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广西工程建设质量安全管理协会网站,购物网站最近浏览怎么做,怎么重建wordpress,东莞市门户网站建设怎么样未经允许#xff0c;不得转载#xff0c;侵权必究目标#xff1a;以数学严谨性 工程可落地性为准则#xff0c;全面重构 ReAct 系统的核心组件——T-A-O 循环、Prompt Schema、解析器、工具契约、并发策略。面向高可靠性、低成本、可审计的生产部署。4.1 T-A-O 循环的形式…未经允许不得转载侵权必究目标以数学严谨性工程可落地性为准则全面重构 ReAct 系统的核心组件——T-A-O 循环、Prompt Schema、解析器、工具契约、并发策略。面向高可靠性、低成本、可审计的生产部署。4.1 T-A-O 循环的形式化定义4.1.0 概览专家陈述4.1.1 形式化模型POMDP 视角下的 ReAct定义 1POMDP 表示法设计权衡说明尽管理论上可以建模为完整的 POMDP实际中由于 LLM 输出不确定性大我们将其视为非平稳策略驱动的模拟器而非严格贝叶斯推断系统。因此重点在于设计状态管理器 UpdateState和解析器 Parser来稳定策略执行路径。4.1.2 回合终止条件与成本度量精确定义定义 2终止条件 Predicate Set定义 3成本向量与标量化函数⚠️ 工程提示在 prompt 中注入当前预算余量如{budget_left: {token: 2000, usd: 0.1}}并要求 LLM 在决策中考虑该约束。4.2 Prompt Schema 与指令工程Prompt Engineering4.2.1 指令段System Prompt设计要素合约化系统 prompt 应作为“策略接口契约”包含以下内容类别内容示例输出格式要求“请仅输出合法 JSON符合指定 schema。”工具列表声明available_tools: [{name: search, signature: q:string}]安全与合规约束“不得访问隐私字段”、“遇到敏感词需请求用户确认”效用与预算指引“剩余 token 数为 2000请优先使用本地缓存工具。”自检与修正指令“若解析失败请在 100 tokens 内返回修正后的 JSON。”✅ 工程实践将 system prompt 版本化存储如system_v1.2.txt并随每次部署进行回归测试。4.2.2 Few-Shot 示例选择原则与动态检索原则覆盖率优先涵盖正常流程、边界情况、错误处理路径。最小上下文开销避免长文本挤占上下文窗口。纠错教学提供“原始错误 → 修正样例”对帮助模型学习。版本同步示例与当前 schema/output contract 同步更新。推荐做法使用 embedding 检索引擎如 FAISS动态匹配最相关 few-shot 示例。缓存高频示例至内存中加速检索。示例质量由人工标注团队定期审核。4.2.3 输出约束严格 schema 字段语义推荐最小字段集JSON Schema{ type: object, properties: { thought: { type: string, maxLength: 500 }, action: { oneOf: [ { type: object, properties: { tool: { enum: [search, calculator, ...] }, input: { type: object } }, required: [tool] }, { type: null } ] }, answer: { type: [string, null] }, confidence: { type: number, minimum: 0, maximum: 1 }, debug: { type: [object, null], additionalProperties: true } }, required: [thought] } 工程提醒所有action.tool必须白名单校验action.input需再次 schema 校验防止注入攻击。4.3 输出解析器Parser设计工程化、可证明的鲁棒性4.3.1 层级解析策略推荐顺序层级方法描述1Strict JSON Parse直接校验纯 JSON最优路径2Lenient Extractor提取首段 JSON 块去除冗余内容3Regex Fallback仅限固定格式最小假设4LLM-assisted Correction Loop最终 fallback带 retry 控制4.3.2 LLM-assist 修正算法与回合限制伪代码def parse_and_validate(raw_text, schema, llm, max_retries2): try: parsed json.loads(raw_text) except Exception: parsed lenient_json_extract(raw_text) if validate_against_schema(parsed, schema): return parsed for i in range(max_retries): fix_prompt build_fix_prompt(raw_text, last_errorsparsed.errors()) corrected llm.generate(fix_prompt, max_tokens100) try: fixed_parsed json.loads(corrected) except Exception: continue if validate_against_schema(fixed_parsed, schema): return fixed_parsed raise ParseFailureException(raw_text)设计要点错误消息必须具体如field missing,invalid enum value。最多重试 1~2 次避免费用膨胀。所有尝试记录到日志系统用于后续训练样本回收。4.3.3 JSON Schema 与类型安全化实践建议使用 Draft-7 或更高版本的 JSON Schema。实施字段 coercion如 string → int, float, datetime。所有外部输入必须经过反注入检查。CI 中加入 schema regression test确保新变更不影响已有行为。4.4 工具抽象与接口规范Tool API Contract4.4.1 工具声明模板JSON/YAMLid: search.v1 name: web_search version: 1.0.2 description: Query web index and return top-k results. inputs_schema: type: object properties: q: { type: string } required: [q] outputs_schema: type: array items: type: object properties: title: string url: string cost: usd_per_call: 0.002 token_est: 80 idempotent: true timeout_ms: 3000 retries: max: 2 backoff: exponential side_effects: none auth: required: true scopes: [search:read]工程要求工具注册中心支持运行时查询与版本控制。工具变更需触发 schema 兼容性检查。工具元数据注入到 system prompt 中供 agent 查询。4.4.2 幂等性、事务语义与失败模型失败分类分类特征处理方式Transient可恢复故障超时、网络抖动Exponential Backoff RetryPermanent不可恢复错误无效参数终止计划并报告错误Partial部分成功标记 partial 并合并结果事务语义对于副作用操作如写入数据库、发起支付采用两阶段提交或 Saga 模式。工具接口需显式标明是否支持幂等。