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Author SHA1 Message Date
zhaojie
e3c68b5133 Update config, core models, and web app; refresh Python 3.11 cache 
This commit includes general updates to application configuration, core data models, and various web application components (knowledge base, error handling, static assets, and websocket server). It also refreshes the Python 3.11 bytecode cache, likely due to underlying code changes and environment alignment.
 2025-12-12 13:39:43 +08:00
240 changed files with 9941 additions and 4740 deletions
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34
.claude/agents/doc-updater.md
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---
name: doc-updater
description: "Use this agent when code has been modified and you need to update the corresponding Markdown documentation to reflect those changes. This is crucial after refactoring a function, changing an API endpoint, adding a new feature, or altering configuration that is documented in `.md` files.\\n<example>\\nContext: The user has just changed a function signature in their Python code.\\nuser: \"I've just refactored the `process_data` function. It now takes a dictionary instead of two separate lists.\"\\nassistant: \"I see that the function signature for `process_data` has changed. This likely impacts the documentation. I will use the Task tool to launch the `doc-updater` agent to find and update any relevant code examples and descriptions in the Markdown files.\"\\n<commentary>\\nSince a core function's signature was modified, the agent correctly identifies the need to update documentation and calls the `doc-updater` to handle the task.\\n</commentary>\\n</example>\\n<example>\\nContext: A user has added a new environment variable to their application's configuration.\\nuser: \"I added `API_TIMEOUT` to the config. It defaults to 30 seconds.\"\\nassistant: \"Okay, a new configuration variable has been added. To ensure our project documentation is current, I'll use the `doc-updater` agent to add details about `API_TIMEOUT` to the `CONFIGURATION.md` file.\"\\n<commentary>\\nThe user modified project configuration, which is a common source of documentation drift. The agent proactively uses the `doc-updater` to keep the documentation synchronized.\\n</commentary>\\n</example>"
model: inherit
---
You are an expert Documentation Synchronizer, a meticulous agent dedicated to keeping project documentation perfectly aligned with its source code. Your primary responsibility is to automatically update Markdown (.md, .mdx) files whenever there are corresponding code changes.
Your operational workflow is as follows:
1. **Analyze the Code Change**: Upon activation, your first step is to thoroughly understand the provided code modifications. Identify the core nature of the change:
* Has a function's signature (parameters, return type) been altered?
* Has a new class, method, or function been added? Or an old one removed?
* Has an API endpoint's request/response structure changed?
* Have configuration details or environment variables been updated?
2. **Locate Relevant Documentation**: Systematically search the project for all Markdown files that reference the modified code. This includes API guides, READMEs, tutorials, and architectural documents.
3. **Assess the Impact and Update**: For each relevant document, determine the precise impact of the code change and perform the necessary edits.
* **Update Code Snippets**: Ensure all code examples accurately reflect the new implementation.
* **Adjust Textual Descriptions**: Modify parameter descriptions, explanations of functionality, and return value details.
* **Preserve Style and Tone**: Maintain the existing writing style, formatting, and voice of the document you are editing. Do not introduce new conventions.
* **Add New Content**: If a new feature is introduced, create a new documentation section for it, meticulously following the structure of existing sections.
4. **Handle Non-Impactful Changes**: If you determine that a code change (e.g., an internal refactor, performance tweak) has no impact on the existing documentation, you must explicitly report this. State clearly that no documentation update is necessary and briefly explain why.
5. **Address Missing Documentation**: If you find that a modified piece of code is not documented at all, you should flag this as a documentation gap. Propose where and how it could be documented.
6. **Verify and Finalize**: Before concluding, conduct a final self-review.
* Does the updated documentation accurately reflect the code?
* Are the code snippets syntactically correct?
* Is the formatting clean and consistent with the rest of the file?
Your output should be the complete, updated content of the modified Markdown file(s). If multiple files are changed, clearly delineate each one. Provide a concise summary of the changes you made for user verification.

31
.claude/agents/frontend-sync.md
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---
name: frontend-sync
description: "Use this agent when back-end code (e.g., APIs, data models, business logic) has been modified, and the corresponding front-end functionality needs to be updated to reflect those changes.\\n<example>\\nContext: The user has just added a new field to a data model on the backend and updated the corresponding API endpoint.\\nuser: \"I've added an 'author' field to the BlogPost model and updated the `/api/posts/:id` endpoint to include it in the response.\"\\nassistant: \"The backend is updated. I will now use the Task tool to launch the `frontend-sync` agent to update the post display component to show the new author field.\"\\n<commentary>\\nSince the backend data structure and API response have changed, the frontend code that consumes this data must be updated. This is the primary trigger for the `frontend-sync` agent.\\n</commentary>\\n</example>\\n<example>\\nContext: The user has changed the method of an API endpoint from GET to POST for security reasons.\\nuser: \"I've changed the `/api/user/settings` endpoint from a GET to a POST request to prevent sensitive data from appearing in URLs.\"\\nassistant: \"Understood. The API contract has changed. I will now use the Task tool to launch the `frontend-sync` agent to refactor the settings page to use a POST request to fetch user settings.\"\\n<commentary>\\nA change in the API's method requires a corresponding change in the frontend's data-fetching logic. The `frontend-sync` agent is designed for this exact task.\\n</commentary>\\n</example>"
model: inherit
---
You are a Senior Full-Stack Synchronizer, an expert agent specializing in keeping front-end applications perfectly in sync with back-end code modifications. Your mission is to analyze recent back-end code changes, identify the impacted front-end components, and apply the necessary updates to ensure seamless integration and functionality.
### Your Workflow
1. **Analyze Back-End Changes**: Begin by meticulously reviewing the description of the back-end code modifications. Understand the core nature of the change: Is it a new API endpoint, a change in a data model, a modification to business logic, or a bug fix?
2. **Identify Front-End Impact**: Based on your analysis, use your knowledge of the project structure to locate all relevant front-end files (e.g., React/Vue components, Svelte files, HTML templates, data-fetching services, state management stores) that are affected by the back-end changes. This includes files that consume the modified API, display the altered data, or depend on the updated logic.
3. **Formulate an Update Plan**: Before writing any code, formulate a clear and concise plan. Your plan should detail:
* Which files you will modify.
* How you will adjust API calls (e.g., change URL, method, headers, request body, or response handling).
* How you will update front-end data structures or types (e.g., TypeScript interfaces) to match new models.
* Which UI components need to be adjusted to display new data or handle new states.
* Any new components or views that need to be created.
4. **Execute the Update**: Implement the planned changes to the identified front-end files. Write clean, maintainable, and high-quality code that strictly adheres to the project's existing coding standards, style guides, and architectural patterns. Ensure your changes are focused and directly address the requirements of the back-end modification.
5. **Verify and Summarize**: After applying the changes, briefly describe how the new front-end code works and confirm that it correctly aligns with the back-end updates. Summarize your work, listing the files you modified and the key changes you made.
### Guiding Principles
* **Precision is Key**: Your changes must be precise. Only modify the code necessary to align with the back-end changes. Avoid unrelated refactoring.
* **Maintain Consistency**: Your code must seamlessly integrate with the existing front-end architecture, state management (e.g., Redux, Vuex, Pinia), and UI component libraries.
* **Seek Clarity**: If the impact of a back-end change is ambiguous or unclear, you MUST ask for clarification before proceeding with any modifications. State what information you need to move forward.
* **Prioritize User Experience**: Ensure that your updates do not degrade the user experience. The UI should remain responsive, intuitive, and visually consistent.

16
.claude/settings.local.json
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{
"permissions": {
"allow": [
"Bash(git add:*)",
"Bash(git commit:*)",
"Bash(git push:*)",
"Bash(git config:*)",
"Bash(python:*)",
"Bash(python3:*)",
"Bash(curl:*)",
"Bash(pip show:*)",
"Bash(find:*)",
"Bash(chmod:*)",
"Bash(open:*)",
"Bash(ls:*)",
"Bash(.gitignore:*)",
"Bash(git reset:*)",
"Bash(lsof:*)"
"Bash(curl:*)"
],
"deny": [],
"ask": []

92
.claude/skills/ai-metrics-report/SKILL.md
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---
Name: ai-metrics-report
Description: 基于现有监控与分析模块，生成一份最近一段时间的 AI 成功率、错误率与 Token 成本的综合报告，用于评估 TSP 智能助手的整体表现。
---
你是一个「AI 指标报告助手」，技能名为 **ai-metrics-report**。
你的职责：当用户希望了解一段时间内 AI 助手的表现（成功率、错误率、响应时间、Token 成本等）时，自动调用配套脚本，基于现有监控与分析模块，生成一份面向运营/技术同学都能看懂的综合报告。
---
## 一、触发条件（什么时候使用 ai-metrics-report）
当用户有类似需求时，应激活本 Skill，例如：
- 「帮我出一份最近 7 天 AI 调用表现的报告」
- 「看一下最近 AI 的成功率和错误率」
- 「近一周 Token 消耗和成本情况如何」
- 「AI 现在效果怎么样，有没有明显波动」
---
## 二、总体流程
1. 从项目根目录执行脚本 `scripts/ai_metrics_report.py`；
2. 读取脚本输出的结构化指标数据（JSON 或结构化文本）；
3. 将关键指标用自然语言转述，并做简要分析（例如是否达标、是否有波动）；
4. 如发现明显异常（高错误率 / 成本突增 / 成功率显著下降），给出 1～3 条排查或优化建议。
---
## 三、脚本调用规范
从项目根目录执行命令（可传入天数参数，默认 7 天）：
```bash
python .claude/skills/ai-metrics-report/scripts/ai_metrics_report.py --days 7
```
脚本行为约定：
- 尝试复用现有模块：
- `src.analytics.ai_success_monitor.AISuccessMonitor`（如提供聚合接口则优先使用）；
- `src.analytics.token_monitor.TokenMonitor.get_system_token_stats(days=...)`。
- 至少输出以下信息（以 JSON 或清晰的文本格式打印到 stdout）：
- 时间范围（例如「最近 7 天」）
- 会话类指标：
- 总调用次数、成功调用次数、失败调用次数
- 成功率、平均响应时间
- Token/成本指标：
- 总 Token 数
- 总成本
- 按模型维度的请求数占比（如 `qwen-plus-latest` 等）
- 简单趋势：
- 按天的调用次数与成本（可选）
你需要：
1. 运行脚本并捕获输出；
2. 解析其中关键字段；
3. 用 5～10 句中文自然语言，对用户生成一份「运营视角」的口头报告。
---
## 四、对用户的输出规范
当成功执行 `ai-metrics-report` 时，应返回如下结构的信息：
1. **时间范围与总体结论**
- 例如：「最近 7 天，AI 总调用 1234 次，成功率约 96%，整体表现稳定。」
2. **关键指标分项**
- 成功率 / 错误率、平均响应时间；
- 总 Token 使用量与总成本；
- 主力模型（如 `qwen-plus-latest`）的占比；
3. **趋势与风险提醒**
- 若发现某几天错误率或成本异常升高，应指出并提醒。
4. **建议（可选）**
- 如「可以考虑优化 prompt 降低平均 Token」「错误集中在某业务接口，建议重点排查」等。
避免：
- 直接把原始 JSON 或 Python repr 整段贴给用户；
- 输出过多技术细节，优先用业务/运营语言阐述。
---
## 五、反模式与边界
- 如脚本运行失败，应告诉用户「ai-metrics-report 脚本运行失败」，简要给出错误原因；
- 不要直接访问数据库执行复杂 SQL，优先复用已有封装好的监控/统计接口；
- 不要修改任何生产配置或监控阈值，仅进行只读分析和报告。

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.claude/skills/ai-metrics-report/scripts/ai_metrics_report.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
AI 指标报告脚本
聚合最近一段时间的 AI 使用与成本指标，供 ai-metrics-report Skill 调用。
当前版本主要复用 TokenMonitor 的系统统计能力。
"""
import argparse
import sys
from pathlib import Path
def add_project_root_to_path():
# 假定脚本位于 .claude/skills/ai-metrics-report/scripts/ 下
script_path = Path(__file__).resolve()
project_root = script_path.parents[4]
if str(project_root) not in sys.path:
sys.path.insert(0, str(project_root))
def main():
parser = argparse.ArgumentParser(description="Generate AI metrics report.")
parser.add_argument(
"--days",
type=int,
default=7,
help="Number of days to include in the report (default: 7).",
)
args = parser.parse_args()
add_project_root_to_path()
from src.analytics.token_monitor import TokenMonitor
monitor = TokenMonitor()
stats = monitor.get_system_token_stats(days=args.days) or {}
print(f"=== AI 指标报告（最近 {args.days} 天） ===\n")
total_tokens = stats.get("total_tokens", 0)
total_cost = stats.get("total_cost", 0.0)
total_requests = stats.get("total_requests", 0)
successful_requests = stats.get("successful_requests", 0)
failed_requests = stats.get("failed_requests", 0)
success_rate = stats.get("success_rate", 0)
print("整体概览：")
print(f" 总请求数 : {total_requests}")
print(f" 成功请求数 : {successful_requests}")
print(f" 失败请求数 : {failed_requests}")
print(f" 成功率 : {success_rate:.2%}" if total_requests else " 成功率 : N/A")
print(f" 总 Token 数量 : {total_tokens}")
print(f" 总成本（估算） : {total_cost:.4f} 元")
print()
# 模型使用分布
model_usage = stats.get("model_usage", {})
if model_usage:
print("按模型维度的请求分布：")
for model_name, count in model_usage.items():
pct = (count / total_requests) * 100 if total_requests else 0
print(f" - {model_name}: {count} 次 ({pct:.1f}%)")
print()
# 按日期的成本趋势（如有）
daily_usage = stats.get("daily_usage", {})
if daily_usage:
print("按日期的 Token 与成本（近几天）：")
# daily_usage: {date_str: {"tokens": ..., "cost": ...}}
for date_str in sorted(daily_usage.keys()):
day_data = daily_usage[date_str]
tokens = day_data.get("tokens", 0)
cost = day_data.get("cost", 0.0)
print(f" {date_str}: tokens={tokens}, cost={cost:.4f} 元")
print()
print("提示：")
print(" - 成本与成功率仅基于 TokenMonitor 收集的调用记录进行估算；")
print(" - 如需更细粒度的会话质量指标，可结合 analytics 模块或自定义报表。")
if __name__ == "__main__":
main()

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.claude/skills/config-audit/SKILL.md
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---
Name: config-audit
Description: 检查 TSP 智能助手当前环境配置是否完整可用，包括数据库、LLM、服务端口等关键配置，并输出人类可读的健康检查报告。
---
你是一个「配置健康检查助手」，技能名为 **config-audit**。
你的职责：在用户想确认当前环境配置是否正确、是否缺少关键变量或错误端口时，调用配套脚本，基于 `src/config/unified_config.py` 与 `.env`/环境变量，输出清晰的配置健康检查报告。
---
## 一、触发条件（什么时候使用 config-audit）
当用户有类似需求时，应激活本 Skill，例如：
- 「帮我检查一下配置有没有问题」
- 「现在这个环境的数据库/LLM 配置正常吗」
- 「我改了 .env，帮我看下有没有缺的」
- 「启动报配置错误，帮我检查 config」
---
## 二、总体流程
1. 从项目根目录执行脚本 `scripts/config_audit.py`。
2. 读取并理解输出的检查结果（包括成功与警告/错误）。
3. 将关键结论用自然语言总结给用户，并提出简单修复建议。
4. 避免泄露敏感值（如完整 URL、API Key），仅报告是否存在与格式是否看起来合理。
---
## 三、脚本调用规范
从项目根目录执行命令：
```bash
python .claude/skills/config-audit/scripts/config_audit.py
```
脚本行为约定：
- 尝试导入 `src.config.unified_config.get_config`。
- 调用 `get_config()`：
- 若成功，说明必需的配置大体齐全；
- 若抛出异常（如缺少 `DATABASE_URL`），捕获异常并报告。
- 对关键配置字段进行检查（不打印敏感具体值）：
- 数据库：是否配置 `DATABASE_URL`，能否看起来是有效的 URL。
- LLM：`LLM_API_KEY` 是否存在，`LLM_PROVIDER` / `LLM_MODEL` 是否有值。
- 服务：`SERVER_PORT`、`WEBSOCKET_PORT` 是否在合理范围（例如 1–65535），是否冲突。
- 飞书与 AI 准确率配置：如有配置则检查字段完整性，如未配置则给出提示。
- 最终打印一份「健康检查报告」，按模块（database / llm / server / feishu / ai_accuracy）分段展示。
---
## 四、对用户的输出规范
当成功执行 `config-audit` 时，你应该向用户返回类似结构的信息：
1. **总体结论**（一句话）
- 例如：「当前环境配置基本健康，仅存在 LLM API Key 未配置的警告。」
2. **按模块总结**
- 数据库配置：是否存在、看起来是否合理（不展示完整 URL）。
- LLM 配置：是否配置了 provider/model/key，未配置时的影响。
- 服务端口：当前 HTTP/WebSocket 端口及是否在合理范围。
- 其他模块（飞书、AI 准确率）：若有明显问题则简要说明。
3. **建议**
- 对每个有问题的模块，给出 1～2 条修改 `.env` 或环境变量的建议。
避免：
- 直接打印完整的敏感信息（如 `DATABASE_URL`、`LLM_API_KEY` 值）；
- 输出过多的 Python Traceback，优先用自然语言解释。
---
## 五、反模式与边界
- 如导入 `src.config.unified_config` 失败，或脚本无法运行：
- 明确告诉用户「config-audit 脚本运行失败」，并简述原因。
- 不要修改 `.env` 或环境变量，仅进行只读检查和报告。
- 避免主观猜测真实密码/API Key 内容，只报告「存在 / 缺失 / 格式异常」。

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.claude/skills/config-audit/scripts/config_audit.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
配置健康检查脚本
基于 src.config.unified_config 对当前环境配置进行简单体检，
输出人类可读的检查结果，供 config-audit Skill 调用。
"""
import os
import sys
from pathlib import Path
def add_project_root_to_path():
# 假定脚本位于 .claude/skills/config-audit/scripts/ 下
script_path = Path(__file__).resolve()
project_root = script_path.parents[4] # 回到项目根目录
if str(project_root) not in sys.path:
sys.path.insert(0, str(project_root))
def main():
add_project_root_to_path()
print("=== TSP Assistant 配置健康检查 ===\n")
try:
from src.config.unified_config import get_config
except Exception as e:
print("[FATAL] 无法导入 src.config.unified_config.get_config：")
print(f" 错误：{e}")
print("\n请检查 Python 路径与项目结构。")
return
try:
cfg = get_config()
except Exception as e:
print("[FATAL] get_config() 调用失败，可能缺少关键环境变量：")
print(f" 错误：{e}")
print("\n请检查 .env / 环境变量是否包含 DATABASE_URL 等必需项。")
return
problems = []
# Database
print("---- 数据库配置 (database) ----")
db_url = os.getenv("DATABASE_URL", "")
if not db_url:
print(" [ERROR] 未设置 DATABASE_URL，无法连接数据库。")
problems.append("缺少 DATABASE_URL")
else:
# 不打印完整 URL，只提示前缀
prefix = db_url.split("://")[0] if "://" in db_url else "未知协议"
print(f" [OK] 已配置 DATABASE_URL（协议前缀：{prefix}://...）")
# LLM
print("\n---- LLM 配置 (llm) ----")
llm_api_key = os.getenv("LLM_API_KEY", "")
if not llm_api_key:
print(" [WARN] 未设置 LLM_API_KEY，AI 功能可能不可用或调用失败。")
problems.append("缺少 LLM_API_KEY")
else:
print(" [OK] 已配置 LLM_API_KEY（具体值已隐藏）。")
provider = os.getenv("LLM_PROVIDER", cfg.llm.provider if hasattr(cfg, "llm") else "")
model = os.getenv("LLM_MODEL", cfg.llm.model if hasattr(cfg, "llm") else "")
print(f" Provider: {provider or '未配置'}")
print(f" Model : {model or '未配置'}")
# Server
print("\n---- 服务配置 (server) ----")
try:
port = int(os.getenv("SERVER_PORT", cfg.server.port))
ws_port = int(os.getenv("WEBSOCKET_PORT", cfg.server.websocket_port))
except Exception:
port = cfg.server.port
ws_port = cfg.server.websocket_port
def _check_port(p: int, name: str):
if not (1 <= p <= 65535):
problems.append(f"{name} 端口不在 1-65535 范围内")
return f"[ERROR] {name} 端口 {p} 非法（应在 1-65535 范围内）。"
return f"[OK] {name} 端口：{p}"
print(" " + _check_port(port, "HTTP"))
print(" " + _check_port(ws_port, "WebSocket"))
# Feishu
print("\n---- 飞书配置 (feishu) ----")
feishu_app_id = os.getenv("FEISHU_APP_ID", "")
feishu_app_secret = os.getenv("FEISHU_APP_SECRET", "")
if feishu_app_id and feishu_app_secret:
print(" [OK] 已配置 FEISHU_APP_ID / FEISHU_APP_SECRET。")
elif feishu_app_id and not feishu_app_secret:
print(" [WARN] 已配置 FEISHU_APP_ID，但缺少 FEISHU_APP_SECRET。")
problems.append("飞书配置不完整：缺少 FEISHU_APP_SECRET")
else:
print(" [INFO] 未配置飞书相关信息（如不使用飞书集成可忽略）。")
# AI Accuracy
print("\n---- AI 准确率配置 (ai_accuracy) ----")
# 使用 cfg.ai_accuracy 中的默认或 env 覆盖值
try:
aa = cfg.ai_accuracy
print(
f" auto_approve_threshold: {aa.auto_approve_threshold}, "
f"manual_review_threshold: {aa.manual_review_threshold}"
)
except Exception:
print(" [INFO] 无法读取 AI 准确率配置，使用默认值。")
# 总结
print("\n=== 检查总结 ===")
if not problems:
print(" [OK] 当前配置整体健康，未发现明显问题。")
else:
print(" 以下问题需要关注：")
for p in problems:
print(f" - {p}")
print("\n 建议：根据提示检查 .env 或部署环境变量，并重新运行 config-audit 以确认问题是否已解决。")
if __name__ == "__main__":
main()

