": "dietary_preferences"}},
{"id": "2", "text": "Alex is allergic to nuts", "metadata": {"category": "allergies"}},
]
# Test the workflow
mem = Memory.from_config(config_dict=mock_config)
assert mem is not None
# Test adding memories
result = mock_add(test_messages, user_id="alice", metadata={"category": "book_recommendations"})
assert "results" in result
assert len(result["results"]) == 2
# Test retrieving memories
all_memories = mock_get_all(user_id="alice")
assert len(all_memories) == 2
assert any("vegetarian" in memory["text"] for memory in all_memories)
assert any("allergic to nuts" in memory["text"] for memory in all_memories)
def test_azure_config_structure():
"""Test that Azure configuration structure is properly formatted"""
# Test Azure configuration structure (without actual credentials)
azure_config = {
"llm": {
"provider": "azure_openai",
"config": {
"model": "gpt-4",
"temperature": 0.1,
"max_tokens": 1500,
"azure_kwargs": {
"azure_deployment": "test-deployment",
"api_version": "2023-12-01-preview",
"azure_endpoint": "https://test.openai.azure.com/",
"api_key": "test-key",
},
},
},
"vector_store": {
"provider": "azure_ai_search",
"config": {
"service_name": "test-service",
"api_key": "test-key",
"collection_name": "test-collection",
"embedding_model_dims": 1536,
},
},
"embedder": {
"provider": "azure_openai",
"config": {
"model": "text-embedding-ada-002",
"api_key": "test-key",
"azure_kwargs": {
"api_version": "2023-12-01-preview",
"azure_deployment": "test-embedding-deployment",
"azure_endpoint": "https://test.openai.azure.com/",
"api_key": "test-key",
},
},
},
}
# Validate configuration structure
assert "llm" in azure_config
assert "vector_store" in azure_config
assert "embedder" in azure_config
# Validate Azure-specific configurations
assert azure_config["llm"]["provider"] == "azure_openai"
assert "azure_kwargs" in azure_config["llm"]["config"]
assert "azure_deployment" in azure_config["llm"]["config"]["azure_kwargs"]
assert azure_config["vector_store"]["provider"] == "azure_ai_search"
assert "service_name" in azure_config["vector_store"]["config"]
assert azure_config["embedder"]["provider"] == "azure_openai"
assert "azure_kwargs" in azure_config["embedder"]["config"]
def test_memory_messages_format():
"""Test that memory messages are properly formatted"""
# Test message format from main.py
messages = [
{"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
{
"role": "assistant",
"content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions.",
},
]
# Validate message structure
assert len(messages) == 2
assert all("role" in msg for msg in messages)
assert all("content" in msg for msg in messages)
# Validate roles
assert messages[0]["role"] == "user"
assert messages[1]["role"] == "assistant"
# Validate content
assert "vegetarian" in messages[0]["content"].lower()
assert "allergic to nuts" in messages[0]["content"].lower()
assert "vegetarian" in messages[1]["content"].lower()
assert "nut allergy" in messages[1]["content"].lower()
def test_safe_update_prompt_constant():
"""Test the SAFE_UPDATE_PROMPT constant from main.py"""
SAFE_UPDATE_PROMPT = """
Based on the user's latest messages, what new preference can be inferred?
Reply only in this json_object format:
"""
# Validate prompt structure
assert isinstance(SAFE_UPDATE_PROMPT, str)
assert "user's latest messages" in SAFE_UPDATE_PROMPT
assert "json_object format" in SAFE_UPDATE_PROMPT
assert len(SAFE_UPDATE_PROMPT.strip()) > 0
|