461 lines
18 KiB
Python
461 lines
18 KiB
Python
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# -*- coding: utf-8 -*-
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"""园区知识库 —— 轻量本地 RAG（embedding + numpy 余弦检索，双路索引）
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升级为「细粒度分块 + LLM 生成问答对 + 双路向量索引（原句 + 问题）+ 余弦检索返回原文」：
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- 知识源：backend/knowledge/park.md（按 H2 节 → 原子事实两级分块）
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- 向量化：dashscope text-embedding-v4（云端 API，索引构建一次后缓存，构建时 L2 归一化）
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- 问答：复用 llm._chat_once 对每块生成 3-5 个口语化问句，缓存到 kb_qa.json（无 key 自动单路降级）
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- 检索：query 同时比对原句向量与问题向量，按块去重，返回 top_k 个原文块
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- 提供：
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- retrieve(query, top_k) → 检索命中的原文块（对话动态注入）
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- full_kb() → 整库原文（小库全量注入，供语音 instructions）
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- brief() → 前 N 字摘要（兼容 /api/kb/brief 与检索无命中回退）
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"""
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import hashlib
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import json
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import logging
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import os
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import re
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from pathlib import Path
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import numpy as np
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log = logging.getLogger("dpm.rag")
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def _trunc(s, n=300):
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"""日志用截断：超长内容只留前 n 字并标注总长。"""
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s = str(s)
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return s if len(s) <= n else s[:n] + f"...（共{len(s)}字）"
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KB_DIR = Path(__file__).resolve().parent / "knowledge"
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KB_GLOB = "*.md" # 知识库 = knowledge/ 下全部 markdown（park.md + opc.md 等）
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META_FILE = KB_DIR / "kb_meta.json" # 元数据（JSON，可读）：model/hash/chunks/questions/embed_texts/embed_owner
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VECTOR_FILE = KB_DIR / "kb_vectors.npy" # 向量矩阵（二进制 numpy float32，快载小体积）
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LEGACY_INDEX_FILE = KB_DIR / "kb_index.json" # 旧版单文件索引（迁移后删除）
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QA_FILE = KB_DIR / "kb_qa.json"
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EMBED_MODEL = "text-embedding-v4"
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EMBED_BATCH = 10 # DashScope embedding 单批上限
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QA_BATCH = 5 # 每批几块生成问答
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CHUNK_MAX = 240 # 单块超过此长度按句拆
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_index = None # 缓存 {chunks, questions, embed_texts, embed_owner, embeddings(归一化 np.ndarray)}
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def _kb_text() -> str:
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"""拼接 knowledge/ 下全部 markdown 内容（按文件名排序，稳定顺序）。"""
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parts = []
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for f in sorted(KB_DIR.glob(KB_GLOB)):
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try:
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parts.append(f.read_text("utf-8"))
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except OSError as e: # noqa: BLE001
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log.warning("rag: 跳过知识文件 %s: %s", f.name, e)
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return "\n\n".join(parts)
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def _data_hash() -> str:
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"""全部知识文件内容签名，用于索引/问答缓存失效判断。"""
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return hashlib.sha1(_kb_text().encode("utf-8")).hexdigest()
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def _split_long(text: str, limit: int = CHUNK_MAX) -> list[str]:
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"""超长块按句号/分号/顿号等边界拆成多块，保留语义完整。"""
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if len(text) <= limit:
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return [text]
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parts: list[str] = []
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cur = ""
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for seg in re.split(r"(?<=[。！？；、])", text):
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if len(cur) + len(seg) > limit and cur:
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parts.append(cur.strip())
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cur = ""
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cur += seg
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if cur.strip():
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parts.append(cur.strip())
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return [p for p in parts if len(p) > 8]
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def _chunk_md(text: str) -> list[str]:
