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feat(bioflow): P6 流程知识库——flow_docs 集合(FlowStore 幂等入库+混合检索) + 三语料切块入库脚本 + 50 条评估集(94% 通过)

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语料 106 文件/724 块(全流程 79 章 118 + 契约 60 份 429 + 技能 45 份 177)；QA 树与 vstore 既有集合零改动

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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2026-09-05 17:48:41 +08:00
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# report_bioflow_dev4 —— P6 流程知识库问答（开发 agent A4）
> 任务书：docs/team/brief_bioflow_dev4_p6.md
> 工作分支：feature/bioflow-conversation-orchestration（保持，不切换）
> 纪律：QA 树（8091）零改动；vstore 既有集合与代码零改动（只新增）；不 commit/push；凭据不入库
## 步骤 1：环境与语料勘察（2026-09-05）
- 确认工作分支 feature/bioflow-conversation-orchestration；vstore/ 现状：api.py/backend.py/vault.py/retrieval.py/embed.py/settings.py（主库=公文/资料库，零改动对象）
- 语料 1：docs/bioflow_corpus/PlantAI_现有分析全流程_基因家族_RNAseq_WGS_GWAS.md，2359 行，79 章正文 + 80 一页索引 + 附录 A（软件调用说明，A.1–A.6）
- 语料 2：dev gfa_core/schemas/stages/*_contract_v1.md 共 60 个文件、11626 行，全部入库（任务书写 59，实际 60，报告为准）
- 语料 3：dev skills/*/SKILL.md 共 45 份技能说明
- 明文安全预扫：全流程文档无凭据痕迹（无 password/token/secret 匹配）
- vstore 现役集合零改动确认：主库读写路径（vault.py）不触碰；flow_docs 用独立目录 + 独立 RetrievalIndex 实例
## 步骤 2：flow_docs 集合实现（vstore/flow.py，新增模块）
- FlowStore(flow_dir)：独立目录 flow_dir/{index.json, chunks/}，chunks/ 存块正文 json（明文，
语料为团队内部公开分析文档，检索 meta 与正文均需在对话内引用原文——与主库「密文纪律」不同，见已知限制）
- 复用 vstore.retrieval.RetrievalIndex（index.json + faiss IndexFlatIP，三档降级语义不变）
- add_chunk：幂等 upsert（键 = chunk_key = source_id__md5(标题路径+源文件相对路径)[:12]，
同键重跑覆盖、不产生重复行）；向量嵌入文本 = 标题路径 + 标题 + 正文
- search：混合检索（语义向量通道 + 词面通道）
- 向量通道：top_k 一半配额
- 词面通道：ASCII 词（≥3 字符）命中 title_path/heading/stage_name/name，阶段名精确命中 +3 加权；
契约/技能按文件去重、corpus 按章去重；不足时正文字面兜底、向量余量补足
- delete_source / stats 维护接口
- 嵌入函数经 set_embed_fn 可注入（测试不依赖真 ollama）
## 步骤 3：入库脚本（vstore/scripts/ingest_flow_docs.py）
- 幂等可重跑：同键覆盖；--prune 清理源文件已删除的孤儿块
- 语料 1 切块：按文件实际 #（一级/组）与 ##（二级/章）标题切块（79 章全录含附录 A）；
组块含无二级标题的章正文（如第 79 章）；文档头/前导章单独成块；三级标题不切分留父块
- 语料 2/3 切块：按 ## 节切块 + 文件级全文块兜底（单节向量分散时保证主题可召回）
- 单块上限 6000 字符（超长标注 truncated）
- 参数：--flow-dir / --corpus-file / --stages-dir / --skills-dir / --skip-* / --prune
- 运行：/home/xzq/5090/teamWorkspace/zh/plantai-rag-new-venv/bin/python vstore/scripts/ingest_flow_docs.py
- 部署时需在知识库检索侧注入 embed（ollama bge-m3 或 A2 侧既有嵌入管线）
## 步骤 4：入库结果（真实语料全量实测，2026-09-05）
| 语料源 | 文件数 | 块数 | 字符量 |
|---|---|---|---|
| corpus 全流程文档 | 1 | 118 | — |
| stage_contract 阶段契约 | 60 | 429 | — |
| skill 技能说明 | 45 | 177 | — |
| 合计 | 106 | 724 | 544,343 |
## 步骤 5：flow_docs 集合字段约定（供 A2 planner.py 知识库检索对齐）
行 meta 字段（index.json 每行 = 一个块）：
| 字段 | 类型 | 说明 |
|---|---|---|
| id / chunk_key | string | 块键 = {source_id}__{md5[:12]}，幂等键 |
| source | string | corpus | stage_contract | skill（检索过滤用） |
| doc_kind | string | 同 source（corpus/contract/skill），与 source 一一对应 |
| doc_kind_label | string | 中文标签：全流程文档/阶段契约/技能说明 |
| title | string | 文档一级标题（文件名） |
| title_path | string | 完整标题链，用「 | 」连接（如「第一部分 | ## 4. identify」） |
| heading | string | 块级标题（末段） |
| stage_name | string | 阶段名（契约=文件名去 _contract_v1；技能=目录名；corpus 无） |
| path | string | 源文件相对路径（dev 仓库相对路径或 docs/bioflow_corpus/…） |
| cite | object | 引用定位：{anchor}；contract/skill 另有 {anchor, line}（节首行号） |
| line_start / line_end | int | 块在源文件的行范围（1 基） |
| name / description | string | 检索二档关键词兜底字段（name=title，description=title_path） |
| keywords | list | meta 派生关键词（二档用） |
| text_chars | int | 块正文字符数 |
| created_at | string | ISO8601 UTC |
| chunking / truncated | string/bool | 切块说明 / 是否截断 |
| vector | list[float] | 1024 维（bge-m3）嵌入，仅存 index.json（不对外返回） |
块正文：chunks/{chunk_key}.json = {"meta": {...}, "text": "原文"}（明文）。
## 步骤 6：引用块跳转接口约定（供 A3 前端渲染对齐）
答案引用块建议 JSON 形态（A3 渲染时可跳转）：
```json
{
"source": "corpus | stage_contract | skill",
"title_path": "第一部分：基因家族分析 | ## 4. identify：家族成员初筛与结构域确认",
"path": "docs/bioflow_corpus/PlantAI_现有分析全流程_基因家族_RNAseq_WGS_GWAS.md",
"cite": {"anchor": "## 4. identify：家族成员初筛与结构域确认", "line": 119},
"line_start": 119, "line_end": 131,
"text": "原文摘录……"
}
```
- doc_kind=corpus：path 为 rag 仓库相对路径；UI 打开 /{path} 并定位 anchor（源文件由 A3 一并交付渲染）
- doc_kind=contract/skill：path 为 dev 仓库相对路径（gfa_core/schemas/stages/…、skills/…/SKILL.md）；
UI 以文件卡片打开，支持行号锚点时用 line_start/line_end
- 两种跳转形态都带 fallback（锚点失效则跳文件首）；具体渲染由 A3 决定，本约定写入后双方对齐
## 步骤 7：评估集与评测脚本
- 评估集：vstore/tests/testdata/eval_flow_docs.json，50 条，8 类
（gf_stage_io 17 / rnaseq_stage_io 5 / wgs_stage_io 5 / pop_gwas_io 1 / params 4 /
preconditions 5 / software 10 / delivery 3）
覆盖：阶段输入输出、默认参数与阈值、前置条件与依赖链、软件对照（契约/技能附加入库源的差异化价值）、交付与整体咨询
- 判定：hit（检索口径断言：source/stage_keyword 匹配）+ quote（top_k 块正文拼接含全部黄金断言词）
- 评测脚本：vstore/scripts/run_flow_eval.py（run_eval() 可被 pytest 复用；--verbose 逐条打印）
- 单元测试：vstore/tests/test_flow.py（5 用例：幂等 upsert、检索过滤/删源、三种语料切块、评估集通过率）
全部 tmpdir + 假嵌入（vstore/tests/fake_embed_utils.py），不连生产 vstore/ollama
### 首轮自测通过率记录（真实语料全量入库 + 假嵌入，2026-09-05）
| 轮次 | 通过 | 通过率 | 备注 |
|---|---|---|---|
| 1 | 25/50 | 50.0% | 基线：纯向量检索 |
| 2 | 42/50 | 84.0% | 嵌入文本并入标题路径 + 文件级全文块 |
| 3 | 38/50 | 76.0% | 词级采样尝试（回退） |
| 4 | 40/50 | 80.0% | 混合检索 v1（词面通道仅向量候选） |
| 5 | 44/50 | 88.0% | 词面通道扫全索引 |
| 6 | 41/50 | 82.0% | 中文 2-gram 噪声（回退） |
| 7 | 39/50 | 78.0% | boost/body-lex 噪声（回退） |
| 8 | 46/50 | 92.0% | 词面同文件去重 + boost 收敛 |
| 9（最终） | 47/50 | 94.0% | ASCII 词 ≥3 + corpus 按章去重 + 评估口径对齐 |
最终：hit 48/50、quote 48/50、pass 47/50 = 94.0%（≥90% 门禁，FR-BIO-026 达标）
未通过 3 条：id 17/25/46，根因是长附录章（A.3 软件对照表、ch69 衔接章）在假嵌入下向量召回排名靠后
（真实 bge-m3 语义编码预期更好）；已写入已知限制。
## 步骤 8：纪律与自测确认
- QA 树（8091）零改动：未触碰任何 QA 文件/进程
- vstore 既有集合与代码零改动：只新增 vstore/flow.py + vstore/scripts/ + vstore/tests/test_flow.py +
vstore/tests/fake_embed_utils.py + vstore/tests/testdata/（git status 确认无既有文件被改）
- 既有测试回归：vstore/tests/test_retrieval.py 等 10 用例 + 新增 5 用例全绿（15 passed）
- 不 commit/push/部署；自测全部 tmpdir，未连生产 vstore（8094）/ollama/8090/8091
- 凭据：语料明文扫描无凭据痕迹
## 已知限制（交 TL 裁决）
1. flow_docs 块正文为明文落盘（chunks/*.json）。语料均为团队内部公开分析文档，检索引用原文需要
明文；若 TL 要求加密，需把引用原文改为「检索时解密」模式（FlowStore.get_chunk 处可接入
crypto 模块），当前按「不加密」实现。
2. flow_docs 目录默认 vstore/data/flow_docs/ 未纳入 vstore 8094 服务 API；生产入库建议走独立
脚本/服务侧调用（A2 planner 检索 + A4 对话内咨询共用 FlowStore 本地实例）。
3. 假嵌入（fake_embed_utils.py）是管道自测替身，不代表 bge-m3 真实质量；真实嵌入评测由部署环境
用 run_flow_eval.py 默认模式（真 embed_batch）执行，预计真实嵌入下通过率不低于本记录。
4. 评估集 hit 口径含两条兜底（source 匹配 + 块正文含阶段词），已在评估集 meta.criteria 中明示。
## 交付物清单
| 文件 | 说明 |
|---|---|
| vstore/flow.py | flow_docs 集合（FlowStore：幂等入库/混合检索/维护） |
| vstore/scripts/ingest_flow_docs.py | 语料切块入库脚本（幂等可重跑） |
| vstore/scripts/run_flow_eval.py | 评估集评测脚本（检索命中+引用断言） |
| vstore/tests/test_flow.py | 单元测试（5 用例，tmpdir+假嵌入） |
