#!/usr/bin/env python3 """ s10: 系统提示词 — 运行时组装提示词并缓存。 运行: python s10_system_prompt/code.py 需要: pip install anthropic python-dotenv + .env 中配置 ANTHROPIC_API_KEY 相对 s09 的变化： - PROMPT_SECTIONS：按主题作为 key 的提示词片段字典 - assemble_system_prompt(context)：根据真实状态选择并拼接片段 - get_system_prompt(context)：通过 json.dumps 实现确定性的缓存 - agent_loop 使用 get_system_prompt(context)，不再使用硬编码 SYSTEM 当 .memory/MEMORY.md 存在时加载记忆片段（基于真实状态，不靠关键词）。 """ import os, subprocess, json from pathlib import Path try: import readline readline.parse_and_bind('set bind-tty-special-chars off') except ImportError: pass from anthropic import Anthropic from dotenv import load_dotenv load_dotenv(override=True) if os.getenv("ANTHROPIC_BASE_URL"): os.environ.pop("ANTHROPIC_AUTH_TOKEN", None) WORKDIR = Path.cwd() MEMORY_DIR = WORKDIR / ".memory" MEMORY_INDEX = MEMORY_DIR / "MEMORY.md" client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL")) MODEL = os.environ["MODEL_ID"] # ── 提示词片段 ── PROMPT_SECTIONS = { "identity": "你是一个编码 Agent。直接行动，不要只解释。", } def assemble_system_prompt(context: dict) -> str: """Select and join prompt sections based on current context.""" sections = [] # 始终加载 — 身份 sections.append(PROMPT_SECTIONS["identity"]) # 动态加载 — 来自上下文的工具和工作区 tools = ", ".join(context.get("enabled_tools", [])) if tools: sections.append(f"可用工具：{tools}.") sections.append(f"工作目录：{context.get('workspace', WORKDIR)}") # 条件加载 — MEMORY.md 存在且有内容时加载记忆 memories = context.get("memories", "") if memories: sections.append(f"Relevant memories:\n{memories}") return "\n\n".join(sections) _last_context_key = None _last_prompt = None def get_system_prompt(context: dict) -> str: """Cache wrapper — reassemble only when context changes. Uses json.dumps for deterministic serialization, not Python's hash() which has process randomization and fails on nested dicts/lists. This cache only avoids redundant string assembly within a process. Real Claude Code additionally protects API-level prompt cache via stable section ordering and SYSTEM_PROMPT_DYNAMIC_BOUNDARY. """ global _last_context_key, _last_prompt key = json.dumps(context, sort_keys=True, ensure_ascii=False, default=str) if key == _last_context_key and _last_prompt: print(" \033[90m[缓存命中] 系统提示词未变化\033[0m") return _last_prompt _last_context_key = key _last_prompt = assemble_system_prompt(context) loaded = ["identity", "tools", "workspace"] if context.get("memories"): loaded.append("memory") print(f" \033[32m[已组装] 片段：{', '.join(loaded)}\033[0m") return _last_prompt # ── 工具 ── def safe_path(p: str) -> Path: path = (WORKDIR / p).resolve() if not path.is_relative_to(WORKDIR): raise ValueError(f"路径逃逸出工作区：{p}") return path def run_bash(command: str) -> str: try: r = subprocess.run(command, shell=True, cwd=WORKDIR, capture_output=True, text=True, timeout=120) out = (r.stdout + r.stderr).strip() return out[:50000] if out else "（无输出）" except subprocess.TimeoutExpired: return "错误：执行超时（120 秒）" def run_read(path: str, limit: int | None = None) -> str: try: lines = safe_path(path).read_text().splitlines() if limit and limit < len(lines): lines = lines[:limit] + [f"... ({len(lines) - limit} 行更多内容）"] return "\n".join(lines) except Exception as e: return f"错误：{e}" def run_write(path: str, content: str) -> str: try: file_path = safe_path(path) file_path.parent.mkdir(parents=True, exist_ok=True) file_path.write_text(content) return f"已写入 {len(content)} 字节到 {path}" except Exception as e: return f"错误：{e}" TOOLS = [ {"name": "bash", "description": "运行一条 shell 命令。", "input_schema": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}}, {"name": "read_file", "description": "读取文件内容。", "input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "limit": {"type": "integer"}}, "required": ["path"]}}, {"name": "write_file", "description": "向文件写入内容。", "input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}}, ] TOOL_HANDLERS = {"bash": run_bash, "read_file": run_read, "write_file": run_write} # ── 上下文 ── def update_context(context: dict, messages: list) -> dict: """从真实状态推导上下文：有哪些工具、是否存在记忆文件。""" memories = "" if MEMORY_INDEX.exists(): content = MEMORY_INDEX.read_text().strip() if content: memories = content return { "enabled_tools": list(TOOL_HANDLERS.keys()), "workspace": str(WORKDIR), "memories": memories, } # ── Agent 循环 ── def agent_loop(messages: list, context: dict): """Main loop — uses assembled system prompt instead of hardcoded SYSTEM.""" system = get_system_prompt(context) while True: response = client.messages.create( model=MODEL, system=system, messages=messages, tools=TOOLS, max_tokens=8000) messages.append({"role": "assistant", "content": response.content}) if response.stop_reason != "tool_use": return results = [] for block in response.content: if block.type != "tool_use": continue print(f"\033[36m> {block.name}\033[0m") handler = TOOL_HANDLERS.get(block.name) output = handler(**block.input) if handler else f"未知工具：{block.name}" print(str(output)[:200]) results.append({"type": "tool_result", "tool_use_id": block.id, "content": output}) messages.append({"role": "user", "content": results}) # 每轮工具调用后重新评估上下文和提示词 context = update_context(context, messages) system = get_system_prompt(context) if __name__ == "__main__": print("s10: 系统提示词 — 运行时组装") print("输入问题后按回车发送。输入 q 退出。\n") history = [] context = update_context({}, []) while True: try: query = input("\033[36ms10 >> \033[0m") except (EOFError, KeyboardInterrupt): break if query.strip().lower() in ("q", "exit", ""): break history.append({"role": "user", "content": query}) agent_loop(history, context) context = update_context(context, history) for block in history[-1]["content"]: if getattr(block, "type", None) == "text": print(block.text) print()