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---
Name: gitupdate
Description: 在对代码仓库进行较大范围修改后，自动检测变更范围，执行 git add / commit / push，并向用户汇报本次提交的摘要和推送结果。
---
你是一个「自动 Git 提交与推送助手」，技能名为 **gitupdate**。
你的职责：在对项目进行 **较大范围代码变更** 后，**自动** 将变更提交到 Git 仓库并推送远程，减少人工操作，同时保证安全、可追踪。
---
## 一、触发条件（什么时候激活 gitupdate）
当本次会话中，你对代码做了满足以下任一条件的修改时，应自动考虑激活本 Skill：
- 修改的文件数 **≥ 3**，或
- 单个文件改动行数 **≥ 50 行**，或
- 用户在对话中明确表达「这次改动比较大」「重构」「重写某个模块」「请帮我一起提交」「记得帮我提交 git」等需求。
如果改动很小（例如只改一两个小 bug、几行配置），可以不自动提交，除非用户明确要求你提交。
---
## 二、总体流程
激活 `gitupdate` 后，严格按以下顺序执行：
1. 确认当前目录为 Git 仓库
2. 查看并总结本次变更内容
3. 可选：运行测试（如果存在测试命令）
4. 将变更加入暂存区（git add）
5. 自动生成规范化的 commit message
6. 执行 git commit
7. 检测并执行 git push
8. 向用户汇报结果（简短、清晰）
每一步都需要有清晰的异常处理与用户反馈。
---
## 三、详细步骤与命令规范
### 1. 确认当前目录为 Git 仓库
- 执行命令：
```bash
git rev-parse --is-inside-work-tree
```
- 若返回非 0，或输出不是 `true`：
- 向用户说明：「当前目录不是 Git 仓库，gitupdate 自动提交流程已跳过」。
- 立即终止本 Skill 后续步骤。
---
### 2. 查看并总结变更
- 执行：
```bash
git status
git diff
git diff --cached || true
```
- 阅读差异内容，尝试用 **1～3 句话** 总结本次变更，例如：
- 「重构了配置模块，迁移到 unified_config」
- 「新增 AI 调用监控（ai_success_monitor / token_monitor）」
- 「修复对话历史中的 Redis 缓存逻辑」
- 该总结将用于后续生成 commit message。
如果 `git status` 显示没有任何变更（工作区干净），则：
- 告诉用户「当前没有可提交的变更」，
- 不再进行后续步骤。
---
### 3. 可选：运行测试（若存在）
按以下优先级尝试检测和运行测试命令（存在就执行，不存在就跳过）：
1. `pytest`
2. `python -m pytest`
3. `npm test` / `pnpm test` / `yarn test`
执行规则：
- 若测试命令存在且 **执行成功（退出码 0）**：
- 记录「测试通过」的结论，稍后在汇报中说明。
- 若测试失败：
- 读取关键错误输出的一小段（不要整屏复制），
- 明确告诉用户：「测试失败，本次 gitupdate 自动提交已取消，请先修复问题」，
- **停止执行 git add / commit / push**，终止本 Skill。
---
### 4. 将修改加入暂存区
默认策略：
```bash
git add -A
```
例外情况：
- 如果用户在会话中明确说「不要提交某些文件 / 目录」，则：
- 改用精确路径，例如：
```bash
git add src/ config/ README.md
```
- 确保不将用户明确排除的文件加入暂存区。
如 `git add` 报错（权限或路径问题），向用户说明并终止本 Skill。
---
### 5. 自动生成规范化 Commit Message
Commit message 必须遵循以下规则：
- 使用常见前缀之一（推荐英文小写）：
- `feat:` 新功能或较大功能增强
- `fix:` 明确的 bug 修复
- `refactor:` 重构（无新功能、无 bug 修复）
- `chore:` 配置、脚本、小改动或文档调整
- 后面跟一句简短说明，**聚焦“做了什么/为什么”**，避免塞入实现细节。
- 尽量使用英文，必要时可使用中英混合，但保持清晰。
示例：
- `feat: add ai success monitoring dashboards`
- `refactor: migrate legacy Config to unified_config`
- `fix: handle missing redis connection in system optimizer`
- `chore: update logging to per-startup folders`
生成逻辑建议：
1. 先根据变更总结判断改动类型（新增功能 / 重构 / 修 bug / 配置调整）。
2. 再提取 1~2 个关键模块或文件名，概括在短语中。
---
### 6. 执行 Commit
- 执行命令示例：
```bash
git commit -m "<自动生成的 commit message>"
```
- 如果 commit 失败：
- 若提示「nothing to commit, working tree clean」：
- 说明当前没有实际变更，不再继续 push。
- 向用户简单说明「没有可提交的变更」。
- 若因 hook 失败：
- 将 hook 的关键错误信息简要反馈给用户，
- 不要反复重试或绕过 hook（除非用户明确要求）。
---
### 7. 推送到远程仓库
1. 检查当前分支及是否有 upstream：
```bash
git status -sb
```
2. 若当前分支 **尚无 upstream**（初次推送）：
```bash
git push -u origin HEAD
```
3. 若已存在 upstream：
```bash
git push
```
4. 如果 push 失败：
- 分析错误类型（如权限不足 / 需要登录 / 需要先 pull / 网络错误等），
- 向用户用自然语言说明原因，
- 不要自动执行 `git pull --rebase` 或 `git push --force` 等危险操作，除非用户在对话中有明确授权。
---
## 四、安全与边界条件
在任何情况下，必须遵守以下规则：
- **禁止** 使用以下命令，除非用户显式、清晰地要求（并且复述确认）：
- `git push --force`
- `git push --force-with-lease`
- 任何会重写公共历史的操作（如 `git rebase -i`）。
- 不要修改全局 Git 配置，例如：
- `git config --global ...`
- 避免提交明显敏感文件：
- 如 `.env`、包含 `secret` / `password` / `token` 等关键词的配置文件。
- 若用户坚持要求提交敏感文件，应先发出风险提示再执行。
- 如 `git status` 显示仓库干净，**不要创建空 commit**，简单提示「无变更，不需要提交」。
---
## 五、对用户的输出格式要求
每次成功执行 `gitupdate` 后，向用户返回一个简洁的小节，包含：
1. **提交概览**（一句话）：
- 例如：「已提交并推送本次大改：重构配置系统并新增 AI 监控模块。」
2. **commit message**：
- 原样展示一次，例如：
`commit: refactor: migrate legacy Config to unified_config`
3. **分支与远程信息**：
- 例如：「当前分支：\`main\`，已推送到 \`origin/main\`。」
4. **测试情况（若有执行）**：
- 例如：「pytest 已通过」或「未检测到测试命令，未执行自动测试」。
避免：
- 粘贴大段 diff 或完整日志；
- 输出过多与用户无关的 Git 内部细节。
---
## 六、反模式（应避免的做法）
- 为了“干净”而强制 push 覆盖远程历史。
- 未经用户允许自动创建新分支或修改远程分支结构。
- 在测试失败或仓库状态异常时仍然继续执行 commit / push。
- 提交前后不向用户交代任何信息，让用户不知道发生了什么。
严格遵守以上规范，以确保 `gitupdate` 自动提交既省心又安全。

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---
Name: http-error-analyzer
Description: 当日志中出现 4xx/5xx HTTP 错误（如 404、500）时，自动提取相关日志上下文，并分析可能的原因与影响范围，给出排查建议。
---
你是一个「HTTP 报错分析助手」，技能名为 **http-error-analyzer**。
你的职责：当系统日志中出现 404、500 等 HTTP 错误时，调用配套脚本，提取这些错误日志的上下文，分析可能的根因（路由/权限/参数/后端异常等），并给出清晰可执行的排查建议。
---
## 一、触发条件（什么时候使用 http-error-analyzer）
在以下场景中，应考虑激活本 Skill：
- 用户直接给出日志片段，包含 `404` / `500` / `502` / `503` 等状态码，并询问「为什么」；
- 用户提到「某个页面/接口 404/500」并附带或引用了日志文件；
- 你通过其他 Skill（例如 `log-summary`）发现最近 4xx/5xx 错误明显增多，需要进一步排查。
---
## 二、总体流程
1. 从项目根目录执行脚本 `scripts/analyze_http_errors.py`；
2. 读取脚本输出的错误汇总与示例上下文；
3. 结合项目结构与路由、后端模块划分，推断每类错误的可能成因；
4. 向用户输出简明的「错误分类 → 可能原因 → 建议排查路径」列表。
---
## 三、脚本调用规范
从项目根目录执行命令：
```bash
python .claude/skills/http-error-analyzer/scripts/analyze_http_errors.py
```
脚本行为约定：
- 扫描 `logs/` 目录下最近若干个 `dashboard.log`（与 `log-summary` 类似，只读）；
- 匹配常见 HTTP 错误状态码：`404`、`500`、`502`、`503` 等；
- 对每种状态码统计出现次数，并从每类中抽取若干代表性日志行（包含 URL/方法/错误信息等）；
- 打印类似如下结构的文本到 stdout：
- 每个状态码一段：
- 状态码 + 次数
- 若干示例行（截断到合理长度，避免过长）
你需要：
1. 运行脚本并捕获输出；
2. 判断 4xx 与 5xx 错误主要集中在哪些 URL / 接口；
3. 根据日志中的路径、报错栈信息等，推理可能的成因。
---
## 四、对用户的输出规范
当成功执行 `http-error-analyzer` 时，你应该向用户返回包括以下内容的简要报告：
1. **错误分布概览**
- 例如：「最近 5 个日志文件中，404 共 32 次，500 共 5 次。」
2. **按错误类型的分析**
- 对每种状态码（如 404、500），用 1～3 句话说明：
- 主要集中在哪些 URL 或模块（如 `/knowledge/...`、`/api/workorders/...`）；
- 可能原因（如路由未配置、静态资源路径错误、模板缺失、后端异常、数据库错误等）。
3. **具体排查建议**
- 比如：
- 404：检查对应 Blueprint/路由是否注册、前端跳转 URL 是否正确、静态资源路径是否匹配 Nginx 配置；
- 500：查看对应视图函数/接口的 Python 代码与 Traceback，重点排查数据库访问/配置读取/第三方服务调用。
避免：
- 一股脑贴出整段日志，只需引用少量代表性行做说明；
- 在没有足够信息的情况下，过度肯定某个具体根因；应以「可能是」「优先排查方向」来表述。
---
## 五、反模式与边界
- 本 Skill 只做分析与建议，不修改任何代码或配置；如需改动，应由用户确认后再进行；
- 若日志中没有任何 4xx/5xx 错误，应明确告知用户「未发现相关 HTTP 错误」，而不是强行分析；
- 如脚本运行失败（路径不对/权限问题等），应提示用户修复后再重试。

129
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
HTTP 错误日志分析脚本
扫描 logs/*/dashboard.log，统计常见 4xx/5xx HTTP 状态码（如 404/500），
并输出按状态码分组的出现次数与若干示例行，供 http-error-analyzer Skill 使用。
"""
import os
import re
from pathlib import Path
from typing import Dict, List, Tuple
LOG_ROOT = Path("logs")
LOG_FILENAME = "dashboard.log"
MAX_FILES = 5 # 最多分析最近 N 个日志文件
# 简单匹配常见 HTTP 错误码；根据你实际日志格式可以再调整
ERROR_CODES = [404, 500, 502, 503]
CODE_PATTERNS: Dict[int, re.Pattern] = {
code: re.compile(rf"\b{code}\b") for code in ERROR_CODES
}
def find_log_files() -> List[Path]:
if not LOG_ROOT.exists():
return []
candidates: List[Tuple[float, Path]] = []
for root, dirs, files in os.walk(LOG_ROOT):
if LOG_FILENAME in files:
p = Path(root) / LOG_FILENAME
try:
mtime = p.stat().st_mtime
except OSError:
continue
candidates.append((mtime, p))
candidates.sort(key=lambda x: x[0], reverse=True)
return [p for _, p in candidates[:MAX_FILES]]
def analyze_file(path: Path):
"""
返回:
counts: {status_code: 次数}
samples: {status_code: [示例行1, 示例行2, ...]}
"""
counts: Dict[int, int] = {code: 0 for code in ERROR_CODES}
samples: Dict[int, List[str]] = {code: [] for code in ERROR_CODES}
try:
with path.open("r", encoding="utf-8", errors="ignore") as f:
for line in f:
for code, pattern in CODE_PATTERNS.items():
if pattern.search(line):
counts[code] += 1
# 只收集每个状态码前若干条示例
if len(samples[code]) < 5:
samples[code].append(line.strip()[:300])
break
except OSError as e:
print(f"[!] 读取日志失败 {path}: {e}")
return None
return counts, samples
def main():
log_files = find_log_files()
if not log_files:
print("未找到任何日志文件（logs/*/dashboard.log）。")
return
overall_counts: Dict[int, int] = {code: 0 for code in ERROR_CODES}
overall_samples: Dict[int, List[str]] = {code: [] for code in ERROR_CODES}
print(f"=== HTTP 错误日志分析（最近最多 {MAX_FILES} 个日志文件）===\n")
for idx, path in enumerate(log_files, start=1):
print(f"[{idx}] 日志文件: {path}")
result = analyze_file(path)
if result is None:
print(" 无法读取该日志文件。\n")
continue
counts, samples = result
any_error = any(counts[code] > 0 for code in ERROR_CODES)
if not any_error:
print(" 未发现 404/500/502/503 等 HTTP 错误。")
print()
continue
for code in ERROR_CODES:
c = counts[code]
if c == 0:
continue
overall_counts[code] += c
overall_samples[code].extend(samples[code])
print(f" 状态码 {code}: {c} 次")
if samples[code]:
print(" 示例：")
for line in samples[code]:
print(f" {line}")
print()
print("=== 汇总统计 ===")
any_overall = any(overall_counts[code] > 0 for code in ERROR_CODES)
if not any_overall:
print(" 未在最近的日志文件中发现任何配置的 HTTP 错误状态码。")
return
for code in ERROR_CODES:
c = overall_counts[code]
if c == 0:
continue
print(f" 状态码 {code}: {c} 次")
print("\n提示：以上为按状态码汇总的次数与部分示例行，")
print("你可以结合 URL 路径、接口名称、堆栈片段来推断可能的路由配置问题、权限问题或后端异常。")
if __name__ == "__main__":
main()

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---
Name: kb-audit
Description: 对知识库条目进行体检，找出命中率低、置信度低或长期未更新的知识点，并给出优化建议，帮助持续提升 TSP 智能助手的知识质量。
---
你是一个「知识库健康检查与优化助手」，技能名为 **kb-audit**。
你的职责：当用户希望检查知识库质量、找出需要优化或归档的知识条目时，调用配套脚本，对当前知识库进行体检，输出一份可执行的优化清单。
---
## 一、触发条件（什么时候使用 kb-audit）
当用户有类似需求时，应激活本 Skill，例如：
- 「帮我看看知识库有没有陈旧/低质量内容」
- 「哪些知识点命中率低需要优化」
- 「清理一下长期不用的知识条目」
- 「做一次知识库体检，看看哪里要改」
---
## 二、总体流程
1. 从项目根目录执行脚本 `scripts/kb_audit.py`；
2. 脚本从数据库中读取 `KnowledgeEntry` 相关字段（如 `confidence_score`、`usage_count`、`updated_at` 等），做简单统计与筛选；
3. 你读取脚本输出，提炼出「高风险/待优化」的知识条目特征和数量；
4. 为用户形成简明的优化建议与优先级排序。
---
## 三、脚本调用规范
从项目根目录执行命令：
```bash
python .claude/skills/kb-audit/scripts/kb_audit.py
```
脚本行为约定：
- 通过 `db_manager.get_session()` 访问数据库，查询 `KnowledgeEntry` 表；
- 至少统计以下内容并打印为清晰的文本：
- 知识库总条目数；
- 置信度较低的条目数量（例如 `confidence_score < 0.7`）；
- 使用次数为 0 或极低（例如 `< 3`）的条目数量；
- 长期未更新的条目数量（例如 `updated_at` 距今超过 90 天）；
- 可列出若干代表性条目的 ID / 标题摘要（不要打印完整答案内容）。
- 脚本不做任何写操作，只读。
你需要：
1. 运行脚本并捕获输出；
2. 根据统计结果，概括知识库当前健康状况（良好 / 一般 / 需要重点治理）；
3. 给出 3～5 条具体的优化建议，如「优先补充高频问题的答案」「合并重复知识点」等。
---
## 四、对用户的输出规范
当成功执行 `kb-audit` 时，应向用户返回包括以下内容的简要报告：
1. **总体健康度**（一句话）
- 例如：「当前知识库共 500 条，其中约 15% 条目置信度偏低，10% 长期未更新。」
2. **问题概览**
- 低置信度条目大致数量与比例；
- 使用次数很少的条目数量与可能的原因；
- 长期未更新条目的数量。
3. **优化建议**
- 分点列出建议（如按优先级：先处理高频但低置信度的条目）。
避免：
- 直接打印或暴露完整的知识答案内容（可能包含敏感信息）；
- 输出过长的 SQL 或技术细节，优先用运营视角解释。
---
## 五、反模式与边界
- 脚本仅做只读操作，**禁止** 修改或删除知识库条目；
- 如数据库连接失败，应提示用户先确认数据库配置与网络，再重试；
- 不要根据少量样本过度推断整体质量，尽量使用统计结果支撑你的结论。

89
.claude/skills/kb-audit/scripts/kb_audit.py
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@@ -1,89 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
知识库体检脚本
对 KnowledgeEntry 做简单统计，供 kb-audit Skill 调用。
"""
import sys
from datetime import datetime, timedelta
from pathlib import Path
def add_project_root_to_path():
# 假定脚本位于 .claude/skills/kb-audit/scripts/ 下
script_path = Path(__file__).resolve()
project_root = script_path.parents[4]
if str(project_root) not in sys.path:
sys.path.insert(0, str(project_root))
def main():
add_project_root_to_path()
from src.core.database import db_manager
from src.core.models import KnowledgeEntry
print("=== 知识库健康检查 ===\n")
with db_manager.get_session() as session:
total = session.query(KnowledgeEntry).count()
print(f"知识条目总数: {total}")
# 低置信度（<0.7）
low_conf = (
session.query(KnowledgeEntry)
.filter(KnowledgeEntry.confidence_score.isnot(None))
.filter(KnowledgeEntry.confidence_score < 0.7)
.count()
)
print(f"低置信度条目数 (confidence_score < 0.7): {low_conf}")
# 使用次数极低（usage_count < 3 或为 NULL）
low_usage = (
session.query(KnowledgeEntry)
.filter(
(KnowledgeEntry.usage_count.is_(None))
| (KnowledgeEntry.usage_count < 3)
)
.count()
)
print(f"使用次数极低条目数 (usage_count < 3 或空): {low_usage}")
# 长期未更新（> 90 天）
cutoff = datetime.now() - timedelta(days=90)
old_entries = (
session.query(KnowledgeEntry)
.filter(
(KnowledgeEntry.updated_at.isnot(None))
& (KnowledgeEntry.updated_at < cutoff)
)
.count()
)
print(f"长期未更新条目数 (updated_at > 90 天未更新): {old_entries}")
print("\n示例问题条目（不含完整答案，仅展示前若干个）：")
sample_entries = (
session.query(KnowledgeEntry)
.order_by(KnowledgeEntry.created_at.desc())
.limit(5)
.all()
)
for e in sample_entries:
q_preview = (e.question or "")[:40]
print(
f" ID={e.id}, category={e.category}, "
f"confidence={e.confidence_score}, usage={e.usage_count}, "
f"Q='{q_preview}...'"
)
print("\n提示：")
print(" - 建议优先审查低置信度且 usage_count 较高的条目；")
print(" - 对长期未更新且 usage_count 较高的条目，可考虑人工复查内容是否过时；")
print(" - 对 usage_count 极低且从未触发的条目，可考虑合并或归档。")
if __name__ == "__main__":
main()

86
.claude/skills/log-summary/SKILL.md
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@@ -1,86 +0,0 @@
---
Name: log-summary
Description: 汇总并分析 TSP 智能助手日志中的 ERROR 与 WARNING，输出最近一次启动以来的错误概览和统计，帮助快速诊断问题。
---
你是一个「日志错误汇总与分析助手」，技能名为 **log-summary**。
你的职责：在用户希望快速了解最近一次或最近几次运行的错误情况时，调用配套脚本，汇总 `logs/` 目录下各启动时间子目录中的日志文件，统计 ERROR / WARNING / CRITICAL，并输出简明的错误概览与分布情况。
---
## 一、触发条件（什么时候使用 log-summary）
当用户有类似需求时，应激活本 Skill，例如：
- 「帮我看看最近运行有没有错误」
- 「总结一下最近日志里的报错」
- 「分析 logs 下面的错误情况」
- 「最近系统老出问题，帮我看看日志」
---
## 二、总体流程
1. 调用脚本 `scripts/log_summary.py`，从项目根目录执行。
2. 读取输出并用自然语言向用户转述关键发现。
3. 对明显频繁的错误类型，给出简单的排查建议。
4. 输出时保持简洁，避免粘贴大段原始日志。
---
## 三、脚本调用规范
从项目根目录（包含 `start_dashboard.py` 的目录）执行命令：
```bash
python .claude/skills/log-summary/scripts/log_summary.py
```
脚本行为约定：
- 自动遍历 `logs/` 目录下所有子目录（例如 `logs/2026-02-10_23-51-10/dashboard.log`）。
- 默认分析最近 N（例如 5）个按时间排序的日志文件，统计：
- 每个文件中的 ERROR / WARNING / CRITICAL 行数
- 按「错误消息前缀」聚类的 Top N 频率最高错误
- 将结果以结构化的文本形式打印到标准输出。
你需要：
1. 运行脚本并捕获输出；
2. 读懂其中的统计数据与 Top 错误信息；
3. 用 3～8 句中文自然语言，对用户进行总结说明。
---
## 四、对用户的输出规范
当成功执行 `log-summary` 时，你应该向用户返回类似结构的信息：
1. **总体健康度**（一句话）
- 例如：「最近 3 次启动中共记录 2 条 ERROR、5 条 WARNING，整体较为稳定。」
2. **每次启动的错误统计**（列表形式）
- 对应每个日志文件（按时间），简要说明：
- 启动时间（从路径或日志中推断）
- ERROR / WARNING / CRITICAL 数量
3. **Top 错误类型**
- 例如：「最频繁的错误是 `No module named 'src.config.config'`，共出现 4 次。」
4. **简单建议（可选）**
- 对明显重复的错误给出 1～3 条排查/优化建议。
避免：
- 直接原样复制整段日志；
- 输出过长的技术细节堆栈，优先摘要。
---
## 五、反模式与边界
- 如果 `logs/` 目录不存在或没有任何日志文件：
- 明确告诉用户当前没有可分析的日志，而不是编造结果。
- 若脚本执行失败（例如 Python 错误、路径错误）：
- 简要粘贴一小段错误信息，说明「log-summary 脚本运行失败」，
- 不要尝试自己扫描所有日志文件（除非用户另外要求）。
- 不要擅自删除或修改日志文件。

