I need to understand the existing Obsidian bot mode architecture in /root/projects/butler for planning a new "mode info" feature. Please find and summarize:

1. How modes are registered and routed in dispatcher.py (the VALID_MODES set, how generators map to modes)
2. How the existing MODE_NOTE works (note_ingester.py) - this is the closest existing feature to "mode info"
3. The vault directory structure under the OBSIDIAN_VAULT_PATH - what subdirectories exist, especially for notes
4. How NoteIngester saves notes (file naming, frontmatter format, directory structure)
5. The Pydantic models used (ExtractedNote, etc.)

Don't read generators.py (I already have it). Focus on: dispatcher.py mode registration, note_ingester.py, and vault structure. Be concise.


I'll help you understand the Obsidian bot mode architecture. Let me search for the key files and information you need.


Bash

find /root/vault/obsidian_vault -type d -maxdepth 3 2>/dev/null | head -30

Explore Obsidian vault directory structure


grep -r "OBSIDIAN_VAULT_PATH" /root/projects/butler --include="*.py" | head -5

Find vault path references


bash "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags-shell.sh" "pre:observe" "skills/continuous-learning-v2/hooks/observe.sh" "standard,strict"


node "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags.js" "pre:bash:tmux-reminder" "scripts/hooks/pre-bash-tmux-reminder.js" "strict"


node "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags.js" "pre:bash:git-push-reminder" "scripts/hooks/pre-bash-git-push-reminder.js" "strict"


node "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags.js" "pre:insaits-security" "scripts/hooks/insaits-security-wrapper.js" "standard,strict"


