Explore /root/projects/butler focusing on:
1. Any existing knowledge management, RAG, or retrieval code
2. The Obsidian vault location and structure (check /root/vault/ directory)
3. Any existing ChromaDB, SQLite-vec, or vector database integration
4. The health analytics system structure (health/ directory) for understanding patterns to follow
5. Any existing note-taking or content generation scripts
6. Check if tavily-python, sentence-transformers, or chromadb are already in requirements.txt or pyproject.toml

Also check /root/vault/obsidian_vault/ structure to understand how notes are organized.


I'll explore the butler project and Obsidian vault structure to understand the existing systems. Let me start with broad searches and then dive into specific areas.


Bash

find /root/projects/butler -type f \( -name "*.txt" -o -name "*.toml" -o -name "*.cfg" \) | head -20

Find requirement/dependency files in butler project


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

List contents of /root/vault directory


/root/projects/butler/health/utils/__init__.py
/root/projects/butler/health/utils/date_utils.py
/root/projects/butler/health/utils/exceptions.py
/root/projects/butler/health/__init__.py
/root/projects/butler/tag20260215/health/utils/__init__.py
/root/projects/butler/tag20260215/health/utils/date_utils.py
/root/projects/butler/tag20260215/health/utils/exceptions.py
/root/projects/butler/tag20260215/health/__init__.py
/root/projects/butler/tag20260302/health/utils/__init__.py
/root/projects/butler/tag20260302/health/utils/date_utils.py
/root/projects/butler/tag20260302/health/utils/exceptions.py
/root/projects/butler/tag20260302/health/__init__.py
/root/projects/butler/health/db/__init__.py
/root/projects/butler/health/db/schema.py
/root/projects/butler/tag20260215/health/db/__init__.py
/root/projects/butler/tag20260215/health/db/schema.py
/root/projects/butler/tag20260302/health/db/__init__.py
/root/projects/butler/tag20260302/health/db/schema.py
/root/projects/butler/health/db/repository.py
/root/projects/butler/tag20260215/health/db/repository.py
/root/projects/butler/tag20260302/health/db/repository.py
/root/projects/butler/health/models/__init__.py
/root/projects/butler/health/models/sync_metadata.py
/root/projects/butler/tag20260215/health/models/__init__.py
/root/projects/butler/tag20260215/health/models/sync_metadata.py
/root/projects/butler/tag20260302/health/models/__init__.py
/root/projects/butler/tag20260302/health/models/sync_metadata.py
/root/projects/butler/health/services/__init__.py
/root/projects/butler/tag20260215/health/services/__init__.py
/root/projects/butler/tag20260302/health/services/__init__.py
/root/projects/butler/tag20260215/health/services/storage.py
/root/projects/butler/tag20260302/health/services/storage.py
/root/projects/butler/health/cli/__init__.py
/root/projects/butler/health/cli/sync.py
/root/projects/butler/tag20260215/health/cli/__init__.py
/root/projects/butler/tag20260215/health/cli/sync.py
/root/projects/butler/tag20260302/health/cli/__init__.py
/root/projects/butler/tag20260302/health/cli/sync.py
/root/projects/butler/scripts/setup_health.py
/root/projects/butler/tag20260215/scripts/setup_health.py
/root/projects/butler/tag20260302/scripts/setup_health.py
/root/projects/butler/skills/health_sync.py
/root/projects/butler/tag20260215/skills/health_sync.py
/root/projects/butler/tag20260302/skills/health_sync.py
/root/projects/butler/health/models/body_metrics.py
/root/projects/butler/tag20260215/health/models/body_metrics.py
/root/projects/butler/tag20260302/health/models/body_metrics.py
/root/projects/butler/scripts/verify_system.py
/root/projects/butler/tag20260215/scripts/verify_system.py
/root/projects/butler/tag20260302/scripts/verify_system.py
/root/projects/butler/health/models/activity.py
/root/projects/butler/tag20260215/health/models/activity.py
/root/projects/butler/tag20260302/health/models/activity.py
/root/projects/butler/tag20260215/health/models/daily_metrics.py
/root/projects/butler/tag20260302/health/models/daily_metrics.py
/root/projects/butler/tag20260215/tests/__init__.py
/root/projects/butler/tests/__init__.py
/root/projects/butler/tag20260302/tests/__init__.py
/root/projects/butler/tag20260215/tests/test_garmin_client_new_features.py
/root/projects/butler/tests/test_garmin_client_new_features.py
/root/projects/butler/tag20260302/tests/test_garmin_client_new_features.py
/root/projects/butler/health/services/query.py
/root/projects/butler/tag20260215/health/services/query.py
/root/projects/butler/tag20260302/health/services/query.py
/root/projects/butler/scripts/query_examples.py
/root/projects/butler/tag20260215/scripts/query_examples.py
/root/projects/butler/tag20260302/scripts/query_examples.py
/root/projects/butler/scripts/resync_steps.py
/root/projects/butler/tag20260215/scripts/resync_steps.py
/root/projects/butler/tag20260302/scripts/resync_steps.py
/root/projects/butler/scripts/daily_sync.py
/root/projects/butler/tag20260215/scripts/daily_sync.py
/root/projects/butler/tag20260302/scripts/daily_sync.py
/root/projects/butler/tag20260215/health/services/garmin_client.py
/root/projects/butler/tag20260302/health/services/garmin_client.py
/root/projects/butler/health/cli/query.py
/root/projects/butler/tag20260215/health/cli/query.py
/root/projects/butler/tag20260302/health/cli/query.py
/root/projects/butler/health/services/data_sync.py
/root/projects/butler/tag20260215/health/services/data_sync.py
/root/projects/butler/tag20260302/health/services/data_sync.py
/root/projects/butler/health/models/manual_log.py
/root/projects/butler/tag20260215/health/models/manual_log.py
/root/projects/butler/tag20260302/health/models/manual_log.py
/root/projects/butler/health/services/manual_log_storage.py
/root/projects/butler/tag20260215/health/services/manual_log_storage.py
/root/projects/butler/tag20260302/health/services/manual_log_storage.py
/root/projects/butler/health/cli/manual.py
/root/projects/butler/tag20260215/health/cli/manual.py
/root/projects/butler/tag20260302/health/cli/manual.py
/root/projects/butler/health/services/obsidian.py
/root/projects/butler/tag20260215/health/services/obsidian.py
/root/projects/butler/tag20260302/health/services/obsidian.py
/root/projects/butler/health/cli/obsidian.py
/root/projects/butler/tag20260215/health/cli/obsidian.py
/root/projects/butler/tag20260302/health/cli/obsidian.py
/root/projects/butler/skills/obsidian_sync.py
/root/projects/butler/tag20260215/skills/obsidian_sync.py
/root/projects/butler/tag20260302/skills/obsidian_sync.py
/root/projects/butler/scripts/analyze_recent_health.py
(Results are truncated. Consider using a more specific path or pattern.)


total 8
drwxr-xr-x  3 root root   45 Feb 26 21:28 .
dr-xr-x--- 17 root root 4096 Mar  3 10:45 ..
drwxr-xr-x  3 root root   22 Feb 26 21:05 obsidian_vault
-rw-r--r--  1 root root  556 Feb 26 21:28 README.md


Let me continue exploring key files:


List Obsidian vault structure


find /root/vault/obsidian_vault -type f -name "*.md" | head -20

Find markdown files in Obsidian vault


ls -la /root/projects/butler/ | grep -E "^d"

List main directories in butler project


     1→# Garmin Health Data Sync Dependencies
     2→
     3→# Garmin Connect API
     4→garminconnect>=0.2.38
     5→garth>=0.5.21
     6→
     7→# Data validation and serialization
     8→pydantic>=2.8.2
     9→
    10→# Environment variables
    11→python-dotenv>=1.0.0
    12→
    13→# CLI framework
    14→click>=8.1.0
    15→
    16→
    17→# Slack Bot Dependencies
    18→slack_bolt>=1.18.0
    19→slack_sdk>=3.27.0
    20→google-generativeai>=0.3.2
    21→openai>=1.12.0  # For Gemini Proxy compatibility
    22→pandas
    23→duckduckgo-search
    24→requests
    25→Pillow
    26→psutil
    27→
    28→# Testing
    29→pytest-cov>=4.0.0
    30→pytest-mock>=3.10.0
    31→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


total 0
drwxr-xr-x 3 root root 22 Feb 26 21:05 .
drwxr-xr-x 3 root root 45 Feb 26 21:28 ..
drwxr-xr-x 4 root root 39 Mar  2 10:00 obsidian


drwxr-xr-x 20 root root    4096 Mar  2 17:44 .
drwxr-xr-x  4  501 games     48 Mar  2 09:16 ..
drwxr-xr-x  3  501 games     37 Jan 22 10:06 .agent
drwxr-xr-x  3  501 games     49 Mar  2 19:08 .claude
drwxr-xr-x  3  501 games     54 Feb  6 15:21 data
drwxr-xr-x  3  501 games   4096 Mar  2 23:18 docs
drwxr-xr-x  2  501 games    168 Mar  2 09:41 experiments
drwxr-xr-x  9  501 games    142 Mar  2 17:26 health
drwxr-xr-x  2 root root    4096 Mar  2 17:44 htmlcov
drwxr-xr-x  2  501 games    179 Mar  3 08:00 logs
drwxr-xr-x  3  501 games     70 Jan 20 08:58 .pytest_cache
drwxr-xr-x  3  501 games   4096 Mar  2 15:45 scripts
drwxr-xr-x  2  501 games    139 Jan 20 17:38 skills
drwxr-xr-x  9  501 games   4096 Mar  2 16:56 slack_bot
drwxr-xr-x 12  501 games   4096 Feb 15 10:47 tag20260215
drwxr-xr-x 14 root root    4096 Mar  2 10:38 tag20260302
drwxr-xr-x  7  501 games   4096 Mar  2 17:42 tests
drwxr-xr-x  2  501 games     24 Feb  5 18:21 tools
drwxr-xr-x  5 root root      74 Mar  2 09:14 venv
drwxr-xr-x  2  501 games    153 Feb  4 10:44 zhihu-sample


/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/Daily Audio/New Recording 1.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/Daily Audio/New Recording 2.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/Daily Audio/New Recording.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/Conversations/句子.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/回复内容/2025-12-22 cocoon.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/2025-03-11 模拟舱分享.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/2025 q1 团队会议.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/新任总监培训的几个问题.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/2025Q3中心大会.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/2025q2团队会议.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/麦肯锡结构性战略思维.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/麦肯锡战略课程.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/战略几个问题.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/计算产品战略.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/决策思维.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/读书笔记/认知觉醒.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/GPU/国产卡.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/寓言/傻逼.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/童言/兵马俑还是兵马桶.md
/root/vault/obsidian_vault/obsidian/obsidian/AppleNotes/童言/愿意担保吗.md


Now let me search for any RAG, knowledge management, or vector database related code:


grep -r "chroma\|chromadb\|vector\|embedding\|rag" /root/projects/butler/requirements.txt 2>/dev/null

Check requirements.txt for vector database or RAG packages


No files found


<persisted-output>
Output too large (84.9KB). Full output saved to: /root/.claude/projects/-root-projects-butler/4b7d6b28-0442-4ea3-9916-3bdf8188e539/tool-results/toolu_014dVMsJXsotKP2fSptyfBtt.txt

