rag_agent / test_scripts.py
Cheh Kit Hong
fixing gradio
aa018e3
raw
history blame
10.8 kB
"""
Test script for RAG Agent logic.
Tests the agent workflow, nodes, state management, and retrieval.
"""
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent))
from langchain_core.messages import HumanMessage, AIMessage
from agent.state import AgentState
from core.rag_agent import RAGAgent
def print_separator(title: str):
"""Print a visual separator."""
print("\n" + "="*70)
print(f" {title}")
print("="*70 + "\n")
def test_agent_initialization():
"""Test RAGAgent can be initialized properly."""
print_separator("TEST 1: Agent Initialization")
try:
agent = RAGAgent()
print("βœ“ RAGAgent initialized successfully")
print(f" - Thread ID: {agent.thread_id}")
print(f" - LLM Model: {agent.llm.model_name if hasattr(agent.llm, 'model_name') else 'initialized'}")
print(f" - Graph: {type(agent.agent_graph).__name__}")
return agent
except Exception as e:
print(f"βœ— Failed to initialize RAGAgent: {e}")
import traceback
traceback.print_exc()
return None
def test_simple_query(agent: RAGAgent):
"""Test a simple query execution."""
print_separator("TEST 2: Simple Query")
if agent is None:
print("βœ— Skipping - agent not initialized")
return False
try:
query = "What is DeepAnalyze?"
print(f"Query: '{query}'")
initial_state = {
"messages": [HumanMessage(content=query)],
}
result = agent.agent_graph.invoke(
initial_state,
config=agent.get_config()
)
messages = result.get("messages", [])
ai_messages = [m for m in messages if isinstance(m, AIMessage)]
if ai_messages:
print(f"βœ“ Query executed successfully")
print(f" Total messages: {len(messages)}")
print(f" Response length: {len(ai_messages[-1].content)} chars")
print(f"\n Response preview:")
print(f" {ai_messages[-1].content[:300]}...")
return True
else:
print(f"βœ— No AI response generated")
return False
except Exception as e:
print(f"βœ— Query execution failed: {e}")
import traceback
traceback.print_exc()
return False
def test_rag_query(agent: RAGAgent):
"""Test a query that should use RAG (local documents)."""
print_separator("TEST 3: RAG Query")
if agent is None:
print("βœ— Skipping - agent not initialized")
return False
try:
query = "Explain the architecture of SAM 3"
print(f"Query: '{query}' (should use local documents)")
initial_state = {
"messages": [HumanMessage(content=query)],
}
result = agent.agent_graph.invoke(
initial_state,
config=agent.get_config()
)
messages = result.get("messages", [])
rag_method = result.get("rag_method", "UNKNOWN")
ai_messages = [m for m in messages if isinstance(m, AIMessage)]
print(f" Routing decision: {rag_method}")
if ai_messages:
print(f"βœ“ RAG query executed")
print(f" Response preview:")
print(f" {ai_messages[-1].content[:300]}...")
return True
else:
print(f"βœ— No response generated")
return False
except Exception as e:
print(f"βœ— RAG query failed: {e}")
import traceback
traceback.print_exc()
return False
def test_web_search_query(agent: RAGAgent):
"""Test a query that should use web search."""
print_separator("TEST 4: Web Search Query")
if agent is None:
print("βœ— Skipping - agent not initialized")
return False
try:
query = "What's the latest news about AI in 2025?"
print(f"Query: '{query}' (should use web search)")
initial_state = {
"messages": [HumanMessage(content=query)],
}
result = agent.agent_graph.invoke(
initial_state,
config=agent.get_config()
)
messages = result.get("messages", [])
rag_method = result.get("rag_method", "UNKNOWN")
ai_messages = [m for m in messages if isinstance(m, AIMessage)]
print(f" Routing decision: {rag_method}")
if ai_messages:
print(f"βœ“ Web search query executed")
print(f" Response preview:")
print(f" {ai_messages[-1].content[:300]}...")
return True
else:
print(f"βœ— No response generated")
return False
except Exception as e:
print(f"βœ— Web search query failed: {e}")
import traceback
traceback.print_exc()
return False
def test_general_query(agent: RAGAgent):
"""Test a general query that doesn't need RAG or web search."""
print_separator("TEST 5: General Query")
if agent is None:
print("βœ— Skipping - agent not initialized")
return False
try:
query = "What is 15 multiplied by 7?"
