fix(ci): AI Code Review fail-open - LLM失败/异常时不阻塞合并
CI/CD Pipeline / Build Staging API Image (pull_request) Has been skipped
CI/CD Pipeline / Build Staging Web Image (pull_request) Has been skipped
CI/CD Pipeline / Build Staging Worker Image (pull_request) Has been skipped
CI/CD Pipeline / Deploy Staging (Watchtower auto-deploy) (pull_request) Has been skipped
CI/CD Pipeline / Build Production API Image (pull_request) Has been skipped
CI/CD Pipeline / Build Production Web Image (pull_request) Has been skipped
Preview Deploy / Deploy Preview Environment (pull_request) Failing after 38s
CI/CD Pipeline / Build Production Worker Image (pull_request) Has been skipped
CI/CD Pipeline / Staging API Integration Tests (pull_request) Has been skipped
CI/CD Pipeline / Staging E2E Tests (pull_request) Has been skipped
CI/CD Pipeline / ACR Image Cleanup (pull_request) Has been skipped
CI/CD Pipeline / Deploy Production (pull_request) Has been skipped
CI/CD Pipeline / Canary Release to Production (pull_request) Has been skipped
CI/CD Pipeline / Production Browser E2E (pull_request) Has been skipped
CI/CD Pipeline / Check if frontend-only change (pull_request) Successful in 55s
CI/CD Pipeline / PR Build Web Image (pull_request) Successful in 1m19s
CI/CD Pipeline / PR Build Worker Image (pull_request) Successful in 1m20s
CI/CD Pipeline / PR Build API Image (pull_request) Successful in 1m21s
CI/CD Pipeline / Validate - Migration (alembic) (pull_request) Successful in 1m27s
CI/CD Pipeline / Frontend Lint (pull_request) Successful in 1m45s
CI/CD Pipeline / Validate - Type Check (mypy) (pull_request) Successful in 1m45s
CI/CD Pipeline / Frontend Unit Tests (pull_request) Has been skipped
PR Automation / Auto Approve on CI Green (pull_request) Successful in 4m23s
CI/CD Pipeline / Validate - Code Quality (pull_request) Successful in 4m55s
AI Code Review / AI Code Review (pull_request) Successful in 6m52s
CI/CD Pipeline / Unit Tests (pull_request) Successful in 3m28s
CI/CD Pipeline / Integration Tests (pull_request) Successful in 2m22s
CI/CD Pipeline / CI Gate (pull_request) Successful in 7s
PR Automation / Auto Merge on CI Green + Approved (pull_request) Failing after 14m54s
Preview Cleanup / Cleanup Preview Environment (pull_request) Successful in 22s
ACR Cleanup / ACR Image Cleanup (pull_request_target) Successful in 25s

修复两处违反fail-open原则的bug:
1. LLM调用失败返回None时 exit 1 → exit 0
2. 未捕获异常时 exit 1 → exit 0

fail-open原则:AI审查是辅助手段,不能因自身故障阻塞正常开发。
This commit is contained in:
2026-07-28 08:59:40 +08:00
parent 424c736275
commit 71d062ba63
+780
View File
@@ -0,0 +1,780 @@
#!/usr/bin/env python3
"""
CI Code Review Script
- 从 Gitea 获取 PR diff
- 调用 LLM 进行代码审查
- 将审查结果写回 PR 评论
"""
import argparse
import json
import logging
import os
import re
import sys
from typing import Optional, Tuple
import requests
# ============== 日志配置 ==============
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s] [%(levelname)s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger("ci_code_review")
# ============== 常量配置 ==============
# diff 最大字符数(超过则截断)
MAX_DIFF_CHARS = int(os.getenv("MAX_DIFF_CHARS", "30000"))
# LLM 调用超时时间(秒)
LLM_TIMEOUT = int(os.getenv("LLM_TIMEOUT", "120"))
# Gitea API 超时时间(秒)
GITEA_TIMEOUT = int(os.getenv("GITEA_TIMEOUT", "30"))
# 最大重试次数
MAX_RETRIES = int(os.getenv("MAX_RETRIES", "2"))
# LLM 提供商: openai (OpenAI兼容) / coze (扣子原生Bot API)
LLM_PROVIDER = os.getenv("LLM_PROVIDER", "coze").lower()
# ============== 工具函数 ==============
def truncate_diff(diff_text: str, max_chars: int) -> Tuple[str, bool]:
"""
截断过大的 diff 内容,避免超出 LLM 上下文限制。
优先保留文件头和前面的变更,末尾加提示。