Preview (first 2KB):
Found 1249 files
slack_bot/tools/health_read.py
health/services/garmin_client.py
health/analytics/engine.py
health/services/storage.py
health/config.py
health/models/daily_metrics.py
slack_bot/dispatcher.py
tests/integration/test_slack_bot.py
tests/integration/test_sync_flow.py
venv/lib/python3.11/site-packages/pytest_cov/__init__.py
venv/lib/python3.11/site-packages/pytest_cov/engine.py
venv/lib/python3.11/site-packages/pytest_cov/plugin.py
venv/lib/python3.11/site-packages/pytest_mock/plugin.py
venv/lib/python3.11/site-packages/coverage/tracer.pyi
venv/lib/python3.11/site-packages/coverage/types.py
venv/lib/python3.11/site-packages/coverage/version.py
venv/lib/python3.11/site-packages/coverage/xmlreport.py
venv/lib/python3.11/site-packages/coverage/sysmon.py
venv/lib/python3.11/site-packages/coverage/templite.py
venv/lib/python3.11/site-packages/coverage/tomlconfig.py
venv/lib/python3.11/site-packages/coverage/report_core.py
venv/lib/python3.11/site-packages/coverage/report.py
venv/lib/python3.11/site-packages/coverage/results.py
venv/lib/python3.11/site-packages/coverage/sqldata.py
venv/lib/python3.11/site-packages/coverage/sqlitedb.py
venv/lib/python3.11/site-packages/coverage/pth_file.py
venv/lib/python3.11/site-packages/coverage/python.py
venv/lib/python3.11/site-packages/coverage/pytracer.py
venv/lib/python3.11/site-packages/coverage/regions.py
venv/lib/python3.11/site-packages/coverage/parser.py
venv/lib/python3.11/site-packages/coverage/patch.py
venv/lib/python3.11/site-packages/coverage/phystokens.py
venv/lib/python3.11/site-packages/coverage/plugin_support.py
venv/lib/python3.11/site-packages/coverage/plugin.py
venv/lib/python3.11/site-packages/coverage/jsonreport.py
venv/lib/python3.11/site-packages/coverage/lcovreport.py
venv/lib/python3.11/site-packages/coverage/misc.py
venv/lib/python3.11/site-packages/coverage/multiproc.py
venv/lib/python3.11/site-packages/coverage/numbits.py
venv/lib/python3.11/site-packages/coverage/execfile.py
...
</persisted-output>


Let me explore the health analytics system and note-taking scripts:


find /root/projects/butler/health -type f -name "*.py" | grep -v __pycache__ | sort

List all Python files in health directory


find /root/projects/butler -type f -name "*.py" | grep -E "(obsidian|note|content|generation)" | head -20

Find note-taking and content generation scripts


Error: EISDIR: illegal operation on a directory, read '/root/projects/butler/health'


/root/projects/butler/health/analytics/engine.py
/root/projects/butler/health/analytics/__init__.py
/root/projects/butler/health/analytics/weekly_report.py
/root/projects/butler/health/cli/__init__.py
/root/projects/butler/health/cli/manual.py
/root/projects/butler/health/cli/obsidian.py
/root/projects/butler/health/cli/query.py
/root/projects/butler/health/cli/sync.py
/root/projects/butler/health/config.py
/root/projects/butler/health/db/__init__.py
/root/projects/butler/health/db/repository.py
/root/projects/butler/health/db/schema.py
/root/projects/butler/health/__init__.py
/root/projects/butler/health/models/activity.py
/root/projects/butler/health/models/body_metrics.py
/root/projects/butler/health/models/daily_metrics.py
/root/projects/butler/health/models/__init__.py
/root/projects/butler/health/models/manual_log.py
/root/projects/butler/health/models/sync_metadata.py
/root/projects/butler/health/services/data_sync.py
/root/projects/butler/health/services/garmin_client.py
/root/projects/butler/health/services/__init__.py
/root/projects/butler/health/services/manual_log_storage.py
/root/projects/butler/health/services/obsidian.py
/root/projects/butler/health/services/query.py
/root/projects/butler/health/services/storage.py
/root/projects/butler/health/utils/date_utils.py
/root/projects/butler/health/utils/env_loader.py
/root/projects/butler/health/utils/exceptions.py
/root/projects/butler/health/utils/__init__.py
/root/projects/butler/health/utils/logging_config.py
/root/projects/butler/health/utils/time_utils.py


/root/projects/butler/health/cli/obsidian.py
/root/projects/butler/health/services/obsidian.py
/root/projects/butler/scripts/obsidian_bot.py
/root/projects/butler/scripts/obsidian_distiller.py
/root/projects/butler/scripts/obsidian_tool.py
/root/projects/butler/scripts/verify_obsidian_tools.py
/root/projects/butler/skills/obsidian_sync.py
/root/projects/butler/slack_bot/obsidian/__init__.py
/root/projects/butler/slack_bot/obsidian/cli.py
/root/projects/butler/slack_bot/obsidian/dispatcher.py
/root/projects/butler/slack_bot/obsidian/generators.py
/root/projects/butler/slack_bot/obsidian/indexer.py
/root/projects/butler/slack_bot/tools/obsidian.py
/root/projects/butler/tag20260215/health/cli/obsidian.py
/root/projects/butler/tag20260215/health/services/obsidian.py
/root/projects/butler/tag20260215/scripts/obsidian_bot.py
/root/projects/butler/tag20260215/scripts/obsidian_distiller.py
/root/projects/butler/tag20260215/scripts/obsidian_tool.py
/root/projects/butler/tag20260215/scripts/verify_obsidian_tools.py
/root/projects/butler/tag20260215/skills/obsidian_sync.py


Excellent! There are substantial Obsidian-related modules. Let me explore them in detail:


