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

chapter9/prompt-auto-optimization/coding_agent.py

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+"""
+Coding Agent：读取系统提示词文件 → 定位相关规则 → 生成精确的搜索/替换编辑 → 真的改写文件。
+
+它的工作方式和真实的编程 Agent（如 Claude Code / Cursor）一致：
+不是让模型整篇重写，而是让模型产出一组 (old_str -> new_str) 的精确编辑，
+由代码逐条做"精确字符串替换"落到文件里；若某条编辑的 old_str 匹配不上，
+把错误反馈回模型让它重试。这样修改是"代码级"的、可审计的（能直接出 diff）。
+"""
+
+import difflib
+import json
+from config import get_client, get_model, get_temperature, record_completion
+
+# 暴露给 Coding Agent 的"文件编辑工具"
+EDIT_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "apply_edits",
+            "description": (
+                "对提示词文件应用一组精确的搜索/替换编辑。每条编辑给出 old_str "
+                "（文件中唯一存在的原文片段）和 new_str（替换后的新文本）。"
+                "old_str 必须与文件内容逐字符完全一致。"
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "edits": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "old_str": {"type": "string"},
+                                "new_str": {"type": "string"},
+                            },
+                            "required": ["old_str", "new_str"],
+                        },
+                    },
+                    "rationale": {
+                        "type": "string",
+                        "description": "简述本次改动如何回应人类反馈。",
+                    },
+                },
+                "required": ["edits"],
+            },
+        },
+    }
+]
+
+
+def _apply_one(content: str, old_str: str, new_str: str) -> tuple[str, str | None]:
+    """尝试应用一条编辑。成功返回(新内容, None)，失败返回(原内容, 错误信息)。"""
+    if old_str is None or new_str is None:
+        return content, "old_str/new_str 不能为 null"
+    count = content.count(old_str)
+    if count == 0:
+        return content, f"old_str 在文件中未找到：{old_str[:60]!r}"
+    if count > 1:
+        return content, f"old_str 在文件中出现 {count} 次（不唯一）：{old_str[:60]!r}"
+    return content.replace(old_str, new_str, 1), None
+
+
+def _apply_edits_from_args(working: str, args: dict) -> tuple[str, int, list, list, list]:
+    """Apply edits; null edits → []; skip non-dict entries with a warning."""
+    edits = args.get("edits")
+    if edits is None:
+        edits = []
+    errors = []
+    warnings = []
+    applied = 0
+    for e in edits:
+        if not isinstance(e, dict):
+            warnings.append(f"跳过非对象编辑项 ({type(e).__name__}): {e!r}")
+            continue
+        working, err = _apply_one(working, e.get("old_str", ""), e.get("new_str", ""))
+        if err:
+            errors.append(err)
+        else:
+            applied += 1
+    return working, applied, errors, warnings, edits
+
+def optimize_prompt(prompt_path: str, feedback, max_rounds: int = 3, verbose: bool = True) -> dict:
+    """
+    让 Coding Agent 根据 human feedback 改写 prompt_path 指向的文件（原地覆盖）。
+
+    返回 {"before": 原文, "after": 新文, "diff": 统一 diff 文本, "rationale": 说明}。
+    """
+    client = get_client()
+    model = get_model()
+
+    with open(prompt_path, "r", encoding="utf-8") as f:
+        original = f.read()
+
+    feedback_text = json.dumps(feedback, ensure_ascii=False, indent=2) if isinstance(feedback, dict) else str(feedback)
+
+    system = (
+        "你是一名资深的提示词工程 Coding Agent。你会收到一份航空客服 Agent 的"
+        "系统提示词文件，以及从失败轨迹生成的结构化诊断。请定位与'人工转接'相关的规则，"
+        "生成精确的搜索/替换编辑来改进它，然后调用 apply_edits 工具落地修改。\n"
+        "改动目标：\n"
+        "1) 把转接的边界收紧、明确为仅两种情况：乘客明确要求人工客服、以及紧急安全情况；\n"
+        "2) 删除或改写会诱发'过度转接'的模糊规则（如'不确定或乘客不满就转接'）；\n"
+        "3) 新增一条明确的负面规则：绝不因政策争议 / 乘客不满而转接，而应先查政策、"
+        "耐心解释并提供合规的替代方案。\n"
+        "只修改与转接策略相关的部分，尽量保留其余内容不动。"
+    )
+
+    messages = [
+        {"role": "system", "content": system},
+        {
+            "role": "user",
+            "content": (
+                f"【失败轨迹诊断】\n{feedback_text}\n\n"
+                f"【当前系统提示词文件内容】\n---\n{original}\n---\n\n"
+                "请调用 apply_edits 提交你的精确编辑。"
+            ),
+        },
+    ]
+
+    working = original
+    rationale = ""
+    submitted_edits = []
+
+    for round_idx in range(max_rounds):
+        resp = record_completion(client, kind="coding_agent",
+            model=model,
+            messages=messages,
+            tools=EDIT_TOOLS,
+            tool_choice={"type": "function", "function": {"name": "apply_edits"}},
+            temperature=get_temperature(),
+        )
+        msg = resp.choices[0].message
+        messages.append(msg)
+
+        if not msg.tool_calls:
+            break
+
+        # 处理（唯一的）apply_edits 调用
+        tc = msg.tool_calls[0]
+        try:
+            args = json.loads(tc.function.arguments or "{}")
+        except json.JSONDecodeError:
+            args = {}
+        rationale = args.get("rationale", rationale)
+        working, applied, errors, warnings, edits = _apply_edits_from_args(working, args)
+        submitted_edits = edits
+
+        if verbose:
+            print(f"  [round {round_idx + 1}] 提交 {len(edits)} 条编辑，成功 {applied}，失败 {len(errors)}，跳过 {len(warnings)}")
+
+        if not errors:
+            # 有效编辑已全部成功应用，落盘
+            msg_content = "所有有效编辑已成功应用。"
+            if warnings:
+                msg_content += "\n以下非对象编辑项已被跳过：\n" + "\n".join(f"- {w}" for w in warnings)
+            messages.append(
+                {"role": "tool", "tool_call_id": tc.id, "content": msg_content}
+            )
+            break
+        else:
+            # 有实际应用失败：回滚到原文，把错误反馈给模型重试（保持编辑的原子性）
+            working = original
+            feedback_msg = (
+                "以下编辑未能应用，请修正后重新提交完整的编辑列表（注意 old_str 必须与文件逐字符一致）：\n"
+                + "\n".join(f"- {er}" for er in errors)
+            )
+            if warnings:
+                feedback_msg += "\n另有以下非对象编辑项已被跳过：\n" + "\n".join(f"- {w}" for w in warnings)
+            messages.append({"role": "tool", "tool_call_id": tc.id, "content": feedback_msg})
+    # 落盘（原地覆盖 prompt 文件）
+    with open(prompt_path, "w", encoding="utf-8") as f:
+        f.write(working)
+
+    diff = "".join(
+        difflib.unified_diff(
+            original.splitlines(keepends=True),
+            working.splitlines(keepends=True),
+            fromfile="system_prompt.txt (before)",
+            tofile="system_prompt.txt (after)",
+        )
+    )
+
+    return {
+        "before": original,
+        "after": working,
+        "diff": diff,
+        "rationale": rationale,
+        "edits": submitted_edits,
+    }