4.5 伪码工程级 ReAct 主循环单线程与并发4.5.1 单线程主循环基础版def react_loop(state, llm, tools, schema, budget, max_steps20): for step in range(max_steps): prompt build_prompt(state) raw_output llm.generate(prompt, max_tokens2048) try: parsed parse_and_validate(raw_output, schema, llm) except ParseFailureException: return failure(PARSE_FAILED) # Token cost estimation state.costs.token estimate_token_count(raw_output) if parsed.answer: if verify_answer(parsed.answer, state): return success(parsed.answer, metastate) else: state update_state(state, thoughtparsed.thought, actionNone, observation{error: verification_failed}) continue if not parsed.action: if should_ask_clarification(state): state update_state(state, thoughtparsed.thought, actionNone, observationNone) continue else: return failure(NO_ACTION_NO_ANSWER) tool tools.get(parsed.action.tool) if not tool: state update_state(state, thoughtparsed.thought, actionparsed.action, observation{error: UNKNOWN_TOOL}) continue try: normalized_input coerce_and_validate(parsed.action.input, tool.schema.input) except ValidationError as ve: state update_state(state, thoughtparsed.thought, actionparsed.action, observation{error: INPUT_INVALID, details: str(ve)}) continue try: obs tool.call(normalized_input, timeouttool.timeout_ms) except TransientError as te: obs {status: TRANSIENT_ERROR, details: str(te)} except PermanentError as pe: obs {status: PERMANENT_ERROR, details: str(pe)} state update_state(state, thoughtparsed.thought, actionparsed.action, observationobs) state.costs.monetary obs.get(monetary_cost, 0) state.costs.latency obs.get(latency_ms, 0) if budget.exceeded(state.costs): return failure(BUDGET_EXCEEDED) if detect_no_progress(state): return failure(NO_PROGRESS_DETECTED) return failure(MAX_STEPS_REACHED)4.5.2 并发 / 批量调用扩展设计与伪码并发适用场景动作之间无依赖关系工具延迟较高总体延迟成为瓶颈。并发伪码async def concurrent_react_loop(state, llm, tools, schema, budget, max_steps20): for step in range(max_steps): prompt build_prompt(state) raw await llm.generate_async(prompt) parsed parse_and_validate(raw, schema, llm) actions parsed.actions or [parsed.action] grouped_actions partition_by_independence(actions, tools) for group in grouped_actions: semaphore asyncio.Semaphore(group.max_concurrency) async def exec_one(action): async with semaphore: tool tools[action.tool] inp coerce_and_validate(action.input, tool.schema.input) return await tool.call_async(inp, timeouttool.timeout_ms) futures [asyncio.create_task(exec_one(a)) for a in group.actions] done, pending await asyncio.wait_for(asyncio.gather(*futures, return_exceptionsTrue), timeoutgroup.group_timeout_ms) observations [res for res in done if not isinstance(res, Exception)] merged_obs group.merge_fn(observations) state update_state(state, thoughtparsed.thought, actionsgroup.actions, observationmerged_obs) estimated_cost sum([obs.get(estimated_cost, 0) for obs in observations]) state.costs.add(**estimated_cost) if budget.exceeded(state.costs): return failure(BUDGET_EXCEEDED) return failure(MAX_STEPS_REACHED)设计权衡与工程建议专家清单组件关键权衡点推荐方案Schema-first是否牺牲灵活性换取稳定性强烈推荐所有接口均需 schema 校验最小信任原则如何平衡 LLM 输出自由度与安全性所有输出必须经 schema 验证后再执行动作解析修正策略如何降低 LLM 辅助修正带来的成本限定最多 12 次修正记录修正日志用于训练并发控制如何在性能提升与复杂度间取得平衡仅对幂等工具启用并发使用 group timeout 控制权限与敏感操作如何保障敏感操作的安全性加入 manual approval flag 或 human-in-loop日志与监控如何追踪失败原因与改进方向完整保留 raw/parsed/observation 流水线日志回归测试如何防止系统升级引发行为退化CI 中加入 few-shot/schemas 回归测试集结论构建工业级 ReAct Agent 的关键维度总结维度技术要点状态建模明确区分 visible state vs internal state策略控制结合 LLM Parser Schema 实现可靠策略成本控制成本向量 权重标量化 Budget-driven prompting工具治理工具注册中心 Schema-first 幂等性保证解析鲁棒性多层级解析器 LLM-assisted correction loop并发调度group-wise concurrency merge strategy timeout control安全合规权限校验 敏感操作拦截 human-in-loop 支持可审计性完整记录 raw → parsed → executed → observed 全链路日志迭代优化解析失败样本回收 → 微调 LLM → 更新 few-shot 示例源码实践文件 1react_agent.py—— 主模块无外部依赖标准库实现可插入真实 LLM 接口文件 2test_react_agent.py—— 单元测试使用unittest/pytest均可运行如何运行把两个文件放在同一目录后运行python -m pytest test_react_agent.py或python test_react_agent.py如果用 unittest。