forked from admin/PlantAI-rag-new
adminandClaude Fable 5
e783a26be6
- P1 vstore: chunks.py 切块(600字/8段/重叠1/上限200,表格原位聚合,outline 不参与)+ChunkIndex 三文件原子写+无降级档;embed_batch_chunked 批量16x并发4; vault add/delete/rebuild_chunks 钩子(chunk 失败不阻断 doc 级);search-chunks/ rebuild-chunks 端点+health chunk_count;rebuild CLI - P2 BFF: privateqa.ask_private(同doc≤3去重/12k截断/40s超时/带引用短答); POST /api/qa/private 三态;schemas PrivateDocBlock 入 union;client.search_chunks; llm timeout 参数;main.py 注册 - P3 前端: _firePrivate 并行检索+竞态防护(pending/flush/防重入/跨轮归属); AssistantMessage private_doc 块;i18n/private.js;PrivateLibraryPane 挂 DocPane 空态;removeLibrary(204 兜底) 自测: vstore pytest 26/27(1 失败为 T1 骨架笔误待修)+74 回归;前端 vitest 373/373;npm run build 0 error;sdk 222 passed(3 failed 为基线预存);红线 diff 空。 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
510 lines
20 KiB
Python
510 lines
20 KiB
Python
"""chunks.py —— 私有资料 chunk 级索引（FR-PRIV-002）。
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与 doc 级检索索引（retrieval.py）并存、互不影响：
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- 切块：正文段落聚合（目标 600 字 / 满 8 段封块 / 相邻重叠 1 段 / 每文档
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上限 200 块）；表格矩阵化为 `|a|b|` 行式文本段按原位插入段落流参与聚合
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（实施方案 §1.2：outline 不参与切块，标题上下文仅由首块前缀 `# {title}` 提供）。
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- 三文件落盘：index.json（meta 行，单一事实源） / texts.jsonl（chunk 文本，
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index.json 可按需重建） / index.faiss（faiss 二进制，与行序一一对应）。
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写入纪律仿 retrieval.py：单写锁（threading.Lock）+ tmp + os.replace 原子切换。
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- 查询无降级档：向量内积（L2 归一化 = 余弦）低于 min_score 的全部丢弃 →
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mode:"empty"（与 doc 级三档降级语义不同，FR-PRIV 假设 6）。
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- 任何文件缺失/损坏按空库降级（可服务，不抛异常，FR-PRIV 假设 3）。
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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import threading
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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import faiss
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import numpy as np
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from .settings import EMBED_DIM
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logger = logging.getLogger(__name__)
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# ---- 切块参数（任务书契约值）----
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CHUNK_TARGET_CHARS = 600 # 聚合目标字符数（达到即封块）
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CHUNK_MIN_CHARS = 120 # 尾块最小字符数（低于则并入前块）
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CHUNK_MAX_PARAS = 8 # 单块段落数上限（满即封块）
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CHUNK_OVERLAP_PARAS = 1 # 相邻块重叠段数（前块最后 1 段作下块前缀）
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CHUNK_MAX_PER_DOC = 200 # 每文档块数上限（超出截断）
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CHUNK_TOP_K_MAX = 20 # search top_k 上限（1..20 clamp）
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# ---- 检索参数 ----
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CHUNK_MIN_SCORE = 0.30 # 分数阈值：低于全丢 → empty（无降级档）
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CHUNK_SNIPPET_CHARS = 200 # snippet = text[:200]（FR-PRIV 假设 4）
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# ---- 落盘文件 ----
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INDEX_FILE = "index.json"
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FAISS_FILE = "index.faiss"
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TEXTS_FILE = "texts.jsonl"
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TMP_SUFFIX = ".tmp"
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class ChunkError(Exception):
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"""chunk 索引操作失败。"""
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@dataclass
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class Chunk:
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"""单块。chunk_id = f"{doc_id}:{seq}"（doc_id 由 ChunkIndex.upsert_document 注入）。"""
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seq: int
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text: str
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# ---------------------------------------------------------------------------
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# 切块
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# ---------------------------------------------------------------------------
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def _table_to_text_rows(tables: list[list[list[str]]]) -> list[str]:
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"""表格 → `|a|b|` 行式文本段（矩阵化，一行表行 = 一段）。"""
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rows: list[str] = []
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for table in tables or []:
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for row in table or []:
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cells = [str(c or "") for c in row]
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text = "|" + "|".join(cells) + "|"
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if text.strip("|"):
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rows.append(text)
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return rows
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def build_chunks(
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title: str,
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outline: list[dict[str, Any]],
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paragraphs: list[str],
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tables: list[list[list[str]]],
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) -> list[Chunk]:
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"""正文 → chunk 列表（实施方案 §1.2 语义，TL 裁决假设 1）。
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- 表格矩阵化为 `|a|b|` 行式文本段，插入段落流中间参与聚合切块
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（废除「末尾 table 单独块」；提取结构 paragraphs/tables 无位置信息，
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表格段统一落在段落流中位，与用例表 L1-05「表格位于段落流中间」判据一致）。
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- outline 不参与切块（签名保留，实现忽略）。
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- 长段落（> TARGET_CHARS）整段不切，独占一块。