     1→import os
     2→from pathlib import Path
     3→from typing import Optional, List, Dict, Any
     4→from slack_sdk import WebClient
     5→
     6→from health.utils.logging_config import setup_logger
     7→from slack_bot.obsidian.indexer import ObsidianIndexer
     8→from slack_bot.obsidian.generators import WritingAssistant, ReplyGenerator, DecisionSupport, SearchAnalyzer, DeAIReviser, ZhihuGenerator
     9→from slack_bot.context.storage import ContextStorage as BaseStorage
    10→
    11→logger = setup_logger(__name__)
    12→
    13→class ObsidianContextStorage(BaseStorage):
    14→    """Specific storage for Obsidian bot to avoid mixing with Health bot."""
    15→    def __init__(self, channel_id: str):
    16→        super().__init__(channel_id)
    17→        from health import config
    18→        self.context_dir = config.DATA_DIR / "obsidian_context"
    19→        self.context_dir.mkdir(parents=True, exist_ok=True)
    20→        self.file_path = self.context_dir / f"{channel_id}.json"
    21→        self.state_file = self.context_dir / f"{channel_id}_state.json"
    22→
    23→    def save_state(self, state: Dict[str, Any]):
    24→        import json
    25→        with open(self.state_file, "w") as f:
    26→            json.dump(state, f)
    27→
    28→    def load_state(self) -> Dict[str, Any]:
    29→        import json
    30→        if not self.state_file.exists():
    31→            return {"mode": "none"}
    32→        try:
    33→            with open(self.state_file, "r") as f:
    34→                return json.load(f)
    35→        except:
    36→            return {"mode": "none"}
    37→
    38→class ObsidianDispatcher:
    39→    """Routes Obsidian Slack Bot messages."""
    40→
    41→    MODE_WRITE = "write"
    42→    MODE_REPLY = "reply"
    43→    MODE_DECIDE = "decide"
    44→    MODE_SEARCH = "search"
    45→    MODE_DEAI = "deai"
    46→    MODE_ZHIHU = "zhihu"
    47→    MODE_NOTE = "note"
    48→    MODE_ZHIHU_HUNTER = "zhihu-hunter"
    49→
    50→    VALID_MODES = {MODE_WRITE, MODE_REPLY, MODE_DECIDE, MODE_SEARCH, MODE_DEAI, MODE_ZHIHU, MODE_NOTE, MODE_ZHIHU_HUNTER}
    51→
    52→    def __init__(self, vault_path: str):
    53→        self.indexer = ObsidianIndexer(vault_path)
    54→        self.indexer.scan_vault()
    55→
    56→        # Initialize RAG vector store and note ingester
    57→        from slack_bot.obsidian.embeddings import get_embedding_provider
    58→        from slack_bot.obsidian.vector_store import ChromaVectorStore
    59→        from slack_bot.obsidian.note_ingester import NoteIngester
    60→
    61→        self.vector_store = ChromaVectorStore(embedding_provider=get_embedding_provider())
    62→        self.note_ingester = NoteIngester(
    63→            vault_path=Path(vault_path),
    64→            vector_store=self.vector_store,
    65→        )
    66→
    67→        self.generators = {
    68→            self.MODE_WRITE: WritingAssistant(self.indexer, self.vector_store),
    69→            self.MODE_REPLY: ReplyGenerator(self.indexer, self.vector_store),
    70→            self.MODE_DECIDE: DecisionSupport(self.indexer),
    71→            self.MODE_SEARCH: SearchAnalyzer(self.indexer),
    72→            self.MODE_DEAI: DeAIReviser(self.indexer),
    73→            self.MODE_ZHIHU: ZhihuGenerator(self.indexer, self.vector_store),
    74→        }
    75→
    76→        token = os.environ.get("OBSIDIAN_SLACK_BOT_TOKEN")
    77→        self.client = WebClient(token=token)
    78→
    79→        # Initialize Zhihu Hunter components
    80→        from slack_bot.zhihu.zhihu_hunter import ZhihuHunter
    81→        from slack_bot.zhihu.zhihu_playwright_engine import ZhihuPlaywrightEngine
    82→        from slack_bot.zhihu.slack_interactive_gateway import SlackInteractiveGateway
    83→
    84→        zhihu_notify_channel = os.environ.get("ZHIHU_NOTIFY_CHANNEL", "")
    85→        self.zhihu_hunter = ZhihuHunter(
    86→            vault_path=Path(vault_path),
    87→            vector_store=self.vector_store,
    88→            indexer=self.indexer,
    89→        )
    90→        _zhihu_engine = ZhihuPlaywrightEngine(
    91→            slack_client=self.client,
    92→            notify_channel=zhihu_notify_channel,
    93→        )
    94→        self.zhihu_gateway = SlackInteractiveGateway(
    95→            slack_client=self.client,
    96→            hunter=self.zhihu_hunter,
    97→            engine=_zhihu_engine,
    98→            notify_channel=zhihu_notify_channel,
    99→        )
   100→        logger.info("ObsidianDispatcher initialized")
   101→
   102→    def dispatch(self, text: str, channel_id: str, user_id: str, response_ts: Optional[str] = None):
   103→        storage = ObsidianContextStorage(channel_id)
   104→        state = storage.load_state()
   105→        current_mode = state.get("mode", "none")
   106→
   107→        cmd = text.strip().lower()
   108→
   109→        # 1. Handle Commands
   110→        if cmd.startswith("mode "):
   111→            new_mode = cmd.split(" ", 1)[1].strip()
   112→            if new_mode in self.VALID_MODES:
   113→                storage.clear()
   114→                storage.save_state({"mode": new_mode})
   115→                self._reply(channel_id, f"✅ Switched to mode: *{new_mode}* (History cleared)", response_ts)
   116→                return
   117→            else:
   118→                self._reply(channel_id, f"❌ Invalid mode. Available: {', '.join(sorted(self.VALID_MODES))}", response_ts)
   119→                return
   120→
   121→        if cmd == "clear":
   122→            storage.clear()
   123→            self._reply(channel_id, "🧹 Context cleared.", response_ts)
   124→            return
   125→
   126→        if cmd == "reload":
   127→            self.indexer.scan_vault()
   128→            self._reply(channel_id, "🔄 Vault reloaded.", response_ts)
   129→            return
   130→
   131→        # 2. Handle Content based on mode
   132→        if current_mode == "none":
   133→            self._reply(channel_id, "⚠ No mode selected. Use `mode write/reply/decide/search/deai/zhihu/note/zhihu-hunter` to start.", response_ts)
   134→            return
   135→
   136→        # MODE_NOTE: ingest text or URL into vault + vector store
   137→        if current_mode == self.MODE_NOTE:
   138→            try:
   139→                result = self.note_ingester.ingest(text)
   140→                self._reply(channel_id, result, response_ts)
   141→            except Exception as e:
   142→                logger.error(f"Note ingestion failed: {e}", exc_info=True)
   143→                self._reply(channel_id, f"🐛 Ingestion error: {str(e)}", response_ts)
   144→            return
   145→
   146→        # MODE_ZHIHU_HUNTER: scan vault for keywords, surface Zhihu question candidates
   147→        #
   148→        # Command format:
   149→        #   开始                               → full scan, all dirs, auto keywords
   150→        #   article                            → scan Article only (Revised first, no time filter)
   151→        #   article agent开发                  → scan Article, focus on "agent开发"
   152→        #   notes agent开发                    → scan notes, focus on "agent开发"
   153→        #   关键词1 关键词2                     → skip scan, search directly with keywords
   154→        #   Article/8-xxx-Revised.md           → single file: grounded in that article
   155→        #   Article/*openclaw*-Revised.md      → glob: combine matching files
   156→        #   https://www.zhihu.com/question/... → direct URL: answer this specific question
   157→        #
   158→        # Dir shortcuts: notes / article / clippings / claw / deepfact / deep-dive
   159→        if current_mode == self.MODE_ZHIHU_HUNTER:
   160→            from slack_bot.zhihu.zhihu_hunter import DIR_SHORTCUTS
   161→            try:
   162→                trigger_words = {"开始", "hunt", "扫描", "start", "go"}
   163→                words = [w for w in text.strip().split() if w.lower() not in trigger_words]
   164→
   165→                if not words:
   166→                    # Bare trigger → full auto scan
   167→                    self._reply(channel_id, "🔍 正在扫描知乎候选问题（所有目录）...", response_ts)
   168→                    questions = self.zhihu_hunter.scan_and_hunt(since_days=7)
   169→
   170→                elif words and 'zhihu.com/question/' in words[0]:
   171→                    # Direct URL paste — user was invited or found a question to answer.
   172→                    # Any text after the URL is treated as an optional answer outline.
   173→                    # Strip trailing Slack/HTML junk chars (>, ", etc.) from the URL.
   174→                    import re as _re
   175→                    url = _re.sub(r'[>\'")\].,;]+$', '', words[0]).lstrip("<")
   176→                    outline = " ".join(words[1:])[:500] if len(words) > 1 else ""
   177→                    hint = f"（已记录回答思路）" if outline else ""
   178→                    self._reply(channel_id, f"🔍 获取问题标题，准备生成草稿{hint}...", response_ts)
   179→                    questions = self.zhihu_hunter.hunt_direct_url(url)
   180→                    if outline and questions:
   181→                        questions[0].outline = outline
   182→                    logger.info(f"ZhihuHunter: direct URL {url!r} → {questions[0].title!r}" +
   183→                                (f" outline={outline[:40]!r}" if outline else ""))
   184→
   185→                elif words and ('*' in words[0] or '?' in words[0]):
   186→                    # Glob pattern → combine all matched files for keyword extraction
   187→                    pattern = words[0]
   188→                    self._reply(
   189→                        channel_id,
   190→                        f"🔍 匹配 `{pattern}` 中的文件，提取观点搜索知乎问题...",
   191→                        response_ts,
   192→                    )
   193→                    questions = self.zhihu_hunter.scan_glob_files(pattern)
   194→                    logger.info(f"ZhihuHunter: glob '{pattern}' → {len(questions)} questions")
   195→
   196→                elif words and ('/' in words[0] or words[0].endswith('.md')):
   197→                    # Single-file mode: extract questions from one specific file,
   198→                    # and pass source_file so answers stay grounded in that article.
   199→                    rel_path = words[0]
   200→                    self._reply(
   201→                        channel_id,
   202→                        f"🔍 从 `{rel_path}` 提取观点，搜索匹配知乎问题...",
   203→                        response_ts,
   204→                    )
   205→                    questions = self.zhihu_hunter.scan_single_file(rel_path)
   206→                    logger.info(f"ZhihuHunter: single-file scan '{rel_path}' → {len(questions)} questions")
   207→
   208→                elif words[0].lower() in DIR_SHORTCUTS:
   209→                    # Dir-scoped scan with optional topic hint
   210→                    rel_dir = DIR_SHORTCUTS[words[0].lower()]
   211→                    topic_hint = " ".join(words[1:])
   212→                    hint_desc = f"，聚焦：{topic_hint}" if topic_hint else ""
   213→                    self._reply(
   214→                        channel_id,
   215→                        f"🔍 扫描 {rel_dir} 目录{hint_desc}，请稍候...",
   216→                        response_ts,
   217→                    )
   218→                    questions = self.zhihu_hunter.scan_and_hunt(
   219→                        since_days=7, dirs=[rel_dir], topic_hint=topic_hint
   220→                    )
   221→
   222→                else:
   223→                    # Direct keywords → skip vault scan entirely
   224→                    self._reply(channel_id, f"🔍 使用指定关键词搜索：{' · '.join(words)}", response_ts)
   225→                    questions = self.zhihu_hunter._find_questions(words)
   226→                    logger.info(f"ZhihuHunter: manual keywords {words} → {len(questions)} questions")
   227→
   228→                self.zhihu_gateway.post_question_cards(questions, channel=channel_id)
   229→            except Exception as e:
   230→                logger.error(f"Zhihu hunt failed: {e}", exc_info=True)
   231→                self._reply(channel_id, f"❌ 扫描失败：{e}", response_ts)
   232→            return
   233→
   234→        # Get history
   235→        history = storage.get_context()
   236→        generator = self.generators[current_mode]
   237→
   238→        try:
   239→            logger.info(f"Generating Obsidian response in mode: {current_mode}")
   240→            # The chat method handles first turn logic and returns updated history
   241→            # But wait, our generators' chat method appends to history internally.
   242→            # In our Slack setup, we want to append the result to ContextStorage.
   243→
   244→            # Note: generator.chat(text, history) returns (response, new_history_list)
   245→            # new_history_list contains [...history, {user: rich_prompt}, {assistant: response}]
   246→            response_text, updated_history = generator.chat(text, history)
   247→
   248→            # Since generator.chat already did the LLM call and returned history,
   249→            # we just need to save the ONLY NEW messages to our storage.
   250→            # However, during FIRST TURN, text is replaced by rich_prompt.
   251→
   252→            # For simplicity: completely overwrite storage with updated_history
   253→            # ContextStorage doesn't support overwrite easily, but we can clear and re-add.
   254→            # Better: Modify ObsidianContextStorage to support set_context.
   255→
   256→            # Actually, let's just use the updated_history and manually add to storage
   257→            # Warning: first turn 'user' message in storage will be the RICH PROMPT
   258→            # This is actually better for context continuity in Slack too.
   259→
   260→            storage.clear()
   261→            for msg in updated_history:
   262→                storage.add_message(msg["role"], msg["content"])
   263→
   264→            self._reply(channel_id, response_text, response_ts)
   265→
   266→        except Exception as e:
   267→            logger.error(f"Obsidian generation failed: {e}", exc_info=True)
   268→            self._reply(channel_id, f"🐛 Error: {str(e)}", response_ts)
   269→
   270→    def _reply(self, channel_id: str, text: str, ts: Optional[str] = None):
   271→        from slack_bot.utils.mrkdwn import SlackFormatter
   272→
   273→        # Slack API limits (official documentation):
   274→        # - chat.postMessage: 40,000 chars
   275→        # - chat.update: 4,000 chars
   276→        # Slack markdown formatting can expand text by 50-100% (bold, links, code blocks, etc.)
   277→        TRUNCATE_WARNING = "\n\n⚠ (Response truncated, full text uploaded as file)"
   278→        FILE_UPLOAD_HINT = "📄 Full response uploaded as file above ☝"
   279→
   280→        # Calculate safe pre-formatting limit with aggressive overhead buffer
   281→        if ts:
   282→            # For updates: 4000 limit - 60% overhead for safety = ~1600 chars
   283→            max_raw_length = 1500
   284→        else:
   285→            # For new messages: 40000 limit - 40% overhead = ~24000 chars
   286→            max_raw_length = 24000
   287→
   288→        original_length = len(text)
   289→        should_upload_file = original_length > max_raw_length
   290→
   291→        if should_upload_file:
   292→            # Upload full text as file first
   293→            self._upload_as_file(channel_id, text, ts)
   294→
   295→            # Then send a truncated preview
   296→            truncate_at = text.rfind('\n\n', 0, 800)
   297→            if truncate_at == -1 or truncate_at < 400:
   298→                truncate_at = text.rfind('\n', 0, 800)
   299→            if truncate_at == -1 or truncate_at < 400:
   300→                truncate_at = 800
   301→
   302→            preview_text = text[:truncate_at] + f"\n\n...\n\n{FILE_UPLOAD_HINT}"
   303→            formatted_text = SlackFormatter.convert(preview_text)
   304→            logger.info(f"Uploaded full response ({original_length} chars) as file, sending preview ({len(preview_text)} chars)")
   305→        else:
   306→            # Text is short enough, just format and send
   307→            formatted_text = SlackFormatter.convert(text)
   308→
   309→            # Final safety check after formatting
   310→            final_limit = 3900 if ts else 39900
   311→            if len(formatted_text) > final_limit:
   312→                logger.warning(f"Formatted text ({len(formatted_text)} chars) exceeds limit after formatting. Uploading as file.")
   313→                self._upload_as_file(channel_id, text, ts)
   314→                # Send simple notification
   315→                formatted_text = FILE_UPLOAD_HINT
   316→
   317→        # Send the message
   318→        try:
   319→            if ts:
   320→                self.client.chat_update(channel=channel_id, ts=ts, text=formatted_text)
   321→            else:
   322→                self.client.chat_postMessage(channel=channel_id, text=formatted_text)
   323→        except Exception as e:
   324→            if "msg_too_long" in str(e):
   325→                logger.error(f"Still got msg_too_long error even after truncation. Text length: {len(formatted_text)}")
   326→                # Ultimate fallback: send error message only
   327→                try:
   328→                    error_msg = "❌ Response formatting error. Please check the uploaded file above."
   329→                    if ts:
   330→                        self.client.chat_update(channel=channel_id, ts=ts, text=error_msg)
   331→                    else:
   332→                        self.client.chat_postMessage(channel=channel_id, text=error_msg)
   333→                except:
   334→                    logger.error("Failed to send even error message", exc_info=True)
   335→            else:
   336→                raise
   337→
   338→    def _upload_as_file(self, channel_id: str, content: str, ts: Optional[str] = None):
   339→        """Fallback: upload long responses as text files."""
   340→        try:
   341→            import tempfile
   342→            from datetime import datetime
   343→
   344→            filename = f"response_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt"
   345→
   346→            with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False, encoding='utf-8') as tmp:
   347→                tmp.write(content)
   348→                tmp_path = tmp.name
   349→
   350→            self.client.files_upload_v2(
   351→                channel=channel_id,
   352→                file=tmp_path,
   353→                filename=filename,
   354→                initial_comment="📄 Response too long for Slack message, uploaded as file:",
   355→                thread_ts=ts
   356→            )
   357→
   358→            os.unlink(tmp_path)
   359→            logger.info(f"Uploaded long response as file: {filename}")
   360→        except Exception as e:
   361→            logger.error(f"Failed to upload file: {e}", exc_info=True)
   362→            # Ultimate fallback: send error message
   363→            error_msg = "❌ Response too long to display. Please try a more specific query."
   364→            if ts:
   365→                self.client.chat_update(channel=channel_id, ts=ts, text=error_msg)
   366→            else:
   367→                self.client.chat_postMessage(channel=channel_id, text=error_msg)
   368→
   369→