115
.claude/skills/log-summary/scripts/log_summary.py
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@@ -1,115 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
简单日志汇总脚本
遍历 logs/ 目录下最近的若干个 dashboard.log 文件，统计 ERROR / WARNING / CRITICAL，
并输出简要汇总信息，供 log-summary Skill 调用。
"""
import os
import re
from pathlib import Path
from typing import List, Tuple
LOG_ROOT = Path("logs")
LOG_FILENAME = "dashboard.log"
MAX_FILES = 5 # 最多分析最近 N 个日志文件
LEVEL_PATTERNS = {
"ERROR": re.compile(r"\bERROR\b"),
"WARNING": re.compile(r"\bWARNING\b"),
"CRITICAL": re.compile(r"\bCRITICAL\b"),
}
def find_log_files() -> List[Path]:
if not LOG_ROOT.exists():
return []
candidates: List[Tuple[float, Path]] = []
for root, dirs, files in os.walk(LOG_ROOT):
if LOG_FILENAME in files:
p = Path(root) / LOG_FILENAME
try:
mtime = p.stat().st_mtime
except OSError:
continue
candidates.append((mtime, p))
# 按修改时间从新到旧排序
candidates.sort(key=lambda x: x[0], reverse=True)
return [p for _, p in candidates[:MAX_FILES]]
def summarize_file(path: Path):
counts = {level: 0 for level in LEVEL_PATTERNS.keys()}
top_messages = {}
try:
with path.open("r", encoding="utf-8", errors="ignore") as f:
for line in f:
for level, pattern in LEVEL_PATTERNS.items():
if pattern.search(line):
counts[level] += 1
# 取日志消息部分做前缀（粗略）
msg = line.strip()
# 截断以防过长
msg = msg[:200]
top_messages[msg] = top_messages.get(msg, 0) + 1
break
except OSError as e:
print(f"[!] 读取日志失败 {path}: {e}")
return None
# 取 Top 5
top_list = sorted(top_messages.items(), key=lambda x: x[1], reverse=True)[:5]
return counts, top_list
def main():
log_files = find_log_files()
if not log_files:
print("未找到任何日志文件（logs/*/dashboard.log）。")
return
print(f"共找到 {len(log_files)} 个最近的日志文件（最多 {MAX_FILES} 个）：\n")
overall = {level: 0 for level in LEVEL_PATTERNS.keys()}
for idx, path in enumerate(log_files, start=1):
print(f"[{idx}] 日志文件: {path}")
result = summarize_file(path)
if result is None:
print(" 无法读取该日志文件。\n")
continue
counts, top_list = result
for level, c in counts.items():
overall[level] += c
print(
" 级别统计: "
+ ", ".join(f"{lvl}={counts[lvl]}" for lvl in LEVEL_PATTERNS.keys())
)
if top_list:
print(" Top 错误/警告消息：")
for msg, n in top_list:
print(f" [{n}次] {msg}")
else:
print(" 未发现 ERROR/WARNING/CRITICAL 级别日志。")
print()
print("总体统计：")
print(
" "
+ ", ".join(f"{lvl}={overall[lvl]}" for lvl in LEVEL_PATTERNS.keys())
)
if __name__ == "__main__":
main()

126
.env
View File

@@ -1,126 +0,0 @@
# .env.example
# This file contains all the environment variables needed to run the application.
# Copy this file to .env and fill in the values for your environment.
# ============================================================================
# SERVER CONFIGURATION
# ============================================================================
# The host the web server will bind to.
SERVER_HOST=0.0.0.0
# The port for the main Flask web server.
SERVER_PORT=5001
# The port for the WebSocket server for real-time chat.
WEBSOCKET_PORT=8765
# Set to "True" for development to enable debug mode and auto-reloading.
# Set to "False" for production.
DEBUG_MODE=False
# Logging level for the application. Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_LEVEL=INFO
# ============================================================================
# DATABASE CONFIGURATION
# ============================================================================
# The connection string for the primary database.
# Format for MySQL: mysql+pymysql://<user>:<password>@<host>:<port>/<dbname>?charset=utf8mb4
# Format for SQLite: sqlite:///./local_test.db
# 使用本地 SQLite（推荐用于开发和测试）
DATABASE_URL=sqlite:///./data/tsp_assistant.db
# 远程 MySQL（生产环境使用，需要时取消注释）
# DATABASE_URL=mysql+pymysql://tsp_assistant:123456@jeason.online/tsp_assistant?charset=utf8mb4
# ============================================================================
# LARGE LANGUAGE MODEL (LLM) CONFIGURATION
# ============================================================================
# The provider of the LLM. Supported: "qwen", "openai", "anthropic"
LLM_PROVIDER=qwen
# The API key for your chosen LLM provider.
LLM_API_KEY=sk-c0dbefa1718d46eaa897199135066f00
# The base URL for the LLM API. This is often needed for OpenAI-compatible endpoints.
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
# The specific model to use, e.g., "qwen-plus-latest", "gpt-3.5-turbo", "claude-3-sonnet-20240229"
LLM_MODEL=qwen-plus-latest
# The temperature for the model's responses (0.0 to 2.0).
LLM_TEMPERATURE=0.7
# The maximum number of tokens to generate in a response.
LLM_MAX_TOKENS=2000
# The timeout in seconds for API calls to the LLM.
LLM_TIMEOUT=30
# ============================================================================
# FEISHU (LARK) INTEGRATION CONFIGURATION
# ============================================================================
# The App ID of your Feishu enterprise application.
FEISHU_APP_ID=cli_a8b50ec0eed1500d
# The App Secret of your Feishu enterprise application.
FEISHU_APP_SECRET=ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK
# The Verification Token for validating event callbacks (if configured).
FEISHU_VERIFICATION_TOKEN=
# The Encrypt Key for decrypting event data (if configured).
FEISHU_ENCRYPT_KEY=
# The App Token of the Feishu multi-dimensional table document.
FEISHU_APP_TOKEN=XXnEbiCmEaMblSs6FDJcFCqsnIg
# The ID of the Feishu multi-dimensional table for data synchronization.
FEISHU_TABLE_ID=tblnl3vJPpgMTSiP
# ============================================================================
# AI ACCURACY CONFIGURATION
# ============================================================================
# The similarity threshold (0.0 to 1.0) for auto-approving an AI suggestion.
AI_AUTO_APPROVE_THRESHOLD=0.95
# The similarity threshold below which the human-provided resolution is preferred.
AI_USE_HUMAN_RESOLUTION_THRESHOLD=0.90
# The similarity threshold for flagging a suggestion for manual review.
AI_MANUAL_REVIEW_THRESHOLD=0.80
# The default confidence score for an AI suggestion.
AI_SUGGESTION_CONFIDENCE=0.95
# The confidence score assigned when a human resolution is used.
AI_HUMAN_RESOLUTION_CONFIDENCE=0.90
# ============================================================================
# REDIS CACHE CONFIGURATION
# ============================================================================
# Redis server host (use localhost for local development)
REDIS_HOST=localhost
# Redis server port
REDIS_PORT=6379
# Redis database number (0-15)
REDIS_DB=0
# Redis password (leave empty if no password)
REDIS_PASSWORD=
# Redis connection pool size
REDIS_POOL_SIZE=10
# Redis cache default TTL in seconds (3600 = 1 hour)
REDIS_DEFAULT_TTL=3600
# Enable Redis cache (set to False to disable caching)
REDIS_ENABLED=True

126
.env.example
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@@ -1,126 +0,0 @@
# .env.example
# This file contains all the environment variables needed to run the application.
# Copy this file to .env and fill in the values for your environment.
# ============================================================================
# SERVER CONFIGURATION
# ============================================================================
# The host the web server will bind to.
SERVER_HOST=0.0.0.0
# The port for the main Flask web server.
SERVER_PORT=5001
# The port for the WebSocket server for real-time chat.
WEBSOCKET_PORT=8765
# Set to "True" for development to enable debug mode and auto-reloading.
# Set to "False" for production.
DEBUG_MODE=False
# Logging level for the application. Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_LEVEL=INFO
# ============================================================================
# DATABASE CONFIGURATION
# ============================================================================
# The connection string for the primary database.
# Format for MySQL: mysql+pymysql://<user>:<password>@<host>:<port>/<dbname>?charset=utf8mb4
# Format for SQLite: sqlite:///./local_test.db
# 使用本地 SQLite（推荐用于开发和测试）
DATABASE_URL=sqlite:///./data/tsp_assistant.db
# 远程 MySQL（生产环境使用，需要时取消注释）
# DATABASE_URL=mysql+pymysql://tsp_assistant:123456@jeason.online/tsp_assistant?charset=utf8mb4
# ============================================================================
# LARGE LANGUAGE MODEL (LLM) CONFIGURATION
# ============================================================================
# The provider of the LLM. Supported: "qwen", "openai", "anthropic"
LLM_PROVIDER=qwen
# The API key for your chosen LLM provider.
LLM_API_KEY=sk-c0dbefa1718d46eaa897199135066f00
# The base URL for the LLM API. This is often needed for OpenAI-compatible endpoints.
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
# The specific model to use, e.g., "qwen-plus-latest", "gpt-3.5-turbo", "claude-3-sonnet-20240229"
LLM_MODEL=qwen-plus-latest
# The temperature for the model's responses (0.0 to 2.0).
LLM_TEMPERATURE=0.7
# The maximum number of tokens to generate in a response.
LLM_MAX_TOKENS=2000
# The timeout in seconds for API calls to the LLM.
LLM_TIMEOUT=30
# ============================================================================
# FEISHU (LARK) INTEGRATION CONFIGURATION
# ============================================================================
# The App ID of your Feishu enterprise application.
FEISHU_APP_ID=cli_a8b50ec0eed1500d
# The App Secret of your Feishu enterprise application.
FEISHU_APP_SECRET=ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK
# The Verification Token for validating event callbacks (if configured).
FEISHU_VERIFICATION_TOKEN=
# The Encrypt Key for decrypting event data (if configured).
FEISHU_ENCRYPT_KEY=
# The App Token of the Feishu multi-dimensional table document.
FEISHU_APP_TOKEN=XXnEbiCmEaMblSs6FDJcFCqsnIg
# The ID of the Feishu multi-dimensional table for data synchronization.
FEISHU_TABLE_ID=tblnl3vJPpgMTSiP
# ============================================================================
# AI ACCURACY CONFIGURATION
# ============================================================================
# The similarity threshold (0.0 to 1.0) for auto-approving an AI suggestion.
AI_AUTO_APPROVE_THRESHOLD=0.95
# The similarity threshold below which the human-provided resolution is preferred.
AI_USE_HUMAN_RESOLUTION_THRESHOLD=0.90
# The similarity threshold for flagging a suggestion for manual review.
AI_MANUAL_REVIEW_THRESHOLD=0.80
# The default confidence score for an AI suggestion.
AI_SUGGESTION_CONFIDENCE=0.95
# The confidence score assigned when a human resolution is used.
AI_HUMAN_RESOLUTION_CONFIDENCE=0.90
# ============================================================================
# REDIS CACHE CONFIGURATION
# ============================================================================
# Redis server host (use localhost for local development)
REDIS_HOST=localhost
# Redis server port
REDIS_PORT=6379
# Redis database number (0-15)
REDIS_DB=0
# Redis password (leave empty if no password)
REDIS_PASSWORD=
# Redis connection pool size
REDIS_POOL_SIZE=10
# Redis cache default TTL in seconds (3600 = 1 hour)
REDIS_DEFAULT_TTL=3600
# Enable Redis cache (set to False to disable caching)
REDIS_ENABLED=True

44
.gitignore vendored
View File

@@ -1,44 +0,0 @@
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
*.egg-info/
dist/
build/
# 环境变量和敏感信息
.env
*.log
# 数据库
*.db
*.sqlite
*.sqlite3
# 日志文件
logs/
*.log
# 临时文件
*.tmp
*.temp
note.txt
database_init_report.json
# IDE
.vscode/
.idea/
*.swp
*.swo
# macOS
.DS_Store
# 测试文件
test_*.py
*_test.py
# 缓存
.cache/

8
.idea/.gitignore generated vendored Normal file
View File

@@ -0,0 +1,8 @@
# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

12
.idea/dataSources.xml generated Normal file
View File

@@ -0,0 +1,12 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
<data-source source="LOCAL" name="@43.134.68.207" uuid="715b070d-f258-43df-a066-49e825a9b04f">
<driver-ref>mysql.8</driver-ref>
<synchronize>true</synchronize>
<jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
<jdbc-url>jdbc:mysql://43.134.68.207:3306</jdbc-url>
<working-dir>$ProjectFileDir$</working-dir>
</data-source>
</component>
</project>

6
.idea/data_source_mapping.xml generated Normal file
View File

@@ -0,0 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DataSourcePerFileMappings">
<file url="file://$APPLICATION_CONFIG_DIR$/consoles/db/715b070d-f258-43df-a066-49e825a9b04f/console.sql" value="715b070d-f258-43df-a066-49e825a9b04f" />
</component>
</project>

6
.idea/inspectionProfiles/profiles_settings.xml generated Normal file
View File

@@ -0,0 +1,6 @@
<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

7
.idea/misc.xml generated Normal file
View File

@@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Black">
<option name="sdkName" value="Python 3.11 (tsp-assistant)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (tsp-assistant)" project-jdk-type="Python SDK" />
</project>

8
.idea/modules.xml generated Normal file
View File

@@ -0,0 +1,8 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/tsp-assistant.iml" filepath="$PROJECT_DIR$/.idea/tsp-assistant.iml" />
</modules>
</component>
</project>

14
.idea/tsp-assistant.iml generated Normal file
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@@ -0,0 +1,14 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/.venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.11 (tsp-assistant)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PyDocumentationSettings">
<option name="format" value="PLAIN" />
<option name="myDocStringFormat" value="Plain" />
</component>
</module>

6
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@@ -0,0 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
</component>
</project>

26
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@@ -0,0 +1,26 @@
{
"files.autoGuessEncoding": false,
"files.encoding": "utf8",
"files.eol": "\n",
"[python]": {
"files.encoding": "utf8",
"files.eol": "\n"
},
"[json]": {
"files.encoding": "utf8"
},
"[javascript]": {
"files.encoding": "utf8"
},
"[html]": {
"files.encoding": "utf8"
},
"[css]": {
"files.encoding": "utf8"
},
"[markdown]": {
"files.encoding": "utf8"
},
"python.defaultInterpreterPath": "${workspaceFolder}/.venv/Scripts/python.exe",
"Codegeex.RepoIndex": true
}

88
CLAUDE.md
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@@ -1,88 +0,0 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## High-Level Architecture
This project is a Python Flask-based web application called "TSP Assistant". It's an intelligent customer service system designed for Telematics Service Providers (TSP).
The backend is built with Flask and utilizes a modular structure with Blueprints. The core application logic resides in the `src/` directory.
Key components of the architecture include:
* **Web Framework**: The web interface and APIs are built using **Flask**. The main Flask app is likely configured in `src/web/app.py`.
* **Modular Routing**: The application uses Flask **Blueprints** for organizing routes. These are located in `src/web/blueprints/`. Each file in this directory corresponds to a feature area (e.g., `agent.py`, `workorders.py`, `analytics.py`).
* **Intelligent Agent**: A core feature is the AI agent. Its logic is contained within the `src/agent/` directory, which includes components for planning (`planner.py`), tool management (`tool_manager.py`), and execution (`executor.py`).
* **Database**: The application uses a relational database (likely MySQL) with **SQLAlchemy** as the ORM. Models are defined in `src/core/models.py`.
* **Configuration**: A unified configuration center (`src/config/unified_config.py`) manages all settings via environment variables and `.env` files.
* **Real-time Communication**: **WebSockets** are used for real-time features like the intelligent chat. The server logic is in `src/web/websocket_server.py`.
* **Data Analytics**: The system has a dedicated data analysis module located in `src/analytics/`.
* **Frontend**: The frontend is built with Bootstrap 5, Chart.js, and vanilla JavaScript (ES6+). Frontend assets are in `src/web/static/` and templates are in `src/web/templates/`.
## Common Commands
### Environment Setup
The project can be run using Docker (recommended) or locally.
**1. Install Dependencies:**
```bash
pip install -r requirements.txt
```
**2. Initialize the Database:**
This script sets up the necessary database tables.
```bash
python init_database.py
```
### Running the Application
**Local Development:**
To start the Flask development server:
```bash
python start_dashboard.py
```
The application will be available at `http://localhost:5000`.
**Docker Deployment:**
The project includes a `docker-compose.yml` for easy setup of all services (application, database, cache, monitoring).
To start all services:
```bash
docker-compose up -d
```
Or use the provided script:
```bash
chmod +x scripts/docker_deploy.sh
./scripts/docker_deploy.sh start
```
To stop services:
```bash
./scripts/docker_deploy.sh stop
```
### Running Tests
The project uses `pytest` for testing.
```bash
pytest
```
To run tests with coverage:
```bash
pytest --cov
```
## Key File Locations
* **Main Application Entry Point**: `start_dashboard.py` (local) or `src/web/app.py` (via WSGI in production).
* **Flask Blueprints (Routes)**: `src/web/blueprints/`
* **Agent Core Logic**: `src/agent/`
* **Database Models**: `src/core/models.py`
* **Frontend Static Assets**: `src/web/static/` (JS, CSS, images)
* **Frontend HTML Templates**: `src/web/templates/`
* **WebSocket Server**: `src/web/websocket_server.py`
* **Configuration Files**: `config/`
* **Deployment Scripts**: `scripts/`
* **Database Initialization**: `init_database.py`

21
README.md
View File

@@ -277,6 +277,18 @@ python start_dashboard.py
## 🔄 部署与更新
### 多环境部署
```bash
# 开发环境
python scripts/update_manager.py auto-update --source . --environment development
# 测试环境
python scripts/update_manager.py auto-update --source . --environment staging
# 生产环境
python scripts/update_manager.py auto-update --source . --environment production
```
### 版本管理
```bash
# 更新版本号
@@ -289,6 +301,15 @@ python version.py changelog --message "新功能描述"
python version.py tag --message "Release v1.3.0"
```
### 热更新
```bash
# 热更新（无需重启）
python scripts/update_manager.py hot-update --source ./new_version --environment production
# 自动更新（智能选择）
python scripts/update_manager.py auto-update --source ./new_version --environment production
```
## 📊 系统监控
### 健康检查

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@echo off
chcp 65001 >nul
echo ========================================
echo TSP智能助手 - 自动推送脚本
echo ========================================
echo.
:: 检查Git状态
echo [1/4] 检查Git状态...
git status --porcelain >nul 2>&1
if %errorlevel% neq 0 (
echo ❌ Git未初始化或不在Git仓库中
pause
exit /b 1
)
:: 显示当前状态
echo 📋 当前Git状态:
git status --short
echo.
:: 询问是否继续
set /p confirm="是否继续推送? (y/n): "
if /i "%confirm%" neq "y" (
echo 操作已取消
pause
exit /b 0
)
:: 检查是否有更改需要提交
echo.
echo [2/4] 检查更改状态...
:: 启用延迟变量扩展
setlocal enabledelayedexpansion
:: 检查未暂存的更改
git diff --quiet
set has_unstaged=%errorlevel%
:: 检查已暂存的更改
git diff --cached --quiet
set has_staged=%errorlevel%
:: 检查未跟踪的文件
git ls-files --others --exclude-standard >nul 2>&1
set has_untracked=%errorlevel%
if %has_unstaged% equ 0 if %has_staged% equ 0 if %has_untracked% neq 0 (
echo ℹ️ 没有检测到任何更改，无需提交
echo.
echo ✅ 工作区干净，无需推送
pause
exit /b 0
)
:: 显示详细状态
echo 📊 详细状态信息:
echo 未暂存更改:
if %has_unstaged% neq 0 (
git diff --name-only
) else (
echo 无
)
echo 已暂存更改:
if %has_staged% neq 0 (
git diff --cached --name-only
) else (
echo 无
)
echo 未跟踪文件:
if %has_untracked% neq 0 (
git ls-files --others --exclude-standard
) else (
echo 无
)
echo.
:: 添加所有更改
echo 添加所有更改到暂存区...
git add .
if %errorlevel% neq 0 (
echo ❌ 添加文件失败
pause
exit /b 1
)
echo ✅ 文件已添加到暂存区
:: 检查markdown文件修改并生成智能提交信息
echo.
echo [3/4] 分析markdown文件并生成提交信息...
:: 检查是否有markdown文件修改
set md_files=
for /f "tokens=*" %%f in ('git diff --name-only --cached 2^>nul ^| findstr /i "\.md$"') do (
set md_files=!md_files! %%f
)
for /f "tokens=*" %%f in ('git diff --name-only 2^>nul ^| findstr /i "\.md$"') do (
set md_files=!md_files! %%f
)
set commit_msg=
if not "%md_files%"=="" (
echo 📝 检测到markdown文件修改:
echo %md_files%
echo.
:: 提取markdown文件的主要内容
set commit_title=
set commit_type=docs
:: 检查是否有修复相关内容
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /i "修复\|解决\|问题\|错误"') do (
set commit_type=fix
set commit_title=修复问题
goto :found_fix
)
)
)
:: 检查是否有新功能相关内容
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /i "功能\|新增\|添加\|实现"') do (
set commit_type=feat
set commit_title=新增功能
goto :found_feature
)
)
)
:: 检查是否有优化相关内容
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /i "优化\|性能\|改进\|提升"') do (
set commit_type=perf
set commit_title=性能优化
goto :found_optimization
)
)
)
:: 提取文件标题
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /n "^#" ^| head -1') do (
set line=%%l
set line=!line:*:=!
set line=!line:# =!
set line=!line:## =!
if "!line!" neq "" (
set commit_title=!line!
goto :found_title
)
)
)
)
:found_fix
:found_feature
:found_optimization
:found_title
if "%commit_title%"=="" (
set commit_title=更新文档记录
)
:: 生成提交信息
set commit_msg=%commit_type%: %commit_title%
echo 📋 生成的提交信息: %commit_msg%
echo.
) else (
echo ℹ️ 没有检测到markdown文件修改
set commit_msg=feat: 自动提交 - %date% %time%
)
:: 询问是否使用生成的提交信息
set /p confirm="是否使用此提交信息? (y/n/e编辑): "
if /i "%confirm%"=="e" (
set /p commit_msg="请输入自定义提交信息: "
) else if /i "%confirm%" neq "y" (
set /p commit_msg="请输入提交信息: "
)
:: 提交更改
echo 提交信息: %commit_msg%
git commit -m "%commit_msg%"
if %errorlevel% neq 0 (
echo ❌ 提交失败
pause
exit /b 1
)
echo ✅ 提交成功
:: 推送到远程仓库
echo.
echo [4/4] 推送到远程仓库...
:: 先尝试拉取最新更改
echo 🔄 检查远程更新...
git fetch origin main
if %errorlevel% neq 0 (
echo ⚠️ 无法获取远程更新，继续推送...
) else (
echo ✅ 远程更新检查完成
)
:: 推送到远程
git push origin main
if %errorlevel% neq 0 (
echo ❌ 推送失败
echo.
echo 💡 可能的原因:
echo - 网络连接问题
echo - 远程仓库权限不足
echo - 分支冲突
echo - 需要先拉取远程更改
echo.
echo 🔧 尝试自动解决冲突...
git pull origin main --rebase
if %errorlevel% equ 0 (
echo ✅ 冲突已解决，重新推送...
git push origin main
if %errorlevel% equ 0 (
echo ✅ 推送成功！
) else (
echo ❌ 重新推送失败
echo.
echo 🔧 建议手动解决:
echo 1. 运行: git pull origin main
echo 2. 解决冲突后运行: git push origin main
pause
exit /b 1
)
) else (
echo ❌ 无法自动解决冲突
echo.
echo 🔧 建议手动解决:
echo 1. 运行: git pull origin main
echo 2. 解决冲突后运行: git push origin main
pause
exit /b 1
)
) else (
echo ✅ 推送成功！
)
echo.
echo ========================================
echo ✅ 推送完成！
echo ========================================
echo 📊 提交统计:
git log --oneline -1
echo.
echo 🌐 远程仓库状态:
git status
echo.
pause

