ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
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# Experiment 5-10: NL ERP Agent (NL → SQL, Artifact Mode) / 实验 5-10：自然语言交互的 ERP Agent（NL → SQL，artifact 模式）
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> Companion lab for *AI Agents in Depth*, Chapter 5 — Chinese NL → SQL executed by DB; LLM only produces the SQL **artifact**, never moves rows itself.
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> 《深入理解 AI Agent》第 5 章：中文自然语言转 SQL 由 DB 执行；LLM 只生成 SQL 制品，不搬运数据。
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← [Chapter 5 index / 返回第 5 章目录](../README.md)
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---
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## English
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### Overview
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Turn Chinese natural-language queries into SQL; the system executes and presents result tables. Core is **artifact mode**: the Agent only produces the SQL artifact; the database runs the query—**LLM never hauls rows**—saves tokens, avoids mental arithmetic errors, and still returns large result sets instantly.
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### Data model (two tables)
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- `employees`: employee id, name, department, level (higher number = higher rank), hire date, leave date (`NULL` = active)
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- `salaries`: employee id, pay date (one row per month, `YYYY-MM-01`), amount
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Data from `seed.py` with fixed seed 42, relative to “today”—**fully reproducible**: ~40 employees across 5 depts/levels including some leavers; salaries = base at hire + fixed annual raise per person (unique raises so Q9 ranking is unique); deliberately drop one month’s pay for one active employee (Q10 “arrears”).
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### 10 auto-answered questions
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1. Average tenure per employee
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2. Active headcount per department
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3. Department with highest average level
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4. New hires this year / last year per department
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5. Dept A average salary from March two years ago through May last year
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6. Last year, which of depts A/B had higher average salary
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7. Average salary per level this year
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8. Average latest-month salary for tenure bands <1y / 1–2y / 2–3y
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9. Top 10 largest raises last year → this year
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10. Any unpaid months while employed
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Dept A = 研发部 (R&D), B = 销售部 (Sales) (fixed in the prompt).
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### Run
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```bash
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# From the repository root: use the shared Chapter 5 environment
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uv sync --locked --python 3.12 --extra ch5
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# Activate it before changing directories:
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# macOS/Linux:
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source .venv/bin/activate
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# Windows PowerShell: .\.venv\Scripts\Activate.ps1
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# Windows cmd: .venv\Scripts\activate.bat
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# pip fallback when uv is not installed:
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# python -m pip install -e ".[ch5]"
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cd chapter5/erp-agent
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# Single-project compatibility path, still supported during migration:
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# python -m pip install -r requirements.txt
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cp env.example .env # OPENAI_API_KEY
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python demo.py # same as python demo.py run
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```
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**OpenRouter fallback**: if `OPENAI_API_KEY` unset, set `OPENROUTER_API_KEY` (`gpt-*` → `openai/*`, else `openai/gpt-5.6-luna`). Default `gpt-5.6-luna` is gpt-5.x (org verification on direct OpenAI), so with `OPENROUTER_API_KEY` OpenRouter is preferred.
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`demo.py` has 4 subcommands (no subcommand = `run`):
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| Subcommand | Needs API | Role |
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| --- | --- | --- |
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| `run` | Yes | Online: Agent SQL → execute → compare to reference; per-question print + pass rate |
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| `gold` | **No** | Offline: built-in gold SQL (`gold.py`) for all 10; proves data model consistency |
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| `ask` | Yes | One NL query → SQL → execute and print table |
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| `initdb` | No | Create tables and seed a SQLite file for manual `sqlite3` inspection |
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Common flags: `--only 1,5,10`, `--db erp.db`, `--model gpt-5.6-luna`, `--output result.json`. Examples:
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```bash
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python demo.py gold # offline 10/10, no API
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python demo.py run --only 2,3,6 # Agent SQL for three questions only
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python demo.py ask "研发部现在有多少在职员工？"
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```
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Online path: in-memory SQLite → seed → per-question Agent SQL → execute → print question / SQL / result / pass → total pass rate.
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### Correctness checking
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`reference.py` is an **independent Python reference** (no SQL)—computes each answer on seed data. `demo.py` compares SQL results to reference with multiset + numeric tolerance. `gold.py` holds 10 hand-written gold SQL statements; `python demo.py gold` runs them offline without API.
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Recent real runs: offline `gold` **10/10**; online `run` (`gpt-5.6-luna`) stable **10/10**.
