ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter5/permission-embedded-data-objects/README.md

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+# Experiment 5-12: Permission-Embedded Data Objects / 实验 5-12：权限内嵌的数据对象（★★★）
+
+This project is the implementation companion for the **dynamic software**
+discussion in Chapter 5. It is adapted from the
+`PermissionEmbeddedDataObjects` prototype: the implementation code, scenarios,
+tests, and the targeted generated-code evaluator are included here; the paper,
+PDF, website, and large result files are intentionally left out.
+
+## English
+
+### Goal
+
+Show how an application whose business code may be generated or rewritten by an
+Agent can still enforce authorization and data integrity. The experiment makes
+the application layer deliberately small: generated code calls a stable object
+store, while permissions, validators, relationships, and consequences are
+declared with the data type and checked by the store on every operation.
+
+The deterministic demo exercises three cases:
+
+1. a valid hiring-pipeline update is accepted;
+2. generated code that skips a candidate status transition or writes an
+   out-of-range salary is rejected by the data-layer validator;
+3. a cross-tenant read is rejected by the permission boundary.
+
+### Technical plan
+
+The prototype is a Python middleware layer over PostgreSQL:
+
+- `pedo/core/models.py` defines `DataObject`, `ObjectType`, `PermissionRule`,
+  `AccessContext`, relationships, and reaction declarations;
+- `pedo/core/store.py` implements the three-tier pipeline: synchronous
+  permission checks and validators, persistence and referential-integrity
+  mechanics, then asynchronous reactions with a bounded depth;
+- `pedo/scenarios/` registers realistic hiring, project-management, and other
+  multi-tenant schemas with state machines and cross-object validators;
+- `run_targeted_eval.py` is an optional live comparison that asks models to
+  generate code for raw SQL and PEDO APIs on adversarial prompts, then checks
+  the resulting database state.
+
+The key comparison is not whether the generated handler contains a correct
+`if` statement. It is whether the same request is accepted or rejected when it
+reaches the stable data layer. The generated layer receives a scoped
+`AccessContext`; it does not receive a privileged database connection.
+
+### Run the deterministic demo
+
+PostgreSQL must be running and the database named by `PEDO_DSN` must be
+reachable. The default is `dbname=pedo_test`.
+
+```bash
+cd chapter5/permission-embedded-data-objects
+python -m pip install -r requirements.txt
+createdb pedo_test                 # if the database does not exist yet
+python demo.py
+pytest -q
+```
+
+Use another connection string with, for example,
+`PEDO_DSN='dbname=pedo_test host=localhost user=postgres'`.
+
+The optional live evaluator needs provider SDKs and credentials in addition to
+the core requirements:
+
+```bash
+python -m pip install anthropic openai
+DATAGUARDBENCH_DSN="$PEDO_DSN" python run_targeted_eval.py
+```
+
+Run that evaluator only in an isolated test database. It executes model-
+generated code by design and is not a production security boundary.
+
+## 中文
+
+### 实验目标
+
+验证业务代码可以动态生成或重写时，系统仍能保证权限和数据完整性。实验把应用层故意做得很薄：生成的代码只调用稳定的对象存储接口；权限规则、校验器、对象关系和后果声明附着在数据类型上，由对象存储在每次操作时统一检查。
+
+确定性演示包含三类操作：合法的招聘流程更新应当成功；跳过候选人状态机或写入超出职位范围的工资应由数据层拒绝；跨租户读取应由权限边界拒绝。
+
+### 技术方案
+
+项目是运行在 PostgreSQL 之上的 Python 中间层。`models.py` 定义数据对象、对象类型、权限规则和访问上下文；`store.py` 实现三层流水线：同步执行权限检查与校验器，完成持久化和引用完整性处理，再以受控深度异步执行 reactions（后果反应）。`scenarios/` 提供招聘、项目管理等带状态机、跨对象校验和多租户隔离的示例 schema。`run_targeted_eval.py` 则是可选的在线评测：让模型分别为裸 SQL 和 PEDO 接口生成代码，再用对抗性请求检查最终数据库状态。
+
+这个实验关注的不是生成的 handler 有没有写出一条正确的 `if`，而是同一请求到达稳定数据层后能否被可靠接受或拒绝。生成代码只能携带受限的 `AccessContext`，不能拿到可绕过规则的高权限数据库连接。
+
+### 运行
+
+先启动 PostgreSQL，并准备 `pedo_test` 数据库，然后执行：
+
+```bash
+cd chapter5/permission-embedded-data-objects
+python -m pip install -r requirements.txt
+createdb pedo_test
+python demo.py
+pytest -q
+```
+
+可通过 `PEDO_DSN` 指定其他 PostgreSQL 连接串。在线评测还需要安装
+`anthropic`、`openai` 并配置对应的 API 凭证；它会在隔离测试库中执行模型生成的代码，不应直接指向生产数据库。
+
+### 文件
+
+- `demo.py`：无需调用 LLM 的确定性演示；
+- `pedo/core/`：权限内嵌对象模型和三层对象存储；
+- `pedo/scenarios/`：招聘、项目管理及其他多租户场景；
+- `tests/`：核心权限、校验、租户隔离和反应机制测试；
+- `run_targeted_eval.py`：可选的 Agent 生成代码安全对照评测。