feat(agent): Wave 1 quick wins (G1/G2/G3/G8) + review fixes #4
|
|
@ -33,6 +33,12 @@ A phase dynamically inserted into a team plan when divergence is detected betwee
|
|||
### Resume
|
||||
The act of rebuilding a crashed pipeline's runtime state from persisted checkpoints. Restores completed and failed phase statuses, rebuilds runtime counters, and re-persists any dynamically inserted phases so the restored plan matches what was executing at crash time.
|
||||
|
||||
### Verify Re-injection
|
||||
The feedback loop triggered when a verification check fails after a final answer is produced. Errors are injected as a new user message into the conversation and the ReAct loop continues for one retry; a second verification failure interrupts execution and returns the error to the user with the verify log. Bounded to one retry to balance auto-correction against token cost, rather than looping until `max_steps`.
|
||||
|
||||
### Three-tier Degradation Chain
|
||||
The agent-level fallback sequence when the primary agent fails: main agent → Recovery tier (reuses `ReflexionEngine` for Evaluate→Reflect→Retry) → Emergency tier (rule-based fallback returning a structured error with suggestions). Each tier is independently configurable; the Recovery tier avoids new infrastructure by reusing the existing reflection engine, and the Emergency tier replaces the previous static-text fallback with actionable error structure.
|
||||
|
||||
## Channels & Caching
|
||||
|
||||
### Per-User Cache Namespace
|
||||
|
|
|
|||
|
|
@ -0,0 +1,186 @@
|
|||
---
|
||||
date: 2026-06-29
|
||||
topic: advanced-agent-gap-optimization
|
||||
type: feature
|
||||
origin: "2026-06-24-004-feat-long-horizon-reliability-optimization-plan.md(增补);Qoder/Codex/Hermes/Trae Work 架构对比(2026-06-29)"
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
为 2026-06-24-004 长程可靠性 plan 补齐 9 个真新缺口——对比 Qoder/Codex/Hermes/Trae Work 后未被覆盖的执行/反馈/效率短板。分 3 波按 ROI/风险交付:Wave 1 自包含快速补强(W1-1~W1-4),Wave 2 中等耦合(W2-1~W2-3),Wave 3 战略级重构(W3-1~W3-2)。每波独立 plan、独立验证、独立发布。
|
||||
|
||||
---
|
||||
|
||||
## Problem Frame
|
||||
|
||||
2026-06-24-004 plan 已落地循环检测(U1)、Headroom 压缩(U3)、SharedWorkspace Redis 化(U4)、中间件管道(U6)、阶段级 checkpoint+resume(U7)等长程可靠性护栏。但对照 Qoder(Spec→Coding→Verify 闭环)、Codex(apply_patch+OS 沙箱)、Hermes(三级降级链+4层记忆)、Trae Work(SOLO 四阶段状态机+Trajectory 持久化)的工程实践,agentkit 仍存在 9 个**已在生产/测试中观察到痛点**的短板,横跨三个维度:
|
||||
|
||||
- **反馈稳定性**:verify 失败不回灌 ReAct 直接退出、工具调用无 schema 校验、主 agent 失败后仅静态 fallback、子任务失败不回滚已写文件
|
||||
- **响应效率**:prompt cache 命中率低(记忆注入破坏前缀)、摘要/压缩用主模型、文件读取无函数级分片、token chunk 无节流
|
||||
- **执行能力**:ReAct 循环无阶段约束(think 阶段即可 write_file)
|
||||
|
||||
验证器已核对 10 项仓库声明,9 项 confirmed,2 处修正(C5:agentkit 无独立 `read_file` 工具,文件读取走 `tools/shell.py` 的 `cat`;C6:`PlanPhase` 定义在 `experts/plan.py:148` 非 `team.py`)。
|
||||
|
||||
---
|
||||
|
||||
## Key Decisions
|
||||
|
||||
**KTD1:G1 verify 回灌一次后中断**
|
||||
|
||||
verify 失败时自动把 errors 注入 conversation 继续 ReAct,但仅重试一次;二次失败则中断返回错误给用户(附 verify log)。平衡自动纠正与 token 成本,而非全自动循环到 max_steps 烧 token。
|
||||
|
||||
**KTD2:3 波按 ROI/风险分次**
|
||||
|
||||
Wave 1(G1/G2/G3/G8)自包含、低风险、可独立验证;Wave 2(G4/G7/G9)中等耦合,需触碰 LLM 层与编排层;Wave 3(G5/G6)战略级,引入新依赖或触 ReAct 核心。优先交付自包含补强,而非按维度或 LLM 层依赖分波。
|
||||
|
||||
**KTD3:G2 三层 prompt 结构跨 provider,记忆注入移到 volatile**
|
||||
|
||||
system prompt 重构为 stable(技能配置/系统指令)/ context(会话上下文)/ volatile(记忆检索+时间戳)三层。`memory_retriever` 当前在 `react.py:1042-1059` 把"## 参考信息"拼到 system prompt 末尾,每次随 query 变化破坏 cache 前缀——移到 volatile 层,stable 层保持不变以命中 prompt cache。cache 策略跨 provider 统一:Anthropic 用原生 `cache_control` 断点(`system_and_3`),OpenAI 等依赖自动前缀缓存(stable 层前置即命中),无需为每个 provider 单独适配。
|
||||
|
||||
**KTD4:G7 复用 ReflexionEngine 作为 Recovery 层**
|
||||
|
||||
主 agent 失败后触发 Recovery 层,复用现有 `ReflexionEngine`(`core/reflexion.py:58`,Evaluate→Reflect→Retry),而非新建 Recovery Agent。Recovery 仍失败则进入 Emergency 层(规则化 fallback,返回结构化错误+建议)。避免新基础设施,最大化复用现有反思机制。
|
||||
|
||||
**KTD5:G9 阶段级 rollback,git checkout 机制**
|
||||
|
||||
`PlanPhase` 增加 `validation_command`/`rollback_command` 可选字段,阶段失败时执行 rollback(默认 `git checkout`),与 U7 checkpoint 协同——checkpoint save 必须在 rollback validation 通过后。不做步骤级 rollback(粒度过细,实现复杂度高)。
|
||||
|
||||
**KTD6:G5 函数级分片引入 tree-sitter(Wave 3 决策可延后)**
|
||||
|
||||
Wave 3 的 G5 需要按 symbol/函数粒度分片读取文件,引入 tree-sitter 作为新依赖。具体集成方式(tree-sitter vs ANTLR4 vs 复用 `quality` 模块)延后到 Wave 3 自己的 plan 决策。
|
||||
|
||||
**KTD7:G6 扩展 PLAN_EXEC 而非新建模式**
|
||||
|
||||
G6 SOLO 四阶段状态机约束通过扩展现有 `ExecutionMode.PLAN_EXEC` 实现,而非新建独立模式。阶段约束(Planning 阶段只允许 think/search,Building 才允许 write_file)作为 PLAN_EXEC 的配置项。
|
||||
|
||||
---
|
||||
|
||||
## Requirements
|
||||
|
||||
### Wave 1 — 自包含快速补强(P0,低风险)
|
||||
|
||||
**G1 Verify 失败回灌 ReAct**
|
||||
|
||||
- R1. verify 失败时,系统自动把 errors 作为新 user 消息注入 conversation,继续 ReAct 循环,而非直接抛 `TaskResult.error_message` 退出。
|
||||
- R2. 回灌后若二次 verify 仍失败,系统中断执行并返回错误给用户,附 verify log(测试输出/schema 错误明细)。
|
||||
- R3. 回灌最大重试次数可配置(默认 1 次),受 `max_steps` 上限约束。
|
||||
|
||||
**G2 prompt cache 断点策略**
|
||||
|
||||
- R4. system prompt 重构为三层:stable(技能配置/系统指令)、context(会话上下文)、volatile(记忆检索+时间戳)。
|
||||
- R5. 记忆检索注入从 system prompt 末尾移到 volatile 层,stable 层保持不变以命中 prompt cache。
|
||||
- R6. 跨 provider 统一 cache 策略:Anthropic 显式插入 `cache_control` 断点(`system_and_3`,最多 4 个);OpenAI 等无原生断点的 provider 依赖自动前缀缓存,通过 stable 层前置保证命中。
|
||||
- R7. 多轮对话输入 token 成本降低(目标降低 ~50% 输入 token,跨 provider 均受益)。
|
||||
|
||||
**G3 工具调用 schema 校验**
|
||||
|
||||
- R8. `_execute_tool` 调用工具前,基于 `tool.input_schema` 校验 LLM 传入参数(类型/必填)。
|
||||
- R9. 校验失败时返回类型化错误码(`tool_call_invalid`/`schema_mismatch`),不执行工具。
|
||||
- R10. 错误以 tool 角色消息回灌 conversation,给 LLM 自我修正机会。
|
||||
|
||||
**G8 delta_flush_interval 调速**
|
||||
|
||||
- R11. `execute_stream` 的 token chunk yield 加可配置节流(默认 `flush_interval_ms`,如 50ms)。
|
||||
- R12. 节流配置化(`agentkit.yaml` 或 runtime 配置),允许客户端调高降低渲染开销。
|
||||
|
||||
### Wave 2 — 中等耦合(P1)
|
||||
|
||||
**G4 辅助 LLM 分流**
|
||||
|
||||
- R13. `ContextCompressor` 摘要任务路由到 auxiliary model(便宜模型,如 Gemini Flash/Doubao lite),而非主 model。
|
||||
- R14. `auxiliary_model` 配置化,与主 `model` 分离。
|
||||
- R15. 摘要质量不降级:auxiliary model 失败时回退主 model。
|
||||
|
||||
**G7 三级降级链**
|
||||
|
||||
- R16. 主 agent 失败后触发 Recovery 层(复用 `ReflexionEngine` 做 Evaluate→Reflect→Retry)。
|
||||
- R17. Recovery 仍失败触发 Emergency 层(规则化 fallback,返回结构化错误+建议)。
|
||||
- R18. 降级链可配置(每层最大重试次数、是否启用 Recovery/Emergency)。
|
||||
|
||||
**G9 原子化子任务 + rollback 绑定**
|
||||
|
||||
- R19. `PlanPhase`(`experts/plan.py:148`)增加 `validation_command` 和 `rollback_command` 可选字段。
|
||||
- R20. 阶段失败时自动执行 `rollback_command`(默认 `git checkout`),与 U7 checkpoint 协同。
|
||||
- R21. checkpoint save 必须在 rollback validation 通过后(避免持久化失败状态)。
|
||||
|
||||
### Wave 3 — 战略级重构(P2,高风险)
|
||||
|
||||
**G5 函数级代码分片**
|
||||
|
||||
- R22. 文件读取支持按 symbol/函数粒度分片(需引入 tree-sitter 或类似,具体方式延后到 Wave 3 plan)。
|
||||
- R23. 分片能力作为工具参数(如 `symbol="function_name"`),向后兼容整文件读取。
|
||||
|
||||
**G6 SOLO 四阶段状态机约束**
|
||||
|
||||
- R24. ReAct 循环加阶段约束:Planning 阶段只允许 think/search,Building 阶段才允许 `write_file`。
|
||||
- R25. 阶段状态可配置(扩展 `ExecutionMode.PLAN_EXEC`,非新建独立模式)。
|
||||
|
||||
### Cross-cutting
|
||||
|
||||
- R26. 所有优化项配置化(`agentkit.yaml` 新增对应配置节,遵循 `ServerConfig.from_dict` 模式)。
|
||||
- R27. 每个优化项附最小自检测试(ponytail 规则,参考 `test_pipeline_state.py` 的 `TestPipelineStateRedis` 模式)。
|
||||
|
||||
---
|
||||
|
||||
## Acceptance Examples
|
||||
|
||||
- **AE1**(Covers R1, R2, R3):用户发起 ReAct 任务,verify 失败(测试不通过)→ 系统自动把 errors 注入 conversation 继续 ReAct → LLM 修正后二次 verify 通过 → 任务完成。若二次 verify 仍失败 → 中断返回错误,附 verify log。
|
||||
- **AE2**(Covers R4, R5, R6, R7):用户发起 50 轮长对话 → stable 层(技能配置)保持不变,volatile 层(记忆检索)随 query 变化 → Anthropic prompt cache 命中 stable 前缀 → 输入 token 成本降低 ~50%。
|
||||
- **AE3**(Covers R8, R9, R10):LLM 调用工具时传错参数类型(如 `count: "abc"` 应为 int)→ schema 校验失败 → 返回 `tool_call_invalid` 错误码 → 错误回灌 conversation → LLM 修正参数类型后重试成功。
|
||||
- **AE4**(Covers R16, R17, R18):主 agent 连续 3 次失败 → 触发 Recovery 层(ReflexionEngine 反思重试)→ Recovery 仍失败 → 触发 Emergency 层(规则化 fallback,返回结构化错误+建议)→ 用户收到清晰错误而非静态文案。
|
||||
- **AE5**(Covers R19, R20, R21):`@team` 任务阶段 3 失败 → 自动执行 `rollback_command`(git checkout 阶段 3 写入的文件)→ rollback validation 通过后才 save checkpoint → 阶段 4 从干净状态继续。
|
||||
- **AE6**(Covers R11, R12):弱网客户端连接 `execute_stream` → token chunk 按 50ms 节流批量 yield → 前端渲染开销降低,无卡顿。
|
||||
|
||||
---
|
||||
|
||||
## Scope Boundaries
|
||||
|
||||
### Deferred for later
|
||||
|
||||
- Wave 3(G5/G6)的具体实现设计延后到 Wave 3 自己的 plan,本文档只锁定战略方向(KTD6/KTD7)。
|
||||
- G5 的具体集成方式(tree-sitter vs ANTLR4 vs 复用 `quality` 模块)延后到 Wave 3 plan 决策。
|
||||
- G7 Emergency 层的具体规则模板(返回什么错误结构、建议什么动作)延后到 Wave 2 plan。
|
||||
- 全局 LLM 并发限制(`LLMGateway` 内 semaphore)——本期只做工具层/编排层,LLM 层并发延后。
|
||||
|
||||
### Outside this product's identity
|
||||
|
||||
- 重写现有编排逻辑(拓扑排序/Board 辩论/4 层记忆保持不变)——继承自 2026-06-24-004 plan。
|
||||
- 节点级 checkpoint(ReAct 循环单步)——继承自 2026-06-24-004 KTD3,阶段级已满足核心需求。
|
||||
- DeerFlow 式磁盘文件系统——继承自 2026-06-24-004 KTD4,复用 Redis。
|
||||
- 全盘迁移 LangGraph——继承自 2026-06-24-004,自研架构保持灵活。
|
||||
- Docker 沙箱默认引入——仅文档化命令级安全边界,作为可选插件未来考虑。
|
||||
|
||||
---
|
||||
|
||||
## Dependencies / Assumptions
|
||||
|
||||
- **依赖**:Wave 2 的 G9 rollback 依赖现有 U7 checkpoint 已落地(`orchestrator/checkpoint.py` + `experts/orchestrator.py:265-268,335`)。
|
||||
- **依赖**:Wave 1 的 G2 prompt cache 断点依赖 Anthropic provider 已支持 `cache_control`(LiteLLM 适配层 U15)。
|
||||
- **假设**:G4 auxiliary model 可用——`agentkit.yaml` 的 llm 段可配置多个 model,auxiliary 复用现有 provider 注册机制。若实际不可用,回退主 model(R15)。
|
||||
- **假设**:G6 阶段约束不影响现有 DIRECT_CHAT/REACT 模式——仅作用于 PLAN_EXEC 模式,向后兼容。
|
||||
|
||||
---
|
||||
|
||||
## Sources & Research
|
||||
|
||||
- **Qoder 架构研究**(2026-06-29):Spec→Coding→Verify 闭环、SSE 事件成对约束、`delta_flush_interval_ms`、prompt cache、自动模型路由
|
||||
- **Codex CLI 架构研究**(2026-06-29):apply_patch 协议、Approval Policy 三层决策、OS 级沙箱、prompt caching 前缀匹配、`/responses/compact` 压缩
|
||||
- **Hermes Agent 架构研究**(2026-06-29):Pydantic+JSON Schema 双校验、`validate_function_call_schema`、三级降级链(主→Recovery→Emergency)、`cache_control: system_and_3` 断点(4 个,~75% token 节省)、auxiliary_client 分流
|
||||
- **Trae Work 架构研究**(2026-06-29):SOLO 四阶段状态机(Planning→Building→Verification→Delivery)、TrajectoryRecorder JSON 持久化、断点续跑、原子化子任务+rollback、函数级分片(ANTLR4+LLVM)
|
||||
- **agentkit 现有架构**(2026-06-29 验证器核对):`verification_loop.py:111-145`、`react.py:1042-1059,1118-1134,1897-1916`、`compressor.py:45,123-154`、`reflexion.py:58-702`、`fallback.py:1-19`、`gateway.py:405-407`、`experts/plan.py:148`、`orchestrator/checkpoint.py`、`experts/orchestrator.py:265-268,335`
|
||||
- **2026-06-24-004 plan**(增补来源):U1-U7 长程可靠性护栏,KTD1-KTD5 决策,U7 阶段级 checkpoint+resume 已落地
|
||||
|
||||
---
|
||||
|
||||
## Outstanding Questions
|
||||
|
||||
### Resolve Before Planning
|
||||
|
||||
(已全部解决——OQ1 确认同 plan 内顺序执行 G2→G8;OQ2 确认跨 provider 统一 cache 策略。)
|
||||
|
||||
### Deferred to Planning
|
||||
|
||||
- G5 的具体集成方式(tree-sitter vs ANTLR4 vs 复用 `quality` 模块)。
|
||||
- G7 Emergency 层的具体规则模板与错误结构。
|
||||
- G6 阶段约束的精确边界(Planning 阶段允许哪些工具、Building 阶段允许哪些)。
|
||||
- G9 rollback_command 的默认值(git checkout 整文件 vs patch 级)。
|
||||
|
|
@ -0,0 +1,275 @@
|
|||
---
|
||||
title: "feat: Agent Wave 1 快速补强 (verify 回灌/prompt cache/schema 校验/delta_flush)"
|
||||
type: feat
|
||||
date: 2026-06-29
|
||||
origin: docs/brainstorms/2026-06-29-advanced-agent-gap-optimization-requirements.md
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
落地 brainstorm Wave 1 的 4 项自包含快速补强:G1 verify 失败回灌 ReAct、G2 prompt cache 三层结构跨 provider、G3 工具调用 schema 校验、G8 delta_flush_interval 调速。四项均作用于 ReAct 引擎层,同 plan 内 G2→G8 顺序执行(共享 `execute_stream` 改动),每项附最小自检测试。
|
||||
|
||||
---
|
||||
|
||||
## Problem Frame
|
||||
|
||||
agentkit 对比 Qoder/Codex/Hermes/Trae 后发现 9 个真新缺口(已在生产/测试观察到痛点),brainstorm 分 3 波交付。本 plan 实现 Wave 1——自包含、低风险的 4 项快速补强,覆盖反馈稳定性(verify 不回灌、工具无 schema 校验)与响应效率(prompt cache 命中率低、token chunk 无节流)。
|
||||
|
||||
验证器已核对仓库现状(见 origin Sources & Research):`verification_loop.py:111-145` verify 失败仅调 `fix_callback` 不回灌 ReAct;`react.py:1042-1059` 记忆注入拼到 system prompt 末尾破坏 cache 前缀;`react.py:1118-1134` 每个 token chunk 都 yield 无节流;`tools/base.py:50-77` `safe_execute` 无 schema 校验直接 `await execute()`。