     1→import os
     2→import random
     3→import re
     4→from typing import List, Dict, Optional, Set
     5→from health.utils.logging_config import setup_logger
     6→
     7→logger = setup_logger(__name__)
     8→
     9→class ObsidianIndexer:
    10→    """
    11→    Scans an Obsidian Vault to index files and extract samples based on tags.
    12→    """
    13→
    14→    WRITING_SAMPLE_TAG = "#writing_sample"
    15→    REPLY_SAMPLE_TAG = "#reply_sample"
    16→
    17→    def __init__(self, vault_path: str):
    18→        self.vault_path = os.path.expanduser(vault_path) if vault_path else None
    19→        if not self.vault_path or not os.path.exists(self.vault_path):
    20→            logger.warning(f"Obsidian Vault path invalid: {self.vault_path}")
    21→
    22→        self.writing_samples: List[str] = []
    23→        self.reply_samples: List[str] = []
    24→        self.file_index: Dict[str, str] = {} # filepath -> lowercase content (for simple search)
    25→        self.file_metadata: Dict[str, float] = {} # filepath -> mtime
    26→        self.files_scanned = 0
    27→
    28→    def scan_vault(self):
    29→        """Scans the vault for markdown files and populates indexes."""
    30→        if not self.vault_path or not os.path.exists(self.vault_path):
    31→            logger.error(f"Cannot scan: Invalid vault path {self.vault_path}")
    32→            return
    33→
    34→        logger.info(f"Scanning Obsidian Vault at: {self.vault_path}")
    35→        self.writing_samples = []
    36→        self.reply_samples = []
    37→        self.file_index = {}
    38→        self.file_metadata = {}
    39→        self.files_scanned = 0
    40→
    41→        for root, dirs, files in os.walk(self.vault_path):
    42→            # Skip hidden folders (like .obsidian, .git)
    43→            dirs[:] = [d for d in dirs if not d.startswith('.')]
    44→
    45→            for file in files:
    46→                if file.endswith(".md"):
    47→                    full_path = os.path.join(root, file)
    48→                    self._process_file(full_path)
    49→                    self.files_scanned += 1
    50→
    51→        logger.info(f"Scan complete. Scanned {self.files_scanned} files.")
    52→        logger.info(f"Found {len(self.writing_samples)} writing samples.")
    53→        logger.info(f"Found {len(self.reply_samples)} reply samples.")
    54→
    55→    def _process_file(self, file_path: str):
    56→        """Reads a file and checks for tags."""
    57→        try:
    58→            # Capture metadata
    59→            mtime = os.path.getmtime(file_path)
    60→            self.file_metadata[file_path] = mtime
    61→
    62→            with open(file_path, "r", encoding="utf-8") as f:
    63→                content = f.read()
    64→
    65→            # Basic tag detection (handles #tag inside text)
    66→            # We don't need a full-blown frontmatter parser for this,
    67→            # as Obsidian tags can be anywhere.
    68→
    69→            if self.WRITING_SAMPLE_TAG in content:
    70→                self.writing_samples.append(file_path)
    71→
    72→            if self.REPLY_SAMPLE_TAG in content:
    73→                self.reply_samples.append(file_path)
    74→
    75→            # Store for search (naive in-memory index)
    76→            # Truncating huge files to avoid memory boom if necessary,
    77→            # but for text notes 100k char limit is generous.
    78→            self.file_index[file_path] = content[:100000].lower()
    79→
    80→        except Exception as e:
    81→            logger.warning(f"Failed to read file {file_path}: {e}")
    82→
    83→    def get_writing_samples(self, count: int = 3) -> List[str]:
    84→        """Returns content of random writing samples."""
    85→        if not self.writing_samples:
    86→            return []
    87→
    88→        selected_paths = random.sample(self.writing_samples, min(len(self.writing_samples), count))
    89→        return self._read_files(selected_paths)
    90→
    91→    def get_reply_samples(self, count: int = 3) -> List[str]:
    92→        """Returns content of random reply samples."""
    93→        if not self.reply_samples:
    94→            return []
    95→
    96→        selected_paths = random.sample(self.reply_samples, min(len(self.reply_samples), count))
    97→        return self._read_files(selected_paths)
    98→
    99→    def search(self, query: str, limit: int = 5) -> List[str]:
   100→        """
   101→        Simple keyword search. Returns content of matching files.
   102→        RAG Strategy: Find notes containing the query keywords.
   103→        """
   104→        query_lower = query.lower()
   105→
   106→        # Split query into tokens
   107→        # Improved regex to handle mixed English/Chinese (e.g. "XLSmart遇到了")
   108→        # Matches:
   109→        # 1. English/Number/Underscore sequences: [a-zA-Z0-9_]+
   110→        # 2. Chinese characters (unigrams): [\u4e00-\u9fff]
   111→        tokens = re.findall(r'[a-zA-Z0-9_]+|[\u4e00-\u9fff]', query_lower)
   112→        if not tokens:
   113→            return []
   114→
   115→        logger.debug(f"Search tokens: {tokens}")
   116→
   117→        # Naive scoring: count token occurrences
   118→        scored_files = []
   119→        for path, content in self.file_index.items():
   120→            score = 0
   121→            unique_matches = 0
   122→
   123→            # Combine filename and content for search context
   124→            filename = os.path.basename(path).lower()
   125→            search_text = f"{filename} {filename} {content}" # Weight filename higher
   126→
   127→            for token in tokens:
   128→                if token in search_text:
   129→                    # Cubic weighting by length to heavily favor longer keywords (like "XLSmart")
   130→                    # over common single characters.
   131→                    count = search_text.count(token)
   132→                    score += count * (len(token) ** 3)
   133→                    unique_matches += 1
   134→
   135→            # Boost score based on how many unique tokens matched
   136→            if unique_matches > 0:
   137→                score = score * (unique_matches ** 2)
   138→                scored_files.append((score, path))
   139→
   140→        # Sort by score desc
   141→        scored_files.sort(key=lambda x: x[0], reverse=True)
   142→
   143→        top_paths = [path for _, path in scored_files[:limit]]
   144→        return self._read_files(top_paths)
   145→
   146→    def get_recent_files(self, days: int = 5, limit: int = 10) -> str:
   147→        """
   148→        Returns a formatted list of files modified in the last N days.
   149→        """
   150→        import time
   151→        current_time = time.time()
   152→        cutoff_time = current_time - (days * 86400)
   153→
   154→        recent_files = []
   155→        for path, mtime in self.file_metadata.items():
   156→            if mtime >= cutoff_time:
   157→                recent_files.append((mtime, path))
   158→
   159→        # Sort by mtime DESC (newest first)
   160→        recent_files.sort(key=lambda x: x[0], reverse=True)
   161→
   162→        # Limit
   163→        recent_files = recent_files[:limit]
   164→
   165→        if not recent_files:
   166→            return f"No files modified in the last {days} days."
   167→
   168→        output = [f"📂 **Updated in last {days} days:**"]
   169→        for mtime, path in recent_files:
   170→            filename = os.path.basename(path)
   171→            date_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(mtime))
   172→            output.append(f"- `{filename}` ({date_str})")
   173→
   174→        return "\n".join(output)
   175→
   176→    def _read_files(self, paths: List[str]) -> List[str]:
   177→        """Helper to read complete content of list of paths."""
   178→        contents = []
   179→        for p in paths:
   180→            try:
   181→                with open(p, "r", encoding="utf-8") as f:
   182→                    # Provide context about the source
   183→                    filename = os.path.basename(p)
   184→                    contents.append(f"--- Source: {filename} ---\n{f.read()}")
   185→            except Exception:
   186→                pass
   187→        return contents
   188→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→import os
     2→from typing import List, Dict, Tuple, Optional
     3→from slack_bot.llm.gemini import GeminiLLM
     4→from slack_bot.obsidian.indexer import ObsidianIndexer
     5→from slack_bot.tools.web import WebSearchTool
     6→from health.utils.logging_config import setup_logger
     7→from health.utils.time_utils import get_current_time_str
     8→
     9→logger = setup_logger(__name__)
    10→
    11→
    12→OBSIDIAN_SYSTEM_PROMPT = """You are Butler (Obsidian Edition), an intelligent knowledge assistant connected to the user's second brain.
    13→Current Time: {current_time}
    14→
    15→Your Role:
    16→You are NOT a health assistant. You are a Knowledge Partner designed to help the user think, write, and communicate. You draw directly from the user's local Obsidian notes, values, and methodology.
    17→
    18→Core Capabilities:
    19→1. Writing: You mimic the user's unique writing style (as defined in `writing_style.md`).
    20→2. Communication: You draft high-EQ, logically rigorous replies for professional contexts (as defined in `REPLY-SAMPLE.md`).
    21→3. Decision: You act as a "Devil's Advocate" and strategic advisor using the user's decision frameworks (GPA, IPO).
    22→
    23→Guidelines:
    24→- **Style Alignment**: Strictly adhere to the tone and sentence structures found in the provided samples.
    25→- **Data Source**: Rely primarily on the provided context (RAG) and loaded markdown files.
    26→- **Identity**: You are pragmatic, rational, and value "technological optimism" and "intellectual honesty".
    27→"""
    28→
    29→class BaseGenerator:
    30→    def __init__(self, indexer: ObsidianIndexer):
    31→        formatted_prompt = OBSIDIAN_SYSTEM_PROMPT.format(current_time=get_current_time_str())
    32→        self.llm = GeminiLLM(system_instruction=formatted_prompt)
    33→        self.indexer = indexer
    34→        self.workspace_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
    35→
    36→    def _read_file(self, filename: str) -> str:
    37→        """Reads a file from the workspace root."""
    38→        path = os.path.join(self.workspace_root, filename)
    39→        try:
    40→            with open(path, "r", encoding="utf-8") as f:
    41→                return f.read()
    42→        except Exception as e:
    43→            logger.error(f"Failed to read {filename}: {e}")
    44→            return ""
    45→
    46→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
    47→        """
    48→        Processes chat input.
    49→        If history is empty, treats input as a Topic/Query and builds a Rich Prompt.
    50→        If history exists, treats input as follow-up instruction.
    51→        Returns (response_text, updated_history)
    52→        """
    53→        raise NotImplementedError
    54→
    55→class WritingAssistant(BaseGenerator):
    56→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
    57→
    58→        is_first_turn = (len(history) == 0)
    59→
    60→        if is_first_turn:
    61→            # === First Turn: Build Rich Prompt ===
    62→            # user_input is treated as "Topic"
    63→
    64→            # Naive splitting for context if formatted as "Topic | Context"
    65→            parts = user_input.split("|", 1)
    66→            topic = parts[0].strip()
    67→            extra_context = parts[1].strip() if len(parts) > 1 else ""
    68→
    69→            # 1. RAG & Samples
    70→            style_samples = self.indexer.get_writing_samples(count=3)
    71→            style_text = "\n\n".join(style_samples)
    72→
    73→            rag_notes = self.indexer.search(topic, limit=5)
    74→            rag_text = "\n\n".join(rag_notes)
    75→
    76→            writing_style_guide = self._read_file("writing_style.md")
    77→            methodology = self._read_file("methodology.md")
    78→
    79→            prompt = f"""You are a ghostwriter for the user. Your goal is to write an article on the TOPIC provided.
    80→
    81→            === YOUR IDENTITY & METHODOLOGY ===
    82→            {methodology}
    83→
    84→            === YOUR WRITING STYLE GUIDE ===
    85→            {writing_style_guide}
    86→
    87→            === YOUR WRITING SAMPLES (MIMIC THIS TONE) ===
    88→            {style_text}
    89→
    90→            === RELEVANT NOTES FROM OBSIDIAN VAULT ===
    91→            {rag_text}
    92→
    93→            === TASK ===
    94→            Topic: {topic}
    95→            Extra Context: {extra_context}
    96→
    97→            Write the article in Chinese (unless the topic implies English).
    98→            Adhere strictly to the "Identity" and "Style Guide".
    99→            """
   100→
   101→            logger.info(f"Generating article for topic: {topic}")
   102→            actual_user_message = prompt # We inject the big prompt
   103→
   104→        else:
   105→            # === Follow-up Turn ===
   106→            # user_input is just instructions like "Make it shorter"
   107→            logger.info(f"Follow-up instruction: {user_input}")
   108→            actual_user_message = user_input