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"""两级切分：H2 节标题作上下文前缀 + 节内原子事实。
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企业条目（加粗公司名行 `**...｜公司名**` + 负责人/简介列表）合并为单个块，
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保证「企业名称 + 企业介绍」作为一个整体可被按名召回。
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"""
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chunks: list[str] = []
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section = ""
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para: list[str] = []
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company: list[str] | None = None # 正在累积的单个企业块
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def flush_para():
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nonlocal para
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if para:
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t = "\n".join(para).strip()
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if len(t) > 8:
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chunks.extend(_split_long(t))
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para = []
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def flush_company():
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nonlocal company
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if company:
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t = "\n".join(company).strip()
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if len(t) > 8:
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chunks.append(t) # 企业块不拆，保证名称+介绍同块
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company = None
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def add_fact(content: str):
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full = f"{section}\n{content}" if section else content
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if len(full) > 8:
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chunks.extend(_split_long(full))
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for line in text.splitlines():
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s = line.rstrip()
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if s.startswith("# "): # H1：不产块
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flush_para(); flush_company()
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continue
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if s.startswith("## "): # H2 小节：切换上下文前缀
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flush_para(); flush_company()
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section = s[3:].strip()
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continue
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if s.startswith("###"): # H3 子节：上下文（如分区），flush 企业
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flush_para(); flush_company()
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para.append(s.strip())
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continue
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stripped = s.strip()
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if not stripped:
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continue
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# 企业条目：加粗公司名行作为单个企业块的起点
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if stripped.startswith("**") and "｜" in stripped:
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flush_para(); flush_company()
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company = [stripped]
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continue
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if company is not None:
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# 企业块的跟随行（负责人/简介等列表项）
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if stripped.startswith("- ") or stripped.startswith("问：") or re.match(r"^\d+[\.、]\s", stripped):
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company.append(stripped)
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else:
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flush_company()
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para.append(stripped)
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continue
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if stripped.startswith("- "): # 列表项 = 原子事实
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flush_para(); add_fact(stripped)
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elif re.match(r"^\d+[\.、]\s", stripped): # 编号步
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flush_para(); add_fact(stripped)
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elif stripped.startswith("问："): # 单条问答
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flush_para(); add_fact(stripped)
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else: # 普通段落：累积后整段成块（超长再拆）
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para.append(stripped)
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flush_para()
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flush_company()
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return chunks
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def _company_name(chunk: str) -> str:
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"""从企业块提取企业名称：`**加速1｜云南派音人工智能科技有限公司**` → `云南派音人工智能科技有限公司`。"""
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m = re.match(r"^\*\*[^｜\n]*｜([^*]+)\*\*", chunk) or re.match(r"^\*\*([^*]+)\*\*", chunk)
