Navigate back to the previous page in browser history. Requires browser_navigate to be called first.

Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e5', '@e12')

Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.

If true, clear the message buffers after reading

JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll("a").length'

Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.

Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.

The URL to navigate to (e.g., 'https://example.com')

Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.

Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')

Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.

Direction to scroll

Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.

If true, returns complete page content. If false (default), returns compact view with interactive elements only.

Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e3')

The text to type into the field

Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA:<screenshot_path> in your response. Requires browser_navigate to be called first.

What you want to know about the page visually. Be specific about what you're looking for.

If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout.

clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.

The question to present to the user.

Choices:
Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), or platform:chat_id:thread_id for a specific destination. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting.

Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.

Provider name (e.g. 'openrouter', 'anthropic'). Omit to use and pin the current provider.

Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')

Optional path to a Python script that runs before each cron job execution. Its stdout is injected into the prompt as context. Use for data collection and change detection. Relative paths resolve under ~/.hermes/scripts/. On update, pass empty string to clear.

Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. Batch (parallel): provide 'tasks' array with up to 3 items. All run concurrently and results are returned together.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- Subagents CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'cronjob', 'file', 'homeassistant', 'image_gen', 'search', 'session_search', 'skills', 'terminal', 'todo', 'tts', 'vision', 'web'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks.

Task goal

Task-specific context

Toolsets for this specific task. Available: 'browser', 'cronjob', 'file', 'homeassistant', 'image_gen', 'search', 'session_search', 'skills', 'terminal', 'todo', 'tts', 'vision', 'web'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction.

Per-task ACP command override (e.g. 'claude'). Overrides the top-level acp_command for this task only.

Per-task ACP args override.

Batch mode: tasks to run in parallel (limit configurable via delegation.max_concurrent_children, default 3). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored.

Max tool-calling turns per subagent (default: 50). Only set lower for simple tasks.

Override ACP command for child agents (e.g. 'claude', 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Enables spawning Claude Code (claude --acp --stdio) or other ACP-capable agents from any parent, including Discord/Telegram/CLI.

Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Example: ['--acp', '--stdio', '--model', 'claude-opus-4-6']

Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).

Use normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.

Available via `from hermes_tools import ...`:

  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout.

memory

Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

patch

Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.

Replace mode (default): find a unique string and replace it.
Patch mode: apply V4A multi-file patches for bulk changes.

Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches

File path to edit (required for 'replace' mode)

Text to find in the file (required for 'replace' mode). Must be unique in the file unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'replace' mode). Can be empty string to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

V4A format patch content (required for 'patch' mode). Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).

list poll log wait kill write submit close

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.

TWO MODES:
1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.

Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match ("docker networking"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.

Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.

Actions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted.

Content for the file. Required for 'write_file'.

Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.

The skill name (use skills_list to see available skills)

OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.
Background: Set background=true to get a session_id. Two patterns:
  (1) Long-lived processes that never exit (servers, watchers).
  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.
Use process(action="poll") for progress checks, process(action="wait") to block until done.
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.

The command to execute on the VM

Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.

Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.

Working directory for this command (absolute path). Defaults to the session working directory.

Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.

When true (and background=true), you'll be automatically notified when the process finishes — no polling needed. Use this for tasks that take a while (tests, builds, deployments) so you can keep working on other things in the meantime.

List of strings to watch for in background process output. When any pattern matches a line of output, you'll be notified with the matching text — like notify_on_complete but triggers mid-process on specific output. Use for monitoring logs, watching for errors, or waiting for specific events (e.g. ["ERROR", "FAIL", "listening on port"]).

Convert text to speech audio. Returns a MEDIA: path that the platform delivers as a voice message. On Telegram it plays as a voice bubble, on Discord/WhatsApp as an audio attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured, not model-selected.

The text to convert to speech. Keep under 4000 characters.

Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3

todo

Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

Analyze images using AI vision. Provides a comprehensive description and answers a specific question about the image content.

Image URL (http/https) or local file path to analyze.

Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question.

Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits.

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file


分析一下：https://www.bmpi.dev/self/my-gpt-reader/


<think>
The user wants me to analyze the URL https://www.bmpi.dev/self/my-gpt-reader/. I should use the browser to navigate to this page and analyze its content.
</think>


我的AI阅读助手 · BMPI

Running WITHOUT residential proxies. Bot detection may be more aggressive. Consider upgrading Browserbase plan for proxy support.

  - main:
    - navigation:
      - link "BMPI" [ref=e1]:
        - /url: https://www.bmpi.dev/
        - listitem:
          - link "GitHub" [ref=e2]:
            - /url: https://github.com/madawei2699
        - listitem:
          - link "Twitter" [ref=e3]:
            - /url: https://twitter.com/madawei2699
        - listitem:
          - link "学习" [ref=e4]:
            - /url: https://www.bmpi.dev/self/
        - listitem:
          - link "技术" [ref=e5]:
            - /url: https://www.bmpi.dev/dev/
        - listitem:
          - link "投资" [ref=e6]:
            - /url: https://www.bmpi.dev/money/
        - listitem: "|"
        - listitem:
          - link "English" [ref=e7]:
            - /url: https://www.bmpi.dev/en/
    - article:
      - heading "我的AI阅读助手" [ref=e8] [level=1]
      - text: 
      - time: 2023-03-26
      - text:  6:49 | 1503 字 | 次阅读 
      - link "chatGPT" [ref=e9]:
        - /url: https://www.bmpi.dev/tags/chatgpt/
      - text: •
      - link "信息输入" [ref=e10]:
        - /url: https://www.bmpi.dev/tags/%E4%BF%A1%E6%81%AF%E8%BE%93%E5%85%A5/
      - text: •
      - link "自我提升" [ref=e11]:
        - /url: https://www.bmpi.dev/tags/%E8%87%AA%E6%88%91%E6%8F%90%E5%8D%87/
      - text: •
      - link "知识管理" [ref=e12]:
        - /url: https://www.bmpi.dev/tags/%E7%9F%A5%E8%AF%86%E7%AE%A1%E7%90%86/
      - link "如何做出更好的投资决策？" [ref=e13]:
        - /url: https://www.myinvestpilot.com/
      - link "品牌赞助" [ref=e14]:
        - /url: https://www.bmpi.dev/_ad/
      - heading "AI降临" [ref=e15] [level=2]
      - paragraph:
        - text: 今年你一定或多或少被chatGPT等AI模型的消息轰炸过。早在去年年底的时候我就被chatGPT的实力震撼过，为此还专门写过一篇文章：
        - link "AI降临" [ref=e16]:
          - /url: https://www.bmpi.dev/self/ai-arrival/
        - text: ，虽说AI模型是被人工训练出来的，但许多测试都表明它或多或少都有一些与人相似的智能，根据算力的演进速度，它的成长可能比很多人想象中的要快，所以所今年是AI降临年也不为过。如果你经常混迹推特，可能对此会更有体会，每天有数不尽的AI研究被公布，新的模型被发布，甚至GPT-4也被OpenAI发布了，而chatGPT只是GPT-3.5的一个调优版！
      - paragraph: 虽然2023年是糟糕的一年，很多公司在裁员，经济比大家预期的要差。但AI技术的突破性成果让这一切看起来像是iPhone发布的初期，iPhone在2008年发布，当年正是全球次贷危机爆发的一年，而且PC的发展在当时看也走到了尽头，是iPhone带领我们走向了移动互联网蓬勃发展的十年，谁也没能预料到iPhone给我们带来生活上的多大改变。所以乐观点看，也许2023年未来十年不错的开端，就算你此刻的处境也很糟糕，希望能坚持向前看，相信未来会更好。
      - paragraph:
        - text: 关注我的朋友都知道，我投入很多的精力在
        - link "知识管理" [ref=e17] [nth=1]:
          - /url: https://www.bmpi.dev/tags/%E7%9F%A5%E8%AF%86%E7%AE%A1%E7%90%86/
        - text: 上，也写了一些和信息获取的文章如
        - link "构建高质量的信息输入渠道" [ref=e18]:
          - /url: https://www.bmpi.dev/self/my-info-input-channel/
        - text: 与
        - link "构建自己的信息简报" [ref=e19]:
          - /url: https://www.bmpi.dev/self/use-rss-email-read/
        - text: 。这段时间我深深的意识到AI将会对我们未来的学习产生很深远的影响，从chatGPT能自由的切换语言到瞬间整理总结知识，这一切都看起来是如此的震撼。我为此专门测试了一些让chatGPT帮助我阅读总结整理书籍、文章、代码仓库，甚至与它结对编写代码。
      - heading "myGPTReader" [ref=e20] [level=2]
      - paragraph:
        - text: 在做完这些测试后，我意识到是时候革新我的知识管理流程和工具了，第一步就是从获取信息的阅读作为切入点，所以我花了十几天时间与chatGPT合作开发了我的个人AI阅读助手：
        - link "myGPTReader" [ref=e21]:
          - /url: https://github.com/madawei2699/myGPTReader
        - text: ，它是一个在slack聊天软件里的bot，可以读取任何网页、电子书与文档，并根据与问题相关的内容做总结与分析处理，无论你给它的是什么语言的版本，它都能按你需要的语言去整理，这对平时阅读大量英文文章的我来说是一个效率的提升，当然这个bot背后还是chatGPT，所以可以询问它任何问题。
      - paragraph: 它能做什么？
      - heading "功能特性" [ref=e22] [level=3]
        - listitem: 在线读取任意网页内容包括视频（YouTube），并根据这些内容回答你提出的相关问题或总结相关内容。
        - listitem: 支持读取电子书与文档（支持PDF、EPUB、DOCX、Markdown、TXT），并根据这些内容回答你提出的相关问题或总结相关内容。
        - listitem: 定时发送每日热榜新闻，无论新闻是中文还是其他语言，它都能使用chatGPT用中文自动总结新闻的内容，方便快速获取热点新闻信息。
          - text: 支持
          - code: prompt
          - text: 模版，能根据消息历史记录的上下文回答你的问题，甚至能和你玩游戏。
        - listitem: 支持多国语音交互（英文、中文、德语与日语），它会根据你的语言使用相关语言的声音来回答你的问题，从而帮助你训练外语能力，可以理解为它是你的私人外教。
      - paragraph:
        - text: 这是一个开源软件，意味着你可以私有部署它到你的workspace中，当然也可以加入我的
        - link "slack" [ref=e23]:
          - /url: https://slack-redirect.i365.tech/
        - text: 频道来免费体验它的强大。
      - heading "AI时代的学习范式" [ref=e24] [level=2]
      - paragraph: 当AlphaGo战胜了人类顶级棋手李世石那一刻，人类认为最复杂的棋类被AI淘汰了，当Stable Diffusion能够在一秒内画出人类画师一天的作品时，人类认为最复杂的艺术领域开始被AI攻克，当chatGPT能够在一秒内回答人类的任何问题时，人类认为最复杂的知识领域开始被AI攻克。
      - paragraph: 如果AI能够迅速掌握很多我们人类需要数年掌握的知识，那学习的意义何在？
      - paragraph: 如果把现今人类的知识领域看作冷兵器时代，那chaGPT的出现就如枪的诞生一样。一个带枪的人与冷兵器时代的人，谁更有优势？这个带枪的人就是超级个体，他能快速去实现自己的想法，而不需要去学习那么多细节性的知识，参加那么多无效的沟通会议，他能在短时间内实现以前一个团队才能实现的事情。
      - paragraph: 如果我们的很多技能注定要被AI淘汰的话，最好的就是提前去与它共舞。当第一辆汽车问世的时候，很多马车夫不以为然，但也有人主动去学习它，去迎接一个新时代的来临。
      - text: "更新时间: 36个月前 版本:"
      - link "4a556e913" [ref=e25]:
        - /url: https://github.com/madawei2699/bmpi.dev/commit/4a556e9135d7ec17e4861f8b4c856acc3a79660a
      - paragraph:
        - text: 本作品采用
        - link "知识共享署名-非商业性使用-禁止演绎4.0国际许可协议" [ref=e26]:
          - /url: http://creativecommons.org/licenses/by-nc-nd/4.0/
        - text: 进行许可
      - heading "相关文章推荐" [ref=e27] [level=2]
      - listitem:
        - link "构建自己的信息简报2.0" [ref=e28]:
          - /url: https://www.bmpi.dev/self/use-rss-email-read-v2/
      - listitem:
        - link "构建高质量的信息输入渠道" [ref=e29] [nth=1]:
          - /url: https://www.bmpi.dev/self/my-info-input-channel/
      - listitem:
        - link "构建终身学习体系进行自我提升" [ref=e30]:
          - /url: https://www.bmpi.dev/self/build-personal-knowledge-system/
      - link "Share on Twitter" [ref=e31]:
        - /url: https://twitter.com/intent/tweet?text=%e5%9f%ba%e4%ba%8e%20chatGPT%20%e7%9a%84%e8%81%8a%e5%a4%a9%e6%9c%ba%e5%99%a8%e4%ba%ba%ef%bc%8c%e5%b8%ae%e5%8a%a9%e4%bd%a0%e9%ab%98%e6%95%88%e9%98%85%e8%af%bb%e3%80%82&url=https%3a%2f%2fwww.bmpi.dev%2fself%2fmy-gpt-reader%2f&via=madawei2699
      - text: ·
      - link "Discuss on Twitter" [ref=e32]:
        - /url: https://twitter.com/search?q=https%3a%2f%2fwww.bmpi.dev%2fself%2fmy-gpt-reader%2f
    - link "策引" [ref=e33]:
      - /url: https://www.myinvestpilot.com/
    - text: ⚡
    - link "i365" [ref=e34]:
      - /url: https://www.i365.tech/
    - text: ⚡
    - link "演讲" [ref=e35]:
      - /url: https://www.bmpi.dev/talk/
    - text: ⚡
    - link "社区" [ref=e36]:
      - /url: https://www.bmpi.dev/community/
    - text: © 2019 - 2026