react_agent.py react_agent.py Engineering-grade Python module draft for a ReAct-style agent: - Parser with LLM-assisted correction loop - Tool registry with metadata and sync/async call helpers - CostCounter for tracking token/monetary/latency costs - A minimal single-threaded react_loop demonstrating usage This module is intentionally dependency-free (standard library only). Replace DummyLLM with a real LLM wrapper for production use. from typing import Any, Dict, Optional, Callable, List import json import time import asyncio import re # --------------------------- # Utilities: JSON extraction # --------------------------- def extract_first_json_object(text: str) - Optional[str]: Extract the first balanced JSON object from text by scanning braces. Returns the JSON substring or None if not found. start text.find({) if start -1: return None depth 0 for i in range(start, len(text)): ch text[i] if ch {: depth 1 elif ch }: depth - 1 if depth 0: return text[start:i1] return None def strict_json_parse(text: str) - Any: Strictly parse JSON -- raises json.JSONDecodeError on failure. return json.loads(text) def lenient_extract_and_parse(text: str) - Any: Attempt to extract a JSON object and parse it. Raises json.JSONDecodeError if parsing the extracted JSON fails, or ValueError if none found. js extract_first_json_object(text) if js is None: raise ValueError(No JSON object found in text) return json.loads(js) # --------------------------- # Schema validation (simple) # --------------------------- def validate_agent_output_schema(obj: Dict[str, Any]) - List[str]: Very small validator for the expected output schema: { thought: str, action: null | {tool: str, input: dict}, answer: null | str, confidence: float in [0,1] } Returns a list of validation error strings (empty if valid). errs [] if not isinstance(obj, dict): errs.append(output must be a JSON object) return errs # thought if thought not in obj: errs.append(missing thought) elif not isinstance(obj[thought], str): errs.append(thought must be a string) # action if action not in obj: errs.append(missing action) else: a obj[action] if a is not None: if not isinstance(a, dict): errs.append(action must be null or an object) else: if tool not in a: errs.append(action missing tool) elif not isinstance(a[tool], str): errs.append(action.tool must be a string) if input not in a: errs.append(action missing input) elif not isinstance(a[input], dict): errs.append(action.input must be an object) # answer if answer not in obj: errs.append(missing answer) else: if obj[answer] is not None and not isinstance(obj[answer], str): errs.append(answer must be null or a string) # confidence if confidence not in obj: errs.append(missing confidence) else: conf obj[confidence] if not (isinstance(conf, float) or isinstance(conf, int)): errs.append(confidence must be a number) else: if not (0.0 float(conf) 1.0): errs.append(confidence must be between 0 and 1) return errs # --------------------------- # LLM interface (pluggable) # --------------------------- class LLMInterface: Minimal LLM interface wrapper. Implement generate(prompt, max_tokens) for synchronous LLM, and generate_async(...) for async usage. def generate(self, prompt: str, max_tokens: int 1024) - str: raise NotImplementedError() async def generate_async(self, prompt: str, max_tokens: int 1024) - str: # default implementation wraps sync generate return self.generate(prompt, max_tokensmax_tokens) class DummyLLM(LLMInterface): Deterministic dummy LLM for unit tests and local debugging. Behavior: - If constructed with a mapping responses (ordered dict like), when a key is substring of the prompt, the first unused matching key will be returned and marked