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return m.group(1).strip() if m else ""
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def _embed(texts: list[str]) -> list[list[float]]:
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"""dashscope text-embedding-v4 批量向量化（每批 ≤10 条，按 text_index 排序拼接）。"""
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import dashscope
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api_key = os.environ.get("DASHSCOPE_API_KEY") or ""
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out: list[list[float]] = []
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for i in range(0, len(texts), EMBED_BATCH):
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batch = texts[i:i + EMBED_BATCH]
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resp = dashscope.TextEmbedding.call(
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model=EMBED_MODEL,
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input=batch,
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api_key=api_key,
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)
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if resp.status_code != 200:
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raise RuntimeError(
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f"embedding API {resp.status_code}: {resp.code} {resp.message}"
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)
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embs = sorted(
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resp.output["embeddings"],
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key=lambda e: e.get("text_index", e.get("index", 0)),
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)
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out.extend(e["embedding"] for e in embs)
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log.info("rag: embedding 完成 %d 条（%s，每批 %d）", len(texts), EMBED_MODEL, EMBED_BATCH)
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return out
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def _normalize(vectors: list[list[float]]) -> np.ndarray:
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"""转为 float32 np.ndarray 并做 L2 行归一化（点积即余弦相似度）。"""
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arr = np.asarray(vectors, dtype=np.float32)
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norms = np.linalg.norm(arr, axis=1, keepdims=True)
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norms[norms == 0] = 1.0
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return arr / norms
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# ── LLM 问答生成 ────────────────────────────────────────────────
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def _parse_json_array(text: str) -> list:
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"""解析模型返回的 JSON 数组：剥 markdown 围栏 → json.loads → 截取首尾方括号兜底。"""
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text = (text or "").strip()
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if text.startswith("```"):
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lines = [l for l in text.splitlines() if not l.strip().startswith("```")]
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text = "\n".join(lines).strip()
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for candidate in (text,):
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try:
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return json.loads(candidate)
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except Exception:
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pass
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i = text.find("[")
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j = text.rfind("]")
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if i != -1 and j > i:
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try:
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return json.loads(text[i:j + 1])
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except Exception:
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pass
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return []
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def _qa_for_batch(chunks: list[str]) -> list[list[str]]:
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"""对一批分块，用 LLM 生成每块的 3-5 个口语化问句（输出 JSON 数组，与块一一对应）。"""
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from .llm import _chat_once
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numbered = "\n\n".join(f"[{i}]\n{c}" for i, c in enumerate(chunks))
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def build_prompt():
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return (
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"下面是园区知识库的若干分块，每块标有 [编号]。"
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"请为每一块生成 3-5 个用户可能会问的自然口语化中文问句，"
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"覆盖该块全部信息点与常见同义问法。"
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f"严格只输出一个 JSON 数组，数组长度必须为 {len(chunks)}，"
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"第 i 个元素是第 i 块的问题字符串数组。不要输出任何其他文字或解释。\n\n"
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+ numbered
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)
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for attempt in range(2):
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try:
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data = _chat_once([{"role": "user", "content": build_prompt()}], with_tools=False)