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@@ -0,0 +1,178 @@
# TSP智能助手配置说明
## 📋 配置文件概述
本目录包含TSP智能助手的核心配置文件，包括LLM配置、集成配置等。
## 🤖 LLM配置
### 千问模型配置
本项目默认使用阿里云千问模型。要使用千问模型，请按以下步骤配置：
#### 1. 获取API密钥
1. 访问 [阿里云百炼平台](https://bailian.console.aliyun.com/)
2. 注册并登录账号
3. 创建应用并获取API密钥
#### 2. 配置API密钥
编辑 `config/llm_config.py` 文件，将 `api_key` 替换为您的实际API密钥：
```python
QWEN_CONFIG = LLMConfig(
provider="openai",
api_key="sk-your-actual-qwen-api-key", # 替换为您的实际密钥
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
model="qwen-turbo",
temperature=0.7,
max_tokens=2000
)
```
#### 3. 可用的千问模型
- `qwen-turbo`: 快速响应，适合一般对话
- `qwen-plus`: 平衡性能和成本
- `qwen-max`: 最强性能，适合复杂任务
#### 4. 环境变量配置（可选）
您也可以使用环境变量来配置：
```bash
export QWEN_API_KEY="sk-your-actual-qwen-api-key"
export QWEN_MODEL="qwen-turbo"
```
#### 5. 其他模型支持
项目也支持其他LLM提供商：
- **OpenAI**: GPT-3.5/GPT-4
- **Anthropic**: Claude系列
- **本地模型**: Ollama等
#### 6. 配置验证
启动系统后，可以在Agent管理页面查看LLM使用统计，确认配置是否正确。
## 📱 飞书集成配置
### 配置文件说明
`integrations_config.json` 文件包含飞书集成的所有配置信息：
```json
{
"feishu": {
"app_id": "cli_a8b50ec0eed1500d",
"app_secret": "ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK",
"app_token": "XXnEbiCmEaMblSs6FDJcFCqsnIg",
"table_id": "tblnl3vJPpgMTSiP",
"last_updated": "2025-09-19T18:27:40.579958",
"status": "active"
},
"system": {
"sync_limit": 10,
"ai_suggestions_enabled": true,
"auto_sync_interval": 0,
"last_sync_time": null
}
}
```
### 配置参数说明
#### 飞书应用配置
- `app_id`: 飞书应用ID
- `app_secret`: 飞书应用密钥
- `app_token`: 飞书多维表格应用Token
- `table_id`: 飞书多维表格ID
- `last_updated`: 最后更新时间
- `status`: 集成状态（active/inactive）
#### 系统配置
- `sync_limit`: 同步记录数量限制
- `ai_suggestions_enabled`: 是否启用AI建议
- `auto_sync_interval`: 自动同步间隔（分钟）
- `last_sync_time`: 最后同步时间
### 获取飞书配置
1. **获取应用凭证**
- 访问 [飞书开放平台](https://open.feishu.cn/)
- 创建企业自建应用
- 获取 `app_id` 和 `app_secret`
2. **获取表格信息**
- 打开飞书多维表格
- 从URL中提取 `app_token` 和 `table_id`
- 例如：`https://my-ichery.feishu.cn/base/XXnEbiCmEaMblSs6FDJcFCqsnIg?table=tblnl3vJPpgMTSiP`
- `app_token`: `XXnEbiCmEaMblSs6FDJcFCqsnIg`
- `table_id`: `tblnl3vJPpgMTSiP`
3. **配置权限**
- 在飞书开放平台中配置应用权限
- 确保应用有读取多维表格的权限
### 字段映射配置
系统会自动映射以下飞书字段到本地数据库：
| 飞书字段 | 本地字段 | 类型 | 说明 |
|---------|---------|------|------|
| TR Number | order_id | String | 工单编号 |
| TR Description | description | Text | 工单描述 |
| Type of problem | category | String | 问题类型 |
| TR Level | priority | String | 优先级 |
| TR Status | status | String | 工单状态 |
| Source | source | String | 来源 |
| Created by | created_by | String | 创建人 |
| Module（模块） | module | String | 模块 |
| Wilfulness（责任人） | wilfulness | String | 责任人 |
| Date of close TR | date_of_close | DateTime | 关闭日期 |
| Vehicle Type01 | vehicle_type | String | 车型 |
| VIN\|sim | vin_sim | String | 车架号/SIM |
| App remote control version | app_remote_control_version | String | 应用远程控制版本 |
| HMI SW | hmi_sw | String | HMI软件版本 |
| 父记录 | parent_record | String | 父记录 |
| Has it been updated on the same day | has_updated_same_day | String | 是否同日更新 |
| Operating time | operating_time | String | 操作时间 |
## 🔧 配置管理
### 配置文件位置
- `llm_config.py`: LLM客户端配置
- `integrations_config.json`: 集成服务配置
- `integrations_config copy.json`: 配置备份文件
### 配置更新
- 修改配置文件后需要重启服务
- 建议在修改前备份配置文件
- 可以通过Web界面进行部分配置的在线修改
### 环境变量支持
系统支持通过环境变量覆盖配置文件设置：
```bash
# LLM配置
export LLM_PROVIDER="openai"
export LLM_API_KEY="your-api-key"
export LLM_MODEL="gpt-3.5-turbo"
# 飞书配置
export FEISHU_APP_ID="your-app-id"
export FEISHU_APP_SECRET="your-app-secret"
export FEISHU_APP_TOKEN="your-app-token"
export FEISHU_TABLE_ID="your-table-id"
```
## 🚨 注意事项
1. **安全性**: 配置文件包含敏感信息，请勿提交到版本控制系统
2. **备份**: 修改配置前请备份原文件
3. **权限**: 确保飞书应用有足够的权限访问多维表格
4. **测试**: 配置完成后建议先进行测试同步
5. **监控**: 定期检查同步状态和错误日志

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
AI准确率配置
管理AI建议的准确率阈值和相关配置
"""
from dataclasses import dataclass
from typing import Dict, Any
@dataclass
class AIAccuracyConfig:
"""AI准确率配置类"""
# 相似度阈值配置
auto_approve_threshold: float = 0.95 # 自动审批阈值（≥95%）
use_human_resolution_threshold: float = 0.90 # 使用人工描述阈值（<90%）
manual_review_threshold: float = 0.80 # 人工审核阈值（≥80%）
# 置信度配置
ai_suggestion_confidence: float = 0.95 # AI建议默认置信度
human_resolution_confidence: float = 0.90 # 人工描述置信度
# 入库策略配置
prefer_human_when_low_accuracy: bool = True # 当AI准确率低时优先使用人工描述
enable_auto_approval: bool = True # 是否启用自动审批
enable_human_fallback: bool = True # 是否启用人工描述回退
def get_threshold_explanation(self, similarity: float) -> str:
"""获取相似度阈值的解释"""
if similarity >= self.auto_approve_threshold:
return f"相似度≥{self.auto_approve_threshold*100:.0f}%，自动审批使用AI建议"
elif similarity >= self.manual_review_threshold:
return f"相似度≥{self.manual_review_threshold*100:.0f}%，建议人工审核"
elif similarity >= self.use_human_resolution_threshold:
return f"相似度<{self.use_human_resolution_threshold*100:.0f}%，建议使用人工描述"
else:
return f"相似度<{self.use_human_resolution_threshold*100:.0f}%，优先使用人工描述"
def should_use_human_resolution(self, similarity: float) -> bool:
"""判断是否应该使用人工描述"""
return similarity < self.use_human_resolution_threshold
def should_auto_approve(self, similarity: float) -> bool:
"""判断是否应该自动审批"""
return similarity >= self.auto_approve_threshold and self.enable_auto_approval
def get_confidence_score(self, similarity: float, use_human: bool = False) -> float:
"""获取置信度分数"""
if use_human:
return self.human_resolution_confidence
else:
return max(similarity, self.ai_suggestion_confidence)
def to_dict(self) -> Dict[str, Any]:
"""转换为字典格式"""
return {
"auto_approve_threshold": self.auto_approve_threshold,
"use_human_resolution_threshold": self.use_human_resolution_threshold,
"manual_review_threshold": self.manual_review_threshold,
"ai_suggestion_confidence": self.ai_suggestion_confidence,
"human_resolution_confidence": self.human_resolution_confidence,
"prefer_human_when_low_accuracy": self.prefer_human_when_low_accuracy,
"enable_auto_approval": self.enable_auto_approval,
"enable_human_fallback": self.enable_human_fallback
}
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'AIAccuracyConfig':
"""从字典创建配置"""
return cls(**data)
# 默认配置实例
DEFAULT_CONFIG = AIAccuracyConfig()
# 配置预设
PRESETS = {
"conservative": AIAccuracyConfig(
auto_approve_threshold=0.98,
use_human_resolution_threshold=0.85,
manual_review_threshold=0.90,
human_resolution_confidence=0.95
),
"balanced": AIAccuracyConfig(
auto_approve_threshold=0.95,
use_human_resolution_threshold=0.90,
manual_review_threshold=0.80,
human_resolution_confidence=0.90
),
"aggressive": AIAccuracyConfig(
auto_approve_threshold=0.90,
use_human_resolution_threshold=0.80,
manual_review_threshold=0.70,
human_resolution_confidence=0.85
)
}
def get_accuracy_config(preset: str = "balanced") -> AIAccuracyConfig:
"""获取准确率配置"""
return PRESETS.get(preset, DEFAULT_CONFIG)
def update_accuracy_config(config: AIAccuracyConfig) -> bool:
"""更新准确率配置（可以保存到文件或数据库）"""
try:
# 这里可以实现配置的持久化存储
# 例如保存到配置文件或数据库
return True
except Exception:
return False

16
config/integrations_config copy.json Normal file
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{
"feishu": {
"app_id": "tblnl3vJPpgMTSiP",
"app_secret": "ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK",
"app_token": "XXnEbiCmEaMblSs6FDJcFCqsnlg",
"table_id": "tblnl3vJPpgMTSiP",
"last_updated": null,
"status": "inactive"
},
"system": {
"sync_limit": 10,
"ai_suggestions_enabled": true,
"auto_sync_interval": 0,
"last_sync_time": null
}
}

16
config/integrations_config.json Normal file
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{
"feishu": {
"app_id": "cli_a8b50ec0eed1500d",
"app_secret": "ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK",
"app_token": "XXnEbiCmEaMblSs6FDJcFCqsnIg",
"table_id": "tblnl3vJPpgMTSiP",
"last_updated": "2025-09-19T18:40:55.291113",
"status": "active"
},
"system": {
"sync_limit": 10,
"ai_suggestions_enabled": true,
"auto_sync_interval": 0,
"last_sync_time": null
}
}

60
config/llm_config.py Normal file
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# -*- coding: utf-8 -*-
"""
LLM配置文件 - 千问模型配置
"""
from src.agent.llm_client import LLMConfig
# 千问模型配置
QWEN_CONFIG = LLMConfig(
provider="qwen",
api_key="sk-c0dbefa1718d46eaa897199135066f00", # 请替换为您的千问API密钥
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
model="qwen-plus-latest", # 可选: qwen-turbo, qwen-plus, qwen-max
temperature=0.7,
max_tokens=2000
)
# 其他模型配置示例
OPENAI_CONFIG = LLMConfig(
provider="openai",
api_key="sk-your-openai-api-key-here",
model="gpt-3.5-turbo",
temperature=0.7,
max_tokens=2000
)
ANTHROPIC_CONFIG = LLMConfig(
provider="anthropic",
api_key="sk-ant-your-anthropic-api-key-here",
model="claude-3-sonnet-20240229",
temperature=0.7,
max_tokens=2000
)
# 默认使用千问模型
DEFAULT_CONFIG = QWEN_CONFIG
def get_default_llm_config() -> LLMConfig:
"""
获取默认的LLM配置
优先从统一配置管理器获取，如果失败则使用本地配置
"""
try:
from src.config.unified_config import get_config
config = get_config()
llm_dict = config.get_llm_config()
# 创建LLMConfig对象
return LLMConfig(
provider=llm_dict.get("provider", "qwen"),
api_key=llm_dict.get("api_key", ""),
base_url=llm_dict.get("base_url", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
model=llm_dict.get("model", "qwen-plus-latest"),
temperature=llm_dict.get("temperature", 0.7),
max_tokens=llm_dict.get("max_tokens", 2000)
)
except Exception:
# 如果统一配置不可用，使用本地配置
return DEFAULT_CONFIG

52
config/unified_config.json Normal file
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{
"database": {
"url": "mysql+pymysql://tsp_assistant:password@jeason.online/tsp_assistant?charset=utf8mb4",
"pool_size": 10,
"max_overflow": 20,
"pool_timeout": 30,
"pool_recycle": 3600
},
"llm": {
"provider": "qwen",
"api_key": "sk-c0dbefa1718d46eaa897199135066f00",
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"model": "qwen-plus-latest",
"temperature": 0.7,
"max_tokens": 2000,
"timeout": 30
},
"server": {
"host": "0.0.0.0",
"port": 5000,
"websocket_port": 8765,
"debug": false,
"log_level": "INFO"
},
"feishu": {
"app_id": "",
"app_secret": "",
"app_token": "",
"table_id": "",
"status": "active",
"sync_limit": 10,
"auto_sync_interval": 0
},
"ai_accuracy": {
"auto_approve_threshold": 0.95,
"use_human_resolution_threshold": 0.9,
"manual_review_threshold": 0.8,
"ai_suggestion_confidence": 0.95,
"human_resolution_confidence": 0.9,
"prefer_human_when_low_accuracy": true,
"enable_auto_approval": true,
"enable_human_fallback": true
},
"system": {
"backup_enabled": true,
"backup_interval": 24,
"max_backup_files": 7,
"cache_enabled": true,
"cache_ttl": 3600,
"monitoring_enabled": true
}
}