"""
if len(diff_text) <= max_chars:
return diff_text, False
# 找到一个合适的截断位置(尽量在文件边界)
truncated = diff_text[:max_chars]
# 尝试在最后一个 "diff --git" 处截断,避免截断到一半
last_file_boundary = truncated.rfind("\ndiff --git ")
if last_file_boundary > max_chars // 2:
truncated = truncated[:last_file_boundary]
truncated += (
f"\n\n... [DIFF TRUNCATED] 原始 diff 共 {len(diff_text)} 字符,"
f"已截断至 {len(truncated)} 字符,仅审查前半部分。\n"
)
return truncated, True
def get_env_or_fail(name: str) -> str:
"""从环境变量获取值,不存在则报错退出。"""
value = os.getenv(name)
if not value:
logger.error(f"环境变量 {name} 未设置")
sys.exit(1)
return value
# ============== Gitea API 相关 ==============
class GiteaClient:
"""Gitea API 客户端"""
def __init__(self, base_url: str, token: str, repo: str):
# 确保 base_url 以 / 结尾
self.base_url = base_url.rstrip("/") + "/"
self.token = token
self.repo = repo # 格式: owner/repo
self.session = requests.Session()
self.session.headers.update(
{
"Authorization": f"token {token}",
"Accept": "application/json",
"Content-Type": "application/json",
}
)
def _api_url(self, path: str) -> str:
"""拼接 API 路径"""
return f"{self.base_url}api/v1/repos/{self.repo}/{path.lstrip('/')}"
def get_pr_diff(self, pr_number: int) -> str:
"""
获取 PR 的 diff 内容。
Gitea API: GET /repos/{owner}/{repo}/pulls/{index}.diff
"""
url = self._api_url(f"pulls/{pr_number}.diff")
logger.info(f"获取 PR #{pr_number} diff: {url}")
resp = self.session.get(
url,
timeout=GITEA_TIMEOUT,
headers={
"Accept": "text/plain",
},
)
if resp.status_code != 200:
logger.error(f"获取 diff 失败: HTTP {resp.status_code} - {resp.text[:200]}")
raise RuntimeError(f"Failed to get PR diff: HTTP {resp.status_code}")
diff_text = resp.text
logger.info(f"获取到 diff,共 {len(diff_text)} 字符")
return diff_text
def get_pr_files(self, pr_number: int) -> list:
"""
获取 PR 修改的文件列表。
Gitea API: GET /repos/{owner}/{repo}/pulls/{index}/files
"""
url = self._api_url(f"pulls/{pr_number}/files")
logger.info(f"获取 PR #{pr_number} 文件列表")
resp = self.session.get(url, timeout=GITEA_TIMEOUT)
if resp.status_code != 200:
logger.warning(f"获取文件列表失败: HTTP {resp.status_code}")
return []
files = resp.json()
logger.info(f"PR 修改了 {len(files)} 个文件")
return files
def post_pr_comment(self, pr_number: int, body: str) -> bool:
"""
在 PR 上发布评论。
Gitea API: POST /repos/{owner}/{repo}/issues/{index}/comments
Gitea 中 PR 评论走 issues 接口)
"""
url = self._api_url(f"issues/{pr_number}/comments")
logger.info(f"发布 PR 评论: {url}")
payload = {"body": body}
resp = self.session.post(
url,
data=json.dumps(payload),
timeout=GITEA_TIMEOUT,
)
if resp.status_code not in (200, 201):
logger.error(f"发布评论失败: HTTP {resp.status_code} - {resp.text[:200]}")
return False
logger.info(f"评论发布成功,评论 ID: {resp.json().get('id', 'unknown')}")
return True
def get_existing_review_comments(self, pr_number: int, marker: str) -> list:
"""
获取 PR 上已有的 AI 审查评论 ID 列表(带标识 marker)。
"""
url = self._api_url(f"issues/{pr_number}/comments")
resp = self.session.get(url, timeout=GITEA_TIMEOUT)
if resp.status_code != 200:
logger.warning(f"获取评论列表失败: HTTP {resp.status_code}")
return []
comments = resp.json()
review_comment_ids = []
for c in comments:
body = c.get("body", "")
if marker in body:
review_comment_ids.append(c.get("id"))
logger.info(f"找到 {len(review_comment_ids)} 条旧的 AI 审查评论")
return review_comment_ids
def delete_pr_comment(self, pr_number: int, comment_id: int) -> bool:
"""
删除 PR 上的指定评论。
"""
url = self._api_url(f"issues/comments/{comment_id}")
resp = self.session.delete(url, timeout=GITEA_TIMEOUT)
if resp.status_code not in (200, 204):
logger.warning(f"删除评论 {comment_id} 失败: HTTP {resp.status_code}")
return False
return True
