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8b30412 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | """Integration tests for Langgraph state manager."""
import pytest
from langchain_core.messages import HumanMessage
from chatassistant_retail.state import ConversationState, LanggraphManager
class MockLLMClient:
"""Mock LLM client for testing."""
async def call_llm(self, messages, tools=None):
"""Mock LLM call - returns dictionary format."""
return {
"choices": [
{
"message": {
"content": "This is a test response.",
"role": "assistant",
"tool_calls": None,
}
}
]
}
async def extract_response_content(self, response):
"""Extract response content from dictionary."""
if isinstance(response, dict) and "choices" in response:
choices = response.get("choices", [])
if choices:
message = choices[0].get("message", {})
return message.get("content", "")
return ""
async def extract_tool_calls(self, response):
"""Extract tool calls from dictionary."""
if isinstance(response, dict) and "choices" in response:
choices = response.get("choices", [])
if choices:
message = choices[0].get("message", {})
tool_calls = message.get("tool_calls", [])
if tool_calls:
return [
{
"name": tc.get("function", {}).get("name", ""),
"arguments": tc.get("function", {}).get("arguments", {}),
}
for tc in tool_calls
]
return []
class MockRAGRetriever:
"""Mock RAG retriever for testing."""
async def retrieve(self, query, top_k=5):
"""Mock retrieval."""
return [
{
"sku": "SKU-10000",
"name": "Test Product",
"category": "Electronics",
"price": 99.99,
"current_stock": 5,
"reorder_level": 10,
}
]
class MockToolExecutor:
"""Mock tool executor for testing."""
async def execute_tool(self, tool_name, args):
"""Mock tool execution."""
return {
"success": True,
"message": f"Executed {tool_name} with args {args}",
}
class TestLanggraphManager:
"""Test Langgraph state management."""
@pytest.mark.asyncio
async def test_greeting_classification(self):
"""Test that greetings are classified correctly."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Hello")],
)
# Classify intent
state = await manager._classify_intent_node(state)
assert state.current_intent == "greeting"
@pytest.mark.asyncio
async def test_rag_classification(self):
"""Test that product queries are classified as RAG."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Find me a wireless mouse")],
)
# Classify intent
state = await manager._classify_intent_node(state)
assert state.current_intent == "rag"
assert state.needs_rag is True
@pytest.mark.asyncio
async def test_tool_classification(self):
"""Test that tool-related queries are classified correctly."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Check low stock items")],
)
# Classify intent
state = await manager._classify_intent_node(state)
assert state.current_intent == "tool"
assert state.needs_tool is True
@pytest.mark.asyncio
async def test_rag_retrieval_node(self):
"""Test RAG retrieval node."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Find wireless mouse")],
)
# Execute retrieval
state = await manager._rag_retrieval_node(state)
assert "products" in state.context
assert len(state.context["products"]) > 0
assert state.context["products"][0]["name"] == "Test Product"
@pytest.mark.asyncio
async def test_generate_response_node(self):
"""Test response generation node."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Hello")],
context={"products": []},
)
# Generate response
state = await manager._generate_response_node(state)
# Should have added an AI message
assert len(state.messages) == 2
assert state.messages[1].content == "This is a test response."
@pytest.mark.asyncio
async def test_full_workflow_greeting(self):
"""Test full workflow for greeting."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Hi there")],
)
# Process through workflow
final_state = await manager.process(state)
# Should have response
assert len(final_state.messages) == 2
assert final_state.current_intent == "greeting"
assert final_state.error is None
@pytest.mark.asyncio
async def test_full_workflow_rag(self):
"""Test full workflow for RAG query."""
llm_client = MockLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Find electronics products")],
)
# Process through workflow
final_state = await manager.process(state)
# Should have retrieved products and generated response
assert "products" in final_state.context
assert len(final_state.messages) == 2
assert final_state.current_intent == "rag"
@pytest.mark.asyncio
async def test_error_handling(self):
"""Test error handling in workflow."""
class FailingLLMClient:
async def call_llm(self, messages, tools=None):
raise Exception("LLM error")
llm_client = FailingLLMClient()
rag_retriever = MockRAGRetriever()
tool_executor = MockToolExecutor()
manager = LanggraphManager(llm_client, rag_retriever, tool_executor)
state = ConversationState(
session_id="test-session",
messages=[HumanMessage(content="Hello")],
)
# Process through workflow (should handle error gracefully)
final_state = await manager.process(state)
# Should have error set
assert final_state.error is not None
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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