feat(chat): 基于 RAG 的笔记内容 AI 问答功能

实现类似 Google NotebookLM 的效果：笔记生成后自动向量化，
用户可针对笔记内容进行 LLM 问答。

### 后端
- 新增 VectorStoreManager（ChromaDB），按标题/转录分块建立向量索引
- 新增 chat_service.py RAG 问答：检索相关片段 → 构建 prompt → 调用 LLM
- 新增 /chat/index, /chat/ask, /chat/status API 端点
- 笔记生成完成后自动建立向量索引

### 前端
- 使用 @ant-design/x Bubble.List + Sender 组件构建聊天面板
- 新增 chatStore（Zustand + persist）持久化聊天记录
- MarkdownViewer 右侧嵌入 ChatPanel，通过"AI 问答"按钮切换
- 首次打开自动检查/触发索引，支持重新索引

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/services/vector_store.py

@@ -0,0 +1,155 @@
+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 名称需满足限制：3-63字符，字母数字开头结尾。"""
+        safe = re.sub(r'[^a-zA-Z0-9_-]', '_', task_id)[:60]
+        if not safe or not safe[0].isalnum():
+            safe = "t" + safe
+        if not safe[-1].isalnum():
+            safe = safe + "0"
+        return safe
+
+    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 ValueError:
+            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 ValueError:
+            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 ValueError:
+            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 ValueError:
+            return False