<think>
I have the page content. Let me analyze this article about "myGPTReader" - an AI reading assistant. Let me provide a comprehensive analysis in the user's preferred style (诸葛亮的说话风格).
</think>

## 文章分析：我的AI阅读助手

**来源：** BMPI.dev（作者 madawei2699）
**发布日期：** 2023-03-26
**字数：** 约 1503 字
**标签：** ChatGPT、信息输入、自我提升、知识管理

---

### 核心内容

**一、项目简介**

作者开发了一款基于 ChatGPT 的 Slack 机器人 **myGPTReader**（开源），可读取任意网页、电子书（PDF/EPUB/DOCX/Markdown/TXT）及 YouTube 视频内容，并针对提问进行总结分析，还支持多语言语音交互。

**二、核心功能**

| 功能 | 说明 |
|------|------|
| 网页/视频阅读 | 读取任何网页和 YouTube 视频内容后问答 |
| 电子书解析 | 支持主流文档格式的阅读与问答 |
| 每日新闻推送 | 自动抓取热榜新闻并用中文总结 |
| Prompt 模板 | 支持上下文连续对话，甚至可玩游戏 |
| 多语言语音交互 | 支持中、英、德、日四国语言，帮助训练外语 |

**三、核心观点**

作者借 AI 发展浪潮（AlphaGo → Stable Diffusion → ChatGPT）阐述了一个理念：

> **ChatGPT = 知识领域的"枪"**

- 马车夫 vs 汽车 → 冷兵器 vs 带枪者
- AI 淘汰的不是人类，而是**无效学习的旧范式**
- 超级个体崛起：一人 + AI，能做过去一个团队的事
- 策略：**提前与 AI 共舞**，而非抗拒

---

### 价值评估

**亮点：**
- 实操性强，作者将 AI 工具直接嵌入自己的知识管理流程
- 开源可私有部署，或加入 Slack 频道免费体验
- 视角宏观，将 AI 提升到「学习范式革命」的高度

**时代背景：**
- 文章写于 2023 年 3 月（GPT-4 发布后不久），彼时 AI 浪潮刚刚开始
- 作者的判断在今天看来颇具前瞻性——"超级个体"概念已成现实

**链接：** [GitHub: myGPTReader](https://github.com/madawei2699/myGPTReader)

---

主公，此文虽逾三年，其核心洞见犹有价值。若有意构建个人 AI 阅读工作流，此开源项目可资参考。臣等正欲死战，陛下何故先降！？


分析一下https://github.com/madawei2699/myGPTReader，用什么技术实现的，如何做到网页/视频阅读、电子书解析、 Prompt 模板、多语言语音交互的


<think>
The user wants me to analyze the myGPTReader GitHub repository to understand its technical implementation. Let me navigate to the GitHub page and examine the codebase.
</think>


GitHub - myreader-io/myGPTReader: A community-driven way to read and chat with AI bots - powered by chatGPT. · GitHub