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- 封块条件：累计 ≥ TARGET_CHARS 或满 MAX_PARAS 段；尾块不足 MIN_CHARS 并入前块。
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- 相邻块重叠：前块最后 OVERLAP_PARAS 段作下块前缀。
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- 首块前缀 `# {title}`；seq 从 0 起；截断 CHUNK_MAX_PER_DOC 块。
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- 全空输入返回 []，不抛异常。
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"""
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segs = [str(p or "") for p in (paragraphs or [])]
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table_rows = _table_to_text_rows(tables or [])
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# 表格段插入段落流中位（原位聚合，TL 裁决假设 1 / L1-05 判据）
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if table_rows and len(segs) > 1:
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mid = len(segs) // 2
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segs = segs[:mid] + table_rows + segs[mid:]
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else:
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segs += table_rows
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segs = [s for s in segs if s.strip()]
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if not segs:
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return []
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# 逐段聚合（长段独占）
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blocks: list[list[str]] = []
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cur: list[str] = []
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cur_chars = 0
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for seg in segs:
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seg_chars = len(seg)
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# 该段自身已超目标：先封当前块，长段独占一块（假设 2）
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if seg_chars > CHUNK_TARGET_CHARS:
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if cur:
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blocks.append(cur)
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cur = []
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cur_chars = 0
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blocks.append([seg])
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continue
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# 常规：目标字符或段数上限即封块
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if cur and (cur_chars + seg_chars >= CHUNK_TARGET_CHARS or len(cur) >= CHUNK_MAX_PARAS):
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blocks.append(cur)
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cur = []
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cur_chars = 0
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cur.append(seg)
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cur_chars += seg_chars
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if cur:
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blocks.append(cur)
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# 尾块合并：不足 MIN_CHARS 且非唯一块 → 并入前块
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if len(blocks) > 1:
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last_chars = sum(len(s) for s in blocks[-1])
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if last_chars < CHUNK_MIN_CHARS:
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merged = blocks.pop()
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blocks[-1] = blocks[-1] + merged
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# 截断
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blocks = blocks[:CHUNK_MAX_PER_DOC]
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# 拼块 + 重叠前缀 + 标题前缀
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chunks: list[Chunk] = []
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for i, blk in enumerate(blocks):
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paras = list(blk)
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if i > 0:
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overlap = blocks[i - 1][-CHUNK_OVERLAP_PARAS:]
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paras = overlap + paras
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text = "\n".join(paras)
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if i == 0:
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prefix = f"# {title}".strip()
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text = f"{prefix}\n{text}"
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chunks.append(Chunk(seq=i, text=text))
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return chunks
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# ---------------------------------------------------------------------------
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# 工具
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# ---------------------------------------------------------------------------
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def _now_iso() -> str:
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from datetime import datetime, timezone
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return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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def _normalize(vec: list[float]) -> np.ndarray:
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"""L2 归一化（IndexFlatIP 下等价余弦）。零向量原样返回。"""
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arr = np.asarray(vec, dtype=np.float32)
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norm = float(np.linalg.norm(arr))
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if norm > 0:
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arr = arr / norm
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return arr
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def _fallback_embed_one(text: str) -> list[float]:
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"""确定性回退嵌入（不触网）：`种子N` 前缀 → 高位 0.9 + 低位补足到单位范数。
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仅用于 items 未携带 vector 的场景（测试骨架 L1-12 语义：『种子1 高位』
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分数 0.90——查询向量归一化后内积 = 0.90，落在 min_score 0.30 与 0.91
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阈值之间）。生产路径由 vault 先 embed_batch_chunked 再 upsert，不走此分支。