used. This allows sequential different responses for similar prompts across multiple calls. - If prompt contains the special marker FIX-RETURN...END-FIX, returns the enclosed text. - Otherwise returns a minimal valid no-op JSON object. def __init__(self, responses: Optional[Dict[str, str]] None): self.responses responses or {} self._used_keys: List[str] [] def generate(self, prompt: str, max_tokens: int 1024) - str: # exact mapping or substring mapping with first unused match for k, v in self.responses.items(): if k in prompt and k not in self._used_keys: self._used_keys.append(k) return v # if all matches used, allow reuse: return first matching key for k, v in self.responses.items(): if k in prompt: return v # special marker support m re.search(rFIX-RETURN(.*?)END-FIX, prompt, flagsre.S) if m: return m.group(1).strip() # fallback: return a polite empty valid object (no action) return json.dumps({thought:no-op,action:None,answer:None,confidence:0.0}) # --------------------------- # Parser with LLM-assisted correction loop # --------------------------- def build_fix_prompt(original: str, validation_errors: List[str], schema_note: str ) - str: Build a short prompt to ask the LLM to only output corrected JSON that satisfies the agent output schema. errs \\n.join(validation_errors) prompt ( The system attempted to parse the following LLM output but failed schema validation.\\n Original output:\\n ----BEGIN ORIGINAL----\\n f{original}\\n ----END ORIGINAL----\\n Validation errors:\\n f{errs}\\n Please output **only** a single JSON object that matches the schema: {thought: string, action: null | {tool:string,input:{}}, answer: null | string, confidence: 0.0} Do not include any prose. If you intend no action, set action to null.\\n ) if schema_note: prompt \\n schema_note return prompt def try_parse_json(raw: str) - Any: Try strict parse first; if fails, try lenient extraction. Raises json.JSONDecodeError or ValueError. try: return strict_json_parse(raw) except Exception: # lenient attempt return lenient_extract_and_parse(raw) def parse_and_validate(raw_text: str, llm: Optional[LLMInterface] None, max_retries: int 2) - Dict[str, Any]: Parse the raw LLM output into JSON and validate against the agent output schema. If validation fails and an LLM is provided, ask the LLM to correct the output. last_raw raw_text attempt 0 while True: attempt 1 parsed None try: parsed try_parse_json(last_raw) errs validate_agent_output_schema(parsed) if not errs: return parsed # success else: parse_err schema errors: ; .join(errs) except Exception as e: parsed None parse_err fparse error: {repr(e)} # at this point parsed is None or has errors if llm is None or attempt max_retries: # give up raise ValueError(fUnparsable or invalid output after {attempt} attempts. Last error: {parse_err}. Raw: {last_raw[:1000]}) # build fix prompt fix_prompt build_fix_prompt(last_raw, errs if parsed is not None else [parse_err]) fixed llm.generate(fix_prompt, max_tokens512) # guard against identical response if fixed.strip() last_raw.strip(): raise ValueError(LLM returned identical output when asked to fix; aborting) last_raw fixed # loop to attempt parse again # --------------------------- # Tool registry and Tool abstraction # --------------------------- class Tool: Tool metadata and a callable interface. In production, call_fn should make network calls or perform the actual tool behavior. def __init__(self, id: str, name: str, inputs_schema: Optional[Dict] None, outputs_schema: Optional[Dict] None, cost_estimate: Optional[Dict] None, idempotent: bool True, timeout_ms: int 3000, call_fn: Optional[Callable] None, call_async_fn: Optional[Callable] None): self.id id self.name name self.inputs_schema