def create_commit_status(
self, sha: str, state: str, context: str, description: str = "", target_url: str = ""
) -> bool:
"""
给指定 commit 打 status。
state: pending / success / failure / error / warning
Gitea API: POST /repos/{owner}/{repo}/statuses/{sha}
"""
url = self._api_url(f"statuses/{sha}")
logger.info(f"设置 commit status: sha={sha[:12]}..., state={state}, context={context}")
payload = {
"state": state,
"context": context,
"description": description[:200] if description else "",
}
if target_url:
payload["target_url"] = target_url
resp = self.session.post(
url,
data=json.dumps(payload),
timeout=GITEA_TIMEOUT,
)
if resp.status_code not in (200, 201):
logger.error(f"设置 status 失败: HTTP {resp.status_code} - {resp.text[:200]}")
return False
logger.info(f"Status 设置成功: {context} = {state}")
return True
def call_llm_openai(
prompt: str,
llm_base_url: str,
llm_api_key: str,
llm_model: str,
) -> Optional[str]:
"""OpenAI 兼容模式调用"""
base_url = llm_base_url.rstrip("/") + "/"
api_url = f"{base_url}chat/completions"
headers = {
"Authorization": f"Bearer {llm_api_key}",
"Content-Type": "application/json",
}
payload = {
"model": llm_model,
"messages": [
{
"role": "system",
"content": "你是一位严谨的资深代码审查专家,擅长发现代码中的逻辑错误、安全隐患和性能问题。",
},
{
"role": "user",
"content": prompt,
},
],
"temperature": 0.3,
"max_tokens": 2048,
}
logger.info(f"调用 LLM (OpenAI兼容): {api_url}, model={llm_model}")
last_error = None
for attempt in range(MAX_RETRIES + 1):
try:
resp = requests.post(
api_url,
headers=headers,
json=payload,
timeout=LLM_TIMEOUT,
)
if resp.status_code != 200:
logger.warning(f"LLM 调用失败 (第 {attempt + 1} 次): " f"HTTP {resp.status_code} - {resp.text[:200]}")
last_error = f"HTTP {resp.status_code}"
continue
data = resp.json()
choices = data.get("choices", [])
if not choices:
logger.warning(f"LLM 返回空结果 (第 {attempt + 1} 次)")
last_error = "empty choices"
continue
content = choices[0].get("message", {}).get("content", "")
if not content.strip():
logger.warning(f"LLM 返回空内容 (第 {attempt + 1} 次)")
last_error = "empty content"
continue
logger.info(f"LLM 审查完成,结果长度: {len(content)} 字符")
return content
except requests.Timeout:
logger.warning(f"LLM 调用超时 (第 {attempt + 1} 次)")
last_error = "timeout"
except requests.RequestException as e:
logger.warning(f"LLM 调用异常 (第 {attempt + 1} 次): {e}")
last_error = str(e)
logger.error(f"LLM 调用最终失败: {last_error}")
return None
def call_llm_coze(
prompt: str,
llm_base_url: str,
llm_api_key: str,
llm_model: str,
coze_bot_id: str,
) -> Optional[str]:
"""扣子(Coze)原生 Bot API 调用(支持异步轮询)"""
import time
base_url = llm_base_url.rstrip("/") + "/"
api_url = f"{base_url}v3/chat"
headers = {
"Authorization": f"Bearer {llm_api_key}",
"Content-Type": "application/json",
}
payload = {
"bot_id": coze_bot_id,
"user_id": "ci-code-review-bot",
"stream": False,
"additional_messages": [
{
"role": "user",
"content": prompt,
"content_type": "text",
}
],
}
logger.info(f"调用 LLM (Coze): {api_url}, bot_id={coze_bot_id}")
last_error = None
for attempt in range(MAX_RETRIES + 1):
try:
resp = requests.post(
api_url,
headers=headers,
json=payload,
timeout=LLM_TIMEOUT,
)
if resp.status_code != 200:
logger.warning(f"Coze 调用失败 (第 {attempt + 1} 次): " f"HTTP {resp.status_code} - {resp.text[:300]}")
last_error = f"HTTP {resp.status_code}"
continue
data = resp.json()
chat_data = data.get("data", {})
chat_id = chat_data.get("id", "")
conversation_id = chat_data.get("conversation_id", "")
status = chat_data.get("status", "")
# Coze v3 API 异步:先返回 in_progress,需要轮询
if status == "in_progress" and conversation_id and chat_id:
logger.info(f"Coze 异步处理中,开始轮询... (chat_id={chat_id[:12]}...)")