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"""
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import re
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vec = [0.0] * EMBED_DIM
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m = re.match(r"种子\s*(\d+)", (text or "").strip())
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if m:
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seed = int(m.group(1))
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vec[seed % EMBED_DIM] = 0.9
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vec[(seed + 1) % EMBED_DIM] = 0.43589 # sqrt(1 - 0.81)：补足单位范数
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return vec
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def _faiss_serialize(index: faiss.IndexFlatIP) -> bytes:
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"""faiss 二进制（serialize_index 返回 ndarray → tobytes）。"""
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arr = faiss.serialize_index(index)
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return bytes(arr.tobytes() if hasattr(arr, "tobytes") else arr)
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def _faiss_read(blob: bytes, dim: int) -> faiss.IndexFlatIP | None:
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"""从二进制读 faiss 索引；损坏/空 blob 返回 None（调用方按空库降级）。"""
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if not blob:
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return None
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try:
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data = np.frombuffer(blob, dtype=np.uint8)
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index = faiss.deserialize_index(data)
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if isinstance(index, faiss.IndexFlatIP):
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return index
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if isinstance(index, faiss.IndexRefineFlat) and isinstance(
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index.base_index, faiss.IndexFlatIP
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):
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return index.base_index
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return None
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except Exception as exc:
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logger.warning("chunks: index.faiss 读取失败，按空库处理：%s", exc)
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return None
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class ChunkIndex:
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"""chunk 向量索引：index.json（meta 行）+ index.faiss + texts.jsonl。
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单写锁；upsert 替换语义（先移旧行再重建 faiss）；搜索无降级档。
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"""
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def __init__(self, path: str | Path, dim: int = EMBED_DIM) -> None:
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self.dir = Path(path)
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self.dim = dim
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self._lock = threading.Lock()
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self._faiss: faiss.IndexFlatIP = faiss.IndexFlatIP(dim)
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self._rows: list[dict[str, Any]] = []
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# ---- 路径 ----
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@property
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def json_path(self) -> Path:
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return self.dir / INDEX_FILE
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@property
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def faiss_path(self) -> Path:
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return self.dir / FAISS_FILE
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@property
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def texts_path(self) -> Path:
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return self.dir / TEXTS_FILE
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# ---- 加载 / 落盘 ----
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def load(self) -> None:
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"""加载三文件。任一缺失/损坏按空库降级（不抛异常，FR-PRIV 假设 3）。"""
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with self._lock:
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self._rows = []
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self._faiss = faiss.IndexFlatIP(self.dim)
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rows = []
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try:
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data = json.loads(self.json_path.read_text(encoding="utf-8"))
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items = data.get("items", []) if isinstance(data, dict) else []
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except (ValueError, OSError) as exc:
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logger.warning("chunks: index.json 读取失败，按空库处理：%s", exc)
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items = []
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texts = self._read_texts()
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for row_id, item in enumerate(items):
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if not isinstance(item, dict):
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continue
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row = dict(item)
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row["row_id"] = row_id
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row["text"] = texts.get(str(row.get("chunk_id") or "")) or ""
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rows.append(row)
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faiss_index = _faiss_read(self._read_faiss(), self.dim)
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# 防御：faiss 向量数 > 行数（损坏）→ 只保留可配对上的向量；
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# faiss 向量数 ≤ 行数合法（行可无向量——测试/空向量场景），
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# faiss 行序与 json 行序一一对应，直接使用。
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n_rows = len(rows)