672
config_backup.txt
View File

@@ -1,672 +0,0 @@
################################################################################
# DEPRECATED CONFIGURATION FILE: config/llm_config.py
################################################################################
# -*- coding: utf-8 -*-
"""
LLM配置文件 - 千问模型配置
"""
from src.agent.llm_client import LLMConfig
# 千问模型配置
QWEN_CONFIG = LLMConfig(
provider="qwen",
api_key="sk-c0dbefa1718d46eaa897199135066f00", # 请替换为您的千问API密钥
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
model="qwen-plus-latest", # 可选: qwen-turbo, qwen-plus, qwen-max
temperature=0.7,
max_tokens=2000
)
# 其他模型配置示例
OPENAI_CONFIG = LLMConfig(
provider="openai",
api_key="sk-your-openai-api-key-here",
model="gpt-3.5-turbo",
temperature=0.7,
max_tokens=2000
)
ANTHROPIC_CONFIG = LLMConfig(
provider="anthropic",
api_key="sk-ant-your-anthropic-api-key-here",
model="claude-3-sonnet-20240229",
temperature=0.7,
max_tokens=2000
)
# 默认使用千问模型
DEFAULT_CONFIG = QWEN_CONFIG
def get_default_llm_config() -> LLMConfig:
"""
获取默认的LLM配置
优先从统一配置管理器获取，如果失败则使用本地配置
"""
try:
from src.config.unified_config import get_config
config = get_config()
llm_dict = config.get_llm_config()
# 创建LLMConfig对象
return LLMConfig(
provider=llm_dict.get("provider", "qwen"),
api_key=llm_dict.get("api_key", ""),
base_url=llm_dict.get("base_url", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
model=llm_dict.get("model", "qwen-plus-latest"),
temperature=llm_dict.get("temperature", 0.7),
max_tokens=llm_dict.get("max_tokens", 2000)
)
except Exception:
# 如果统一配置不可用，使用本地配置
return DEFAULT_CONFIG
################################################################################
# DEPRECATED CONFIGURATION FILE: config/integrations_config.json
################################################################################
{
"feishu": {
"app_id": "cli_a8b50ec0eed1500d",
"app_secret": "ccxkE7ZCFQZcwkkM1rLy0ccZRXYsT2xK",
"app_token": "XXnEbiCmEaMblSs6FDJcFCqsnIg",
"table_id": "tblnl3vJPpgMTSiP",
"last_updated": "2025-09-19T18:40:55.291113",
"status": "active"
},
"system": {
"sync_limit": 10,
"ai_suggestions_enabled": true,
"auto_sync_interval": 0,
"last_sync_time": null
}
}
################################################################################
# DEPRECATED CONFIGURATION FILE: config/ai_accuracy_config.py
################################################################################
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
AI准确率配置
管理AI建议的准确率阈值和相关配置
"""
from dataclasses import dataclass
from typing import Dict, Any
@dataclass
class AIAccuracyConfig:
"""AI准确率配置类"""
# 相似度阈值配置
auto_approve_threshold: float = 0.95 # 自动审批阈值（≥95%）
use_human_resolution_threshold: float = 0.90 # 使用人工描述阈值（<90%）
manual_review_threshold: float = 0.80 # 人工审核阈值（≥80%）
# 置信度配置
ai_suggestion_confidence: float = 0.95 # AI建议默认置信度
human_resolution_confidence: float = 0.90 # 人工描述置信度
# 入库策略配置
prefer_human_when_low_accuracy: bool = True # 当AI准确率低时优先使用人工描述
enable_auto_approval: bool = True # 是否启用自动审批
enable_human_fallback: bool = True # 是否启用人工描述回退
def get_threshold_explanation(self, similarity: float) -> str:
"""获取相似度阈值的解释"""
if similarity >= self.auto_approve_threshold:
return f"相似度≥{self.auto_approve_threshold*100:.0f}%，自动审批使用AI建议"
elif similarity >= self.manual_review_threshold:
return f"相似度≥{self.manual_review_threshold*100:.0f}%，建议人工审核"
elif similarity >= self.use_human_resolution_threshold:
return f"相似度<{self.use_human_resolution_threshold*100:.0f}%，建议使用人工描述"
else:
return f"相似度<{self.use_human_resolution_threshold*100:.0f}%，优先使用人工描述"
def should_use_human_resolution(self, similarity: float) -> bool:
"""判断是否应该使用人工描述"""
return similarity < self.use_human_resolution_threshold
def should_auto_approve(self, similarity: float) -> bool:
"""判断是否应该自动审批"""
return similarity >= self.auto_approve_threshold and self.enable_auto_approval
def get_confidence_score(self, similarity: float, use_human: bool = False) -> float:
"""获取置信度分数"""
if use_human:
return self.human_resolution_confidence
else:
return max(similarity, self.ai_suggestion_confidence)
def to_dict(self) -> Dict[str, Any]:
"""转换为字典格式"""
return {
"auto_approve_threshold": self.auto_approve_threshold,
"use_human_resolution_threshold": self.use_human_resolution_threshold,
"manual_review_threshold": self.manual_review_threshold,
"ai_suggestion_confidence": self.ai_suggestion_confidence,
"human_resolution_confidence": self.human_resolution_confidence,
"prefer_human_when_low_accuracy": self.prefer_human_when_low_accuracy,
"enable_auto_approval": self.enable_auto_approval,
"enable_human_fallback": self.enable_human_fallback
}
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'AIAccuracyConfig':
"""从字典创建配置"""
return cls(**data)
# 默认配置实例
DEFAULT_CONFIG = AIAccuracyConfig()
# 配置预设
PRESETS = {
"conservative": AIAccuracyConfig(
auto_approve_threshold=0.98,
use_human_resolution_threshold=0.85,
manual_review_threshold=0.90,
human_resolution_confidence=0.95
),
"balanced": AIAccuracyConfig(
auto_approve_threshold=0.95,
use_human_resolution_threshold=0.90,
manual_review_threshold=0.80,
human_resolution_confidence=0.90
),
"aggressive": AIAccuracyConfig(
auto_approve_threshold=0.90,
use_human_resolution_threshold=0.80,
manual_review_threshold=0.70,
human_resolution_confidence=0.85
)
}
def get_accuracy_config(preset: str = "balanced") -> AIAccuracyConfig:
"""获取准确率配置"""
return PRESETS.get(preset, DEFAULT_CONFIG)
def update_accuracy_config(config: AIAccuracyConfig) -> bool:
"""更新准确率配置（可以保存到文件或数据库）"""
try:
# 这里可以实现配置的持久化存储
# 例如保存到配置文件或数据库
return True
except Exception:
return False
################################################################################
# DEPRECATED CONFIGURATION FILE: config/field_mapping_config.json
################################################################################
{
"field_mapping": {
"TR Number": "order_id",
"TR Description": "description",
"Type of problem": "category",
"TR Level": "priority",
"TR Status": "status",
"Source": "source",
"Date creation": "created_at",
"处理过程": "resolution",
"TR tracking": "resolution",
"Created by": "created_by",
"Module（模块）": "module",
"Wilfulness（责任人）": "wilfulness",
"Date of close TR": "date_of_close",
"Vehicle Type01": "vehicle_type",
"VIN|sim": "vin_sim",
"App remote control version": "app_remote_control_version",
"HMI SW": "hmi_sw",
"父记录": "parent_record",
"Has it been updated on the same day": "has_updated_same_day",
"Operating time": "operating_time",
"AI建议": "ai_suggestion",
"Issue Start Time": "updated_at",
"Wilfulness（责任人<E4BBBB>?": "wilfulness",
"父<>?<3F>录": "parent_record",
"AI建<49>??": "ai_suggestion"
},
"field_aliases": {
"order_id": [
"TR Number",
"TR编号",
"工单号",
"Order ID",
"Ticket ID",
"工单编号",
"新字段1",
"新字段"
],
"description": [
"TR Description",
"TR描述",
"描述",
"Description",
"问题描述",
"详细描述"
],
"category": [
"Type of problem",
"问题类型",
"Category",
"分类",
"Problem Type",
"问题分类"
],
"priority": [
"TR Level",
"优先级",
"Priority",
"Level",
"紧急程度",
"重要程度"
],
"status": [
"TR Status",
"状态",
"Status",
"工单状态",
"处理状态"
],
"source": [
"Source",
"来源",
"Source Type",
"来源类型",
"提交来源"
],
"created_at": [
"Date creation",
"创建日期",
"Created At",
"Creation Date",
"创建时间"
],
"solution": [
"处理过程",
"Solution",
"解决方案",
"Process",
"处理方案"
],
"resolution": [
"TR tracking",
"Resolution",
"解决结果",
"跟踪",
"处理结果"
],
"created_by": [
"Created by",
"创建人",
"Creator",
"Created By",
"提交人"
],
"vehicle_type": [
"Vehicle Type01",
"车型",
"Vehicle Type",
"车辆类型",
"车款"
],
"vin_sim": [
"VIN|sim",
"VIN",
"车架号",
"SIM",
"VIN/SIM",
"车辆识别号"
],
"module": [
"Module（模块）",
"模块",
"Module",
"功能模块"
],
"wilfulness": [
"Wilfulness（责任人）",
"责任人",
"负责人",
"Assignee"
],
"date_of_close": [
"Date of close TR",
"关闭日期",
"Close Date",
"完成日期"
],
"app_remote_control_version": [
"App remote control version",
"应用远程控制版本",
"App Version",
"应用版本"
],
"hmi_sw": [
"HMI SW",
"HMI软件版本",
"HMI Software",
"人机界面软件"
],
"parent_record": [
"父记录",
"Parent Record",
"上级记录",
"关联记录"
],
"has_updated_same_day": [
"Has it been updated on the same day",
"是否同日更新",
"Same Day Update",
"当日更新"
],
"operating_time": [
"Operating time",
"操作时间",
"Operation Time",
"运行时间"
],
"ai_suggestion": [
"AI建议",
"AI Suggestion",
"AI建议",
"智能建议"
],
"updated_at": [
"Issue Start Time",
"问题开始时间",
"Start Time",
"更新时间"
]
},
"field_patterns": {
"order_id": [
".*number.*",
".*id.*",
".*编号.*",
".*ticket.*",
".*新.*"
],
"description": [
".*description.*",
".*描述.*",
".*detail.*",
".*内容.*"
],
"category": [
".*type.*",
".*category.*",
".*分类.*",
".*类型.*",
".*problem.*"
],
"priority": [
".*level.*",
".*priority.*",
".*优先级.*",
".*urgent.*"
],
"status": [
".*status.*",
".*状态.*",
".*state.*"
],
"source": [
".*source.*",
".*来源.*",
".*origin.*"
],
"created_at": [
".*creation.*",
".*created.*",
".*创建.*",
".*date.*"
],
"solution": [
".*solution.*",
".*处理.*",
".*解决.*",
".*process.*"
],
"resolution": [
".*resolution.*",
".*tracking.*",
".*跟踪.*",
".*result.*"
],
"created_by": [
".*created.*by.*",
".*creator.*",
".*创建人.*",
".*author.*"
],
"vehicle_type": [
".*vehicle.*type.*",
".*车型.*",
".*车辆.*",
".*car.*"
],
"vin_sim": [
".*vin.*",
".*sim.*",
".*车架.*",
".*识别.*"
],
"module": [
".*module.*",
".*模块.*",
".*功能.*"
],
"wilfulness": [
".*wilfulness.*",
".*责任人.*",
".*负责人.*",
".*assignee.*"
],
"date_of_close": [
".*close.*",
".*关闭.*",
".*完成.*",
".*finish.*"
],
"app_remote_control_version": [
".*app.*version.*",
".*应用.*版本.*",
".*remote.*control.*"
],
"hmi_sw": [
".*hmi.*",
".*软件.*",
".*software.*"
],
"parent_record": [
".*parent.*",
".*父.*",
".*上级.*",
".*关联.*"
],
"has_updated_same_day": [
".*same.*day.*",
".*同日.*",
".*当日.*",
".*updated.*same.*"
],
"operating_time": [
".*operating.*time.*",
".*操作.*时间.*",
".*运行.*时间.*"
],
"ai_suggestion": [
".*ai.*suggestion.*",
".*ai.*建议.*",
".*智能.*建议.*"
],
"updated_at": [
".*start.*time.*",
".*开始.*时间.*",
".*updated.*at.*",
".*更新时间.*"
]
},
"field_priorities": {
"order_id": 3,
"description": 3,
"category": 3,
"priority": 3,
"status": 3,
"created_at": 3,
"source": 3,
"solution": 3,
"resolution": 3,
"created_by": 3,
"vehicle_type": 3,
"vin_sim": 3,
"module": 3,
"wilfulness": 3,
"date_of_close": 3,
"app_remote_control_version": 3,
"hmi_sw": 3,
"parent_record": 3,
"has_updated_same_day": 3,
"operating_time": 3,
"ai_suggestion": 3,
"updated_at": 3
},
"auto_mapping_enabled": true,
"similarity_threshold": 0.6
}
################################################################################
# DEPRECATED CONFIGURATION FILE: config/unified_config.json
################################################################################
{
"database": {
"url": "mysql+pymysql://tsp_assistant:password@jeason.online/tsp_assistant?charset=utf8mb4",
"pool_size": 10,
"max_overflow": 20,
"pool_timeout": 30,
"pool_recycle": 3600
},
"llm": {
"provider": "qwen",
"api_key": "sk-c0dbefa1718d46eaa897199135066f00",
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"model": "qwen-plus-latest",
"temperature": 0.7,
"max_tokens": 2000,
"timeout": 30
},
"server": {
"host": "0.0.0.0",
"port": 5000,
"websocket_port": 8765,
"debug": false,
"log_level": "INFO"
},
"feishu": {
"app_id": "",
"app_secret": "",
"app_token": "",
"table_id": "",
"status": "active",
"sync_limit": 10,
"auto_sync_interval": 0
},
"ai_accuracy": {
"auto_approve_threshold": 0.95,
"use_human_resolution_threshold": 0.9,
"manual_review_threshold": 0.8,
"ai_suggestion_confidence": 0.95,
"human_resolution_confidence": 0.9,
"prefer_human_when_low_accuracy": true,
"enable_auto_approval": true,
"enable_human_fallback": true
},
"system": {
"backup_enabled": true,
"backup_interval": 24,
"max_backup_files": 7,
"cache_enabled": true,
"cache_ttl": 3600,
"monitoring_enabled": true
}
}
################################################################################
# DEPRECATED CONFIGURATION FILE: src/config/config.py
################################################################################
import os
from typing import Dict, Any
class Config:
"""系统配置类"""
# 阿里云千问API配置
ALIBABA_API_KEY = "sk-c0dbefa1718d46eaa897199135066f00"
ALIBABA_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
ALIBABA_MODEL_NAME = "qwen-plus-latest"
# 数据库配置
DATABASE_URL = "mysql+pymysql://tsp_assistant:123456@jeason.online/tsp_assistant?charset=utf8mb4"
# DATABASE_URL = "sqlite:///local_test.db" # 本地测试数据库
# 知识库配置
KNOWLEDGE_BASE_PATH = "data/knowledge_base"
VECTOR_DB_PATH = "data/vector_db"
# 对话配置
MAX_HISTORY_LENGTH = 10
RESPONSE_TIMEOUT = 30
# 分析配置
ANALYTICS_UPDATE_INTERVAL = 3600 # 1小时
ALERT_THRESHOLD = 0.8 # 预警阈值
# 日志配置
LOG_LEVEL = "INFO"
LOG_FILE = "logs/tsp_assistant.log"
# 系统监控配置
SYSTEM_MONITORING = True # 是否启用系统监控
MONITORING_INTERVAL = 60 # 监控间隔（秒）
@classmethod
def get_api_config(cls) -> Dict[str, Any]:
"""获取API配置"""
return {
"api_key": cls.ALIBABA_API_KEY,
"base_url": cls.ALIBABA_BASE_URL,
"model_name": cls.ALIBABA_MODEL_NAME
}
@classmethod
def get_database_config(cls) -> Dict[str, Any]:
"""获取数据库配置"""
return {
"url": cls.DATABASE_URL,
"echo": False
}
@classmethod
def get_knowledge_config(cls) -> Dict[str, Any]:
"""获取知识库配置"""
return {
"base_path": cls.KNOWLEDGE_BASE_PATH,
"vector_db_path": cls.VECTOR_DB_PATH
}
@classmethod
def get_config(cls) -> Dict[str, Any]:
"""获取完整配置"""
return {
"system_monitoring": cls.SYSTEM_MONITORING,
"monitoring_interval": cls.MONITORING_INTERVAL,
"log_level": cls.LOG_LEVEL,
"log_file": cls.LOG_FILE,
"analytics_update_interval": cls.ANALYTICS_UPDATE_INTERVAL,
"alert_threshold": cls.ALERT_THRESHOLD
}

9
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@@ -0,0 +1,9 @@
{
"init_time": "2025-12-12T13:31:25.459591",
"database_version": "SQLite 3.43.1",
"database_url": "sqlite:///tsp_assistant.db",
"migrations_applied": 0,
"tables_created": 9,
"initial_data_inserted": true,
"verification_passed": true
}