   109→            prompt = user_input
   110→
   111→        # Call LLM
   112→        response, _ = self.llm.generate_response(prompt, history)
   113→
   114→        # Update History
   115→        new_history = history.copy()
   116→        new_history.append({"role": "user", "content": actual_user_message})
   117→        new_history.append({"role": "assistant", "content": response})
   118→
   119→        return response, new_history
   120→
   121→class ReplyGenerator(BaseGenerator):
   122→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   123→
   124→        is_first_turn = (len(history) == 0)
   125→
   126→        if is_first_turn:
   127→             # === First Turn ===
   128→            static_samples = self._read_file("REPLY-SAMPLE.md")
   129→            dynamic_samples = self.indexer.get_reply_samples(count=3)
   130→            dynamic_samples_text = "\n\n".join(dynamic_samples)
   131→            methodology = self._read_file("methodology.md")
   132→
   133→            prompt = f"""You are an experienced Product/R&D Manager.
   134→            Your task is to draft a reply to a challenging situation.
   135→
   136→            === CORE PHILOSOPHY ===
   137→            {methodology}
   138→
   139→            === REPLY GUIDELINES ===
   140→            1. Logic: Be clear and structured (e.g., numbered lists).
   141→            2. Responsibility: Don't take unnecessary blame, but don't shirk core duties. Explain objective causes.
   142→            3. Emotion: Be empathetic but professional.
   143→            4. Conciseness: No fluff.
   144→
   145→            === REFERENCE SAMPLES (FEW-SHOT) ===
   146→            {static_samples}
   147→
   148→            {dynamic_samples_text}
   149→
   150→            === THE SITUATION ===
   151→            {user_input}
   152→
   153→            Draft a reply. If the context implies a specific role (PM, Dev Lead), assume that role.
   154→            """
   155→            logger.info(f"Generating reply for query: {user_input}")
   156→            actual_user_message = prompt
   157→
   158→        else:
   159→             # === Follow-up Turn ===
   160→            logger.info(f"Follow-up reply instruction: {user_input}")
   161→            actual_user_message = user_input
   162→            prompt = user_input
   163→
   164→        response, _ = self.llm.generate_response(prompt, history)
   165→
   166→        new_history = history.copy()
   167→        new_history.append({"role": "user", "content": actual_user_message})
   168→        new_history.append({"role": "assistant", "content": response})
   169→
   170→        return response, new_history
   171→
   172→class DecisionSupport(BaseGenerator):
   173→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   174→
   175→        is_first_turn = (len(history) == 0)
   176→
   177→        if is_first_turn:
   178→            decision_guide = self._read_file("decision.md")
   179→            methodology = self._read_file("methodology.md")
   180→
   181→            rag_notes = self.indexer.search(user_input, limit=3)
   182→            rag_text = "\n\n".join(rag_notes)
   183→
   184→            prompt = f"""You are a Decision Support Assistant.
   185→            Your goal is to help the user think through a complex decision.
   186→
   187→            === DECISION FRAMEWORK ===
   188→            {decision_guide}
   189→
   190→            === CORE PHILOSOPHY ===
   191→            {methodology}
   192→
   193→            === RELEVANT CONTEXT FROM NOTES ===
   194→            {rag_text}
   195→
   196→            === THE DECISION / ISSUE ===
   197→            {user_input}
   198→
   199→            === TASK ===
   200→            1. Challenge the premise: Is this the right problem to solve?
   201→            2. Apply the Framework: Use GPA (Goal, Priority, Alternatives) and IPO (Information, People, Objective reasoning) models.
   202→            3. Pre-mortem: What is the worst that could happen?
   203→            4. Provide a recommendation or a set of questions to clarify.
   204→            """
   205→
   206→            logger.info(f"Analyzing decision: {user_input}")
   207→            actual_user_message = prompt
   208→        else:
   209→            logger.info(f"Follow-up decision instruction: {user_input}")
   210→            actual_user_message = user_input
   211→            prompt = user_input
   212→
   213→        response, _ = self.llm.generate_response(prompt, history)
   214→
   215→        new_history = history.copy()
   216→        new_history.append({"role": "user", "content": actual_user_message})
   217→        new_history.append({"role": "assistant", "content": response})
   218→
   219→        return response, new_history
   220→
   221→class SearchAnalyzer(BaseGenerator):
   222→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   223→
   224→        # Tools definition for the LLM
   225→        tools = [
   226→            {
   227→                "type": "function",
   228→                "function": {
   229→                    "name": "search_web",
   230→                    "description": "Search the public web using DuckDuckGo. Use this when the internal notes are insufficient or when you need up-to-date external information.",
   231→                    "parameters": {
   232→                        "type": "object",
   233→                        "properties": {
   234→                            "query": {
   235→                                "type": "string",
   236→                                "description": "The search query"
   237→                            }
   238→                        },
   239→                        "required": ["query"]
   240→                    }
   241→                }
   242→            },
   243→            {
   244→                "type": "function",
   245→                "function": {
   246→                    "name": "list_recent_files",
   247→                    "description": "List files in the Obsidian vault that have been modified recently. Use this when the user asks for 'recent updates', 'what's new', or changes in the last X days.",
   248→                    "parameters": {
   249→                        "type": "object",
   250→                        "properties": {
   251→                            "days": {
   252→                                "type": "integer",
   253→                                "description": "Number of days to check (default 5)",
   254→                                "default": 5
   255→                            }
   256→                        },
   257→                        "required": []
   258→                    }
   259→                }
   260→            }
   261→        ]
   262→
   263→        is_first_turn = (len(history) == 0)
   264→
   265→        # Current Turn Message
   266→        if is_first_turn:
   267→            rag_notes = self.indexer.search(user_input, limit=5)
   268→            rag_text = "\n\n".join(rag_notes)
   269→
   270→            logger.info(f"SearchAnalyzer retrieved {len(rag_notes)} local notes.")
   271→            for note in rag_notes:
   272→                # Extract source line for logging
   273→                first_line = note.split('\n')[0]
   274→                logger.debug(f" - Retrieved: {first_line}")
   275→
   276→            prompt = f"""You are an Analyst using Butler (Obsidian Edition).
   277→
   278→            === TASK ===
   279→            Answer the user's question.
   280→            CRITICAL: You MUST explicitly cite your sources in the output.
   281→
   282→            === STRATEGY ===
   283→            1. first: Check "RELEVANT LOCAL NOTES" provided below.
   284→            2. second: If the notes answer the question, answer directly.
   285→            3. third: If the notes are missing info, ambiguos, or outdated, USE THE `search_web` TOOL to find external info.
   286→
   287→            === OUTPUT FORMAT ===
   288→            - Begin with a summary.
   289→            - When stating facts, append the source in brackets, e.g., "The server failed [Source: XLSmart 2025-12-25.md]" or "Market share is 20% [Source: Web Search]".
   290→            - If data comes from BOTH, mention both.
   291→
   292→            === RELEVANT LOCAL NOTES ===
   293→            {rag_text}
   294→
   295→            === USER QUESTION ===
   296→            {user_input}
   297→            """
   298→            actual_user_message = prompt
   299→        else:
   300→            actual_user_message = user_input
   301→
   302→        # Prepare execution context
   303→        # We don't want to mutate the input history directly until the end
   304→        exec_history = history.copy()
   305→        exec_history.append({"role": "user", "content": actual_user_message})
   306→
   307→        logger.info(f"Analyzer thinking on: {user_input}")
   308→
   309→        # === ReAct Loop (Max 2 turns to prevent infinite loops) ===
   310→        for i in range(2):
   311→            response_text, tool_calls = self.llm.generate_response(
   312→                message="", # Message is already in history
   313→                context=exec_history,
   314→                tools=tools
   315→            )
   316→
   317→            if tool_calls:
   318→                # LLM wants to use tools
   319→                logger.info(f"LLM requested tool calls: {tool_calls}")
   320→
   321→                # Append the assistant's request (with tool calls) to history
   322→                exec_history.append({
   323→                    "role": "assistant",
   324→                    "content": f"[Tool Call Request: {tool_calls}]"
   325→                })
   326→
   327→                for tc in tool_calls:
   328→                    if tc["name"] == "search_web":
   329→                        query = tc["args"].get("query")
   330→                        search_result = WebSearchTool.search_web(query)
   331→
   332→                        # Feed result back
   333→                        exec_history.append({
   334→                            "role": "user", # Using 'function' role would be better if our LLM class supports it fully, but 'user' works as system injection
   335→                            "content": f"--- TOOL OUTPUT (search_web) ---\n{search_result}"
   336→                        })
   337→
   338→                    elif tc["name"] == "list_recent_files":
   339→                        days = tc["args"].get("days", 5)
   340→                        # Call indexer method
   341→                        recent_list = self.indexer.get_recent_files(days=days)
   342→
   343→                        exec_history.append({
   344→                            "role": "user",
   345→                            "content": f"--- TOOL OUTPUT (list_recent_files) ---\n{recent_list}"
   346→                        })
   347→
   348→                # Loop again to get the final answer using tool outputs
   349→                try:
   350→                     # Second pass with tool outputs
   351→                    response_text, _ = self.llm.generate_response(
   352→                        message="",
   353→                        context=exec_history,
   354→                        tools=tools
   355→                    )
   356→                    return response_text, exec_history
   357→                except Exception as e:
   358→                    logger.error(f"Error in second ReAct loop: {e}")
   359→                    return response_text, exec_history
   360→
   361→            else:
   362→                # Final answer (no tools)
   363→                return response_text, exec_history
   364→
   365→        # Fallback if loop exhausted
   366→        return response_text, exec_history
   367→
   368→
   369→class DeAIReviser(BaseGenerator):
   370→    """Remove AI tone from articles - text-in, text-out mode."""
   371→
   372→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   373→
   374→        is_first_turn = (len(history) == 0)
   375→
   376→        if is_first_turn:
   377→            # === First Turn: User pastes the AI-generated article ===
   378→            # Load the POWELL_REVISE.md guide
   379→            powell_revise_guide = self._read_file("POWELL_REVISE.md")
   380→
   381→            # Load user's writing samples for style reference
   382→            style_samples = self.indexer.get_writing_samples(count=3)
   383→            style_text = "\n\n".join(style_samples)
   384→
   385→            prompt = f"""You are an expert editor specializing in removing AI-generated tone from articles.
   386→
   387→Your task is to revise the article provided by the user to make it sound more human and authentic.
   388→
   389→=== REVISION GUIDE ===
   390→{powell_revise_guide}
   391→
   392→=== USER'S AUTHENTIC WRITING SAMPLES (for style reference) ===
   393→{style_text}
   394→
   395→=== ARTICLE TO REVISE ===
   396→{user_input}
   397→
   398→=== TASK ===
   399→1. Analyze the article and identify AI characteristics (过渡词堆积, 排比对仗, 空洞修饰词等)
   400→2. Rewrite the article following the POWELL_REVISE.md principles
   401→3. Preserve:
   402→   - Original meaning and key information
   403→   - Technical terms and proper nouns
   404→   - Code blocks, lists, and formatting
   405→   - Original language (Chinese/English)
   406→4. Output the revised article directly (no meta-commentary like "Here's the revised version...")
   407→
   408→CRITICAL: Output ONLY the revised article text. Do NOT add explanations before or after.
   409→"""
   410→