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### Files
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| File | Role |
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| --- | --- |
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| `demo.py` | CLI (run/gold/ask/initdb): DB, seed, questions, SQL exec, compare, pass rate |
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| `seed.py` | Reproducible seed + schema load |
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| `reference.py` | Independent Python answers for 10 questions |
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| `gold.py` | Hand-written gold SQL (SQLite dialect) for offline `gold` |
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| `questions.py` | 10 NL questions + column/business-hint prompts for the Agent |
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| `agent.py` | NL→SQL Agent (OpenAI SDK; `OPENAI_API_KEY` or `OPENROUTER_API_KEY`; default `gpt-5.6-luna`) |
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| `schema_postgres.sql` | Book’s PostgreSQL DDL (for real Postgres migration) |
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### About the database
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This project uses **SQLite** (zero deps, easy reproduce). The book uses **PostgreSQL**; SQL is mostly portable. Date differences: here `strftime('%Y','now')`, `julianday()`, `date('now','-1 year')`; on Postgres use `EXTRACT(YEAR FROM now())`, `AGE()` / date subtract, `now() - interval '1 year'`, etc.
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### Notes and caveats
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- Prompt includes schema-level hints: expected columns/order, business rules (active = empty leave_date; this/last year via `strftime(...,'now',...)`; A/B dept mapping); **no hard-coded years** (else “last year / year before” drifts). Hints do not leak answers.
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- Q8 and Q10 are harder (tenure bands + latest month; recursive months for gaps)—prompt gives recommended SQL structure templates for weaker models.
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- `temperature=0` for stability, but LLM is not strictly deterministic; re-run or stronger model (`OPENAI_MODEL`) if a question flukes.
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---
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## 中文
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### 概述
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把中文自然语言查询自动转成 SQL，由系统执行并直接呈现结果表。核心是 **artifact（制品）模式**：
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Agent 只负责「生成 SQL」这个制品，真正的数据查询交给数据库执行，**LLM 不亲自搬运数据**——
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既省 token、又避免大模型手算出错，几万行结果也能秒回。
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### 数据模型（两张表）
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- `employees`：员工ID、姓名、部门、级别（数字越大越高）、入职日期、离职日期（NULL = 在职）
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- `salaries`：员工ID、发薪日期（每月一条，`YYYY-MM-01`）、工资
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数据由 `seed.py` 用固定随机种子（42）生成、以「今天」为基准相对生成，**完全可复现**：
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约 40 名员工跨 5 个部门/多级别，含若干已离职者；工资按「入职基准 + 每年固定涨薪额」逐月生成，
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每人涨薪额互不相同（保证问题 9 排名唯一）；并刻意为一名在职员工删掉某月工资（制造问题 10 的「拖欠」）。
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### 10 个自动回答的问题
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1. 平均每个员工在职多久 2. 每个部门有多少在职员工 3. 哪个部门平均级别最高
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4. 每个部门今年/去年各新入职多少人 5. 前年3月到去年5月 A 部门平均工资
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6. 去年 A/B 部门平均工资哪个高 7. 今年每个级别平均工资
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8. 入职一年内 / 一到两年 / 两到三年员工的最近一月平均工资
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9. 去年到今年涨薪最大的 10 位员工 10. 有没有拖欠工资（某月在职却没发薪）
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其中 A 部门 = 研发部，B 部门 = 销售部（在 prompt 中约定）。
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### 运行
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```bash
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# 在仓库根目录使用统一的第 5 章环境
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uv sync --locked --python 3.12 --extra ch5
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# 切换目录前先激活环境：
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# macOS/Linux：
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source .venv/bin/activate
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# Windows PowerShell：.\.venv\Scripts\Activate.ps1
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# Windows cmd：.venv\Scripts\activate.bat
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# 未安装 uv 时可用 pip 兜底：
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# python -m pip install -e ".[ch5]"
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cd chapter5/erp-agent
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# 迁移期间仍支持单项目兼容路径：
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# python -m pip install -r requirements.txt
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cp env.example .env # 填入 OPENAI_API_KEY
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python demo.py # 等价于 python demo.py run