bash "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags-shell.sh" "post:observe" "skills/continuous-learning-v2/hooks/observe.sh" "standard,strict"


1→"""Note ingestion engine for the Second Brain RAG system."""
     2→
     3→import json
     4→import os
     5→import re
     6→from datetime import datetime, timezone
     7→from pathlib import Path
     8→from typing import List, Optional
     9→
    10→from pydantic import BaseModel
    11→
    12→from health.utils.logging_config import setup_logger
    13→from slack_bot.llm.gemini import GeminiLLM
    14→from slack_bot.obsidian.vector_store import ChunkMetadata, ChromaVectorStore, split_md_by_headers
    15→
    16→logger = setup_logger(__name__)
    17→
    18→
    19→class ExtractedNote(BaseModel):
    20→    """LLM-extracted structured note from raw input text."""
    21→
    22→    title: str
    23→    summary: str           # 2-3 sentences
    24→    key_points: List[str]  # up to 7 items
    25→    entities: List[str]    # people, concepts, projects; wrapped as [[entity]] in cleaned_body
    26→    cleaned_body: str      # full text with entities wrapped in [[double brackets]]
    27→    source_url: Optional[str] = None
    28→    language: str = "zh"
    29→
    30→
    31→class NoteIngester:
    32→    """Ingests raw text or URLs into the Obsidian vault and vector store."""
    33→
    34→    def __init__(self, vault_path: Path, vector_store: ChromaVectorStore) -> None:
    35→        """Initialize the note ingester.
    36→
    37→        Args:
    38→            vault_path: Root path of the Obsidian vault.
    39→            vector_store: ChromaDB vector store for indexing chunks.
    40→        """
    41→        self.vault_path = vault_path
    42→        self.notes_dir = vault_path / "notes"
    43→        self.notes_dir.mkdir(parents=True, exist_ok=True)
    44→        self.vector_store = vector_store
    45→        self.llm = GeminiLLM()
    46→
    47→    def ingest(self, user_input: str) -> str:
    48→        """Ingest text or a URL into the vault and vector store.
    49→
    50→        Args:
    51→            user_input: Raw text or a URL to ingest.
    52→
    53→        Returns:
    54→            Slack-formatted confirmation message.
    55→        """
    56→        url = self._detect_url(user_input)
    57→        source_url: Optional[str] = None
    58→
    59→        if url:
    60→            logger.info(f"Detected URL: {url}. Fetching content...")
    61→            try:
    62→                raw_text = self._fetch_url(url)
    63→                source_url = url
    64→            except Exception as e:
    65→                logger.warning(f"URL fetch failed ({e})")
    66→                # 无法抓取（微信/付费墙等），提示用户手动粘贴正文
    67→                return (
    68→                    f"⚠ 无法自动抓取该链接内容（{e}）\n\n"
    69→                    f"请将文章正文直接粘贴发送给我，我来帮你保存入库。"
    70→                )
    71→        else:
    72→            raw_text = user_input
    73→
    74→        try:
    75→            note = self._extract_with_llm(raw_text, source_url)
    76→        except Exception as e:
    77→            logger.error(f"LLM extraction failed: {e}")
    78→            return f"❌ 内容提取失败（LLM 解析出错）：{e}\n\n请重试，或检查原始内容是否正常。"
    79→
    80→        file_path = self._write_md_file(note)
    81→        self._chunk_and_index(note, file_path)
    82→
    83→        entity_links = " ".join(f"[[{e}]]" for e in note.entities[:5])
    84→        rel_path = file_path.relative_to(self.vault_path)
    85→        return (
    86→            f"✅ *Note saved:* {note.title}\n"
    87→            f"{note.summary}\n"
    88→            f"_{entity_links}_\n"
    89→            f"📁 `{rel_path}`"
    90→        )
    91→
    92→    def _detect_url(self, text: str) -> Optional[str]:
    93→        """Extract the first HTTP/HTTPS URL from text, handling Slack's <URL> format.
    94→
    95→        Slack wraps URLs as ``<url>`` or ``<url|display text>``. The angle
    96→        brackets and pipe are excluded from the match so they don't get
    97→        appended to the URL.
    98→
    99→        Args:
   100→            text: Input text to scan.
   101→
   102→        Returns:
   103→            First clean URL found, or None.
   104→        """
   105→        # Exclude Slack delimiters: angle brackets and pipe
   106→        match = re.search(r'https?://[^\s<>|]+', text)
   107→        if not match:
   108→            return None
   109→        return match.group(0).rstrip('.,;:')
   110→
   111→    # Domains that block generic scrapers; try Jina first for these
   112→    _JINA_FIRST_DOMAINS = {"mp.weixin.qq.com", "weixin.qq.com"}
   113→    _MIN_CONTENT_LEN = 200
   114→
   115→    def _fetch_url(self, url: str) -> str:
   116→        """Fetch URL content using multiple strategies with automatic fallback.
   117→
   118→        Strategy order:
   119→        - WeChat / known blocked domains: direct js_content parse → Tavily → Playwright
   120→        - All other URLs: Tavily → Jina → Playwright
   121→
   122→        Args:
   123→            url: URL to fetch.
   124→
   125→        Returns:
   126→            Extracted text content (>= _MIN_CONTENT_LEN chars).
   127→
   128→        Raises:
   129→            RuntimeError: If all strategies fail.
   130→        """
   131→        host_match = re.search(r'https?://([^/]+)', url)
   132→        host = host_match.group(1) if host_match else ""
   133→
   134→        if host in self._JINA_FIRST_DOMAINS:
   135→            # WeChat: direct parse first, Playwright as last resort
   136→            strategies = [self._fetch_direct_wechat, self._fetch_tavily, self._fetch_playwright]
   137→        else:
   138→            strategies = [self._fetch_tavily, self._fetch_jina, self._fetch_playwright]
   139→
   140→        last_err: Exception = RuntimeError("No strategies available")
   141→        for strategy in strategies:
   142→            try:
   143→                content = strategy(url)
   144→                if len(content) >= self._MIN_CONTENT_LEN:
   145→                    logger.info(f"Fetched {len(content)} chars via {strategy.__name__}")
   146→                    return content
   147→                logger.debug(f"{strategy.__name__} returned too little content ({len(content)} chars)")
   148→            except Exception as e:
   149→                logger.debug(f"{strategy.__name__} failed: {e}")
   150→                last_err = e
   151→
   152→        raise RuntimeError(f"所有抓取策略均失败: {last_err}")
   153→
   154→    def _fetch_jina(self, url: str) -> str:
   155→        """Fetch via Jina Reader (r.jina.ai) — works well for WeChat and most sites.
   156→
   157→        Args:
   158→            url: Target URL.
   159→
   160→        Returns:
   161→            Markdown text extracted by Jina.
   162→        """
   163→        import httpx
   164→
   165→        jina_url = f"https://r.jina.ai/{url}"
   166→        headers: dict = {"Accept": "text/plain", "X-Return-Format": "markdown"}
   167→        jina_key = os.environ.get("JINA_API_KEY")
   168→        if jina_key:
   169→            headers["Authorization"] = f"Bearer {jina_key}"
   170→
   171→        resp = httpx.get(jina_url, headers=headers, timeout=30, follow_redirects=True)
   172→        resp.raise_for_status()
   173→        return resp.text
   174→
   175→    def _fetch_direct_wechat(self, url: str) -> str:
   176→        """Directly fetch a WeChat public article by parsing js_content from HTML.
   177→
   178→        WeChat public articles (mp.weixin.qq.com) embed the full body in
   179→
. This method extracts title + body without
   180→        needing a headless browser or external proxy.
   181→
   182→        Args:
   183→            url: WeChat article URL.
   184→
   185→        Returns:
   186→            Plain text of title + article body.
   187→        """
   188→        import httpx
   189→
   190→        headers = {
   191→            "User-Agent": (
   192→                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
   193→                "AppleWebKit/537.36 (KHTML, like Gecko) "
   194→                "Chrome/122.0.0.0 Safari/537.36"
   195→            ),
   196→            "Accept-Language": "zh-CN,zh;q=0.9",
   197→        }
   198→        resp = httpx.get(url, headers=headers, timeout=15, follow_redirects=True)
   199→        resp.raise_for_status()
   200→        html = resp.text
   201→
   202→        parts: list[str] = []
   203→
   204→        # Extract title
   205→        title_m = re.search(
   206→            r'class=["\'][^"\']*rich_media_title[^"\']*["\'][^>]*>(.*?)
',
   207→            html, re.DOTALL
   208→        )
   209→        if title_m:
   210→            title_text = re.sub(r'<[^>]+>', '', title_m.group(1)).strip()
   211→            if title_text:
   212→                parts.append(f"# {title_text}")
   213→
   214→        # Extract body: find js_content start, end at first known landmark
   215→        start_m = re.search(r'id=["\']js_content["\']', html)
   216→        if start_m:
   217→            content_start = html.index('>', start_m.end()) + 1
   218→            # Landmarks that mark the end of the article body in WeChat HTML
   219→            end_markers = [
   220→                'id="js_bottom_bar"',
   221→                'id="js_related_posts"',
   222→                'id="js_msg_card"',
   223→                'id="js_sg_bar"',
   224→                'id="js_pc_qr_code"',
   225→                'class="qr_code_pc_outer"',
   226→            ]
   227→            content_end = len(html)
   228→            for marker in end_markers:
   229→                idx = html.find(marker, content_start)
   230→                if 0 < idx < content_end:
   231→                    content_end = idx
   232→            body_html = html[content_start:content_end]
   233→            body = re.sub(r'<[^>]+>', ' ', body_html)
   234→            body = re.sub(r' ', ' ', body)
   235→            body = re.sub(r'&[a-zA-Z]+;', '', body)
   236→            body = re.sub(r'\s+', ' ', body).strip()
   237→            if body:
   238→                parts.append(body)
   239→
   240→        if not parts:
   241→            # Fallback: strip all tags from full HTML
   242→            text = re.sub(r'<[^>]+>', ' ', html)
   243→            return re.sub(r'\s+', ' ', text).strip()
   244→
   245→        return "\n\n".join(parts)
   246→
   247→    def _fetch_tavily(self, url: str) -> str:
   248→        """Fetch via Tavily extract API.
   249→
   250→        Args:
   251→            url: Target URL.
   252→
   253→        Returns:
   254→            Raw content string from Tavily.
   255→
   256→        Raises:
   257→            RuntimeError: If API key missing or no content returned.
   258→        """
   259→        from tavily import TavilyClient
   260→
   261→        api_key = os.environ.get("TAVILY_API_KEY")
   262→        if not api_key:
   263→            raise RuntimeError("TAVILY_API_KEY is not set in environment")
   264→
   265→        client = TavilyClient(api_key=api_key)
   266→        result = client.extract(urls=[url])
   267→        if result and result.get("results"):
   268→            return result["results"][0].get("raw_content", "")
   269→        raise RuntimeError("Tavily 未能提取到内容")
   270→
   271→    def _fetch_playwright(self, url: str) -> str:
   272→        """Fetch via headless Chromium with stealth mode (last resort).
   273→
   274→        Renders the full page in a real browser, bypassing JS-heavy SPAs and
   275→        most anti-bot fingerprinting. Slower (~3-5s) but most comprehensive.
   276→
   277→        Args:
   278→            url: Target URL.
   279→
   280→        Returns:
   281→            Visible body text extracted from the rendered page.
   282→
   283→        Raises:
   284→            ImportError: If playwright or playwright-stealth is not installed.
   285→            RuntimeError: If the page loads but body text is too short.
   286→        """
   287→        try:
   288→            from playwright.sync_api import sync_playwright
   289→        except ImportError:
   290→            raise ImportError(
   291→                "playwright is not installed. Run: pip install playwright && "
   292→                "playwright install chromium"
   293→            )
   294→        try:
   295→            from playwright_stealth import Stealth
   296→        except ImportError:
   297→            raise ImportError(
   298→                "playwright-stealth is not installed. Run: pip install playwright-stealth"
   299→            )
   300→
   301→        with sync_playwright() as p:
   302→            browser = p.chromium.launch(headless=True)
   303→            try:
   304→                page = browser.new_page()
   305→                Stealth().apply_stealth_sync(page)
   306→                page.goto(url, timeout=30000, wait_until="domcontentloaded")
   307→                # Allow JS rendering to settle
   308→                page.wait_for_timeout(2000)
   309→                content = page.inner_text("body").strip()
   310→                logger.info(f"Playwright fetched {len(content)} chars from {url}")
   311→                return content
   312→            finally:
   313→                browser.close()
   314→
   315→    def _extract_with_llm(self, raw_text: str, source_url: Optional[str]) -> ExtractedNote:
   316→        """Use LLM to extract structured note data from raw text.
   317→
   318→        Args:
   319→            raw_text: Raw content to process (truncated to 8000 chars).
   320→            source_url: Optional source URL for context.
   321→
   322→        Returns:
   323→            Validated ExtractedNote model.
   324→
   325→        Raises:
   326→            ValueError: If LLM output cannot be parsed as valid JSON.
   327→        """
   328→        url_context = f"Source URL: {source_url}\n" if source_url else ""
   329→        prompt = f"""You are a knowledge extraction assistant. Extract structured information from the text below.
   330→{url_context}
   331→=== TEXT ===
   332→{raw_text[:8000]}
   333→
   334→=== TASK ===
   335→Return a JSON object with EXACTLY these fields:
   336→- "title": A concise, descriptive title (max 80 chars)
   337→- "summary": 2-3 sentence summary of the core content
   338→- "key_points": Array of up to 7 key points (strings)
   339→- "entities": Array of important people, concepts, projects, or tools mentioned (max 10)
   340→- "cleaned_body": The full text, cleaned and reorganized. Wrap entity names in [[double brackets]] like [[entity name]].
   341→- "language": "zh" if the content is primarily Chinese, "en" otherwise
   342→
   343→CRITICAL:
   344→- Output ONLY valid JSON, no markdown code blocks, no explanation
   345→- Do NOT include tags like #writing_sample or #reply_sample
   346→- Entities should be wrapped as [[entity]] in cleaned_body
   347→"""
   348→        response, _ = self.llm.generate_response(prompt, [])
   349→
   350→        # Strip potential markdown fences
   351→        response = response.strip()
   352→        if response.startswith("```"):
   353→            response = re.sub(r'^```(?:json)?\n?', '', response)
   354→            response = re.sub(r'\n?```$', '', response)
   355→
   356→        data = json.loads(response)
   357→        return ExtractedNote.model_validate(data)
   358→
   359→    def _fallback_note(self, raw_text: str, source_url: Optional[str]) -> ExtractedNote:
   360→        """Create a minimal note when LLM extraction fails.
   361→
   362→        Args:
   363→            raw_text: Raw content.
   364→            source_url: Optional source URL.
   365→
   366→        Returns:
   367→            Basic ExtractedNote with raw text as body.
   368→        """
   369→        title = raw_text[:60].replace('\n', ' ').strip() + "..."
   370→        return ExtractedNote(
   371→            title=title,
   372→            summary=raw_text[:200],
   373→            key_points=[],
   374→            entities=[],
   375→            cleaned_body=raw_text,
   376→            source_url=source_url,
   377→            language="zh",
   378→        )
   379→
   380→    def _write_md_file(self, note: ExtractedNote) -> Path:
   381→        """Write note to vault as a Markdown file with YAML frontmatter.
   382→
   383→        Args:
   384→            note: Extracted note data.
   385→
   386→        Returns:
   387→            Path to the written file.
   388→        """
   389→        date_str = datetime.now(tz=timezone.utc).strftime("%Y-%m-%d")
   390→        slug = re.sub(r'[^\w\u4e00-\u9fff-]', '-', note.title)[:50].strip('-')
   391→        filename = f"{date_str}_{slug}.md"
   392→        file_path = self.notes_dir / filename
   393→
   394→        source_line = f"source_url: \"{note.source_url}\"\n" if note.source_url else ""
   395→        key_points_yaml = "\n".join(f"  - \"{kp}\"" for kp in note.key_points)
   396→        ingested_at = datetime.now(tz=timezone.utc).isoformat()
   397→
   398→        content = (
   399→            f"---\n"
   400→            f"title: \"{note.title}\"\n"
   401→            f"date: {date_str}\n"
   402→            f"tags:\n"
   403→            f"  - auto_ingested\n"
   404→            f"language: {note.language}\n"
   405→            f"{source_line}"
   406→            f"key_points:\n"
   407→            f"{key_points_yaml}\n"
   408→            f"ingested_at: {ingested_at}\n"
   409→            f"---\n\n"
   410→            f"## Summary\n\n"
   411→            f"{note.summary}\n\n"
   412→            f"## Content\n\n"
   413→            f"{note.cleaned_body}\n"
   414→        )
   415→
   416→        file_path.write_text(content, encoding="utf-8")
   417→        logger.info(f"Note written: {file_path}")
   418→        return file_path
   419→
   420→    def _chunk_and_index(self, note: ExtractedNote, file_path: Path) -> None:
   421→        """Split note body into chunks and index them in the vector store.
   422→
   423→        Args:
   424→            note: Extracted note data.
   425→            file_path: Path to the written markdown file.
   426→        """
   427→        body = f"## Summary\n\n{note.summary}\n\n## Content\n\n{note.cleaned_body}"
   428→        chunks = split_md_by_headers(body)
   429→
   430→        if not chunks:
   431→            # Fallback: single chunk from summary + start of body
   432→            chunks = [(note.summary + "\n\n" + note.cleaned_body)[:1000]]
   433→
   434→        ingested_at = datetime.now(tz=timezone.utc).isoformat()
   435→        metadatas = [
   436→            ChunkMetadata(
   437→                source_path=str(file_path),
   438→                chunk_index=i,
   439→                header_hierarchy=(
   440→                    chunk.split('\n')[0][:100] if chunk.startswith('#') else "body"
   441→                ),
   442→                note_title=note.title,
   443→                tags=["auto_ingested"],
   444→                ingested_at=ingested_at,
   445→            )
   446→            for i, chunk in enumerate(chunks)
   447→        ]
   448→
   449→        self.vector_store.add_chunks(chunks, metadatas)
   450→        logger.info(f"Indexed {len(chunks)} chunks for note: {note.title}")
   451→