   411→            logger.info(f"DeAI Reviser: Processing article ({len(user_input)} chars)")
   412→            actual_user_message = prompt
   413→
   414→        else:
   415→            # === Follow-up Turn: User gives refinement instructions ===
   416→            logger.info(f"DeAI Reviser: Follow-up instruction - {user_input}")
   417→            actual_user_message = user_input
   418→            prompt = user_input
   419→
   420→        # Call LLM
   421→        response, _ = self.llm.generate_response(prompt, history)
   422→
   423→        # Update History
   424→        new_history = history.copy()
   425→        new_history.append({"role": "user", "content": actual_user_message})
   426→        new_history.append({"role": "assistant", "content": response})
   427→
   428→        return response, new_history
   429→
   430→
   431→class ZhihuGenerator(BaseGenerator):
   432→    """Generate Zhihu-style answers based on style guide and user's knowledge base."""
   433→
   434→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   435→
   436→        is_first_turn = (len(history) == 0)
   437→
   438→        if is_first_turn:
   439→            # === First Turn: Build Rich Prompt ===
   440→            # Expected input format: "Question | Core Ideas"
   441→            parts = user_input.split("|", 1)
   442→            question = parts[0].strip()
   443→            core_ideas = parts[1].strip() if len(parts) > 1 else ""
   444→
   445→            # 1. Load Style Guide (zhihu-style.md serves as writing standard, NOT as material)
   446→            zhihu_style_guide = self._read_file("zhihu-style.md")
   447→
   448→            # 2. RAG from Obsidian vault for actual content material
   449→            # Search Article directory for relevant revised.md files
   450→            rag_notes = self.indexer.search(question, limit=5)
   451→            rag_text = "\n\n".join(rag_notes)
   452→
   453→            # 3. Load methodology for consistent identity
   454→            methodology = self._read_file("methodology.md")
   455→
   456→            prompt = f"""You are answering a Zhihu question. Your goal is to write an authentic, experience-driven answer.
   457→
   458→=== YOUR IDENTITY & METHODOLOGY ===
   459→{methodology}
   460→
   461→=== ZHIHU STYLE GUIDE (FOLLOW THESE WRITING PRINCIPLES) ===
   462→{zhihu_style_guide}
   463→
   464→CRITICAL: The style guide above defines HOW to write (tone, structure, language patterns).
   465→Do NOT treat zhihu-sample content as factual material. It's ONLY for style reference.
   466→
   467→=== RELEVANT KNOWLEDGE FROM YOUR OBSIDIAN VAULT (USE AS ACTUAL MATERIAL) ===
   468→{rag_text}
   469→
   470→=== THE QUESTION ===
   471→{question}
   472→
   473→=== YOUR CORE IDEAS ===
   474→{core_ideas}
   475→
   476→=== TASK ===
   477→1. Answer the question based on YOUR knowledge (from Obsidian vault) and core ideas
   478→2. Follow the Zhihu style guide strictly for tone and structure
   479→3. Share real experiences, examples, and insights from the vault material
   480→4. Write in Chinese (unless question implies English)
   481→5. Use first person ("我", "我们") frequently
   482→6. Be professional yet approachable, technical yet relatable
   483→7. Adhere to the "七个避免事项" (avoid AI tone, avoid empty buzzwords, etc.)
   484→
   485→OUTPUT FORMAT:
   486→- If the answer is over 300 characters, append this at the end:
   487→
   488→---
   489→
   490→**更多关于这些问题的思考可以在公众号搜索账户: AI Manifest**
   491→
   492→Begin writing the answer:
   493→"""
   494→
   495→            logger.info(f"Generating Zhihu answer for: {question[:50]}...")
   496→            actual_user_message = prompt
   497→
   498→        else:
   499→            # === Follow-up Turn: User gives refinement instructions ===
   500→            logger.info(f"Follow-up Zhihu instruction: {user_input}")
   501→            actual_user_message = user_input
   502→            prompt = user_input
   503→
   504→        # Call LLM
   505→        response, _ = self.llm.generate_response(prompt, history)
   506→
   507→        # Post-processing: Add signature if length > 300 and not already present
   508→        if is_first_turn and len(response) > 300:
   509→            signature = "\n\n---\n\n**更多关于这些问题的思考可以在公众号搜索账户: AI Manifest**"
   510→            if "AI Manifest" not in response:
   511→                response += signature
   512→
   513→        # Update History
   514→        new_history = history.copy()
   515→        new_history.append({"role": "user", "content": actual_user_message})
   516→        new_history.append({"role": "assistant", "content": response})
   517→
   518→        return response, new_history
   519→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→import re
     2→import os
     3→from datetime import date
     4→from pathlib import Path
     5→from typing import Dict, List, Optional, Any
     6→from dataclasses import dataclass
     7→
     8→from health.services.manual_log_storage import ManualLogStorage
     9→from health.models.manual_log import SupplementEntry, AlcoholEntry
    10→from health.utils.logging_config import setup_logger
    11→
    12→logger = setup_logger(__name__)
    13→
    14→# Default Obsidian Daily Notes Path (can be overridden)
    15→DEFAULT_OBSIDIAN_PATH = Path.home() / "Library/Mobile Documents/iCloud~md~obsidian/Documents/obsidian/daily"
    16→
    17→# Mapping from Obsidian YAML keys to Supplement names
    18→SUPPLEMENT_MAPPING = {
    19→    "MED_001": "Medication 001",
    20→    "nac": "NAC",
    21→    "magnesium": "Magnesium",
    22→    "fish_oil": "Fish Oil",
    23→    "coq10": "CoQ10",
    24→    "berberine": "Berberine",
    25→    "b_complex": "Vitamin B Complex",
    26→    "v_d3_k2": "Vitamin D3+K2"
    27→}
    28→
    29→# Mapping for Fasting Modes
    30→FASTING_MAPPING = {
    31→    "PSMF": "PSMF",
    32→    "OMAD": "OMAD",
    33→    "WaterFast": "Water Fast"
    34→}
    35→
    36→class ObsidianSyncService:
    37→    """Service to sync data from Obsidian daily notes."""
    38→
    39→    def __init__(self, obsidian_dir: Optional[Path] = None):
    40→        self.obsidian_dir = obsidian_dir or DEFAULT_OBSIDIAN_PATH
    41→        self.storage = ManualLogStorage()
    42→
    43→    def _get_note_path(self, target_date: date) -> Path:
    44→        """Get the path to the daily note for a given date."""
    45→        filename = f"{target_date.isoformat()}.md"
    46→        return self.obsidian_dir / filename
    47→
    48→    def _parse_frontmatter(self, content: str) -> Dict[str, Any]:
    49→        """Parse YAML frontmatter from markdown content manually."""
    50→        frontmatter = {}
    51→
    52→        # Regex to find content between ---
    53→        match = re.search(r"^---\n(.*?)\n---", content, re.DOTALL)
    54→        if not match:
    55→            return frontmatter
    56→
    57→        yaml_content = match.group(1)
    58→
    59→        # Parse line by line
    60→        for line in yaml_content.split('\n'):
    61→            line = line.strip()
    62→            if not line or line.startswith('#'):
    63→                continue
    64→
    65→            if ':' in line:
    66→                key, value = line.split(':', 1)
    67→                key = key.strip()
    68→                value = value.strip()
    69→
    70→                # Handle booleans
    71→                if value.lower() == 'true':
    72→                    value = True
    73→                elif value.lower() == 'false':
    74→                    value = False
    75→                # Handle numbers
    76→                elif value.replace('.', '', 1).isdigit():
    77→                    try:
    78→                        value = float(value) if '.' in value else int(value)
    79→                    except ValueError:
    80→                        pass
    81→                # Handle nulls
    82→                elif value.lower() in ('null', 'nil', ''):
    83→                    value = None
    84→
    85→                frontmatter[key] = value
    86→
    87→        return frontmatter
    88→
    89→    def sync_daily_note(self, target_date: date) -> bool:
    90→        """Sync data from Obsidian for a specific date.
    91→
    92→        Returns:
    93→            True if data was found and synced, False otherwise.
    94→        """
    95→        note_path = self._get_note_path(target_date)
    96→        if not note_path.exists():
    97→            logger.warning(f"No Obsidian note found for {target_date} at {note_path}")
    98→            return False
    99→
   100→        try:
   101→            with open(note_path, 'r', encoding='utf-8') as f:
   102→                content = f.read()
   103→
   104→            data = self._parse_frontmatter(content)
   105→            if not data:
   106→                logger.info(f"No frontmatter found in {note_path}")
   107→                return False
   108→
   109→            log = self.storage.load_log(target_date)
   110→            changes_made = False
   111→
   112→            # 1. Sync Fasting Mode
   113→            for key, mode in FASTING_MAPPING.items():
   114→                if data.get(key) is True:
   115→                    if log.fasting_mode != mode:
   116→                        log.fasting_mode = mode
   117→                        changes_made = True
   118→                        logger.info(f"Synced Fasting Mode: {mode}")
   119→                    break # Only one fasting mode per day
   120→
   121→            if data.get("fasting") is False and log.fasting_mode:
   122→                # If explicitly false, maybe clear it?
   123→                # Decided to safer: only set if True.
   124→                pass
   125→
   126→            # 2. Sync Supplements
   127→            for key, name in SUPPLEMENT_MAPPING.items():
   128→                if data.get(key) is True:
   129→                    # Check if already exists
   130→                    exists = any(
   131→                        s.supplement_name == name and s.time == "00:00"
   132→                        for s in log.supplement_entries
   133→                    )
   134→
   135→                    if not exists:
   136→                        entry = SupplementEntry(
   137→                            time="00:00", # Default time
   138→                            supplement_name=name,
   139→                            dosage=None, # Dosage not tracked in Obsidian YAML boolean
   140→                            timing="obsidian_sync"
   141→                        )
   142→                        log.supplement_entries.append(entry)
   143→                        changes_made = True
   144→                        logger.info(f"Synced Supplement: {name}")
   145→
   146→            # 3. Sync Alcohol
   147→            if data.get("alcohol") is True:
   148→                # Check for existing alcohol entry from Obsidian (generic one)
   149→                alcohol_ml = data.get("alcohol_ml", 0)
   150→                amount_str = f"{alcohol_ml}ml pure alcohol" if alcohol_ml else "See Obsidian"
   151→
   152→                # Check if we already have a 'wine' entry at 00:00 (our default)
   153→                exists = any(
   154→                    (a.time == "00:00" and a.type == "wine")
   155→                    for a in log.alcohol_entries
   156→                )
   157→
   158→                if not exists:
   159→                    entry = AlcoholEntry(
   160→                        time="00:00",
   161→                        type="wine", # Default type per user request
   162→                        amount=amount_str,
   163→                        notes=f"Synced from Obsidian (alcohol_ml: {alcohol_ml})"
   164→                    )
   165→                    log.alcohol_entries.append(entry)
   166→                    changes_made = True
   167→                    logger.info(f"Synced Alcohol: {amount_str}")
   168→
   169→            if changes_made:
   170→                self.storage.save_log(log)
   171→                logger.info(f"Successfully synced Obsidian data for {target_date}")
   172→                return True
   173→            else:
   174→                logger.info(f"No new data to sync for {target_date}")
   175→                return True
   176→
   177→        except Exception as e:
   178→            logger.error(f"Failed to sync Obsidian note for {target_date}: {e}")
   179→            return False
   180→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→from datetime import date
     2→from health.services.obsidian import ObsidianSyncService
     3→from health.utils.logging_config import setup_logger
     4→
     5→logger = setup_logger(__name__)
     6→
     7→class ObsidianTool:
     8→    @staticmethod
     9→    def sync_obsidian(target_date: str) -> str:
    10→        """Sync Obsidian daily note for a date."""
    11→        try:
    12→            date_obj = date.fromisoformat(target_date)
    13→            service = ObsidianSyncService()
    14→            success = service.sync_daily_note(date_obj)
    15→
    16→            if success:
    17→                return f"Successfully synced Obsidian note for {target_date}"
    18→            else:
    19→                return f"Failed to sync Obsidian note for {target_date} (File not found or no data)"
    20→        except Exception as e:
    21→            logger.error(f"Obsidian sync error: {e}")
    22→            return f"Error syncing Obsidian: {str(e)}"
    23→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Now let me explore more Obsidian-related modules and the Slack bot integration:


ls -la /root/vault/obsidian_vault/obsidian/obsidian/ | head -20

List top-level directories in Obsidian vault


     1→import os
     2→from typing import Optional, List, Dict, Any
     3→from slack_sdk import WebClient
     4→
     5→from health.utils.logging_config import setup_logger
     6→from slack_bot.obsidian.indexer import ObsidianIndexer
     7→from slack_bot.obsidian.generators import WritingAssistant, ReplyGenerator, DecisionSupport, SearchAnalyzer, DeAIReviser, ZhihuGenerator
     8→from slack_bot.context.storage import ContextStorage as BaseStorage
     9→
    10→logger = setup_logger(__name__)
    11→
    12→class ObsidianContextStorage(BaseStorage):
    13→    """Specific storage for Obsidian bot to avoid mixing with Health bot."""
    14→    def __init__(self, channel_id: str):
    15→        super().__init__(channel_id)
    16→        from health import config
    17→        self.context_dir = config.DATA_DIR / "obsidian_context"
    18→        self.context_dir.mkdir(parents=True, exist_ok=True)
    19→        self.file_path = self.context_dir / f"{channel_id}.json"
    20→        self.state_file = self.context_dir / f"{channel_id}_state.json"
    21→
    22→    def save_state(self, state: Dict[str, Any]):
    23→        import json
    24→        with open(self.state_file, "w") as f:
    25→            json.dump(state, f)
    26→
    27→    def load_state(self) -> Dict[str, Any]:
    28→        import json
    29→        if not self.state_file.exists():
    30→            return {"mode": "none"}
    31→        try:
    32→            with open(self.state_file, "r") as f:
    33→                return json.load(f)
    34→        except:
    35→            return {"mode": "none"}
    36→
    37→class ObsidianDispatcher:
    38→    """Routes Obsidian Slack Bot messages."""
    39→
    40→    MODE_WRITE = "write"
    41→    MODE_REPLY = "reply"
    42→    MODE_DECIDE = "decide"
    43→    MODE_SEARCH = "search"
    44→    MODE_DEAI = "deai"
    45→    MODE_ZHIHU = "zhihu"
    46→
    47→    def __init__(self, vault_path: str):
    48→        self.indexer = ObsidianIndexer(vault_path)
    49→        self.indexer.scan_vault()
    50→
    51→        self.generators = {
    52→            self.MODE_WRITE: WritingAssistant(self.indexer),
    53→            self.MODE_REPLY: ReplyGenerator(self.indexer),
    54→            self.MODE_DECIDE: DecisionSupport(self.indexer),
    55→            self.MODE_SEARCH: SearchAnalyzer(self.indexer),
    56→            self.MODE_DEAI: DeAIReviser(self.indexer),
    57→            self.MODE_ZHIHU: ZhihuGenerator(self.indexer)
    58→        }
    59→
    60→        token = os.environ.get("OBSIDIAN_SLACK_BOT_TOKEN")
    61→        self.client = WebClient(token=token)
    62→        logger.info("ObsidianDispatcher initialized")
    63→
    64→    def dispatch(self, text: str, channel_id: str, user_id: str, response_ts: Optional[str] = None):
    65→        storage = ObsidianContextStorage(channel_id)
    66→        state = storage.load_state()
    67→        current_mode = state.get("mode", "none")
    68→
    69→        cmd = text.strip().lower()
    70→
    71→        # 1. Handle Commands
    72→        if cmd.startswith("mode "):
    73→            new_mode = cmd.split(" ", 1)[1].strip()
    74→            if new_mode in self.generators:
    75→                storage.clear()
    76→                storage.save_state({"mode": new_mode})
    77→                self._reply(channel_id, f"✅ Switched to mode: *{new_mode}* (History cleared)", response_ts)
    78→                return
    79→            else:
    80→                self._reply(channel_id, f"❌ Invalid mode. Available: {', '.join(self.generators.keys())}", response_ts)
    81→                return
    82→
    83→        if cmd == "clear":
    84→            storage.clear()
    85→            self._reply(channel_id, "🧹 Context cleared.", response_ts)
    86→            return
    87→
    88→        if cmd == "reload":
    89→            self.indexer.scan_vault()
    90→            self._reply(channel_id, "🔄 Vault reloaded.", response_ts)
    91→            return
    92→
    93→        # 2. Handle Content based on mode
    94→        if current_mode == "none":
    95→            self._reply(channel_id, "⚠ No mode selected. Use `mode write/reply/decide/search/deai/zhihu` to start.", response_ts)
    96→            return
    97→
    98→        # Get history
    99→        history = storage.get_context()
   100→        generator = self.generators[current_mode]
   101→
   102→        try:
   103→            logger.info(f"Generating Obsidian response in mode: {current_mode}")
   104→            # The chat method handles first turn logic and returns updated history
   105→            # But wait, our generators' chat method appends to history internally.
   106→            # In our Slack setup, we want to append the result to ContextStorage.
   107→
   108→            # Note: generator.chat(text, history) returns (response, new_history_list)
   109→            # new_history_list contains [...history, {user: rich_prompt}, {assistant: response}]
   110→            response_text, updated_history = generator.chat(text, history)
   111→
   112→            # Since generator.chat already did the LLM call and returned history,
   113→            # we just need to save the ONLY NEW messages to our storage.
   114→            # However, during FIRST TURN, text is replaced by rich_prompt.
   115→
   116→            # For simplicity: completely overwrite storage with updated_history
   117→            # ContextStorage doesn't support overwrite easily, but we can clear and re-add.
   118→            # Better: Modify ObsidianContextStorage to support set_context.
   119→
   120→            # Actually, let's just use the updated_history and manually add to storage
   121→            # Warning: first turn 'user' message in storage will be the RICH PROMPT
   122→            # This is actually better for context continuity in Slack too.
   123→
   124→            storage.clear()
   125→            for msg in updated_history:
   126→                storage.add_message(msg["role"], msg["content"])
   127→
   128→            self._reply(channel_id, response_text, response_ts)
   129→
   130→        except Exception as e:
   131→            logger.error(f"Obsidian generation failed: {e}", exc_info=True)
   132→            self._reply(channel_id, f"🐛 Error: {str(e)}", response_ts)
   133→
   134→    def _reply(self, channel_id: str, text: str, ts: Optional[str] = None):
   135→        from slack_bot.utils.mrkdwn import SlackFormatter
   136→
   137→        # Slack API limits (official documentation):
   138→        # - chat.postMessage: 40,000 chars
   139→        # - chat.update: 4,000 chars
   140→        # Slack markdown formatting can expand text by 50-100% (bold, links, code blocks, etc.)
   141→        TRUNCATE_WARNING = "\n\n⚠ (Response truncated, full text uploaded as file)"
   142→        FILE_UPLOAD_HINT = "📄 Full response uploaded as file above ☝"
   143→
   144→        # Calculate safe pre-formatting limit with aggressive overhead buffer
   145→        if ts:
   146→            # For updates: 4000 limit - 60% overhead for safety = ~1600 chars
   147→            max_raw_length = 1500
   148→        else:
   149→            # For new messages: 40000 limit - 40% overhead = ~24000 chars
   150→            max_raw_length = 24000
   151→
   152→        original_length = len(text)
   153→        should_upload_file = original_length > max_raw_length
   154→
   155→        if should_upload_file:
   156→            # Upload full text as file first
   157→            self._upload_as_file(channel_id, text, ts)
   158→
   159→            # Then send a truncated preview
   160→            truncate_at = text.rfind('\n\n', 0, 800)
   161→            if truncate_at == -1 or truncate_at < 400:
   162→                truncate_at = text.rfind('\n', 0, 800)
   163→            if truncate_at == -1 or truncate_at < 400:
   164→                truncate_at = 800
   165→
   166→            preview_text = text[:truncate_at] + f"\n\n...\n\n{FILE_UPLOAD_HINT}"
   167→            formatted_text = SlackFormatter.convert(preview_text)
   168→            logger.info(f"Uploaded full response ({original_length} chars) as file, sending preview ({len(preview_text)} chars)")
   169→        else:
   170→            # Text is short enough, just format and send
   171→            formatted_text = SlackFormatter.convert(text)
   172→
   173→            # Final safety check after formatting
   174→            final_limit = 3900 if ts else 39900
   175→            if len(formatted_text) > final_limit:
   176→                logger.warning(f"Formatted text ({len(formatted_text)} chars) exceeds limit after formatting. Uploading as file.")
   177→                self._upload_as_file(channel_id, text, ts)
   178→                # Send simple notification
   179→                formatted_text = FILE_UPLOAD_HINT
   180→
   181→        # Send the message
   182→        try:
   183→            if ts:
   184→                self.client.chat_update(channel=channel_id, ts=ts, text=formatted_text)
   185→            else:
   186→                self.client.chat_postMessage(channel=channel_id, text=formatted_text)
   187→        except Exception as e:
   188→            if "msg_too_long" in str(e):
   189→                logger.error(f"Still got msg_too_long error even after truncation. Text length: {len(formatted_text)}")
   190→                # Ultimate fallback: send error message only
   191→                try:
   192→                    error_msg = "❌ Response formatting error. Please check the uploaded file above."
   193→                    if ts:
   194→                        self.client.chat_update(channel=channel_id, ts=ts, text=error_msg)
   195→                    else:
   196→                        self.client.chat_postMessage(channel=channel_id, text=error_msg)
   197→                except:
   198→                    logger.error("Failed to send even error message", exc_info=True)
   199→            else:
   200→                raise
   201→
   202→    def _upload_as_file(self, channel_id: str, content: str, ts: Optional[str] = None):
   203→        """Fallback: upload long responses as text files."""
   204→        try:
   205→            import tempfile
   206→            from datetime import datetime
   207→
   208→            filename = f"response_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt"
   209→
   210→            with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False, encoding='utf-8') as tmp:
   211→                tmp.write(content)
   212→                tmp_path = tmp.name
   213→
   214→            self.client.files_upload_v2(
   215→                channel=channel_id,
   216→                file=tmp_path,
   217→                filename=filename,
   218→                initial_comment="📄 Response too long for Slack message, uploaded as file:",
   219→                thread_ts=ts
   220→            )
   221→
   222→            os.unlink(tmp_path)
   223→            logger.info(f"Uploaded long response as file: {filename}")
   224→        except Exception as e:
   225→            logger.error(f"Failed to upload file: {e}", exc_info=True)
   226→            # Ultimate fallback: send error message
   227→            error_msg = "❌ Response too long to display. Please try a more specific query."
   228→            if ts:
   229→                self.client.chat_update(channel=channel_id, ts=ts, text=error_msg)
   230→            else:
   231→                self.client.chat_postMessage(channel=channel_id, text=error_msg)
   232→
   233→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→try:
     2→    from duckduckgo_search import DDGS
     3→except ImportError:
     4→    DDGS = None
     5→
     6→from health.utils.logging_config import setup_logger
     7→
     8→logger = setup_logger(__name__)
     9→
    10→class WebSearchTool:
    11→    @staticmethod
    12→    def search_web(query: str, max_results: int = 5) -> str:
    13→        """
    14→        Search the web using DuckDuckGo.
    15→        Use this for:
    16→        1. Retrieval of latest scientific research or studies (e.g. "latest study on NAD+")
    17→        2. Real-time news or events.
    18→        3. External knowledge verification (e.g. "reviews of Oura Ring Gen 4")
    19→        4. "Biohacker" community experiences (Reddit, forums, etc).
    20→
    21→        Args:
    22→            query: The search query string.
    23→            max_results: Max number of results (default 5).
    24→        """
    25→        try:
    26→            logger.info(f"Searching web for: {query}")
    27→            results = DDGS().text(query, max_results=max_results)
    28→
    29→            if not results:
    30→                return f"No results found for query: {query}"
    31→
    32→            # Format results nicely for the LLM
    33→            formatted = [f"Search Results for '{query}':"]
    34→            for i, res in enumerate(results, 1):
    35→                title = res.get('title', 'No Title')
    36→                link = res.get('href', '#')
    37→                snippet = res.get('body', 'No content')
    38→                formatted.append(f"\n{i}. [{title}]({link})\n   {snippet}")
    39→
    40→            return "\n".join(formatted)
    41→
    42→        except Exception as e:
    43→            logger.error(f"Web search error: {e}")
    44→            return f"Error performing web search: {str(e)}"
    45→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→import pandas as pd
     2→import numpy as np
     3→from datetime import date, timedelta
     4→from typing import Dict, Any, List, Optional
     5→from health.services.query import HealthDataQuery
     6→from health.services.manual_log_storage import ManualLogStorage
     7→from health.utils.logging_config import setup_logger
     8→
     9→logger = setup_logger(__name__)
    10→
    11→class HealthAnalyst:
    12→    """
    13→    Advanced analytics engine for health data using Pandas.
    14→    Calculates correlations, trends, and lifestyle impacts.
    15→    """
    16→
    17→    def __init__(self):
    18→        self.query = HealthDataQuery()
    19→        self.manual_storage = ManualLogStorage()
    20→
    21→    def get_dataframe(self, days: int = 30) -> pd.DataFrame:
    22→        """
    23→        Fetch all relevant data for the last N days and merge into a single DataFrame.
    24→        Includes Garmin metrics, manual logs, and Garmin Lifestyle Logging data.
    25→        """
    26→        end_date = date.today()
    27→        start_date = end_date - timedelta(days=days)
    28→
    29→        metrics_to_fetch = ["sleep", "heart_rate", "stress", "body_battery", "steps", "hrv", "rhr"]
    30→
    31→        data_map = {}
    32→        curr = start_date
    33→        while curr <= end_date:
    34→            data_map[curr.isoformat()] = {"date": curr.isoformat()}
    35→            curr += timedelta(days=1)
    36→
    37→        for metric in metrics_to_fetch:
    38→            points = self.query.get_metric_range(metric, start_date, end_date)
    39→            for p in points:
    40→                val = np.nan
    41→                if hasattr(p, 'value'): val = p.value
    42→                elif isinstance(p, dict):
    43→                    if 'average_heart_rate' in p: val = p['average_heart_rate']
    44→                    elif 'overall_sleep_score' in p: val = p['overall_sleep_score']
    45→                    elif 'resting_heart_rate' in p: val = p['resting_heart_rate']
    46→                    elif 'average_stress_level' in p: val = p['average_stress_level']
    47→                    elif 'charged' in p: val = p.get('charged', 0)
    48→
    49→                d_str = p.get('calendar_date') or p.get('date')
    50→                if d_str and d_str in data_map:
    51→                    data_map[d_str][metric] = val
    52→
    53→        # Manual logs (alcohol, fasting, supplements)
    54→        logs = self.manual_storage.get_logs_in_range(start_date, end_date)
    55→        for log in logs:
    56→            d_str = log.log_date
    57→            if d_str in data_map:
    58→                data_map[d_str]["alcohol_units"] = sum([1 for _ in log.alcohol_entries])
    59→                data_map[d_str]["has_alcohol"] = 1 if log.alcohol_entries else 0
    60→                data_map[d_str]["fasting_mode"] = log.fasting_mode or "Normal"
    61→                data_map[d_str]["has_magnesium"] = 1 if any("magnesium" in s.supplement_name.lower() for s in log.supplement_entries) else 0
    62→
    63→        # Garmin Lifestyle Logging (alcohol, caffeine, meals, fasting, light exercise)
    64→        lifestyle_points = self.query.get_metric_range("lifestyle_logging", start_date, end_date)
    65→        for p in lifestyle_points:
    66→            if not isinstance(p, dict):
    67→                continue
    68→            d_str = p.get('date')
    69→            if not d_str or d_str not in data_map:
    70→                continue
    71→            row = data_map[d_str]
    72→            # Alcohol (from Garmin — finer granularity than manual logs)
    73→            row["gl_alcohol"] = 1 if p.get('alcohol_logged') else 0
    74→            row["gl_alcohol_total"] = (
    75→                p.get('alcohol_beer', 0) + p.get('alcohol_wine', 0) +
    76→                p.get('alcohol_spirit', 0) + p.get('alcohol_other', 0)
    77→            )
    78→            # Caffeine
    79→            row["gl_morning_caffeine"] = 1 if p.get('morning_caffeine_logged') else 0
    80→            row["gl_morning_coffee"] = p.get('morning_caffeine_coffee', 0)
    81→            row["gl_late_caffeine"] = 1 if p.get('late_caffeine_logged') else 0
    82→            # Meal quality
    83→            row["gl_healthy_meals"] = 1 if p.get('healthy_meals') else 0
    84→            row["gl_heavy_meals"] = 1 if p.get('heavy_meals') else 0
    85→            row["gl_late_meals"] = 1 if p.get('late_meals') else 0
    86→            # Light exercise & fasting
    87→            row["gl_light_exercise"] = 1 if p.get('light_exercise') else 0
    88→            row["gl_fasting"] = 1 if p.get('intermittent_fasting') else 0
    89→
    90→        df = pd.DataFrame(list(data_map.values()))
    91→        df['date'] = pd.to_datetime(df['date'])
    92→        df = df.sort_values('date').set_index('date')
    93→        return df
    94→
    95→    def analyze_recovery_correlations(self, days: int = 90) -> Dict[str, Any]:
    96→        """
    97→        Analyze impact of Alcohol and Activity on Next-Day Recovery (Sleep, HRV, RHR).
    98→        """
    99→        df = self.get_dataframe(days)
   100→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


