Pine
107727ea5e
- Moved card rendering logic from DpmOverlays to a new ShowCard component for better reusability. - Introduced ToolStatusToast to display real-time operation statuses in the top right corner. - Updated PageHeader to conditionally render credits based on the current path. - Modified PromptPanel to change tool names and update prompt titles. - Enhanced ScreenLayout to include ToolStatusToast. - Updated styles for new components and adjusted existing styles for consistency. - Implemented statusBus utility for dispatching tool status events. - Updated useMqttControl to integrate tool status notifications during navigation and card display actions.
64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""知识库入库脚本：分块 → LLM 生成问答 → 双路向量索引 → 落盘 kb_index.json / kb_qa.json。
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知识源 = backend/knowledge/ 下全部 *.md（park.md / opc.md / park_data.md …）。
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- 幂等：缓存有效时直接加载（不重新构建）；--force 强制删除缓存重建。
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- 首次运行会调用 DashScope（text-embedding-v4 向量化 + qwen-plus 生成问答），
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耗时约数分钟，仅构建一次，之后命中缓存秒开。
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用法（在 backend/ 目录下执行）：
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python scripts/ingest_kb.py # 有缓存则加载，无缓存则构建
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python scripts/ingest_kb.py --force # 强制重建（任一知识文件变更后）
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"""
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import argparse
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import sys
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from pathlib import Path
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BACKEND_DIR = Path(__file__).resolve().parent.parent
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if str(BACKEND_DIR) not in sys.path:
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sys.path.insert(0, str(BACKEND_DIR))
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def main() -> None:
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parser = argparse.ArgumentParser(description="知识库入库（分块 + 问答 + 双路向量索引）")
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parser.add_argument("--force", action="store_true", help="删除缓存并强制重建")
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parser.add_argument("--dry", action="store_true", help="只做分块预览，不调 API、不落盘")
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args = parser.parse_args()
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from app import rag
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if args.dry:
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chunks = rag._chunk_md(rag._kb_text())
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print(f"[dry] 知识源 {len(list(rag.KB_DIR.glob(rag.KB_GLOB)))} 个文件，全库 "
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f"{len(rag._kb_text())} 字 → {len(chunks)} 块（最长 {max(map(len, chunks))} 字）")
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return
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if args.force:
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for p in (rag.META_FILE, rag.VECTOR_FILE, rag.QA_FILE, rag.LEGACY_INDEX_FILE):
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if p.exists():
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p.unlink()
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print(f"[ingest] 已删除缓存 {p.name}")
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print("[ingest] 知识源：")
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for f in sorted(rag.KB_DIR.glob(rag.KB_GLOB)):
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print(f" - {f.name} ({f.stat().st_size} B)")
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idx = rag._load_index()
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n_chunk = len(idx["chunks"])
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n_vec = len(idx["embed_texts"])
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dim = idx["embeddings"].shape[1]
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n_q = sum(1 for qs in idx["questions"] for _ in qs)
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print("[ingest] 入库完成：")
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print(f" - 分块数: {n_chunk}")
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print(f" - 生成问句数: {n_q}")
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print(f" - 向量数: {n_vec}（dim={dim}，含原句+问句双路）")
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print(f" - 向量矩阵: {rag.VECTOR_FILE}")
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print(f" - 元数据: {rag.META_FILE}")
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print(f" - 问答缓存: {rag.QA_FILE}")
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print("[ingest] 成功。")
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if __name__ == "__main__":
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main()
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