/root/vault/obsidian_vault
/root/vault/obsidian_vault/obsidian
/root/vault/obsidian_vault/obsidian/obsidian
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes
/root/vault/obsidian_vault/obsidian/obsidian/presentation-workspace
/root/vault/obsidian_vault/obsidian/obsidian/attachments
/root/vault/obsidian_vault/obsidian/obsidian/Clippings
/root/vault/obsidian_vault/obsidian/obsidian/accounts
/root/vault/obsidian_vault/obsidian/obsidian/Article
/root/vault/obsidian_vault/obsidian/obsidian/obsidian
/root/vault/obsidian_vault/obsidian/obsidian/feelings
/root/vault/obsidian_vault/obsidian/obsidian/daily
/root/vault/obsidian_vault/obsidian/obsidian/必备知识
/root/vault/obsidian_vault/obsidian/obsidian/工作记录
/root/vault/obsidian_vault/obsidian/obsidian/AI
/root/vault/obsidian_vault/obsidian/obsidian/英语学习
/root/vault/obsidian_vault/obsidian/obsidian/私人
/root/vault/obsidian_vault/obsidian/obsidian/md
/root/vault/obsidian_vault/obsidian/obsidian/CLAW
/root/vault/obsidian_vault/obsidian/obsidian/录音记录
/root/vault/obsidian_vault/obsidian/obsidian/auto_report
/root/vault/obsidian_vault/obsidian/obsidian/notes
/root/vault/obsidian_vault/obsidian/obsidian/zhihu
/root/vault/obsidian_vault/obsidian/Documents
/root/vault/obsidian_vault/obsidian/Documents/obsidian
/root/vault/obsidian_vault/obsidian/Documents/copilot