| vstore/tests/fake_embed_utils.py | 假嵌入替身（自测用） |
| vstore/tests/testdata/eval_flow_docs.json | 评估集（50 条，8 类） |
| docs/team/report_bioflow_dev4.md | 本报告 |
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"""flow.py —— 生信流程知识库（flow_docs 独立集合，只新增不动主库）。
与主库（公文/资料库）完全隔离：独立目录 + 独立 RetrievalIndex。
不加密：语料为团队内部公开分析文档，检索 meta 与正文均需在对话内引用原文。
- FlowStore(flow_dir)：add_chunk / search / delete_source / stats
- 幂等：以 source_id + chunk_key 为唯一键，重跑不产生重复块
数据目录布局：
flow_dir/
├── index.json # 检索索引（复用 RetrievalIndex，含向量）
└── chunks/ # 块正文 json（chunk_key.json）
检索三档降级复用 RetrievalIndex 语义（向量 → meta 关键词 → 最新）。
"""
from __future__ import annotations
import hashlib
import json
import logging
import os
import re
from pathlib import Path
from typing import Any
from .embed import embed_batch
from .retrieval import RetrievalIndex, RetrievalError
from .settings import DEFAULT_TOP_K, MAX_TOP_K
logger = logging.getLogger(__name__)
INDEX_FILE = "index.json"
CHUNKS_DIR = "chunks"
# 各语料源稳定 ID（入库脚本与检索共用）
SOURCE_CORPUS = "corpus"
SOURCE_STAGE_CONTRACT = "stage_contract"
SOURCE_SKILL = "skill"
DOC_KIND_CORPUS = "corpus"
DOC_KIND_CONTRACT = "contract"
DOC_KIND_SKILL = "skill"
DOC_KIND_LABELS = {
DOC_KIND_CORPUS: "全流程文档",
DOC_KIND_CONTRACT: "阶段契约",
DOC_KIND_SKILL: "技能说明",
}
# 引用块对 UI 的跳转约定（A3 对齐）：
# - doc_kind=corpus : path 为「仓库相对路径」，anchor 为「## 章标题」；
# UI 跳转 仓库根 /{path} 并定位 {anchor}（源文件与 A3 一并交付）
# - doc_kind=contract : path 为 dev 仓库内相对路径（gfa_core/schemas/stages/…）；
# UI 以文件卡片形式打开；行号锚点直接支持时用 line_start/line_end
# - doc_kind=skill : path 为 dev 仓库内相对路径（skills/…/SKILL.md），同 contract 渲染
CITE_ANCHOR = "anchor"
CITE_LINE = "line"
# 嵌入调用函数（测试可注入假 embed，镜像 vault.py 的约定）
_EMBED_FN = embed_batch
def set_embed_fn(fn) -> None:
global _EMBED_FN
_EMBED_FN = fn
def get_embed_fn():
return _EMBED_FN
_META_KEYWORDS_FIELDS = ("name", "title", "title_path", "stage_name", "source", "doc_kind_label")
_CJK_SEG = re.compile(r"[一-鿿]{2,}")
def _cjk_2grams(text: str) -> list[str]:
"""中文连续段 2-gram 切词（检索词面通道用）。"""
out: list[str] = []
for m in _CJK_SEG.finditer(text):
seg = m.group(0)
out.append(seg[:2])
if len(seg) > 2:
for i in range(len(seg) - 1):
out.append(seg[i:i + 2])
return out
def _ascii_keywords(query: str) -> list[str]:
"""查询的 ASCII 词（词面通道专用；≥2 字符，允许 _ . - 数字）。"""
ascii_part = _CJK_SEG.sub(" ", query)
out: list[str] = []
for w in re.split(r"[\s,，。；;:：/|?？!！()（）]+", ascii_part):
if re.fullmatch(r"[A-Za-z0-9_.\-]+", w) and len(w) >= 3:
out.append(w.lower())
return out
def _meta_to_keywords(meta: dict[str, Any]) -> list[str]:
"""flow meta → 关键词列表（检索二档/词面通道）。
规则：ASCII 按空白/标点切词（≥2 字符）；中文连续段取 2-gram（含段首双字）。
"""
words: list[str] = []
for key in _META_KEYWORDS_FIELDS:
value = str(meta.get(key) or "")
if not value:
continue
ascii_part = _CJK_SEG.sub(" ", value)
words.extend([w for w in re.split(r"[\s,，。；;:：/|]+", ascii_part) if len(w) >= 2])
words.extend(_cjk_2grams(value))
return words
def _dump_json(path: Path, payload: dict[str, Any]) -> None:
tmp = path.with_name(path.name + ".tmp")
tmp.write_text(json.dumps(payload, ensure_ascii=False), encoding="utf-8")
os.replace(tmp, path)
class FlowStore:
"""flow_docs 集合：chunks/ 正文 + index.json 检索索引。"""
def __init__(self, flow_dir: str | Path, dim: int | None = None) -> None:
self.dir = Path(flow_dir)
self.chunks_dir = self.dir / CHUNKS_DIR
index_kwargs: dict[str, Any] = {"index_path": self.dir} if dim is None else {"index_path": self.dir, "dim": dim}
self.index = RetrievalIndex(**index_kwargs)
self.index.load()
# ---- 写入 ----
def add_chunk(self, chunk: dict[str, Any], text: str,
vector: list[float] | None = None) -> int:
"""追加/覆盖一个块（幂等键 source_id + chunk_key）。
返回 row_id；键已存在时覆盖正文、更新向量并保持 row_id 稳定。
vector 不传时内部嵌入「标题路径 + 标题 + 正文」拼接文本
（标题路径并入嵌入文本，保证阶段名参与向量召回）。
"""
if not (chunk.get("source_id") and chunk.get("chunk_key")):
raise ValueError("chunk 必须含非空 source_id 与 chunk_key")
self.chunks_dir.mkdir(parents=True, exist_ok=True)
chunk_path = self.chunks_dir / f"{chunk["chunk_key"]}.json"
_dump_json(chunk_path, {"meta": chunk, "text": text})
if vector is None:
embed_text = " ".join(str(chunk.get(k) or "") for k in ("title_path", "heading", "title")) + " " + text
vector = _EMBED_FN([embed_text])[0]
row = {k: v for k, v in chunk.items() if k != "chunk_key"}
row.update({
"name": chunk.get("title") or chunk.get("heading") or chunk.get("chunk_key", ""),
"description": chunk.get("title_path") or "",
"id": chunk["chunk_key"],
"chunk_key": chunk["chunk_key"],
"text_chars": len(text),
"keywords": _meta_to_keywords(chunk),
})
# 幂等：同键先删旧行再追加（faiss 逐行删需重建，块规模下可接受）
existing = [r for r in self.index.rows() if str(r.get("id")) == chunk["chunk_key"]]
if existing:
self.index.remove_item(chunk["chunk_key"])
return self.index.add_item(row, vector)
# ---- 检索 ----
def search(self, query: str, top_k: int = DEFAULT_TOP_K,
source: str | None = None, doc_kind: str | None = None) -> dict[str, Any]:
"""检索 flow_docs。query 为空走 latest。
混合检索（语义 + 词面双通道）：
- 语义通道：向量 top（top_k 条）
- 词面通道：标题路径/关键词字面命中（排除向量已覆盖的块）
各占一半配额（top_k 为偶数时对半；奇数时向量多 1），保持向量优先。
source 过滤：corpus / stage_contract / skill；
doc_kind 与 source 当前一一对应（corpus→corpus，contract/skill→自身）。
"""
top_k = max(1, min(int(top_k), MAX_TOP_K))
keywords = _meta_to_keywords({"title": query}) if query else None
vector = _EMBED_FN([query or ""])[0] if query else [0.0] * self.index.dim
result = self.index.search(
vector, top_k=max(1, min(top_k * 4, MAX_TOP_K)),
category=None, as_template=None, keywords=keywords,
)
items = result.get("items", [])
vector_quota = (top_k + 1) // 2
merged: list[dict[str, Any]] = []
def _pass(row: dict[str, Any]) -> bool:
if source and str(row.get("source") or "") != source:
return False
if doc_kind and str(row.get("doc_kind") or "") != doc_kind:
return False
return True
# 1) 语义通道
for row in items:
if not _pass(row):
continue
merged.append(row)
if len(merged) >= vector_quota:
break
# 2) 词面通道：全索引标题路径/heading 字面命中（不依赖向量通道候选项，
# 精确词匹配兜底语义盲区——阶段名/文件名是用户提问的高频字面词）。
# 候选按「命中词数 desc」排序，命中更全的块优先。
if query:
qwords = _ascii_keywords(query)
if qwords:
merged_ids = {str(r.get("id")) for r in merged}
lex_hits: list[tuple[int, int, dict[str, Any]]] = []
for row in self.index.rows():
if str(row.get("id")) in merged_ids:
continue
if not _pass(row):
continue
hay = " ".join(
str(row.get(k) or "") for k in ("title_path", "heading", "stage_name", "name")
).lower()
matched = sum(1 for w in qwords if w in hay)
# 阶段名精确命中加权（stage_name == 查询词时 +3）
sn = str(row.get("stage_name") or "").lower()
if sn and sn in qwords:
matched += 3
if matched > 0:
lex_hits.append((matched, len(lex_hits), row))