  - link "Skip to content" [ref=e1]:
    - /url: "#start-of-content"
  - banner:
    - heading "Navigation Menu" [ref=e2] [level=2]
    - link "Homepage" [ref=e3]:
      - /url: /
    - navigation "Global" [ref=e4]:
        - listitem:
          - button "Platform" [ref=e5]
        - listitem:
          - button "Solutions" [ref=e6]
        - listitem:
          - button "Resources" [ref=e7]
        - listitem:
          - button "Open Source" [ref=e8]
        - listitem:
          - button "Enterprise" [ref=e9]
        - listitem:
          - link "Pricing" [ref=e10]:
            - /url: https://github.com/pricing
    - button "Search or jump to…" [ref=e11]
    - link "Sign in" [ref=e12]:
      - /url: /login?return_to=https%3A%2F%2Fgithub.com%2Fmyreader-io%2FmyGPTReader
    - link "Sign up" [ref=e13]:
      - /url: /signup?ref_cta=Sign+up&ref_loc=header+logged+out&ref_page=%2F%3Cuser-name%3E%2F%3Crepo-name%3E&source=header-repo&source_repo=myreader-io%2FmyGPTReader
    - button "Appearance settings" [ref=e14]
  - main:
    - link "myreader-io" [ref=e15]:
      - /url: /myreader-io
    - text: /
    - strong:
      - link "myGPTReader" [ref=e16]:
        - /url: /myreader-io/myGPTReader
    - text: Public
      - listitem:
        - button "Sponsor myreader-io/myGPTReader" [ref=e17]: Sponsor
      - listitem:
        - link "You must be signed in to change notification settings" [ref=e18]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
          - text: Notifications
      - listitem:
        - link "Fork 444" [ref=e19]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
      - listitem:
        - link "You must be signed in to star a repository" [ref=e20]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
          - text: Star 4.4k
    - navigation "Repository" [ref=e21]:
        - listitem:
          - link "Code" [ref=e22]:
            - /url: /myreader-io/myGPTReader
        - listitem:
          - link "Issues 14" [ref=e23]:
            - /url: /myreader-io/myGPTReader/issues
        - listitem:
          - link "Pull requests 1" [ref=e24]:
            - /url: /myreader-io/myGPTReader/pulls
        - listitem:
          - link "Discussions" [ref=e25]:
            - /url: /myreader-io/myGPTReader/discussions
        - listitem:
          - link "Actions" [ref=e26]:
            - /url: /myreader-io/myGPTReader/actions
        - listitem:
          - link "Projects" [ref=e27]:
            - /url: /myreader-io/myGPTReader/projects
        - listitem:
          - link "Security and quality" [ref=e28]:
            - /url: /myreader-io/myGPTReader/security
        - listitem:
          - link "Insights" [ref=e29]:
            - /url: /myreader-io/myGPTReader/pulse
    - heading "myreader-io/myGPTReader" [ref=e30] [level=1]
    - button "main branch" [ref=e31]: main
    - link "1 Branch" [ref=e32]:
      - /url: /myreader-io/myGPTReader/branches
      - strong: "1"
      - text: Branch
    - link "0 Tags" [ref=e33]:
      - /url: /myreader-io/myGPTReader/tags
      - strong: "0"
      - text: Tags
    - combobox "Go to file" [ref=e34]
    - button "Code" [ref=e35]
    - heading "Folders and files" [ref=e36] [level=2]
    - table "Folders and files":
        - row "Name Last commit message Last commit date":
          - columnheader "Name" [ref=e37]
          - columnheader "Last commit message" [ref=e38]
          - columnheader "Last commit date" [ref=e39]
        - row "Latest commit madawei2699 commits by madawei2699 Update README with MyInvestPilot maintenance info Open commit details Commit 4ecc0b1 · Feb 5, 20263 months ago History 305 Commits":
          - cell "Latest commit madawei2699 commits by madawei2699 Update README with MyInvestPilot maintenance info Open commit details Commit 4ecc0b1 · Feb 5, 20263 months ago History 305 Commits" [ref=e40]:
            - heading "Latest commit" [ref=e41] [level=2]
            - link "madawei2699" [ref=e42]:
              - /url: /madawei2699
            - link "commits by madawei2699" [ref=e43]:
              - /url: /myreader-io/myGPTReader/commits?author=madawei2699
              - text: madawei2699
            - link "Update README with MyInvestPilot maintenance info" [ref=e44]:
              - /url: /myreader-io/myGPTReader/commit/4ecc0b13260eb70e6b9e41e64e59d012583d5da5
            - button "Open commit details" [ref=e45]
            - link "Commit 4ecc0b1" [ref=e46]:
              - /url: /myreader-io/myGPTReader/commit/4ecc0b13260eb70e6b9e41e64e59d012583d5da5
              - text: 4ecc0b1
            - text: · Feb 5, 20263 months ago
            - heading "History" [ref=e47] [level=2]
            - link "305 Commits" [ref=e48]:
              - /url: /myreader-io/myGPTReader/commits/main/
        - row ".github, (Directory) u May 28, 20233 years ago":
          - cell ".github, (Directory)" [ref=e49]:
            - link ".github, (Directory)" [ref=e50]:
              - /url: /myreader-io/myGPTReader/tree/main/.github
              - text: .github
          - cell "u" [ref=e51]:
            - link "u" [ref=e52]:
              - /url: /myreader-io/myGPTReader/commit/821dcd4c0d434d28468d7c042c31eb5d4464962c
          - cell "May 28, 20233 years ago" [ref=e53]
        - row "app, (Directory) file path auto adaptation Apr 24, 20242 years ago":
          - cell "app, (Directory)" [ref=e54]:
            - link "app, (Directory)" [ref=e55]:
              - /url: /myreader-io/myGPTReader/tree/main/app
              - text: app
          - cell "file path auto adaptation" [ref=e56]:
            - link "file path auto adaptation" [ref=e57]:
              - /url: /myreader-io/myGPTReader/commit/1a36475553c43c0753ab41537c4052635613fa19
          - cell "Apr 24, 20242 years ago" [ref=e58]
        - 'row "docs, (Directory) fix: youtube url issue Apr 23, 20233 years ago"':
          - cell "docs, (Directory)" [ref=e59]:
            - link "docs, (Directory)" [ref=e60]:
              - /url: /myreader-io/myGPTReader/tree/main/docs
              - text: docs
              - /url: /myreader-io/myGPTReader/commit/84a4b75e3baf354557bd4314bb4b8b3239f992b5
          - cell "Apr 23, 20233 years ago" [ref=e61]
        - row "web/landing, (Directory) u Nov 19, 20233 years ago":
          - cell "web/landing, (Directory)" [ref=e62]:
            - link "web/landing, (Directory)" [ref=e63]:
              - /url: /myreader-io/myGPTReader/tree/main/web/landing
              - text: web/landing
          - cell "u" [ref=e64] [nth=1]:
            - link "u" [ref=e65] [nth=1]:
              - /url: /myreader-io/myGPTReader/commit/9266b552261fa50061314dd581d6de2c1a3be185
          - cell "Nov 19, 20233 years ago" [ref=e66]
        - row ".dockerignore, (File) add api server and deploy to the fly.io Mar 8, 20233 years ago":
          - cell ".dockerignore, (File)" [ref=e67]:
            - link ".dockerignore, (File)" [ref=e68]:
              - /url: /myreader-io/myGPTReader/blob/main/.dockerignore
              - text: .dockerignore
          - cell "add api server and deploy to the fly.io" [ref=e69]:
            - link "add api server and deploy to the fly.io" [ref=e70]:
              - /url: /myreader-io/myGPTReader/commit/12a2efd7c047a414551531f65ce6ceb05459396b
          - cell "Mar 8, 20233 years ago" [ref=e71]
        - 'row ".env_sample, (File) feat: WIP, voice interact with bot Mar 22, 20233 years ago"':
          - cell ".env_sample, (File)" [ref=e72]:
            - link ".env_sample, (File)" [ref=e73]:
              - /url: /myreader-io/myGPTReader/blob/main/.env_sample
              - text: .env_sample
              - /url: /myreader-io/myGPTReader/commit/f0b1c00f845a3c5cb14dd6ad84c52ef98a128751
          - cell "Mar 22, 20233 years ago" [ref=e74]
        - row ".gitignore, (File) Initial commit Mar 8, 20233 years ago":
          - cell ".gitignore, (File)" [ref=e75]:

[... 369 more lines truncated, use browser_snapshot for full content]


<think>
Let me get a better view of the repository structure and then look at the main code files to understand the implementation.
</think>