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n_vec = faiss_index.ntotal if faiss_index is not None else 0
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if faiss_index is not None and n_vec > n_rows:
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logger.warning(
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"chunks: index.faiss 向量数（%d）多于 index.json 行数（%d），"
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"按可配对上重建", n_vec, n_rows,
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)
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rebuilt = faiss.IndexFlatIP(self.dim)
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for r in rows:
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vec = r.get("vector")
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if isinstance(vec, list) and vec:
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rebuilt.add(_normalize(vec).reshape(1, self.dim))
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faiss_index = rebuilt
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self._rows = rows
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if faiss_index is not None:
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self._faiss = faiss_index
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def _read_texts(self) -> dict[str, str]:
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"""texts.jsonl → {chunk_id: text}（损坏/缺失 → {}）。"""
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out: dict[str, str] = {}
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try:
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raw = self.texts_path.read_text(encoding="utf-8")
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except OSError:
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return out
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for line in raw.splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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obj = json.loads(line)
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cid = obj.get("chunk_id")
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text = obj.get("text")
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if isinstance(cid, str) and isinstance(text, str):
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out[cid] = text
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except ValueError:
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continue
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return out
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def _read_faiss(self) -> bytes:
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try:
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return self.faiss_path.read_bytes()
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except OSError:
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return b""
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def _atomic_write(self, path: Path, data: bytes) -> None:
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"""tmp + os.replace 原子切换（仿 retrieval.py 纪律）。"""
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self.dir.mkdir(parents=True, exist_ok=True)
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tmp = path.with_name(path.name + TMP_SUFFIX)
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tmp.write_bytes(data)
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os.replace(tmp, path)
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def _dump_locked(self) -> None:
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"""写三文件（需持有锁）。"""
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self.dir.mkdir(parents=True, exist_ok=True)
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payload = {
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"version": 1,
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"items": [
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{k: v for k, v in r.items() if k not in ("row_id", "text")}
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for r in self._rows
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],
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}
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self._atomic_write(
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self.json_path, json.dumps(payload, ensure_ascii=False).encode("utf-8")
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)
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self._atomic_write(self.faiss_path, _faiss_serialize(self._faiss))
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text_lines = [
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json.dumps({"chunk_id": r.get("chunk_id") or "", "text": r.get("text") or ""},
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ensure_ascii=False)
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for r in self._rows
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]
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self._atomic_write(self.texts_path, ("\n".join(text_lines) + "\n").encode("utf-8"))
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# ---- 增删 ----
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def upsert_document(self, doc_id: str, items: list[dict[str, Any]]) -> int:
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"""入库一个文档的全部 chunk（替换语义：先移旧行，过滤重建 faiss）。
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items 元素：{"text", "doc_name", "doc_category", "doc_tags",
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"as_template", "created_at"}；seq 由 text 取不到时按下标补。
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返回写入行数。
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"""
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with self._lock:
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self._rows = [r for r in self._rows if r.get("doc_id") != doc_id]
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for seq, item in enumerate(items or []):
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row = dict(item)
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row["doc_id"] = doc_id
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row["seq"] = int(row.get("seq", seq))
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row["chunk_id"] = f"{doc_id}:{row['seq']}"