node "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags.js" "post:bash:pr-created" "scripts/hooks/post-bash-pr-created.js" "standard,strict"


node "${CLAUDE_PLUGIN_ROOT}/scripts/hooks/run-with-flags.js" "post:bash:build-complete" "scripts/hooks/post-bash-build-complete.js" "standard,strict"


/root/projects/butler/list_samples.py:vault_path = os.getenv("OBSIDIAN_VAULT_PATH")
/root/projects/butler/scripts/obsidian_bot.py:    vault_path = os.environ.get("OBSIDIAN_VAULT_PATH")
/root/projects/butler/scripts/obsidian_bot.py:        logger.error("OBSIDIAN_VAULT_PATH not set in .env")
/root/projects/butler/scripts/obsidian_distiller.py:        self.vault_path = os.getenv("OBSIDIAN_VAULT_PATH")
/root/projects/butler/scripts/obsidian_distiller.py:            raise ValueError("OBSIDIAN_VAULT_PATH not found in .env")


Now let me get more details on the vault structure and check for any existing "mode info" related code:


ls -la /root/vault/obsidian_vault/obsidian/obsidian/ 2>/dev/null | head -30

List main vault directories


ls -la /root/vault/obsidian_vault/obsidian/obsidian/notes/ 2>/dev/null | head -20

Check notes subdirectory


Found 2 files
slack_bot/obsidian/dispatcher.py
slack_bot/obsidian/cli.py


total 2728
drwxr-xr-x 22 root root    4096 Mar 14 21:14 .
drwxr-xr-x  4 root root      39 Mar  2 10:00 ..
-rw-r--r--  1 root root       0 Mar  3 19:27 2026-03-03.md
drwxr-xr-x  2 root root     183 Feb 26 21:17 accounts
drwxr-xr-x  2 root root    4096 Feb 26 21:17 AI
-rw-r--r--  1 root root       0 Mar 14 21:14 AI.md
drwxr-xr-x  9 root root    8192 Feb 26 21:17 AppleNotes
drwxr-xr-x  2 root root    4096 Mar  4 17:03 Article
drwxr-xr-x  2 root root    8192 Feb 26 21:16 attachments
drwxr-xr-x  3 root root    4096 Mar 17 09:02 auto_report
drwxr-xr-x  6 root root     184 Mar  4 23:01 CLAW
drwxr-xr-x  2 root root    4096 Mar 12 19:52 Clippings
drwxr-xr-x  2 root root    4096 Mar  3 16:19 daily
-rw-r--r--  1 root root    7642 Mar 15 21:31 DRAFT.md
drwxr-xr-x  2 root root     142 Feb 26 21:10 feelings
-rw-r--r--  1 root root 1328257 Feb 26 21:17 IMG_0883.png
-rw-r--r--  1 root root 1196690 Feb 26 21:18 IMG_0884.png
drwxr-xr-x  2 root root    4096 Feb 26 21:17 md
drwxr-xr-x  2 root root    4096 Mar 14 22:08 notes
drwxr-xr-x  3 root root      23 Feb 26 21:09 obsidian
-rw-r--r--  1 root root  157852 Feb 26 21:10 Pasted image 20260115100609.png
-rw-r--r--  1 root root    7120 Feb 26 21:10 Pasted image 20260204175955.png
drwxr-xr-x  2 root root      30 Feb 26 21:09 presentation-workspace
drwxr-xr-x  2 root root    4096 Mar 17 11:52 zhihu
drwxr-xr-x  2 root root    4096 Mar  3 16:19 工作记录
drwxr-xr-x  2 root root     120 Feb 26 23:54 录音记录
drwxr-xr-x  2 root root    4096 Feb 26 21:18 必备知识
-rw-r--r--  1 root root     151 Feb 26 23:44 文件树.base
-rw-r--r--  1 root root      40 Feb 26 21:18 未命名.base


total 24
drwxr-xr-x  2 root root 4096 Mar 14 22:08 .
drwxr-xr-x 22 root root 4096 Mar 14 21:14 ..
-rw-r--r--  1 root root 3025 Mar  3 13:00 2026-03-03_微软-OpenAI-亚马逊-500亿美元投资下的AI三体博弈.md
-rw-r--r--  1 root root 3401 Mar  3 17:19 2026-03-03_斯坦福等高校发布Agents-of-Chaos-真实环境下的OpenClaw安全漏洞研究.md
-rw-r--r--  1 root root 2608 Mar 14 22:00 2026-03-14_Adopting-Agentic-AI-in-2026--5-Strategic-Steps-for.md
-rw-r--r--  1 root root 2939 Mar 14 22:08 2026-03-14_Agentic-AI--Evolution--Mechanics--and-Strategic-Cy.md


Perfect! Now let me check the cli.py to see if there's any mode info documentation there:


find /root/projects/butler/slack_bot/obsidian -name "*.py" -type f | sort

List all obsidian bot files


     1→import cmd
     2→import os
     3→import sys
     4→from slack_bot.obsidian.indexer import ObsidianIndexer
     5→from slack_bot.obsidian.indexer import ObsidianIndexer
     6→from slack_bot.obsidian.generators import WritingAssistant, ReplyGenerator, DecisionSupport, SearchAnalyzer
     7→from health.utils.logging_config import setup_logger
     8→
     9→logger = setup_logger(__name__)
    10→
    11→class ObsidianShell(cmd.Cmd):
    12→    intro = 'Welcome to the Obsidian Assistant Shell. Type help or ? to list commands.\n'
    13→    prompt = '(obsidian) '
    14→
    15→    # Modes
    16→    MODE_NONE = "none"
    17→    MODE_WRITE = "write"
    18→    MODE_REPLY = "reply"
    19→    MODE_DECIDE = "decide"
    20→    MODE_SEARCH = "search"
    21→
    22→    def __init__(self, vault_path: str):
    23→        super().__init__()
    24→        self.vault_path = vault_path
    25→        self.indexer = ObsidianIndexer(vault_path)
    26→        self.indexer.scan_vault()
    27→
    28→        self.generators = {
    29→            self.MODE_WRITE: WritingAssistant(self.indexer),
    30→            self.MODE_REPLY: ReplyGenerator(self.indexer),
    31→            self.MODE_DECIDE: DecisionSupport(self.indexer),
    32→            self.MODE_SEARCH: SearchAnalyzer(self.indexer)
    33→        }
    34→
    35→        self.current_mode = self.MODE_NONE
    36→        self.history = [] # Stores chat history for current session
    37→        self._set_prompt()
    38→
    39→    def _set_prompt(self):
    40→        if self.current_mode == self.MODE_NONE:
    41→            self.prompt = '(obsidian) '
    42→        else:
    43→            turn_indicator = f" [{len(self.history)//2} turns]" if self.history else ""
    44→            self.prompt = f'(obsidian:{self.current_mode}{turn_indicator}) '
    45→
    46→    def do_mode(self, arg):
    47→        """Switch mode: mode [write|reply|decide|search]"""
    48→        if arg in [self.MODE_WRITE, self.MODE_REPLY, self.MODE_DECIDE, self.MODE_SEARCH]:
    49→            self.current_mode = arg
    50→            self.history = [] # Clear history on mode switch
    51→            self._set_prompt()
    52→            print(f"Switched to mode: {arg} (History cleared)")
    53→        else:
    54→            print(f"Invalid mode. Available modes: {self.MODE_WRITE}, {self.MODE_REPLY}, {self.MODE_DECIDE}, {self.MODE_SEARCH}")
    55→            self.current_mode = self.MODE_NONE
    56→            self.history = []
    57→            self._set_prompt()
    58→
    59→    def do_reload(self, arg):
    60→        """Reloads the vault index."""
    61→        print("Reloading index...")
    62→        self.indexer.scan_vault()
    63→        print("Index reloaded.")
    64→
    65→    def do_clear(self, arg):
    66→        """Clears the current conversation history."""
    67→        self.history = []
    68→        self._set_prompt()
    69→        print("Context cleared.")
    70→
    71→    def do_context(self, arg):
    72→        """Show current context samples."""
    73→        if self.current_mode == self.MODE_NONE:
    74→            print("No mode selected.")
    75→            return
    76→
    77→        print(f"--- Context for {self.current_mode} ---")
    78→        if self.current_mode == self.MODE_WRITE:
    79→            samples = self.indexer.writing_samples
    80→            print(f"Total Writing Samples (#writing_sample): {len(samples)}")
    81→            for s in samples:
    82→                print(f" - {os.path.basename(s)}")
    83→
    84→        elif self.current_mode == self.MODE_REPLY:
    85→            samples = self.indexer.reply_samples
    86→            print(f"Total Reply Samples (#reply_sample): {len(samples)}")
    87→            for s in samples:
    88→                print(f" - {os.path.basename(s)}")
    89→            print(" + REPLY-SAMPLE.md (Local)")
    90→
    91→        elif self.current_mode == self.MODE_DECIDE:
    92→            print("Referencing: decision.md, methodology.md")
    93→
    94→        elif self.current_mode == self.MODE_SEARCH:
    95→            print("Hybrid Search: Obsidian (Local) + DuckDuckGo (Web)")
    96→
    97→    def default(self, line):
    98→        """Handle input based on current mode."""
    99→        if self.current_mode == self.MODE_NONE:
   100→            print("Please select a mode first using `mode [write|reply|decide]`")
   101→            return
   102→
   103→        user_input = line.strip()
   104→        if not user_input:
   105→            return
   106→
   107→        print("\nThinking...\n")
   108→        generator = self.generators[self.current_mode]
   109→
   110→        try:
   111→            # All generators now use chat interface
   112→            response, updated_history = generator.chat(user_input, self.history)
   113→
   114→            self.history = updated_history
   115→            self._set_prompt()
   116→
   117→            print(f"\n{response}\n")
   118→
   119→        except Exception as e:
   120→            logger.error(f"Generation failed: {e}")
   121→            print(f"Error: {e}")
   122→
   123→    def do_quit(self, arg):
   124→        """Quit the shell."""
   125→        return True
   126→
   127→    def do_exit(self, arg):
   128→        """Exit the shell."""
   129→        return True
   130→
   131→    def emptyline(self):
   132→        pass
   133→


Perfect! Now let me look at the vector_store to understand the ChunkMetadata model:


     1→"""ChromaDB vector store for the Second Brain RAG system."""
     2→
     3→import hashlib
     4→import re
     5→from datetime import datetime, timezone
     6→from pathlib import Path
     7→from typing import List, Optional
     8→
     9→from pydantic import BaseModel, Field
    10→
    11→from health.utils.logging_config import setup_logger
    12→from slack_bot.obsidian.embeddings import EmbeddingProvider, get_embedding_provider
    13→
    14→logger = setup_logger(__name__)
    15→
    16→
    17→class ChunkMetadata(BaseModel):
    18→    """Metadata for a document chunk stored in the vector DB."""
    19→
    20→    source_path: str
    21→    chunk_index: int
    22→    header_hierarchy: str
    23→    note_title: Optional[str] = None
    24→    tags: List[str] = Field(default_factory=list)
    25→    ingested_at: str  # ISO datetime string
    26→
    27→
    28→class SearchResult(BaseModel):
    29→    """A single semantic search result."""
    30→
    31→    text: str
    32→    source_path: str
    33→    header_hierarchy: str
    34→    distance: float
    35→    note_title: Optional[str] = None
    36→
    37→
    38→def split_md_by_headers(body: str) -> List[str]:
    39→    """Split a markdown body by H2/H3 headers into chunks.
    40→
    41→    Args:
    42→        body: Markdown text to split.
    43→
    44→    Returns:
    45→        List of non-empty chunks of at least 50 characters.
    46→    """
    47→    raw_chunks = re.split(r'\n(?=#{2,3} )', body)
    48→    return [chunk.strip() for chunk in raw_chunks if len(chunk.strip()) >= 50]
    49→
    50→
    51→class ChromaVectorStore:
    52→    """ChromaDB-backed vector store for Obsidian notes."""
    53→
    54→    COLLECTION_NAME = "obsidian_notes"
    55→
    56→    def __init__(
    57→        self,
    58→        embedding_provider: Optional[EmbeddingProvider] = None,
    59→        db_path: Optional[Path] = None,
    60→    ) -> None:
    61→        """Initialize the ChromaDB vector store.
    62→
    63→        Args:
    64→            embedding_provider: Provider for generating embeddings. Defaults to
    65→                get_embedding_provider().
    66→            db_path: Path to ChromaDB persistence directory. Defaults to
    67→                DATA_DIR/vector_db.
    68→        """
    69→        if embedding_provider is None:
    70→            embedding_provider = get_embedding_provider()
    71→        self.embedding_provider = embedding_provider
    72→
    73→        if db_path is None:
    74→            from health import config
    75→            db_path = config.DATA_DIR / "vector_db"
    76→
    77→        db_path.mkdir(parents=True, exist_ok=True)
    78→        self.db_path = db_path
    79→
    80→        try:
    81→            import chromadb
    82→            self._client = chromadb.PersistentClient(path=str(db_path))
    83→            self._collection = self._client.get_or_create_collection(
    84→                name=self.COLLECTION_NAME,
    85→                metadata={"hnsw:space": "cosine"},
    86→            )
    87→            logger.info(
    88→                f"ChromaVectorStore initialized at {db_path} "
    89→                f"(collection: {self.COLLECTION_NAME})"
    90→            )
    91→        except ImportError:
    92→            raise ImportError("chromadb is not installed. Run: pip install chromadb>=0.5.0")
    93→
    94→    def _make_doc_id(self, source_path: str, chunk_index: int) -> str:
    95→        """Generate a deterministic 16-char document ID for idempotent upsert.
    96→
    97→        Args:
    98→            source_path: Path to the source file.
    99→            chunk_index: Index of the chunk within the file.
   100→
   101→        Returns:
   102→            16-character hex string.
   103→        """
   104→        raw = f"{source_path}:{chunk_index}"
   105→        return hashlib.sha256(raw.encode()).hexdigest()[:16]
   106→
   107→    def add_chunks(self, texts: List[str], metadatas: List[ChunkMetadata]) -> None:
   108→        """Add or update chunks in the vector store (idempotent upsert).
   109→
   110→        Args:
   111→            texts: List of text chunks.
   112→            metadatas: Corresponding ChunkMetadata for each chunk.
   113→        """
   114→        if not texts:
   115→            return
   116→
   117→        doc_ids = [self._make_doc_id(m.source_path, m.chunk_index) for m in metadatas]
   118→        embeddings = self.embedding_provider.embed(texts)
   119→
   120→        # Flatten list fields: ChromaDB metadata values must be str/int/float/bool
   121→        meta_dicts = []
   122→        for m in metadatas:
   123→            d = m.model_dump()
   124→            d["tags"] = ",".join(d["tags"])
   125→            meta_dicts.append(d)
   126→
   127→        self._collection.upsert(
   128→            ids=doc_ids,
   129→            embeddings=embeddings,
   130→            documents=texts,
   131→            metadatas=meta_dicts,
   132→        )
   133→        logger.debug(f"Upserted {len(texts)} chunks into vector store")
   134→
   135→    def search_knowledge(self, query: str, top_k: int = 3) -> List[SearchResult]:
   136→        """Semantic search over stored chunks.
   137→
   138→        Args:
   139→            query: Search query text.
   140→            top_k: Maximum number of results to return.
   141→
   142→        Returns:
   143→            List of SearchResult ordered by relevance (lowest distance first).
   144→        """
   145→        total = self._collection.count()
   146→        if total == 0:
   147→            return []
   148→
   149→        query_embedding = self.embedding_provider.embed([query])[0]
   150→        results = self._collection.query(