myGPTReader/app at main · myreader-io/myGPTReader · GitHub

  - link "Skip to content" [ref=e1]:
    - /url: "#start-of-content"
  - banner:
    - heading "Navigation Menu" [ref=e2] [level=2]
    - link "Homepage" [ref=e3]:
      - /url: /
    - navigation "Global" [ref=e4]:
        - listitem:
          - button "Platform" [ref=e5]
        - listitem:
          - button "Solutions" [ref=e6]
        - listitem:
          - button "Resources" [ref=e7]
        - listitem:
          - button "Open Source" [ref=e8]
        - listitem:
          - button "Enterprise" [ref=e9]
        - listitem:
          - link "Pricing" [ref=e10]:
            - /url: https://github.com/pricing
    - button "Search or jump to…" [ref=e11]
    - link "Sign in" [ref=e12]:
      - /url: /login?return_to=https%3A%2F%2Fgithub.com%2Fmyreader-io%2FmyGPTReader%2Ftree%2Fmain%2Fapp
    - link "Sign up" [ref=e13]:
      - /url: /signup?ref_cta=Sign+up&ref_loc=header+logged+out&ref_page=%2F%3Cuser-name%3E%2F%3Crepo-name%3E%2Ffiles%2Fdisambiguate&source=header-repo&source_repo=myreader-io%2FmyGPTReader
    - button "Appearance settings" [ref=e14]
  - main:
    - link "myreader-io" [ref=e15]:
      - /url: /myreader-io
    - text: /
    - strong:
      - link "myGPTReader" [ref=e16]:
        - /url: /myreader-io/myGPTReader
    - text: Public
      - listitem:
        - button "Sponsor myreader-io/myGPTReader" [ref=e17]: Sponsor
      - listitem:
        - link "You must be signed in to change notification settings" [ref=e18]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
          - text: Notifications
      - listitem:
        - link "Fork 444" [ref=e19]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
      - listitem:
        - link "You must be signed in to star a repository" [ref=e20]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
          - text: Star 4.4k
    - navigation "Repository" [ref=e21]:
        - listitem:
          - link "Code" [ref=e22]:
            - /url: /myreader-io/myGPTReader
        - listitem:
          - link "Issues 14" [ref=e23]:
            - /url: /myreader-io/myGPTReader/issues
        - listitem:
          - link "Pull requests 1" [ref=e24]:
            - /url: /myreader-io/myGPTReader/pulls
        - listitem:
          - link "Discussions" [ref=e25]:
            - /url: /myreader-io/myGPTReader/discussions
        - listitem:
          - link "Actions" [ref=e26]:
            - /url: /myreader-io/myGPTReader/actions
        - listitem:
          - link "Projects" [ref=e27]:
            - /url: /myreader-io/myGPTReader/projects
        - listitem:
          - link "Security and quality" [ref=e28]:
            - /url: /myreader-io/myGPTReader/security
        - listitem:
          - link "Insights" [ref=e29]:
            - /url: /myreader-io/myGPTReader/pulse
    - heading "Collapse file tree" [ref=e30] [level=2]:
      - button "Collapse file tree" [ref=e31] [expanded]
    - heading "Files" [ref=e32] [level=2]
    - button "main branch" [ref=e33]: main
    - button "Search this repository (forward slash)" [ref=e34]
    - combobox "Go to file" [ref=e35]
    - navigation "File Tree Navigation" [ref=e36]:
      - tree "Files":
        - treeitem ".github" [ref=e37] [level=1]
        - treeitem "app" [ref=e38] [expanded] [level=1]:
          - text: app
          - group "app":
            - treeitem "data" [ref=e39] [level=2]
            - treeitem "__init__.py" [ref=e40] [level=2]
            - treeitem "daily_hot_news.py" [ref=e41] [level=2]
            - treeitem "fetch_web_post.py" [ref=e42] [level=2]
            - treeitem "gpt.py" [ref=e43] [level=2]
            - treeitem "prompt.py" [ref=e44] [level=2]
            - treeitem "rate_limiter.py" [ref=e45] [level=2]
            - treeitem "server.py" [ref=e46] [level=2]
            - treeitem "slash_command.py" [ref=e47] [level=2]
            - treeitem "ttl_set.py" [ref=e48] [level=2]
            - treeitem "user.py" [ref=e49] [level=2]
            - treeitem "util.py" [ref=e50] [level=2]
        - treeitem "docs" [ref=e51] [level=1]
        - treeitem "web" [ref=e52] [level=1]
        - treeitem ".dockerignore" [ref=e53] [level=1]
        - treeitem ".env_sample" [ref=e54] [level=1]
        - treeitem ".gitignore" [ref=e55] [level=1]
        - treeitem "LICENSE" [ref=e56] [level=1]
        - treeitem "Procfile" [ref=e57] [level=1]
        - treeitem "README-CN.md" [ref=e58] [level=1]
        - treeitem "README.md" [ref=e59] [level=1]
        - treeitem "fly.toml" [ref=e60] [level=1]
        - treeitem "requirements.txt" [ref=e61] [level=1]
    - slider "Draggable pane splitter" [ref=e62]
    - navigation "Breadcrumbs" [ref=e63]:
      - heading "Breadcrumbs" [ref=e64] [level=2]
        - listitem:
          - link "myGPTReader" [ref=e65] [nth=1]:
            - /url: /myreader-io/myGPTReader/tree/main
    - heading "app" [ref=e66] [level=1]
    - button "Copy path" [ref=e67]
    - heading "Directory actions" [ref=e68] [level=2]
    - heading "More options" [ref=e69] [level=2]
    - button "More options" [ref=e70]
    - heading "Latest commit" [ref=e71] [level=2]
    - link "waveBoom" [ref=e72]:
      - /url: /waveBoom
    - link "commits by waveBoom" [ref=e73]:
      - /url: /myreader-io/myGPTReader/commits?author=waveBoom
      - text: waveBoom
    - link "file path auto adaptation" [ref=e74]:
      - /url: /myreader-io/myGPTReader/commit/1a36475553c43c0753ab41537c4052635613fa19
    - link "Commit 1a36475" [ref=e75]:
      - /url: /myreader-io/myGPTReader/commit/1a36475553c43c0753ab41537c4052635613fa19
      - text: 1a36475
    - text: · Apr 24, 20242 years ago
    - heading "History" [ref=e76] [level=2]
    - link "History" [ref=e77]:
      - /url: /myreader-io/myGPTReader/commits/main/app
    - heading "Folders and files" [ref=e78] [level=2]
    - table "Folders and files":
        - row "Name Last commit message Last commit date":
          - columnheader "Name" [ref=e79]
          - columnheader "Last commit message" [ref=e80]
          - columnheader "Last commit date" [ref=e81]
        - row "parent directory Parent directory":
          - cell "parent directory Parent directory" [ref=e82]:
            - heading "parent directory" [ref=e83] [level=3]
            - link "Parent directory" [ref=e84]:
              - /url: /myreader-io/myGPTReader/tree/main
              - text: ..
        - 'row "data, (Directory) feat: update premium user check Apr 1, 20233 years ago"':
          - cell "data, (Directory)" [ref=e85]:
            - link "data, (Directory)" [ref=e86]:
              - /url: /myreader-io/myGPTReader/tree/main/app/data
              - text: data
              - /url: /myreader-io/myGPTReader/commit/11faee0412419425a69a29ed87ac56926acf03d3
          - cell "Apr 1, 20233 years ago" [ref=e87]
        - 'row "__init__.py, (File) refactor: project structure optimize Mar 19, 20233 years ago"':
          - cell "__init__.py, (File)" [ref=e88]:
            - link "__init__.py, (File)" [ref=e89]:
              - /url: /myreader-io/myGPTReader/blob/main/app/__init__.py
              - text: __init__.py
              - /url: /myreader-io/myGPTReader/commit/1299a7a0c2180df764c8fa1c67b7bd4880b70646
          - cell "Mar 19, 20233 years ago" [ref=e90]
        - row "daily_hot_news.py, (File) file path auto adaptation Apr 24, 20242 years ago":
          - cell "daily_hot_news.py, (File)" [ref=e91]:
            - link "daily_hot_news.py, (File)" [ref=e92]:
              - /url: /myreader-io/myGPTReader/blob/main/app/daily_hot_news.py
              - text: daily_hot_news.py
          - cell "file path auto adaptation" [ref=e93]:
            - link "file path auto adaptation" [ref=e94] [nth=1]:
              - /url: /myreader-io/myGPTReader/commit/1a36475553c43c0753ab41537c4052635613fa19
          - cell "Apr 24, 20242 years ago" [ref=e95]

[... 71 more lines truncated, use browser_snapshot for full content]


<think>
Now let me look at the key source files. I can see the main structure:
- `app/` - main Python application
  - `fetch_web_post.py` - likely handles web fetching
  - `gpt.py` - likely handles GPT interaction
  - `prompt.py` - likely handles prompt templates
  - `daily_hot_news.py` - daily news feature
  - `server.py` - likely the Slack bot server
  - `user.py` - user management
  - `util.py` - utilities

Let me look at the key files to understand the implementation.
</think>

Let me深入查看核心源码文件：


<think>
The raw URL didn't work well. Let me try the GitHub blob URL instead.
</think>