|
||||
|
||||
---
|
||||
|
||||
## Requirements
|
||||
|
||||
### G1 Verify 失败回灌 ReAct (origin R1-R3)
|
||||
|
||||
- R1. verify 失败时,系统自动把 errors 作为新 user 消息注入 conversation,继续 ReAct 循环,而非直接退出。
|
||||
- R2. 回灌后若二次 verify 仍失败,系统中断执行并返回错误给用户,附 verify log。
|
||||
- R3. 回灌最大重试次数可配置(默认 1 次),受 `max_steps` 上限约束。
|
||||
|
||||
### G2 Prompt Cache 三层结构 (origin R4-R7)
|
||||
|
||||
- R4. system prompt 重构为双块结构:stable(技能配置/系统指令)+volatile(记忆检索+时间戳)。原 context(会话上下文)层由 conversation messages 承载,不进 system prompt。
|
||||
- R5. 记忆检索注入从 system prompt 末尾移到 volatile 层,stable 层保持不变。
|
||||
- R6. 跨 provider 统一 cache 策略:Anthropic 显式插入 `cache_control` 断点;OpenAI 等依赖自动前缀缓存。
|
||||
- R7. 多轮对话输入 token 成本降低(目标 ~50%)。
|
||||
|
||||
### G3 工具调用 Schema 校验 (origin R8-R10)
|
||||
|
||||
- R8. `safe_execute` 调用 `execute()` 前,基于 `tool.input_schema` 校验参数(类型/必填)。
|
||||
- R9. 校验失败时返回类型化错误码(`tool_call_invalid`/`schema_mismatch`),不执行工具。
|
||||
- R10. 错误以 tool 角色消息回灌 conversation,给 LLM 自我修正机会。
|
||||
|
||||
### G8 delta_flush_interval 调速 (origin R11-R12)
|
||||
|
||||
- R11. `execute_stream` 的 token chunk yield 加可配置节流(默认 `flush_interval_ms`)。
|
||||
- R12. 节流配置化,允许客户端调高降低渲染开销。
|
||||
|
||||
### Cross-cutting (origin R26-R27)
|
||||
|
||||
- R13. 所有优化项配置化(`agentkit.yaml` 新增对应配置节)。
|
||||
- R14. 每个优化项附最小自检测试(ponytail 规则)。
|
||||
|
||||
---
|
||||
|
||||
## Key Technical Decisions
|
||||
|
||||
**KTD1:G3 schema 校验放在 `Tool.safe_execute`(base.py),而非 `_execute_tool`(react.py)**
|
||||
|
||||
校验在工具基类层,所有调用方(ReActEngine、ExpertTeam、StandaloneAgent)统一受益。以 `tool.input_schema`(JSON Schema dict)为契约源,`input_schema=None` 时跳过校验保持向后兼容。用 `jsonschema` 库(已是 Python 生态标准,无新依赖)。
|
||||
|
||||
**KTD2:G2 双块结构用 content blocks + cache_control 标记,Anthropic provider 需改 _convert_messages**
|
||||
|
||||
system message 的 `content` 从字符串改为 content blocks 列表(`[{"type":"text","text":stable,"cache_control":{"type":"ephemeral"}},{"type":"text","text":volatile}]`)。**注意**:`src/agentkit/llm/providers/anthropic.py` 是 httpx 直连实现(非 LiteLLM),其 `_convert_messages`(`:102-197`)假设 system content 为字符串(`:116`),需修改以支持 list-type system content 并透传 `cache_control` blocks。OpenAI 等 provider 的 chat completions API 不支持 list-type system content,`_build_system_message` 需按 provider 能力检测:Anthropic 返回 blocks,其余返回字符串拼接(stable+volatile),依赖 stable 前缀命中自动前缀缓存。不改 gateway 方法签名。
|
||||
|
||||
**KTD3:G1 verify 回灌包进 ReAct 主循环,而非外层 wrapper**
|
||||
|
||||
verify 当前在循环外 final answer 后运行(`react.py:887` execute / `:1603` execute_stream)。回灌改为:检测到 final answer(无 tool_calls)→ 运行 verify → 失败则把 errors 作为 user 消息 append 到 conversation → `continue` 主循环(LLM 自纠正)→ 二次 final answer 再 verify → 仍失败则 break 带 verify log。保留现有 `VerificationLoop` 类与 `verify_and_retry` 方法不动(向后兼容),回灌逻辑在 ReActEngine 内。
|
||||
|
||||
**KTD4:G8 delta_flush 用 time.monotonic 节流,非计数器**
|
||||
|
||||
在 `execute_stream` chunk 循环内累积 chunks,按 `flush_interval_ms` 间隔批量 yield。`flush_interval_ms=0` 时退化为逐 chunk yield(向后兼容)。流结束 mid-interval 时最终 flush。用 `time.monotonic()`(不受系统时钟跳变影响)。
|
||||
|
||||
**KTD5:G2→G8 同 plan 内顺序执行,共享 execute_stream 改动**
|
||||
|
||||
G2 改 system prompt 构造(循环前),G8 改 chunk yield 逻辑(循环内),两者不冲突但都触 `execute_stream`。G2 先落地确保 stable 前缀结构稳定,G8 再加节流避免在未稳定结构上叠加。
|
||||
|
||||
**KTD6:ServerConfig 到 ReActEngine 的接线用独立构造参数(带默认值)**
|
||||
|
||||
`ReActEngine.__init__`(`react.py:154-198`)不接受 `ServerConfig` 对象,采用独立构造参数:`prompt_cache_enable: bool = True`、`flush_interval_ms: int = 0`、`max_reinjections: int = 1`(默认值保当前行为,向后兼容)。`ServerConfig` 在 agent 工厂/handler 层(`chat/handler.py` 等 ReActEngine 构造点)读取并传入。实现时需列出所有构造 ReActEngine 的调用点并更新。
|
||||
|
||||
---
|
||||
|
||||
## Implementation Units
|
||||
|
||||
### U1. G3 工具调用 Schema 校验
|
||||
|
||||
**Goal:** 在 `Tool.safe_execute` 调用 `execute()` 前校验参数,失败返回类型化错误。
|
||||
|
||||
**Requirements:** R8, R9, R10, R14
|
||||
|
||||
**Dependencies:** 无(独立,奠基性——定义错误回灌模式)
|
||||
|
||||
**Files:**
|
||||
- 修改: `src/agentkit/tools/base.py`(`safe_execute` 加校验 + `ToolValidationError`)
|
||||
- 修改: `src/agentkit/core/react.py`(`_execute_tool` `:1897-1916` 捕获 `ToolValidationError` 并 append tool 角色消息)
|
||||
- 测试: `tests/unit/test_tool_schema_validation.py`
|
||||
|
||||
**Approach:**
|
||||
- 新增 `ToolValidationError(Exception)`,带 `error_code`(`tool_call_invalid`/`schema_mismatch`)与 `details`。
|
||||
- `safe_execute` 在 `before_execute` 后、`execute` 前:若 `self.input_schema` 非 None,用 `jsonschema.validate(kwargs, self.input_schema)`;校验失败抛 `ToolValidationError`。
|
||||
- `input_schema=None` → 跳过(向后兼容,旧工具无 schema)。
|
||||
- `_execute_tool`(react.py:1897-1916)在现有 `except Exception` **之前**加 `except ToolValidationError as e:` 优先捕获,返回 `{"error": str(e), "error_code": e.error_code, "details": e.details}`(保留类型化错误码,不被通用 except 平坦化为字符串)。现有调用方 `_build_tool_result_message` 把返回 dict 转为 tool 角色消息 append 到 conversation,给 LLM 自纠正机会。
|
||||
|
||||
**Patterns to follow:** `VerificationResult` 的类型化错误模式(`verification_loop.py:18-24`);`jsonschema` 标准用法。
|
||||
|
||||
**Test scenarios:**
|
||||
- Covers R8. Happy path: tool 有 `input_schema={"type":"object","properties":{"count":{"type":"integer"}},"required":["count"]}`,传 `count=5` → 校验通过,execute 正常执行。
|
||||
- Covers R9. Edge: `input_schema=None` → 跳过校验,execute 正常(向后兼容)。
|
||||
- Covers R9. Error: 传 `count="abc"`(类型错)→ 抛 `ToolValidationError(error_code="tool_call_invalid")`,execute 不调用。
|
||||
- Covers R9. Error: 缺 `count`(必填)→ 抛 `ToolValidationError(error_code="schema_mismatch")`。
|
||||
- Covers R10. Integration: `_execute_tool` 捕获 `ToolValidationError` → conversation append tool 角色消息 → LLM 下一轮看到错误并修正参数 → 重试成功。
|
||||
|
||||
**Verification:** `python3 -m pytest tests/unit/test_tool_schema_validation.py -x -q` 通过;现有工具测试不回归。
|
||||
|
||||
---
|
||||
|
||||
### U2. G2 Prompt Cache 双块结构
|
||||
|
||||
**Goal:** system prompt 重构为 stable/volatile 双块结构,记忆注入移到 volatile,加 cache_control 断点。
|
||||
|
||||
**Requirements:** R4, R5, R6, R7, R13, R14
|
||||
|
||||
**Dependencies:** 无(独立,与 U1 不触同代码区)
|
||||
|
||||
**Files:**
|
||||
- 修改: `src/agentkit/core/react.py`(execute_stream `:1042-1059` 记忆注入 + system message 构造;execute 同路径若有,可选 — 见 Scope Boundaries)
|
||||
- 修改: `src/agentkit/llm/providers/anthropic.py`(`_convert_messages` `:102-197` 支持 list-type system content,透传 cache_control blocks)
|
||||
- 配置: `src/agentkit/config.py` 或 `ServerConfig`(`prompt_cache.enable: bool`)
|
||||
- 测试: `tests/unit/test_prompt_cache_layers.py`
|
||||
|
||||
**Approach:**
|
||||
- 新增 `_build_system_message(base_prompt, memory_context, enable_cache, provider)` 工具方法:Anthropic provider 返回 content blocks 列表,stable 块在前(带 `cache_control: {"type":"ephemeral"}`),volatile 块(记忆+时间戳)在后;非 Anthropic provider 返回字符串拼接(stable+volatile),依赖 stable 前缀命中自动前缀缓存。
|
||||
- execute_stream `:1042-1059`:记忆注入从 `system_prompt += "## 参考信息"` 改为收集 `memory_context`,传给 `_build_system_message`。
|
||||
- conversation 的 system 消息 content:Anthropic 用 blocks 列表,其余用字符串;gateway `chat_stream` 经 `**kwargs` 透传。
|
||||
- `anthropic.py` 的 `_convert_messages`(`:102-197`)需修改:`:116` 从 `system_prompt = content` 改为支持 list-type content 直接透传(payload `["system"]` 接受字符串或 content blocks)。
|
||||
- 配置 `prompt_cache.enable: bool`(默认 True)。断点数硬编码为 1(stable 层),不暴露配置(YAGNI — 2 块结构下 >1 无语义)。
|
||||
- `enable_cache=False` 或 provider 非 Anthropic → 退化为字符串拼接(向后兼容)。
|
||||
|
||||
**Patterns to follow:** LiteLLM/Anthropic `cache_control` content block 规范;现有 `memory_retriever.get_context_string` 调用不变。
|
||||
|
||||
**Test scenarios:**
|
||||
- Covers R4, R5. Happy path: 多轮对话,stable 层(技能配置)跨轮不变,volatile 层(记忆)随 query 变 → system message content blocks 结构正确,stable 在前 volatile 在后。
|
||||
- Covers R6. Integration: Anthropic provider 收到带 `cache_control` 的 content blocks → `_convert_messages` 透传 → cache 命中 stable 前缀。
|
||||
- Covers R6. Edge: OpenAI provider(provider != anthropic)→ `_build_system_message` 返回字符串拼接,不报错;stable 前缀命中自动前缀缓存。
|
||||
- Covers R5. Edge: `memory_retriever` 返回空 → 无 volatile 块,system message 仅 stable。
|
||||
- Covers R13. Config: `prompt_cache.enable=False` → 退化为字符串拼接,行为同改动前。
|
||||
|
||||
**Verification:** `python3 -m pytest tests/unit/test_prompt_cache_layers.py -x -q` 通过;多轮对话 system message 结构符合预期。
|
||||
|
||||
---
|
||||
|
||||
### U3. G8 delta_flush_interval 调速
|
||||
|
||||
**Goal:** execute_stream token chunk yield 加时间节流。
|
||||
|
||||
**Requirements:** R11, R12, R13, R14
|
||||
|
||||
**Dependencies:** U2(共享 execute_stream,G2 先改 system prompt 结构,G8 后改 yield 逻辑)
|
||||
|
||||
**Files:**
|
||||
- 修改: `src/agentkit/core/react.py`(`execute_stream` chunk 循环 `:1118-1134`;execute 同路径若有)
|
||||
- 配置: `ServerConfig`(`streaming.flush_interval_ms`)
|
||||
- 测试: `tests/unit/test_delta_flush.py`
|
||||
|
||||
**Approach:**
|
||||
- chunk 循环内:累积 `stream_content_chunks` 同时累积 `_flush_buffer`;用 `time.monotonic()` 记 `_last_flush_ts`。
|
||||
- 当 `now - _last_flush_ts >= flush_interval_ms/1000` 时:yield 合并后的 buffer,清空,更新 ts。
|
||||
- `flush_interval_ms=0` → 逐 chunk yield(向后兼容,当前行为)。
|
||||
- 流结束(for 循环退出)→ 最终 flush 剩余 buffer。
|
||||
|
||||
**Patterns to follow:** `time.monotonic()` 用法(已在 `:1080` `_stream_start` 使用);现有 `ReActEvent(event_type="token")` 结构不变。
|
||||
|
||||
**Test scenarios:**
|
||||
- Covers R11. Happy path: `flush_interval_ms=50`,模拟连续 chunks → 按 50ms 间隔批量 yield,合并 content。
|
||||
- Covers R12. Config: `flush_interval_ms=0` → 逐 chunk yield(向后兼容)。
|
||||
- Edge: 流结束 mid-interval → 最终 flush 剩余 buffer,不丢 content。
|
||||
- Edge: 单个 chunk 后流结束 → 立即 flush。
|
||||
- Covers R14. Self-check: 断言 yield 的合并 content 等于原始 chunks 拼接(不丢字符)。
|
||||
|
||||
**Verification:** `python3 -m pytest tests/unit/test_delta_flush.py -x -q` 通过;token 流无字符丢失。
|
||||
|
||||
---
|
||||
|
||||
### U4. G1 Verify 失败回灌 ReAct
|
||||
|
||||
**Goal:** verify 失败时把 errors 注入 conversation 继续 ReAct,二次失败中断带 log。
|
||||
|
||||
**Requirements:** R1, R2, R3, R13, R14
|
||||
|
||||
**Dependencies:** U1(复用错误回灌模式;U1 的 `ToolValidationError` 回灌与 G1 verify 回灌同模式)
|
||||
|
||||
**Files:**
|
||||
- 修改: `src/agentkit/core/react.py`(`execute` `:886-907` verify 块;`execute_stream` `:1601-1629` verify 块)
|
||||
- 配置: `ServerConfig`(`verification.max_reinjections: int = 1`)
|
||||
- 测试: `tests/unit/test_verify_reinjection.py`
|
||||
|
||||
**Approach:**
|
||||
- 把 verify 从"循环后一次性运行"改为"final answer 检测点 + 回灌重试"。
|
||||
- 主循环内检测到 final answer(无 tool_calls)时:`if self._verification_enabled` → 运行 verify。
|
||||
- verify 通过 → `break`,正常结束。
|
||||
- verify 失败且 `reinjections < max_reinjections`:append `{"role":"user","content":f"验证失败,错误如下:\n{vresult.errors}"}` 到 conversation,`continue` 主循环(LLM 见 errors 自纠正)。
|
||||
- verify 失败且 `reinjections >= max`:记录 verify log 到 trajectory,`break` 返回失败结果。
|
||||
- 保留现有 `VerificationLoop` 类与 `verify_and_retry` 不动(向后兼容,外部调用方仍可用)。
|
||||
|
||||
**Patterns to follow:** 现有 verify 块的 trajectory/event 记录模式;U1 的 `ToolValidationError` 回灌模式。
|
||||
|
||||
**Execution note:** 先加 characterization 测试覆盖现有 verify 行为(失败仅记录),再改实现确保不回归。
|
||||
|
||||
**Test scenarios:**
|
||||
- Covers R1. Happy path: verify 首次失败 → errors 注入 conversation → LLM 自纠正 → 二次 verify 通过 → 任务完成。
|
||||
- Covers R2. Error: verify 二次失败 → 中断,返回错误附 verify log(测试输出 + errors 列表)。
|
||||
- Covers R3. Config: `max_reinjections=0` → 等价于不回灌(当前行为),verify 失败直接退出。
|
||||
- Covers R3. Edge: 回灌期间达到 `max_steps` → 中断(不无限循环)。
|
||||
- Covers R1. Integration: 回灌的 user 消息出现在 conversation,LLM 下一轮 input 含 errors 文本。
|
||||
- Covers R14. Self-check: `max_reinjections` 默认值为 1。
|
||||
|
||||
**Verification:** `python3 -m pytest tests/unit/test_verify_reinjection.py -x -q` 通过;`pytest tests/unit/ -x -q` 全量不回归。
|
||||
|
||||
---
|
||||
|
||||
## Scope Boundaries
|
||||
|
||||
### Deferred for later
|
||||
|
||||
- Wave 2(G4 辅助 LLM 分流 / G7 三级降级链 / G9 原子化 rollback)——见 origin Wave 2 section,单独 plan。
|
||||
- Wave 3(G5 函数级代码分片 / G6 SOLO 状态机)——见 origin Wave 3 section,单独 plan。
|
||||
- G7 Emergency 层规则模板、G5 tree-sitter 集成方式——origin Deferred to Planning。
|
||||
|
||||
### Deferred to Follow-Up Work
|
||||
|
||||
- `execute()`(非流式)的 G2/G8 改动:本 plan 优先 `execute_stream`(WebSocket 主路径),`execute()` 同步改动可顺带做但非必须,若拆 PR 则归 follow-up。
|
||||
- prompt cache 命中率指标化(R7 的 ~50% 目标):本 plan 落结构,埋点量化归 follow-up。
