添加Feishu通道 #1336
添加Feishu通道 #1336
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添加Feishu通道的bot实例化 添加Feishu通道的依赖包 添加Feishu通道的bot基础功能（未完成）
![sourcery-ai[bot]](https://avatars.githubusercontent.com/in/48477?s=60&v=4){kind=small}
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Hey @IceThunder - I've reviewed your changes and they look great!
General suggestions:
- Consider refactoring to reduce code duplication and improve maintainability.
- Verify the asynchronous handling of functions to ensure they are correctly defined and used.
- Review the implementation for potential concurrency issues given the use of global variables and threading.
Here's what I looked at during the review
- 🟡 General issues: 3 issues found
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟡 Complexity: 1 issue found
- 🟢 Docstrings: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment to tell me if it was helpful.
| class FeishuBot(BaseModel): | ||
| port: int = 9880 | ||
| """飞书回调端口号, 默认9880""" | ||
| debug: bool = False | ||
| """是否开启debug，错误时展示日志""" | ||
| app_id: str | ||
| """飞书应用 的 App ID""" | ||
| app_secret: str | ||
| """飞书应用 的 Secret""" | ||
| token: str | ||
| """飞书应用 API 加密策略 的 Verification Token""" | ||
| encrypt_key: Optional[str] = None |
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suggestion (code_refinement): Consider adding type hints for clarity and consistency.
Adding type hints for the class attributes would enhance code readability and maintain consistency with the rest of the codebase.
| @@ -48,6 +48,11 @@ | |||
| logger.info("检测到 Wecom 配置，将启动 Wecom Bot 模式……") | |||
| from platforms.wecom_bot import start_task | |||
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| bots.append(loop.create_task(start_task())) | |||
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issue (bug_risk): Duplicate task creation for bots may lead to unintended behavior.
It appears that start_task() is being called and appended to bots twice in succession without any conditional checks in between. This might result in the same task being started twice unintentionally.
| bot_request.set_result_status(RESPONSE_DONE) | ||
| bot_request.done = True | ||
| logger.debug(f"Bot request {bot_request.request_time} done.") | ||
| await reply(bot_request) |
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issue (bug_risk): Verify the necessity of awaiting a non-async function.
The reply function does not appear to be an asynchronous function, yet it's being awaited. This might be an oversight. If reply is intended to be asynchronous, its definition needs to be updated accordingly.
| @@ -78,6 +78,21 @@ class WecomBot(BaseModel): | |||
| """企业微信应用 API 令牌 的 EncodingAESKey""" | |||
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| class FeishuBot(BaseModel): | |||
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issue (complexity): Consider refactoring common bot configuration properties into a base class.
The addition of the FeishuBot class and its integration into the Config class introduces a higher level of complexity, primarily due to the increase in configuration parameters and the pattern of adding platform-specific bot configurations. While the implementation is consistent with the existing pattern, it's worth considering strategies to manage and potentially reduce complexity as the configuration grows.
One approach could be to refactor common properties among bot configurations into a base class. This base class could encapsulate properties like token, debug, and others that are shared across different bot configurations. Subsequent platform-specific configurations, like FeishuBot, could then extend this base class, adding or overriding properties as necessary. This would not only reduce redundancy but also simplify the addition of new bot configurations in the future.
Here's a simplified example of how this could be structured:
class BaseBotConfig(BaseModel):
port: int
debug: bool = False
token: str
app_id: Optional[str] = None
app_secret: Optional[str] = None
encrypt_key: Optional[str] = None
class FeishuBot(BaseBotConfig):
port: int = 9880
app_id: str
app_secret: str
# Inherits debug, token, and encrypt_key from BaseBotConfig
class Config(BaseModel):
# Other platform settings...
feishu: Optional[FeishuBot] = None
# Account Settings...This approach could make the codebase easier to maintain and extend, especially as more configurations are added. It's a suggestion to consider how the current implementation might evolve and how to manage complexity effectively.
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