myGPTReader/app/fetch_web_post.py at main · myreader-io/myGPTReader · GitHub

  - link "Skip to content" [ref=e1]:
    - /url: "#start-of-content"
  - banner:
    - heading "Navigation Menu" [ref=e2] [level=2]
    - link "Homepage" [ref=e3]:
      - /url: /
    - navigation "Global" [ref=e4]:
        - listitem:
          - button "Platform" [ref=e5]
        - listitem:
          - button "Solutions" [ref=e6]
        - listitem:
          - button "Resources" [ref=e7]
        - listitem:
          - button "Open Source" [ref=e8]
        - listitem:
          - button "Enterprise" [ref=e9]
        - listitem:
          - link "Pricing" [ref=e10]:
            - /url: https://github.com/pricing
    - button "Search or jump to…" [ref=e11]
    - link "Sign in" [ref=e12]:
      - /url: /login?return_to=https%3A%2F%2Fgithub.com%2Fmyreader-io%2FmyGPTReader%2Fblob%2Fmain%2Fapp%2Ffetch_web_post.py
    - link "Sign up" [ref=e13]:
      - /url: /signup?ref_cta=Sign+up&ref_loc=header+logged+out&ref_page=%2F%3Cuser-name%3E%2F%3Crepo-name%3E%2Fblob%2Fshow&source=header-repo&source_repo=myreader-io%2FmyGPTReader
    - button "Appearance settings" [ref=e14]
  - main:
    - link "myreader-io" [ref=e15]:
      - /url: /myreader-io
    - text: /
    - strong:
      - link "myGPTReader" [ref=e16]:
        - /url: /myreader-io/myGPTReader
    - text: Public
      - listitem:
        - button "Sponsor myreader-io/myGPTReader" [ref=e17]: Sponsor
      - listitem:
        - link "You must be signed in to change notification settings" [ref=e18]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
          - text: Notifications
      - listitem:
        - link "Fork 444" [ref=e19]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
      - listitem:
        - link "You must be signed in to star a repository" [ref=e20]:
          - /url: /login?return_to=%2Fmyreader-io%2FmyGPTReader
          - text: Star 4.4k
    - navigation "Repository" [ref=e21]:
        - listitem:
          - link "Code" [ref=e22]:
            - /url: /myreader-io/myGPTReader
        - listitem:
          - link "Issues 14" [ref=e23]:
            - /url: /myreader-io/myGPTReader/issues
        - listitem:
          - link "Pull requests 1" [ref=e24]:
            - /url: /myreader-io/myGPTReader/pulls
        - listitem:
          - link "Discussions" [ref=e25]:
            - /url: /myreader-io/myGPTReader/discussions
        - listitem:
          - link "Actions" [ref=e26]:
            - /url: /myreader-io/myGPTReader/actions
        - listitem:
          - link "Projects" [ref=e27]:
            - /url: /myreader-io/myGPTReader/projects
        - listitem:
          - link "Security and quality" [ref=e28]:
            - /url: /myreader-io/myGPTReader/security
        - listitem:
          - link "Insights" [ref=e29]:
            - /url: /myreader-io/myGPTReader/pulse
    - heading "Collapse file tree" [ref=e30] [level=2]:
      - button "Collapse file tree" [ref=e31] [expanded]
    - heading "Files" [ref=e32] [level=2]
    - button "main branch" [ref=e33]: main
    - button "Search this repository (forward slash)" [ref=e34]
    - combobox "Go to file" [ref=e35]
    - navigation "File Tree Navigation" [ref=e36]:
      - tree "Files":
        - treeitem ".github" [ref=e37] [level=1]
        - treeitem "app" [ref=e38] [expanded] [level=1]:
          - text: app
          - group "app":
            - treeitem "data" [ref=e39] [level=2]
            - treeitem "__init__.py" [ref=e40] [level=2]
            - treeitem "daily_hot_news.py" [ref=e41] [level=2]
            - treeitem "fetch_web_post.py" [ref=e42] [level=2]
            - treeitem "gpt.py" [ref=e43] [level=2]
            - treeitem "prompt.py" [ref=e44] [level=2]
            - treeitem "rate_limiter.py" [ref=e45] [level=2]
            - treeitem "server.py" [ref=e46] [level=2]
            - treeitem "slash_command.py" [ref=e47] [level=2]
            - treeitem "ttl_set.py" [ref=e48] [level=2]
            - treeitem "user.py" [ref=e49] [level=2]
            - treeitem "util.py" [ref=e50] [level=2]
        - treeitem "docs" [ref=e51] [level=1]
        - treeitem "web" [ref=e52] [level=1]
        - treeitem ".dockerignore" [ref=e53] [level=1]
        - treeitem ".env_sample" [ref=e54] [level=1]
        - treeitem ".gitignore" [ref=e55] [level=1]
        - treeitem "LICENSE" [ref=e56] [level=1]
        - treeitem "Procfile" [ref=e57] [level=1]
        - treeitem "README-CN.md" [ref=e58] [level=1]
        - treeitem "README.md" [ref=e59] [level=1]
        - treeitem "fly.toml" [ref=e60] [level=1]
        - treeitem "requirements.txt" [ref=e61] [level=1]
    - slider "Draggable pane splitter" [ref=e62]
    - navigation "Breadcrumbs" [ref=e63]:
      - heading "Breadcrumbs" [ref=e64] [level=2]
        - listitem:
          - link "myGPTReader" [ref=e65] [nth=1]:
            - /url: /myreader-io/myGPTReader/tree/main
        - listitem:
          - link "app" [ref=e66]:
            - /url: /myreader-io/myGPTReader/tree/main/app
    - heading "fetch_web_post.py" [ref=e67] [level=1]
    - button "Copy path" [ref=e68]
    - button "More file actions" [ref=e69]
    - heading "Latest commit" [ref=e70] [level=2]
    - link "madawei2699" [ref=e71]:
      - /url: /madawei2699
    - link "commits by madawei2699" [ref=e72]:
      - /url: /myreader-io/myGPTReader/commits?author=madawei2699
      - text: madawei2699
      - /url: /myreader-io/myGPTReader/commit/29b33d7ca95cc7d1214425735d331d49a6195401
    - link "Commit 29b33d7" [ref=e73]:
      - /url: /myreader-io/myGPTReader/commit/29b33d7ca95cc7d1214425735d331d49a6195401
      - text: 29b33d7
    - text: · Apr 18, 20233 years ago
    - heading "History" [ref=e74] [level=2]
    - link "History" [ref=e75]:
      - /url: /myreader-io/myGPTReader/commits/main/app/fetch_web_post.py
    - heading "File metadata and controls" [ref=e76] [level=2]
    - list "File view":
      - listitem:
        - button "Code" [ref=e77]
      - listitem:
        - button "Blame" [ref=e78]
    - link "Raw" [ref=e79]:
      - /url: https://github.com/myreader-io/myGPTReader/raw/refs/heads/main/app/fetch_web_post.py
    - button "Copy raw file" [ref=e80]
    - button "Download raw file" [ref=e81]
    - button "Open symbols panel" [ref=e82]
    - region "fetch_web_post.py" [ref=e83]:

[... 1 more lines truncated, use browser_snapshot for full content]


<think>
I can see the file tree on the left, but I need to see the actual code content. Let me try using the terminal to clone the repo and examine the files directly. That would be more efficient.
</think>

用终端直接 clone 并分析代码，更高效：

terminal

cd /tmp && git clone --depth 1 https://github.com/myreader-io/myGPTReader.git 2>&1 | tail -3


Output: Cloning into 'myGPTReader'...


<think>
Now let me look at the key files.
</think>

Explanation: Now let me look at the key files.

cd /tmp/myGPTReader && ls -la app/ && echo "--- requirements.txt ---" && cat requirements.txt


Output:
total 84
drwxr-xr-x 3 root root  4096 May  7 08:57 .
drwxr-xr-x 7 root root  4096 May  7 08:57 ..
-rw-r--r-- 1 root root  5707 May  7 08:57 daily_hot_news.py
drwxr-xr-x 2 root root    50 May  7 08:57 data
-rw-r--r-- 1 root root  3765 May  7 08:57 fetch_web_post.py
-rw-r--r-- 1 root root  9360 May  7 08:57 gpt.py
-rw-r--r-- 1 root root     0 May  7 08:57 __init__.py
-rw-r--r-- 1 root root   799 May  7 08:57 prompt.py
-rw-r--r-- 1 root root   533 May  7 08:57 rate_limiter.py
-rw-r--r-- 1 root root 21902 May  7 08:57 server.py
-rw-r--r-- 1 root root  8097 May  7 08:57 slash_command.py
-rw-r--r-- 1 root root  1074 May  7 08:57 ttl_set.py
-rw-r--r-- 1 root root  2859 May  7 08:57 user.py
-rw-r--r-- 1 root root  1023 May  7 08:57 util.py
--- requirements.txt ---
Flask==2.2.3
gunicorn==20.1.0
llama-index==0.6.9
langchain==0.0.154
httpx==0.23.3
requests==2.28.2
openai==0.27.1
trafilatura==1.4.1
slack-bolt==1.16.4
urllib3==1.26.14
bs4==0.0.1
requests==2.28.2
html2text==2020.1.16
feedparser==6.0.10
validators==0.20.0
Flask-APScheduler==1.12.4
EbookLib==0.18
PyPDF2==3.0.1
docx2txt==0.8
azure-cognitiveservices-speech==1.26.0
langdetect==1.0.9
youtube-transcript-api==0.5.0
pypdf==3.9.0
Werkzeug==2.2.2


<think>
Now let me look at the key files to understand the implementation.
</think>

cd /tmp/myGPTReader && cat app/fetch_web_post.py


Output:
import logging
import os
import json
import html2text
import requests
import feedparser
import validators
import fnmatch
from youtube_transcript_api import YouTubeTranscriptApi

CF_ACCESS_CLIENT_ID = os.environ.get('CF_ACCESS_CLIENT_ID')
CF_ACCESS_CLIENT_SECRET=os.env...ET')

PHANTOMJSCLOUD_API_KEY=os.env...EY')
PHANTOMJSCLOUD_WEBSITES = ['https://twitter.com/', 'https://t.co/', 'https://medium.com/', 'https://app.mailbrew.com/', 'https://us12.campaign-archive.com', 'https://news.ycombinator.com', 'https://www.bloomberg.com', 'https://*.substack.com/*', 'https://*.1point3acres.com/*', 'https://www.v2ex.com', 'https://www.producthunt.com', 'http://xueqiu.com', 'https://www.jisilu.cn', 'https://www.163.com']

def check_if_need_use_phantomjscloud(url):
    for site in PHANTOMJSCLOUD_WEBSITES:
        if fnmatch.fnmatch(url, site):
                return True
        elif url.startswith(site):
            return True
    return False

def check_if_youtube_url(url):
    return 'youtube.com' in url or 'youtu.be' in url

def get_urls(urls):
    rss_urls = []
    page_urls = []
    phantomjscloud_urls = []
    youtube_urls = []
    for url in urls:
        if validators.url(url):
            feed = feedparser.parse(url)
            if hasattr(feed, 'version') and feed.version:
                rss_urls.append(url)
            elif check_if_need_use_phantomjscloud(url):
                phantomjscloud_urls.append(url)
            elif check_if_youtube_url(url):
                youtube_urls.append(url)
            else:
                page_urls.append(url)
    return {'rss_urls': rss_urls, 'page_urls': page_urls, 'phantomjscloud_urls': phantomjscloud_urls, 'youtube_urls': youtube_urls}

def format_text(text):
    text_without_html_tag = html2text.html2text(text)
    fix_chinese_split_chunk_size_error = text_without_html_tag.replace('，', '， ')
    return fix_chinese_split_chunk_size_error

def scrape_website(url: str) -> str:
    endpoint_url = f"https://web-scraper.i365.tech/?url={url}&selector=div"
    headers = {
        'CF-Access-Client-Id': CF_ACCESS_CLIENT_ID,
        'CF-Access-Client-Secret': CF_ACCESS_CLIENT_SECRET,
    }
    response = requests.get(endpoint_url, headers=headers)
    if response.status_code == 200:
        try:
            json_response = response.json()
            tag_array = json_response['result']['div']
            text = ''.join(tag_array)
            return format_text(text)
        except:
            return "Error: Unable to parse JSON response"
    else:
        return f"Error: {response.status_code} - {response.reason}"

def scrape_website_by_phantomjscloud(url: str) -> str:
    endpoint_url = f"https://PhantomJsCloud.com/api/browser/v2/{PHANTOMJSCLOUD_API_KEY}/"
    data ={
        "url": url,
        "renderType" : "plainText",
        "requestSettings":{
            "doneWhen":[
                {
                    "event": "domReady"
                },
            ],
        }
    }
    response = requests.post(endpoint_url, data=json.dumps(data))
    if response.status_code == 200:
        try:
            return response.content.decode('utf-8')
        except:
            return "Error: Unable to fetch content"
    else:
        return f"Error: {response.status_code} - {response.reason}"

def get_youtube_transcript(video_id: str) -> str:
    try:
        transcripts = YouTubeTranscriptApi.list_transcripts(video_id)
        transcript = next((transcript.fetch() for transcript in transcripts if transcript), None)
        if transcript:
            return "\n".join(chunk["text"] for chunk in transcript)
    except Exception as e:
        logging.warning(f"Error: {e} - {video_id}")
    return None


<think>
Now let me look at the GPT module.
</think>

Explanation: Now let me look at the GPT module.