inputs_schema or {} self.outputs_schema outputs_schema or {} self.cost_estimate cost_estimate or {} self.idempotent idempotent self.timeout_ms timeout_ms self._call_fn call_fn self._call_async_fn call_async_fn def call(self, inp: Dict[str, Any]) - Dict[str, Any]: Synchronous call wrapper. Raises exceptions on error. if self._call_fn is None: raise RuntimeError(No call_fn configured for tool self.name) start time.time() out self._call_fn(inp) out_meta dict(out) out_meta.setdefault(_meta, {}) out_meta[_meta][latency_ms] int((time.time() - start) * 1000) return out_meta async def call_async(self, inp: Dict[str, Any]) - Dict[str, Any]: if self._call_async_fn is not None: start time.time() out await self._call_async_fn(inp) out_meta dict(out) out_meta.setdefault(_meta, {}) out_meta[_meta][latency_ms] int((time.time() - start) * 1000) return out_meta # wrap sync in threadpool to not block event loop loop asyncio.get_event_loop() return await loop.run_in_executor(None, self.call, inp) class ToolRegistry: def __init__(self): self._tools {} def register(self, tool: Tool): if tool.name in self._tools: raise KeyError(Tool already registered: tool.name) self._tools[tool.name] tool def get(self, name: str) - Optional[Tool]: return self._tools.get(name) # --------------------------- # Cost counter # --------------------------- class CostCounter: def __init__(self): self.tokens 0 self.monetary 0.0 self.latency_ms 0 def add_tokens(self, n: int): self.tokens int(n) def add_monetary(self, usd: float): self.monetary float(usd) def add_latency(self, ms: int): self.latency_ms int(ms) def add_from_tool_obs(self, obs: Dict[str, Any]): meta obs.get(_meta, {}) self.add_latency(meta.get(latency_ms, 0)) # if tool provides monetary_cost in obs, add it if monetary_cost in obs: try: self.add_monetary(float(obs[monetary_cost])) except Exception: pass def exceeded(self, budget: Dict[str, Any]) - bool: budget example: {tokens:10000, monetary: 1.0} if not budget: return False if tokens in budget and self.tokens budget[tokens]: return True if monetary in budget and self.monetary budget[monetary]: return True return False # --------------------------- # Minimal single-threaded react loop # --------------------------- def build_prompt_from_state(state: Dict[str, Any]) - str: Simple prompt builder. For production, replace with system prompt few-shot state summary. # Allow tests to inject explicit prompt override if prompt_override in state: return state[prompt_override] return State summary: json.dumps(state.get(summary, {})) \\nProduce JSON output. def react_single_thread(initial_state: Dict[str, Any], llm: LLMInterface, tool_registry: ToolRegistry, budget: Optional[Dict[str, Any]] None, max_steps: int 10) - Dict[str, Any]: S dict(initial_state) # shallow copy costs CostCounter() for step in range(max_steps): prompt build_prompt_from_state(S) raw llm.generate(prompt, max_tokens1024) costs.add_tokens(len(raw.split())) try: parsed parse_and_validate(raw, llmllm, max_retries1) except Exception as e: return {status:error, reason:parse_failure, detail: str(e), state: S} # record thought S.setdefault(history, []).append({thought: parsed.get(thought)}) # if answer - verify (here we accept as final) if parsed.get(answer) is not None: return {status:success, answer: parsed[answer], steps: step1, costs: {tokens: costs.tokens, monetary: costs.monetary, latency_ms: costs.latency_ms}} act parsed.get(action) if act is None: # nothing to do - terminate as no-op return {status:no_action, steps: step1, state: S} # validate tool exists tool tool_registry.get(act.get(tool)) if tool is None: S[history].append({observation: {error:unknown_tool, tool: act.get(tool)}}) continue # call tool (sync) try: obs