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my-project-public — gitlawb

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

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