Output:
import os
import logging
import hashlib
import random
import uuid
import openai
from pathlib import Path
from llama_index import ServiceContext, GPTVectorStoreIndex, LLMPredictor, RssReader, SimpleDirectoryReader, StorageContext, load_index_from_storage
from llama_index.readers.schema.base import Document
from langchain.chat_models import ChatOpenAI
from azure.cognitiveservices.speech import SpeechConfig, SpeechSynthesizer, ResultReason, CancellationReason, SpeechSynthesisOutputFormat
from azure.cognitiveservices.speech.audio import AudioOutputConfig

from app.fetch_web_post import get_urls, get_youtube_transcript, scrape_website, scrape_website_by_phantomjscloud
from app.prompt import get_prompt_template
from app.util import get_language_code, get_youtube_video_id

OPENAI_API_KEY=os.env...EY')
SPEECH_KEY = os.environ.get('SPEECH_KEY')
SPEECH_REGION = os.environ.get('SPEECH_REGION')
openai.api_key = OPENAI_API_KEY

index_cache_web_dir = Path('/tmp/myGPTReader/cache_web/')
index_cache_file_dir = Path('/data/myGPTReader/file/')
index_cache_voice_dir = Path('/tmp/myGPTReader/voice/')

if not index_cache_web_dir.is_dir():
    index_cache_web_dir.mkdir(parents=True, exist_ok=True)

if not index_cache_voice_dir.is_dir():
    index_cache_voice_dir.mkdir(parents=True, exist_ok=True)

if not index_cache_file_dir.is_dir():
    index_cache_file_dir.mkdir(parents=True, exist_ok=True)

llm_predictor = LLMPredictor(llm=ChatOpenAI(
    temperature=0, model_name="gpt-3.5-turbo"))

service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)

web_storage_context = StorageContext.from_defaults()
file_storage_context = StorageContext.from_defaults()

def get_unique_md5(urls):
    urls_str = ''.join(sorted(urls))
    hashed_str = hashlib.md5(urls_str.encode('utf-8')).hexdigest()
    return hashed_str

def format_dialog_messages(messages):
    return "\n".join(messages)

def get_document_from_youtube_id(video_id):
    if video_id is None:
        return None
    transcript = get_youtube_transcript(video_id)
    if transcript is None:
        return None
    return Document(transcript)

def remove_prompt_from_text(text):
    return text.replace('chatGPT:', '').strip()

def get_documents_from_urls(urls):
    documents = []
    for url in urls['page_urls']:
        document = Document(scrape_website(url))
        documents.append(document)
    if len(urls['rss_urls']) > 0:
        rss_documents = RssReader().load_data(urls['rss_urls'])
        documents = documents + rss_documents
    if len(urls['phantomjscloud_urls']) > 0:
        for url in urls['phantomjscloud_urls']:
            document = Document(scrape_website_by_phantomjscloud(url))
            documents.append(document)
    if len(urls['youtube_urls']) > 0:
        for url in urls['youtube_urls']:
            video_id = get_youtube_video_id(url)
            document = get_document_from_youtube_id(video_id)
            if (document is not None):
                documents.append(document)
            else:
                documents.append(Document(f"Can't get transcript from youtube video: {url}"))
    return documents

def get_index_from_web_cache(name):
    try:
        index = load_index_from_storage(web_storage_context, index_id=name)
    except Exception as e:
        logging.error(e)
        return None
    return index

def get_index_from_file_cache(name):
    try:
        index = load_index_from_storage(file_storage_context, index_id=name)
    except Exception as e:
        logging.error(e)
        return None
    return index

def get_index_name_from_file(file: str):
    file_md5_with_extension = str(Path(file).relative_to(index_cache_file_dir).name)
    file_md5 = file_md5_with_extension.split('.')[0]
    return file_md5

def get_answer_from_chatGPT(messages):
    dialog_messages = format_dialog_messages(messages)
    logging.info('=====> Use chatGPT to answer!')
    logging.info(dialog_messages)
    completion = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": dialog_messages}]
    )
    logging.info(completion.usage)
    total_tokens = completion.usage.total_tokens
    return completion.choices[0].message.content, total_tokens, None

def get_answer_from_llama_web(messages, urls):
    dialog_messages = format_dialog_messages(messages)
    lang_code = get_language_code(remove_prompt_from_text(messages[-1]))
    combained_urls = get_urls(urls)
    logging.info(combained_urls)
    index_file_name = get_unique_md5(urls)
    index = get_index_from_web_cache(index_file_name)
    if index is None:
        logging.info(f"=====> Build index from web!")
        documents = get_documents_from_urls(combained_urls)
        logging.info(documents)
        index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
        index.set_index_id(index_file_name)
        index.storage_context.persist()
        logging.info(
            f"=====> Save index to disk path: {index_cache_web_dir / index_file_name}")
    prompt = get_prompt_template(lang_code)
    logging.info('=====> Use llama web with chatGPT to answer!')
    logging.info('=====> dialog_messages')
    logging.info(dialog_messages)
    logging.info('=====> text_qa_template')
    logging.info(prompt.prompt)
    answer = index.as_query_engine(text_qa_template=prompt).query(dialog_messages)
    total_llm_model_tokens = llm_predictor.last_token_usage
    total_embedding_model_tokens = service_context.embed_model.last_token_usage
    return answer, total_llm_model_tokens, total_embedding_model_tokens

def get_answer_from_llama_file(messages, file):
    dialog_messages = format_dialog_messages(messages)
    lang_code = get_language_code(remove_prompt_from_text(messages[-1]))
    index_name = get_index_name_from_file(file)
    index = get_index_from_file_cache(index_name)
    if index is None:
        logging.info(f"=====> Build index from file!")
        documents = SimpleDirectoryReader(input_files=[file]).load_data()
        index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
        index.set_index_id(index_name)
        index.storage_context.persist()
        logging.info(
            f"=====> Save index to disk path: {index_cache_file_dir / index_name}")
    prompt = get_prompt_template(lang_code)
    logging.info('=====> Use llama file with chatGPT to answer!')
    logging.info('=====> dialog_messages')
    logging.info(dialog_messages)
    logging.info('=====> text_qa_template')
    logging.info(prompt)
    answer = answer = index.as_query_engine(text_qa_template=prompt).query(dialog_messages)
    total_llm_model_tokens = llm_predictor.last_token_usage
    total_embedding_model_tokens = service_context.embed_model.last_token_usage
    return answer, total_llm_model_tokens, total_embedding_model_tokens

def get_text_from_whisper(voice_file_path):
    with open(voice_file_path, "rb") as f:
        transcript = openai.Audio.transcribe("whisper-1", f)
    return transcript.text

lang_code_voice_map = {
    'zh': ['zh-CN-XiaoxiaoNeural', 'zh-CN-XiaohanNeural', 'zh-CN-YunxiNeural', 'zh-CN-YunyangNeural'],
    'en': ['en-US-JennyNeural', 'en-US-RogerNeural', 'en-IN-NeerjaNeural', 'en-IN-PrabhatNeural', 'en-AU-AnnetteNeural', 'en-AU-CarlyNeural', 'en-GB-AbbiNeural', 'en-GB-AlfieNeural'],
    'ja': ['ja-JP-AoiNeural', 'ja-JP-DaichiNeural'],
    'de': ['de-DE-AmalaNeural', 'de-DE-BerndNeural'],
}

def convert_to_ssml(text, voice_name=None):
    try:
        logging.info("=====> Convert text to ssml!")
        logging.info(text)
        text = remove_prompt_from_text(text)
        lang_code = get_language_code(text)
        if voice_name is None:
            voice_name = random.choice(lang_code_voice_map[lang_code])
    except Exception as e:
        logging.warning(f"Error: {e}. Using default voice.")
        voice_name = random.choice(lang_code_voice_map['zh'])
    ssml = '<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="zh-CN">'
    ssml += f'<voice name="{voice_name}">{text}</voice>'
    ssml += '</speak>'

    return ssml

def get_voice_file_from_text(text, voice_name=None):
    speech_config = SpeechConfig(subscription=SPEECH_KEY, region=SPEECH_REGION)
    speech_config.set_speech_synthesis_output_format(
        SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)
    speech_config.speech_synthesis_language = "zh-CN"
    file_name = f"{index_cache_voice_dir}{uuid.uuid4()}.mp3"
    file_config = AudioOutputConfig(filename=file_name)
    synthesizer = SpeechSynthesizer(
        speech_config=speech_config, audio_config=file_config)
    ssml = convert_to_ssml(text, voice_name)
    result = synthesizer.speak_ssml_async(ssml).get()
    if result.reason == ResultReason.SynthesizingAudioCompleted:
        logging.info("Speech synthesized for text [{}], and the audio was saved to [{}]".format(
            text, file_name))
    elif result.reason == ResultReason.Canceled:
        cancellation_details = result.cancellation_details
        logging.info("Speech synthesis canceled: {}".format(
            cancellation_details.reason))
        if cancellation_details.reason == CancellationReason.Error:
            logging.error("Error details: {}".format(
                cancellation_details.error_details))
    return file_name