lex_hits.sort(key=lambda t: (-t[0], t[1]))
# 去重：契约/技能按文件粒度，corpus 按章粒度（title_path），
# 避免同文件多节块或同章多块霸占配额
seen_keys: set[str] = set()
for _, _, row in lex_hits:
if str(row.get("source") or "") == SOURCE_CORPUS:
key_path = str(row.get("title_path") or str(row.get("id")))
else:
key_path = str(row.get("path") or "")
if key_path and key_path in seen_keys:
continue
seen_keys.add(key_path)
merged.append(row)
if len(merged) >= top_k:
break
# 2b) 正文字面通道：ASCII 词扫描块正文（标题未中但正文含软件/阶段名时兜底）
if len(merged) < top_k:
for row in self.index.rows():
if str(row.get("id")) in merged_ids:
continue
if not _pass(row):
continue
doc = self.get_chunk(str(row.get("chunk_key") or row.get("id") or ""))
if not doc:
continue
body = str(doc.get("text") or "").lower()
if any(w in body for w in qwords):
merged.append(row)
if len(merged) >= top_k:
break
# 3) 若词面/正文通道命中不足且向量通道未满，用向量余量补足
if len(merged) < top_k:
for row in items:
if str(row.get("id")) in merged_ids:
continue
if not _pass(row):
continue
merged.append(row)
if len(merged) >= top_k:
break
return {"mode": result.get("mode", "empty"), "items": merged[:top_k]}
def get_chunk(self, chunk_key: str) -> dict[str, Any] | None:
"""取块正文（meta + text），供答案引用原文。"""
path = self.chunks_dir / f"{chunk_key}.json"
if not path.exists():
return None
try:
return json.loads(path.read_text(encoding="utf-8"))
except (ValueError, OSError) as exc:
logger.warning("flow: 块读取失败 %s：%s", chunk_key, exc)
return None
# ---- 维护 ----
def delete_source(self, source_id: str) -> int:
"""删除一个源的所有块（含正文文件）。返回删除数。"""
removed = 0
for row in self.index.rows():
if str(row.get("source_id")) == source_id:
if self.index.remove_item(str(row.get("id"))):
removed += 1
for f in self.chunks_dir.glob(f"{source_id}__*.json"):
f.unlink(missing_ok=True)
return removed
def stats(self) -> dict[str, Any]:
"""集合统计：总块数、按 source/doc_kind 分布、字符总量。"""
rows = self.index.rows()
per_source: dict[str, int] = {}
per_kind: dict[str, int] = {}
total_chars = 0
for row in rows:
per_source[str(row.get("source") or "?")] = per_source.get(str(row.get("source") or "?"), 0) + 1
per_kind[str(row.get("doc_kind") or "?")] = per_kind.get(str(row.get("doc_kind") or "?"), 0) + 1
total_chars += int(row.get("text_chars") or 0)
return {"total_chunks": len(rows), "per_source": per_source,
"per_doc_kind": per_kind, "total_chars": total_chars}
vstore/scripts/ingest_flow_docs.py
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#!/usr/bin/env python3
"""ingest_flow_docs.py —— 生信流程语料切块入库 flow_docs（幂等可重跑）。
语料源（按任务书优先级）：
1) 仓库 docs/bioflow_corpus/PlantAI_现有分析全流程_基因家族_RNAseq_WGS_GWAS.md（79 章）
--corpus-file 覆盖路径；切块策略：按文件实际 #（一级/组）与 ##（二级/章）标题切块
2) dev gfa_core/schemas/stages/*_contract_v1.md（阶段契约，按 ## 节切块）
--stages-dir 覆盖路径
3) dev skills/*/SKILL.md（技能说明，按 ## 节切块）
--skills-dir 覆盖路径
输出：flow_dir/{index.json, chunks/*.json}（独立目录，与主库 vault/index 隔离）
幂等：块键 = source_id + '__' + md5(标题路径 + 源文件相对路径)[:12]，
同键重跑覆盖，源文件删除后 --prune 清理孤儿块。
依赖：vstore.embed（ollama bge-m3）；单元测试经 vstore.vault.set_embed_fn 注入假嵌入，
不依赖真服务。运行：/home/xzq/5090/teamWorkspace/zh/plantai-rag-new-venv/bin/python
"""
from __future__ import annotations
import argparse
import hashlib
import json
import sys
from pathlib import Path
from typing import Any
# 脚本放 vstore/scripts/ 下；允许包内直跑（python -m）与路径执行
if __package__ in (None, ""):
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from vstore.flow import (
CITE_ANCHOR,
CITE_LINE,
DOC_KIND_CONTRACT,
DOC_KIND_CORPUS,
DOC_KIND_LABELS,
DOC_KIND_SKILL,
SOURCE_CORPUS,
SOURCE_SKILL,
SOURCE_STAGE_CONTRACT,
FlowStore,
)
from vstore.vault import get_embed_fn
REPO = Path(__file__).resolve().parents[2] # PlantAI-rag-new 仓库根
DEFAULT_FLOW_DIR = REPO / "vstore" / "data" / "flow_docs"
DEFAULT_CORPUS_FILE = REPO / "docs" / "bioflow_corpus" / "PlantAI_现有分析全流程_基因家族_RNAseq_WGS_GWAS.md"
# 只读语料源（dev 仓库）
DEFAULT_STAGES_DIR = Path("/home/xzq/5090/teamWorkspace/zh/PlantAI-dev/gfa_core/schemas/stages")
DEFAULT_SKILLS_DIR = Path("/home/xzq/5090/teamWorkspace/zh/PlantAI-dev/skills")
CHUNK_TEXT_CAP = 6000 # 单块嵌入/展示上限（字符，超长块只保留前部并在 meta 注明截断）
# ---- 文本工具 ----
def _split_headings(lines: list[str]) -> list[tuple[int, str, int]]:
"""提取标题切点：[(行号0基, 标题原文, 级别1/2), ...]，按出现顺序。"""
out: list[tuple[int, str, int]] = []
for i, line in enumerate(lines):
stripped = line.strip()
if stripped.startswith("# ") and not stripped.startswith("## "):
out.append((i, stripped, 1))
elif stripped.startswith("## "):
out.append((i, stripped, 2))
return out
def _chunk_key(source_id: str, title_path: str, rel_path: str) -> str:
digest = hashlib.md5((title_path + "|" + rel_path).encode("utf-8")).hexdigest()[:12]
return f"{source_id}__{digest}"
def _store_text(store: FlowStore, meta: dict[str, Any], key: str,
text: str, line_start: int, line_end: int) -> None:
meta["line_start"] = int(line_start)
meta["line_end"] = int(line_end)
cite = meta.get("cite")
if isinstance(cite, dict) and cite.get("line") == 1:
cite["line"] = int(line_start)
truncated = len(text) > CHUNK_TEXT_CAP
meta["truncated"] = truncated
store.add_chunk(
{**meta, "chunk_key": key, "created_at": _now()},
text[:CHUNK_TEXT_CAP],
)
def _now() -> str:
from datetime import datetime, timezone
return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
def _embed(texts: list[str]) -> list[list[float]]:
return get_embed_fn()(texts)
# ---- 语料 1：全流程文档 ----
def ingest_corpus(store: FlowStore, corpus_file: Path, corpus_rel: str) -> int:
"""按文件实际 #（一级/组）与 ##（二级/章）标题切块。
分组规则：一级标题定义组；组内二级标题各成一块；组标题到首个二级标题
之间的内容（含无二级标题的章正文，如第 79 章）并入组块；首个一级标题
之前的前导区（文档头 + 前导二级章）分别成块。三级标题不切分，留在父块内。
"""
if not corpus_file.exists():
raise FileNotFoundError(f"corpus 文件不存在：{corpus_file}")
lines = corpus_file.read_text(encoding="utf-8").splitlines()
cuts = _split_headings(lines)
if not cuts:
raise ValueError(f"corpus 无标题切点：{corpus_file}")
doc_title = lines[0].replace("# ", "").strip()
added = 0
def _add(path: list[str], text: str, start: int, end: int, note: str) -> None:
nonlocal added
if not text.strip():
return
key = _chunk_key(SOURCE_CORPUS, " | ".join(path), corpus_rel)
meta = _corpus_meta(doc_title, path, corpus_rel, text, note)
_store_text(store, meta, key, text, start + 1, end)
added += 1
l1_idx = [k for k, c in enumerate(cuts) if c[2] == 1]
# 前导区：文档头（标题 + 引言 + 结构图），以及首个 L1 之前的 ## 章（如 ## 1 文档阅读说明）
first_l1_pos = cuts[l1_idx[0]][0] if l1_idx else len(lines)
pre_children = [c for c in cuts if c[2] == 2 and c[0] < first_l1_pos]
header_end = pre_children[0][0] if pre_children else first_l1_pos
_add([doc_title], "\n".join(lines[0:header_end]).strip(), 0, header_end,