total 2716
drwxr-xr-x 20 root root    4096 Mar  2 23:33 .
drwxr-xr-x  4 root root      39 Mar  2 10:00 ..
drwxr-xr-x  2 root root     183 Feb 26 21:17 accounts
drwxr-xr-x  2 root root    4096 Feb 26 21:17 AI
drwxr-xr-x  9 root root    8192 Feb 26 21:17 AppleNotes
drwxr-xr-x  2 root root    4096 Feb 26 21:17 Article
drwxr-xr-x  2 root root    8192 Feb 26 21:16 attachments
drwxr-xr-x  3 root root     107 Mar  3 07:02 auto_report
drwxr-xr-x  5 root root     164 Feb 28 18:48 CLAW
drwxr-xr-x  2 root root    4096 Feb 26 21:09 Clippings
drwxr-xr-x  2 root root    4096 Feb 28 00:22 daily
-rw-r--r--  1 root root    6130 Feb 28 20:20 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  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


Let me check for more analytics, note files, and any LLM integration patterns:


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

List all Slack bot modules


find /root/projects/butler -type f \( -name "writing_style.md" -o -name "methodology.md" -o -name "decision.md" \) | head -10

Find user guide files for content generation


     1→import os
     2→import json
     3→from datetime import date
     4→from typing import List, Dict, Any, Optional, Tuple
     5→from .base import BaseLLM
     6→from health.utils.logging_config import setup_logger
     7→
     8→logger = setup_logger(__name__)
     9→
    10→# Base system prompt for health assistant
    11→BUTLER_SYSTEM_INSTRUCTION_BASE = """You are Butler, a personal health assistant for tracking and analyzing health data.
    12→
    13→You help the user manage:
    14→- Sleep quality and patterns (from Garmin watch)
    15→- Exercise data: steps, activities, heart rate
    16→- Diet logs: meals, supplements, fasting modes (OMAD, PSMF)
    17→- Diet logs: meals, supplements, fasting modes (OMAD, PSMF)
    18→- Body feelings and symptoms
    19→
    20→CRITICAL ANTI-HALLUCINATION RULES:
    21→1. **TOOL-FIRST POLICY**: When user asks about CURRENT or SPECIFIC health data (e.g., "how did I sleep last night?", "what was my steps yesterday?"), you MUST call the appropriate tool FIRST. NEVER answer based on conversation history or memory.
    22→2. **NO DATA INVENTION**: If a tool returns "No data found" or null values, YOU MUST SAY "I cannot find data for this date" or "No data available". Do NOT guess, estimate, or invent numbers.
    23→3. **VISION LIMITATIONS**: When analyzing images, if unclear or unreadable, say "I cannot identify the food in this image". Do NOT guess ingredients you do not see.
    24→4. **EXPLICIT UNCERTAINTY**: If you're unsure whether data exists, call the tool to check. Say "Let me check your data" rather than guessing.
    25→
    26→Guidelines:
    27→- Be concise and data-driven
    28→- When user wants to log something (diet, supplement, etc), use the appropriate tool
    29→- Provide actionable insights based ONLY on retrieved data
    30→- Support both English and Chinese
    31→
    32→CONVERSATIONAL CONTEXT HANDLING:
    33→- When user gives vague confirmation replies like "好的，可以记录" / "ok, log it" / "yes, record that":
    34→  1. FIRST check the conversation history to see what they were discussing
    35→  2. If they mentioned food/diet in recent messages, extract the details and call `log_diet` with:
    36→     - description: the food items they mentioned
    37→     - meal_type: infer from context (breakfast/lunch/dinner/snack)
    38→     - target_date: today unless specified otherwise
    39→  3. If you cannot extract clear details, politely ask: "请问要记录什么内容？可以告诉我具体吃了什么吗？"
    40→- NEVER return empty responses. If unsure what to do, ask a clarifying question.
    41→
    42→WEB SEARCH USAGE:
    43→- You have access to real-time web search via `search_web`.
    44→- USE IT WHEN:
    45→  1. User asks for "latest" research, news, or biohacking trends.
    46→  2. You need to verify external facts (e.g. "benefits of berberine").
    47→  3. User specifically asks to check online.
    48→- DO NOT USE IT WHEN:
    49→  1. User asks for PERSONAL data (steps, sleep). Use `health_read` tools.
    50→
    51→Available data sources:
    52→- Garmin health metrics (sleep, steps, heart rate, etc.)
    53→- Manual logs (diet, supplements, fasting, body feelings)
    54→- Real-time Web Search
    55→- Obsidian daily notes (can be synced)
    56→
    57→5. **ACTION COMMANDS**: If the user asks to "sync", "update", or "fetch" data, you MUST call the corresponding tool (e.g., `sync_garmin`). Do NOT say "done" without calling the tool. If the tool call fails or you cannot find the tool, report the error.
    58→
    59→IMPORTANT: Today's date is {today}. When logging data without a specified date, use today's date."""
    60→
    61→
    62→from health.utils.time_utils import get_current_time_str
    63→
    64→def get_system_instruction() -> str:
    65→    """Get system instruction with current date and health profile."""
    66→    # Use UTC+8 string directly
    67→    base_prompt = BUTLER_SYSTEM_INSTRUCTION_BASE.format(today=get_current_time_str())
    68→
    69→    # Try to load custom health profile
    70→    try:
    71→        health_md_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "health.md")
    72→        if os.path.exists(health_md_path):
    73→            with open(health_md_path, "r", encoding="utf-8") as f:
    74→                health_profile = f.read()
    75→            return f"{base_prompt}\n\n=== USER HEALTH PROTOCOLS ===\n{health_profile}"
    76→    except Exception as e:
    77→        logger.error(f"Failed to load health.md: {e}")
    78→
    79→    return base_prompt
    80→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


/root/projects/butler/slack_bot/context/compressor.py
/root/projects/butler/slack_bot/context/storage.py
/root/projects/butler/slack_bot/dispatcher.backup.py
/root/projects/butler/slack_bot/dispatcher.py
/root/projects/butler/slack_bot/__init__.py
/root/projects/butler/slack_bot/llm/base.py
/root/projects/butler/slack_bot/llm/gemini.py
/root/projects/butler/slack_bot/main.py
/root/projects/butler/slack_bot/obsidian/cli.py
/root/projects/butler/slack_bot/obsidian/dispatcher.py
/root/projects/butler/slack_bot/obsidian/generators.py
/root/projects/butler/slack_bot/obsidian/indexer.py
/root/projects/butler/slack_bot/obsidian/__init__.py
/root/projects/butler/slack_bot/shell/help_text.py
/root/projects/butler/slack_bot/shell/__init__.py
/root/projects/butler/slack_bot/shell_main.py
/root/projects/butler/slack_bot/shell/manager.py
/root/projects/butler/slack_bot/shell/session.py
/root/projects/butler/slack_bot/tools/analytics.py
/root/projects/butler/slack_bot/tools/file_ops.py
/root/projects/butler/slack_bot/tools/garmin.py
/root/projects/butler/slack_bot/tools/groups.py
/root/projects/butler/slack_bot/tools/health_read.py
/root/projects/butler/slack_bot/tools/health_write.py
/root/projects/butler/slack_bot/tools/obsidian.py
/root/projects/butler/slack_bot/tools/process_ops.py
/root/projects/butler/slack_bot/tools/registry.py
/root/projects/butler/slack_bot/tools/shell_tool.py
/root/projects/butler/slack_bot/tools/system_ops.py
/root/projects/butler/slack_bot/tools/web.py
/root/projects/butler/slack_bot/utils/mrkdwn.py


/root/projects/butler/decision.md
/root/projects/butler/methodology.md
/root/projects/butler/tag20260215/decision.md
/root/projects/butler/tag20260215/methodology.md
/root/projects/butler/tag20260215/writing_style.md
/root/projects/butler/writing_style.md
/root/projects/butler/tag20260302/decision.md
/root/projects/butler/tag20260302/methodology.md
/root/projects/butler/tag20260302/writing_style.md