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content = data["choices"][0]["message"].get("content") or ""
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arr = _parse_json_array(content)
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if isinstance(arr, list) and arr:
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out: list[list[str]] = []
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for i in range(len(chunks)):
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qs = arr[i] if i < len(arr) and isinstance(arr[i], list) else []
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out.append([str(q).strip() for q in qs if str(q).strip()][:5])
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return out
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except Exception as e: # noqa: BLE001
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log.warning("rag: QA 批生成失败（尝试 %d/%d）: %s", attempt + 1, 2, e)
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return [[] for _ in chunks]
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def _first_line(chunk: str) -> str:
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"""取块首行（去标题），用作无问句时的兜底检索问题。"""
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for ln in chunk.splitlines():
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ln = ln.strip()
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if ln and not ln.startswith("#"):
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return ln
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return chunk[:60]
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def _ensure_questions(chunks: list[str], questions) -> list[list[str]]:
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"""确保每块都有至少一个检索问题：未生成问句的块，用块首句兜底（保证全量覆盖）。"""
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qs: list[list[str]] = [list(q or []) for q in (questions or [])]
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if len(qs) < len(chunks):
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qs.extend([] for _ in range(len(chunks) - len(qs)))
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for i, c in enumerate(chunks):
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if not qs[i]:
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qs[i] = [_first_line(c)]
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return qs
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def _load_questions(chunks: list[str]) -> list[list[str]]:
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"""加载/生成问答缓存；返回与 chunks 对齐的问题数组（保证每块 ≥1 个问题）。无 key 时单路回退。"""
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from .config import settings
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if not settings.DASHSCOPE_API_KEY:
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log.warning("rag: 无 DASHSCOPE_API_KEY，跳过问答生成（单路检索）")
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return _ensure_questions(chunks, [[] for _ in chunks])
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h = _data_hash()
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if QA_FILE.exists():
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try:
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data = json.loads(QA_FILE.read_text("utf-8"))
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if data.get("data_hash") == h and data.get("chunks") == chunks:
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log.info("rag: 问答缓存已加载（%d 块）", len(chunks))
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return _ensure_questions(chunks, data["questions"])
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except Exception: # noqa: BLE001
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log.warning("rag: 问答缓存损坏，重建")
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questions: list[list[str]] = []
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total = len(chunks)
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for i in range(0, total, QA_BATCH):
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questions.extend(_qa_for_batch(chunks[i:i + QA_BATCH]))
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log.info("rag: 问答生成进度 %d/%d", min(i + QA_BATCH, total), total)
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questions = _ensure_questions(chunks, questions)
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QA_FILE.write_text(
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json.dumps({"data_hash": h, "chunks": chunks, "questions": questions}, ensure_ascii=False),
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|
"utf-8",
|
|||
|
|
)
|
|||
|
|
log.info("rag: 问答缓存已生成（%d 块）-> %s", len(chunks), QA_FILE)
|
|||
|
|
return questions
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ── 双路向量索引 ────────────────────────────────────────────────
|
|||
|
|
|
|||
|
|
def _migrate_legacy():
|
|||
|
|
"""把旧版单文件 kb_index.json（向量内嵌在 JSON）就地转为 kb_vectors.npy + kb_meta.json，再删除旧文件。"""
|
|||
|
|
if not LEGACY_INDEX_FILE.exists() or META_FILE.exists():
|
|||
|
|
return
|
|||
|
|
try:
|
|||
|
|
d = json.loads(LEGACY_INDEX_FILE.read_text("utf-8"))
|
|||
|
|
np.save(VECTOR_FILE, np.asarray(d["embeddings"], dtype=np.float32))
|
|||
|
|
META_FILE.write_text(
|
|||
|
|
json.dumps({
|
|||
|
|
"model": d.get("model"),
|
|||
|
|
"data_hash": d.get("data_hash"),
|
|||
|
|
"chunks": d["chunks"],
|
|||
|
|
"questions": d.get("questions", []),
|
|||
|
|
"embed_texts": d["embed_texts"],