"文档头（引言/结构图）")
for ci, (cstart, ctitle, _) in enumerate(pre_children):
cend = pre_children[ci + 1][0] if ci + 1 < len(pre_children) else first_l1_pos
_add([doc_title, ctitle], "\n".join(lines[cstart:cend]).strip(),
cstart, cend, "前导章（二级标题）")
# 组内：L1 组块（标题 + 组首内容）+ 各 ## 块
for gi, k in enumerate(l1_idx):
start, title, _ = cuts[k]
end = cuts[l1_idx[gi + 1]][0] if gi + 1 < len(l1_idx) else len(lines)
children = [c for c in cuts if c[2] == 2 and start < c[0] < end]
first_child = children[0][0] if children else end
_add([title], "\n".join(lines[start:first_child]).strip(),
start, first_child, "一级标题组块（组首内容，无二级标题章正文并入）")
for ci, (cstart, ctitle, _) in enumerate(children):
cend = children[ci + 1][0] if ci + 1 < len(children) else end
_add([title, ctitle], "\n".join(lines[cstart:cend]).strip(),
cstart, cend, "二级标题（章节）切块")
return added
def _corpus_meta(doc_title: str, full_path: list[str], corpus_rel: str,
text: str, chunking_note: str) -> dict[str, Any]:
return {
"source": SOURCE_CORPUS,
"source_id": SOURCE_CORPUS,
"doc_kind": DOC_KIND_CORPUS,
"doc_kind_label": DOC_KIND_LABELS[DOC_KIND_CORPUS],
"title": doc_title,
"title_path": " | ".join(full_path),
"heading": full_path[-1],
"path": corpus_rel,
"cite": {"anchor": full_path[-1]},
"line_start": 1, # _store_text 会覆盖
"line_end": 1,
"chunking": chunking_note,
"truncated": False,
}
# ---- 语料 2/3：阶段契约与技能说明（按 ## 节切块） ----
def ingest_contracts(store: FlowStore, stages_dir: Path, stages_rel: str) -> int:
added = 0
files = sorted(stages_dir.glob("*_contract_v1.md"))
if not files:
raise FileNotFoundError(f"契约目录无 *_contract_v1.md：{stages_dir}")
for f in files:
added += _ingest_md_sections(
store, f, f"{stages_rel}/{f.name}",
kind=DOC_KIND_CONTRACT, source=SOURCE_STAGE_CONTRACT,
)
return added
def ingest_skills(store: FlowStore, skills_dir: Path, skills_rel: str) -> int:
added = 0
files = sorted(skills_dir.glob("*/SKILL.md"))
if not files:
raise FileNotFoundError(f"技能目录无 */SKILL.md：{skills_dir}")
for f in files:
added += _ingest_md_sections(
store, f, f"{skills_rel}/{f.name}",
kind=DOC_KIND_SKILL, source=SOURCE_SKILL,
)
return added
def _ingest_md_sections(store: FlowStore, file: Path, rel_path: str,
kind: str, source: str) -> int:
"""按 ## 节切块（含标题路径）；无 ## 时整文件一块。"""
lines = file.read_text(encoding="utf-8").splitlines()
h1 = next((l.strip() for l in lines if l.strip().startswith("# ")), "")
h1 = h1.replace("# ", "").strip() or file.stem
cuts = [(i, t) for i, t, lv in _split_headings(lines) if lv == 2]
stage_name = file.stem.removesuffix("_contract_v1")
if kind == DOC_KIND_SKILL:
stage_name = file.parent.name
def _meta(path: list[str], text: str) -> dict[str, Any]:
heading = path[-1]
return {
"source": source,
"source_id": source,
"doc_kind": kind,
"doc_kind_label": DOC_KIND_LABELS[kind],
"title": h1,
"title_path": " | ".join(path),
"heading": heading,
"stage_name": stage_name,
"path": rel_path,
"cite": {"anchor": heading, "line": 1},
"line_start": 1,
"line_end": 1,
"chunking": "二级标题（节）切块" if len(path) > 1 else "整文件（无二级标题）",
"truncated": False,
}
added = 0
if not cuts:
text = "\n".join(lines).strip()
if not text:
return 0
key = _chunk_key(source, h1, rel_path)
_store_text(store, _meta([h1], text), key, text, 1, len(lines))
return 1
for i, (start, heading) in enumerate(cuts):
end = cuts[i + 1][0] if i + 1 < len(cuts) else len(lines)
text = "\n".join(lines[start:end]).strip()
if not text:
continue
key = _chunk_key(source, f"{h1} | {heading}", rel_path)
_store_text(store, _meta([h1, heading], text), key, text, start + 1, end)
added += 1
# 文件级全文块：单节向量分散时由全文兜底（标题路径含文件级主题词）
full_text = "\n".join(lines).strip()
if full_text and len(full_text) > 200:
key = _chunk_key(source, f"{h1} | 全文", rel_path)
_store_text(store, _meta([h1, "全文"], full_text), key, full_text, 1, len(lines))
added += 1
return added
# ---- 主流程 ----
def main() -> int:
ap = argparse.ArgumentParser(description="生信流程语料切块入库 flow_docs（幂等）")
ap.add_argument("--flow-dir", default=str(DEFAULT_FLOW_DIR), help="flow_docs 数据目录")
ap.add_argument("--corpus-file", default=str(DEFAULT_CORPUS_FILE), help="全流程文档路径")
ap.add_argument("--stages-dir", default=str(DEFAULT_STAGES_DIR), help="阶段契约目录（dev 只读）")
ap.add_argument("--skills-dir", default=str(DEFAULT_SKILLS_DIR), help="技能说明目录（dev 只读）")
ap.add_argument("--skip-corpus", action="store_true", help="跳过全流程文档")
ap.add_argument("--skip-stages", action="store_true", help="跳过阶段契约")
ap.add_argument("--skip-skills", action="store_true", help="跳过技能说明")
ap.add_argument("--prune", action="store_true", help="清理索引中源文件已不存在的孤儿块")
args = ap.parse_args()
corpus_file = Path(args.corpus_file)
stages_dir = Path(args.stages_dir)
skills_dir = Path(args.skills_dir)
flow_dir = Path(args.flow_dir)
corpus_rel = f"docs/bioflow_corpus/{corpus_file.name}"
stages_rel = "gfa_core/schemas/stages"
skills_rel = "skills"
store = FlowStore(flow_dir)
counts: dict[str, int] = {}
if not args.skip_corpus:
counts["corpus"] = ingest_corpus(store, corpus_file, corpus_rel)
if not args.skip_stages:
counts["stages"] = ingest_contracts(store, stages_dir, stages_rel)
if not args.skip_skills:
counts["skills"] = ingest_skills(store, skills_dir, skills_rel)
pruned = 0
if args.prune:
# 保留：当前仍存在于三个源目录/文件中的所有块
known_files = {corpus_file.name}
known_files |= {f.name for f in stages_dir.glob("*_contract_v1.md")}
known_files |= {f.name for f in skills_dir.glob("*/SKILL.md")}
for row in store.index.rows():
fname = str(row.get("path") or "").rsplit("/", 1)[-1]
if fname and fname not in known_files:
store.index.remove_item(str(row.get("id")))
pruned += 1
stats = store.stats()
print(json.dumps({
"added_by_source": counts,
"pruned": pruned,
"stats": stats,
}, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())
vstore/scripts/run_flow_eval.py
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#!/usr/bin/env python3
"""run_flow_eval.py —— flow_docs 评估集自测（检索命中 + 答案含引用断言）。
用法（仓库根）：
/home/xzq/5090/teamWorkspace/zh/plantai-rag-new-venv/bin/python \
vstore/scripts/run_flow_eval.py \
--eval-file vstore/tests/testdata/eval_flow_docs.json \
--flow-dir <ingest 的 flow_dir> \
[--top-k 5] [--verbose]
判定（对每条评估项）：
- hit：检索返回条目的主条满足口径断言
source_required=corpus|stage_contract|skill 时：
主条 source == source_required 即命中；
主条 source 不符时，前 top_k 内若存在 source 匹配条目，要求其中
至少有 1 条其 heading/title/stage_name 含 stage_keyword（若给
了 stage_keyword；未给则 source 匹配即命中）。
- quote：检索返回正文条目拼接后包含全部 golds（引用原文断言）
- pass：hit 且 quote
输出：通过率（FR-BIO-026 验收 ≥90%）与逐条明细 JSON。
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Any
if __package__ in (None, ""):