|
||||
|
||||
### Outside this product's identity
|
||||
|
||||
- 重写编排逻辑/拓扑排序/Board 辩论——继承自 2026-06-24-004。
|
||||
- 节点级 checkpoint——继承自 2026-06-24-004 KTD3。
|
||||
- 全盘迁移 LangGraph——继承自 2026-06-24-004。
|
||||
|
||||
---
|
||||
|
||||
## Risks & Dependencies
|
||||
|
||||
- **风险:G2 content blocks 改动破坏现有 message 序列化。** gateway/messages 假设 `content: str`,改为 blocks 列表需核对所有序列化路径(WebSocket、日志、trace)。缓解:仅 system 消息用 blocks,user/assistant 保持 str;`enable_cache=False` 退化路径保底。
|
||||
- **风险:G1 回灌增加 token 消耗。** 二次 verify 循环会多跑一轮 LLM。缓解:`max_reinjections` 默认 1,受 `max_steps` 上限约束;KTD3 设计为循环内 continue 而非递归,无栈溢出风险。
|
||||
- **依赖:U3 依赖 U2 完成。** 两者共享 `execute_stream`,G2 先改 system prompt 结构稳定后再加 G8 节流(KTD5)。
|
||||
- **依赖:G2 需修改 `anthropic.py` 的 `_convert_messages` 支持 list-type system content。** `anthropic.py` 是 httpx 直连实现(非 LiteLLM),需手动改 `:116` 从 `system_prompt = content` 改为支持 content blocks 透传。非 Anthropic provider 走字符串拼接退化路径,不报错。
|
||||
- **依赖:G3 的 `jsonschema` 库。** `jsonschema>=4.0` 已在 `pyproject.toml` 核心依赖中(line 22),无需新增依赖。
|
||||
|
||||
---
|
||||
|
||||
## Acceptance Examples
|
||||
|
||||
- **AE1**(Covers R1, R2, R3):ReAct 任务 final answer 后 verify 失败 → errors 注入 conversation 继续 ReAct → LLM 修正后二次 verify 通过 → 完成。若二次失败 → 中断返回错误附 verify log。
|
||||
- **AE2**(Covers R4, R5, R6, R7):50 轮长对话 → stable 层不变,volatile 随 query 变 → Anthropic cache 命中 stable 前缀,OpenAI 命中自动前缀缓存 → 输入 token 降低。
|
||||
- **AE3**(Covers R8, R9, R10):LLM 传错参数类型 → schema 校验失败 → `tool_call_invalid` → 错误回灌 conversation → LLM 修正后重试成功。
|
||||
- **AE4**(Covers R11, R12):弱网客户端 → token chunk 按 50ms 批量 yield → 前端渲染无卡顿,无字符丢失。
|
||||
|
||||
---
|
||||
|
||||
## Sources & Research
|
||||
|
||||
- **Origin:** `docs/brainstorms/2026-06-29-advanced-agent-gap-optimization-requirements.md` — Wave 1 需求文档(KTD1-KTD7、R1-R27)
|
||||
- **上游 plan:** `docs/plans/2026-06-24-004-feat-long-horizon-reliability-optimization-plan.md` — U1-U7 长程可靠性护栏(本 plan 增补)
|
||||
- **Learnings:** `docs/solutions/logic-errors/long-horizon-reliability-code-review-fixes.md` — 14 个 finding 教训(新字段默认值保契约、跨模块契约显式化、清理方法接入生命周期)
|
||||
- **代码 grounding(验证器核对):**
|
||||
- `src/agentkit/core/verification_loop.py:111-145` — `verify_and_retry` 接 `fix_callback`,不回灌 ReAct
|
||||
- `src/agentkit/core/react.py:1042-1059` — 记忆注入拼到 system prompt 末尾
|
||||
- `src/agentkit/core/react.py:1118-1134` — chunk 循环逐 token yield 无节流
|
||||
- `src/agentkit/core/react.py:886-907, 1601-1629` — verify 在循环后运行,失败仅记录 trajectory
|
||||
- `src/agentkit/core/react.py:1897-1916` — `_execute_tool` 无 schema 校验
|
||||
- `src/agentkit/tools/base.py:21,28,50-77` — `Tool.input_schema`(JSON Schema dict,可选),`safe_execute` 无校验
|
||||
- `src/agentkit/llm/gateway.py:268-281` — `chat_stream(**kwargs)` 可透传 cache_control
|
||||
- `src/agentkit/llm/providers/anthropic.py:316` — Anthropic provider chat_stream
|
||||
- **外部研究(brainstorm 阶段):** Qoder(Spec→Verify 闭环、delta_flush_interval)、Hermes(cache_control system_and_3、validate_function_call_schema)、Codex(prompt caching 前缀匹配)
|
||||
|
|
@ -0,0 +1,509 @@
|
|||
#!/usr/bin/env bash
|
||||
# =============================================================================
|
||||
# Fischer AgentKit — 本地开发环境一键启动
|
||||
# =============================================================================
|
||||
#
|
||||
# 启动：后端服务（+ 可选 Tauri 桌面客户端）
|
||||
# 停止：Ctrl+C 或运行 scripts/dev-stop.sh
|
||||
#
|
||||
# 用法：
|
||||
# bash scripts/dev-start.sh # Web 模式（agentkit gui）
|
||||
# bash scripts/dev-start.sh --tauri # Web + Tauri 桌面客户端
|
||||
# bash scripts/dev-start.sh --serve # 仅后端 API
|
||||
# bash scripts/dev-start.sh --init # 首次运行（初始化 DB）
|
||||
# bash scripts/dev-start.sh --help # 帮助
|
||||
#
|
||||
# 依赖：
|
||||
# Python >= 3.11, Node.js >= 18, Redis, PostgreSQL (均自动检查)
|
||||
# --tauri 需要：Rust 工具链（rustup / brew install rust）
|
||||
#
|
||||
# =============================================================================
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
|
||||
cd "$PROJECT_ROOT"
|
||||
|
||||
# ── 颜色 ────────────────────────────────────────────────────────────────────
|
||||
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
CYAN='\033[0;36m'
|
||||
NC='\033[0m'
|
||||
|
||||
# ── 帮助 ────────────────────────────────────────────────────────────────────
|
||||
|
||||
show_help() {
|
||||
cat <<-'EOF'
|
||||
Fischer AgentKit — 本地开发环境启动
|
||||
|
||||
用法: bash scripts/dev-start.sh [选项]
|
||||
|
||||
选项:
|
||||
--tauri 启动 Tauri 桌面客户端（Vite 热重载 + 原生窗口）
|
||||
--serve 仅启动后端 API（无前端）
|
||||
--init 首次运行，初始化数据库和测试用户
|
||||
--help 显示此帮助
|
||||
|
||||
模式说明:
|
||||
默认 Web 模式：agentkit gui (前后端 + 内置静态服务)
|
||||
--tauri Tauri 模式：后端 API + Vite (:5173) + Tauri 桌面窗口
|
||||
|
||||
端口映射:
|
||||
8000 — 后端 API
|
||||
8002 — Web GUI / 前端静态服务
|
||||
5173 — Vite 开发服务器（--tauri 模式）
|
||||
EOF
|
||||
}
|
||||
|
||||
# ── 参数解析 ────────────────────────────────────────────────────────────────
|
||||
|
||||
MODE="gui"
|
||||
INIT_DB=0
|
||||
|
||||
while [[ $# -gt 0 ]]; do
|
||||
case $1 in
|
||||
--tauri) MODE="tauri"; shift ;;
|
||||
--serve) MODE="serve"; shift ;;
|
||||
--init) INIT_DB=1; shift ;;
|
||||
--help|-h) show_help; exit 0 ;;
|
||||
*) shift ;;
|
||||
esac
|
||||
done
|
||||
|
||||
# ── 日志函数 ────────────────────────────────────────────────────────────────
|
||||
|
||||
ok() { echo -e " ${GREEN}✓${NC} $*"; }
|
||||
info() { echo -e " ${CYAN}→${NC} $*"; }
|
||||
warn() { echo -e " ${YELLOW}!${NC} $*"; }
|
||||
fail() { echo -e " ${RED}✗${NC} $*" >&2; }
|
||||
|
||||
section() {
|
||||
echo ""
|
||||
echo -e "${CYAN}────────────────────────────────────────${NC}"
|
||||
echo -e "${CYAN} $*${NC}"
|
||||
echo -e "${CYAN}────────────────────────────────────────${NC}"
|
||||
}
|
||||
|
||||
# ── 进度状态机 ──────────────────────────────────────────────────────────────
|
||||
|
||||
# 0=未开始, 1=进行中, 2=成功, 3=失败
|
||||
S_DEPS=0 S_ENV=0 S_REDIS=0 S_PG=0 S_BACKEND=0 S_FRONTEND=0 S_TAURI=0
|
||||
|
||||
set_status() {
|
||||
local step=$1 val=$2
|
||||
case $step in
|
||||
deps) S_DEPS=$val ;;
|
||||
env) S_ENV=$val ;;
|
||||
redis) S_REDIS=$val ;;
|
||||
pg) S_PG=$val ;;
|
||||
backend) S_BACKEND=$val ;;
|
||||
frontend) S_FRONTEND=$val ;;
|
||||
tauri) S_TAURI=$val ;;
|
||||
esac
|
||||
}
|
||||
|
||||
print_status() {
|
||||
echo ""
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo -e "${CYAN} 启动状态总览${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo -e "$([[ $S_DEPS -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_DEPS -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") 依赖检查"
|
||||
echo -e "$([[ $S_ENV -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_ENV -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") 环境配置"
|
||||
echo -e "$([[ $S_REDIS -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_REDIS -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") Redis"
|
||||
echo -e "$([[ $S_PG -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_PG -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") PostgreSQL"
|
||||
echo -e "$([[ $S_BACKEND -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_BACKEND -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") 后端服务 (:8000)"
|
||||
if [[ $MODE == "gui" || $MODE == "tauri" ]]; then
|
||||
echo -e "$([[ $S_FRONTEND -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_FRONTEND -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") 前端服务 (:8002)"
|
||||
fi
|
||||
if [[ $MODE == "tauri" ]]; then
|
||||
echo -e "$([[ $S_TAURI -eq 2 ]] && echo " ${GREEN}✓${NC}" || [[ $S_TAURI -eq 3 ]] && echo " ${RED}✗${NC}" || echo " ${YELLOW}○${NC}") Tauri 客户端"
|
||||
fi
|
||||
}
|
||||
|
||||
# ── 前置检查 ────────────────────────────────────────────────────────────────
|
||||
|
||||
check_deps() {
|
||||
section "检查依赖"
|
||||
set_status deps 1
|
||||
|
||||
for cmd in python3 node npm; do
|
||||
if ! command -v "$cmd" &>/dev/null; then
|
||||
fail "缺少依赖: $cmd"
|
||||
set_status deps 3
|
||||
return 1
|
||||
fi
|
||||
done
|
||||
ok "Python $(python3 --version 2>&1 | awk '{print $2}')"
|
||||
ok "Node $(node --version 2>&1)"
|
||||
ok "npm $(npm --version 2>&1)"
|
||||
|
||||
# Python 版本 >= 3.11
|
||||
if ! python3 -c 'import sys; sys.exit(0 if (sys.version_info.major, sys.version_info.minor) >= (3, 11) else 1)'; then
|
||||
fail "Python 版本需 >= 3.11"
|
||||
set_status deps 3
|
||||
return 1
|
||||
fi
|
||||
|
||||
# Tauri 模式需要 Rust
|
||||
if [[ $MODE == "tauri" ]]; then
|
||||
if ! command -v rustc &>/dev/null; then
|
||||
warn "未检测到 Rust（Tauri 需要）：请运行 brew install rust 或 curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh"
|
||||
else
|
||||
ok "Rust $(rustc --version 2>&1 | awk '{print $2}')"
|
||||
fi
|
||||
fi
|
||||
|
||||
set_status deps 2
|
||||
}
|
||||
|
||||
check_env() {
|
||||
section "检查环境配置"
|
||||
set_status env 1
|
||||
|
||||
if [[ ! -f .env ]]; then
|
||||
warn "未找到 .env，将使用环境变量默认值"
|
||||
if [[ -f .env.example ]]; then
|
||||
cp .env.example .env
|
||||
ok "已从 .env.example 生成 .env"
|
||||
fi
|
||||
else
|
||||
ok ".env 存在"
|
||||
fi
|
||||
|
||||
set_status env 2
|
||||
}
|
||||
|
||||
check_redis() {
|
||||
section "检查 Redis"
|
||||
set_status redis 1
|
||||
|
||||
if command -v redis-cli &>/dev/null && redis-cli ping 2>/dev/null | grep -q PONG; then
|
||||
ok "Redis 运行中"
|
||||
set_status redis 2
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Docker 方式
|
||||
local name="fischer-redis-dev"
|
||||
if docker ps --format '{{.Names}}' 2>/dev/null | grep -q "^${name}$"; then
|
||||
ok "Redis 容器运行中"
|
||||
set_status redis 2
|
||||
return 0
|
||||
fi
|
||||
|
||||
warn "Redis 未运行，尝试启动 Docker 容器..."
|
||||
if docker run -d --name "$name" -p 6379:6379 redis:7-alpine &>/dev/null; then
|
||||
sleep 2
|
||||
if docker exec "$name" redis-cli ping 2>/dev/null | grep -q PONG; then
|
||||
ok "Redis Docker 容器启动成功 (:6379)"
|
||||
set_status redis 2
|
||||
return 0
|
||||
fi
|
||||
fi
|
||||
fail "Redis 启动失败（请确保 Docker 运行中，或手动启动 Redis）"
|
||||
set_status redis 3
|
||||
return 1
|
||||
}
|
||||
|
||||
check_postgres() {
|
||||
section "检查 PostgreSQL"
|
||||
set_status pg 1
|
||||
|
||||
if lsof -i :5432 2>/dev/null | grep -q LISTEN; then
|
||||
ok "PostgreSQL 已在 :5432 监听"
|
||||
set_status pg 2
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Docker 方式
|
||||
local name="fischer-pg-dev"
|
||||
if docker ps --format '{{.Names}}' 2>/dev/null | grep -q "^${name}$"; then
|
||||
ok "PostgreSQL Docker 容器运行中"
|
||||
set_status pg 2
|
||||
return 0
|
||||
fi
|
||||
|
||||
warn "PostgreSQL 未在 :5432 运行，尝试启动 Docker 容器..."
|
||||
if docker run -d --name "$name" \
|
||||
-p 5432:5432 \
|
||||
-e POSTGRES_USER=agentkit \
|
||||
-e POSTGRES_PASSWORD=agentkit \
|
||||
-e POSTGRES_DB=agentkit \
|
||||
pgvector/pgvector:pg15 &>/dev/null; then
|
||||
sleep 3
|
||||
if docker exec "$name" pg_isready -U agentkit &>/dev/null; then
|
||||
ok "PostgreSQL Docker 容器启动成功 (:5432)"
|
||||
set_status pg 2
|
||||
return 0
|
||||
fi
|
||||
fi
|
||||
warn "PostgreSQL 启动失败（bitable 等功能可能受限，继续启动后端...）"
|
||||
set_status pg 2 # 不阻塞，继续
|
||||
return 0
|
||||
}
|
||||
|
||||
# ── 安装依赖 ────────────────────────────────────────────────────────────────
|
||||
|
||||
install_deps() {
|
||||
section "安装依赖"
|
||||
info "后端 Python 依赖..."
|
||||
|
||||
# 虚拟环境
|
||||
if [[ ! -d .venv ]]; then
|
||||
python3 -m venv .venv
|
||||
ok "虚拟环境 .venv 创建完成"
|
||||
fi
|
||||
|
||||
source .venv/bin/activate
|
||||
pip install -q -U pip
|
||||
pip install -e ".[dev]" -q
|
||||
ok "后端依赖安装完成"
|
||||
|
||||
# 前端 npm 依赖
|
||||
local FE_DIR="$PROJECT_ROOT/src/agentkit/server/frontend"
|
||||
if [[ ! -d "$FE_DIR/node_modules" ]]; then
|
||||
info "前端 npm 依赖..."
|
||||
cd "$FE_DIR"
|
||||
npm install
|
||||
cd "$PROJECT_ROOT"
|
||||
ok "前端依赖安装完成"
|
||||
else
|
||||
ok "前端 node_modules 已存在"
|
||||
fi
|
||||
}
|
||||
|
||||
# ── 启动后端 ────────────────────────────────────────────────────────────────
|
||||
|
||||
start_backend() {
|
||||
section "启动后端服务"
|
||||
set_status backend 1
|
||||
|
||||
info "启动后端 API (:8000)..."
|
||||
source .venv/bin/activate
|
||||
agentkit serve --port 8000 &
|
||||
BACKEND_PID=$!