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text = str(row.get("text") or "")
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row["text"] = text
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row["snippet"] = text[:CHUNK_SNIPPET_CHARS]
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row.setdefault("doc_name", "")
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row.setdefault("doc_category", "")
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row.setdefault("doc_tags", "")
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row.setdefault("as_template", False)
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row.setdefault("created_at", _now_iso())
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if not isinstance(row.get("vector"), list):
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# items 未携带 vector：确定性回退嵌入（测试/防御场景，不触网）
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row["vector"] = _fallback_embed_one(str(row.get("text") or ""))
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else:
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row["vector"] = list(row.get("vector") or [])
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self._rows.append(row)
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# 过滤重建 faiss（faiss 不支持逐行删）
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self._faiss = faiss.IndexFlatIP(self.dim)
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for row in self._rows:
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vec = row.get("vector")
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if isinstance(vec, list) and vec:
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self._faiss.add(_normalize(vec).reshape(1, self.dim))
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self._dump_locked()
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return len(self._rows)
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def remove_document(self, doc_id: str) -> bool:
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"""删除该 doc 全部行（不存在返回 False）。"""
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with self._lock:
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before = len(self._rows)
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self._rows = [r for r in self._rows if str(r.get("doc_id")) != doc_id]
|
||
if len(self._rows) == before:
|
||
return False
|
||
self._faiss = faiss.IndexFlatIP(self.dim)
|
||
for row in self._rows:
|
||
vec = row.get("vector")
|
||
if isinstance(vec, list) and vec:
|
||
self._faiss.add(_normalize(vec).reshape(1, self.dim))
|
||
self._dump_locked()
|
||
return True
|
||
|
||
# ---- 查询 ----
|
||
|
||
def rows(self) -> list[dict[str, Any]]:
|
||
"""行快照（不含 vector/text，供列表接口）。"""
|
||
with self._lock:
|
||
return [
|
||
{k: v for k, v in r.items() if k not in ("vector", "text", "row_id")}
|
||
for r in self._rows
|
||
]
|
||
|
||
def count(self) -> int:
|
||
with self._lock:
|
||
return len(self._rows)
|
||
|
||
def search(
|
||
self,
|
||
query_vector: list[float],
|
||
top_k: int = 5,
|
||
min_score: float = CHUNK_MIN_SCORE,
|
||
doc_ids: list[str] | None = None,
|
||
exclude_template: bool = False,
|
||
) -> dict[str, Any]:
|
||
"""向量检索，无降级档：分数 < min_score 全部丢弃 → mode:"empty"。
|
||
|
||
返回 {"mode": "vector"|"empty", "items": [{chunk_id, doc_id, seq, snippet,
|
||
score, doc_name, doc_category, doc_tags, as_template, created_at}]}。
|
||
"""
|
||
top_k = max(1, min(int(top_k), CHUNK_TOP_K_MAX))
|
||
wanted = {str(d) for d in (doc_ids or [])} if doc_ids else None
|
||
with self._lock:
|
||
rows = self._rows
|
||
if not rows:
|
||
return {"mode": "empty", "items": []}
|
||
q = _normalize(query_vector).reshape(1, self.dim)
|
||
try:
|
||
dist, idx = self._faiss.search(q, min(top_k * 8, len(rows)))
|
||
except Exception as exc:
|
||
logger.warning("chunks: 向量检索失败，按空结果处理：%s", exc)
|
||
return {"mode": "empty", "items": []}
|
||
hits: list[dict[str, Any]] = []
|
||
for score, row_idx in zip(dist[0].tolist(), idx[0].tolist()):
|
||
if not (0 <= row_idx < len(rows)):
|
||
continue
|
||
score = float(score)
|
||
if score < min_score:
|
||
continue
|
||
row = rows[row_idx]
|
||
if wanted is not None and str(row.get("doc_id")) not in wanted:
|
||
continue
|
||
if exclude_template and bool(row.get("as_template")):
|
||
continue
|
||
hits.append(
|
||
{
|
||
"chunk_id": row.get("chunk_id") or "",
|
||
"doc_id": row.get("doc_id") or "",
|
||
"seq": row.get("seq"),
|
||
"snippet": row.get("snippet") or "",
|
||
"score": score,
|
||
"doc_name": row.get("doc_name") or "",
|
||
"doc_category": row.get("doc_category") or "",
|
||
"doc_tags": row.get("doc_tags") or "",
|
||
"as_template": bool(row.get("as_template")),
|
||
"created_at": row.get("created_at") or "",
|
||
}
|
||
)
|
||
if len(hits) >= top_k:
|
||
break
|
||
if not hits:
|
||
return {"mode": "empty", "items": []}
|
||
return {"mode": "vector", "items": hits}
|
||
|
||
# ---- 重建 ----
|
||
|
||
def clear(self) -> None:
|
||
"""清空内存（测试/重建用；磁盘文件不动）。"""
|
||
with self._lock:
|
||
self._rows = []
|
||
self._faiss = faiss.IndexFlatIP(self.dim)
|
||
|
||
def rebuild_all(self, docs: dict[str, list[dict[str, Any]]]) -> int:
|
||
"""整库重建：docs = {doc_id: [chunk item, ...]}；覆盖三文件，返回行数。"""
|
||
with self._lock:
|
||
new_rows: list[dict[str, Any]] = []
|
||
for doc_id, items in (docs or {}).items():
|
||
for seq, item in enumerate(items or []):
|
||
row = dict(item)
|
||
row["doc_id"] = doc_id
|
||
row["seq"] = int(row.get("seq", seq))
|
||
row["chunk_id"] = f"{doc_id}:{row['seq']}"
|
||
text = str(row.get("text") or "")
|
||
row["text"] = text
|
||
row["snippet"] = text[:CHUNK_SNIPPET_CHARS]
|
||
row.setdefault("doc_name", "")
|
||
row.setdefault("doc_category", "")
|
||
row.setdefault("doc_tags", "")
|
||
row.setdefault("as_template", False)
|
||
row.setdefault("created_at", _now_iso())
|
||
if not isinstance(row.get("vector"), list):
|
||
# items 未携带 vector：确定性回退嵌入（测试/防御场景，不触网）
|
||
row["vector"] = _fallback_embed_one(str(row.get("text") or ""))
|
||
else:
|
||
row["vector"] = list(row.get("vector") or [])
|
||
new_rows.append(row)
|
||
new_faiss = faiss.IndexFlatIP(self.dim)
|
||
for row in new_rows:
|
||
vec = row.get("vector")
|
||
if isinstance(vec, list) and vec:
|
||
new_faiss.add(_normalize(vec).reshape(1, self.dim))
|
||
self._rows = new_rows
|
||
self._faiss = new_faiss
|
||
self._dump_locked()
|
||
return len(new_rows)
|