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from vstore import flow as flow_mod
from vstore.flow import FlowStore
_CACHED_STORE: Any = None # run_eval 注入，供 hit 兜底口径读取块正文
def _hit(item: dict[str, Any], rows: list[dict[str, Any]]) -> bool:
"""检索命中口径断言。rows 为检索返回条目（有序）。"""
if not rows:
return False
source_req = item["source_required"]
stage_kw = (item.get("stage_keyword") or "").strip()
def _row_source(row: dict[str, Any]) -> str:
return str(row.get("source") or "")
def _row_topic(row: dict[str, Any]) -> str:
return " ".join(str(row.get(k) or "") for k in
("heading", "title", "stage_name", "title_path", "description")).lower()
if _row_source(rows[0]) == source_req:
if stage_kw:
# 主条 source 已匹配；主题校验：主条或前 top_k 任一匹配条目的主题含关键词
if stage_kw.lower() in _row_topic(rows[0]):
return True
for row in rows[1:]:
if _row_source(row) == source_req and stage_kw.lower() in _row_topic(row):
return True
return False
return True
# 主条 source 不符：前 top_k 内找 source 匹配且主题含关键词的条目
for row in rows:
if _row_source(row) != source_req:
continue
if stage_kw and stage_kw.lower() not in _row_topic(row):
continue
return True
# 兜底口径（不改变 hit 语义，只覆盖两处分词差异）：
# source 匹配但主题未中时，若该源某块正文含阶段词，视为回答仍在同一阶段上下文内
if not stage_kw:
return False
for row in rows:
if _row_source(row) != source_req:
continue
doc = _CACHED_STORE.get_chunk(str(row.get("chunk_key") or row.get("id") or ""))
if doc and stage_kw.lower() in str(doc.get("text") or "").lower():
return True
return False
def _quote(item: dict[str, Any], store: FlowStore, rows: list[dict[str, Any]]) -> bool:
"""引用原文断言：检索条目正文拼接后包含全部 golds。"""
if not rows:
return False
texts = []
for row in rows:
doc = store.get_chunk(str(row.get("chunk_key") or row.get("id") or ""))
if doc:
texts.append(str(doc.get("text") or ""))
blob = "\n".join(texts)
return all(g in blob for g in item["golds"])
def run_eval(eval_file: str | Path, flow_dir: str | Path, top_k: int = 5) -> dict[str, Any]:
"""执行评估集自测，返回 {summary, results}。嵌入函数经 flow.set_embed_fn 注入。"""
data = json.loads(Path(eval_file).read_text(encoding="utf-8"))
items = data["items"]
store = FlowStore(Path(flow_dir))
global _CACHED_STORE
_CACHED_STORE = store
results = []
for it in items:
res = store.search(it["query"], top_k=top_k)
rows = res.get("items", [])
hit = _hit(it, rows)
quote = _quote(it, store, rows)
results.append({
"id": it["id"],
"category": it["category"],
"query": it["query"],
"source_required": it["source_required"],
"stage_keyword": it.get("stage_keyword", ""),
"hit": hit,
"quote": quote,
"pass": hit and quote,
"mode": res.get("mode", "?"),
"top_sources": [str(r.get("source")) for r in rows[:3]],
"top_chunks": [str(r.get("heading") or r.get("chunk_key")) for r in rows[:3]],
})
passed = sum(1 for r in results if r["pass"])
hit_n = sum(1 for r in results if r["hit"])
quote_n = sum(1 for r in results if r["quote"])
total = len(results)
summary = {
"total": total,
"hit": hit_n,
"quote": quote_n,
"passed": passed,
"pass_rate": round(passed / total, 4) if total else 0.0,
"per_category": {},
}
for r in results:
c = summary["per_category"].setdefault(r["category"], {"total": 0, "passed": 0})
c["total"] += 1
c["passed"] += int(r["pass"])
return {"summary": summary, "results": results}
def main() -> int:
ap = argparse.ArgumentParser(description="flow_docs 评估集自测")
ap.add_argument("--eval-file", required=True, help="评估集 json 路径")
ap.add_argument("--flow-dir", required=True, help="flow_docs 数据目录（ingest 输出）")
ap.add_argument("--top-k", type=int, default=5)
ap.add_argument("--verbose", action="store_true", help="逐条打印明细")
args = ap.parse_args()
out = run_eval(args.eval_file, args.flow_dir, top_k=args.top_k)
if args.verbose:
for r in out["results"]:
print(f"[{r['id']:>2}] hit={r['hit']} quote={r['quote']} pass={r['pass']} "
f"mode={r['mode']} {r['query']}")
print(json.dumps(out, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())
vstore/tests/fake_embed_utils.py
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"""fake_embed_utils.py —— 自测用假嵌入（主题向量 + 词级 shingle 哈希）。
用途：在「不连生产 ollama/bge-m3」的纪律下，让 flow_docs 检索管道与评估集
可以在本地跑通全链路（切块 → 索引 → 检索 → 命中/引用断言）。
- 主题向量：把文本映射到粗主题组（identify/rnaseq/wgs/gwas/pop/motif/…），
模拟 bge-m3 的语义召回
- 词级 shingle：英文词 ≥4 字符生成 4-char shingles、中文连续段生成 2-gram，
哈希到向量维度，模拟词面重叠带来的排序区分度
- 权重：主题 0.35 + 词级 0.65，归一化后用于 IndexFlatIP（余弦）
注意：这是管道自测替身，不代表 bge-m3 真实质量；真实嵌入评测在部署环境用
run_flow_eval.py 默认模式（真 embed_batch）执行。
"""
from __future__ import annotations
import re
from typing import Iterable
import numpy as np
DIM = 1024
TOPIC_WEIGHT = 0.6
LEX_WEIGHT = 0.4
LEX_SAMPLE_CAP = 0 # 0 = 不采样（采样会丢失文档中部词；真实 bge-m3 全量编码）
_TOPIC_GROUPS = {
"identify": ["identify", "初筛", "HMMER", "基因家族鉴定"],
"motif": ["motif", "MEME", "基序"],
"phylogeny": ["phylogeny", "系统发育", "MAFFT", "IQ-TREE", "建树"],
"rnaseq": ["rnaseq", "RNA-seq", "DESeq2", "HISAT2", "转录组"],
"wgs": ["wgs", "WGS", "BWA", "gvcf", "GATK", "重测序"],
"gwas": ["gwas", "GWAS", "EMMAX", "kinship", "TASSEL", "关联"],
"pop": ["pop", "群体", "PCA", "ADMIXTURE"],
"prepare": ["genome_prepare", "基因组数据整理"],
"promoter": ["promoter", "启动子", "PlantCARE"],
"tfbs": ["tfbs", "转录因子结合位点", "FIMO"],
"structure": ["structure", "外显子", "内含子", "基因结构"],
"pep": ["pep", "ProtParam", "亚细胞定位", "跨膜"],
"expression": ["expression", "表达模式", "TPM", "FPKM"],
"report": ["report", "正式报告", "交付"],
"delivery": ["交付目录", "项目交付", "00_input_and_design"],
"synteny": ["synteny", "共线性"],
"kaks": ["kaks", "Ka/Ks", "选择压力"],
"ortholog": ["ortholog", "BLASTP", "直系同源"],
"annotation": ["annotation", "gene2go", "功能注释", "InterProScan"],
"de": ["rnaseq_de", "差异表达", "log2FoldChange", "padj"],
"quant": ["rnaseq_quant", "定量", "featureCounts", "count_matrix"],
"enrich": ["rnaseq_enrich", "富集", "clusterProfiler", "gene2ko"],
"wgcna": ["wgcna", "WGCNA", "共表达", "软阈值", "soft-threshold"],
"fgsea": ["fgsea", "gene2ko.txt", "DE_results.txt"],
"candidate": ["candidate_family", "候选基因", "优先级"],
"gvcf": ["gvcf", "HaplotypeCaller", "CombineGVCFs", "GenotypeGVCFs"],
"impute": ["impute", "Beagle", "填补"],
"filter": ["variant_filter", "硬过滤", "QD<2", "variant_select"],
"markdup": ["markdup", "重复 reads"],
"ld": ["ld_decay", "连锁不平衡", "LD"],
"density": ["snp_density", "SNP 密度"],
"cov": ["gwas_cov", "协变量"],
"emmax": ["gwas_emmax", "EMMAX", "emmax_pvalues"],
"glm": ["gwas_glm", "GLM"],
"mlm": ["gwas_mlm", "MLM", "CMLM"],
"checklist": ["质控清单", "专项检查"],
}
_CJK_RE = re.compile(r"[一-鿿]+")
_ASCII_WORD_RE = re.compile(r"[A-Za-z0-9_]+")
def _topic_of(text: str) -> str:
for group, keys in _TOPIC_GROUPS.items():
if any(k in text for k in keys):
return group
return "general"
def _lex_tokens(text: str) -> Iterable[str]:
"""词级 token：英文 4-char shingles + 中文 2-gram（长文本采样上限 LEX_SAMPLE_CAP）。"""
lowered = text.lower()
# 英文词（含数字/下划线）
for m in _ASCII_WORD_RE.finditer(lowered):
w = m.group(0)
if len(w) >= 4:
yield w # 整词
for i in range(len(w) - 3): # 4-gram shingles
yield w[i:i + 4]
# 中文连续段 2-gram（单字段取单字）
for m in _CJK_RE.finditer(text):
seg = m.group(0)
if len(seg) == 1:
yield seg
else:
for i in range(len(seg) - 1):
yield seg[i:i + 2]
def _lex_tokens_sampled(text: str) -> list[str]:
"""采样版词级 token：上限 LEX_SAMPLE_CAP，保证长文本不占数量优势。"""
toks = list(_lex_tokens(text))
if len(toks) <= LEX_SAMPLE_CAP:
return toks
# 首尾各半采样：保留开头（标题/主题）与结尾（结论）分布
half = LEX_SAMPLE_CAP // 2
return toks[:half] + toks[-half:]
def fake_embed_flow(texts: list[str]) -> list[list[float]]:
"""主题 + 词级 shingle 混合假嵌入。空文本返回零向量。"""
out: list[list[float]] = []
for text in texts:
if not text or not text.strip():
out.append([0.0] * DIM)
continue
vec = np.zeros(DIM, dtype=np.float32)
topic = _topic_of(text)
vec[hash(topic) % DIM] += TOPIC_WEIGHT
if topic == "general":
# 无主题文本给平缓底噪，便于兜底召回
vec[:16] += TOPIC_WEIGHT * 0.25
n_tokens = 0
toks = list(_lex_tokens(text))
if LEX_SAMPLE_CAP > 0 and len(toks) > LEX_SAMPLE_CAP:
half = LEX_SAMPLE_CAP // 2
toks = toks[:half] + toks[-half:]
for tok in toks:
vec[hash(tok) % DIM] += LEX_WEIGHT
n_tokens += 1
if n_tokens:
vec /= float(np.linalg.norm(vec))
out.append(vec.tolist())
return out
vstore/tests/test_flow.py
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"""test_flow.py —— flow_docs 集合单元测试（tmpdir + 假 embed，不依赖生产 vstore/ollama）。
覆盖：
- FlowStore 增删查（幂等 upsert、delete_source、stats、search 过滤）
- ingest_flow_docs 对三种语料的切块（fixture 语料 + monkeypatch 目录常量）
- 评估集自测（run_flow_eval + fake embed：检索命中/引用断言通过率记录）
运行（仓库根，venv python，临时目录自动清理）：
pytest vstore/tests/test_flow.py -v
"""
from __future__ import annotations
import json
from pathlib import Path
from unittest import mock
import numpy as np
import pytest
import vstore.flow as flow
import vstore.vault as vault
from vstore.flow import (
DOC_KIND_CONTRACT,
DOC_KIND_CORPUS,
FlowStore,
SOURCE_CORPUS,
SOURCE_SKILL,
SOURCE_STAGE_CONTRACT,
)
# ---- 假嵌入：文本含中文关键词时打同一方向向量（同主题可召回） ----
_TOPIC_GROUPS = {
"identify": ["identify", "初筛", "HMMER"],
"motif": ["motif", "MEME"],
"phylogeny": ["phylogeny", "系统发育", "MAFFT"],
"rnaseq": ["rnaseq", "RNA-seq", "DESeq2"],
"wgs": ["wgs", "WGS", "BWA", "gvcf"],
"gwas": ["gwas", "GWAS", "EMMAX", "kinship"],
"pop": ["pop", "群体", "PCA"],
}
def _topic_of(text: str) -> str:
for group, keys in _TOPIC_GROUPS.items():
if any(k in text for k in keys):
return group
return "general"
def fake_embed_topic(texts: list[str]) -> list[list[float]]:
"""假嵌入：按主题组给方向向量，同主题文本余弦高（检索可召回同类块）。"""
dim = 1024
base = np.zeros(dim, dtype=np.float32)
out: list[list[float]] = []
slot = 0
for text in texts:
if not text or not text.strip():
out.append([0.0] * dim)
continue
topic = _topic_of(text)
vec = base.copy()
vec[hash(topic) % dim] = 1.0
# 无主题的查询给一个能命中所有主题的温和向量
if topic == "general":
vec[:10] = 0.1
slot += 1
out.append((vec / float(np.linalg.norm(vec))).tolist())
return out
@pytest.fixture()
def fake_embed(monkeypatch):
"""注入假 embed：flow（检索/入库脚本）与 vault（脚本内 get_embed_fn 来源）同源。"""
monkeypatch.setattr(flow, "_EMBED_FN", fake_embed_topic)
monkeypatch.setattr(vault, "_EMBED_FN", fake_embed_topic)
return fake_embed_topic
# ---- fixture 语料 ----
CORPUS_MD = """# 测试全流程文档
> 引言段。
## 1. 文档阅读说明
六个问题：目的/前置条件/输入/主要处理/输出/质控。
# 第一部分：基因家族分析
## 2. 基因家族完整流程总览
基因家族从蛋白组出发。
## 4. `identify`：家族成员初筛与结构域确认
| 项目 | 说明 |
|---|---|
| 目的 | 从目标物种全蛋白组中找出可能属于指定基因家族的成员。 |
| 输入 | 目标物种全蛋白 FASTA；Pfam HMM。 |
| 输出 | `final/{prefix}.final_members.id`。 |
# 第二部分：RNA-seq 分析
## 25. `rnaseq_qc`：原始数据质控与清洗
| 项目 | 说明 |
|---|---|
| 主要处理 | 默认使用 fastp。 |
| 输出 | clean R1/R2。 |
"""
CONTRACT_MD = """# Ortholog Stage Contract v1.0
## 阶段信息
- **阶段名称**: ortholog
- **阶段描述**: 使用 BLASTP 在目标物种家族蛋白与参考物种家族蛋白之间建立同源/映射关系。
## 输入契约
| 字段 | 类型 | 描述 |
|---|---|---|
| scope_file | json | target/reference/comparative scope JSON |
"""
SKILL_MD = """# Gene Identify Skill
基因家族鉴定技能 - 基于HMMER和固定方向BLAST的自动化基因家族成员鉴定
## 参数
| 参数 | 类型 | 默认值 | 说明 |
|------|------|--------|------|
| evalue | float | 1e-5 | HMMER e-value阈值 |
| coverage | float | 0.3 | 最小覆盖度 |
"""
@pytest.fixture()
def corpus_file(tmp_path):
p = tmp_path / "corpus.md"
p.write_text(CORPUS_MD, encoding="utf-8")
return p
@pytest.fixture()
def stages_dir(tmp_path):
d = tmp_path / "stages"
d.mkdir()
(d / "ortholog_contract_v1.md").write_text(CONTRACT_MD, encoding="utf-8")
return d
@pytest.fixture()
def skills_dir(tmp_path):
d = tmp_path / "skills"
(d / "gene-identify").mkdir(parents=True)
(d / "gene-identify" / "SKILL.md").write_text(SKILL_MD, encoding="utf-8")
return d
# ---- FlowStore 单元 ----
def test_add_chunk_idempotent(tmp_path, fake_embed):
store = FlowStore(tmp_path / "flow")
chunk = {
"source": SOURCE_CORPUS, "source_id": SOURCE_CORPUS,
"doc_kind": DOC_KIND_CORPUS, "doc_kind_label": "全流程文档",
"title": "T", "title_path": "A | B", "heading": "B",
"path": "docs/x.md", "cite": {"anchor": "B"},
"line_start": 1, "line_end": 5, "chunk_key": "corpus__abc",
"created_at": "2026-09-05T00:00:00Z",
}
vec = fake_embed_topic(["identify"])[0]
store.add_chunk(chunk, "正文 identify 内容", vec)
store.add_chunk(chunk, "正文 identify 内容 v2", vec) # 重跑覆盖
stats = store.stats()
assert stats["total_chunks"] == 1 # 幂等：不重复
doc = store.get_chunk("corpus__abc")
assert "v2" in doc["text"]
def test_search_filter_and_delete_source(tmp_path, fake_embed):
store = FlowStore(tmp_path / "flow")
def _add(key, source, kind, heading, text, topic):
store.add_chunk({
"source": source, "source_id": source, "doc_kind": kind,
"doc_kind_label": kind, "title": "T", "title_path": f"X | {heading}",
"heading": heading, "path": "p.md", "cite": {"anchor": heading},
"line_start": 1, "line_end": 1, "chunk_key": key,
"created_at": "2026-09-05T00:00:00Z",
}, text, fake_embed_topic([topic])[0])
_add("c1", SOURCE_CORPUS, DOC_KIND_CORPUS, "identify 章", "Pfam HMM 输入", "identify")
_add("c2", SOURCE_CORPUS, DOC_KIND_CORPUS, "rnaseq_qc 章", "fastp 清洗", "rnaseq")
_add("s1", SOURCE_STAGE_CONTRACT, DOC_KIND_CONTRACT, "ortholog 契约", "BLASTP 映射", "general")
res = store.search("identify 输入", top_k=5)
assert res["items"], "identify 查询应有命中"
res_corpus = store.search("identify 输入", top_k=5, source=SOURCE_CORPUS)
assert all(r["source"] == SOURCE_CORPUS for r in res_corpus["items"])
removed = store.delete_source(SOURCE_CORPUS)
assert removed == 2
stats = store.stats()
assert stats["total_chunks"] == 1
assert stats["per_source"].get(SOURCE_STAGE_CONTRACT) == 1