|
||||
|
||||
# 等待健康检查就绪（最多 60 秒）
|
||||
info "等待后端就绪..."
|
||||
local attempt=0
|
||||
while [[ $attempt -lt 60 ]]; do
|
||||
if curl -sf http://127.0.0.1:8000/api/v1/health &>/dev/null; then
|
||||
ok "后端 API 就绪 (http://127.0.0.1:8000, PID $BACKEND_PID)"
|
||||
set_status backend 2
|
||||
return 0
|
||||
fi
|
||||
# 检查进程是否还活着
|
||||
if ! kill -0 $BACKEND_PID 2>/dev/null; then
|
||||
fail "后端进程意外退出"
|
||||
set_status backend 3
|
||||
return 1
|
||||
fi
|
||||
sleep 1
|
||||
((attempt++))
|
||||
[[ $((attempt % 10)) -eq 0 ]] && info "等待中... (${attempt}s)"
|
||||
done
|
||||
|
||||
fail "后端启动超时（60s 内未响应健康检查）"
|
||||
kill $BACKEND_PID 2>/dev/null || true
|
||||
set_status backend 3
|
||||
return 1
|
||||
}
|
||||
|
||||
# ── 启动 Web GUI ────────────────────────────────────────────────────────────
|
||||
|
||||
start_gui() {
|
||||
section "启动 Web GUI"
|
||||
set_status frontend 1
|
||||
|
||||
info "启动 Web GUI (:8002)..."
|
||||
source .venv/bin/activate
|
||||
agentkit gui --port 8002 &
|
||||
GUI_PID=$!
|
||||
|
||||
# 等待就绪
|
||||
info "等待 Web GUI 就绪..."
|
||||
local attempt=0
|
||||
while [[ $attempt -lt 60 ]]; do
|
||||
if curl -sf http://127.0.0.1:8002/api/v1/health &>/dev/null; then
|
||||
ok "Web GUI 就绪 (http://127.0.0.1:8002, PID $GUI_PID)"
|
||||
set_status frontend 2
|
||||
return 0
|
||||
fi
|
||||
if ! kill -0 $GUI_PID 2>/dev/null; then
|
||||
fail "Web GUI 进程意外退出"
|
||||
set_status frontend 3
|
||||
return 1
|
||||
fi
|
||||
sleep 1
|
||||
((attempt++))
|
||||
[[ $((attempt % 10)) -eq 0 ]] && info "等待中... (${attempt}s)"
|
||||
done
|
||||
|
||||
fail "Web GUI 启动超时"
|
||||
kill $GUI_PID 2>/dev/null || true
|
||||
set_status frontend 3
|
||||
return 1
|
||||
}
|
||||
|
||||
# ── 启动 Tauri ─────────────────────────────────────────────────────────────
|
||||
|
||||
start_tauri() {
|
||||
section "启动 Tauri 桌面客户端"
|
||||
set_status tauri 1
|
||||
|
||||
local FE_DIR="$PROJECT_ROOT/src/agentkit/server/frontend"
|
||||
|
||||
if ! command -v tauri &>/dev/null; then
|
||||
warn "Tauri CLI 未安装，跳过桌面客户端"
|
||||
info "安装方式：npm install -g @tauri-apps/cli"
|
||||
set_status tauri 2
|
||||
return 0
|
||||
fi
|
||||
|
||||
info "启动 Tauri 桌面客户端..."
|
||||
info " (Vite → :5173, 后端 API → :8000)"
|
||||
cd "$FE_DIR"
|
||||
npm run tauri dev &
|
||||
TAURI_PID=$!
|
||||
|
||||
info "Tauri 启动中（首次运行需要编译 Rust，可能需要几分钟）..."
|
||||
ok "Tauri 进程已启动 (PID $TAURI_PID)"
|
||||
info "桌面窗口将自动打开，如未打开请手动查看终端输出"
|
||||
|
||||
set_status tauri 2
|
||||
cd "$PROJECT_ROOT"
|
||||
}
|
||||
|
||||
# ── 数据库初始化 ────────────────────────────────────────────────────────────
|
||||
|
||||
init_db() {
|
||||
section "初始化数据库"
|
||||
info "创建测试用户..."
|
||||
|
||||
local SETUP_SCRIPT="$PROJECT_ROOT/src/agentkit/server/frontend/e2e/setup-test-user.py"
|
||||
if [[ -f "$SETUP_SCRIPT" ]]; then
|
||||
source .venv/bin/activate
|
||||
if python3 "$SETUP_SCRIPT" 2>/dev/null; then
|
||||
ok "测试用户创建完成"
|
||||
else
|
||||
warn "测试用户创建失败（可能已存在或数据库未就绪）"
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
||||
# ── 停止服务 ────────────────────────────────────────────────────────────────
|
||||
|
||||
stop_services() {
|
||||
echo ""
|
||||
info "正在停止所有服务..."
|
||||
|
||||
for port in 8000 8001 8002 5173; do
|
||||
local pid=$(lsof -ti :$port 2>/dev/null || true)
|
||||
if [[ -n "$pid" ]]; then
|
||||
kill $pid 2>/dev/null && ok "端口 $port 已停止" || true
|
||||
fi
|
||||
done
|
||||
|
||||
# Vite 进程
|
||||
pkill -f "vite" 2>/dev/null && ok "Vite 进程已停止" || true
|
||||
|
||||
echo ""
|
||||
ok "所有服务已停止。感谢使用！"
|
||||
}
|
||||
|
||||
# ── 主流程 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
echo ""
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo -e "${CYAN} Fischer AgentKit — 本地开发环境启动${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo ""
|
||||
|
||||
# 检查依赖
|
||||
if ! check_deps; then
|
||||
echo ""
|
||||
fail "依赖检查失败，请先安装缺失的依赖"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
check_env
|
||||
|
||||
# 初始化数据库
|
||||
[[ $INIT_DB -eq 1 ]] && init_db
|
||||
|
||||
# 安装依赖
|
||||
install_deps
|
||||
|
||||
# 检查中间件
|
||||
check_redis || true
|
||||
check_postgres || true
|
||||
|
||||
echo ""
|
||||
|
||||
# ── 启动服务 ────────────────────────────────────────────────────────────────
|
||||
|
||||
FAILED=0
|
||||
|
||||
case $MODE in
|
||||
serve)
|
||||
if ! start_backend; then FAILED=1; fi
|
||||
;;
|
||||
gui)
|
||||
if ! start_backend; then FAILED=1; fi
|
||||
if [[ $FAILED -eq 0 ]] && ! start_gui; then FAILED=1; fi
|
||||
;;
|
||||
tauri)
|
||||
if ! start_backend; then FAILED=1; fi
|
||||
if [[ $FAILED -eq 0 ]]; then
|
||||
start_tauri
|
||||
fi
|
||||
;;
|
||||
esac
|
||||
|
||||
# ── 状态总览 + 启动完成 ──────────────────────────────────────────────────────
|
||||
|
||||
print_status
|
||||
|
||||
echo ""
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
if [[ $FAILED -eq 0 ]]; then
|
||||
echo -e "${GREEN} 所有服务启动成功！${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo ""
|
||||
if [[ $MODE == "gui" ]]; then
|
||||
echo -e " Web GUI: ${GREEN}http://localhost:8002${NC}"
|
||||
echo " (在浏览器中打开，或直接在 http://localhost:8002 访问)"
|
||||
elif [[ $MODE == "tauri" ]]; then
|
||||
echo -e " 后端 API: ${GREEN}http://localhost:8000${NC}"
|
||||
echo -e " Vite 热重载: ${GREEN}http://localhost:5173${NC}"
|
||||
echo " Tauri 桌面窗口应已自动打开"
|
||||
elif [[ $MODE == "serve" ]]; then
|
||||
echo -e " 后端 API: ${GREEN}http://localhost:8000${NC}"
|
||||
fi
|
||||
echo ""
|
||||
echo -e " ${YELLOW}按 Ctrl+C 停止所有服务${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
else
|
||||
echo -e "${RED} 服务启动失败，请查看上方错误信息${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo ""
|
||||
echo -e " 诊断命令："
|
||||
echo -e " 查看日志: ${CYAN}curl http://127.0.0.1:8000/api/v1/health${NC}"
|
||||
echo -e " 停止服务: ${CYAN}bash scripts/dev-stop.sh${NC}"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# 注册退出钩子
|
||||
trap stop_services EXIT INT TERM
|
||||
|
||||
# 保持脚本运行
|
||||
if [[ $MODE == "tauri" ]]; then
|
||||
# Tauri 模式：等待 Tauri 进程
|
||||
wait
|
||||
elif [[ $MODE == "gui" || $MODE == "serve" ]]; then
|
||||
# 等待后端进程
|
||||
wait $BACKEND_PID 2>/dev/null || true
|
||||
fi
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
#!/usr/bin/env bash
|
||||
# =============================================================================
|
||||
# Fischer AgentKit — 本地开发环境一键停止脚本
|
||||
# =============================================================================
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
|
||||
cd "$PROJECT_ROOT"
|
||||
|
||||
echo "停止 Fischer AgentKit 开发服务..."
|
||||
|
||||
# 停止后端/API/GUI 进程（按端口）
|
||||
for port in 8000 8001 8002 5173; do
|
||||
PID=$(lsof -ti :$port 2>/dev/null || true)
|
||||
if [[ -n "$PID" ]]; then
|
||||
kill $PID 2>/dev/null && echo " [OK] 端口 $port 已停止 (PID $PID)" || true
|
||||
fi
|
||||
done
|
||||
|
||||
# 停止 Vite/Node 进程
|
||||
pkill -f "vite" 2>/dev/null && echo " [OK] Vite 进程已停止" || true
|
||||
|
||||
# 停止 Docker 容器（开发专用）
|
||||
for container in fischer-redis-dev fischer-pg-dev; do
|
||||
if docker ps --format '{{.Names}}' 2>/dev/null | grep -q "^${container}$"; then
|
||||
docker stop "$container" 2>/dev/null && echo " [OK] 容器 $container 已停止" || true
|
||||
fi
|
||||
done
|
||||
|
||||
echo "停止完成。"
|
||||
|
|
@ -0,0 +1,218 @@
|
|||
#!/usr/bin/env bash
|
||||
# =============================================================================
|
||||
# Fischer AgentKit — 生产环境一键启动脚本
|
||||
# =============================================================================
|
||||
#
|
||||
# 启动：Docker Compose（agentkit + Redis + PostgreSQL）
|
||||
# 要求：.env 文件存在（含 LLM API Key、JWT Secret）
|
||||
#
|
||||
# 用法：
|
||||
# bash scripts/prod-start.sh # 启动所有服务
|
||||
# bash scripts/prod-start.sh --init # 首次启动（初始化 DB）
|
||||
# bash scripts/prod-start.sh --build # 构建镜像后启动
|
||||
# bash scripts/prod-start.sh --help # 帮助
|
||||
# =============================================================================
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
|
||||
cd "$PROJECT_ROOT"
|
||||
|
||||
: "${COMPOSE_FILE:=docker-compose.yaml}"
|
||||
|
||||
# ── 颜色 ────────────────────────────────────────────────────────────────────
|
||||
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
BLUE='\033[0;34m'
|
||||
CYAN='\033[0;36m'
|
||||
NC='\033[0m'
|
||||
|
||||
# ── 帮助 ────────────────────────────────────────────────────────────────────
|
||||
|
||||
show_help() {
|
||||
cat <<-'EOF'
|
||||
Fischer AgentKit — 生产环境启动
|
||||
|
||||
用法: bash scripts/prod-start.sh [选项]
|
||||
|
||||
选项:
|
||||
--init 首次启动，初始化数据库表
|
||||
--build 先构建/拉取镜像，再启动
|
||||
--pull 仅拉取最新镜像（不启动）
|
||||
--help 显示此帮助
|
||||
|
||||
Docker Compose 文件: docker-compose.yaml
|
||||
|
||||
服务端口:
|
||||
8001 — AgentKit API 服务
|
||||
6379 — Redis
|
||||
5432 — PostgreSQL
|
||||
|
||||
访问: http://localhost:8001
|
||||
EOF
|
||||
}
|
||||
|
||||
# ── 参数解析 ────────────────────────────────────────────────────────────────
|
||||
|
||||
INIT_DB=0
|
||||
BUILD=0
|
||||
PULL_ONLY=0
|
||||
|
||||
while [[ $# -gt 0 ]]; do
|
||||
case $1 in
|
||||
--init) INIT_DB=1; shift ;;
|
||||
--build) BUILD=1; shift ;;
|
||||
--pull) PULL_ONLY=1; shift ;;
|
||||
--help|-h) show_help; exit 0 ;;
|
||||
*) shift ;;
|
||||
esac
|
||||
done
|
||||
|
||||
# ── 日志函数 ────────────────────────────────────────────────────────────────
|
||||
|
||||
info() { echo -e "${BLUE}[INFO]${NC} $*"; }
|
||||
ok() { echo -e "${GREEN}[OK]${NC} $*"; }
|
||||
warn() { echo -e "${YELLOW}[WARN]${NC} $*"; }
|
||||
fail() { echo -e "${RED}[FAIL]${NC} $*"; }
|
||||
|
||||
# ── 前置检查 ────────────────────────────────────────────────────────────────
|
||||
|
||||
check_deps() {
|
||||
info "检查依赖..."
|
||||
for cmd in docker; do
|
||||
if ! command -v "$cmd" &>/dev/null; then
|
||||
fail "缺少依赖: $cmd"
|
||||
exit 1
|
||||
fi
|
||||
done
|
||||
# docker compose v2+ (no separate docker-compose binary needed)
|
||||
if ! command -v docker-compose &>/dev/null && docker compose version &>/dev/null; then
|
||||
ok "docker compose 可用（v2+）"
|
||||
elif command -v docker-compose &>/dev/null; then
|
||||
ok "docker-compose 可用"
|
||||
else
|
||||
fail "缺少 docker 或 docker compose v2"
|
||||
exit 1
|
||||
fi
|
||||
ok "依赖检查通过"
|
||||
}
|
||||
|
||||
check_env() {
|
||||
info "检查环境配置..."
|
||||
if [[ ! -f .env ]]; then
|
||||
fail "未找到 .env 文件"
|
||||
echo " 请先创建 .env 文件（参考 .env.example）"
|
||||
exit 1
|
||||
fi
|
||||
# 检查关键变量
|
||||
if ! grep -q "DASHSCOPE_API_KEY\|DEEPSEEK_API_KEY" .env 2>/dev/null; then
|
||||
warn ".env 中可能缺少 LLM API Key"
|
||||
fi
|
||||
ok ".env 存在"
|
||||
}
|
||||
|
||||
# ── 数据库初始化 ────────────────────────────────────────────────────────────
|
||||
|
||||
init_db() {
|
||||
info "初始化数据库..."
|
||||
docker compose -f "$COMPOSE_FILE" exec -T postgres \
|
||||
psql -U agentkit -d agentkit -c "SELECT 1" &>/dev/null || {
|
||||
fail "PostgreSQL 容器未就绪，等待后重试..."
|
||||
sleep 5
|
||||
}
|
||||
|
||||
# 等待 PostgreSQL 完全就绪
|
||||
local attempt=0
|
||||
while [[ $attempt -lt 30 ]]; do
|
||||
if docker compose -f "$COMPOSE_FILE" exec -T postgres \
|
||||
pg_isready -U agentkit &>/dev/null; then
|
||||
break
|
||||
fi
|
||||
sleep 1
|
||||
((attempt++))
|
||||
done
|
||||
|
||||
ok "数据库就绪"
|
||||
}
|
||||
|
||||
# ── 主流程 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
echo ""
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo -e "${CYAN} Fischer AgentKit — 生产环境启动${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo ""
|
||||
|
||||
check_deps
|
||||
check_env
|
||||
|
||||
if [[ $PULL_ONLY -eq 1 ]]; then
|
||||
info "拉取最新镜像..."
|
||||
docker compose -f "$COMPOSE_FILE" pull
|
||||
ok "镜像拉取完成"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo -e "${GREEN}─────────────────────────────────────────────────${NC}"
|
||||
echo -e "${GREEN} 启动 Docker 服务...${NC}"
|
||||
echo -e "${GREEN}─────────────────────────────────────────────────${NC}"
|
||||
echo ""
|
||||
|
||||
if [[ $BUILD -eq 1 ]]; then
|
||||
info "构建/拉取镜像..."
|
||||
docker compose -f "$COMPOSE_FILE" build --pull
|
||||
ok "镜像构建完成"
|
||||
fi
|
||||
|
||||
# 创建网络（如不存在）
|
||||
docker network ls 2>/dev/null | grep -q "fischer-agentkit_default" || \
|
||||
docker network create fischer-agentkit_default &>/dev/null || true
|
||||
|
||||
info "启动服务（docker compose up -d）..."
|
||||
docker compose -f "$COMPOSE_FILE" up -d --remove-orphans
|
||||
|
||||
# 等待健康检查
|
||||
info "等待服务就绪（健康检查最多 120s）..."
|
||||
local attempt=0
|
||||
while [[ $attempt -lt 60 ]]; do
|
||||
if curl -sf http://127.0.0.1:8001/api/v1/health &>/dev/null; then
|
||||
break
|
||||
fi
|
||||
sleep 2
|
||||
((attempt++))
|
||||
echo -n "."
