陈曦
f41c2113dc
为 info_extraction.py、import_openie.py、delete_lpmm_items.py 以及新增的 lpmm_manager.py 引入统一的 --non-interactive 参数，使其能够在 CI 和自动化场景下实现完全脚本化、无交互运行。新增了一个功能完整的命令行管理脚本（lpmm_manager.py）以及一份详细的用户指南（lpmm_pipelines_guide.md）。同时，更新了 test_lpmm_retrieval.py，以支持通过 CLI 自定义测试用例，并改进了整个流水线中的错误处理和用户提示。最后，从文档和代码中移除了 ppr_relation_cap 参数。
123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
import argparse
|
|
import asyncio
|
|
import os
|
|
import sys
|
|
from typing import List, Dict, Any, Optional
|
|
|
|
# 强制使用 utf-8，避免控制台编码报错影响 Embedding 加载
|
|
try:
|
|
if hasattr(sys.stdout, "reconfigure"):
|
|
sys.stdout.reconfigure(encoding="utf-8")
|
|
if hasattr(sys.stderr, "reconfigure"):
|
|
sys.stderr.reconfigure(encoding="utf-8")
|
|
except Exception:
|
|
pass
|
|
|
|
# 确保能导入 src.*
|
|
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
|
|
|
from src.common.logger import get_logger
|
|
from src.config.config import global_config
|
|
from src.chat.knowledge import lpmm_start_up
|
|
from src.memory_system.retrieval_tools.query_lpmm_knowledge import query_lpmm_knowledge
|
|
|
|
logger = get_logger("test_lpmm_retrieval")
|
|
|
|
|
|
DEFAULT_TEST_CASES: List[Dict[str, Any]] = [
|
|
{
|
|
"name": "回滚一批知识",
|
|
"query": "LPMM是什么?",
|
|
"expect_keywords": ["哈希列表", "删除脚本", "OpenIE"],
|
|
},
|
|
{
|
|
"name": "调整 LPMM 检索参数",
|
|
"query": "不同用词习惯带来的检索偏差该如何解决",
|
|
"expect_keywords": ["bot_config.toml", "lpmm_knowledge", "qa_paragraph_search_top_k"],
|
|
},
|
|
]
|
|
|
|
|
|
async def run_tests(test_cases: Optional[List[Dict[str, Any]]] = None) -> None:
|
|
"""简单测试 LPMM 知识库检索能力"""
|
|
if not global_config.lpmm_knowledge.enable:
|
|
logger.warning("当前配置中 lpmm_knowledge.enable 为 False，检索测试可能直接返回“未启用”。")
|
|
|
|
logger.info("开始初始化 LPMM 知识库...")
|
|
lpmm_start_up()
|
|
logger.info("LPMM 知识库初始化完成，开始执行测试用例。")
|
|
|
|
cases = test_cases if test_cases is not None else DEFAULT_TEST_CASES
|
|
|
|
for case in cases:
|
|
name = case["name"]
|
|
query = case["query"]
|
|
expect_keywords: List[str] = case.get("expect_keywords", [])
|
|
|
|
print("\n" + "=" * 60)
|
|
print(f"[TEST] {name}")
|
|
print(f"[Q] {query}")
|
|
|
|
result = await query_lpmm_knowledge(query, limit=3)
|
|
|
|
print("\n[RAW RESULT]")
|
|
print(result)
|
|
|
|
status = "UNKNOWN"
|
|
hit_keywords: List[str] = []
|
|
|
|
if isinstance(result, str):
|
|
if "未启用" in result or "未初始化" in result or "查询失败" in result:
|
|
status = "ERROR"
|
|
elif "未找到与" in result:
|
|
status = "NO_HIT"
|
|
else:
|
|
if expect_keywords:
|
|
hit_keywords = [kw for kw in expect_keywords if kw in result]
|
|
status = "PASS" if hit_keywords else "WARN"
|
|
else:
|
|
status = "PASS"
|
|
|
|
print("\n[CHECK]")
|
|
print(f"Status: {status}")
|
|
if expect_keywords:
|
|
print(f"Expected keywords: {expect_keywords}")
|
|
print(f"Hit keywords: {hit_keywords}")
|
|
|
|
print("\n" + "=" * 60)
|
|
print("LPMM 检索测试完成。请根据每条用例的 Status 和命中关键词判断检索效果是否符合预期。")
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(
|
|
description=(
|
|
"测试 LPMM 知识库检索能力。\n"
|
|
"如不提供参数，则执行内置的默认用例；\n"
|
|
"也可以通过 --query 与 --expect-keyword 自定义一条测试用例。"
|
|
)
|
|
)
|
|
parser.add_argument(
|
|
"--query",
|
|
help="自定义测试问题（单条）。提供该参数时，将仅运行这一条用例。",
|
|
)
|
|
parser.add_argument(
|
|
"--expect-keyword",
|
|
action="append",
|
|
help="期望在检索结果中出现的关键字，可重复多次指定；仅在提供 --query 时生效。",
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
if args.query:
|
|
custom_case = {
|
|
"name": "custom",
|
|
"query": args.query,
|
|
"expect_keywords": args.expect_keyword or [],
|
|
}
|
|
asyncio.run(run_tests([custom_case]))
|
|
else:
|
|
asyncio.run(run_tests())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|