ab1589921a
## 安全修复 (12项) - Webhook接口添加全局Token认证，过滤敏感请求头 - 修复JWT Base64 padding公式，防止签名验证绕过 - 数据库密码/飞书Token从源码移除，改为环境变量 - 工作流引擎添加路径遍历防护 (_resolve_safe_path) - eval()添加模板长度上限检查 - 审批API添加认证依赖 - 前端v-html增强XSS转义，console.log仅开发模式输出 - 500错误不再暴露内部异常详情 ## Agent运行时修复 (7项) - 删除_inject_knowledge_context中未定义db变量的finally块 - 工具执行添加try/except保护，异常不崩溃Agent - LLM重试计入budget计数器 - self_review异常时passed=False - max_iterations截断标记success=False - 工具参数JSON解析失败时记录警告日志 - run()开始时重置_llm_invocations计数器 ## 配置与基础设施 - DEBUG默认False，SQL_ECHO独立配置项 - init_db()补全13个缺失模型导入 - 新增WEBHOOK_AUTH_TOKEN/SQL_ECHO配置项 - 新增.env.example模板文件 ## 前端修复 (12项) - 登录改用URLSearchParams替代FormData - 401拦截器通过Pinia store统一清理状态 - SSE流超时从60s延长至300s - final/error事件时清除streamTimeout - localStorage聊天记录添加24h TTL - safeParseArgCount替代模板中裸JSON.parse - fetchUser 401时同时清除user对象 ## 新增模块 - 知识进化: knowledge_extractor/retriever/tasks - 数字孪生: shadow_executor/comparison模型 - 行为采集: behavior_middleware/collector/fingerprint_engine - 代码审查: code_review_agent/document_review_agent - 反馈学习: feedback_learner - 瓶颈检测/优化引擎/成本估算/需求估算 - 速率限制器 (rate_limiter) - Alembic迁移 015-020 ## 文档 - 商业化落地计划 - 8篇docs文档 (架构/API/部署/开发/贡献等) - Docker Compose生产配置 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
48 lines
2.3 KiB
Python
48 lines
2.3 KiB
Python
"""add knowledge_entries table
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Revision ID: 017_add_knowledge_entries
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Revises: 016_add_user_behavior_logs
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Create Date: 2026-05-10
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"""
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from alembic import op
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import sqlalchemy as sa
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from sqlalchemy.dialects.mysql import CHAR
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revision = "017_add_knowledge_entries"
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down_revision = "016_add_user_behavior_logs"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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op.create_table(
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"knowledge_entries",
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sa.Column("id", CHAR(36), primary_key=True, comment="知识条目ID"),
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sa.Column("title", sa.String(500), nullable=False, comment="知识标题"),
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sa.Column("category", sa.String(30), nullable=False, comment="类别"),
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sa.Column("tags", sa.JSON, nullable=True, comment="标签列表"),
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sa.Column("situation", sa.Text, nullable=True, comment="适用场景"),
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sa.Column("solution", sa.Text, nullable=True, comment="解决方案"),
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sa.Column("caveats", sa.Text, nullable=True, comment="注意事项"),
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sa.Column("source_execution_ids", sa.JSON, nullable=True, comment="原始执行日志ID"),
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sa.Column("source_agent_name", sa.String(200), nullable=True, comment="来源Agent"),
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sa.Column("source_model", sa.String(100), nullable=True, comment="来源模型"),
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sa.Column("embedding_text", sa.Text, nullable=True, comment="embedding文本"),
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sa.Column("embedding", sa.Text, nullable=True, comment="embedding向量"),
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sa.Column("retrieval_count", sa.Integer, default=0, comment="被检索次数"),
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sa.Column("success_rate", sa.Float, nullable=True, comment="应用成功率"),
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sa.Column("extracted_by", sa.String(100), nullable=True, comment="提取方式"),
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sa.Column("confidence", sa.Float, default=0.5, comment="提取置信度"),
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sa.Column("is_active", sa.Boolean, default=True, comment="是否启用"),
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sa.Column("created_at", sa.DateTime, comment="创建时间"),
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sa.Column("updated_at", sa.DateTime, comment="更新时间"),
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)
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op.create_index("ix_knowledge_entries_cat", "knowledge_entries", ["category"])
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op.create_index("ix_knowledge_entries_active", "knowledge_entries", ["is_active"])
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def downgrade() -> None:
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op.drop_index("ix_knowledge_entries_active", table_name="knowledge_entries")
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op.drop_index("ix_knowledge_entries_cat", table_name="knowledge_entries")
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op.drop_table("knowledge_entries")
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