|
||||
done
|
||||
echo ""
|
||||
|
||||
# 显示状态
|
||||
echo ""
|
||||
echo -e "${GREEN}─────────────────────────────────────────────────${NC}"
|
||||
echo -e "${GREEN} Docker 服务状态${NC}"
|
||||
echo -e "${GREEN}─────────────────────────────────────────────────${NC}"
|
||||
docker compose -f "$COMPOSE_FILE" ps
|
||||
|
||||
# 状态检查
|
||||
if curl -sf http://127.0.0.1:8001/api/v1/health &>/dev/null; then
|
||||
echo ""
|
||||
ok "AgentKit API 就绪 -> http://127.0.0.1:8001"
|
||||
echo ""
|
||||
echo -e "${GREEN}═══════════════════════════════════════════════════${NC}"
|
||||
echo -e "${GREEN} 生产服务全部启动成功！${NC}"
|
||||
echo -e "${GREEN}─────────────────────────────────────────────────${NC}"
|
||||
echo -e " AgentKit API: ${GREEN}http://localhost:8001${NC}"
|
||||
echo -e " Redis: ${GREEN}localhost:6379${NC}"
|
||||
echo -e " PostgreSQL: ${GREEN}localhost:5432${NC}"
|
||||
echo -e "${GREEN}─────────────────────────────────────────────────${NC}"
|
||||
echo -e " 查看日志: ${CYAN}docker compose -f $COMPOSE_FILE logs -f${NC}"
|
||||
echo -e " 停止服务: ${CYAN}bash scripts/prod-stop.sh${NC}"
|
||||
echo -e "${GREEN}═══════════════════════════════════════════════════${NC}"
|
||||
else
|
||||
echo ""
|
||||
fail "AgentKit API 启动超时，查看日志诊断："
|
||||
echo -e " ${CYAN}docker compose -f $COMPOSE_FILE logs --tail=50${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
#!/usr/bin/env bash
|
||||
# =============================================================================
|
||||
# Fischer AgentKit — 生产环境一键停止脚本
|
||||
# =============================================================================
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
|
||||
cd "$PROJECT_ROOT"
|
||||
|
||||
COMPOSE_FILE="${COMPOSE_FILE:-docker-compose.yaml}"
|
||||
|
||||
echo "停止 Fischer AgentKit 生产服务..."
|
||||
|
||||
docker compose -f "$COMPOSE_FILE" down 2>/dev/null && echo " [OK] 容器已停止并移除" || echo " [OK] 无运行中的容器"
|
||||
|
||||
echo ""
|
||||
echo "注意：数据卷（Redis/PostgreSQL 数据）已保留。"
|
||||
echo "如需清除所有数据运行：docker volume rm fischer-agentkit_*"
|
||||
echo "停止完成。"
|
||||
|
|
@ -0,0 +1,192 @@
|
|||
#!/usr/bin/env bash
|
||||
# =============================================================================
|
||||
# Fischer AgentKit — 一键测试环境脚本
|
||||
# =============================================================================
|
||||
#
|
||||
# 启动完整测试环境：后端 API + E2E 测试（使用独立端口，不影响开发环境）
|
||||
#
|
||||
# 用法：
|
||||
# bash scripts/test-start.sh # 启动测试环境并运行全部测试
|
||||
# bash scripts/test-start.sh --unit # 仅单元测试
|
||||
# bash scripts/test-start.sh --e2e # 仅 E2E 测试
|
||||
# bash scripts/test-start.sh --bitable # 仅 bitable 模块测试
|
||||
# bash scripts/test-start.sh --skip-server # 跳过服务启动（使用已有服务）
|
||||
# bash scripts/test-start.sh --help # 帮助
|
||||
# =============================================================================
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
|
||||
cd "$PROJECT_ROOT"
|
||||
|
||||
: "${E2E_PORT:=18765}"
|
||||
: "${E2E_API_KEY:=ak_live_e2e_test_key_000000000000000000000000000000000000000000000000}"
|
||||
|
||||
# ── 颜色 ────────────────────────────────────────────────────────────────────
|
||||
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
BLUE='\033[0;34m'
|
||||
CYAN='\033[0;36m'
|
||||
NC='\033[0m'
|
||||
|
||||
# ── 帮助 ────────────────────────────────────────────────────────────────────
|
||||
|
||||
show_help() {
|
||||
cat <<-'EOF'
|
||||
Fischer AgentKit — 测试环境
|
||||
|
||||
用法: bash scripts/test-start.sh [选项]
|
||||
|
||||
选项:
|
||||
--unit 仅运行单元测试（pytest -m "not integration"）
|
||||
--e2e 仅运行 E2E 测试
|
||||
--bitable 仅运行 bitable 模块测试
|
||||
--skip-server 跳过服务启动（使用已有服务）
|
||||
--help 显示帮助
|
||||
|
||||
环境变量:
|
||||
E2E_PORT 测试服务器端口（默认: 18765）
|
||||
E2E_API_KEY 测试 API Key（默认: 内置测试 Key）
|
||||
EOF
|
||||
}
|
||||
|
||||
# ── 参数解析 ────────────────────────────────────────────────────────────────
|
||||
|
||||
RUN_MODE="all"
|
||||
SKIP_SERVER=0
|
||||
|
||||
while [[ $# -gt 0 ]]; do
|
||||
case $1 in
|
||||
--unit) RUN_MODE="unit"; shift ;;
|
||||
--e2e) RUN_MODE="e2e"; shift ;;
|
||||
--bitable) RUN_MODE="bitable"; shift ;;
|
||||
--skip-server) SKIP_SERVER=1; shift ;;
|
||||
--help|-h) show_help; exit 0 ;;
|
||||
*) shift ;;
|
||||
esac
|
||||
done
|
||||
|
||||
# ── 日志函数 ────────────────────────────────────────────────────────────────
|
||||
|
||||
info() { echo -e "${BLUE}[INFO]${NC} $*"; }
|
||||
ok() { echo -e "${GREEN}[OK]${NC} $*"; }
|
||||
warn() { echo -e "${YELLOW}[WARN]${NC} $*"; }
|
||||
fail() { echo -e "${RED}[FAIL]${NC} $*"; }
|
||||
|
||||
# ── 服务管理 ────────────────────────────────────────────────────────────────
|
||||
|
||||
start_test_server() {
|
||||
if [[ $SKIP_SERVER -eq 1 ]]; then
|
||||
info "SKIP_SERVER=1，跳过服务启动"
|
||||
return 0
|
||||
fi
|
||||
|
||||
info "启动测试服务器（端口 $E2E_PORT）..."
|
||||
if lsof -i :$E2E_PORT 2>/dev/null | grep -q LISTEN; then
|
||||
ok "测试服务器已在端口 $E2E_PORT 运行"
|
||||
return 0
|
||||
fi
|
||||
|
||||
export AGENTKIT_E2E_MODE=1
|
||||
export AGENTKIT_WS_TIMEOUT=0
|
||||
export AGENTKIT_API_KEY="$E2E_API_KEY"
|
||||
|
||||
source .venv/bin/activate 2>/dev/null || true
|
||||
python3 -m agentkit.cli.main serve --host 127.0.0.1 --port "$E2E_PORT" &
|
||||
SERVER_PID=$!
|
||||
|
||||
# 等待就绪
|
||||
local attempt=0
|
||||
while [[ $attempt -lt 60 ]]; do
|
||||
if curl -sf "http://127.0.0.1:$E2E_PORT/api/v1/health" &>/dev/null; then
|
||||
ok "测试服务器就绪 (PID $SERVER_PID，端口 $E2E_PORT)"
|
||||
return 0
|
||||
fi
|
||||
sleep 0.5
|
||||
((attempt++))
|
||||
done
|
||||
|
||||
fail "测试服务器启动超时"
|
||||
kill $SERVER_PID 2>/dev/null || true
|
||||
exit 1
|
||||
}
|
||||
|
||||
stop_test_server() {
|
||||
if [[ $SKIP_SERVER -eq 1 ]]; then return 0; fi
|
||||
PID=$(lsof -ti :$E2E_PORT 2>/dev/null || true)
|
||||
if [[ -n "$PID" ]]; then
|
||||
kill $PID 2>/dev/null && ok "测试服务器已停止" || true
|
||||
fi
|
||||
}
|
||||
|
||||
# ── 测试执行 ────────────────────────────────────────────────────────────────
|
||||
|
||||
run_unit_tests() {
|
||||
info "运行单元测试..."
|
||||
source .venv/bin/activate 2>/dev/null || true
|
||||
pytest -m "not integration" -q --tb=short
|
||||
ok "单元测试完成"
|
||||
}
|
||||
|
||||
run_bitable_tests() {
|
||||
info "运行 bitable 模块测试..."
|
||||
source .venv/bin/activate 2>/dev/null || true
|
||||
pytest tests/unit/bitable/ -v --tb=short
|
||||
ok "bitable 测试完成"
|
||||
}
|
||||
|
||||
run_e2e_tests() {
|
||||
info "运行 E2E 测试..."
|
||||
FE_DIR="$PROJECT_ROOT/src/agentkit/server/frontend"
|
||||
cd "$FE_DIR"
|
||||
|
||||
if [[ ! -d node_modules ]]; then
|
||||
info "安装前端依赖..."
|
||||
npm install
|
||||
fi
|
||||
|
||||
export AGENTKIT_SERVER_URL="http://127.0.0.1:$E2E_PORT"
|
||||
export AGENTKIT_API_KEY="$E2E_API_KEY"
|
||||
|
||||
npm run test:e2e
|
||||
ok "E2E 测试完成"
|
||||
cd "$PROJECT_ROOT"
|
||||
}
|
||||
|
||||
# ── 主流程 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
echo ""
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo -e "${CYAN} Fischer AgentKit — 测试环境${NC}"
|
||||
echo -e "${CYAN}═══════════════════════════════════════════════════${NC}"
|
||||
echo ""
|
||||
|
||||
trap stop_test_server EXIT INT TERM
|
||||
|
||||
case $RUN_MODE in
|
||||
unit)
|
||||
run_unit_tests
|
||||
;;
|
||||
e2e)
|
||||
start_test_server
|
||||
run_e2e_tests
|
||||
;;
|
||||
bitable)
|
||||
run_bitable_tests
|
||||
;;
|
||||
all)
|
||||
info "运行全部测试..."
|
||||
run_unit_tests
|
||||
echo ""
|
||||
run_bitable_tests
|
||||
echo ""
|
||||
start_test_server
|
||||
run_e2e_tests
|
||||
;;
|
||||
esac
|
||||
|
||||
echo ""
|
||||
ok "测试完成！"
|
||||
|
|
@ -18,7 +18,7 @@ from agentkit.core.exceptions import LoopDetectedError, TaskCancelledError, Task
|
|||
from agentkit.core.protocol import CancellationToken
|
||||
from agentkit.llm.gateway import LLMGateway
|
||||
from agentkit.llm.protocol import LLMResponse
|
||||
from agentkit.tools.base import Tool
|
||||
from agentkit.tools.base import Tool, ToolValidationError
|
||||
from agentkit.telemetry.tracing import start_span, _OTEL_AVAILABLE
|
||||
from agentkit.telemetry.metrics import (
|
||||
agent_request_counter,
|
||||
|
|
@ -119,7 +119,9 @@ class ReActResult:
|
|||
trajectory: list[ReActStep]
|
||||
total_steps: int
|
||||
total_tokens: int
|
||||
status: str = "success" # "success" | "timeout" | "cancelled" | "partial"
|
||||
status: str = (
|
||||
"success" # "success"|"timeout"|"cancelled"|"partial"|"empty_fallback"|"verify_failed"
|
||||
)
|
||||
fallback_strategy: str | None = None # e.g. "simplified_rewoo", "react", "direct"
|
||||
|
||||
|
||||
|
|
@ -163,6 +165,9 @@ class ReActEngine:
|
|||
core_tool_names: list[str] | None = None,
|
||||
enable_tool_search: bool = True,
|
||||
middleware_chain: "MiddlewareChain | None" = None,
|
||||
prompt_cache_enable: bool = True,
|
||||
flush_interval_ms: int = 0,
|
||||
max_reinjections: int = 1,
|
||||
):
|
||||
if max_steps < 1:
|
||||
raise ValueError(f"max_steps must be >= 1, got {max_steps}")
|
||||
|
|
@ -176,6 +181,16 @@ class ReActEngine:
|
|||
self._parallel_tools = parallel_tools
|
||||
self._verification_enabled = verification_enabled
|
||||
self._verification_commands = verification_commands
|
||||
# U2/G2: prompt cache 双块结构开关(True 时 Anthropic 用 cache_control blocks,
|
||||
# 其他 provider 走字符串拼接依赖自动前缀缓存)
|
||||
self._prompt_cache_enable = prompt_cache_enable
|
||||
# U3/G8: token chunk 节流间隔(ms)。0 = 逐 chunk yield(向后兼容)。
|
||||
# 用 time.monotonic() 不受系统时钟跳变影响。
|
||||
self._flush_interval_ms = flush_interval_ms
|
||||
# U4/G1: verify 失败回灌最大重试次数。0 = 不回灌(当前行为,仅记录 trajectory);
|
||||
# 1 = 首次失败回灌一次 errors 给 LLM 自纠正,二次失败中断。
|
||||
# 受 max_steps 上限约束(不无限循环)。verification_enabled=False 时无效。
|
||||
self._max_reinjections = max_reinjections
|
||||
# Tiered tool description injection config
|
||||
self._core_tool_names: tuple[str, ...] | None = (
|
||||
tuple(core_tool_names) if core_tool_names is not None else None
|
||||
|
|
@ -429,22 +444,22 @@ class ReActEngine:
|
|||
skill_name=task_type or None,
|
||||
)
|
||||
|
||||
# Memory retrieval: 执行前检索相关上下文注入 system_prompt
|
||||
# Memory retrieval: 执行前检索相关上下文,作为 volatile 层注入 system message
|
||||
# U2/G2: 不再拼到 stable(system_prompt)末尾,改由 _build_system_message 组装双块结构
|
||||
memory_context = ""
|
||||
if memory_retriever:
|
||||
try:
|
||||
query = str(messages[-1].get("content", "")) if messages else ""
|
||||
top_k = (retrieval_config or {}).get("top_k", 5)
|
||||
token_budget = (retrieval_config or {}).get("token_budget", 2000)
|
||||
memory_context = await memory_retriever.get_context_string(
|
||||
query=query,
|
||||
top_k=top_k,
|
||||
token_budget=token_budget,
|
||||
memory_context = (
|
||||
await memory_retriever.get_context_string(
|
||||
query=query,
|
||||
top_k=top_k,
|
||||
token_budget=token_budget,
|
||||
)
|
||||
or ""
|
||||
)
|
||||
if memory_context:
|
||||
if system_prompt:
|
||||
system_prompt += f"\n\n## 参考信息\n{memory_context}"
|
||||
else:
|
||||
system_prompt = f"## 参考信息\n{memory_context}"
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Memory retrieval failed, continuing without context: {e}", exc_info=True
|
||||
|
|
@ -452,8 +467,13 @@ class ReActEngine:
|
|||
|
||||
# 构建初始消息
|
||||
conversation: list[dict[str, Any]] = []
|
||||
if system_prompt:
|
||||
conversation.append({"role": "system", "content": system_prompt})
|
||||
system_content = self._build_system_message(
|
||||
stable=system_prompt or "",
|
||||
volatile=memory_context,
|
||||
model=model,
|
||||
)
|
||||
if system_content is not None:
|
||||
conversation.append({"role": "system", "content": system_content})
|
||||
conversation.extend(messages)
|
||||
|
||||
# Context compression: 压缩超长对话历史
|
||||
|
|
@ -468,6 +488,8 @@ class ReActEngine:
|
|||
trace_outcome = "success"
|
||||
step = 0
|
||||
output = ""
|
||||
# U4/G1: verify 失败回灌计数器。受 max_steps 上限约束(不无限循环)。
|
||||
reinjections = 0
|
||||
|
||||
while step < self._max_steps:
|
||||
step += 1
|
||||
|
|
@ -837,6 +859,29 @@ class ReActEngine:
|
|||
except Exception as e:
|
||||
logger.warning(f"Incremental compression failed: {e}")
|
||||
else:
|
||||
# ponytail: 检查是否为畸形工具调用（含 <tool_use> 但解析失败）
|
||||
# 如果是，注入纠正消息让模型重试，而不是把原始 XML 作为最终答案泄漏
|
||||
if "<tool_use>" in (response.content or ""):
|
||||
logger.warning(
|
||||
f"Step {step}: content contains <tool_use> but "
|
||||
f"parsing failed — injecting correction"
|
||||
)
|
||||
conversation.append({"role": "assistant", "content": response.content})
|
||||
conversation.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"你上一次的工具调用格式有误，无法解析。"
|
||||
"请使用正确的格式重新调用工具：\n"
|
||||
"<tool_use>\n"
|
||||
'{"name": "工具名", "arguments": {"参数名": "参数值"}}\n'
|
||||
"</tool_use>\n"
|
||||
"确保 JSON 完整且不要混入其他标签。"
|
||||
),
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
# Final answer: LLM 没有调用工具，返回最终答案
|
||||
react_step = ReActStep(
|
||||
step=step,
|
||||
|
|
@ -856,36 +901,65 @@ class ReActEngine:
|
|||
duration_ms=llm_duration_ms,
|
||||
tokens_used=step_tokens,
|
||||
)
|
||||
break
|
||||
|
||||
# Verification: 如果启用验证，在 final answer 后运行测试
|
||||
if self._verification_enabled and output:
|
||||
try:
|
||||
from agentkit.core.verification_loop import VerificationLoop
|
||||
# U4/G1: verify at final-answer point with reinjection.
|
||||
# 原为循环后一次性运行;现改为循环内检测 final answer 后立即 verify,
|
||||
# 失败则把 errors 作为 user 消息回灌 conversation,continue 主循环让 LLM 自纠正。
|
||||
# max_reinjections=0 等价于原行为(仅记录 trajectory,不回灌)。
|
||||
if self._verification_enabled and output:
|
||||
try:
|
||||
from agentkit.core.verification_loop import VerificationLoop
|
||||
|
||||
vloop = VerificationLoop(commands=self._verification_commands)
|
||||
vresult = await vloop.verify()
|
||||
if not vresult.passed:
|
||||
# 将验证失败信息作为 ReActStep 添加到轨迹
|
||||
verification_step = ReActStep(
|
||||
step=step + 1,
|
||||
action="tool_call",
|
||||
tool_name="verification",
|
||||
arguments={"commands": self._verification_commands},
|
||||
result={
|
||||
"passed": vresult.passed,
|
||||
"errors": vresult.errors,
|
||||
"test_output": vresult.test_output,
|
||||
},
|
||||
content=(f"Verification failed:\n{vresult.test_output[:2000]}"),
|
||||
)
|
||||
trajectory.append(verification_step)
|
||||
logger.info(
|
||||
"Verification failed after final answer, "
|
||||
"appended feedback to trajectory"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Verification loop failed: {e}")
|
||||
vloop = VerificationLoop(commands=self._verification_commands)
|
||||
vresult = await vloop.verify()
|
||||
if not vresult.passed:
|
||||
if (
|
||||
reinjections < self._max_reinjections
|
||||
and step < self._max_steps
|
||||
):
|
||||
# 回灌 errors 作为 user 消息,让 LLM 自纠正
|
||||
errors_text = "\n".join(vresult.errors)
|
||||
conversation.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": (f"验证失败,错误如下:\n{errors_text}"),
|
||||
}
|
||||
)
|
||||
reinjections += 1
|
||||
logger.info(
|
||||
"Verification failed (reinjection %d/%d), "
|
||||
"errors injected into conversation",
|
||||
reinjections,
|
||||
self._max_reinjections,
|
||||
)
|
||||
continue
|
||||
# 达到 max_reinjections 或 max_steps → 记录 verify log 并中断
|
||||
verification_step = ReActStep(
|
||||
step=step,
|
||||
action="tool_call",
|
||||
tool_name="verification",
|
||||
arguments={"commands": self._verification_commands},
|
||||
result={
|
||||
"passed": vresult.passed,
|
||||
"errors": vresult.errors,
|
||||
"test_output": vresult.test_output,
|
||||
},
|
||||
content=(
|
||||
f"Verification failed:\n{vresult.test_output[:2000]}"
|
||||
),
|
||||
)
|
||||
trajectory.append(verification_step)
|
||||
trace_outcome = "verify_failed"
|
||||
logger.info(
|
||||
"Verification failed after %d reinjections, "
|
||||
"interrupting with verify log",
|
||||
reinjections,
|
||||
)
|
||||
break
|
||||
except Exception as e:
|
||||
logger.warning(f"Verification loop failed: {e}")
|
||||
|
||||
break # verify 通过或未启用 → 正常退出
|
||||
|
||||
# 达到 max_steps 时，返回当前最佳输出
|
||||
if step >= self._max_steps and not output:
|
||||
|
|
@ -929,6 +1003,7 @@ class ReActEngine:
|
|||
trajectory=trajectory,
|
||||
total_steps=len(trajectory),
|
||||
total_tokens=total_tokens,
|
||||
status=trace_outcome,
|
||||
)
|
||||
finally:
|
||||
# Telemetry: end span and record duration — always runs
|
||||
|
|
@ -1014,28 +1089,34 @@ class ReActEngine:
|
|||
skill_name=task_type or None,
|
||||
)
|
||||
|
||||
# Memory retrieval: 执行前检索相关上下文注入 system_prompt
|
||||
# Memory retrieval: 执行前检索相关上下文,作为 volatile 层注入 system message
|
||||
# U2/G2: 不再拼到 stable(system_prompt)末尾破坏 cache 前缀,改由 _build_system_message
|
||||
# 组装双块结构(stable + volatile),Anthropic provider 在 stable 上加 cache_control。
|
||||
memory_context = ""
|
||||
if memory_retriever:
|
||||
try:
|
||||
query = str(messages[-1].get("content", "")) if messages else ""
|
||||
top_k = (retrieval_config or {}).get("top_k", 5)
|
||||
token_budget = (retrieval_config or {}).get("token_budget", 2000)
|
||||
memory_context = await memory_retriever.get_context_string(
|
||||
query=query,
|
||||
top_k=top_k,
|
||||
token_budget=token_budget,
|
||||
memory_context = (
|
||||
await memory_retriever.get_context_string(
|
||||
query=query,
|
||||
top_k=top_k,
|
||||
token_budget=token_budget,
|
||||
)
|
||||
or ""
|
||||
)
|
||||
if memory_context:
|
||||
if system_prompt:
|
||||
system_prompt += f"\n\n## 参考信息\n{memory_context}"
|
||||
else:
|
||||
system_prompt = f"## 参考信息\n{memory_context}"
|
||||
except Exception as e:
|
||||
logger.warning(f"Memory retrieval failed, continuing without context: {e}")
|
||||
|
||||
conversation: list[dict[str, Any]] = []
|
||||
if system_prompt:
|
||||
conversation.append({"role": "system", "content": system_prompt})
|
||||
system_content = self._build_system_message(
|
||||
stable=system_prompt or "",
|
||||
volatile=memory_context,
|
||||
model=model,
|
||||
)
|
||||
if system_content is not None:
|
||||
conversation.append({"role": "system", "content": system_content})
|
||||
conversation.extend(messages)
|
||||
|
||||
# Context compression: 压缩超长对话历史
|
||||
|
|
@ -1052,6 +1133,8 @@ class ReActEngine:
|
|||
step = 0
|
||||
output = ""
|
||||
trace_outcome = "success"
|
||||
# U4/G1: verify 失败回灌计数器(execute_stream 版)。受 max_steps 上限约束。
|
||||
reinjections = 0
|
||||
_stream_start = time.monotonic()
|
||||
effective_timeout = (
|
||||
timeout_seconds if timeout_seconds is not None else self._default_timeout
|
||||
|
|
@ -1089,6 +1172,9 @@ class ReActEngine:
|
|||
stream_usage = None
|
||||
stream_tool_calls: list[Any] = []
|
||||
stream_model = model
|
||||
# U3/G8: delta_flush 节流 buffer,按 flush_interval_ms 批量 yield
|
||||
_flush_buffer: list[str] = []
|
||||
_last_flush_ts = time.monotonic()
|
||||
|
||||
async for chunk in _ensure_async_iterable(
|
||||
self._llm_gateway.chat_stream(
|
||||
|
|
@ -1102,11 +1188,20 @@ class ReActEngine:
|
|||
):
|
||||
if chunk.content:
|
||||
stream_content_chunks.append(chunk.content)
|
||||
yield ReActEvent(
|
||||
event_type="token",
|
||||
step=step,
|
||||
data={"content": chunk.content},
|
||||
)
|
||||
_flush_buffer.append(chunk.content)
|
||||
now = time.monotonic()
|
||||
# flush_interval_ms=0 → 逐 chunk yield(向后兼容,条件短路为 True)
|
||||
if (
|
||||
self._flush_interval_ms == 0
|
||||
or now - _last_flush_ts >= self._flush_interval_ms / 1000
|
||||
):
|
||||
yield ReActEvent(
|
||||
event_type="token",
|
||||
step=step,
|
||||
data={"content": "".join(_flush_buffer)},
|
||||
)
|
||||
_flush_buffer = []
|
||||
_last_flush_ts = now
|
||||
if chunk.usage:
|
||||
stream_usage = chunk.usage
|
||||
if chunk.tool_calls:
|
||||
|
|
@ -1114,6 +1209,15 @@ class ReActEngine:
|
|||
if chunk.model:
|
||||
stream_model = chunk.model
|
||||
|
||||
# U3/G8: 流结束 mid-interval → 最终 flush 剩余 buffer(不丢字符)
|
||||
if _flush_buffer:
|
||||
yield ReActEvent(
|
||||
event_type="token",
|
||||
step=step,
|
||||
data={"content": "".join(_flush_buffer)},
|
||||
)
|
||||
_flush_buffer = []
|
||||
|
||||
# Build response-like object from stream
|
||||
stream_content = "".join(stream_content_chunks)
|
||||
response = self._build_response_from_stream(
|
||||
|
|
@ -1513,6 +1617,34 @@ class ReActEngine:
|
|||
except Exception as e:
|
||||
logger.warning(f"Incremental compression failed: {e}")
|
||||
else:
|
||||
# ponytail: 检查是否为畸形工具调用（含 <tool_use> 但解析失败）
|
||||
# 如果是，注入纠正消息让模型重试，而不是把原始 XML 作为最终答案泄漏
|
||||
if "<tool_use>" in (response.content or ""):
|
||||
logger.warning(
|
||||
f"Step {step}: content contains <tool_use> but "
|
||||
f"parsing failed — injecting correction (stream)"
|
||||
)
|
||||
conversation.append({"role": "assistant", "content": response.content})
|
||||
conversation.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"你上一次的工具调用格式有误，无法解析。"
|
||||
"请使用正确的格式重新调用工具：\n"
|
||||
"<tool_use>\n"
|
||||
'{"name": "工具名", "arguments": {"参数名": "参数值"}}\n'
|
||||
"</tool_use>\n"
|
||||
"确保 JSON 完整且不要混入其他标签。"
|
||||
),
|
||||
}
|
||||
)
|
||||
yield ReActEvent(
|
||||
event_type="step",
|
||||
step=step,
|
||||
data={"message": "工具调用格式异常，已注入纠正消息"},
|
||||
)
|
||||
continue
|
||||
|
||||
# Final answer
|
||||
react_step = ReActStep(
|
||||
step=step,
|
||||
|
|
@ -1533,6 +1665,80 @@ class ReActEngine:
|
|||
tokens_used=step_tokens,
|
||||
)
|
||||
|
||||
# U4/G1: verify at final-answer point with reinjection (stream 版)。
|
||||
# 与 execute() 同模式:失败回灌 errors 作为 user 消息,continue 主循环。
|
||||
# max_reinjections=0 等价于原行为(仅记录 trajectory,不回灌)。
|
||||
# 注意:final_answer 事件在 verify 通过后才 yield,避免客户端过早收到完成信号。
|
||||
if self._verification_enabled and output:
|
||||
try:
|
||||
from agentkit.core.verification_loop import VerificationLoop
|
||||
|
||||
vloop = VerificationLoop(commands=self._verification_commands)
|
||||
vresult = await vloop.verify()
|
||||
if not vresult.passed:
|
||||
if (
|
||||
reinjections < self._max_reinjections
|
||||
and step < self._max_steps
|
||||
):
|
||||
# 回灌 errors,不发 final_answer 事件,继续循环
|
||||
errors_text = "\n".join(vresult.errors)
|
||||
conversation.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": (f"验证失败,错误如下:\n{errors_text}"),
|
||||
}
|
||||
)
|
||||
reinjections += 1
|
||||
yield ReActEvent(
|
||||
event_type="step",
|
||||
step=step,
|
||||
data={
|
||||
"message": (
|
||||
f"验证失败,已注入错误信息让 LLM 自纠正 "
|
||||
f"(reinjection {reinjections}/{self._max_reinjections})"
|
||||
),
|
||||
"verify_errors": vresult.errors,
|
||||
},
|
||||
)
|
||||
continue
|
||||
# 达到 max_reinjections 或 max_steps → 记录 verify log 并中断
|
||||
verification_step = ReActStep(
|
||||
step=step,
|
||||
action="tool_call",
|
||||
tool_name="verification",
|
||||
arguments={"commands": self._verification_commands},
|
||||
result={
|
||||
"passed": vresult.passed,
|
||||
"errors": vresult.errors,
|
||||
"test_output": vresult.test_output,
|
||||
},
|
||||
content=(
|
||||
f"Verification failed:\n{vresult.test_output[:2000]}"
|
||||
),
|
||||
)
|
||||
trajectory.append(verification_step)
|
||||
trace_outcome = "verify_failed"
|
||||
yield ReActEvent(
|
||||
event_type="tool_result",
|
||||
step=step,
|
||||
data={
|
||||
"tool_name": "verification",
|
||||
"result": {
|
||||
"passed": vresult.passed,
|
||||
"errors": vresult.errors,
|
||||
"test_output": vresult.test_output,
|
||||
},
|
||||
},
|
||||
)
|
||||
logger.info(
|
||||
"Verification failed after %d reinjections, "
|
||||
"interrupting with verify log",
|
||||
reinjections,
|
||||
)
|
||||
break
|
||||
except Exception as e:
|
||||
logger.warning(f"Verification loop failed: {e}")
|
||||
|
||||
yield ReActEvent(
|
||||
event_type="final_answer",
|
||||
step=step,
|
||||
|
|
@ -1542,47 +1748,7 @@ class ReActEngine:
|
|||
"total_tokens": total_tokens,
|
||||
},
|
||||
)
|
||||
break
|
||||
|
||||
# Verification: 如果启用验证，在 final answer 后运行测试
|
||||
if self._verification_enabled and output:
|
||||
try:
|
||||
from agentkit.core.verification_loop import VerificationLoop
|
||||
|
||||
vloop = VerificationLoop(commands=self._verification_commands)
|
||||
vresult = await vloop.verify()
|
||||
if not vresult.passed:
|
||||
verification_step = ReActStep(
|
||||
step=step + 1,
|
||||
action="tool_call",
|
||||
tool_name="verification",
|
||||
arguments={"commands": self._verification_commands},
|
||||
result={
|
||||
"passed": vresult.passed,
|
||||
"errors": vresult.errors,
|
||||
"test_output": vresult.test_output,
|
||||
},
|
||||
content=(f"Verification failed:\n{vresult.test_output[:2000]}"),
|
||||
)
|
||||
trajectory.append(verification_step)
|
||||
yield ReActEvent(
|
||||
event_type="tool_result",
|
||||
step=step + 1,
|
||||
data={
|
||||
"tool_name": "verification",
|
||||
"result": {
|
||||
"passed": vresult.passed,
|
||||
"errors": vresult.errors,
|
||||
"test_output": vresult.test_output,
|
||||
},
|
||||
},
|
||||
)
|
||||
logger.info(
|
||||
"Verification failed after final answer, "
|
||||
"appended feedback to trajectory"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Verification loop failed: {e}")
|
||||
break # verify 通过或未启用 → 正常退出
|
||||
|
||||
if step >= self._max_steps and not output:
|
||||
trace_outcome = "partial"
|
||||
|
|
@ -1666,6 +1832,66 @@ class ReActEngine:
|
|||
schemas.append(schema)
|
||||
return schemas
|
||||
|
||||
def _build_system_message(
|
||||
self,
|
||||
stable: str,
|
||||
volatile: str,
|
||||
*,
|
||||
model: str,
|
||||
) -> str | list[dict[str, Any]] | None:
|
||||
"""构建双块结构 system message(stable + volatile)。
|
||||
|
||||
- prompt_cache_enable=False 或无 stable+volatile → 返回 str(或 None)
|
||||
- Anthropic provider → 返回 content blocks 列表,stable 块带 cache_control
|
||||
- 其他 provider → 返回字符串拼接(stable + volatile),依赖 stable 前缀命中自动前缀缓存
|
||||
|
||||
ponytail: 断点数硬编码为 1(stable 层),不暴露配置(YAGNI — 双块结构 >1 无语义)。
|
||||
"""
|
||||
if not stable and not volatile:
|
||||
return None
|
||||
if not self._prompt_cache_enable:
|
||||
# 退化为字符串拼接(向后兼容,行为同改动前)
|
||||
if stable and volatile:
|
||||
return f"{stable}\n\n## 参考信息\n{volatile}"
|
||||
if volatile:
|
||||
return f"## 参考信息\n{volatile}"
|
||||
return stable
|
||||
|
||||
provider_name = self._get_provider_name(model)
|
||||
if provider_name == "anthropic":
|
||||
blocks: list[dict[str, Any]] = []
|
||||
if stable:
|
||||
blocks.append(
|
||||
{
|
||||
"type": "text",
|
||||
"text": stable,
|
||||
"cache_control": {"type": "ephemeral"},
|
||||
}
|
||||
)
|
||||
if volatile:
|
||||
blocks.append(
|
||||
{
|
||||
"type": "text",
|
||||
"text": f"## 参考信息\n{volatile}",
|
||||
}
|
||||
)
|
||||
return blocks if blocks else None
|
||||
|
||||
# 非 Anthropic:字符串拼接,stable 前缀命中 OpenAI/DashScope 自动前缀缓存
|
||||
if stable and volatile:
|
||||
return f"{stable}\n\n## 参考信息\n{volatile}"
|
||||
if volatile:
|
||||
return f"## 参考信息\n{volatile}"
|
||||
return stable
|
||||
|
||||
def _get_provider_name(self, model: str) -> str | None:
|
||||
"""通过 gateway 查询 model 对应的 provider 名。失败回退 None(字符串拼接)。"""
|
||||
try:
|
||||
return self._llm_gateway.get_provider_name_for_model(model)
|
||||
except Exception:
|
||||
# ponytail: 测试中 gateway 可能是 MagicMock,无该方法;回退保守路径
|
||||
return None
|
||||
|
||||
def _build_tool_use_prompt(self, tools: list[Tool]) -> str:
|
||||
"""Build prompt-based tool calling instructions with tiered injection.