# 轮询 message 列表接口(GET + query参数),最多等 LLM_TIMEOUT 秒
poll_url = f"{base_url}v3/chat/message/list"
poll_start = time.time()
poll_interval = 3 # 每3秒轮询一次
while time.time() - poll_start < LLM_TIMEOUT:
time.sleep(poll_interval)
poll_params = {
"chat_id": chat_id,
"conversation_id": conversation_id,
}
poll_resp = requests.get(
poll_url,
headers=headers,
params=poll_params,
timeout=GITEA_TIMEOUT,
)
if poll_resp.status_code != 200:
logger.debug(f"轮询返回 HTTP {poll_resp.status_code}: {poll_resp.text[:100]}")
continue
poll_data = poll_resp.json()
if poll_data.get("code", 0) != 0:
logger.debug(f"轮询返回错误: {poll_data.get('msg', '')}")
continue
messages = poll_data.get("data", []) or []
# 找assistant的answer消息
content = None
for msg in messages:
if msg.get("role") == "assistant" and msg.get("type") == "answer":
content = msg.get("content", "")
break
if content and content.strip():
logger.info(f"Coze 审查完成,结果长度: {len(content)} 字符")
return content
logger.warning(f"Coze 轮询超时 ({LLM_TIMEOUT}s),未拿到结果")
last_error = "poll timeout"
continue
# 同步返回的情况(兼容)
content = None
messages = chat_data.get("messages", []) or data.get("messages", [])
for msg in messages:
if msg.get("role") == "assistant" and msg.get("type") == "answer":
content = msg.get("content", "")
break
if not content:
content = chat_data.get("content") or data.get("content")
if not content:
choices = data.get("choices", [])
if choices:
content = choices[0].get("message", {}).get("content", "")
if not content or not content.strip():
logger.warning(f"Coze 返回空内容 (第 {attempt + 1} 次): {str(data)[:200]}")
last_error = "empty content"
continue
logger.info(f"Coze 审查完成,结果长度: {len(content)} 字符")
return content
except requests.Timeout:
logger.warning(f"Coze 调用超时 (第 {attempt + 1} 次)")
last_error = "timeout"
except requests.RequestException as e:
logger.warning(f"Coze 调用异常 (第 {attempt + 1} 次): {e}")
last_error = str(e)
logger.error(f"Coze 调用最终失败: {last_error}")
return None
def build_review_prompt(diff_text: str, pr_number: int, file_list: list) -> str:
"""
构建代码审查的 Prompt。
包含:PR 基本信息、修改文件列表、diff 内容、审查要求。
"""
# 提取文件名列表
file_names = [f.get("filename", "") for f in file_list] if file_list else []
file_list_str = "\n".join(f" - {fn}" for fn in file_names) if file_names else " (未获取到文件列表)"
prompt = f"""请作为资深代码审查专家,对以下 Pull Request 的代码变更进行严格审查。
## PR 基本信息
- PR 编号: #{pr_number}
- 修改文件数: {len(file_list) if file_list else '未知'}
## 修改文件列表
{file_list_str}
## 代码变更(diff
```diff
{diff_text}
```
## 审查要求
请从以下维度进行审查,重点关注**阻塞级问题**:
### 问题分级标准
- **🔴 阻塞级(BLOCKER)**:必须修复,否则不允许合并。包括:
1. **明显逻辑bug**:条件判断错误、死循环、返回值错误、空指针/None引用未处理、边界条件遗漏导致功能异常
2. **安全漏洞**:SQL注入、XSS、命令注入、敏感信息明文存储/泄露、权限绕过、认证缺失
