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ErrorDetecting/backend/tests/test_llm.py

124 lines
5.6 KiB

import asyncio
import os
import sys
# Add backend directory to sys.path to import app modules
# Current file: backend/tests/test_llm.py
# Parent: backend/tests
# Grandparent: backend
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from app.services.llm import LLMClient
from app.services.ops_tools import openai_tools_schema, tool_web_search, tool_start_cluster, tool_stop_cluster
from app.db import SessionLocal
from dotenv import load_dotenv
import json
async def main():
# Load .env from backend directory
env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), ".env")
load_dotenv(env_path)
print("Testing LLMClient with REAL Tools...")
try:
llm = LLMClient()
print(f"Provider: {llm.provider}")
print(f"Endpoint: {llm.endpoint}")
print(f"Model: {llm.model}")
print(f"Timeout: {llm.timeout}")
messages = [{"role": "user", "content": "停止集群 5c43a9c7-e2a9-4756-b75d-6813ac55d3ba"}]
# 1. Get tools definition
chat_tools = openai_tools_schema()
print(f"Tools loaded: {[t['function']['name'] for t in chat_tools]}")
print("Sending initial request...")
resp = await llm.chat(messages, tools=chat_tools)
if "choices" in resp and resp["choices"]:
msg = resp["choices"][0].get("message", {})
tool_calls = msg.get("tool_calls")
if tool_calls:
print(f"Tool calls triggered: {len(tool_calls)}")
# Append assistant message with tool_calls
messages.append(msg)
async with SessionLocal() as db:
for tc in tool_calls:
fn = tc.get("function", {})
name = fn.get("name")
args_str = fn.get("arguments", "{}")
print(f"Executing REAL tool: {name} with args: {args_str}")
if name == "web_search":
try:
args = json.loads(args_str)
tool_result = await tool_web_search(args.get("query"), args.get("max_results", 5))
messages.append({
"role": "tool",
"tool_call_id": tc.get("id"),
"name": name,
"content": json.dumps(tool_result, ensure_ascii=False)
})
print("Tool execution completed.")
except Exception as e:
print(f"Tool execution failed: {e}")
elif name == "start_cluster":
try:
args = json.loads(args_str)
cluster_uuid = args.get("cluster_uuid")
# Execute REAL tool
tool_result = await tool_start_cluster(db, "admin", cluster_uuid)
messages.append({
"role": "tool",
"tool_call_id": tc.get("id"),
"name": name,
"content": json.dumps(tool_result, ensure_ascii=False)
})
print(f"REAL tool start_cluster execution completed: {tool_result.get('status')}")
except Exception as e:
print(f"REAL tool execution failed: {e}")
elif name == "stop_cluster":
try:
args = json.loads(args_str)
cluster_uuid = args.get("cluster_uuid")
# Execute REAL tool
tool_result = await tool_stop_cluster(db, "admin", cluster_uuid)
messages.append({
"role": "tool",
"tool_call_id": tc.get("id"),
"name": name,
"content": json.dumps(tool_result, ensure_ascii=False)
})
print(f"REAL tool stop_cluster execution completed: {tool_result.get('status')}")
except Exception as e:
print(f"REAL tool execution failed: {e}")
# 2. Send follow-up request with tool results
print("Sending follow-up request...")
resp = await llm.chat(messages, tools=chat_tools)
if "choices" in resp and resp["choices"]:
final_msg = resp["choices"][0].get("message", {})
print("\nFinal Reply:")
print(final_msg.get('content'))
if "reasoning_content" in final_msg:
print(f"\nReasoning:\n{final_msg.get('reasoning_content')}")
else:
print("No tool calls triggered.")
print(f"Reply: {msg.get('content')}")
else:
print(resp)
except Exception as e:
import traceback
traceback.print_exc()
print(f"Error: {repr(e)}")
if __name__ == "__main__":
asyncio.run(main())