Now let me check a few more key system files and look at the overall structure:


List all markdown documentation files in root


     1→# 写作风格指南 (Writing Style Guide)
     2→
     3→## 1. 核心语感基因 (Style DNA)
     4→*   **结构化思考，碎片化表达**：
     5→    *   习惯使用清晰的列表（1, 2, 3...）和子弹点。
     6→    *   即便是复杂论述，也要拆解为明确的标题（如“现状”、“挑战”、“结语”）。
     7→*   **技术理想主义的浪漫**：
     8→    *   可以使用宏大的历史或文学隐喻（如：西西弗斯、巴别塔、福特生产线、魔法），但每篇文章**最多出现 1-2 个比喻**，不重复使用同一个。
     9→    *   将枯燥的技术概念”人格化”或”生活化”（如：服务器是宠物还是牛群）。
    10→*   **真诚且透明的“自黑”**：
    11→    *   不避讳谈及局限性或个人背景（“小镇做题家”）。以坦诚建立信任，反对官僚套话。
    12→*   **冷静的宿命感与乐观**：
    13→    *   语气中常带有“接受最坏情况，但坚持背水一战”的韧性。
    14→
    15→## 2. 沟通与词汇 (Communication & Vocabulary)
    16→*   **中英技术混排 (Bilingual Accuracy)**：
    17→    *   在中文叙述中自然嵌入技术或商务术语（如：btw, todo, hosting, computing, infra, winback, poc, roadmap）。
    18→    *   专业名词严格保留英文，追求表达的精确性。
    19→*   **结论先行 & “为什么”驱动**：
    20→    *   首先概述核心结论。
    21→    *   强调行动的合理性，解释“为什么要解决这个问题”。
    22→*   **零废话原则 (Zero Fluff)**：
    23→    *   省略无意义的寒暄和重复。直接输出核心逻辑。
    24→
    25→## 3. 输出规范 (Output Standards)
    26→*   **简洁有序**：逻辑结构必须是第一位的，多用子弹点。
    27→*   **去概念化**：擅长将复杂技术“人话化”。
    28→*   **纯文本倾向**：优先用自然语言分段，不用 Markdown 加粗（**xxx**）装饰普通词汇，不用 `---` 分割线制造仪式感，标题和有序列表仅在结构真正需要时使用。
    29→*   **减少解释**：尽量不要用括号来解释术语或者比喻，减少对流畅性的打断
    30→
    31→
    32→## 4. 典型句式 (Patterns to Mimic)
    33→*   “其实我知道大家觉得...但是我也对此持有非常疑虑的态度，我必须要明确承认这一点。”
    34→*   “我们可以从两个极端角度来回答这些所有问题。”
    35→*   “云的理想非常直接，就是我们要建设一个IT的巴别塔。”
    36→*   “这不仅是炫技，更是为了解决...的矛盾。”
    37→
    38→## 5. 禁忌 (Strict Don'ts)
    39→*   **禁止**：说教式的语气（如”记得要注意...”）。
    40→*   **禁止**：在没有具体方案时使用空洞的鼓励话语。
    41→*   **禁止**：使用官僚色彩浓厚的行政套话。
    42→*   **禁止**：在输出文本中提及内部决策框架名称（GPA、IPO 等）——这些是内部推理工具，直接按原则行事即可，不需要向读者点名。
    43→*   **禁止**：在同一篇文章里重复使用同一个比喻；每篇文章比喻总量不超过 1-2 个，超出则删减。
    44→*   **永久禁用比喻**：”简装房”——这是特定历史场景下的表达，不再在任何生成内容中使用。
    45→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→# 个人方法论与价值观 (Methodology & Values)
     2→
     3→## 1. 核心身份与愿景 (Identity & Vision)
     4→*   **角色定位**：从研发转型产品经营的技术布道者。自称“小镇做题家”，技术信仰者。
     5→*   **终极理想：“IT 巴别塔”**：追求 IT 基础设施的极限标准化和产品化，消除技术隔阂，让计算能力像水电一样触手可得。
     6→*   **人生信条**：
     7→    *   **抱朴守一**：在复杂环境中保持对技术本质的追求。
     8→    *   **求仁得仁**：为自己的选择负责，接受因果。
     9→    *   **推石上山**：接受世界的“荒诞”和不确定性，但在行动上保持悲观的预演与乐观的执行。
    10→
    11→## 2. 产品与经营哲学 (Product & Business)
    12→*   **工业化本质**：反复引用福特汽车生产线。认为云的价值在于“部件标准化”和“生产流水线”。
    13→*   **“简装房”理论**：CVM 是毛坯房；Lighthouse（轻量云）是“良心云/简装房”，通过牺牲部分灵活性换取极致的易用性。
    14→*   **可持续经营**：反对单纯为了规模而牺牲毛利。通过技术突破（热迁移、锁核升频）实现低成本运营。
    15→*   **需求本位**：叙事始于“痛点洞察”，而非产品功能。
    16→
    17→## 3. 决策方法论 (Decision Methodology)
    18→*   **最坏情况假设**：先设定决策的底线，如果底线可承受，则行动不再迟疑。
    19→*   **”写下来”**：通过书写推演逻辑，抵制灵机一动。
    20→*   **GPA 原则**：目标 (Goal)、优先级 (Priority)、可选方案 (Alternatives)。
    21→*   **IPO 原则**：信息 (Information)、人员 (People)、客观推理 (Objective Reasoning)。
    22→*   **零基思维**：如果产品不对客户产生真实价值，则拒绝制造伪需求。
    23→
    24→> **注意（给 Bot 的指令）**：GPA 和 IPO 是内部推理框架，用于结构化思考过程，**不要在输出文本中提及这两个框架名称**。直接按原则行事即可，不需要向读者解释框架本身。
    25→
    26→## 4. 协作与管理思维 (Management)
    27→*   **“对人编程” (To-Human Programming)**：像理解 API 一样理解人的反馈。程序员需要建立连接、沟通，解决人的问题。
    28→*   **“对自己编程”**：通过习惯和认知升级重构个人的“软硬件”。
    29→*   **成就他人**：管理者的成功在于团队成长。在利益冲突时，主动平衡以换取协作顺畅。
    30→*   **反“学生思维”**：警惕被动指令和虚假努力，推崇“成人逻辑”和职业化。
    31→
    32→## 5. 核心名词百科 (Knowledge Glossary)
    33→*   **Lighthouse (轻量云)**：开箱即用的产品哲学载体，代表“良心云”品牌。
    34→*   **VStation**：计算产品的核心调度底座。
    35→*   **分布式云**：算力延伸至客户本地。
    36→*   **长安链/至信链**：国家级区块链主链与司法存证应用。
    37→
    38→## 6. 工程偏好
    39→*   **实用主义**：推崇 "Batteries Included"，反对过度设计。
    40→*   **可追溯性**：系统可追溯性优于灵活性，故障回滚能力至关重要。
    41→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


-rw-r--r-- 1 root root  24803 Mar  2 10:58 /root/projects/butler/BOT_FEATURES_REFERENCE.md
-rw-r--r-- 1  501 games  2812 Feb  2 10:56 /root/projects/butler/BOT_FIX_SUMMARY.md
-rw-r--r-- 1  501 games  5114 Jan 22 11:30 /root/projects/butler/Butler-Bot使用指南.md
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-rw-r--r-- 1  501 games  4188 Jan 19 13:46 /root/projects/butler/health.md
-rw-r--r-- 1  501 games  5361 Jan 20 14:49 /root/projects/butler/mannul-data-input.md
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     1→"""
     2→Pydantic models for daily health metrics.
     3→
     4→Includes models for steps, heart rate, sleep, stress, body battery, and more.
     5→"""
     6→
     7→from datetime import date as date_type, time as time_type
     8→from typing import Optional, List, Dict, Any
     9→from pydantic import BaseModel, Field
    10→
    11→
    12→class StepsData(BaseModel):
    13→    """Daily step count and related metrics."""
    14→
    15→    date: date_type = Field(..., description="Date of the data")
    16→    total_steps: Optional[int] = Field(default=None, description="Total steps taken")
    17→    total_distance_meters: Optional[float] = Field(
    18→        default=None, description="Total distance in meters"
    19→    )
    20→    calories_burned: Optional[int] = Field(
    21→        default=None, description="Calories burned from steps"
    22→    )
    23→    step_goal: Optional[int] = Field(default=None, description="Daily step goal")
    24→    raw_data: Optional[Dict[str, Any]] = Field(
    25→        default=None, description="Raw API response"
    26→    )
    27→
    28→
    29→class HeartRateData(BaseModel):
    30→    """Daily heart rate metrics."""
    31→
    32→    date: date_type = Field(..., description="Date of the data")
    33→    resting_heart_rate: Optional[int] = Field(
    34→        default=None, description="Resting heart rate in bpm"
    35→    )
    36→    min_heart_rate: Optional[int] = Field(
    37→        default=None, description="Minimum heart rate in bpm"
    38→    )
    39→    max_heart_rate: Optional[int] = Field(
    40→        default=None, description="Maximum heart rate in bpm"
    41→    )
    42→    average_heart_rate: Optional[int] = Field(
    43→        default=None, description="Average heart rate in bpm"
    44→    )
    45→    heart_rate_zones: Optional[Dict[str, int]] = Field(
    46→        default=None, description="Time in each heart rate zone (minutes)"
    47→    )
    48→    raw_data: Optional[Dict[str, Any]] = Field(
    49→        default=None, description="Raw API response"
    50→    )
    51→
    52→
    53→class SleepData(BaseModel):
    54→    """Daily sleep metrics."""
    55→
    56→    date: date_type = Field(..., description="Date of the sleep (night)")
    57→    total_sleep_seconds: Optional[int] = Field(
    58→        default=None, description="Total sleep time in seconds"
    59→    )
    60→    deep_sleep_seconds: Optional[int] = Field(
    61→        default=None, description="Deep sleep time in seconds"
    62→    )
    63→    light_sleep_seconds: Optional[int] = Field(
    64→        default=None, description="Light sleep time in seconds"
    65→    )
    66→    rem_sleep_seconds: Optional[int] = Field(
    67→        default=None, description="REM sleep time in seconds"
    68→    )
    69→    awake_seconds: Optional[int] = Field(
    70→        default=None, description="Time awake in seconds"
    71→    )
    72→    sleep_score: Optional[int] = Field(
    73→        default=None, description="Overall sleep quality score (0-100)"
    74→    )
    75→    sleep_start_time: Optional[time_type] = Field(
    76→        default=None, description="Sleep start time"
    77→    )
    78→    sleep_end_time: Optional[time_type] = Field(default=None, description="Sleep end time")
    79→    sleep_levels: Optional[List[Dict[str, Any]]] = Field(
    80→        default=None, description="Detailed sleep level timeline"

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Perfect! Let me check one final key component - the weekly report generation:


find /root/vault/obsidian_vault/obsidian/obsidian -type d | head -15

Show main directory structure of Obsidian vault


wc -l /root/projects/butler/health/services/*.py | tail -1

Count total lines of code in health services