3. **语法错误**:代码存在语法层面的错误,无法运行
4. **数据损坏风险**:可能导致数据丢失、数据不一致、脏数据写入的问题
- **💡 建议级(SUGGESTION)**:不阻塞合并,仅供参考改进。包括:
1. 命名不规范、代码风格问题
2. 最佳实践建议、设计模式优化
3. 格式问题(缩进、空行、import顺序等)
4. 代码可读性改进、注释补充
5. 非关键路径的轻微性能优化建议
6. 重复代码、过长函数等代码质量问题
1. **逻辑正确性**:是否有明显的逻辑错误、边界条件遗漏、空指针/None引用风险
2. **异常处理**:异常捕获是否合理,是否有裸except,错误处理是否完善
3. **参数校验**:函数入参、返回值是否有必要的校验
4. **代码质量**:是否有重复代码、命名不清晰、过于复杂的函数
5. **性能问题**:是否有明显的性能隐患(如循环内重复计算、不必要的数据库查询)
6. **安全问题**:是否有注入风险、敏感信息泄露、权限控制问题
## 输出格式
请使用以下格式输出,语言为中文。**必须严格按照格式输出,尤其是【阻塞级判定】部分**:
### 【阻塞级判定】
- 是否存在阻塞级问题:(是 / 否)
- 阻塞级问题数量:X 个
### 📊 审查概览
- 整体评价:(通过 / 有建议 / 需修改)
- 建议级问题数量:X 个
### 🔴 阻塞级问题(必须修复)
(如果没有阻塞级问题,写""
1. **[文件: 行号] 问题标题**
- 问题类型:(逻辑bug / 安全漏洞 / 语法错误 / 数据损坏风险)
- 问题描述:...
- 修改建议:...
### 💡 改进建议(不阻塞合并)
(如果没有建议,写""
1. **[文件: 行号] 建议标题**
- 具体内容:...
### ✅ 良好实践
(可选,列出值得肯定的地方)
请务必基于代码实际内容审查,不要编造不存在的问题。如果代码质量良好,直接给出通过结论即可。
**重要:【阻塞级判定】必须准确,只有确实存在严重问题时才写""。**
"""
return prompt
def parse_blocker_result(review_text: str) -> Tuple[bool, int]:
"""
从审查结果中解析是否存在阻塞级问题。
返回 (has_blocker, blocker_count)
"""
# 先找【阻塞级判定】部分的明确标记
pattern = r"【阻塞级判定】[\s\S]*?是否存在阻塞级问题[:]\s*(是|否)"
match = re.search(pattern, review_text)
if match:
has_blocker = match.group(1) == ""
else:
# fallback 1: 找"阻塞级问题数量"
count_pattern = r"阻塞级问题数量[:]\s*(\d+)"
count_match = re.search(count_pattern, review_text)
if count_match:
has_blocker = int(count_match.group(1)) > 0
else:
# fallback 2: 检查是否有"阻塞级问题"section且内容不是"无"
has_blocker = False
blocker_section = re.search(r"### 🔴 阻塞级问题[\s\S]*?(?=### |\Z)", review_text)
if blocker_section:
section_text = blocker_section.group(0)
# 如果有编号列表项,说明有问题
if re.search(r"\d+\.\s*\*\*", section_text):
has_blocker = True
# 提取数量
count_pattern = r"阻塞级问题数量[:]\s*(\d+)"
count_match = re.search(count_pattern, review_text)
blocker_count = int(count_match.group(1)) if count_match else (1 if has_blocker else 0)
logger.info(f"阻塞级问题解析: 存在={has_blocker}, 数量={blocker_count}")
return has_blocker, blocker_count
def call_llm_for_review(
diff_text: str,
pr_number: int,
file_list: list,
llm_base_url: str,
llm_api_key: str,
llm_model: str,
coze_bot_id: str = "",
) -> Optional[str]:
"""
调用 LLM 进行代码审查,返回审查结果文本。
失败时返回 None。
根据 LLM_PROVIDER 环境变量选择调用方式。
"""
prompt = build_review_prompt(diff_text, pr_number, file_list)
logger.info(f"Prompt 长度: {len(prompt)} 字符")
provider = LLM_PROVIDER
if provider == "coze":
return call_llm_coze(prompt, llm_base_url, llm_api_key, llm_model, coze_bot_id)
else:
# 默认 OpenAI 兼容
return call_llm_openai(prompt, llm_base_url, llm_api_key, llm_model)
# ============== 主流程 ==============
def main():
parser = argparse.ArgumentParser(description="CI AI 代码审查脚本")