print(f"Query: '{query}' (should use general LLM)")
initial_state = {
"messages": [HumanMessage(content=query)],
}
result = agent.agent_graph.invoke(
initial_state,
config=agent.get_config()
)
messages = result.get("messages", [])
rag_method = result.get("rag_method", "UNKNOWN")
ai_messages = [m for m in messages if isinstance(m, AIMessage)]
print(f" Routing decision: {rag_method}")
if ai_messages:
print(f"βœ“ General query executed")
print(f" Response: {ai_messages[-1].content}")
return True
else:
print(f"βœ— No response generated")
return False
except Exception as e:
print(f"βœ— General query failed: {e}")
import traceback
traceback.print_exc()
return False
def test_conversation_memory(agent: RAGAgent):
"""Test multi-turn conversation with memory."""
print_separator("TEST 6: Conversation Memory")
if agent is None:
print("βœ— Skipping - agent not initialized")
return False
try:
# Reset thread for clean test
agent.reset_thread()
# First turn
print("Turn 1: 'What is DeepAnalyze?'")
state1 = {
"messages": [HumanMessage(content="What is DeepAnalyze?")],
}
result1 = agent.agent_graph.invoke(state1, config=agent.get_config())
ai_msg_1 = [m for m in result1["messages"] if isinstance(m, AIMessage)]
if not ai_msg_1:
print("βœ— No response in turn 1")
return False
print(f"βœ“ Turn 1 response: {ai_msg_1[-1].content[:100]}...")
# Second turn - follow-up question
print("\nTurn 2: 'What are its main features?' (requires context)")
state2 = {
"messages": [HumanMessage(content="What are its main features?")],
}
result2 = agent.agent_graph.invoke(state2, config=agent.get_config())
ai_msg_2 = [m for m in result2["messages"] if isinstance(m, AIMessage)]
if not ai_msg_2:
print("βœ— No response in turn 2")
return False
print(f"βœ“ Turn 2 response: {ai_msg_2[-1].content[:100]}...")
# Check if response makes sense in context
response = ai_msg_2[-1].content.lower()
if "deepanalyze" in response or "feature" in response or "agent" in response:
print("βœ“ Conversation memory working - response uses context")
return True
else:
print("⚠ Response may not be using conversation context properly")
return True # Still pass, as it generated a response
except Exception as e:
print(f"βœ— Conversation memory test failed: {e}")
import traceback
traceback.print_exc()
return False
def test_thread_reset(agent: RAGAgent):
"""Test thread reset functionality."""
print_separator("TEST 7: Thread Reset")
if agent is None:
print("βœ— Skipping - agent not initialized")
return False
try:
old_thread_id = agent.thread_id
print(f"Old thread ID: {old_thread_id}")
agent.reset_thread()
new_thread_id = agent.thread_id
print(f"New thread ID: {new_thread_id}")
if old_thread_id != new_thread_id:
print("βœ“ Thread reset successfully")
return True
else:
print("βœ— Thread ID unchanged after reset")
return False
except Exception as e:
print(f"βœ— Thread reset failed: {e}")
import traceback
traceback.print_exc()
return False
def run_all_tests():
"""Run all tests and provide summary."""
print("\n" + "β–ˆ"*70)
print(" RAG AGENT TEST SUITE")
print("β–ˆ"*70)
# Initialize agent once
agent = test_agent_initialization()
if agent is None:
print("\nβœ— Cannot proceed - agent initialization failed")
return False
tests = [
("Simple Query", lambda: test_simple_query(agent)),
("RAG Query", lambda: test_rag_query(agent)),
("Web Search Query", lambda: test_web_search_query(agent)),
("General Query", lambda: test_general_query(agent)),
("Conversation Memory", lambda: test_conversation_memory(agent)),
("Thread Reset", lambda: test_thread_reset(agent)),
]
results = {}
for name, test_func in tests:
try:
results[name] = test_func()
except Exception as e:
print(f"\nβœ— Test '{name}' crashed: {e}")
import traceback
traceback.print_exc()
results[name] = False
# Print summary
print_separator("TEST SUMMARY")
passed = sum(results.values())
total = len(results)
for name, passed_test in results.items():
status = "βœ“ PASS" if passed_test else "βœ— FAIL"
print(f"{status}: {name}")
print(f"\n{'='*70}")
print(f" TOTAL: {passed}/{total} tests passed ({passed/total*100:.1f}%)")
print(f"{'='*70}\n")
return passed == total
if __name__ == "__main__":
success = run_all_tests()
sys.exit(0 if success else 1)