|
||||
|
||||
|
|
@ -1855,6 +2081,15 @@ class ReActEngine:
|
|||
try:
|
||||
result = await tool.safe_execute(**clean_args)
|
||||
return result
|
||||
except ToolValidationError as e:
|
||||
# 保留类型化错误码,不被通用 except 平坦化为字符串
|
||||
error_msg = f"Tool '{tool_name}' schema validation failed: {e}"
|
||||
logger.warning(error_msg)
|
||||
return {
|
||||
"error": str(e),
|
||||
"error_code": e.error_code,
|
||||
"details": e.details,
|
||||
}
|
||||
except Exception as e:
|
||||
error_msg = f"Tool '{tool_name}' execution failed: {e}"
|
||||
logger.warning(error_msg)
|
||||
|
|
@ -2052,4 +2287,101 @@ class ReActEngine:
|
|||
else:
|
||||
logger.warning(f"Failed to parse tool_use block: {json_str[:200]}")
|
||||
|
||||
if calls:
|
||||
return calls
|
||||
|
||||
# 格式 4: 畸形 <tool_use> — 缺少闭合标签或 JSON 被截断/混入杂标签
|
||||
# 兜底解析：从 <tool_use> 后提取 JSON 片段，用大括号匹配法恢复完整 JSON
|
||||
open_pattern = re.compile(r"<tool_use>\s*", re.IGNORECASE)
|
||||
for match in open_pattern.finditer(content):
|
||||
remainder = content[match.end() :]
|
||||
parsed = self._extract_tool_call_from_malformed(remainder)
|
||||
if parsed:
|
||||
calls.append(parsed)
|
||||
|
||||
return calls
|
||||
|
||||
@staticmethod
|
||||
def _extract_tool_call_from_malformed(text: str) -> dict[str, Any] | None:
|
||||
"""从畸形文本中尝试提取工具调用。
|
||||
|
||||
处理场景：
|
||||
1. JSON 被截断（缺少闭合大括号）
|
||||
2. JSON 中混入 <parameter> 等 XML 标签
|
||||
3. 完全无法解析时返回 None
|
||||
"""
|
||||
# 尝试用大括号匹配提取第一个 JSON 对象
|
||||
brace_start = text.find("{")
|
||||
if brace_start == -1:
|
||||
return None
|
||||
|
||||
depth = 0
|
||||
json_end = -1
|
||||
in_string = False
|
||||
escape = False
|
||||
for i in range(brace_start, len(text)):
|
||||
ch = text[i]
|
||||
if escape:
|
||||
escape = False
|
||||
continue
|
||||
if ch == "\\":
|
||||
escape = True
|
||||
continue
|
||||
if ch == '"':
|
||||
in_string = not in_string
|
||||
continue
|
||||
if in_string:
|
||||
continue
|
||||
if ch == "{":
|
||||
depth += 1
|
||||
elif ch == "}":
|
||||
depth -= 1
|
||||
if depth == 0:
|
||||
json_end = i + 1
|
||||
break
|
||||
|
||||
if json_end == -1:
|
||||
# JSON 被截断 — 尝试补全大括号后解析
|
||||
json_str = text[brace_start:].strip()
|
||||
# 截断掉非 JSON 尾部（如 </parameter>, <function> 等）
|
||||
cut = json_str.find("}")
|
||||
if cut != -1:
|
||||
json_str = json_str[: cut + 1]
|
||||
else:
|
||||
# 补全缺失的大括号
|
||||
open_braces = json_str.count("{") - json_str.count("}")
|
||||
json_str = json_str + "}" * max(open_braces, 0)
|
||||
else:
|
||||
json_str = text[brace_start:json_end]
|
||||
|
||||
try:
|
||||
parsed = json.loads(json_str)
|
||||
name = parsed.get("name", "")
|
||||
arguments = parsed.get("arguments", {})
|
||||
if name:
|
||||
return {"name": name, "arguments": arguments}
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# 最终兜底：用正则提取 name 和已知的参数字段
|
||||
name_match = re.search(r'"name"\s*:\s*"([^"]+)"', text)
|
||||
if not name_match:
|
||||
return None
|
||||
name = name_match.group(1)
|
||||
|
||||
arguments: dict[str, Any] = {}
|
||||
# 提取 "key": "value" 模式
|
||||
for kv_match in re.finditer(r'"(\w+)"\s*:\s*"([^"]*)"', text):
|
||||
key = kv_match.group(1)
|
||||
if key in ("name",):
|
||||
continue
|
||||
arguments[key] = kv_match.group(2)
|
||||
|
||||
# 提取 <parameter=key>value</parameter> 模式
|
||||
for pm in re.finditer(r"<parameter=(\w+)>\s*(.*?)\s*</parameter>", text, re.DOTALL):
|
||||
arguments[pm.group(1)] = pm.group(2).strip()
|
||||
|
||||
if name:
|
||||
return {"name": name, "arguments": arguments}
|
||||
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -412,6 +412,24 @@ class LLMGateway:
|
|||
return self._config.model_aliases[model]
|
||||
return model
|
||||
|
||||
def get_provider_name_for_model(self, model: str) -> str | None:
|
||||
"""返回 model 对应的 provider 名(用于 provider-specific 优化如 cache_control)。
|
||||
|
||||
ponytail: 仅做 alias 解析 + provider 前缀提取,不查内部状态。
|
||||
升级路径:ServerConfig 显式声明 provider per model。
|
||||
返回 None 表示无法确定(多 provider + 无 "/" 前缀),调用方应回退到字符串拼接。
|
||||
"""
|
||||
resolved = self._resolve_model_alias(model)
|
||||
if "/" in resolved:
|
||||
provider_name = resolved.split("/", 1)[0]
|
||||
if provider_name in self._providers:
|
||||
return provider_name
|
||||
return None
|
||||
# 无 "/" 前缀:仅当只有一个 provider 时能确定
|
||||
if len(self._providers) == 1:
|
||||
return next(iter(self._providers))
|
||||
return None
|
||||
|
||||
def _resolve_model(self, model: str) -> tuple[LLMProvider, str]:
|
||||
"""解析模型为 (provider, actual_model_name)"""
|
||||
# model 格式: "provider/model_name" 或 "model_name"
|
||||
|
|
|
|||
|
|
@ -99,13 +99,18 @@ class AnthropicProvider(LLMProvider):
|
|||
"content-type": "application/json",
|
||||
}
|
||||
|
||||
def _convert_messages(self, messages: list[dict[str, str]]) -> tuple[str | None, list[dict[str, Any]]]:
|
||||
def _convert_messages(self, messages: list[dict[str, str]]) -> tuple[str | list[dict[str, Any]] | None, list[dict[str, Any]]]:
|
||||
"""将 OpenAI 风格消息转换为 Anthropic 格式
|
||||
|
||||
Returns:
|
||||
(system_prompt, anthropic_messages)
|
||||
|
||||
system_prompt 可为:
|
||||
- str: 传统字符串(向后兼容)
|
||||
- list[dict]: Anthropic content blocks(支持 cache_control,U2/G2)
|
||||
- None: 无 system 消息
|
||||
"""
|
||||
system_prompt: str | None = None
|
||||
system_prompt: str | list[dict[str, Any]] | None = None
|
||||
anthropic_messages: list[dict[str, Any]] = []
|
||||
|
||||
for msg in messages:
|
||||
|
|
@ -113,6 +118,8 @@ class AnthropicProvider(LLMProvider):
|
|||
content = msg.get("content", "")
|
||||
|
||||
if role == "system":
|
||||
# U2/G2: content 可为 str(传统)或 list[dict](content blocks)
|
||||
# content blocks 直接透传(包含 cache_control 标记)
|
||||
system_prompt = content
|
||||
continue
|
||||
|
||||
|
|
|
|||
|
|
@ -43,6 +43,9 @@ class AuthMiddleware(BaseHTTPMiddleware):
|
|||
"""
|
||||
|
||||
WHITELIST_PATHS = (
|
||||
"/",
|
||||
"/login",
|
||||
"/assets", # Static assets (exact match covers root; prefix below covers sub-paths)
|
||||
"/api/v1/health",
|
||||
"/api/v1/auth/login",
|
||||
"/api/v1/auth/refresh",
|
||||
|
|
@ -74,16 +77,15 @@ class AuthMiddleware(BaseHTTPMiddleware):
|
|||
"""Return True if ``path`` matches a whitelisted route.
|
||||
|
||||
Uses exact match for auth routes (so ``/auth/logout`` does NOT
|
||||
whitelist ``/auth/logout-others``) and prefix match for docs.
|
||||
whitelist ``/auth/logout-others``) and prefix match for docs and assets.
|
||||
"""
|
||||
for prefix in self.WHITELIST_PATHS:
|
||||
if path == prefix:
|
||||
return True
|
||||
# Prefix match only for documentation paths (trailing slash
|
||||
# or sub-path is fine). Auth paths require exact match to
|
||||
# avoid accidentally whitelisting sibling routes like
|
||||
# /auth/logout-others under /auth/logout.
|
||||
if prefix in ("/docs", "/openapi.json", "/redoc") and path.startswith(prefix):
|
||||
# Prefix match for documentation paths and static assets.
|
||||
# Auth paths require exact match to avoid accidentally whitelisting
|
||||
# sibling routes like /auth/logout-others under /auth/logout.
|
||||
if path.startswith(prefix) and prefix in ("/docs", "/openapi.json", "/redoc", "/assets"):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
|
|
|||
|
|
@ -116,6 +116,9 @@ class ServerConfig:
|
|||
evolution: dict[str, Any] | None = None,
|
||||
expert_paths: list[str] | None = None,
|
||||
board: dict[str, Any] | None = None,
|
||||
prompt_cache: dict[str, Any] | None = None,
|
||||
streaming: dict[str, Any] | None = None,
|
||||
verification: dict[str, Any] | None = None,
|
||||
on_change: Callable[["ServerConfig"], None] | None = None,
|
||||
):
|
||||
self.host = host
|
||||
|
|
@ -144,6 +147,12 @@ class ServerConfig:
|
|||
self.evolution = evolution or {}
|
||||
self.expert_paths = expert_paths or []
|
||||
self.board = board or {}
|
||||
self.prompt_cache = prompt_cache or {}
|
||||
# U3/G8: streaming.flush_interval_ms 控制 token chunk 节流(默认 0 = 逐 chunk yield)
|
||||
self.streaming = streaming or {}
|
||||
# U4/G1: verification.max_reinjections 控制 verify 失败回灌次数(默认 1)
|
||||
# verification_enabled=False 时此配置无效
|
||||
self.verification = verification or {}
|
||||
self.on_change = on_change
|
||||
|
||||
# Config watching state
|
||||
|
|
@ -227,6 +236,11 @@ class ServerConfig:
|
|||
# Board meeting config (max_rounds, default_template, etc.)
|
||||
board_data = data.get("board", {})
|
||||
|
||||
# U2/U3/U4: prompt_cache / streaming / verification 配置(从 YAML 读取)
|
||||
prompt_cache_data = data.get("prompt_cache", {})
|
||||
streaming_data = data.get("streaming", {})
|
||||
verification_data = data.get("verification", {})
|
||||
|
||||
return cls(
|
||||
host=server.get("host", "0.0.0.0"),
|
||||
port=server.get("port", 8001),
|
||||
|
|
@ -254,6 +268,9 @@ class ServerConfig:
|
|||
evolution=evolution_data,
|
||||
expert_paths=expert_paths,
|
||||
board=board_data,
|
||||
prompt_cache=prompt_cache_data,
|
||||
streaming=streaming_data,
|
||||
verification=verification_data,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ declare module 'vue' {
|
|||
AListItem: typeof import('ant-design-vue/es')['ListItem']
|
||||
AListItemMeta: typeof import('ant-design-vue/es')['ListItemMeta']
|
||||
AMenu: typeof import('ant-design-vue/es')['Menu']
|
||||
AMenuDivider: typeof import('ant-design-vue/es')['MenuDivider']
|
||||
AMenuItem: typeof import('ant-design-vue/es')['MenuItem']
|
||||
AModal: typeof import('ant-design-vue/es')['Modal']
|
||||
APageHeader: typeof import('ant-design-vue/es')['PageHeader']
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@
|
|||
<template
|
||||
v-for="f in fields"
|
||||
:key="`hdr_${f.id}`"
|
||||
#[`header_${f.id}`]"
|
||||
#[`header_${f.id}`]
|
||||
>
|
||||
<ColumnHeaderMenu
|
||||
:field="f"
|
||||
|
|
|
|||
|
|
@ -5,8 +5,8 @@
|
|||
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Fischer AgentKit</title>
|
||||
<script type="module" crossorigin src="/assets/index-DQi4f51B.js"></script>
|
||||
<link rel="stylesheet" crossorigin href="/assets/index-BdbfOrU3.css">
|
||||
<script type="module" crossorigin src="/assets/index-a-0N3I41.js"></script>
|
||||
<link rel="stylesheet" crossorigin href="/assets/index-Mld5F0pG.css">
|
||||
</head>
|
||||
<body>
|
||||
<div id="app"></div>
|
||||
|
|
|
|||
|
|
@ -4,10 +4,32 @@ import time
|
|||
from abc import ABC, abstractmethod
|
||||
from typing import Any
|
||||
|
||||
import jsonschema
|
||||
|
||||
from agentkit.telemetry.tracing import start_span
|
||||
from agentkit.telemetry.metrics import tool_duration_histogram
|
||||
|
||||
|
||||
class ToolValidationError(Exception):
|
||||
"""工具参数 schema 校验失败。
|
||||
|
||||
error_code:
|
||||
- "tool_call_invalid" 类型不匹配(jsonschema.ValidationError.path 末段为字段名)
|
||||
- "schema_mismatch" 必填缺失 / 结构性错误(默认兜底)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
message: str,
|
||||
*,
|
||||
error_code: str = "schema_mismatch",
|
||||
details: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
super().__init__(message)
|
||||
self.error_code = error_code
|
||||
self.details = details or {}
|
||||
|
||||
|
||||
class Tool(ABC):
|
||||
"""工具抽象基类
|
||||
|
||||
|
|
@ -57,6 +79,7 @@ class Tool(ABC):
|
|||
_start = time.monotonic()
|
||||
try:
|
||||
await self.before_execute(**kwargs)
|
||||
self._validate_input(kwargs)
|
||||
result = await self.execute(**kwargs)
|
||||
await self.after_execute(result, **kwargs)
|
||||
_duration_ms = int((time.monotonic() - _start) * 1000)
|
||||
|
|
@ -76,6 +99,37 @@ class Tool(ABC):
|
|||
finally:
|
||||
_span_cm.__exit__(None, None, None)
|
||||
|
||||
def _validate_input(self, kwargs: dict[str, Any]) -> None:
|
||||
"""校验 kwargs 是否符合 self.input_schema。
|
||||
|
||||
- input_schema=None → 跳过(向后兼容,旧工具无 schema)
|
||||
- 类型不匹配 → error_code="tool_call_invalid"
|
||||
- 必填缺失 / 结构性错误 → error_code="schema_mismatch"
|
||||
"""
|
||||
if self.input_schema is None:
|
||||
return
|
||||
# 过滤内部 kwargs(下划线开头,如 _skip_dangerous_check),不参与 schema 校验
|
||||
# W3 fix: 防止 additionalProperties:false 的 schema 拒绝内部控制参数
|
||||
user_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
|
||||
try:
|
||||
jsonschema.validate(instance=user_kwargs, schema=self.input_schema)
|
||||
except jsonschema.ValidationError as e:
|
||||
field_path = ".".join(str(p) for p in e.absolute_path) or "<root>"
|
||||
# required 缺失走 schema_mismatch;类型不符走 tool_call_invalid
|
||||
if e.validator == "required":
|
||||
code = "schema_mismatch"
|
||||
else:
|
||||
code = "tool_call_invalid"
|
||||
raise ToolValidationError(
|
||||
f"Tool '{self.name}' argument validation failed: {e.message}",
|
||||
error_code=code,
|
||||
details={
|
||||
"field": field_path,
|
||||
"validator": e.validator,
|
||||
"schema_path": list(e.absolute_schema_path),
|
||||
},
|
||||
) from e
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
"name": self.name,
|
||||
|
|
|
|||
|
|
@ -50,6 +50,13 @@ class FunctionTool(Tool):
|
|||
for param_name, param in sig.parameters.items():
|
||||
if param_name in ("self", "cls"):
|
||||
continue
|
||||
# ponytail: VAR_KEYWORD(**kwargs)/VAR_POSITIONAL(*args) 是 catch-all,
|
||||
# 不是具体参数,不进 schema。否则 schema 校验会要求 kwargs 字段必填。
|
||||
if param.kind in (
|
||||
inspect.Parameter.VAR_KEYWORD,
|
||||
inspect.Parameter.VAR_POSITIONAL,
|
||||
):
|
||||
continue
|
||||
|
||||
param_type = "string"
|
||||
if param.annotation != inspect.Parameter.empty:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,148 @@
|
|||
"""U3 / G8 delta_flush_interval 调速测试。
|
||||
|
||||
覆盖 R11-R12, R14:
|
||||
- R11 chunk 按 flush_interval_ms 间隔批量 yield
|
||||
- R12 配置化(flush_interval_ms=0 退化为逐 chunk yield)
|
||||
- R14 自检:合并 content 等于原始 chunks 拼接(不丢字符)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
|
||||
from agentkit.core.react import ReActEngine
|
||||
from agentkit.llm.protocol import StreamChunk
|
||||
|
||||
|
||||
class _StubGateway:
|
||||
"""模拟 LLMGateway,yield 一串 StreamChunk 后结束。"""
|
||||
|
||||
def __init__(self, chunks: list[str]):
|
||||
self._chunks = chunks
|
||||
|
||||
def get_provider_name_for_model(self, model: str) -> str | None:
|
||||
return None
|
||||
|
||||
async def chat_stream(self, **kwargs):
|
||||
for c in self._chunks:
|
||||
yield StreamChunk(content=c, model="test")
|
||||
|
||||
|
||||
def _collect_token_events(events) -> list[str]:
|
||||
return [e.data["content"] for e in events if e.event_type == "token"]
|
||||
|
||||
|
||||
# ---- R12 Config: flush_interval_ms=0 → 逐 chunk yield(向后兼容) ----
|
||||
|
||||
|
||||
async def test_flush_interval_zero_yields_per_chunk():
|
||||
chunks = ["H", "e", "l", "l", "o"]
|
||||
gw = _StubGateway(chunks)
|
||||
engine = ReActEngine(llm_gateway=gw, flush_interval_ms=0)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="test",
|
||||
):
|
||||
events.append(ev)
|
||||
tokens = _collect_token_events(events)
|
||||
# 5 chunks → 5 token events(每个内容 = 单 chunk)
|
||||
assert tokens == ["H", "e", "l", "l", "o"]
|
||||
|
||||
|
||||
# ---- R11 Happy path: flush_interval_ms > 0 → 批量合并 ----
|
||||
|
||||
|
||||
async def test_flush_interval_batches_chunks_by_interval():
|
||||
chunks = ["a", "b", "c", "d", "e", "f"]
|
||||
gw = _StubGateway(chunks)
|
||||
# 间隔设很大(10s),所有 chunks 在第一个 interval 内累积,流结束后最终 flush
|
||||
engine = ReActEngine(llm_gateway=gw, flush_interval_ms=10000)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="test",
|
||||
):
|
||||
events.append(ev)
|
||||
tokens = _collect_token_events(events)
|
||||
# 所有 chunk 累积到流结束,最终 flush 一次 → 1 个 token event,content = 全拼接
|
||||
assert len(tokens) == 1
|
||||
assert tokens[0] == "abcdef"
|
||||
|
||||
|
||||
# ---- R14 Self-check: 合并 content 等于原始 chunks 拼接(不丢字符) ----
|
||||
|
||||
|
||||
async def test_no_character_loss_after_merge():
|
||||
chunks = ["Hello", " ", "World", "!", "你好", "世界"]