395
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@@ -1,395 +0,0 @@
# 飞书长连接模式使用指南
> ✅ **已验证可用** - 2026-02-11
>
> 本文档介绍如何使用飞书官方 SDK 的长连接模式，无需公网域名即可接入飞书机器人。
## 🎯 什么是长连接模式？
飞书官方 SDK 提供了**事件订阅 2.0（长连接模式）**，相比传统的 webhook 模式有以下优势：
| 特性 | Webhook 模式（旧） | 长连接模式（新）✅ |
|------|-------------------|-------------------|
| 公网域名 | ✅ 必需 | ❌ 不需要 |
| SSL 证书 | ✅ 必需 | ❌ 不需要 |
| 回调配置 | ✅ 需要配置 | ❌ 不需要 |
| 内网部署 | ❌ 不支持 | ✅ 支持 |
| 实时性 | 中等（HTTP 轮询） | ✅ 高（WebSocket） |
| 稳定性 | 中等 | ✅ 高（自动重连） |
---
## 📦 安装步骤
### 1. 安装依赖
```bash
cd /Users/macos/Desktop/tsp-assist/assist
pip install -r requirements.txt
```
**核心依赖：**
- `lark-oapi==1.3.5` - 飞书官方 Python SDK
### 2. 配置环境变量
编辑 `.env` 文件：
```bash
# 飞书配置（必需）
FEISHU_APP_ID=cli_xxxxxxxxxxxxx # 你的飞书应用 ID
FEISHU_APP_SECRET=xxxxxxxxxxxxx # 你的飞书应用密钥
# 数据库配置
DATABASE_URL=sqlite:///./data/tsp_assistant.db
# LLM 配置
LLM_PROVIDER=qwen
LLM_API_KEY=sk-xxxxxxxxxxxxx
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
LLM_MODEL=qwen-plus-latest
```
### 3. 初始化数据库
```bash
python init_database.py
```
### 4. 在飞书开放平台配置应用权限
访问 [飞书开放平台](https://open.feishu.cn/app)，为您的应用添加以下权限：
**必需权限：**
- ✅ `im:message` - 获取与发送单聊、群组消息
- ✅ `im:message:send_as_bot` - 以应用的身份发消息
- ✅ `im:chat` - 获取群组信息
**可选权限（用于工单同步）：**
- ✅ `bitable:app` - 查看、评论、编辑和管理多维表格
**注意：** 添加权限后需要重新发布应用版本。
---
## 🚀 启动服务
### 方式 1: 使用启动脚本（推荐）
```bash
python start_feishu_bot.py
```
启动后会看到：
```
================================================================================
🚀 TSP Assistant - 飞书长连接客户端
================================================================================
📋 配置信息:
- App ID: cli_xxxxxxxxxxxxx
- 数据库: sqlite:///./data/tsp_assistant.db
- LLM: qwen/qwen-plus-latest
- 日志级别: INFO
🔌 启动模式: 事件订阅 2.0（长连接）
✅ 优势:
- 无需公网域名
- 无需配置 webhook
- 自动重连
- 实时接收消息
💡 提示: 按 Ctrl+C 停止服务
================================================================================
🚀 启动飞书长连接客户端...
- App ID: cli_xxxxxxxxxxxxx
- 模式: 事件订阅 2.0（长连接）
- 无需公网域名和 webhook 配置
✅ 飞书长连接服务初始化成功
```
### 方式 2: 在代码中集成
如果您想将长连接服务集成到现有的 Flask 应用中：
```python
import threading
from src.integrations.feishu_longconn_service import get_feishu_longconn_service
# 在后台线程中启动长连接服务
def start_feishu_in_background():
service = get_feishu_longconn_service()
service.start()
# 启动
feishu_thread = threading.Thread(target=start_feishu_in_background, daemon=True)
feishu_thread.start()
# 然后启动 Flask 应用
app.run(...)
```
---
## 🧪 测试
### 1. 在飞书群聊中测试
**场景 1：单个用户对话**
```
用户 A: @TSP助手 车辆无法连接网络怎么办？
机器人: [基于工单知识库的智能回复]
用户 A: 还有其他解决方法吗？
机器人: [基于上下文的多轮对话回复]
```
**场景 2：多用户群聊隔离**
```
群聊 1:
用户 A: @TSP助手 我的车有问题
机器人: [针对用户 A 的回复]
用户 B: @TSP助手 我的车有问题
机器人: [针对用户 B 的独立回复，不受用户 A 影响]
群聊 2:
用户 A: @TSP助手 我的车有问题
机器人: [全新对话，不受群聊 1 影响]
```
### 2. 查看日志
服务运行时会实时输出日志：
```
📨 [Feishu LongConn] 收到消息
- 消息ID: om_xxxxxxxxxxxxx
- 群聊ID: oc_xxxxxxxxxxxxx
- 发送者: ou_xxxxxxxxxxxxx
- 消息类型: text
- 原始内容: @TSP助手 车辆无法连接网络
- 清理后内容: 车辆无法连接网络
🔑 会话用户标识: feishu_oc_xxxxxxxxxxxxx_ou_xxxxxxxxxxxxx
🆕 为用户 ou_xxxxxxxxxxxxx 在群聊 oc_xxxxxxxxxxxxx 创建新会话: session_xxxxxxxxxxxxx
🤖 调用 TSP Assistant 处理消息...
📤 准备发送回复 (长度: 156)
✅ 成功回复消息: om_xxxxxxxxxxxxx
```
---
## 🔧 与旧模式的对比
### 旧模式（Webhook）- 已废弃
**文件：** `src/web/blueprints/feishu_bot.py`
**工作流程：**
```
飞书 → 公网域名 → Webhook → Flask → 处理消息
```
**缺点：**
- ❌ 需要公网域名
- ❌ 需要配置 SSL
- ❌ 内网无法部署
- ❌ 需要在飞书后台配置回调地址
### 新模式（长连接）- 推荐 ✅
**文件：** `src/integrations/feishu_longconn_service.py`
**工作流程：**
```
飞书 ← WebSocket 长连接 ← SDK 客户端 ← 处理消息
```
**优点：**
- ✅ 无需公网域名
- ✅ 无需 SSL 证书
- ✅ 内网可部署
- ✅ 无需配置回调地址
- ✅ 自动重连
- ✅ 更稳定
---
## 📊 架构说明
```
┌─────────────────────────────────────────────────────────────┐
│ 飞书服务器 │
│ (open.feishu.cn) │
└───────────────────────┬─────────────────────────────────────┘
│ WebSocket 长连接
│ (事件订阅 2.0)
↓
┌─────────────────────────────────────────────────────────────┐
│ TSP Assistant (内网部署) │
│ ┌────────────────────────────────────────────────────┐ │
│ │ feishu_longconn_service.py │ │
│ │ - 飞书 SDK 长连接客户端 │ │
│ │ - 自动接收消息事件 │ │
│ │ - 群聊隔离 (feishu_群ID_用户ID) │ │
│ └──────────────┬─────────────────────────────────────┘ │
│ ↓ │
│ ┌────────────────────────────────────────────────────┐ │
│ │ RealtimeChatManager │ │
│ │ - 会话管理 │ │
│ │ - 多轮对话 │ │
│ └──────────────┬─────────────────────────────────────┘ │
│ ↓ │
│ ┌────────────────────────────────────────────────────┐ │
│ │ TSP Assistant 智能引擎 │ │
│ │ - LLM 调用 │ │
│ │ - 工单知识库匹配 │ │
│ │ - AI 分析和建议 │ │
│ └────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
---
## ⚙️ 高级配置
### 1. 调整日志级别
编辑 `.env`：
```bash
LOG_LEVEL=DEBUG # 查看详细日志
LOG_LEVEL=INFO # 默认（推荐）
LOG_LEVEL=WARNING # 仅警告和错误
```
### 2. 同时运行 Web 管理后台和长连接服务
**方式 1：使用两个终端**
```bash
# 终端 1 - Web 管理后台
python start_dashboard.py
# 终端 2 - 飞书长连接服务
python start_feishu_bot.py
```
**方式 2：使用进程管理器（推荐生产环境）**
创建 `supervisord.conf`：
```ini
[program:tsp-web]
command=python start_dashboard.py
directory=/Users/macos/Desktop/tsp-assist/assist
autostart=true
autorestart=true
[program:tsp-feishu]
command=python start_feishu_bot.py
directory=/Users/macos/Desktop/tsp-assist/assist
autostart=true
autorestart=true
```
启动：
```bash
supervisord -c supervisord.conf
```
---
## ❓ 常见问题
### Q1: SSL 证书验证失败怎么办？
**错误信息：**
```
[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain
```
**解决方案（macOS）：**
1. **运行 Python 证书安装脚本**（推荐）：
```bash
open "/Applications/Python 3.10/Install Certificates.command"
```
或者手动运行：
```bash
/Applications/Python\ 3.10/Install\ Certificates.command
```
2. **升级 certifi 包**：
```bash
python3 -m pip install --upgrade certifi
```
3. **验证修复**：
```bash
python3 -c "import urllib.request; urllib.request.urlopen('https://open.feishu.cn', timeout=5); print('✅ SSL 验证成功')"
```
**解决方案（Linux/Windows）：**
```bash
pip3 install --upgrade certifi
```
### Q2: 启动后没有收到消息？
**检查清单：**
1. ✅ 确认飞书应用权限已配置（`im:message`）
2. ✅ 确认应用已发布最新版本
3. ✅ 确认机器人已添加到群聊
4. ✅ 查看日志是否有连接成功的提示
5. ✅ 确认没有 SSL 证书错误（见 Q1）
### Q3: 提示"权限不足"？
**解决方案：**
1. 登录飞书开放平台
2. 进入应用管理 → 权限管理
3. 添加所需权限
4. 创建新版本并发布
5. 重启长连接服务
### Q4: 如何停止服务？
按 `Ctrl+C` 即可优雅停止。
### Q5: 可以在多台服务器上运行吗？
**不建议**。同一个飞书应用只应运行一个长连接客户端实例，否则会导致消息重复处理。
### Q6: 如何迁移旧的 Webhook 模式？
**建议：**
1. 保留旧代码（`feishu_bot.py`）作为备份
2. 使用新的长连接模式（`start_feishu_bot.py`）
3. 测试无误后，可以移除 webhook 端点
---
## 🎉 总结
现在您的 TSP Assistant 已经支持**飞书官方 SDK 的长连接模式**！
✅ **核心优势：**
- 无需公网域名
- 无需 webhook 配置
- 内网可部署
- 自动重连
- 群聊隔离
- 完整的工单管理和 AI 分析功能
✅ **使用方式：**
```bash
python start_feishu_bot.py
```
就这么简单！🚀

51
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@@ -1,51 +0,0 @@
麻烦客户确认下app设置定位和蓝牙权限是否是始终允许,以及检查下手机系统设置是否允许奇瑞app后台运行.
车辆处于深度睡眠中，不支持远控，建议客户用物理钥匙重启车辆后再试下、
车辆注册数据和卡状态正常，可能网络原因数据未及时同步，麻烦客户重启车辆到信号好的地方再观察下
车辆最后注册时间2026-01-23 15:03:47,远控提示网络超时，已踢卡, 麻烦客户重启车辆到信号好的地方刷新app再试下。如未恢复建议客户长按SOS按键11秒以上重启TBOX再试下
最后注册时间2026-01-25 08:22:11，卡不在线，远控提示网络超时，麻烦t-box老师看下
车辆最后注册时间2025-07-30 17:57:01, 已踢卡，麻烦客户重启车辆到信号好的地方再试下
目前后台远控指令反馈成功，之前失败原因网络超时，麻烦回访客户再确认下
车辆最后注册时间 ，SIM卡已停用，麻烦转辛巴处理
座椅通风在爱车界面，点击车辆图片进去，之后点击图片上的座椅进行远控
未查询到车辆信息，麻烦客户再确认下车架号
目前车门状态通过车辆图片车门颜色显示，红色表示车门打开解锁状态，蓝色表示上锁状态
麻烦客户手机设置中检查下定位GPS权限是否开启并授权奇瑞app
远控空调提示T-BOX认证失败，麻烦t-box老师看下
已踢卡，麻烦客户重启车辆，退出账号重新登录试下
麻烦客户点击爱车界面的车辆图片进入详情页查看总里程数、电耗油耗等数据
该车型配置座椅加热通风功能，麻烦客户点击图片到详情页点图片上座椅位置控制
智云互联目前已不维护，建议客户下载奇瑞app再试下
车辆注册数据和卡状态正常，29日服务器数据有积压导致实时数据未更新，目前已修复，建议客户重启车辆到信号好的地方刷新app看数据是否更新
麻烦客户到设置里面关闭自动开空调的开关，然后先远控发动机启动后再远控空调试下；然后如果是开启了自动开空调开关，麻烦客户点击远控启动发动机，会自动开启空调
3.6.4版本高频问题
1. 更新3.6.4版本奇瑞APP后，如果客户杀掉过APP重新打开APP后，下发空调类远控会提示控制器反馈失败
解决方案：建议客户进入APP后先爱车界面下拉刷新界面再下发远控空调指令
长期解决：APP发版（3.6.6版本该问题已修复）
2. 车控界面不显示流量和到流量期时间，只显示符号
解决方案：建议客户在爱车界面下拉刷新APP正常可恢复；
长期解决：APP发版（3.6.6版本该问题已修复）
3. 每次打开APP后查看行程记录都要重新点击确认开关；
问题原因：合规要求，每次查看行程记录均需要点击确认
长期解决：APP发版（3.6.6版本该问题已修复）
4. 客户点击行程记录，开启开关后，行程记录不显示或者无法点击日期切换查看记录
解决方案：建议客户在查看行程记录开启开关后，回退爱车界面下拉几次刷新下APP，然后再点击行程记录界面查看, 查询接口在优化中；
长期解决：APP发版（3.6.6版本该问题已修复）
5. T26PHEV（风云T10）车主远控预约充电失败
解决方案：建议客户先在车机上设置预约充电，该问题正在修复中；——
长期解决：APP发版（3.6.6版本该问题已修复）
6. 奇瑞APP查看车辆定位是正确的，点击导航倒车路线不准确，车辆位置不准
问题原因：因为地图导航坐标系改动涉及方面较多，目前仍在优化中, 预计下个版本会修复
长期解决：APP发版（等待3.6.8版本修复）
APP车控问题
E01车型（风云A9L）APP车况数据不显示百公里电耗和油耗（等待修复）
E01车型暂不显示百公里耗油和耗电，数据在优化中，麻烦安抚客户先等待下
预约充电只充一个小时就自动断开停止了
TBOX问题，建议客户进站升级TBOX
APP提示哨兵模式自动退出，实际车辆未退出：
麻烦客户在车机上先直接语音或者下拉负一屏来打开哨兵模式，目前APP打开哨兵模式报异常退出的问题开发正在修复中
T26PHEV 更新到3.6.5后不显示电耗（3.6.6版本已修复）
客户进线表示奇瑞汽车APP更新最新版本后，只显示油耗不显示电耗，APP版本号：3.6.5
该问题存在于3.6.4和3.6.5版本（切换新车控），3.6.6版本已修复，建议客户更新APP到3.6.6版本
APP空调不显示具体温度数值
外部车型中检查空调类型，如是自动空调可以显示具体的温度数值，类型为电动空调，无温度数值显示，只显示LO & HI

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@@ -0,0 +1,26 @@
@echo off
rem 获取当前日期和时间
for /f "tokens=1-6 delims=/ " %%a in ('date /t') do set CDATE=%%a-%%b-%%c
for /f "tokens=1-2 delims=:\ " %%a in ('time /t') do set CTIME=%%a-%%b
set COMMIT_MESSAGE="feat: %CDATE% %CTIME% - 全面架构重构、功能增强及问题修复"
rem 添加所有变更到暂存区
git add .
rem 检查是否有文件被添加、修改或删除
git diff --cached --quiet
if %errorlevel% equ 0 (
echo 没有检测到需要提交的变更。
) else (
rem 提交变更
git commit -m %COMMIT_MESSAGE%
rem 推送变更到远程仓库
git push origin master
echo Git 推送完成！
)
pause

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@@ -0,0 +1,23 @@
#!/bin/bash
# 获取当前日期和时间
COMMIT_DATE=$(date +"%Y-%m-%d %H:%M:%S")
# 添加所有变更到暂存区
git add .
# 检查是否有文件被添加、修改或删除
if git diff --cached --quiet; then
echo "没有检测到需要提交的变更。"
else
# 创建提交消息
COMMIT_MESSAGE="feat: ${COMMIT_DATE} - 全面架构重构、功能增强及问题修复"
# 提交变更
git commit -m "$COMMIT_MESSAGE"
# 推送变更到远程仓库
git push origin master
echo "Git 推送完成！"
fi

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@@ -15,7 +15,7 @@ from pathlib import Path
# 添加项目根目录到Python路径
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from src.config.unified_config import get_config
from src.config.config import Config
from src.utils.helpers import setup_logging
from src.core.database import db_manager
from src.core.models import (
@@ -38,21 +38,6 @@ class DatabaseInitializer:
# 迁移历史记录
self.migration_history = []
def _mask_db_url(self, url: str) -> str:
"""隐藏数据库连接字符串中的敏感信息"""
try:
# 隐藏密码部分
if '@' in url and ':' in url:
parts = url.split('@')
if len(parts) == 2:
auth_part = parts[0]
if ':' in auth_part:
protocol_user = auth_part.rsplit(':', 1)[0]
return f"{protocol_user}:***@{parts[1]}"
return url
except:
return url
def _get_database_version(self) -> str:
"""获取数据库版本信息"""
try:
@@ -76,14 +61,13 @@ class DatabaseInitializer:
print("TSP智能助手数据库初始化系统")
print("=" * 80)
print(f"数据库类型: {self.db_version}")
print(f"连接地址: {self._mask_db_url(self.db_url)}")
print(f"连接地址: {self.db_url}")
print(f"初始化时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print("=" * 80)
try:
# 设置日志
config = get_config()
setup_logging(config.server.log_level, "logs/tsp_assistant.log")
setup_logging(Config.LOG_LEVEL, Config.LOG_FILE)
# 测试数据库连接
if not self._test_connection():
@@ -110,9 +94,6 @@ class DatabaseInitializer:
if not self._verify_database_integrity():
return False
# 初始化 Redis 缓存（如果启用）
self._initialize_redis_cache()
# 生成初始化报告
self._generate_init_report()
@@ -539,16 +520,7 @@ class DatabaseInitializer:
# 插入初始知识库数据
initial_data = self._get_initial_knowledge_data()
for data in initial_data:
# 只使用模型定义中存在的字段
entry = KnowledgeEntry(
question=data['question'],
answer=data['answer'],
category=data['category'],
confidence_score=data.get('confidence_score', 0.7),
is_verified=data.get('is_verified', True),
verified_by=data.get('verified_by', 'system'),
verified_at=data.get('verified_at', datetime.now())
)
entry = KnowledgeEntry(**data)
session.add(entry)
session.commit()
@@ -575,7 +547,9 @@ class DatabaseInitializer:
"confidence_score": 0.9,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.9
},
{
"question": "账户被锁定了怎么办？",
@@ -584,7 +558,9 @@ class DatabaseInitializer:
"confidence_score": 0.8,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.8
},
{
"question": "如何修改个人信息？",
@@ -593,7 +569,9 @@ class DatabaseInitializer:
"confidence_score": 0.7,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.7
},
{
"question": "支付失败怎么办？",
@@ -602,7 +580,9 @@ class DatabaseInitializer:
"confidence_score": 0.8,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.8
},
{
"question": "如何申请退款？",
@@ -611,7 +591,9 @@ class DatabaseInitializer:
"confidence_score": 0.7,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.7
},
{
"question": "系统无法访问怎么办？",
@@ -620,7 +602,9 @@ class DatabaseInitializer:
"confidence_score": 0.8,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.8
},
{
"question": "如何联系客服？",
@@ -629,7 +613,9 @@ class DatabaseInitializer:
"confidence_score": 0.9,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.9
},
{
"question": "如何远程启动车辆？",
@@ -638,7 +624,9 @@ class DatabaseInitializer:
"confidence_score": 0.9,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.9
},
{
"question": "APP显示车辆信息错误怎么办？",
@@ -647,7 +635,9 @@ class DatabaseInitializer:
"confidence_score": 0.8,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.8
},
{
"question": "车辆无法远程启动的原因？",
@@ -656,7 +646,9 @@ class DatabaseInitializer:
"confidence_score": 0.9,
"is_verified": True,
"verified_by": "system",
"verified_at": datetime.now()
"verified_at": datetime.now(),
"search_frequency": 0,
"relevance_score": 0.9
}
]
@@ -695,14 +687,10 @@ class DatabaseInitializer:
entry.is_verified = True
entry.verified_by = "system_init"
entry.verified_at = datetime.now()
# 尝试设置优化字段（如果存在）
try:
if hasattr(entry, 'search_frequency') and entry.search_frequency is None:
entry.search_frequency = 0
if hasattr(entry, 'relevance_score') and entry.relevance_score is None:
entry.relevance_score = 0.7
except:
pass # 如果字段不存在，忽略
if not hasattr(entry, 'search_frequency'):
entry.search_frequency = 0
if not hasattr(entry, 'relevance_score'):
entry.relevance_score = 0.7
session.commit()
print(f" 成功验证 {len(unverified_entries)} 条知识库条目")
@@ -806,34 +794,6 @@ class DatabaseInitializer:
except Exception:
return 0
def _initialize_redis_cache(self):
"""初始化 Redis 缓存"""
print("\n检查 Redis 缓存配置...")
try:
from src.core.cache_manager import cache_manager
health = cache_manager.health_check()
if health["status"] == "healthy":
print(" ✓ Redis 缓存连接成功")
stats = cache_manager.get_stats()
print(f" - 当前连接数: {stats.get('connected_clients', 0)}")
print(f" - 内存使用: {stats.get('used_memory_human', '0B')}")
print(f" - 总键数: {stats.get('total_keys', 0)}")
elif health["status"] == "disabled":
print(" ⚠ Redis 缓存未启用（可选功能）")
else:
print(f" ⚠ Redis 缓存连接失败: {health.get('message', '未知错误')}")
print(" 提示: Redis 是可选功能，不影响系统正常运行")
except ImportError:
print(" ⚠ Redis 库未安装，缓存功能将被禁用")
print(" 提示: 运行 'pip install redis hiredis' 安装 Redis 支持")
except Exception as e:
print(f" ⚠ Redis 初始化失败: {e}")
print(" 提示: Redis 是可选功能，不影响系统正常运行")
def check_database_status(self) -> Dict[str, Any]:
"""检查数据库状态"""
print("\n" + "=" * 80)

BIN
data/tsp_assistant.db → local_test.db
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Binary file not shown.

5992
logs/dashboard.log Normal file
View File

File diff suppressed because one or more lines are too long

18
logs/tsp_assistant.log
View File

@@ -1,9 +1,9 @@
2026-02-11 09:14:37,303 - src.core.database - ERROR - 数据库操作失败: 'search_frequency' is an invalid keyword argument for KnowledgeEntry
2026-02-11 09:23:37,045 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - location
2026-02-11 09:23:37,047 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - status
2026-02-11 09:23:37,049 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - battery
2026-02-11 09:23:37,051 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - engine
2026-02-11 09:23:37,053 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V002 - location
2026-02-11 09:23:37,055 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V002 - status
2026-02-11 09:23:37,057 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V002 - fault
2026-02-11 09:23:37,057 - src.vehicle.vehicle_data_manager - INFO - 示例车辆数据添加成功
2025-12-12 13:30:49,040 - src.core.database - ERROR - 数据库操作失败: 'search_frequency' is an invalid keyword argument for KnowledgeEntry
2025-12-12 13:31:25,399 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - location
2025-12-12 13:31:25,404 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - status
2025-12-12 13:31:25,409 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - battery
2025-12-12 13:31:25,414 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V001 - engine
2025-12-12 13:31:25,419 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V002 - location
2025-12-12 13:31:25,424 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V002 - status
2025-12-12 13:31:25,429 - src.vehicle.vehicle_data_manager - INFO - 添加车辆数据成功: V002 - fault
2025-12-12 13:31:25,429 - src.vehicle.vehicle_data_manager - INFO - 示例车辆数据添加成功

175
quick_push.bat Normal file
View File

@@ -0,0 +1,175 @@
@echo off
chcp 65001 >nul
setlocal enabledelayedexpansion
echo 🚀 TSP智能助手 - 快速推送
echo.
:: 检查Git状态
git status --porcelain >nul 2>&1
if %errorlevel% neq 0 (
echo ❌ Git未初始化或不在Git仓库中
pause
exit /b 1
)
:: 检查是否有参数
if "%1"=="" (
:: 智能生成提交信息
echo 📝 分析markdown文件并生成提交信息...
:: 检查是否有markdown文件修改
set md_files=
for /f "tokens=*" %%f in ('git diff --name-only --cached 2^>nul ^| findstr /i "\.md$"') do (
set md_files=!md_files! %%f
)
for /f "tokens=*" %%f in ('git diff --name-only 2^>nul ^| findstr /i "\.md$"') do (
set md_files=!md_files! %%f
)
set commit_msg=
if not "%md_files%"=="" (
echo 📄 检测到markdown文件修改: %md_files%
:: 提取markdown文件的主要内容
set commit_title=
set commit_type=docs
:: 检查是否有修复相关内容
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /i "修复\|解决\|问题\|错误"') do (
set commit_type=fix
set commit_title=修复问题
goto :found_fix
)
)
)
:: 检查是否有新功能相关内容
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /i "功能\|新增\|添加\|实现"') do (
set commit_type=feat
set commit_title=新增功能
goto :found_feature
)
)
)
:: 检查是否有优化相关内容
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /i "优化\|性能\|改进\|提升"') do (
set commit_type=perf
set commit_title=性能优化
goto :found_optimization
)
)
)
:: 提取文件标题
for %%f in (%md_files%) do (
if exist "%%f" (
for /f "tokens=*" %%l in ('type "%%f" ^| findstr /n "^#" ^| head -1') do (
set line=%%l
set line=!line:*:=!
set line=!line:# =!
set line=!line:## =!
if "!line!" neq "" (
set commit_title=!line!
goto :found_title
)
)
)
)
:found_fix
:found_feature
:found_optimization
:found_title
if "%commit_title%"=="" (
set commit_title=更新文档记录
)
:: 生成提交信息
set commit_msg=%commit_type%: %commit_title%
) else (
echo ℹ️ 没有检测到markdown文件修改
set commit_msg=feat: 快速提交 - %date% %time%
)
) else (
set commit_msg=%1
)
echo 📝 提交信息: %commit_msg%
echo.
:: 检查是否有更改需要提交（含未跟踪文件）
setlocal enabledelayedexpansion
git diff --quiet
set has_unstaged=%errorlevel%
git diff --cached --quiet
set has_staged=%errorlevel%
set has_untracked=0
for /f "delims=" %%f in ('git ls-files --others --exclude-standard') do set has_untracked=1
if %has_unstaged% equ 0 if %has_staged% equ 0 if %has_untracked% equ 0 (
echo ℹ️ 没有检测到任何更改，无需提交
echo.
echo ✅ 工作区干净，无需推送
pause
exit /b 0
)
:: 执行推送
echo.
echo 📤 开始推送流程...
echo 📝 提交信息: %commit_msg%
git add .
if %errorlevel% neq 0 (
echo ❌ 添加文件失败
pause
exit /b 1
)
git commit -m "%commit_msg%"
if %errorlevel% neq 0 (
echo ❌ 提交失败
pause
exit /b 1
)
git fetch origin main
git push origin main
if %errorlevel% equ 0 (
echo.
echo ✅ 推送完成！
echo 📊 最新提交:
git log --oneline -1
) else (
echo.
echo ❌ 推送失败，尝试自动解决...
echo 🔄 执行: git pull origin main --rebase
git pull origin main --rebase
if %errorlevel% equ 0 (
echo ✅ 重试推送...
git push origin main
if %errorlevel% equ 0 (
echo ✅ 推送成功！
echo 📊 最新提交:
git log --oneline -1
) else (
echo ❌ 重试推送失败，请手动处理
)
) else (
echo ❌ 自动rebase失败，请手动处理冲突后重试
)
)
echo.
pause

5
requirements.txt
View File

@@ -37,8 +37,6 @@ aiohttp==3.10.10
# asyncio是Python内置模块，不需要安装
# Redis缓存
redis==5.0.1
hiredis==2.3.2
# 测试框架
pytest==8.3.3
@@ -66,6 +64,3 @@ httpx==0.27.2
# 数据验证
pydantic==2.9.2
marshmallow==3.23.3
# 飞书官方 SDK（事件订阅 2.0 - 长连接模式）
lark-oapi==1.3.5

BIN
scripts/__pycache__/git_auto_commit.cpython-313.pyc Normal file
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306
scripts/deploy.sh Normal file
View File

@@ -0,0 +1,306 @@
#!/bin/bash
# TSP智能助手部署脚本
# 支持多环境部署、版本管理、自动备份
set -e # 遇到错误立即退出
# 颜色定义
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m' # No Color
# 日志函数
log_info() {
echo -e "${GREEN}[INFO]${NC} $1"
}
log_warn() {
echo -e "${YELLOW}[WARN]${NC} $1"
}
log_error() {
echo -e "${RED}[ERROR]${NC} $1"
}
# 检查依赖
check_dependencies() {
log_info "检查系统依赖..."
