docs/bot-command_EN.md
This document covers the bot module architecture, supported commands, webhook routes, and how to configure platform integrations.
Glossary: "Enterprise bot" in this context means a chatbot that receives commands via webhook from a messaging platform (Feishu / DingTalk / WeChat Work / Telegram) and calls the analysis pipeline to reply inline.
flowchart TB
subgraph Platforms [Messaging Platforms]
FS[Feishu]
DT[DingTalk]
WC[WeChat Work]
TG[Telegram]
More[More platforms...]
end
subgraph BotModule [bot/ module]
WH[Webhook Server]
Adapters[Platform Adapters]
Dispatcher[Command Dispatcher]
Commands[Command Handlers]
end
subgraph Core [Core Modules]
AS[AnalysisService]
MA[MarketAnalyzer]
NS[NotificationService]
end
FS -->|POST /bot/feishu| WH
DT -->|POST /bot/dingtalk| WH
WC -->|POST /bot/wecom| WH
TG -->|POST /bot/telegram| WH
WH --> Adapters
Adapters -->|Unified message format| Dispatcher
Dispatcher --> Commands
Commands --> AS
Commands --> MA
Commands --> NS
bot/
├── __init__.py # Module entry, exports main classes
├── models.py # Unified message/response models
├── dispatcher.py # Command dispatcher (core)
├── handler.py # Webhook handler functions (one per platform)
├── commands/ # Command handlers
│ ├── __init__.py
│ ├── base.py # Abstract base class for commands
│ ├── analyze.py # /analyze — stock analysis
│ ├── ask.py # /ask — single-turn question
│ ├── batch.py # /batch — batch watchlist analysis
│ ├── chat.py # /chat — multi-turn strategy chat
│ ├── market.py # /market — market review
│ ├── help.py # /help — help text
│ └── status.py # /status — system status
└── platforms/ # Platform adapters
├── __init__.py
├── base.py # Abstract base class for platforms
├── dingtalk.py # DingTalk bot
├── dingtalk_stream.py # DingTalk Stream bot
└── feishu_stream.py # Feishu (Lark) Stream bot
bot/models.py)@dataclass
class BotMessage:
platform: str # Platform ID: feishu / dingtalk / wecom / telegram
user_id: str # Sender ID
user_name: str # Sender display name
chat_id: str # Conversation ID (group or DM)
chat_type: str # Conversation type: group / private
content: str # Message text
raw_data: Dict # Raw request data (platform-specific)
timestamp: datetime
mentioned: bool = False # Whether the bot was @-mentioned
@dataclass
class BotResponse:
text: str
markdown: bool = False # Whether the response is Markdown
at_user: bool = True # Whether to @-mention the sender
bot/platforms/base.py)class BotPlatform(ABC):
@property
@abstractmethod
def platform_name(self) -> str: ...
@abstractmethod
def verify_request(self, headers: Dict, body: bytes) -> bool:
"""Verify request signature (security check)"""
...
@abstractmethod
def parse_message(self, data: Dict) -> Optional[BotMessage]:
"""Parse platform message into unified format"""
...
@abstractmethod
def format_response(self, response: BotResponse, message: BotMessage) -> WebhookResponse:
"""Convert unified response to platform format"""
...
bot/commands/base.py)class BotCommand(ABC):
@property
@abstractmethod
def name(self) -> str: ... # e.g. 'analyze'
@property
@abstractmethod
def aliases(self) -> List[str]: ... # e.g. ['a', 'analyse']
@property
@abstractmethod
def description(self) -> str: ...
@property
@abstractmethod
def usage(self) -> str: ...
@abstractmethod
def execute(self, message: BotMessage, args: List[str]) -> BotResponse: ...
| Command | Description | Example |
|---|---|---|
/analyze | Analyze a specific stock | /analyze AAPL or /analyze 600519 |
/ask | Single-turn question about a stock or the market | /ask what is RSI for AAPL |
/batch | Batch-analyze your configured watchlist | /batch |
/chat | Multi-turn strategy chat (maintains conversation context) | /chat |
/market | Market review (A-shares / US stocks) | /market |
/help | Show help text | /help |
/status | Show system status | /status |
Stock code formats: A-shares use 6-digit codes (e.g.
600519); HK stocks prefixhk(e.g.hk00700); US stocks use ticker symbols (e.g.AAPL,TSLA).
/status and LLM configuration diagnostics/status/status follows runtime precedence:
LITELLM_CONFIG (LiteLLM YAML)LLM_CHANNELSGEMINI_API_KEY / OPENAI_API_KEY / ANTHROPIC_API_KEY / DEEPSEEK_API_KEY)LITELLM_MODEL or AGENT_LITELLM_MODEL) has no configured source in the active layer, /status shows AI 服务未配置 and keeps the explicit reason line.litellm>=1.80.10,<1.82.7; current status semantics are aligned with this window.GET /api/v1/system/config/setup/status for LLM checks: channels/yaml are active higher priority than legacy keys, and no silent migration is performed when toggling modes.LITELLM_CONFIG or LLM_CHANNELS is active, lower-priority legacy provider keys are ignored as the active source for that run (no silent downgrade).Handler functions for each platform live in bot/handler.py.
These routes are not yet wired into the FastAPI application — you must mount them manually.
| Route | Method | Status | Notes |
|---|---|---|---|
/bot/dingtalk | POST | Ready | DingtalkPlatform is registered in ALL_PLATFORMS |
/bot/feishu | POST | Stream only | Use feishu_stream.py; no Webhook adapter in ALL_PLATFORMS |
/bot/wecom | POST | Not implemented | Handler exists but no platform adapter |
/bot/telegram | POST | Not implemented | Handler exists but no platform adapter |
To mount the DingTalk webhook in your FastAPI app:
from bot.handler import handle_dingtalk_webhook
@app.post("/bot/dingtalk")
async def dingtalk_webhook(request: Request):
headers = dict(request.headers)
body = await request.body()
return handle_dingtalk_webhook(headers, body)
Add the following to your .env. Some of these bot-specific keys are already listed in .env.example (for example the DingTalk and Feishu app credentials), while others are not, so treat this section as a consolidated reference for bot setup:
# --- Bot general ---
BOT_ENABLED=false
BOT_COMMAND_PREFIX=/
# --- Feishu (Lark) bot ---
FEISHU_APP_ID=
FEISHU_APP_SECRET=
FEISHU_VERIFICATION_TOKEN= # Event verification token
FEISHU_ENCRYPT_KEY= # Encryption key (optional)
# --- DingTalk bot ---
DINGTALK_APP_KEY=
DINGTALK_APP_SECRET=
# --- WeChat Work bot (in development) ---
WECOM_TOKEN=
WECOM_ENCODING_AES_KEY=
# --- Telegram bot ---
TELEGRAM_BOT_TOKEN= # Get from @BotFather
TELEGRAM_WEBHOOK_SECRET= # Webhook secret token
bot/platforms/.BotPlatform and implement verify_request, parse_message, format_response.api/app.py) instead of api/v1/router.py, so the callback path stays /bot/<platform> rather than /api/v1/bot/<platform>.bot/commands/.BotCommand and implement the execute method.