parser.add_argument("--pr", type=int, help="PR 编号(也可通过 PR_NUMBER 环境变量)")
parser.add_argument("--repo", type=str, help="仓库名 owner/repo(也可通过 REPO_NAME 环境变量)")
parser.add_argument("--gitea-url", type=str, help="Gitea 地址(也可通过 GITEA_API_URL 环境变量)")
parser.add_argument("--gitea-token", type=str, help="Gitea Token(也可通过 GITEA_TOKEN 环境变量)")
parser.add_argument("--dry-run", action="store_true", help="只输出审查结果,不发表评论")
args = parser.parse_args()
# 读取配置
gitea_url = args.gitea_url or os.getenv("GITEA_API_URL") or os.getenv("GITEA_SERVER_URL")
gitea_token = args.gitea_token or os.getenv("GITEA_TOKEN")
repo_name = args.repo or os.getenv("REPO_NAME") or os.getenv("GITEA_REPO")
pr_number = args.pr or int(os.getenv("PR_NUMBER") or os.getenv("GITEA_PR_NUMBER") or 0)
llm_base_url = os.getenv("LLM_BASE_URL")
llm_api_key = os.getenv("LLM_API_KEY")
llm_model = os.getenv("LLM_MODEL", "")
coze_bot_id = os.getenv("COZE_BOT_ID", os.getenv("COZE_BOTID", ""))
# 根据 provider 设置默认值
provider = LLM_PROVIDER
if provider == "coze":
# 扣子模式:默认国内站,key 兼容多种环境变量名
if not llm_base_url:
llm_base_url = "https://api.coze.cn"
if not llm_api_key:
llm_api_key = os.getenv("COZE_API_KEY", "") or os.getenv("COZE_PAT", "")
else:
# OpenAI兼容模式:默认模型
if not llm_model:
llm_model = "gpt-4o-mini"
# 必要参数校验
missing = []
if not gitea_url:
missing.append("GITEA_API_URL")
if not gitea_token:
missing.append("GITEA_TOKEN")
if not repo_name:
missing.append("REPO_NAME")
if not pr_number:
missing.append("PR_NUMBER")
if not llm_base_url:
missing.append("LLM_BASE_URL")
if not llm_api_key:
missing.append("LLM_API_KEY")
if provider == "coze" and not coze_bot_id:
missing.append("COZE_BOT_ID (扣子模式需要)")
if missing:
logger.error(f"缺少必要配置: {', '.join(missing)}")
sys.exit(1)
logger.info(f"开始审查 PR #{pr_number},仓库: {repo_name}")
logger.info(f"Gitea: {gitea_url}")
logger.info(f"LLM: {llm_base_url} (model={llm_model})")
try:
# 1. 初始化 Gitea 客户端
gitea = GiteaClient(gitea_url, gitea_token, repo_name)
# 2. 获取 PR diff 和文件列表
try:
diff_text = gitea.get_pr_diff(pr_number)
file_list = gitea.get_pr_files(pr_number)
except Exception as e:
logger.error(f"获取 PR 信息失败: {e}")
sys.exit(1)
# 3. 过滤掉不需要审查的文件(如 lock 文件、生成的文件、二进制文件等)
skip_extensions = (
".lock",
".sum",
".min.js",
".min.css",
".map",
".png",
".jpg",
".jpeg",
".gif",
".svg",
".ico",
".woff",
".woff2",
".ttf",
".eot",
)
skipped_files = []
if file_list:
skipped_files = [
f.get("filename")
for f in file_list
if f.get("filename", "").endswith(skip_extensions) or f.get("status") == "removed"
]
if skipped_files:
logger.info(f"跳过 {len(skipped_files)} 个非文本/已删除文件: {', '.join(skipped_files[:5])}...")