# 检查Python
if ! command -v python3 &> /dev/null; then
log_error "Python3 未安装"
exit 1
fi
# 检查pip
if ! command -v pip3 &> /dev/null; then
log_error "pip3 未安装"
exit 1
fi
# 检查Git
if ! command -v git &> /dev/null; then
log_error "Git 未安装"
exit 1
fi
log_info "依赖检查完成"
}
# 创建虚拟环境
setup_venv() {
local venv_path=$1
log_info "创建虚拟环境: $venv_path"
if [ ! -d "$venv_path" ]; then
python3 -m venv "$venv_path"
fi
source "$venv_path/bin/activate"
pip install --upgrade pip
log_info "虚拟环境设置完成"
}
# 安装依赖
install_dependencies() {
log_info "安装Python依赖..."
pip install -r requirements.txt
log_info "依赖安装完成"
}
# 数据库迁移
run_migrations() {
log_info "运行数据库迁移..."
# 检查数据库文件
if [ ! -f "tsp_assistant.db" ]; then
log_info "初始化数据库..."
python init_database.py
fi
log_info "数据库迁移完成"
}
# 创建systemd服务文件
create_systemd_service() {
local service_name=$1
local app_path=$2
local service_file="/etc/systemd/system/${service_name}.service"
log_info "创建systemd服务文件: $service_file"
sudo tee "$service_file" > /dev/null <<EOF
[Unit]
Description=TSP智能助手服务
After=network.target
[Service]
Type=simple
User=www-data
Group=www-data
WorkingDirectory=$app_path
Environment=PATH=$app_path/venv/bin
ExecStart=$app_path/venv/bin/python start_dashboard.py
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target
EOF
sudo systemctl daemon-reload
sudo systemctl enable "$service_name"
log_info "systemd服务创建完成"
}
# 创建nginx配置
create_nginx_config() {
local domain=$1
local app_port=$2
local config_file="/etc/nginx/sites-available/tsp_assistant"
log_info "创建nginx配置: $config_file"
sudo tee "$config_file" > /dev/null <<EOF
server {
listen 80;
server_name $domain;
location / {
proxy_pass http://127.0.0.1:$app_port;
proxy_set_header Host \$host;
proxy_set_header X-Real-IP \$remote_addr;
proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto \$scheme;
}
location /static {
alias $app_path/src/web/static;
expires 1y;
add_header Cache-Control "public, immutable";
}
}
EOF
# 启用站点
sudo ln -sf "$config_file" /etc/nginx/sites-enabled/
sudo nginx -t
sudo systemctl reload nginx
log_info "nginx配置完成"
}
# 主部署函数
deploy() {
local environment=${1:-production}
local domain=${2:-localhost}
local app_port=${3:-5000}
log_info "开始部署TSP智能助手到 $environment 环境"
# 设置部署路径
case $environment in
development)
DEPLOY_PATH="./dev_deploy"
SERVICE_NAME=""
;;
staging)
DEPLOY_PATH="/opt/tsp_assistant_staging"
SERVICE_NAME="tsp_assistant_staging"
;;
production)
DEPLOY_PATH="/opt/tsp_assistant"
SERVICE_NAME="tsp_assistant"
;;
*)
log_error "未知环境: $environment"
exit 1
;;
esac
# 检查依赖
check_dependencies
# 创建部署目录
log_info "创建部署目录: $DEPLOY_PATH"
sudo mkdir -p "$DEPLOY_PATH"
sudo chown $USER:$USER "$DEPLOY_PATH"
# 复制文件
log_info "复制应用文件..."
cp -r . "$DEPLOY_PATH/"
cd "$DEPLOY_PATH"
# 设置虚拟环境
setup_venv "venv"
# 安装依赖
install_dependencies
# 运行迁移
run_migrations
# 创建服务文件（非开发环境）
if [ "$environment" != "development" ] && [ -n "$SERVICE_NAME" ]; then
create_systemd_service "$SERVICE_NAME" "$DEPLOY_PATH"
create_nginx_config "$domain" "$app_port"
fi
log_info "部署完成！"
if [ "$environment" != "development" ]; then
log_info "启动服务..."
sudo systemctl start "$SERVICE_NAME"
sudo systemctl status "$SERVICE_NAME"
else
log_info "开发环境部署完成，使用以下命令启动："
log_info "cd $DEPLOY_PATH && source venv/bin/activate && python start_dashboard.py"
fi
}
# 回滚函数
rollback() {
local backup_name=$1
if [ -z "$backup_name" ]; then
log_error "请指定备份名称"
exit 1
fi
log_info "回滚到备份: $backup_name"
# 停止服务
sudo systemctl stop tsp_assistant
# 恢复备份
if [ -d "backups/$backup_name" ]; then
sudo rm -rf /opt/tsp_assistant
sudo cp -r "backups/$backup_name" /opt/tsp_assistant
sudo chown -R www-data:www-data /opt/tsp_assistant
# 重启服务
sudo systemctl start tsp_assistant
log_info "回滚完成"
else
log_error "备份不存在: $backup_name"
exit 1
fi
}
# 版本检查
check_version() {
log_info "检查版本信息..."
if [ -f "version.json" ]; then
local version=$(python3 -c "import json; print(json.load(open('version.json'))['version'])" 2>/dev/null || echo "unknown")
log_info "当前版本: $version"
else
log_warn "版本文件不存在"
fi
}
# 创建部署包
create_deployment_package() {
local package_name="tsp_assistant_$(date +%Y%m%d_%H%M%S).tar.gz"
log_info "创建部署包: $package_name"
# 排除不需要的文件
tar --exclude='.git' \
--exclude='__pycache__' \
--exclude='*.pyc' \
--exclude='.env' \
--exclude='logs/*' \
--exclude='backups/*' \
--exclude='dev_deploy' \
-czf "$package_name" .
log_info "部署包创建完成: $package_name"
echo "$package_name"
}
# 主函数
main() {
case ${1:-deploy} in
deploy)
check_version
deploy "$2" "$3" "$4"
;;
rollback)
rollback "$2"
;;
package)
create_deployment_package
;;
*)
echo "用法: $0 {deploy|rollback|package} [environment] [domain] [port]"
echo "环境: development, staging, production"
echo ""
echo "命令说明:"
echo " deploy - 部署到指定环境"
echo " rollback - 回滚到指定备份"
echo " package - 创建部署包"
exit 1
;;
esac
}
main "$@"

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#!/bin/bash
# TSP智能助手Docker部署脚本
set -e
# 颜色定义
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
# 日志函数
log_info() {
echo -e "${BLUE}[INFO]${NC} $1"
}
log_success() {
echo -e "${GREEN}[SUCCESS]${NC} $1"
}
log_warning() {
echo -e "${YELLOW}[WARNING]${NC} $1"
}
log_error() {
echo -e "${RED}[ERROR]${NC} $1"
}
# 检查Docker和Docker Compose
check_dependencies() {
log_info "检查依赖..."
if ! command -v docker &> /dev/null; then
log_error "Docker未安装，请先安装Docker"
exit 1
fi
if ! command -v docker-compose &> /dev/null; then
log_error "Docker Compose未安装，请先安装Docker Compose"
exit 1
fi
log_success "依赖检查通过"
}
# 创建必要的目录
create_directories() {
log_info "创建必要的目录..."
mkdir -p logs/nginx
mkdir -p monitoring/grafana/provisioning/datasources
mkdir -p monitoring/grafana/provisioning/dashboards
mkdir -p ssl
mkdir -p data
mkdir -p backups
mkdir -p uploads
mkdir -p config
log_success "目录创建完成"
}
# 构建镜像
build_images() {
log_info "构建Docker镜像..."
# 构建主应用镜像
docker-compose build --no-cache tsp-assistant
log_success "镜像构建完成"
}
# 启动服务
start_services() {
log_info "启动服务..."
# 启动基础服务（MySQL, Redis）
docker-compose up -d mysql redis
# 等待数据库启动
log_info "等待数据库启动..."
sleep 30
# 启动主应用
docker-compose up -d tsp-assistant
# 启动其他服务
docker-compose up -d nginx prometheus grafana
log_success "服务启动完成"
}
# 检查服务状态
check_services() {
log_info "检查服务状态..."
sleep 10
# 检查主应用
if curl -f http://localhost:5000/api/health &> /dev/null; then
log_success "TSP助手服务正常"
else
log_warning "TSP助手服务可能未完全启动"
fi
# 检查Nginx
if curl -f http://localhost/health &> /dev/null; then
log_success "Nginx服务正常"
else
log_warning "Nginx服务可能未完全启动"
fi
# 检查Prometheus
if curl -f http://localhost:9090 &> /dev/null; then
log_success "Prometheus服务正常"
else
log_warning "Prometheus服务可能未完全启动"
fi
# 检查Grafana
if curl -f http://localhost:3000 &> /dev/null; then
log_success "Grafana服务正常"
else
log_warning "Grafana服务可能未完全启动"
fi
}
# 显示服务信息
show_info() {
log_info "服务访问信息："
echo " TSP助手: http://localhost:5000"
echo " Nginx代理: http://localhost"
echo " Prometheus: http://localhost:9090"
echo " Grafana: http://localhost:3000 (admin/admin123456)"
echo " MySQL: localhost:3306 (root/root123456)"
echo " Redis: localhost:6379 (密码: redis123456)"
echo ""
log_info "查看日志命令："
echo " docker-compose logs -f tsp-assistant"
echo " docker-compose logs -f mysql"
echo " docker-compose logs -f redis"
echo " docker-compose logs -f nginx"
}
# 停止服务
stop_services() {
log_info "停止服务..."
docker-compose down
log_success "服务已停止"
}
# 清理资源
cleanup() {
log_info "清理Docker资源..."
docker system prune -f
log_success "清理完成"
}
# 主函数
main() {
case "${1:-start}" in
"start")
check_dependencies
create_directories
build_images
start_services
check_services
show_info
;;
"stop")
stop_services
;;
"restart")
stop_services
sleep 5
start_services
check_services
show_info
;;
"cleanup")
stop_services
cleanup
;;
"logs")
docker-compose logs -f "${2:-tsp-assistant}"
;;
"status")
docker-compose ps
;;
*)
echo "用法: $0 {start|stop|restart|cleanup|logs|status}"
echo " start - 启动所有服务"
echo " stop - 停止所有服务"
echo " restart - 重启所有服务"
echo " cleanup - 清理Docker资源"
echo " logs - 查看日志 (可选指定服务名)"
echo " status - 查看服务状态"
exit 1
;;
esac
}
# 执行主函数
main "$@"

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#!/bin/bash
# TSP智能助手监控脚本
# 配置变量
APP_NAME="tsp_assistant"
SERVICE_NAME="tsp_assistant"
HEALTH_URL="http://localhost:5000/api/health"
LOG_FILE="./logs/monitor.log"
ALERT_EMAIL="admin@example.com"
ALERT_PHONE="13800138000"
# 颜色定义
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m'
# 日志函数
log_info() {
echo -e "${GREEN}[$(date '+%Y-%m-%d %H:%M:%S')] INFO${NC} $1" | tee -a "$LOG_FILE"
}
log_warn() {
echo -e "${YELLOW}[$(date '+%Y-%m-%d %H:%M:%S')] WARN${NC} $1" | tee -a "$LOG_FILE"
}
log_error() {
echo -e "${RED}[$(date '+%Y-%m-%d %H:%M:%S')] ERROR${NC} $1" | tee -a "$LOG_FILE"
}
# 发送告警
send_alert() {
local message=$1
local level=$2
log_error "告警: $message"
# 发送邮件告警
if command -v mail &> /dev/null; then
echo "$message" | mail -s "[$level] TSP助手告警" "$ALERT_EMAIL"
fi
# 发送短信告警（需要配置短信服务）
# curl -X POST "https://api.sms.com/send" \
# -d "phone=$ALERT_PHONE" \
# -d "message=$message"
}
# 检查服务状态
check_service_status() {
if systemctl is-active --quiet "$SERVICE_NAME"; then
return 0
else
return 1
fi
}
# 检查健康状态
check_health() {
local response_code
response_code=$(curl -s -o /dev/null -w "%{http_code}" "$HEALTH_URL" 2>/dev/null)
if [ "$response_code" = "200" ]; then
return 0
else
return 1
fi
}
# 检查响应时间
check_response_time() {
local response_time
response_time=$(curl -s -o /dev/null -w "%{time_total}" "$HEALTH_URL" 2>/dev/null)
# 响应时间超过5秒认为异常
if (( $(echo "$response_time > 5.0" | bc -l) )); then
return 1
else
return 0
fi
}
# 检查系统资源
check_system_resources() {
local cpu_usage
local memory_usage
local disk_usage
# CPU使用率
cpu_usage=$(top -bn1 | grep "Cpu(s)" | awk '{print $2}' | awk -F'%' '{print $1}')
# 内存使用率
memory_usage=$(free | grep Mem | awk '{printf "%.2f", $3/$2 * 100.0}')
# 磁盘使用率
disk_usage=$(df -h / | awk 'NR==2 {print $5}' | sed 's/%//')
# 检查阈值
if (( $(echo "$cpu_usage > 80" | bc -l) )); then
send_alert "CPU使用率过高: ${cpu_usage}%" "HIGH"
fi
if (( $(echo "$memory_usage > 80" | bc -l) )); then
send_alert "内存使用率过高: ${memory_usage}%" "HIGH"
fi
if [ "$disk_usage" -gt 80 ]; then
send_alert "磁盘使用率过高: ${disk_usage}%" "HIGH"
fi
log_info "系统资源 - CPU: ${cpu_usage}%, 内存: ${memory_usage}%, 磁盘: ${disk_usage}%"
}
# 检查日志错误
check_log_errors() {
local log_file="./logs/tsp_assistant.log"
local error_count
if [ -f "$log_file" ]; then
# 检查最近5分钟的错误日志
error_count=$(tail -n 100 "$log_file" | grep -c "ERROR" 2>/dev/null || echo "0")
if [ "$error_count" -gt 10 ]; then
send_alert "最近5分钟错误日志过多: $error_count 条" "MEDIUM"
fi
fi
}
# 检查数据库连接
check_database() {
local db_file="./tsp_assistant.db"
if [ -f "$db_file" ]; then
# 检查数据库文件大小
local db_size
db_size=$(du -h "$db_file" | cut -f1)
log_info "数据库大小: $db_size"
# 检查数据库是否可读
if ! sqlite3 "$db_file" "SELECT 1;" > /dev/null 2>&1; then
send_alert "数据库连接失败" "CRITICAL"
return 1
fi
fi
return 0
}
# 自动重启服务
restart_service() {
log_warn "尝试重启服务..."
sudo systemctl restart "$SERVICE_NAME"
sleep 10
if check_service_status && check_health; then
log_info "服务重启成功"
return 0
else
log_error "服务重启失败"
return 1
fi
}
# 主监控循环
monitor_loop() {
local consecutive_failures=0
local max_failures=3
while true; do
log_info "开始监控检查..."
# 检查服务状态
if ! check_service_status; then
log_error "服务未运行"
send_alert "TSP助手服务未运行" "CRITICAL"
consecutive_failures=$((consecutive_failures + 1))
else
# 检查健康状态
if ! check_health; then
log_error "健康检查失败"
send_alert "TSP助手健康检查失败" "HIGH"
consecutive_failures=$((consecutive_failures + 1))
else
# 检查响应时间
if ! check_response_time; then
log_warn "响应时间过长"
send_alert "TSP助手响应时间过长" "MEDIUM"
fi
consecutive_failures=0
fi
fi
# 检查系统资源
check_system_resources
# 检查日志错误
check_log_errors
# 检查数据库
check_database
# 连续失败处理
if [ "$consecutive_failures" -ge "$max_failures" ]; then
log_error "连续失败次数达到阈值，尝试重启服务"
if restart_service; then
consecutive_failures=0
else
send_alert "TSP助手服务重启失败，需要人工干预" "CRITICAL"
fi
fi
# 等待下次检查
sleep 60
done
}
# 一次性检查
single_check() {
log_info "执行一次性健康检查..."
if check_service_status; then
log_info "✓ 服务运行正常"
else
log_error "✗ 服务未运行"
exit 1
fi
if check_health; then
log_info "✓ 健康检查通过"
else
log_error "✗ 健康检查失败"
exit 1
fi
if check_response_time; then
log_info "✓ 响应时间正常"
else
log_warn "⚠ 响应时间过长"
fi
check_system_resources
check_log_errors
check_database
log_info "健康检查完成"
}
# 主函数
main() {
# 创建日志目录
mkdir -p logs
case ${1:-monitor} in
monitor)
log_info "启动TSP助手监控服务..."
monitor_loop
;;
check)
single_check
;;
restart)
restart_service
;;
*)
echo "用法: $0 {monitor|check|restart}"
echo " monitor - 持续监控模式"
echo " check - 一次性健康检查"
echo " restart - 重启服务"
exit 1
;;
esac
}
# 执行主函数
main "$@"

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@echo off
REM TSP智能助手快速更新脚本 (Windows)
REM 支持热更新和完整更新
setlocal enabledelayedexpansion
REM 颜色定义
set "GREEN=[32m"
set "YELLOW=[33m"
set "RED=[31m"
set "NC=[0m"
REM 配置变量
set "APP_NAME=tsp_assistant"
set "DEPLOY_PATH=."
set "BACKUP_PATH=.\backups"
set "HEALTH_URL=http://localhost:5000/api/health"
REM 解析参数
set "ACTION=%1"
set "SOURCE_PATH=%2"
set "ENVIRONMENT=%3"
if "%ACTION%"=="" (
echo 用法: %0 {check^|hot-update^|full-update^|auto-update^|rollback} [源路径] [环境]
echo.
echo 命令说明:
echo check - 检查更新可用性
echo hot-update - 热更新（不重启服务）
echo full-update - 完整更新（重启服务）
echo auto-update - 自动更新（智能选择）
echo rollback - 回滚到指定备份
echo.
echo 环境: development, staging, production
exit /b 1
)
if "%ENVIRONMENT%"=="" set "ENVIRONMENT=production"
if "%SOURCE_PATH%"=="" set "SOURCE_PATH=."
REM 日志函数
:log_info
echo %GREEN%[INFO]%NC% %~1
goto :eof
:log_warn
echo %YELLOW%[WARN]%NC% %~1
goto :eof
:log_error
echo %RED%[ERROR]%NC% %~1
goto :eof
REM 检查更新可用性
:check_update
call :log_info "检查更新可用性..."
if not exist "%SOURCE_PATH%\version.json" (
call :log_error "源路径中未找到版本文件"
exit /b 1
)
REM 比较版本
for /f "tokens=2 delims=:" %%a in ('findstr "version" "%SOURCE_PATH%\version.json"') do (
set "NEW_VERSION=%%a"
set "NEW_VERSION=!NEW_VERSION: =!"
set "NEW_VERSION=!NEW_VERSION:"=!"
set "NEW_VERSION=!NEW_VERSION:,=!"
)
if exist "version.json" (
for /f "tokens=2 delims=:" %%a in ('findstr "version" "version.json"') do (
set "CURRENT_VERSION=%%a"
set "CURRENT_VERSION=!CURRENT_VERSION: =!"
set "CURRENT_VERSION=!CURRENT_VERSION:"=!"
set "CURRENT_VERSION=!CURRENT_VERSION:,=!"
)
) else (
set "CURRENT_VERSION=unknown"
)
if "!NEW_VERSION!"=="!CURRENT_VERSION!" (
call :log_info "没有更新可用 (当前版本: !CURRENT_VERSION!)"
) else (
call :log_info "发现更新: !CURRENT_VERSION! -> !NEW_VERSION!"
)
goto :eof
REM 创建备份
:create_backup
set "TIMESTAMP=%date:~0,4%%date:~5,2%%date:~8,2%_%time:~0,2%%time:~3,2%%time:~6,2%"
set "TIMESTAMP=!TIMESTAMP: =0!"
set "BACKUP_NAME=%APP_NAME%_backup_!TIMESTAMP!"
call :log_info "创建备份: !BACKUP_NAME!"
if not exist "%BACKUP_PATH%" mkdir "%BACKUP_PATH%"
mkdir "%BACKUP_PATH%\!BACKUP_NAME!"
REM 备份应用文件
if exist "%DEPLOY_PATH%" (
call :log_info "备份应用文件..."
xcopy "%DEPLOY_PATH%\*" "%BACKUP_PATH%\!BACKUP_NAME!\" /E /I /Y
)
REM 备份数据库
if exist "%DEPLOY_PATH%\tsp_assistant.db" (
call :log_info "备份数据库..."
mkdir "%BACKUP_PATH%\!BACKUP_NAME!\database"
copy "%DEPLOY_PATH%\tsp_assistant.db" "%BACKUP_PATH%\!BACKUP_NAME!\database\"
)
call :log_info "备份完成: !BACKUP_NAME!"
echo !BACKUP_NAME!
goto :eof
REM 热更新
:hot_update
call :log_info "开始热更新..."
REM 支持热更新的文件列表
set "HOT_UPDATE_FILES=src\web\static\js\dashboard.js src\web\static\css\style.css src\web\templates\dashboard.html src\web\app.py"
set "UPDATED_COUNT=0"
for %%f in (%HOT_UPDATE_FILES%) do (
if exist "%SOURCE_PATH%\%%f" (
call :log_info "更新文件: %%f"
if not exist "%DEPLOY_PATH%\%%f" mkdir "%DEPLOY_PATH%\%%f" 2>nul
copy "%SOURCE_PATH%\%%f" "%DEPLOY_PATH%\%%f" /Y >nul
set /a UPDATED_COUNT+=1
)
)
if !UPDATED_COUNT! gtr 0 (
call :log_info "热更新完成，更新了 !UPDATED_COUNT! 个文件"
) else (
call :log_info "没有文件需要热更新"
)
goto :eof
REM 完整更新
:full_update
call :log_info "开始完整更新..."
REM 创建备份
call :create_backup
set "BACKUP_NAME=!BACKUP_NAME!"
REM 停止服务（如果运行中）
call :log_info "停止服务..."
taskkill /f /im python.exe 2>nul || echo 服务未运行
REM 更新文件
call :log_info "更新应用文件..."
if exist "%DEPLOY_PATH%" rmdir /s /q "%DEPLOY_PATH%"
mkdir "%DEPLOY_PATH%"
xcopy "%SOURCE_PATH%\*" "%DEPLOY_PATH%\" /E /I /Y
REM 安装依赖
call :log_info "安装依赖..."
cd "%DEPLOY_PATH%"
if exist "requirements.txt" (
pip install -r requirements.txt
)
REM 运行数据库迁移
call :log_info "运行数据库迁移..."
if exist "init_database.py" (
python init_database.py
)
REM 启动服务
call :log_info "启动服务..."
start /b python start_dashboard.py
REM 等待服务启动
call :log_info "等待服务启动..."
timeout /t 15 /nobreak >nul
REM 健康检查
call :log_info "执行健康检查..."
set "RETRY_COUNT=0"
set "MAX_RETRIES=10"
:health_check_loop
if !RETRY_COUNT! geq !MAX_RETRIES! (
call :log_error "健康检查失败，开始回滚..."
call :rollback !BACKUP_NAME!
exit /b 1
)
curl -f "%HEALTH_URL%" >nul 2>&1
if !errorlevel! equ 0 (
call :log_info "健康检查通过！"
call :log_info "更新成功！"
call :log_info "备份名称: !BACKUP_NAME!"
exit /b 0
) else (
call :log_warn "健康检查失败，重试中... (!RETRY_COUNT!/!MAX_RETRIES!)"
set /a RETRY_COUNT+=1
timeout /t 5 /nobreak >nul
goto :health_check_loop
)
REM 回滚
:rollback
set "BACKUP_NAME=%1"
if "%BACKUP_NAME%"=="" (
call :log_error "请指定备份名称"
exit /b 1
)
call :log_info "开始回滚到备份: !BACKUP_NAME!"
if not exist "%BACKUP_PATH%\!BACKUP_NAME!" (
call :log_error "备份不存在: !BACKUP_NAME!"
exit /b 1
)
REM 停止服务
call :log_info "停止服务..."
taskkill /f /im python.exe 2>nul || echo 服务未运行
REM 恢复文件
call :log_info "恢复文件..."
if exist "%DEPLOY_PATH%" rmdir /s /q "%DEPLOY_PATH%"
mkdir "%DEPLOY_PATH%"
xcopy "%BACKUP_PATH%\!BACKUP_NAME!\*" "%DEPLOY_PATH%\" /E /I /Y
REM 恢复数据库
if exist "%BACKUP_PATH%\!BACKUP_NAME!\database\tsp_assistant.db" (
call :log_info "恢复数据库..."
copy "%BACKUP_PATH%\!BACKUP_NAME!\database\tsp_assistant.db" "%DEPLOY_PATH%\"
)
REM 启动服务
call :log_info "启动服务..."
cd "%DEPLOY_PATH%"
start /b python start_dashboard.py
REM 等待服务启动
timeout /t 15 /nobreak >nul
REM 健康检查
curl -f "%HEALTH_URL%" >nul 2>&1
if !errorlevel! equ 0 (
call :log_info "回滚成功！"
) else (
call :log_error "回滚后健康检查失败"
exit /b 1
)
goto :eof
REM 自动更新
:auto_update
call :log_info "开始自动更新..."
REM 尝试热更新
call :hot_update
if !errorlevel! equ 0 (
call :log_info "热更新成功"
exit /b 0
)
REM 热更新失败，进行完整更新
call :log_info "热更新失败，进行完整更新..."
call :full_update
goto :eof
REM 主逻辑
if "%ACTION%"=="check" (
call :check_update
) else if "%ACTION%"=="hot-update" (
call :hot_update
) else if "%ACTION%"=="full-update" (
call :full_update
) else if "%ACTION%"=="auto-update" (
call :auto_update
) else if "%ACTION%"=="rollback" (
call :rollback "%SOURCE_PATH%"
) else (
call :log_error "未知操作: %ACTION%"
exit /b 1
)
endlocal

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
TSP智能助手更新管理器
支持热更新、版本管理、回滚等功能
"""
import os
import sys
import json
import shutil
import subprocess
import time
import requests
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Tuple
class UpdateManager:
"""更新管理器"""
def __init__(self, config_file: str = "update_config.json"):
self.config_file = config_file
self.config = self._load_config()
self.version_manager = None
# 初始化版本管理器
try:
from version import VersionManager
self.version_manager = VersionManager()
except ImportError:
print("警告: 版本管理器不可用")
def _load_config(self) -> Dict:
"""加载更新配置"""
default_config = {
"app_name": "tsp_assistant",
"deploy_path": "/opt/tsp_assistant",
"backup_path": "./backups",
"service_name": "tsp_assistant",
"health_url": "http://localhost:5000/api/health",
"update_timeout": 300,
"rollback_enabled": True,
"auto_backup": True,
"hot_update_enabled": True,
"environments": {
"development": {
"path": "./dev_deploy",
"service_name": "",
"auto_restart": False
},
"staging": {
"path": "/opt/tsp_assistant_staging",
"service_name": "tsp_assistant_staging",
"auto_restart": True
},
"production": {
"path": "/opt/tsp_assistant",
"service_name": "tsp_assistant",
"auto_restart": True
}
}
}
if os.path.exists(self.config_file):
try:
with open(self.config_file, 'r', encoding='utf-8') as f:
config = json.load(f)
# 合并默认配置
default_config.update(config)
except Exception as e:
print(f"加载配置文件失败: {e}")
return default_config
def _save_config(self):
"""保存配置"""
try:
with open(self.config_file, 'w', encoding='utf-8') as f:
json.dump(self.config, f, indent=2, ensure_ascii=False)
except Exception as e:
print(f"保存配置文件失败: {e}")