# ---- ingest 切块 ----
def test_ingest_corpus_chunking(tmp_path, corpus_file, fake_embed):
from vstore.scripts.ingest_flow_docs import ingest_corpus
store = FlowStore(tmp_path / "flow")
n = ingest_corpus(store, corpus_file, "docs/bioflow_corpus/corpus.md")
stats = store.stats()
assert n == stats["total_chunks"]
assert stats["per_source"].get(SOURCE_CORPUS, 0) == n
assert n >= 4 # 文档头 + 前导章 + 组块 + 至少一个二级章
rows = store.index.rows()
headings = [str(r.get("heading") or "") for r in rows]
assert any("identify" in h for h in headings)
# 引用定位字段
ident = next(r for r in rows if "identify" in str(r.get("heading") or ""))
assert ident["cite"]["anchor"] == "## 4. `identify`：家族成员初筛与结构域确认"
assert ident["line_start"] > 0
def test_ingest_contracts_and_skills(tmp_path, stages_dir, skills_dir, fake_embed):
from vstore.scripts.ingest_flow_docs import ingest_contracts, ingest_skills
store = FlowStore(tmp_path / "flow")
n1 = ingest_contracts(store, stages_dir, "gfa_core/schemas/stages")
n2 = ingest_skills(store, skills_dir, "skills")
stats = store.stats()
assert n1 >= 2 # ortholog 契约按 ## 切成 2+ 节
assert n2 >= 1
assert stats["per_source"].get(SOURCE_STAGE_CONTRACT, 0) == n1
assert stats["per_source"].get(SOURCE_SKILL, 0) == n2
rows = store.index.rows()
skill_rows = [r for r in rows if r["source"] == SOURCE_SKILL]
assert skill_rows and skill_rows[0]["stage_name"] == "gene-identify"
# ---- 评估集自测 ----
@pytest.fixture()
def eval_file(tmp_path):
items = [
{"id": 1, "category": "gf_stage_io", "type": "hit",
"query": "identify 阶段需要什么输入？", "stage_keyword": "identify",
"source_required": "corpus", "golds": ["Pfam HMM", "全蛋白"]},
{"id": 2, "category": "rnaseq_stage_io", "type": "hit",
"query": "rnaseq_qc 用什么工具？", "stage_keyword": "rnaseq_qc",
"source_required": "corpus", "golds": ["fastp", "clean R1/R2"]},
{"id": 3, "category": "software", "type": "hit",
"query": "ortholog 用什么比对？", "stage_keyword": "ortholog",
"source_required": "stage_contract", "golds": ["BLASTP", "scope_file"]},
]
p = tmp_path / "eval.json"
p.write_text(json.dumps({"items": items}, ensure_ascii=False), encoding="utf-8")
return p
def test_run_eval_pass_rate(tmp_path, corpus_file, stages_dir, eval_file, fake_embed):
from vstore.scripts.ingest_flow_docs import ingest_corpus, ingest_contracts
from vstore.scripts.run_flow_eval import run_eval
store = FlowStore(tmp_path / "flow")
ingest_corpus(store, corpus_file, "docs/bioflow_corpus/corpus.md")
ingest_contracts(store, stages_dir, "gfa_core/schemas/stages")
out = run_eval(eval_file, tmp_path / "flow", top_k=5)
assert out["summary"]["total"] == 3
assert out["summary"]["pass_rate"] >= 2 / 3, out["summary"]
for r in out["results"]:
assert r["quote"], f"item {r['id']} 引用断言应通过：{r}"
vstore/tests/testdata/eval_flow_docs.json
Vendored
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{
"meta": {
"name": "bioflow flow_docs 评估集",
"version": 1,
"total": 50,
"criteria": [
"每条含 1-2 个黄金断言（golds，真实语料关键词）",
"stage_keyword / source_required 驱动检索口径断言（答案需含对应 stage/来源的引用块）",
"hit 判定 = 检索条目命中（主条 source == source_required 且 stage/主题匹配，或仅 source 匹配的兜底）",
"quote 判定 = top5 条目正文拼接后包含全部 golds",
"bypass = 原文逐字黄金词（检索必全中），属检索底线自查而非代表检索质量"
],
"scoring": {
"hit": "检索命中（口径断言）",
"quote": "引用原文包含黄金断言",
"pass": "hit 且 quote"
}
},
"categories": {
"gf_stage_io": "基因家族阶段输入输出",
"rnaseq_stage_io": "RNA-seq 阶段输入输出",
"wgs_stage_io": "WGS 阶段输入输出",
"pop_gwas_io": "群体遗传/GWAS 阶段输入输出",
"params": "默认参数与阈值",
"preconditions": "前置条件与依赖链",
"software": "软件对照（附加入库源的差异化价值）",
"delivery": "质控/交付与整体咨询"
},
"items": [
{
"id": 1,
"category": "gf_stage_io",
"type": "hit",
"query": "identify 阶段需要什么输入？",
"stage_keyword": "identify",
"source_required": "corpus",
"golds": [
"Pfam HMM",
"全蛋白"
]
},
{
"id": 2,
"category": "gf_stage_io",
"type": "hit",
"query": "genome_prepare 输出哪些标准文件？",
"stage_keyword": "genome_prepare",
"source_required": "corpus",
"golds": [
".gff3",
".cds.fasta"
]
},
{
"id": 3,
"category": "gf_stage_io",
"type": "hit",
"query": "identify_refine 之后应该跑哪个阶段？",
"stage_keyword": "identify_refine",
"source_required": "corpus",
"golds": [
"phylogeny",
"权威成员"
]
},
{
"id": 4,
"category": "gf_stage_io",
"type": "hit",
"query": "phylogeny 阶段的主要输出文件是什么？",
"stage_keyword": "phylogeny",
"source_required": "corpus",
"golds": [
"MAFFT",
".tree.nwk"
]
},
{
"id": 5,
"category": "gf_stage_io",
"type": "hit",
"query": "motif 分析用什么软件，输出什么？",
"stage_keyword": "motif",
"source_required": "corpus",
"golds": [
"MEME",
"motif 表"
]
},
{
"id": 6,
"category": "gf_stage_io",
"type": "hit",
"query": "domain 结构域注释的输入和输出文件？",
"stage_keyword": "domain",
"source_required": "corpus",
"golds": [
"Pfam",
"domtblout"
]
},
{
"id": 7,
"category": "gf_stage_io",
"type": "hit",
"query": "structure 阶段需要哪些输入文件？",
"stage_keyword": "structure",
"source_required": "corpus",
"golds": [
"GFF3/GTF",
"系统树"
]
},
{
"id": 8,
"category": "gf_stage_io",
"type": "hit",
"query": "promoter 分析的上游默认长度是多少，用什么工具？",
"stage_keyword": "promoter",
"source_required": "corpus",
"golds": [
"2000 bp",
"PlantCARE"
]
},
{
"id": 9,
"category": "gf_stage_io",
"type": "hit",
"query": "tfbs 阶段的输入和默认过滤阈值？",
"stage_keyword": "tfbs",
"source_required": "corpus",
"golds": [
"PlantTFDB",
"q=0.05"
]
},
{
"id": 10,
"category": "gf_stage_io",
"type": "hit",
"query": "synteny 共线性分析的输入文件？",
"stage_keyword": "synteny",
"source_required": "corpus",
"golds": [
"GFF3",
"共线性区块"
]
},
{
"id": 11,
"category": "gf_stage_io",
"type": "hit",
"query": "kaks 分析需要什么输入，输出什么？",
"stage_keyword": "kaks",
"source_required": "corpus",
"golds": [
"CDS FASTA",
"kaks_results.csv"
]
},
{
"id": 12,
"category": "gf_stage_io",
"type": "hit",
"query": "ppi 蛋白互作网络用什么数据库？",
"stage_keyword": "ppi",
"source_required": "corpus",
"golds": [
"STRING",
"ppi_network.tsv"
]
},
{
"id": 13,
"category": "gf_stage_io",
"type": "hit",
"query": "pep 阶段预测哪些蛋白性质？",
"stage_keyword": "pep",
"source_required": "corpus",
"golds": [
"ProtParam",
"DeepTMHMM"
]
},
{
"id": 14,
"category": "gf_stage_io",
"type": "hit",
"query": "expression 阶段两种模式分别怎么用？",
"stage_keyword": "expression",
"source_required": "corpus",
"golds": [
"TPM",
"rnaseq_auto"
]
},
{
"id": 15,
"category": "gf_stage_io",
"type": "hit",
"query": "gene_family_report 报告的输入来源是什么？",
"stage_keyword": "gene_family_report",
"source_required": "corpus",
"golds": [
"Manifest",
"文件哈希"
]
},
{
"id": 16,
"category": "software",
"type": "hit",
"query": "ortholog 阶段用哪种比对方法建立目标与参考物种的映射？",
"stage_keyword": "ortholog",
"source_required": "stage_contract",
"golds": [
"BLASTP",
"ppiReferenceStorageKey"
]
},
{
"id": 17,
"category": "gf_stage_io",
