mirror of
https://github.com/JefferyHcool/BiliNote.git
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3e9f908d7b
之前各来源各取 n_results 条再按距离排序取 top-n， markdown 距离普遍更近导致 transcript 被挤掉。 改为固定配额：meta 1 条、markdown 2 条、transcript 3 条。 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
227 lines
7.4 KiB
Python
227 lines
7.4 KiB
Python
import json
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import os
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import re
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from typing import Optional
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import chromadb
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from chromadb.config import Settings
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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NOTE_OUTPUT_DIR = os.getenv("NOTE_OUTPUT_DIR", "note_results")
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VECTOR_DB_DIR = os.getenv("VECTOR_DB_DIR", "vector_db")
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def _chunk_markdown(markdown: str) -> list[dict]:
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"""按 H2/H3 标题拆分 markdown 为语义块。"""
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sections = re.split(r'(?=^#{2,3}\s)', markdown, flags=re.MULTILINE)
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chunks = []
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for section in sections:
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section = section.strip()
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if not section or len(section) < 30:
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continue
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heading_match = re.match(r'^(#{2,3})\s+(.+)', section)
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title = heading_match.group(2).strip() if heading_match else "intro"
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chunks.append({
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"text": section,
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"metadata": {"source_type": "markdown", "section_title": title},
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})
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return chunks
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def _chunk_transcript(segments: list[dict], window_size: int = 15, overlap: int = 3) -> list[dict]:
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"""将转录 segments 按滑动窗口分组。"""
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if not segments:
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return []
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chunks = []
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step = max(window_size - overlap, 1)
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for i in range(0, len(segments), step):
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window = segments[i:i + window_size]
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if not window:
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break
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text = "\n".join(
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f"[{seg.get('start', 0):.0f}s] {seg.get('text', '')}" for seg in window
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)
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chunks.append({
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"text": text,
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"metadata": {
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"source_type": "transcript",
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"start_time": window[0].get("start", 0),
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"end_time": window[-1].get("end", 0),
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},
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})
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return chunks
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def _build_meta_chunk(audio_meta: dict) -> list[dict]:
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"""将视频元信息（标题、作者、描述、标签等）构建为可检索的 chunk。"""
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if not audio_meta:
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return []
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raw = audio_meta.get("raw_info", {}) or {}
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parts = []
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title = audio_meta.get("title") or raw.get("title", "")
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if title:
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parts.append(f"视频标题：{title}")
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uploader = raw.get("uploader", "")
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if uploader:
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parts.append(f"视频作者/UP主：{uploader}")
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desc = raw.get("description", "")
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if desc:
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parts.append(f"视频简介：{desc[:500]}")
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tags = raw.get("tags", [])
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if tags and isinstance(tags, list):
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parts.append(f"标签：{', '.join(str(t) for t in tags[:20])}")
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duration = audio_meta.get("duration", 0)
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if duration:
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m, s = divmod(int(duration), 60)
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parts.append(f"视频时长：{m}分{s}秒")
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platform = audio_meta.get("platform", "")
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if platform:
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parts.append(f"平台：{platform}")
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url = raw.get("webpage_url", "")
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if url:
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parts.append(f"链接：{url}")
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if not parts:
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return []
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return [{
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"text": "\n".join(parts),
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"metadata": {"source_type": "meta"},
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}]
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class VectorStoreManager:
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"""基于 ChromaDB 的笔记向量存储管理器。"""
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def __init__(self):
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os.makedirs(VECTOR_DB_DIR, exist_ok=True)
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self._client = chromadb.PersistentClient(
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path=VECTOR_DB_DIR,
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settings=Settings(anonymized_telemetry=False),
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)
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def _collection_name(self, task_id: str) -> str:
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"""ChromaDB collection 名称：直接使用 task_id（UUID 格式合法）。"""
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return task_id
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def index_task(self, task_id: str) -> None:
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"""读取笔记结果并建立向量索引。"""
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result_path = os.path.join(NOTE_OUTPUT_DIR, f"{task_id}.json")
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if not os.path.exists(result_path):
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logger.warning(f"笔记文件不存在，跳过索引: {result_path}")
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return
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with open(result_path, "r", encoding="utf-8") as f:
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note_data = json.load(f)
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markdown = note_data.get("markdown", "")
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transcript = note_data.get("transcript", {})
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segments = transcript.get("segments", [])
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audio_meta = note_data.get("audio_meta", {})
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meta_chunks = _build_meta_chunk(audio_meta)
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md_chunks = _chunk_markdown(markdown)
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tr_chunks = _chunk_transcript(segments)
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all_chunks = meta_chunks + md_chunks + tr_chunks
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if not all_chunks:
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logger.warning(f"笔记内容为空，跳过索引: {task_id}")
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return
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col_name = self._collection_name(task_id)
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# 删除旧 collection（幂等）
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try:
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self._client.delete_collection(col_name)
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except Exception:
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pass
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collection = self._client.create_collection(
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name=col_name,
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metadata={"hnsw:space": "cosine"},
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)
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documents = [c["text"] for c in all_chunks]
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metadatas = [c["metadata"] for c in all_chunks]
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ids = [f"{task_id}_{i}" for i in range(len(all_chunks))]
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collection.add(documents=documents, metadatas=metadatas, ids=ids)
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logger.info(f"向量索引完成: task_id={task_id}, chunks={len(all_chunks)}")
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def _parse_results(self, results: dict) -> list[dict]:
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"""将 ChromaDB query 结果转换为 chunk 列表。"""
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chunks = []
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if not results or not results.get("documents") or not results["documents"][0]:
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return chunks
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for i in range(len(results["documents"][0])):
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chunks.append({
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"text": results["documents"][0][i],
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"metadata": results["metadatas"][0][i] if results["metadatas"] else {},
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"distance": results["distances"][0][i] if results["distances"] else None,
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})
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return chunks
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def query(self, task_id: str, query_text: str, n_results: int = 6) -> list[dict]:
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"""
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按固定配额从各来源检索：meta 1 条、markdown 2 条、transcript 3 条，
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确保三种来源都被召回。
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"""
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col_name = self._collection_name(task_id)
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try:
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collection = self._client.get_collection(col_name)
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except Exception:
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logger.warning(f"Collection 不存在: {col_name}")
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return []
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all_chunks = []
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# 每种来源的配额
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quotas = {"meta": 1, "markdown": 2, "transcript": 3}
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for source_type, quota in quotas.items():
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try:
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results = collection.query(
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query_texts=[query_text],
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n_results=quota,
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where={"source_type": source_type},
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)
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all_chunks.extend(self._parse_results(results))
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except Exception:
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pass
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return all_chunks
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def delete_index(self, task_id: str) -> None:
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"""删除指定任务的向量索引。"""
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col_name = self._collection_name(task_id)
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try:
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self._client.delete_collection(col_name)
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logger.info(f"已删除向量索引: {task_id}")
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except Exception:
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pass
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def is_indexed(self, task_id: str) -> bool:
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"""检查指定任务是否已建立完整索引（含 meta 信息）。"""
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col_name = self._collection_name(task_id)
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try:
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col = self._client.get_collection(col_name)
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if col.count() == 0:
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return False
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# 检查是否包含 meta chunk，旧索引可能缺失
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meta = col.get(where={"source_type": "meta"}, limit=1)
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return len(meta["ids"]) > 0
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except Exception:
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return False
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