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985f3ee | 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 | import os
import sys
import re
import time
from pathlib import Path
from dotenv import load_dotenv
load_dotenv()
BASE_DIR = Path(__file__).resolve().parent.parent
QUESTIONS_FILE = BASE_DIR / "scripts" / "evaluation_questions.md"
RESULTS_FILE = BASE_DIR / "scripts" / "evaluation_results.md"
sys.path.insert(0, str(BASE_DIR))
from backend.app.core.agent import init_agent
def load_questions_from_markdown() -> list:
if not QUESTIONS_FILE.exists():
print(f"[ERROR] {QUESTIONS_FILE} not found!")
sys.exit(1)
with open(QUESTIONS_FILE, "r", encoding="utf-8") as f:
text = f.read()
questions = []
category = "General"
for line in text.splitlines():
line_str = line.strip()
if line_str.startswith("## "):
category = line_str.replace("## ", "").strip()
elif re.match(r"^\d+\.\s+", line_str):
q_text = re.sub(r"^\d+\.\s+", "", line_str).strip()
if q_text:
questions.append({
"id": len(questions) + 1,
"category": category,
"question": q_text,
})
print(f"Loaded {len(questions)} evaluation questions from {QUESTIONS_FILE.name}")
return questions
def run_evaluation():
print("=" * 70)
print("🚀 ArunCore Agent Multi-Turn Evaluation Suite (7-Iteration ReAct Loop)")
print("=" * 70)
questions = load_questions_from_markdown()
if not questions:
print("[ERROR] No valid questions found.")
return
main_llm, prompt, memory, tools = init_agent(temperature=0.3)
tool_map = {t.name: t for t in tools}
results_md_blocks = [
"# 🧪 ArunCore AI Assistant — Evaluation Results & Traces",
f"\n**Evaluated Date:** {time.strftime('%Y-%m-%d %H:%M:%S')}",
f"**Total Questions Evaluated:** {len(questions)}\n",
"---",
]
for item in questions:
qid = item["id"]
cat = item["category"]
q = item["question"]
print(f"\n[Q{qid:02d} | {cat}] '{q}'")
t0 = time.time()
scratchpad = []
tools_called = []
max_turns = 7
turn_count = 0
final_answer = ""
while turn_count < max_turns:
turn_count += 1
messages = prompt.format_messages(
running_summary="",
chat_history=[],
input=q,
agent_scratchpad=scratchpad
)
try:
ai_msg = main_llm.invoke(messages)
except Exception as le:
final_answer = f"LLM Error: {le}"
break
if ai_msg.tool_calls:
scratchpad.append(ai_msg)
for tc in ai_msg.tool_calls:
tname = tc["name"]
targs = tc.get("args", {})
tools_called.append(f"{tname}({json.dumps(targs)})")
print(f" ├─ Turn {turn_count}: Tool Call -> {tname}({targs})")
tool_func = tool_map.get(tname)
if tool_func:
try:
t_res = tool_func.invoke(targs)
except Exception as te:
t_res = f"Tool error: {te}"
else:
t_res = f"Unknown tool: {tname}"
scratchpad.append({
"role": "tool",
"name": tname,
"tool_call_id": tc.get("id", f"tc_{turn_count}"),
"content": str(t_res)[:3000]
})
else:
final_answer = (ai_msg.content or "").strip()
break
elapsed = round(time.time() - t0, 2)
print(f" └─ Final Answer ({elapsed}s, {len(tools_called)} tools used): {final_answer[:120]}...")
tools_str = "\n".join([f"- `{t}`" for t in tools_called]) if tools_called else "*No tools called (Direct Answer)*"
block = f"""
### Q{qid:02d} [{cat}]: {q}
**Execution Time:** `{elapsed}s` | **ReAct Turns:** `{turn_count}`
#### 🛠️ Tools Called:
{tools_str}
#### 🤖 AI Response:
{final_answer}
---
"""
results_md_blocks.append(block)
with open(RESULTS_FILE, "w", encoding="utf-8") as f:
f.write("\n".join(results_md_blocks))
print("\n" + "=" * 70)
print(f"✅ Evaluation complete! Saved full traces and responses to {RESULTS_FILE.name}")
print("=" * 70)
if __name__ == "__main__":
import json
run_evaluation()
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