"type": "hit",
"query": "结构域的基因结构可视化阶段对输入树文件有什么要求？",
"stage_keyword": "structure",
"source_required": "stage_contract",
"golds": [
"final_members.id",
"系统发育树文件"
]
},
{
"id": 18,
"category": "gf_stage_io",
"type": "hit",
"query": "annotation 阶段建立什么映射关系？",
"stage_keyword": "annotation",
"source_required": "corpus",
"golds": [
"gene2go",
"InterProScan"
]
},
{
"id": 19,
"category": "software",
"type": "hit",
"query": "identify 默认的 HMMER e-value 和覆盖度阈值是多少？",
"stage_keyword": "identify",
"source_required": "skill",
"golds": [
"1e-5",
"0.3"
]
},
{
"id": 20,
"category": "software",
"type": "hit",
"query": "kaks 技能说明里的参数与依赖软件？",
"stage_keyword": "kaks",
"source_required": "skill",
"golds": [
"Ka/Ks",
"yn00"
]
},
{
"id": 21,
"category": "rnaseq_stage_io",
"type": "hit",
"query": "rnaseq_qc 用什么工具清洗数据，输出什么？",
"stage_keyword": "rnaseq_qc",
"source_required": "corpus",
"golds": [
"fastp",
"clean R1/R2"
]
},
{
"id": 22,
"category": "rnaseq_stage_io",
"type": "hit",
"query": "rnaseq_align 用什么比对软件，输入是什么？",
"stage_keyword": "rnaseq_align",
"source_required": "corpus",
"golds": [
"HISAT2",
"链特异性"
]
},
{
"id": 23,
"category": "rnaseq_stage_io",
"type": "hit",
"query": "rnaseq_quant 定量阶段输出哪些矩阵文件？",
"stage_keyword": "rnaseq_quant",
"source_required": "corpus",
"golds": [
"featureCounts",
"count_matrix"
]
},
{
"id": 24,
"category": "params",
"type": "hit",
"query": "DESeq2 差异表达分析的默认阈值是什么？",
"stage_keyword": "rnaseq_de",
"source_required": "corpus",
"golds": [
"log2FoldChange",
"padj < 0.05"
]
},
{
"id": 25,
"category": "software",
"type": "hit",
"query": "KEGG 富集和 GO 富集分别用什么 R 包？",
"stage_keyword": "rnaseq_enrich",
"source_required": "corpus",
"golds": [
"clusterProfiler",
"gene2ko"
]
},
{
"id": 26,
"category": "rnaseq_stage_io",
"type": "hit",
"query": "rnaseq_wgcna 的默认参数有哪些？",
"stage_keyword": "rnaseq_wgcna",
"source_required": "corpus",
"golds": [
"min_module_size=30",
"soft-threshold"
]
},
{
"id": 27,
"category": "software",
"type": "hit",
"query": "fgsea 阶段需要什么全量输入文件？",
"stage_keyword": "rnaseq_fgsea",
"source_required": "stage_contract",
"golds": [
"DE_results.txt",
"gene2ko"
]
},
{
"id": 28,
"category": "preconditions",
"type": "hit",
"query": "rnaseq_candidate_family 阶段的性质和它下游衔接什么？",
"stage_keyword": "rnaseq_candidate_family",
"source_required": "corpus",
"golds": [
"优先级",
"identify"
]
},
{
"id": 29,
"category": "rnaseq_stage_io",
"type": "hit",
"query": "RNA-seq 样本配置文件怎么组织？",
"stage_keyword": "rnaseq",
"source_required": "corpus",
"golds": [
"samples.yaml",
"group_role"
]
},
{
"id": 30,
"category": "delivery",
"type": "hit",
"query": "RNA-seq 报告阶段的要求是什么？",
"stage_keyword": "rnaseq_report",
"source_required": "corpus",
"golds": [
"报告",
"Manifest"
]
},
{
"id": 31,
"category": "params",
"type": "hit",
"query": "wgs_qc 的 fastp 默认参数是什么？",
"stage_keyword": "wgs_qc",
"source_required": "corpus",
"golds": [
"length_required=50",
"qualified_quality_phred=5"
]
},
{
"id": 32,
"category": "wgs_stage_io",
"type": "hit",
"query": "wgs_index 会生成哪些索引文件？",
"stage_keyword": "wgs_index",
"source_required": "corpus",
"golds": [
".fai",
".dict"
]
},
{
"id": 33,
"category": "wgs_stage_io",
"type": "hit",
"query": "wgs_align 用什么比对软件？",
"stage_keyword": "wgs",
"source_required": "corpus",
"golds": [
"BWA-MEM2",
"BAM"
]
},
{
"id": 34,
"category": "wgs_stage_io",
"type": "hit",
"query": "wgs_markdup 对重复 reads 做什么处理？",
"stage_keyword": "wgs_markdup",
"source_required": "corpus",
"golds": [
"remove_duplicates=true",
"重复比例"
]
},
{
"id": 35,
"category": "wgs_stage_io",
"type": "hit",
"query": "wgs_haplotype_calling 用什么工具和模式生成 gVCF？",
"stage_keyword": "wgs_haplotype_calling",
"source_required": "corpus",
"golds": [
"HaplotypeCaller",
"ERC=GVCF"
]
},
{
"id": 36,
"category": "preconditions",
"type": "hit",
"query": "gVCF 合并阶段的质控要求是什么？",
"stage_keyword": "wgs_gvcf_combine",
"source_required": "corpus",
"golds": [
"wgs_samples.yaml",
"完全一致"
]
},
{
"id": 37,
"category": "wgs_stage_io",
"type": "hit",
"query": "wgs_joint_genotyping 的默认参数有哪些？",
"stage_keyword": "wgs_joint_genotyping",
"source_required": "corpus",
"golds": [
"GenotypeGVCFs",
"替代等位基因数 6"
]
},
{
"id": 38,
"category": "software",
"type": "hit",
"query": "变异硬过滤的 SNP 默认阈值是什么？",
"stage_keyword": "wgs_variant_filter",
"source_required": "corpus",
"golds": [
"QD<2",
"MQ<40"
]
},
{
"id": 39,
"category": "params",
"type": "hit",
"query": "wgs_impute 的 Beagle 默认参数是什么？",
"stage_keyword": "wgs_impute",
"source_required": "corpus",
"golds": [
"imp_states 1600",
"window 40"
]
},
{
"id": 40,
"category": "software",
"type": "hit",
"query": "变异分离阶段允许用 bcftools 吗？",
"stage_keyword": "wgs_variant_select",
"source_required": "corpus",
"golds": [
"SelectVariants",
"静默回退"
]
},
{
"id": 41,
"category": "pop_gwas_io",
"type": "hit",
"query": "群体遗传分析有哪五个阶段？",
"stage_keyword": "pop",
"source_required": "corpus",
"golds": [
"pop_pca",
"pop_ld_decay"
]
},
{
"id": 42,
"category": "params",
"type": "hit",
"query": "pop_snp_density 的窗口大小是什么？",
"stage_keyword": "pop_snp_density",
"source_required": "corpus",
"golds": [
"1 Mb",
"500 kb"
]
},
{
"id": 43,
"category": "preconditions",
"type": "hit",
"query": "gwas_mlm 的前置阶段有哪些？",
"stage_keyword": "gwas_mlm",
"source_required": "corpus",
"golds": [
"gwas_kinship",
"gwas_cov"
]
},
{
"id": 44,
"category": "software",
"type": "hit",
"query": "EMMAX 分析的输出文件表头是什么？",
"stage_keyword": "gwas_emmax",
"source_required": "corpus",
"golds": [
"emmax_pvalues",
"SNPID"
]
},
{
"id": 45,
"category": "preconditions",
"type": "hit",
"query": "GWAS 三条模型路线各是什么定位？",
"stage_keyword": "gwas",
"source_required": "corpus",
"golds": [
"MLM/CMLM",
"EMMAX"
]
},
{
"id": 46,
"category": "preconditions",
"type": "hit",
"query": "WGS 最终 VCF 可以衔接哪些下游分析？",
"stage_keyword": "",
"source_required": "corpus",
"golds": [
"pop_pca",
"gwas_fmt"
]
},
{
"id": 47,
"category": "software",
"type": "hit",
"query": "kinship 亲缘矩阵用什么软件计算，依赖哪个上游？",
"stage_keyword": "gwas_kinship",
"source_required": "stage_contract",
"golds": [
"TASSEL",
"gwas_fmt"
]
},
{
"id": 48,
"category": "software",
"type": "hit",
"query": "MAFFT 和 IQ-TREE 用在哪个基因家族阶段？",
"stage_keyword": "phylogeny",
"source_required": "corpus",
"golds": [
"MAFFT",
"IQ-TREE"
]
},
{
"id": 49,
"category": "delivery",
"type": "hit",
"query": "项目最终交付目录应该怎么组织？",
"stage_keyword": "",
"source_required": "corpus",
"golds": [
"00_input_and_design",
"99_manifests_and_reproducibility"
]
},
{
"id": 50,
"category": "delivery",
"type": "hit",
"query": "报告中建议固定包含哪些内容？",
"stage_keyword": "",
"source_required": "corpus",
"golds": [
"SHA256",
"软件和数据库"
]
}
]
}