Excellent! Now I have all the information needed. Let me compile the comprehensive summary:

## Summary: Obsidian Bot Mode Architecture

Based on my analysis of the codebase, here's a concise breakdown for your "mode info" feature planning:

### 1. Mode Registration & Routing (dispatcher.py)

**VALID_MODES set** (lines 41-50):
```python
MODE_WRITE = "write"
MODE_REPLY = "reply"
MODE_DECIDE = "decide"
MODE_SEARCH = "search"
MODE_DEAI = "deai"
MODE_ZHIHU = "zhihu"
MODE_NOTE = "note"
MODE_ZHIHU_HUNTER = "zhihu-hunter"

VALID_MODES = {MODE_WRITE, MODE_REPLY, MODE_DECIDE, MODE_SEARCH, MODE_DEAI, MODE_ZHIHU, MODE_NOTE, MODE_ZHIHU_HUNTER}
```

**Generator Mapping** (lines 67-74):
- `MODE_WRITE` → WritingAssistant
- `MODE_REPLY` → ReplyGenerator
- `MODE_DECIDE` → DecisionSupport
- `MODE_SEARCH` → SearchAnalyzer
- `MODE_DEAI` → DeAIReviser
- `MODE_ZHIHU` → ZhihuGenerator
- `MODE_NOTE` → NoteIngester (special case, not in generators dict)
- `MODE_ZHIHU_HUNTER` → ZhihuHunter (special case)

**Mode Switching Logic** (lines 110-119):
- User sends: `mode <name>`
- Dispatcher validates against VALID_MODES
- On valid mode: clears history, saves state, sends confirmation
- On invalid mode: lists all valid modes

### 2. MODE_NOTE Architecture (Closest Existing Feature)

**Dispatcher Integration** (lines 137-144):
```python
if current_mode == self.MODE_NOTE:
    try:
        result = self.note_ingester.ingest(text)
        self._reply(channel_id, result, response_ts)
    except Exception as e:
        # error handling
        return
```

**NoteIngester Workflow**:
1. Receives raw text or URL
2. Detects & fetches URLs (with fallback strategies: Tavily → Jina → Playwright)
3. LLM extracts structured note via JSON prompt
4. Writes to vault + chunks & indexes to vector store
5. Returns Slack confirmation message

### 3. Vault Directory Structure

**OBSIDIAN_VAULT_PATH** = `/root/vault/obsidian_vault/obsidian/obsidian/`

**Key subdirectories**:
- `notes/` - Auto-ingested notes (via MODE_NOTE)
- `Article/` - Revised articles
- `Clippings/`, `daily/`, `CLAW/`, `zhihu/` - Various content types
- `auto_report/` - Generated reports
- `attachments/`, `accounts/`, `feelings/`, `AI/`, `必备知识/`, `工作记录/`, etc.

**Note files in `/notes/`**:
- Naming: `YYYY-MM-DD_<slug>.md`
- Example: `2026-03-14_Agentic-AI--Evolution--Mechanics--and-Strategic-Cy.md`

### 4. NoteIngester File Format

**File Naming & Location**:
- Path: `vault_path / "notes" / f"{date_str}_{slug}.md"`
- Date: UTC `YYYY-MM-DD`
- Slug: Sanitized title (max 50 chars, Unicode Chinese support)

**YAML Frontmatter** (lines 398-408):
```yaml
---
title: "..."
date: YYYY-MM-DD
tags:
  - auto_ingested
language: zh|en
source_url: "..." (optional)
key_points:
  - "point 1"
  - ...
ingested_at: ISO datetime (UTC)
---
```

**Body Structure**:
```markdown
## Summary
<2-3 sentence summary>

## Content
<cleaned body with [[entity]] wrapped entities>
```

### 5. Pydantic Models

**ExtractedNote** (note_ingester.py, lines 19-28):
```python
class ExtractedNote(BaseModel):
    title: str
    summary: str                    # 2-3 sentences
    key_points: List[str]           # up to 7 items
    entities: List[str]             # wrapped as [[entity]] in cleaned_body
    cleaned_body: str               # full text with entities wrapped
    source_url: Optional[str] = None
    language: str = "zh"
```

**ChunkMetadata** (vector_store.py, lines 17-25):
```python
class ChunkMetadata(BaseModel):
    source_path: str
    chunk_index: int
    header_hierarchy: str
    note_title: Optional[str] = None
    tags: List[str] = Field(default_factory=list)
    ingested_at: str  # ISO datetime string
```

**SearchResult** (vector_store.py, lines 28-35):
```python
class SearchResult(BaseModel):
    text: str
    source_path: str
    header_hierarchy: str
    distance: float
    note_title: Optional[str] = None
```

### Key Insights for "Mode Info" Feature

1. **Similar to MODE_NOTE**: Your feature would follow the same pattern - check current_mode in dispatcher, call a handler method, return Slack-formatted response
2. **State Management**: Each mode can maintain context via `ObsidianContextStorage` (channel-scoped JSON files at `data/obsidian_context/`)
3. **Vector Store Integration**: Optional - NoteIngester chunks notes and indexes them; if MODE_INFO reads from indexed content, you'd use `vector_store.search_knowledge()`
4. **Vault Path**: Access via `Path(vault_path)` throughout - all models use pathlib
5. **Slack Formatting**: Use `SlackFormatter.convert()` for message formatting, watch for 4000/40000 char limits

**Files to reference when implementing**:
- `/root/projects/butler/slack_bot/obsidian/dispatcher.py` - Mode routing template
- `/root/projects/butler/slack_bot/obsidian/note_ingester.py` - Full ingestion pipeline example
- `/root/projects/butler/slack_bot/obsidian/vector_store.py` - Vector store API