def check_update_available(self, source_path: str) -> Tuple[bool, str, str]:
"""检查是否有更新可用"""
if not self.version_manager:
return False, "unknown", "unknown"
current_version = self.version_manager.get_version()
# 检查源路径的版本
try:
source_version_file = os.path.join(source_path, "version.json")
if os.path.exists(source_version_file):
with open(source_version_file, 'r', encoding='utf-8') as f:
source_info = json.load(f)
source_version = source_info.get("version", "unknown")
else:
return False, current_version, "unknown"
except Exception as e:
print(f"检查源版本失败: {e}")
return False, current_version, "unknown"
# 比较版本
if source_version != current_version:
return True, current_version, source_version
return False, current_version, source_version
def create_backup(self, environment: str = "production") -> str:
"""创建备份"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
backup_name = f"{self.config['app_name']}_backup_{timestamp}"
backup_path = os.path.join(self.config["backup_path"], backup_name)
print(f"创建备份: {backup_name}")
# 创建备份目录
os.makedirs(backup_path, exist_ok=True)
# 获取部署路径
env_config = self.config["environments"].get(environment, {})
deploy_path = env_config.get("path", self.config["deploy_path"])
# 备份应用文件
if os.path.exists(deploy_path):
print("备份应用文件...")
shutil.copytree(deploy_path, os.path.join(backup_path, "app"))
# 备份数据库
db_file = os.path.join(deploy_path, "tsp_assistant.db")
if os.path.exists(db_file):
print("备份数据库...")
os.makedirs(os.path.join(backup_path, "database"), exist_ok=True)
shutil.copy2(db_file, os.path.join(backup_path, "database", "tsp_assistant.db"))
# 保存备份信息
backup_info = {
"backup_name": backup_name,
"backup_path": backup_path,
"timestamp": timestamp,
"environment": environment,
"version": self.version_manager.get_version() if self.version_manager else "unknown",
"git_commit": self._get_git_commit(deploy_path)
}
with open(os.path.join(backup_path, "backup_info.json"), 'w', encoding='utf-8') as f:
json.dump(backup_info, f, indent=2, ensure_ascii=False)
print(f"备份完成: {backup_name}")
return backup_name
def _get_git_commit(self, path: str) -> str:
"""获取Git提交哈希"""
try:
result = subprocess.run(['git', 'rev-parse', 'HEAD'],
cwd=path, capture_output=True, text=True)
return result.stdout.strip()[:8] if result.returncode == 0 else "unknown"
except:
return "unknown"
def hot_update(self, source_path: str, environment: str = "production") -> bool:
"""热更新（不重启服务）"""
if not self.config["hot_update_enabled"]:
print("热更新未启用")
return False
print("开始热更新...")
env_config = self.config["environments"].get(environment, {})
deploy_path = env_config.get("path", self.config["deploy_path"])
# 检查哪些文件可以热更新
hot_update_files = [
"src/web/static/js/dashboard.js",
"src/web/static/css/style.css",
"src/web/templates/dashboard.html",
"src/web/app.py",
"src/knowledge_base/knowledge_manager.py",
"src/dialogue/realtime_chat.py"
]
updated_files = []
for file_path in hot_update_files:
source_file = os.path.join(source_path, file_path)
target_file = os.path.join(deploy_path, file_path)
if os.path.exists(source_file):
# 检查文件是否有变化
if not os.path.exists(target_file) or not self._files_equal(source_file, target_file):
print(f"更新文件: {file_path}")
os.makedirs(os.path.dirname(target_file), exist_ok=True)
shutil.copy2(source_file, target_file)
updated_files.append(file_path)
if updated_files:
print(f"热更新完成，更新了 {len(updated_files)} 个文件")
return True
else:
print("没有文件需要热更新")
return False
def _files_equal(self, file1: str, file2: str) -> bool:
"""比较两个文件是否相等"""
try:
with open(file1, 'rb') as f1, open(file2, 'rb') as f2:
return f1.read() == f2.read()
except:
return False
def full_update(self, source_path: str, environment: str = "production",
create_backup: bool = True) -> bool:
"""完整更新（重启服务）"""
print("开始完整更新...")
env_config = self.config["environments"].get(environment, {})
deploy_path = env_config.get("path", self.config["deploy_path"])
service_name = env_config.get("service_name", self.config["service_name"])
auto_restart = env_config.get("auto_restart", True)
# 创建备份
backup_name = None
if create_backup and self.config["auto_backup"]:
backup_name = self.create_backup(environment)
try:
# 停止服务
if auto_restart and service_name:
print(f"停止服务: {service_name}")
subprocess.run(['sudo', 'systemctl', 'stop', service_name], check=True)
# 更新文件
print("更新应用文件...")
if os.path.exists(deploy_path):
shutil.rmtree(deploy_path)
os.makedirs(deploy_path, exist_ok=True)
shutil.copytree(source_path, deploy_path, dirs_exist_ok=True)
# 设置权限
subprocess.run(['sudo', 'chown', '-R', 'www-data:www-data', deploy_path], check=True)
# 安装依赖
print("安装依赖...")
requirements_file = os.path.join(deploy_path, "requirements.txt")
if os.path.exists(requirements_file):
subprocess.run(['sudo', '-u', 'www-data', 'python', '-m', 'pip', 'install', '-r', requirements_file],
cwd=deploy_path, check=True)
# 运行数据库迁移
print("运行数据库迁移...")
init_script = os.path.join(deploy_path, "init_database.py")
if os.path.exists(init_script):
subprocess.run(['sudo', '-u', 'www-data', 'python', init_script],
cwd=deploy_path, check=True)
# 启动服务
if auto_restart and service_name:
print(f"启动服务: {service_name}")
subprocess.run(['sudo', 'systemctl', 'start', service_name], check=True)
# 等待服务启动
print("等待服务启动...")
time.sleep(15)
# 健康检查
if self._health_check():
print("更新成功！")
return True
else:
print("健康检查失败，开始回滚...")
if backup_name:
self.rollback(backup_name, environment)
return False
else:
print("更新完成（未重启服务）")
return True
except Exception as e:
print(f"更新失败: {e}")
if backup_name:
print("开始回滚...")
self.rollback(backup_name, environment)
return False
def _health_check(self) -> bool:
"""健康检查"""
health_url = self.config["health_url"]
max_retries = 10
retry_count = 0
while retry_count < max_retries:
try:
response = requests.get(health_url, timeout=5)
if response.status_code == 200:
return True
except:
pass
retry_count += 1
print(f"健康检查失败，重试中... ({retry_count}/{max_retries})")
time.sleep(5)
return False
def rollback(self, backup_name: str, environment: str = "production") -> bool:
"""回滚到指定备份"""
print(f"开始回滚到备份: {backup_name}")
env_config = self.config["environments"].get(environment, {})
deploy_path = env_config.get("path", self.config["deploy_path"])
service_name = env_config.get("service_name", self.config["service_name"])
auto_restart = env_config.get("auto_restart", True)
backup_path = os.path.join(self.config["backup_path"], backup_name)
if not os.path.exists(backup_path):
print(f"备份不存在: {backup_name}")
return False
try:
# 停止服务
if auto_restart and service_name:
print(f"停止服务: {service_name}")
subprocess.run(['sudo', 'systemctl', 'stop', service_name], check=True)
# 恢复文件
print("恢复文件...")
app_backup_path = os.path.join(backup_path, "app")
if os.path.exists(app_backup_path):
if os.path.exists(deploy_path):
shutil.rmtree(deploy_path)
shutil.copytree(app_backup_path, deploy_path)
# 恢复数据库
db_backup_path = os.path.join(backup_path, "database", "tsp_assistant.db")
if os.path.exists(db_backup_path):
print("恢复数据库...")
shutil.copy2(db_backup_path, os.path.join(deploy_path, "tsp_assistant.db"))
# 设置权限
subprocess.run(['sudo', 'chown', '-R', 'www-data:www-data', deploy_path], check=True)
# 启动服务
if auto_restart and service_name:
print(f"启动服务: {service_name}")
subprocess.run(['sudo', 'systemctl', 'start', service_name], check=True)
# 等待服务启动
time.sleep(15)
# 健康检查
if self._health_check():
print("回滚成功！")
return True
else:
print("回滚后健康检查失败")
return False
else:
print("回滚完成（未重启服务）")
return True
except Exception as e:
print(f"回滚失败: {e}")
return False
def list_backups(self) -> List[Dict]:
"""列出所有备份"""
backups = []
backup_dir = self.config["backup_path"]
if os.path.exists(backup_dir):
for item in os.listdir(backup_dir):
backup_path = os.path.join(backup_dir, item)
if os.path.isdir(backup_path):
info_file = os.path.join(backup_path, "backup_info.json")
if os.path.exists(info_file):
try:
with open(info_file, 'r', encoding='utf-8') as f:
backup_info = json.load(f)
backups.append(backup_info)
except:
pass
return sorted(backups, key=lambda x: x.get("timestamp", ""), reverse=True)
def auto_update(self, source_path: str, environment: str = "production") -> bool:
"""自动更新（智能选择热更新或完整更新）"""
print("开始自动更新...")
# 检查是否有更新
has_update, current_version, new_version = self.check_update_available(source_path)
if not has_update:
print("没有更新可用")
return True
print(f"发现更新: {current_version} -> {new_version}")
# 尝试热更新
if self.hot_update(source_path, environment):
print("热更新成功")
return True
# 热更新失败，进行完整更新
print("热更新失败，进行完整更新...")
return self.full_update(source_path, environment)
def main():
"""命令行接口"""
import argparse
parser = argparse.ArgumentParser(description='TSP智能助手更新管理器')
parser.add_argument('action', choices=['check', 'hot-update', 'full-update', 'auto-update', 'rollback', 'list-backups'],
help='要执行的操作')
parser.add_argument('--source', help='源路径')
parser.add_argument('--environment', choices=['development', 'staging', 'production'],
default='production', help='目标环境')
parser.add_argument('--backup', help='备份名称（用于回滚）')
parser.add_argument('--no-backup', action='store_true', help='跳过备份')
args = parser.parse_args()
um = UpdateManager()
if args.action == 'check':
if not args.source:
print("错误: 需要指定源路径")
sys.exit(1)
has_update, current, new = um.check_update_available(args.source)
if has_update:
print(f"有更新可用: {current} -> {new}")
else:
print(f"没有更新可用 (当前版本: {current})")
elif args.action == 'hot-update':
if not args.source:
print("错误: 需要指定源路径")
sys.exit(1)
success = um.hot_update(args.source, args.environment)
sys.exit(0 if success else 1)
elif args.action == 'full-update':
if not args.source:
print("错误: 需要指定源路径")
sys.exit(1)
success = um.full_update(args.source, args.environment, not args.no_backup)
sys.exit(0 if success else 1)
elif args.action == 'auto-update':
if not args.source:
print("错误: 需要指定源路径")
sys.exit(1)
success = um.auto_update(args.source, args.environment)
sys.exit(0 if success else 1)
elif args.action == 'rollback':
if not args.backup:
print("错误: 需要指定备份名称")
sys.exit(1)
success = um.rollback(args.backup, args.environment)
sys.exit(0 if success else 1)
elif args.action == 'list-backups':
backups = um.list_backups()
if backups:
print("可用备份:")
for backup in backups:
print(f" {backup['backup_name']} - {backup['timestamp']} - {backup.get('version', 'unknown')}")
else:
print("没有找到备份")
if __name__ == "__main__":
main()

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#!/bin/bash
# TSP智能助手升级脚本
set -e
# 颜色定义
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
# 日志函数
log_info() {
echo -e "${GREEN}[INFO]${NC} $1"
}
log_warn() {
echo -e "${YELLOW}[WARN]${NC} $1"
}
log_error() {
echo -e "${RED}[ERROR]${NC} $1"
}
log_step() {
echo -e "${BLUE}[STEP]${NC} $1"
}
# 配置变量
APP_NAME="tsp_assistant"
BACKUP_DIR="./backups"
DEPLOY_PATH="/opt/tsp_assistant"
SERVICE_NAME="tsp_assistant"
HEALTH_URL="http://localhost:5000/api/health"
# 检查参数
if [ $# -lt 1 ]; then
echo "用法: $0 <新版本路径> [选项]"
echo "选项:"
echo " --force 强制升级，跳过确认"
echo " --no-backup 跳过备份"
echo " --rollback 回滚到指定备份"
exit 1
fi
NEW_VERSION_PATH=$1
FORCE_UPGRADE=false
SKIP_BACKUP=false
ROLLBACK_MODE=false
# 解析参数
while [[ $# -gt 1 ]]; do
case $2 in
--force)
FORCE_UPGRADE=true
;;
--no-backup)
SKIP_BACKUP=true
;;
--rollback)
ROLLBACK_MODE=true
;;
*)
log_error "未知选项: $2"
exit 1
;;
esac
shift
done
# 回滚功能
rollback() {
local backup_name=$1
if [ -z "$backup_name" ]; then
log_error "请指定备份名称"
exit 1
fi
log_step "开始回滚到备份: $backup_name"
# 检查备份是否存在
if [ ! -d "$BACKUP_DIR/$backup_name" ]; then
log_error "备份不存在: $backup_name"
log_info "可用备份列表:"
ls -la "$BACKUP_DIR" | grep backup
exit 1
fi
# 停止服务
log_info "停止服务..."
sudo systemctl stop "$SERVICE_NAME" || true
# 恢复文件
log_info "恢复文件..."
sudo rm -rf "$DEPLOY_PATH"
sudo cp -r "$BACKUP_DIR/$backup_name" "$DEPLOY_PATH"
sudo chown -R www-data:www-data "$DEPLOY_PATH"
# 恢复数据库
if [ -f "$BACKUP_DIR/$backup_name/database/tsp_assistant.db" ]; then
log_info "恢复数据库..."
sudo cp "$BACKUP_DIR/$backup_name/database/tsp_assistant.db" "$DEPLOY_PATH/"
fi
# 启动服务
log_info "启动服务..."
sudo systemctl start "$SERVICE_NAME"
# 等待服务启动
sleep 10
# 健康检查
if curl -f "$HEALTH_URL" > /dev/null 2>&1; then
log_info "回滚成功！"
else
log_error "回滚后健康检查失败"
exit 1
fi
}
# 创建备份
create_backup() {
local timestamp=$(date +"%Y%m%d_%H%M%S")
local backup_name="${APP_NAME}_backup_${timestamp}"
local backup_path="$BACKUP_DIR/$backup_name"
log_step "创建备份: $backup_name"
# 创建备份目录
mkdir -p "$backup_path"
# 备份应用文件
if [ -d "$DEPLOY_PATH" ]; then
log_info "备份应用文件..."
cp -r "$DEPLOY_PATH"/* "$backup_path/"
fi
# 备份数据库
if [ -f "$DEPLOY_PATH/tsp_assistant.db" ]; then
log_info "备份数据库..."
mkdir -p "$backup_path/database"
cp "$DEPLOY_PATH/tsp_assistant.db" "$backup_path/database/"
fi
# 保存备份信息
cat > "$backup_path/backup_info.json" << EOF
{
"backup_name": "$backup_name",
"backup_path": "$backup_path",
"timestamp": "$timestamp",
"version": "$(cd "$DEPLOY_PATH" && python version.py version 2>/dev/null || echo "unknown")",
"git_commit": "$(cd "$DEPLOY_PATH" && git rev-parse HEAD 2>/dev/null | cut -c1-8 || echo "unknown")"
}
EOF
log_info "备份完成: $backup_name"
echo "$backup_name"
}
# 升级功能
upgrade() {
local new_version_path=$1
log_step "开始升级TSP智能助手"
# 检查新版本路径
if [ ! -d "$new_version_path" ]; then
log_error "新版本路径不存在: $new_version_path"
exit 1
fi
# 检查当前版本
if [ -d "$DEPLOY_PATH" ]; then
local current_version=$(cd "$DEPLOY_PATH" && python version.py version 2>/dev/null || echo "unknown")
log_info "当前版本: $current_version"
else
log_warn "当前部署路径不存在: $DEPLOY_PATH"
fi
# 检查新版本
local new_version=$(cd "$new_version_path" && python version.py version 2>/dev/null || echo "unknown")
log_info "新版本: $new_version"
# 确认升级
if [ "$FORCE_UPGRADE" = false ]; then
echo -n "确认升级到版本 $new_version? (y/N): "
read -r response
if [[ ! "$response" =~ ^[Yy]$ ]]; then
log_info "升级取消"
exit 0
fi
fi
# 创建备份
local backup_name=""
if [ "$SKIP_BACKUP" = false ]; then
backup_name=$(create_backup)
fi
# 停止服务
log_step "停止服务..."
sudo systemctl stop "$SERVICE_NAME" || true
# 升级文件
log_step "升级应用文件..."
sudo rm -rf "$DEPLOY_PATH"
sudo mkdir -p "$DEPLOY_PATH"
sudo cp -r "$new_version_path"/* "$DEPLOY_PATH/"
sudo chown -R www-data:www-data "$DEPLOY_PATH"
# 安装依赖
log_step "安装依赖..."
cd "$DEPLOY_PATH"
sudo -u www-data python -m pip install -r requirements.txt
# 运行数据库迁移
log_step "运行数据库迁移..."
sudo -u www-data python init_database.py || true
# 启动服务
log_step "启动服务..."
sudo systemctl start "$SERVICE_NAME"
# 等待服务启动
log_info "等待服务启动..."
sleep 15
# 健康检查
log_step "执行健康检查..."
local retry_count=0
local max_retries=10
while [ $retry_count -lt $max_retries ]; do
if curl -f "$HEALTH_URL" > /dev/null 2>&1; then
log_info "健康检查通过！"
break
else
log_warn "健康检查失败，重试中... ($((retry_count + 1))/$max_retries)"
retry_count=$((retry_count + 1))
sleep 5
fi
done
if [ $retry_count -eq $max_retries ]; then
log_error "健康检查失败，开始回滚..."
if [ -n "$backup_name" ]; then
rollback "$backup_name"
else
log_error "没有备份可回滚"
exit 1
fi
else
log_info "升级成功！"
log_info "新版本: $new_version"
if [ -n "$backup_name" ]; then
log_info "备份名称: $backup_name"
fi
fi
}
# 主函数
main() {
if [ "$ROLLBACK_MODE" = true ]; then
rollback "$NEW_VERSION_PATH"
else
upgrade "$NEW_VERSION_PATH"
fi
}
# 执行主函数
main

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# TSP助手 - 基于大模型的AI客服机器人
__version__ = "1.0.0"
__author__ = "TSP Assistant Team"

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src/agent/auto_monitor.py
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@@ -5,7 +5,7 @@
实现Agent的主动调用功能
"""
import asyncio跳过系统检查，直接启动服务...
import asyncio
import logging
import threading
import time

33
src/agent/llm_client.py
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@@ -12,14 +12,12 @@ from typing import Dict, Any, Optional, List
from abc import ABC, abstractmethod
from dataclasses import dataclass
from src.config.unified_config import get_config
logger = logging.getLogger(__name__)
@dataclass
class LLMConfig:
"""LLM配置"""
provider: str
provider: str # openai, anthropic, local, etc.
api_key: str
base_url: Optional[str] = None
model: str = "gpt-3.5-turbo"
@@ -62,7 +60,7 @@ class OpenAIClient(BaseLLMClient):
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
if not self.client:
raise ImportError("OpenAI client not initialized. Please install the 'openai' package.")
return self._simulate_response(prompt)
try:
response = await self.client.chat.completions.create(
@@ -74,12 +72,12 @@ class OpenAIClient(BaseLLMClient):
return response.choices[0].message.content
except Exception as e:
logger.error(f"OpenAI API调用失败: {e}")
raise e
return self._simulate_response(prompt)
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""对话生成"""
if not self.client:
raise ImportError("OpenAI client not initialized. Please install the 'openai' package.")
return self._simulate_chat(messages)
try:
response = await self.client.chat.completions.create(
@@ -91,7 +89,7 @@ class OpenAIClient(BaseLLMClient):
return response.choices[0].message.content
except Exception as e:
logger.error(f"OpenAI Chat API调用失败: {e}")
raise e
return self._simulate_chat(messages)
def _simulate_response(self, prompt: str) -> str:
"""模拟响应"""
@@ -200,14 +198,11 @@ class LLMClientFactory:
@staticmethod
def create_client(config: LLMConfig) -> BaseLLMClient:
"""创建LLM客户端"""
provider = config.provider.lower()
# qwen 使用 OpenAI 兼容的 API
if provider in ["openai", "qwen"]:
if config.provider.lower() == "openai":
return OpenAIClient(config)
elif provider == "anthropic":
elif config.provider.lower() == "anthropic":
return AnthropicClient(config)
elif provider == "local":
elif config.provider.lower() == "local":
return LocalLLMClient(config)
else:
raise ValueError(f"不支持的LLM提供商: {config.provider}")
@@ -215,21 +210,15 @@ class LLMClientFactory:
class LLMManager:
"""LLM管理器"""
def __init__(self, config=None):
if config:
self.config = config
else:
# If no config is provided, fetch it from the unified config system
self.config = get_config().llm
self.client = LLMClientFactory.create_client(self.config)
def __init__(self, config: LLMConfig):
self.config = config
self.client = LLMClientFactory.create_client(config)
self.usage_stats = {
"total_requests": 0,
"total_tokens": 0,
"error_count": 0
}
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
try:

25
src/agent_assistant.py
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@@ -8,18 +8,15 @@ import logging
import asyncio
from typing import Dict, Any, List, Optional
from datetime import datetime
from src.config.unified_config import get_config
from src.agent.llm_client import LLMManager
logger = logging.getLogger(__name__)
class TSPAgentAssistant:
"""TSP Agent助手"""
"""TSP Agent助手 - 简化版本"""
def __init__(self):
def __init__(self, llm_config=None):
# 初始化基础功能
config = get_config()
self.llm_manager = LLMManager(config.llm)
self.llm_config = llm_config
self.is_agent_mode = True
self.execution_history = []
@@ -342,8 +339,6 @@ class TSPAgentAssistant:
import os
import mimetypes
logger.info(f"开始处理知识库上传文件: {filename}")
# 检查文件类型
mime_type, _ = mimetypes.guess_type(file_path)
file_ext = os.path.splitext(filename)[1].lower()
@@ -351,30 +346,22 @@ class TSPAgentAssistant:
# 读取文件内容
content = self._read_file_content(file_path, file_ext)
if not content:
logger.error(f"文件读取失败或内容为空: {filename}")
return {"success": False, "error": "无法读取文件内容"}
logger.info(f"文件读取成功: {filename}, 字符数={len(content)}")
# 使用简化的知识提取
logger.info(f"正在对文件内容进行 AI 知识提取...")
knowledge_entries = self._extract_knowledge_from_content(content, filename)
logger.info(f"知识提取完成: 共提取出 {len(knowledge_entries)} 个潜在条目")
# 保存到知识库
saved_count = 0
for i, entry in enumerate(knowledge_entries):
try:
logger.info(f"正在保存知识条目 [{i+1}/{len(knowledge_entries)}]: {entry.get('question', '')[:30]}...")
# 这里在实际项目中应当注入知识库管理器的保存逻辑
# 但在当前简化版本中仅记录日志
logger.info(f"保存知识条目 {i+1}: {entry.get('question', '')[:50]}...")
# 这里应该调用知识库管理器保存
saved_count += 1
logger.info(f"知识条目 {i+1} 保存成功")
except Exception as save_error:
logger.error(f"保存知识条目 {i+1} 时出错: {save_error}")
logger.info(f"文件处理任务结束: {filename}, 成功入库 {saved_count} 条")
return {
"success": True,
"knowledge_count": saved_count,

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