<think>
Now let me look at the prompt.py and server.py files to complete the picture.
</think>

cd /tmp/myGPTReader && cat app/prompt.py && echo "=== server.py (first 150 lines) ===" && head -150 app/server.py


Output:
import logging
from llama_index.prompts.prompts import QuestionAnswerPrompt

QUESTION_ANSWER_PROMPT_TMPL_CN = (
    "上下文信息如下所示： \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "我的问题是：{query_str}\n"
)

QUESTION_ANSWER_PROMPT_TMPL_EN = (
    "Context information is below. \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "My question is {query_str}\n"
)

def get_prompt_template(language='zh'):
    if language == 'en':
        logging.info('=====> Use English prompt template!')
        return QuestionAnswerPrompt(QUESTION_ANSWER_PROMPT_TMPL_EN)
    else:
        logging.info('=====> Use Chinese prompt template!')
        return QuestionAnswerPrompt(QUESTION_ANSWER_PROMPT_TMPL_CN)=== server.py (first 150 lines) ===
import logging
import re
import os
from datetime import datetime
import requests
from urllib.parse import urlparse
from flask import Flask, request
from flask_apscheduler import APScheduler
from slack_bolt import App, BoltResponse
from slack_bolt.adapter.flask import SlackRequestHandler
from slack_bolt.error import BoltUnhandledRequestError
import concurrent.futures
from app.daily_hot_news import build_all_news_block
from app.gpt import get_answer_from_chatGPT, get_answer_from_llama_file, get_answer_from_llama_web, get_text_from_whisper, get_voice_file_from_text, index_cache_file_dir
from app.rate_limiter import RateLimiter
from app.user import get_user, is_premium_user, is_active_user, update_message_token_usage
from app.util import md5

class Config:
    SCHEDULER_API_ENABLED = True

executor = concurrent.futures.ThreadPoolExecutor(max_workers=20)

schedule_channel = "#daily-news"

app = Flask(__name__)

slack_app = App(
    token=os.environ.get("SLACK_TOKEN"),
    signing_secret=os.environ.get("SLACK_SIGNING_SECRET"),
    raise_error_for_unhandled_request=True
)
slack_handler = SlackRequestHandler(slack_app)

@slack_app.error
def handle_errors(error):
    if isinstance(error, BoltUnhandledRequestError):
        return BoltResponse(status=200, body="")
    else:
        return BoltResponse(status=500, body="Something Wrong")

scheduler = APScheduler()
scheduler.api_enabled = True
scheduler.init_app(app)

def send_daily_news(client, news):
    for news_item in news:
        try:
            r = client.chat_postMessage(
                channel=schedule_channel,
                text="🔥🔥🔥 Daily Hot News 🔥🔥🔥",
                blocks=news_item,
                reply_broadcast=True,
                unfurl_links=False,
            )
            logging.info(r)
        except Exception as e:
            logging.error(e)

@scheduler.task('cron', id='daily_news_task', hour=1, minute=30)
def schedule_news():
    logging.info("=====> Start to send daily news!")
    all_news_blocks = build_all_news_block()
    send_daily_news(slack_app.client, all_news_blocks)

@app.route("/slack/events", methods=["POST"])
def slack_events():
    return slack_handler.handle(request)

def insert_space(text):

    # Handling the case between English words and Chinese characters
    text = re.sub(r'([a-zA-Z])([\u4e00-\u9fa5])', r'\1 \2', text)
    text = re.sub(r'([\u4e00-\u9fa5])([a-zA-Z])', r'\1 \2', text)

    # Handling the situation between numbers and Chinese
    text = re.sub(r'(\d)([\u4e00-\u9fa5])', r'\1 \2', text)
    text = re.sub(r'([\u4e00-\u9fa5])(\d)', r'\1 \2', text)

    # handling the special characters
    text = re.sub(r'([\W_])([\u4e00-\u9fa5])', r'\1 \2', text)
    text = re.sub(r'([\u4e00-\u9fa5])([\W_])', r'\1 \2', text)

    text = text.replace('  ', ' ')

    return text

thread_message_history = {}
MAX_THREAD_MESSAGE_HISTORY = 10

def update_thread_history(thread_ts, message_str=None, urls=None, file=None):
    if urls is not None:
        thread_message_history[thread_ts]['context_urls'].update(urls)
    if message_str is not None:
        if thread_ts in thread_message_history:
            dialog_texts = thread_message_history[thread_ts]['dialog_texts']
            dialog_texts.append(message_str)
            if len(dialog_texts) > MAX_THREAD_MESSAGE_HISTORY:
                dialog_texts = dialog_texts[-MAX_THREAD_MESSAGE_HISTORY:]
            thread_message_history[thread_ts]['dialog_texts'] = dialog_texts
        else:
            thread_message_history[thread_ts]['dialog_texts'] = [message_str]
    if file is not None:
        thread_message_history[thread_ts]['file'] = file

def extract_urls_from_event(event):
    if 'blocks' not in event:
        return None
    urls = set()
    for block in event['blocks']:
        for element in block['elements']:
            for e in element['elements']:
                if e['type'] == 'link':
                    url = urlparse(e['url']).geturl()
                    urls.add(url)
    return list(urls)

filetype_extension_allowed = ['epub', 'pdf', 'text', 'docx', 'markdown', 'm4a', 'webm', 'mp3', 'wav']
filetype_voice_extension_allowed = ['m4a', 'webm', 'mp3', 'wav']
max_file_size = 3 * 1024 * 1024

limiter_message_per_user = 5
limiter_time_period = 24 * 3600
limiter = RateLimiter(limit=limiter_message_per_user, period=limiter_time_period)

def dialog_context_keep_latest(dialog_texts, max_length=1):
    if len(dialog_texts) > max_length:
        dialog_texts = dialog_texts[-max_length:]
    return dialog_texts

def remove_url_from_text(text, urls):
    # only remove youtube url
    for url in urls:
        if 'youtube.com' in url or 'youtu.be' in url:
            text = text.replace('<' + url + '>', '')
    return text

def format_dialog_text(text, voicemessage=None):
    if text is None:
        return voicemessage if voicemessage else ''
    return insert_space(text.replace("<@U051JKES6Q1>", "")) + ('\n' + voicemessage if voicemessage else '')

def generate_message_id(channel, thread_ts):
    return f"{channel}-{thread_ts}"

def update_token_usage(event, total_llm_model_tokens, total_embedding_model_tokens):
    logging.info("=====> Start to update token usage!")
    try:
        user = event["user"]
        message_id = generate_message_id(event["channel"], event["ts"])


<think>
Now let me look at the slash_command.py to understand how commands work, and then look at the util.py and daily_hot_news.py.
</think>

<think>
Now let me look at the slash_command.py to understand how commands work, and then look at the util.py and daily_hot_news.py.
</think>

cd /tmp/myGPTReader && cat app/util.py && echo "=== daily_hot_news.py ===" && cat app/daily_hot_news.py


Output:
import hashlib
import logging
from langdetect import detect

def md5(file_path):
    hash_md5 = hashlib.md5()
    with open(file_path, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_md5.update(chunk)
    return hash_md5.hexdigest()

def get_youtube_video_id(url):
    if url is None:
        return None
    if 'youtube.com/watch?v=' in url:
        return url.split('v=')[-1].split('&')[0]
    elif 'youtu.be/' in url:
        return url.split('/')[-1].split('?')[0]
    elif 'youtube.com/embed/' in url:
        return url.split('/')[-1].split('?')[0]
    elif 'youtube.com/v/' in url:
        return url.split('/')[-1].split('?')[0]
    elif 'youtube.com/live/' in url:
        if '?v=' in url:
            return url.split('?v=')[-1].split('&')[0]
        else:
            return url.split('/')[-1].split('?')[0]
    return None

def get_language_code(text):
    try:
        return detect(text).split('-')[0]
    except Exception as e:
        logging.error(e)
        return 'zh'
=== daily_hot_news.py ===
import json
from datetime import date
import logging
import feedparser
import html2text
import concurrent.futures
import os
from app.gpt import get_answer_from_llama_web

current_file_path = os.path.abspath(__file__)
current_dir_path = os.path.dirname(current_file_path)
hot_news_file_path = os.path.join(current_dir_path, "data", "hot_news_rss.json")

with open(hot_news_file_path, "r") as f:
    rss_urls = json.load(f)