tool.call(act.get(input, {})) except Exception as e: obs {error: tool_exception, detail: str(e)} S[history].append({action: act, observation: obs}) costs.add_from_tool_obs(obs) # check budget if costs.exceeded(budget or {}): return {status:budget_exceeded, costs: {tokens: costs.tokens, monetary: costs.monetary}} return {status:max_steps_reached, steps: max_steps, costs: {tokens: costs.tokens, monetary: costs.monetary}}test_react_agent.py Unit tests for react_agent.py import unittest import json import asyncio import time from react_agent import ( extract_first_json_object, strict_json_parse, lenient_extract_and_parse, validate_agent_output_schema, DummyLLM, parse_and_validate, Tool, ToolRegistry, CostCounter, react_single_thread ) class TestJSONExtraction(unittest.TestCase): def test_extract_balanced_json(self): text prefix {\a\:1, \b\:{\c\:2}} suffix js extract_first_json_object(text) self.assertIsNotNone(js) self.assertEqual(json.loads(js), {a:1,b:{c:2}}) def test_no_json(self): text no json here js extract_first_json_object(text) self.assertIsNone(js) class TestParsingAndValidation(unittest.TestCase): def test_strict_parse_valid(self): raw json.dumps({thought:t,action:None,answer:None,confidence:0.5}) parsed parse_and_validate(raw, llmDummyLLM(), max_retries0) self.assertEqual(parsed[thought], t) def test_lenient_extract_and_parse(self): raw Some explanation... {\thought\:\lenient\,\action\:null,\answer\:null,\confidence\:0.1} end parsed parse_and_validate(raw, llmDummyLLM(), max_retries0) self.assertEqual(parsed[thought], lenient) def test_llm_assist_fix(self): # raw is invalid; DummyLLM will return corrected JSON if requests contains special mapping raw I will not output JSON fixed json.dumps({thought:fixed,action:None,answer:done,confidence:0.9}) llm DummyLLM(responses{Please output: fixed}) # We craft parse_and_validate to call llm when max_retries0 -- here single retry parsed parse_and_validate(raw, llmllm, max_retries1) self.assertEqual(parsed[answer], done) class TestToolRegistryAndCost(unittest.TestCase): def test_tool_call_and_cost_accumulation(self): def search_fn(inp): q inp.get(q,) return {results:[{title:r,url:u,snippet:s}], monetary_cost: 0.005} search_tool Tool(idsearch.v1, namesearch, call_fnsearch_fn) reg ToolRegistry() reg.register(search_tool) tool reg.get(search) obs tool.call({q:test}) self.assertIn(results, obs) cost CostCounter() cost.add_from_tool_obs(obs) self.assertAlmostEqual(cost.monetary, 0.005, places6) class TestReactLoop(unittest.TestCase): def test_react_single_thread_answer(self): # LLM will return a direct valid JSON with an answer fixed json.dumps({thought:final,action:None,answer:42,confidence:1.0}) llm DummyLLM(responses{Produce JSON output.: fixed}) reg ToolRegistry() state {summary:{goal:compute 6*7}} res react_single_thread(state, llm, reg, budget{tokens:1000, monetary:1.0}, max_steps3) self.assertEqual(res[status], success) self.assertEqual(res[answer], 42) def test_react_single_thread_tool_call(self): # LLM returns an action calling search, then second LLM call returns answer first json.dumps({thought:do search,action:{tool:search,input:{q:x}},answer:None,confidence:0.6}) second json.dumps({thought:got it,action:None,answer:found,confidence:0.9}) llm DummyLLM(responses{Produce JSON output.: first, State summary: second}) def search_fn(inp): return {results:[{title:r}], monetary_cost: 0.002} reg ToolRegistry() reg.register(Tool(idsearch.v1, namesearch, call_fnsearch_fn)) state {summary:{goal:find x}} res react_single_thread(state, llm, reg, budget{tokens:1000, monetary:1.0}, max_steps5) self.assertEqual(res[status], success) self.assertEqual(res[answer], found) if __name__ __main__: unittest.main(verbosity2)