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```
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**通用 OpenRouter 兜底**：未配置 `OPENAI_API_KEY` 时，设置 `OPENROUTER_API_KEY` 即自动
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改走 OpenRouter（`gpt-*` → `openai/*`，其它 → `openai/gpt-5.6-luna`）。默认模型
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`gpt-5.6-luna` 属 gpt-5.x，直连 OpenAI 需组织实名认证，故设置了 `OPENROUTER_API_KEY`
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时会优先走 OpenRouter。
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`demo.py` 提供 4 个子命令（不带子命令时等价于 `run`）：
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| 子命令 | 是否需要 API | 作用 |
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| --- | --- | --- |
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| `run` | 需要 | 在线：Agent 生成 SQL → 执行 → 与参考实现比对，逐题打印并给出总通过率 |
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| `gold` | **不需要** | 离线自检：执行内置「标准 SQL」（`gold.py`）跑 10 题并比对，证明数据模型自洽 |
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| `ask` | 需要 | 单条自然语言查询 → 生成 SQL → 执行并打印结果表 |
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| `initdb` | 不需要 | 建表并把种子数据灌入一个 SQLite 文件，便于用 `sqlite3` 手工查看 |
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常用参数：`--only 1,5,10`（只跑指定题号）、`--db erp.db`（用文件库而非内存库）、
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`--model gpt-5.6-luna`（覆盖模型）、`--output result.json`（导出逐题明细）。示例：
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```bash
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python demo.py gold # 离线跑通 10 题，无需 API
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python demo.py run --only 2,3,6 # 只让 Agent 生成这 3 题的 SQL 并校验
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python demo.py ask "研发部现在有多少在职员工？"
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```
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在线模式会：建 SQLite 内存库 → 灌种子数据 → 逐题让 Agent 生成 SQL → 执行 → 打印
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「问题 / 生成的 SQL / 查询结果 / 是否通过」，最后给出总通过率。
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### 正确性校验
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`reference.py` 是**独立的 Python 参考实现**：不走 SQL，直接在种子数据上把每题答案算一遍。
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`demo.py` 把 SQL 的执行结果与参考答案按「多重集合 + 数值容差」比对，逐题打印 通过/不通过。
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`gold.py` 是人工编写的 10 条「标准 SQL」，`python demo.py gold` 离线执行它们即可自检，无需 API。
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最近一次真实运行：离线 `gold` 通过率 **10/10**；在线 `run`（`gpt-5.6-luna`）
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全部 10 题稳定通过，总通过率 **10/10**。
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### 文件
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| 文件 | 作用 |
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| --- | --- |
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| `demo.py` | 命令行入口（run/gold/ask/initdb）：建库、灌数据、跑题、执行 SQL、比对、打印通过率 |
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| `seed.py` | 可复现的种子数据生成 + 建表灌数 |
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| `reference.py` | 10 题的独立 Python 参考实现（校验基准） |
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| `gold.py` | 10 题人工编写的「标准 SQL」（SQLite 方言），供 `gold` 离线自检 |
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| `questions.py` | 10 个自然语言问题 + 给 Agent 的「返回列/业务口径」提示 |
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| `agent.py` | NL→SQL Agent（OpenAI SDK，读 `OPENAI_API_KEY` 或 `OPENROUTER_API_KEY` 兜底，默认 `gpt-5.6-luna`） |
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| `schema_postgres.sql` | 书中 PostgreSQL 版建表 DDL（迁移到真实 Postgres 时参考） |
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### 关于数据库
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本项目用 **SQLite**（零依赖、可直接复现）。书中示例用 **PostgreSQL**，SQL 大体通用，
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差异主要在日期函数：本项目用 SQLite 的 `strftime('%Y','now')`、`julianday()`、`date('now','-1 year')` 等；
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迁到 PostgreSQL 时对应换成 `EXTRACT(YEAR FROM now())`、`AGE()`/日期相减、`now() - interval '1 year'` 等即可。
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### 说明与注意事项
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- Agent 的 prompt 里补充了 schema 级提示：期望返回哪些列/顺序、业务口径（在职=leave_date 为空、
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今年/去年如何用 `strftime(...,'now',...)` 推导、A/B 部门映射），以及**禁止硬编码年份**
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（否则模型不知道「今天」是哪年，会把「前年/去年」猜错）。这些是合理的 schema 提示，不泄露具体答案。
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- 问题 8、10 较复杂（工龄分档取最近一月工资、递归生成在职月份找空缺），在提示里给了推荐的
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SQL 结构模板，帮助较小模型稳定产出正确 SQL。
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- `temperature=0` 让输出尽量稳定，但 LLM 仍非严格确定性；若个别题偶发偏差，重跑即可，
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也可换更强的模型（设 `OPENAI_MODEL`）。
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---
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## Notes / 说明
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- Prefer `python demo.py gold` without a key. / 无 Key 优先 `python demo.py gold`。
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- Commands/code/paths/env vars are identical in both language sections. / 命令、代码、路径与环境变量在中英文两侧保持一致。
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