TODAY = today = date.today()
MAX_DESCRIPTION_LENGTH = 300
MAX_POSTS = 3

def cut_string(text):
    words = text.split()
    new_text = ""
    count = 0
    for word in words:
        if len(new_text + word) > MAX_DESCRIPTION_LENGTH:
            break
        new_text += word + " "
        count += 1

    return new_text.strip() + '...'

def get_summary_from_gpt_thread(url):
    news_summary_prompt = '请用中文简短概括这篇文章的内容。'
    gpt_response, total_llm_model_tokens, total_embedding_model_tokens = get_answer_from_llama_web([news_summary_prompt], [url])
    logging.info(f"=====> GPT response: {gpt_response} (total_llm_model_tokens: {total_llm_model_tokens}, total_embedding_model_tokens: {total_embedding_model_tokens}")
    return str(gpt_response)

def get_summary_from_gpt(url):
    with concurrent.futures.ThreadPoolExecutor() as executor:
        future = executor.submit(get_summary_from_gpt_thread, url)
        return future.result(timeout=300)

def get_description(entry):
    gpt_answer = None
    try:
        gpt_answer = get_summary_from_gpt(entry.link)
    except Exception as e:
        logging.error(e)
    if gpt_answer is not None:
        summary = 'AI: ' + gpt_answer
    else:
        summary = cut_string(get_text_from_html(entry.summary))
    return summary

def get_text_from_html(html):
    text_maker = html2text.HTML2Text()
    text_maker.ignore_links = True
    text_maker.ignore_tables = False
    text_maker.ignore_images = True
    return text_maker.handle(html)

def get_post_urls_with_title(rss_url):
    feed = feedparser.parse(rss_url)
    updated_posts = []

    for entry in feed.entries:
        published_time = entry.published_parsed if 'published_parsed' in entry else None
        # published_date = date(published_time.tm_year,
        #                       published_time.tm_mon, published_time.tm_mday)
        updated_post = {}
        updated_post['title'] = entry.title
        updated_post['summary'] = get_description(entry)
        updated_post['url'] = entry.link
        updated_post['publish_date'] = published_time
        updated_posts.append(updated_post)
        if len(updated_posts) >= MAX_POSTS:
            break

    return updated_posts

def build_slack_blocks(title, news):
    blocks = [
        {
            "type": "header",
            "text": {
                "type": "plain_text",
                "text": f"{title} # {TODAY.strftime('%Y-%m-%d')}"
            }
        }]
    for news_item in news:
        blocks.extend([{
            "type": "section",
            "text": {
				"text": f"*{news_item['title']}*",
				"type": "mrkdwn"
			},
        },{
            "type": "section",
            "text": {
				"text": f"{news_item['summary']}",
				"type": "plain_text"
			},
        },{
            "type": "section",
            "text": {
				"text": f"原文链接：<{news_item['url']}>",
				"type": "mrkdwn"
			},
        },{
            "type": "divider"
        }])
    return blocks

def build_hot_news_blocks(news_key):
    rss = rss_urls[news_key]['rss']['hot']
    hot_news = get_post_urls_with_title(rss['url'])
    hot_news_blocks = build_slack_blocks(
        rss['name'], hot_news)
    return hot_news_blocks

def build_zhihu_hot_news_blocks():
    return build_hot_news_blocks('zhihu')

def build_v2ex_hot_news_blocks():
    return build_hot_news_blocks('v2ex')

def build_1point3acres_hot_news_blocks():
    return build_hot_news_blocks('1point3acres')

def build_reddit_news_hot_news_blocks():
    return build_hot_news_blocks('reddit-news')

def build_hackernews_news_hot_news_blocks():
    return build_hot_news_blocks('hackernews')

def build_producthunt_news_hot_news_blocks():
    return build_hot_news_blocks('producthunt')

def build_xueqiu_news_hot_news_blocks():
    return build_hot_news_blocks('xueqiu')

def build_jisilu_news_hot_news_blocks():
    return build_hot_news_blocks('jisilu')

def build_all_news_block():
    with concurrent.futures.ThreadPoolExecutor() as executor:
        zhihu_news = executor.submit(build_zhihu_hot_news_blocks)
        v2ex_news = executor.submit(build_v2ex_hot_news_blocks)
        onepoint3acres_news = executor.submit(build_1point3acres_hot_news_blocks)
        reddit_news = executor.submit(build_reddit_news_hot_news_blocks)
        hackernews_news = executor.submit(build_hackernews_news_hot_news_blocks)
        producthunt_news = executor.submit(build_producthunt_news_hot_news_blocks)
        xueqiu_news = executor.submit(build_xueqiu_news_hot_news_blocks)
        jisilu_news = executor.submit(build_jisilu_news_hot_news_blocks)

        zhihu_news_block = zhihu_news.result()
        v2ex_news_block = v2ex_news.result()
        onepoint3acres_news_block = onepoint3acres_news.result()
        reddit_news_block = reddit_news.result()
        hackernews_news_block = hackernews_news.result()
        producthunt_news_block = producthunt_news.result()
        xueqiu_news_block = xueqiu_news.result()
        jisilu_news_block = jisilu_news.result()

        return [zhihu_news_block, v2ex_news_block, onepoint3acres_news_block,
                           reddit_news_block, hackernews_news_block, producthunt_news_block,
                           xueqiu_news_block, jisilu_news_block]


<think>
Now I have a comprehensive understanding of the codebase. Let me also check the slash_command.py to see how the voice interaction works.
</think>

cd /tmp/myGPTReader && cat app/slash_command.py


Output:
import json
import os

current_file_path = os.path.abspath(__file__)
current_dir_path = os.path.dirname(current_file_path)
slash_propmt_file_path = os.path.join(current_dir_path, "data", "prompt.json")

with open(slash_propmt_file_path, "r") as f:
    prompt_data = json.load(f)

def register_slack_slash_commands(slack_app):
    slack_app.command("/gpt-as-novelist")(handle_command_gpt_as_novelist)
    slack_app.command("/gpt-as-terminal")(handle_command_gpt_as_terminal)
    slack_app.command("/gpt-as-en-translator")(handle_command_gpt_as_en_translator)
    slack_app.command("/gpt-as-en-dict")(handle_command_gpt_as_en_dict)
    slack_app.command("/gpt-as-interviewer")(handle_command_gpt_as_interviewer)
    slack_app.command("/gpt-as-js-console")(handle_command_gpt_as_js_console)
    slack_app.command("/gpt-as-travel-guide")(handle_command_gpt_as_travel_guide)
    slack_app.command("/gpt-as-story-teller")(handle_command_gpt_as_story_teller)
    slack_app.command("/gpt-as-math-teacher")(handle_command_gpt_as_math_teacher)
    slack_app.command("/gpt-as-ai-doctor")(handle_command_gpt_as_ai_doctor)
    slack_app.command("/gpt-as-financer")(handle_command_gpt_as_financer)
    slack_app.command("/gpt-as-investor")(handle_command_gpt_as_investor)
    slack_app.command("/gpt-as-encoverage-book")(handle_command_gpt_as_encoverage_book)
    slack_app.command("/gpt-as-text-gamer")(handle_command_gpt_as_text_gamer)
    slack_app.command("/gpt-as-it-architect")(handle_command_gpt_as_it_architect)
    slack_app.command("/gpt-as-fullstack-dev")(handle_command_gpt_as_fullstack_dev)
    slack_app.command("/gpt-as-regex-master")(handle_command_gpt_as_regex_master)

def get_command_name(command):
    return command["command"].replace("/", "")

def build_prompt_blocks(prompt_key):
    return [
        {
            "type": "header",
            "text": {
                "type": "plain_text",
                "text": f"{prompt_data[prompt_key]['name']['cn']}"
            }
        },
        {
            "type": "section",
            "fields": [
                {
                    "type": "mrkdwn",
                    "text": f"{prompt_data[prompt_key]['prompt']['cn']}"
                },
            ]
        },
        {
            "type": "section",
            "fields": [
                {
                    "type": "mrkdwn",
                    "text": "---\nPlease copy the prompt to reply to me."
                }
            ]
        },
    ]

def handle_command_gpt_as_novelist(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_terminal(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_en_translator(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_en_dict(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_interviewer(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_js_console(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_travel_guide(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_story_teller(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_math_teacher(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_ai_doctor(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_financer(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_investor(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_encoverage_book(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_text_gamer(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_it_architect(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_fullstack_dev(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )

def handle_command_gpt_as_regex_master(ack, say, command):
    ack()
    channel_id = command["channel_id"]
    user_id = command["user_id"]
    blocks = build_prompt_blocks(get_command_name(command))

    say(channel=channel_id,
        text=f"<@{user_id}>, let's talk!",
        blocks=blocks,
        reply_broadcast=True
    )


<think>
Now I have a comprehensive understanding of the codebase. Let me also look at the prompt.json to understand the Prompt Templates, and the hot_news_rss.json to understand the news sources.
</think>

<think>
Now I have a comprehensive understanding of the codebase. Let me also look at the prompt.json and hot_news_rss.json data files.
</think>

Explanation:
Now I have a comprehensive understanding of the codebase. Let me also look at the prompt.json to understand the Prompt Templates, and the hot_news_rss.json to understand the news sources.

Now I have a comprehensive understanding of the codebase. Let me also look at the prompt.json and hot_news_rss.json data files.

cd /tmp/myGPTReader && cat app/data/prompt.json | python3 -m json.tool 2>/dev/null | head -60 && echo "..." && cat app/data/hot_news_rss.json | python3 -m json.tool 2>/dev/null | head -40