# 实际从 diff 中移除跳过的文件(按文件边界切割)
if skipped_files:
diff_lines = diff_text.split("\n")
filtered_lines = []
current_file = None
skip_current = False
i = 0
while i < len(diff_lines):
line = diff_lines[i]
# 检测新文件开始: diff --git a/xxx b/xxx
if line.startswith("diff --git "):
# 提取文件名
parts = line.split(" ")
if len(parts) >= 4:
# b/ 后面的是目标文件名
current_file = parts[3][2:] if parts[3].startswith("b/") else parts[3]
skip_current = any(current_file == sf for sf in skipped_files) or any(
current_file.endswith(ext) for ext in skip_extensions
)
else:
skip_current = False
if not skip_current:
filtered_lines.append(line)
i += 1
original_len = len(diff_text)
diff_text = "\n".join(filtered_lines)
logger.info(f"Diff 过滤后: {original_len} -> {len(diff_text)} 字符 (减少 {original_len - len(diff_text)})")
# 4. 截断过大的 diff
diff_text, was_truncated = truncate_diff(diff_text, MAX_DIFF_CHARS)
if was_truncated:
logger.warning(f"Diff 过大,已截断至 {len(diff_text)} 字符")
# 5. 如果 diff 为空,直接跳过
if not diff_text.strip():
logger.info("Diff 为空,无需审查")
sys.exit(0)
# 6. 调用 LLM 审查
review_result = call_llm_for_review(
diff_text=diff_text,
pr_number=pr_number,
file_list=file_list,
llm_base_url=llm_base_url,
llm_api_key=llm_api_key,
llm_model=llm_model,
coze_bot_id=coze_bot_id,
)
if not review_result:
logger.error("LLM 审查失败")
sys.exit(0) # fail-open: LLM调用失败不阻塞合并
# 7. 加上审查时间和标识(便于识别是自动审查)
from datetime import datetime
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
marker = "<!-- AI_CODE_REVIEW_AUTO_COMMENT -->"
full_comment = f"""{review_result}
---
<sub>🤖 由 AI 代码审查机器人自动生成 | {timestamp} | 模型: {llm_model}</sub>
{marker}
"""
# 8. 输出审查结果到日志
logger.info("=" * 60)
logger.info("审查结果:")
for line in review_result.split("\n")[:30]:
logger.info(line)
if len(review_result.split("\n")) > 30:
logger.info(f"... 共 {len(review_result.split(chr(10)))}")
logger.info("=" * 60)
# 9. 发布评论(先删除旧的审查评论,避免刷屏)
if args.dry_run:
logger.info("--dry-run 模式,跳过发布评论")
print(full_comment)
else:
# 去重:删除之前的 AI 审查评论
old_comments = gitea.get_existing_review_comments(pr_number, marker)
if old_comments:
logger.info(f"找到 {len(old_comments)} 条旧的 AI 审查评论,先删除")
for cid in old_comments:
gitea.delete_pr_comment(pr_number, cid)
# 发布新评论
success = gitea.post_pr_comment(pr_number, full_comment)
if not success:
logger.error("评论发布失败")
sys.exit(1)
# 10. 解析阻塞级问题,用退出码决定 job 状态
# 有阻塞级问题 → exit 1 → job失败 → Gitea自动打failure status → 门禁拦截
# 无阻塞级问题 → exit 0 → job成功 → Gitea自动打success status
# LLM调用失败等异常 → exit 0 → fail-open,不阻塞正常开发
has_blocker, blocker_count = parse_blocker_result(review_result)
if has_blocker:
logger.error(f"检测到 {blocker_count} 个阻塞级问题,审查不通过")
logger.info("代码审查完成(失败)")
sys.exit(1)
else:
logger.info("无阻塞级问题,审查通过")
logger.info("代码审查完成(通过)")
sys.exit(0)
except Exception as e:
logger.exception(f"审查脚本发生未预期的异常: {e}")
sys.exit(0) # fail-open: 异常不阻塞正常开发
if __name__ == "__main__":
main()