|
|||
|
|
"embed_owner": d["embed_owner"],
|
|||
|
|
}, ensure_ascii=False),
|
|||
|
|
"utf-8",
|
|||
|
|
)
|
|||
|
|
LEGACY_INDEX_FILE.unlink()
|
|||
|
|
log.info("rag: 已迁移旧 kb_index.json -> kb_vectors.npy + kb_meta.json（无重建，未调 API）")
|
|||
|
|
except Exception as e: # noqa: BLE001
|
|||
|
|
log.warning("rag: 旧索引迁移失败（将重建）: %s", e)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _load_index():
|
|||
|
|
"""构建/加载双路向量索引（原句 + 生成问题）。向量存二进制 .npy，元数据存 JSON。"""
|
|||
|
|
global _index
|
|||
|
|
if _index is not None:
|
|||
|
|
return _index
|
|||
|
|
_migrate_legacy()
|
|||
|
|
h = _data_hash()
|
|||
|
|
if META_FILE.exists() and VECTOR_FILE.exists():
|
|||
|
|
try:
|
|||
|
|
data = json.loads(META_FILE.read_text("utf-8"))
|
|||
|
|
valid = (
|
|||
|
|
data.get("model") == EMBED_MODEL
|
|||
|
|
and data.get("data_hash") == h
|
|||
|
|
and len(data.get("embed_texts", [])) == len(data.get("embed_owner", []))
|
|||
|
|
)
|
|||
|
|
if valid:
|
|||
|
|
vectors = np.load(VECTOR_FILE) # 已归一化 float32
|
|||
|
|
if len(vectors) == len(data["embed_texts"]):
|
|||
|
|
_index = {
|
|||
|
|
"chunks": data["chunks"],
|
|||
|
|
"questions": data.get("questions", []),
|
|||
|
|
"embed_texts": data["embed_texts"],
|
|||
|
|
"embed_owner": data["embed_owner"],
|
|||
|
|
"embeddings": vectors,
|
|||
|
|
}
|
|||
|
|
log.info("rag: 双路知识索引已加载（%d 块 → %d 向量, %s）",
|
|||
|
|
len(_index["chunks"]), len(_index["embed_texts"]), EMBED_MODEL)
|
|||
|
|
return _index
|
|||
|
|
log.warning("rag: 索引失效（model/hash/长度不符），重建")
|
|||
|
|
except Exception: # noqa: BLE001
|
|||
|
|
log.warning("rag: 索引损坏，重建")
|
|||
|
|
# 构建
|
|||
|
|
chunks = _chunk_md(_kb_text())
|
|||
|
|
questions = _load_questions(chunks)
|
|||
|
|
embed_texts: list[str] = []
|
|||
|
|
embed_owner: list[int] = []
|
|||
|
|
for i, c in enumerate(chunks):
|
|||
|
|
embed_texts.append(c)
|
|||
|
|
embed_owner.append(i)
|
|||
|
|
# 企业块：把「企业名称」作为确定性问题加入索引，保证按名称可召回（名称为问、介绍为答）
|
|||
|
|
cname = _company_name(c)
|
|||
|
|
if cname:
|
|||
|
|
embed_texts.append(cname)
|
|||
|
|
embed_owner.append(i)
|
|||
|
|
for q in questions[i]:
|
|||
|
|
embed_texts.append(q)
|
|||
|
|
embed_owner.append(i)
|
|||
|
|
log.info("rag: 构建双路索引（%d 块 → %d 向量，%s embedding 中…）",
|
|||
|
|
len(chunks), len(embed_texts), EMBED_MODEL)
|
|||
|
|
arr = _normalize(_embed(embed_texts))
|
|||
|
|
_index = {
|
|||
|
|
"chunks": chunks,
|
|||
|
|
"questions": questions,
|
|||
|
|
"embed_texts": embed_texts,
|
|||
|
|
"embed_owner": embed_owner,
|
|||
|
|
"embeddings": arr,
|
|||
|
|
}
|
|||
|
|
np.save(VECTOR_FILE, arr)
|
|||
|
|
META_FILE.write_text(
|
|||
|
|
json.dumps({
|
|||
|
|
"model": EMBED_MODEL,
|
|||
|
|
"data_hash": h,
|
|||
|
|
"chunks": chunks,
|
|||
|
|
"questions": questions,
|
|||
|
|
"embed_texts": embed_texts,
|
|||
|
|
"embed_owner": embed_owner,
|
|||
|
|
}, ensure_ascii=False),
|
|||
|
|
"utf-8",
|
|||
|
|
)
|
|||
|
|
log.info("rag: 双路索引构建完成（%d 块, dim=%d）-> %s + %s",
|
|||
|
|
len(chunks), arr.shape[1], VECTOR_FILE, META_FILE)
|
|||
|
|
return _index
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ── 对外接口 ────────────────────────────────────────────────────
|
|||
|
|
|
|||
|
|
def retrieve(query: str, top_k: int = 3, threshold: float = 0.3) -> list[str]:
|
|||
|
|
"""双路检索：query 同时比对原句与问题向量，按块去重，返回 top_k 个原文块。"""
|
|||
|
|
if not query or not query.strip():
|
|||
|
|
return []
|
|||
|
|
idx = _load_index()
|
|||
|
|
q = _normalize([_embed([query])[0]])[0]
|
|||
|
|
sims = np.dot(idx["embeddings"], q)
|
|||
|
|
order = np.argsort(-sims)
|
|||
|
|
best: dict[int, float] = {} # owner -> 该块最高分
|
|||
|
|
for e in order:
|
|||
|
|
owner = idx["embed_owner"][int(e)]
|
|||
|
|
sc = float(sims[int(e)])
|
|||
|
|
if sc < threshold:
|
|||
|
|
break
|
|||
|
|
if owner not in best:
|
|||
|
|
best[owner] = sc
|
|||
|
|
if len(best) >= top_k:
|
|||
|
|
break
|
|||
|
|
ordered = sorted(best.items(), key=lambda kv: kv[1], reverse=True)
|
|||
|
|
hits = [idx["chunks"][o] for o, _ in ordered]
|
|||
|
|
log.info("rag: retrieve() query=%s top_k=%d threshold=%.2f -> 命中 %d 段",
|
|||
|
|
_trunc(query, 120), top_k, threshold, len(hits))
|
|||
|
|
for j, (o, sc) in enumerate(ordered):
|
|||
|
|
log.info("rag: [%d] %.4f %s", j, sc, _trunc(idx["chunks"][o], 300))
|
|||
|
|
return hits
|
|||
|
|
|
|||
|
|
|
|||
|
|
def brief(max_chars: int = 1200) -> str:
|
|||
|
|
"""固定知识摘要（前 N 字；兼容 /api/kb/brief 与检索无命中回退）。"""
|
|||
|
|
idx = _load_index()
|
|||
|
|
out = []
|
|||
|
|
total = 0
|
|||
|
|
for c in idx["chunks"]:
|
|||
|
|
if total + len(c) > max_chars:
|
|||
|
|
break
|
|||
|
|
out.append(c)
|
|||
|
|
total += len(c)
|
|||
|
|
text = "\n\n".join(out)
|
|||
|
|
log.info("rag: brief() 知识库摘要 %d 字 -> %s", total, _trunc(text, 300))
|
|||
|
|
return text
|
|||
|
|
|
|||
|
|
|
|||
|
|
def full_kb() -> str:
|
|||
|
|
"""整库原文（小库全量注入，用于语音 instructions，修复尾部知识丢失）。"""
|
|||
|
|
idx = _load_index()
|
|||
|
|
text = "\n\n".join(idx["chunks"])
|
|||
|
|
log.info("rag: full_kb() 全库 %d 字 / %d 块", len(text), len(idx["chunks"]))
|
|||
|
|
return text
|
|||
|
|
|
|||
|
|
|
|||
|
|
def build_instructions() -> str:
|
|||
|
|
"""语音助手的完整基础提示词 = 配置提示词 + 整库知识（服务端统一组装）。"""
|
|||
|
|
from .config import settings
|
|||
|
|
base = settings.S2S_INSTRUCTIONS.strip()
|
|||
|
|
try:
|
|||
|
|
kb = full_kb()
|
|||
|
|
if kb:
|
|||
|
|
base += f"\n\n【园区知识库】\n{kb}"
|
|||
|
|
except Exception as e: # noqa: BLE001
|
|||
|
|
log.warning("rag: 知识库拼接失败（仅用基础提示词）: %s", e)
|
|||
|
|
log.info("rag: build_instructions() 语音系统提示词=%s", _trunc(base, 600))
|
|||
|
|
return base
|