BiliNote/backend/app/services/vector_store.py

import json
import os
import re
from typing import Optional

import chromadb
from chromadb.config import Settings

from app.utils.logger import get_logger

logger = get_logger(__name__)

NOTE_OUTPUT_DIR = os.getenv("NOTE_OUTPUT_DIR", "note_results")
VECTOR_DB_DIR = os.getenv("VECTOR_DB_DIR", "vector_db")


def _chunk_markdown(markdown: str) -> list[dict]:
    """按 H2/H3 标题拆分 markdown 为语义块。"""
    sections = re.split(r'(?=^#{2,3}\s)', markdown, flags=re.MULTILINE)
    chunks = []
    for section in sections:
        section = section.strip()
        if not section or len(section) < 30:
            continue
        heading_match = re.match(r'^(#{2,3})\s+(.+)', section)
        title = heading_match.group(2).strip() if heading_match else "intro"
        chunks.append({
            "text": section,
            "metadata": {"source_type": "markdown", "section_title": title},
        })
    return chunks


def _chunk_transcript(segments: list[dict], window_size: int = 15, overlap: int = 3) -> list[dict]:
    """将转录 segments 按滑动窗口分组。"""
    if not segments:
        return []
    chunks = []
    step = max(window_size - overlap, 1)
    for i in range(0, len(segments), step):
        window = segments[i:i + window_size]
        if not window:
            break
        text = "\n".join(
            f"[{seg.get('start', 0):.0f}s] {seg.get('text', '')}" for seg in window
        )
        chunks.append({
            "text": text,
            "metadata": {
                "source_type": "transcript",
                "start_time": window[0].get("start", 0),
                "end_time": window[-1].get("end", 0),
            },
        })
    return chunks


class VectorStoreManager:
    """基于 ChromaDB 的笔记向量存储管理器。"""

    def __init__(self):
        os.makedirs(VECTOR_DB_DIR, exist_ok=True)
        self._client = chromadb.PersistentClient(
            path=VECTOR_DB_DIR,
            settings=Settings(anonymized_telemetry=False),
        )

    def _collection_name(self, task_id: str) -> str:
        """ChromaDB collection 名称：直接使用 task_id（UUID 格式合法）。"""
        return task_id

    def index_task(self, task_id: str) -> None:
        """读取笔记结果并建立向量索引。"""
        result_path = os.path.join(NOTE_OUTPUT_DIR, f"{task_id}.json")
        if not os.path.exists(result_path):
            logger.warning(f"笔记文件不存在，跳过索引: {result_path}")
            return

        with open(result_path, "r", encoding="utf-8") as f:
            note_data = json.load(f)

        markdown = note_data.get("markdown", "")
        transcript = note_data.get("transcript", {})
        segments = transcript.get("segments", [])

        md_chunks = _chunk_markdown(markdown)
        tr_chunks = _chunk_transcript(segments)
        all_chunks = md_chunks + tr_chunks

        if not all_chunks:
            logger.warning(f"笔记内容为空，跳过索引: {task_id}")
            return

        col_name = self._collection_name(task_id)

        # 删除旧 collection（幂等）
        try:
            self._client.delete_collection(col_name)
        except Exception:
            pass

        collection = self._client.create_collection(
            name=col_name,
            metadata={"hnsw:space": "cosine"},
        )

        documents = [c["text"] for c in all_chunks]
        metadatas = [c["metadata"] for c in all_chunks]
        ids = [f"{task_id}_{i}" for i in range(len(all_chunks))]

        collection.add(documents=documents, metadatas=metadatas, ids=ids)
        logger.info(f"向量索引完成: task_id={task_id}, chunks={len(all_chunks)}")

    def query(self, task_id: str, query_text: str, n_results: int = 5) -> list[dict]:
        """检索与查询最相关的文档片段。"""
        col_name = self._collection_name(task_id)
        try:
            collection = self._client.get_collection(col_name)
        except Exception:
            logger.warning(f"Collection 不存在: {col_name}")
            return []

        results = collection.query(query_texts=[query_text], n_results=n_results)

        chunks = []
        for i in range(len(results["documents"][0])):
            chunks.append({
                "text": results["documents"][0][i],
                "metadata": results["metadatas"][0][i] if results["metadatas"] else {},
                "distance": results["distances"][0][i] if results["distances"] else None,
            })
        return chunks

    def delete_index(self, task_id: str) -> None:
        """删除指定任务的向量索引。"""
        col_name = self._collection_name(task_id)
        try:
            self._client.delete_collection(col_name)
            logger.info(f"已删除向量索引: {task_id}")
        except Exception:
            pass

    def is_indexed(self, task_id: str) -> bool:
        """检查指定任务是否已建立索引。"""
        col_name = self._collection_name(task_id)
        try:
            col = self._client.get_collection(col_name)
            return col.count() > 0
        except Exception:
            return False