Mr.Xia
c2da507f43
- pyproject 注册 nonebot_plugin_wtfllm 插件 - 新增 wtfllm_gate 插件:仅在群 621016172 启用 wtfllm 响应 - 新增 kb_build:抓取 danding.vip 文档并导入 wtfllm 知识库的脚本 - requirements: 升级 nonebot2 2.5.0 / pydantic 2.13.5,补 aiohttp 与 wtfllm 依赖
139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
"""把 kb_build/pages/*.txt 的站点文档写入 nonebot-plugin-wtfllm 知识库。
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用法:
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python kb_build/import_kb.py --agent-id <机器人QQ号>
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首次运行会自动下载 Qdrant 与 embedding 模型,需要几分钟。
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"""
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import argparse
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import asyncio
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import re
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import sys
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import time
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import uuid
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from pathlib import Path
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# NoneBot 必须先初始化,wtfllm 的配置才可用
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import nonebot
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nonebot.init()
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sys.path.insert(0, str(Path(__file__).parent.parent))
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PAGES_DIR = Path(__file__).parent / "pages"
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# 每条知识的目标长度(字符),按标题切块后超过再按段落切
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CHUNK_SIZE = 600
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CATEGORY_BY_DIR = {
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"功能帮助": "功能帮助",
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"脚本进阶": "脚本进阶",
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"简单使用": "简单使用",
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"其它": "其它",
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}
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def parse_page(path: Path) -> list[dict]:
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"""把单页文本按标题切成知识条目。"""
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raw = path.read_text(encoding="utf-8")
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lines = raw.splitlines()
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page_title = lines[0].lstrip("# ").replace("\u200b", "") if lines else path.stem
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source = lines[1].lstrip("> 来源: ").strip() if len(lines) > 1 else ""
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parts: list[dict] = []
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cur_head = page_title
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cur_body: list[str] = []
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def flush() -> None:
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text = "\n".join(cur_body).strip()
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if text:
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parts.append({"title": cur_head, "content": text})
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for line in lines[2:]:
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m = re.match(r"^(#{1,4})\s+(.+)$", line)
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if m:
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flush()
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cur_body = []
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h = m.group(2).replace("\u200b", "").strip()
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cur_head = h if h == page_title else f"{page_title} · {h}"
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else:
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cur_body.append(line)
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flush()
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# 超长条目按段落二次切分
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chunks: list[dict] = []
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for p in parts:
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if len(p["content"]) <= CHUNK_SIZE * 1.5:
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chunks.append(p)
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continue
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paras = p["content"].split("\n\n")
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buf: list[str] = []
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for para in paras:
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if buf and sum(len(b) for b in buf) + len(para) > CHUNK_SIZE:
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chunks.append({"title": p["title"], "content": "\n\n".join(buf)})
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buf = []
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buf.append(para)
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if buf:
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chunks.append({"title": p["title"], "content": "\n\n".join(buf)})
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# 附上页面来源与分类
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first_dir = path.stem.split("_")[0]
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category = CATEGORY_BY_DIR.get(first_dir, "general")
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for c in chunks:
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c["category"] = category
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c["source"] = source
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c["page_title"] = page_title
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return chunks
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async def main(agent_id: str, dry_run: bool) -> None:
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entries_spec: list[dict] = []
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for f in sorted(PAGES_DIR.glob("*.txt")):
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entries_spec.extend(parse_page(f))
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print(f"共解析出 {len(entries_spec)} 条知识条目")
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if dry_run:
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for e in entries_spec[:10]:
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print(f" [{e['category']}] {e['title']} ({len(e['content'])} chars)")
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return
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# 必须先以插件形式加载,让 localstore 能定位数据目录
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nonebot.load_plugin("nonebot_plugin_wtfllm")
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from nonebot_plugin_wtfllm.memory.items.knowledge_base import KnowledgeEntry
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from nonebot_plugin_wtfllm.v_db.lifecycle import on_startup as vdb_startup
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from nonebot_plugin_wtfllm.v_db.repositories import KnowledgeBaseRepository
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print("初始化 Qdrant(首次会自动下载)...")
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await vdb_startup()
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now = int(time.time())
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entries = [
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KnowledgeEntry.create(
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content=e["content"],
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title=e["title"],
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agent_id=agent_id,
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category=e["category"],
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tags=[e["page_title"], "danding.vip"],
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token_count=len(e["content"]),
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)
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for e in entries_spec
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]
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repo = KnowledgeBaseRepository()
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ids = await repo.save_many_knowledge(entries)
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print(f"已写入 {len(ids)} 条知识")
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# 验证检索
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for q in ["怎么设置定时调度", "积分怎么获得", "下载安装"]:
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results = await repo.search_relevant(agent_id=agent_id, query=q, limit=3)
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print(f"\n检索「{q}」:")
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for r in results:
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print(f" {r.score:.3f} {r.item.title}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--agent-id", required=True, help="机器人 QQ 号(wtfllm 的 agent_id)")
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parser.add_argument("--dry-run", action="store_true", help="只解析不写入")
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args = parser.parse_args()
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asyncio.run(main(args.agent_id, args.dry_run))
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