|
||||
gw = _StubGateway(chunks)
|
||||
engine = ReActEngine(llm_gateway=gw, flush_interval_ms=10000)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="test",
|
||||
):
|
||||
events.append(ev)
|
||||
tokens = _collect_token_events(events)
|
||||
# 合并所有 token events 的 content 等于原始 chunks 拼接
|
||||
merged = "".join(tokens)
|
||||
assert merged == "".join(chunks) == "Hello World!你好世界"
|
||||
|
||||
|
||||
# ---- Edge: 流结束 mid-interval → 最终 flush 剩余 buffer ----
|
||||
|
||||
|
||||
async def test_final_flush_on_stream_end():
|
||||
chunks = ["x", "y", "z"]
|
||||
gw = _StubGateway(chunks)
|
||||
engine = ReActEngine(llm_gateway=gw, flush_interval_ms=10000)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="test",
|
||||
):
|
||||
events.append(ev)
|
||||
tokens = _collect_token_events(events)
|
||||
# mid-interval 累积 → 流结束最终 flush 一次
|
||||
assert tokens == ["xyz"]
|
||||
|
||||
|
||||
# ---- Edge: 单个 chunk 后流结束 → 立即 flush ----
|
||||
|
||||
|
||||
async def test_single_chunk_immediate_flush():
|
||||
chunks = ["only"]
|
||||
gw = _StubGateway(chunks)
|
||||
engine = ReActEngine(llm_gateway=gw, flush_interval_ms=10000)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="test",
|
||||
):
|
||||
events.append(ev)
|
||||
tokens = _collect_token_events(events)
|
||||
assert tokens == ["only"]
|
||||
|
||||
|
||||
# ---- Edge: chunks 含空 content(usage-only chunk)不进 buffer ----
|
||||
|
||||
|
||||
async def test_empty_content_chunk_not_buffered():
|
||||
chunks = ["a", "", "b"] # 中间 chunk 空
|
||||
gw = _StubGateway(chunks)
|
||||
engine = ReActEngine(llm_gateway=gw, flush_interval_ms=10000)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="test",
|
||||
):
|
||||
events.append(ev)
|
||||
tokens = _collect_token_events(events)
|
||||
# 空 chunk 跳过 buffer,最终 flush "ab"
|
||||
assert tokens == ["ab"]
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
"""U2 / G2 Prompt Cache 双块结构测试。
|
||||
|
||||
覆盖 R4-R7, R13:
|
||||
- R4 stable/volatile 双块结构
|
||||
- R5 记忆注入从 system_prompt 末尾移到 volatile 层
|
||||
- R6 跨 provider 统一 cache 策略(Anthropic blocks / OpenAI 字符串)
|
||||
- R7 多轮 stable 不变(由构造保证)
|
||||
- R13 配置化(prompt_cache_enable=False 退化)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
|
||||
class _StubGateway:
|
||||
"""模拟 LLMGateway,记录最后一次 chat_stream 调用的 messages。"""
|
||||
|
||||
def __init__(self, provider_name: str | None = "anthropic"):
|
||||
self._provider_name = provider_name
|
||||
self.captured_messages: list[dict[str, Any]] | None = None
|
||||
|
||||
def get_provider_name_for_model(self, model: str) -> str | None:
|
||||
return self._provider_name
|
||||
|
||||
async def chat_stream(self, **kwargs):
|
||||
self.captured_messages = list(kwargs.get("messages", []))
|
||||
return
|
||||
yield # makes this an async generator
|
||||
|
||||
|
||||
def _make_engine(provider_name: str | None = "anthropic", *, cache_enable: bool = True) -> tuple[ReActEngine, _StubGateway]:
|
||||
gw = _StubGateway(provider_name=provider_name)
|
||||
engine = ReActEngine.__new__(ReActEngine)
|
||||
engine._llm_gateway = gw
|
||||
engine._prompt_cache_enable = cache_enable
|
||||
return engine, gw
|
||||
|
||||
|
||||
# ---- R4/R6 Anthropic: stable + volatile → content blocks ----
|
||||
|
||||
|
||||
def test_anthropic_provider_returns_content_blocks_with_cache_control():
|
||||
engine, _ = _make_engine("anthropic")
|
||||
result = engine._build_system_message(
|
||||
stable="You are a helpful assistant.",
|
||||
volatile="Memory: foo",
|
||||
model="claude-sonnet-4",
|
||||
)
|
||||
assert isinstance(result, list)
|
||||
assert result[0] == {
|
||||
"type": "text",
|
||||
"text": "You are a helpful assistant.",
|
||||
"cache_control": {"type": "ephemeral"},
|
||||
}
|
||||
assert result[1] == {"type": "text", "text": "## 参考信息\nMemory: foo"}
|
||||
|
||||
|
||||
# ---- R5 Anthropic: empty volatile → only stable block ----
|
||||
|
||||
|
||||
def test_anthropic_empty_volatile_returns_only_stable_block():
|
||||
engine, _ = _make_engine("anthropic")
|
||||
result = engine._build_system_message(
|
||||
stable="base prompt",
|
||||
volatile="",
|
||||
model="claude",
|
||||
)
|
||||
assert isinstance(result, list)
|
||||
assert len(result) == 1
|
||||
assert result[0]["text"] == "base prompt"
|
||||
assert "cache_control" in result[0]
|
||||
|
||||
|
||||
# ---- R6 Non-Anthropic: returns string concat ----
|
||||
|
||||
|
||||
def test_non_anthropic_returns_string_concat():
|
||||
engine, _ = _make_engine("openai")
|
||||
result = engine._build_system_message(
|
||||
stable="base",
|
||||
volatile="ctx",
|
||||
model="gpt-4",
|
||||
)
|
||||
assert isinstance(result, str)
|
||||
assert result == "base\n\n## 参考信息\nctx"
|
||||
|
||||
|
||||
def test_unknown_provider_returns_string_concat():
|
||||
"""provider_name 无法确定时(gateway 返回 None),回退字符串拼接不报错。"""
|
||||
engine, _ = _make_engine(None)
|
||||
result = engine._build_system_message(
|
||||
stable="base",
|
||||
volatile="ctx",
|
||||
model="default",
|
||||
)
|
||||
assert isinstance(result, str)
|
||||
assert "## 参考信息" in result
|
||||
|
||||
|
||||
# ---- R13 Config: prompt_cache_enable=False → 退化字符串 ----
|
||||
|
||||
|
||||
def test_prompt_cache_disabled_falls_back_to_string():
|
||||
engine, _ = _make_engine("anthropic", cache_enable=False)
|
||||
result = engine._build_system_message(
|
||||
stable="base",
|
||||
volatile="ctx",
|
||||
model="claude",
|
||||
)
|
||||
# 即便是 Anthropic,enable=False 时也返回字符串(行为同改动前)
|
||||
assert isinstance(result, str)
|
||||
assert "cache_control" not in result
|
||||
|
||||
|
||||
# ---- R4 Edge: no stable + no volatile → None ----
|
||||
|
||||
|
||||
def test_empty_stable_and_volatile_returns_none():
|
||||
engine, _ = _make_engine("anthropic")
|
||||
result = engine._build_system_message(stable="", volatile="", model="claude")
|
||||
assert result is None
|
||||
|
||||
|
||||
# ---- R5 Edge: empty stable + volatile → only volatile block (Anthropic) ----
|
||||
|
||||
|
||||
def test_anthropic_empty_stable_returns_only_volatile_block():
|
||||
engine, _ = _make_engine("anthropic")
|
||||
result = engine._build_system_message(
|
||||
stable="",
|
||||
volatile="only memory",
|
||||
model="claude",
|
||||
)
|
||||
assert isinstance(result, list)
|
||||
assert len(result) == 1
|
||||
assert result[0]["text"] == "## 参考信息\nonly memory"
|
||||
assert "cache_control" not in result[0] # volatile 块无 cache_control
|
||||
|
||||
|
||||
# ---- Integration: execute_stream uses _build_system_message end-to-end ----
|
||||
|
||||
|
||||
async def test_execute_stream_with_anthropic_uses_content_blocks():
|
||||
"""execute_stream 把双块 system content 透传给 gateway。"""
|
||||
from agentkit.tools.base import Tool
|
||||
|
||||
class _NoopTool(Tool):
|
||||
def __init__(self):
|
||||
super().__init__(name="noop", description="noop")
|
||||
|
||||
async def execute(self, **kwargs):
|
||||
return {}
|
||||
|
||||
class _MockGateway:
|
||||
def __init__(self):
|
||||
self.captured_messages = None
|
||||
|
||||
def get_provider_name_for_model(self, model: str) -> str | None:
|
||||
return "anthropic"
|
||||
|
||||
async def chat_stream(self, **kwargs):
|
||||
self.captured_messages = list(kwargs.get("messages", []))
|
||||
# yield one chunk then end
|
||||
from agentkit.llm.protocol import StreamChunk
|
||||
yield StreamChunk(content="done", model="claude")
|
||||
|
||||
class _MemRetriever:
|
||||
async def get_context_string(self, **kw):
|
||||
return "retrieved context"
|
||||
|
||||
gw = _MockGateway()
|
||||
engine = ReActEngine(llm_gateway=gw, prompt_cache_enable=True)
|
||||
events = []
|
||||
async for ev in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
tools=[],
|
||||
model="claude",
|
||||
system_prompt="base",
|
||||
memory_retriever=_MemRetriever(),
|
||||
):
|
||||
events.append(ev)
|
||||
|
||||
assert gw.captured_messages is not None
|
||||
sys_msg = gw.captured_messages[0]
|
||||
assert sys_msg["role"] == "system"
|
||||
assert isinstance(sys_msg["content"], list)
|
||||
assert sys_msg["content"][0]["text"] == "base"
|
||||
assert "cache_control" in sys_msg["content"][0]
|
||||
assert sys_msg["content"][1]["text"] == "## 参考信息\nretrieved context"
|
||||
|
||||
|
||||
# ---- Anthropic provider _convert_messages passes through list-type system ----
|
||||
|
||||
|
||||
def test_anthropic_convert_messages_passes_through_list_system_content():
|
||||
"""AnthropicProvider._convert_messages 应直接透传 list-type system content。"""
|
||||
from agentkit.llm.providers.anthropic import AnthropicProvider
|
||||
|
||||
provider = AnthropicProvider.__new__(AnthropicProvider)
|
||||
blocks = [
|
||||
{"type": "text", "text": "stable", "cache_control": {"type": "ephemeral"}},
|
||||
{"type": "text", "text": "volatile"},
|
||||
]
|
||||
messages = [{"role": "system", "content": blocks}]
|
||||
system_prompt, anthropic_messages = provider._convert_messages(messages)
|
||||
assert system_prompt is blocks # same object, transparent passthrough
|
||||
assert anthropic_messages == []
|
||||
|
||||
|
||||
def test_anthropic_convert_messages_string_system_still_works():
|
||||
"""传统 string system content 仍能透传(向后兼容)。"""
|
||||
from agentkit.llm.providers.anthropic import AnthropicProvider
|
||||
|
||||
provider = AnthropicProvider.__new__(AnthropicProvider)
|
||||
messages = [{"role": "system", "content": "old style"}]
|
||||
system_prompt, _ = provider._convert_messages(messages)
|
||||
assert system_prompt == "old style"
|
||||
|
|
@ -883,3 +883,109 @@ class ContextCompressorStub:
|
|||
|
||||
def should_compress(self, messages: list[dict]) -> bool:
|
||||
return self._compress
|
||||
|
||||
|
||||
class TestParseMalformedToolUse:
|
||||
"""畸形 <tool_use> 文本的容错解析"""
|
||||
|
||||
def test_missing_closing_tag_still_parses(self):
|
||||
"""模型输出 <tool_use> 但没有 </tool_use> 闭合标签"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
engine = ReActEngine(llm_gateway=MagicMock(spec=LLMGateway))
|
||||
content = (
|
||||
'<tool_use>\n'
|
||||
'{"name": "shell", "arguments": {"command": "ls -la"}}\n'
|
||||
)
|
||||
calls = engine._parse_text_tool_calls(content)
|
||||
assert len(calls) == 1
|
||||
assert calls[0]["name"] == "shell"
|
||||
assert calls[0]["arguments"]["command"] == "ls -la"
|
||||
|
||||
def test_malformed_json_with_stray_tags(self):
|
||||
"""JSON 中混入 <parameter> 等标签时仍能提取工具名和参数"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
engine = ReActEngine(llm_gateway=MagicMock(spec=LLMGateway))
|
||||
content = (
|
||||
'<tool_use>\n'
|
||||
'{"name": "shell", "arguments": {"command": "sudo chown -R $USER /tmp"}}\n'
|
||||
'</parameter>\n'
|
||||
'<parameter=timeout>30</parameter>\n'
|
||||
'<function>\n'
|
||||
)
|
||||
calls = engine._parse_text_tool_calls(content)
|
||||
assert len(calls) == 1
|
||||
assert calls[0]["name"] == "shell"
|
||||
assert "chown" in calls[0]["arguments"]["command"]
|
||||
|
||||
def test_truncated_json_still_extracts_name(self):
|
||||
"""JSON 被截断时仍能提取工具名"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
engine = ReActEngine(llm_gateway=MagicMock(spec=LLMGateway))
|
||||
content = '<tool_use>\n{"name": "web_search", "arguments": {"query": "test"'
|
||||
calls = engine._parse_text_tool_calls(content)
|
||||
assert len(calls) == 1
|
||||
assert calls[0]["name"] == "web_search"
|
||||
|
||||
def test_completely_unparseable_tool_use_returns_empty(self):
|
||||
"""完全无法解析的 <tool_use> 返回空列表"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
engine = ReActEngine(llm_gateway=MagicMock(spec=LLMGateway))
|
||||
content = '<tool_use>\ngarbage not json at all\n'
|
||||
calls = engine._parse_text_tool_calls(content)
|
||||
assert calls == []
|
||||
|
||||
|
||||
class TestMalformedToolUseNotLeakedAsFinalAnswer:
|
||||
"""畸形 <tool_use> 不应作为 final_answer 泄漏给用户"""
|
||||
|
||||
async def test_malformed_tool_use_triggers_correction_not_leak(self):
|
||||
"""模型输出畸形 <tool_use> 时，不应把原始 XML 作为最终答案返回"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
tool = FakeTool(name="shell", result={"output": "done", "exit_code": 0})
|
||||
malformed_content = (
|
||||
'<tool_use>\n'
|
||||
'{"name": "shell", "arguments": {"command": "ls"}}\n'
|
||||
'</parameter>\n<function>\n'
|
||||
)
|
||||
gateway = make_mock_gateway([
|
||||
make_response(content=malformed_content),
|
||||
make_response(content="Done successfully"),
|
||||
])
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "list files"}],
|
||||
tools=[tool],
|
||||
)
|
||||
|
||||
# 不应把原始 XML 作为最终答案
|
||||
assert "<tool_use>" not in result.output
|
||||
assert "</parameter>" not in result.output
|
||||
assert "<function>" not in result.output
|
||||
|
||||
async def test_completely_unparseable_tool_use_injects_correction(self):
|
||||
"""<tool_use> 完全无法解析时，注入纠正消息让模型重试"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
tool = FakeTool(name="search", result={"results": ["data"]})
|
||||
gateway = make_mock_gateway([
|
||||
# 第一次：完全无法解析的 <tool_use>
|
||||
make_response(content="<tool_use>\nnot json at all just words\n"),
|
||||
# 第二次：模型纠正后正常回答
|
||||
make_response(content="Search completed"),
|
||||
])
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "search something"}],
|
||||
tools=[tool],
|
||||
)
|
||||
|
||||
# 不应把原始 XML 作为最终答案
|
||||
assert "<tool_use>" not in result.output
|
||||
assert result.output == "Search completed"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,140 @@
|
|||
"""U1 / G3 工具调用 schema 校验测试。
|
||||
|
||||
覆盖 R8-R10:
|
||||
- R8 schema 校验在 execute 前运行
|
||||
- R9 类型化错误码 (tool_call_invalid / schema_mismatch)
|
||||
- R10 _execute_tool 捕获后回灌 conversation(结构化 dict 保留 error_code)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from agentkit.tools.base import Tool, ToolValidationError
|
||||
|
||||
|
||||
class _StubTool(Tool):
|
||||
"""测试用 stub,记录调用与参数。"""
|
||||
|
||||
def __init__(self, *, schema=None, payload=None):
|
||||
super().__init__(name="stub", description="stub", input_schema=schema)
|
||||
self.calls: list[dict] = []
|
||||
self._payload = payload or {"ok": True}
|
||||
|
||||
async def execute(self, **kwargs) -> dict:
|
||||
self.calls.append(kwargs)
|
||||
return self._payload
|
||||
|
||||
|
||||
_SCHEMA = {
|
||||
"type": "object",
|
||||
"properties": {"count": {"type": "integer"}},
|
||||
"required": ["count"],
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
|
||||
# ---- R8 happy path ----
|
||||
|
||||
|
||||
async def test_schema_valid_passes_through_to_execute():
|
||||
tool = _StubTool(schema=_SCHEMA)
|
||||
result = await tool.safe_execute(count=5)
|
||||
assert result == {"ok": True}
|
||||
assert tool.calls == [{"count": 5}]
|
||||
|
||||
|
||||
# ---- R9 backward compat: input_schema=None skips ----
|
||||
|
||||
|
||||
async def test_none_schema_skips_validation():
|
||||
tool = _StubTool(schema=None)
|
||||
await tool.safe_execute(anything="ok", even_invalid_types=123)
|
||||
assert tool.calls == [{"anything": "ok", "even_invalid_types": 123}]
|
||||
|
||||
|
||||
# ---- R9 type mismatch → tool_call_invalid ----
|
||||
|
||||
|
||||
async def test_type_mismatch_raises_tool_call_invalid():
|
||||
tool = _StubTool(schema=_SCHEMA)
|
||||
with pytest.raises(ToolValidationError) as ei:
|
||||
await tool.safe_execute(count="abc")
|
||||
assert ei.value.error_code == "tool_call_invalid"
|
||||
assert ei.value.details["field"] == "count"
|
||||
assert tool.calls == [] # execute not called
|
||||
|
||||
|
||||
# ---- R9 missing required → schema_mismatch ----
|
||||
|
||||
|
||||
async def test_missing_required_raises_schema_mismatch():
|
||||
tool = _StubTool(schema=_SCHEMA)
|
||||
with pytest.raises(ToolValidationError) as ei:
|
||||
await tool.safe_execute()
|
||||
assert ei.value.error_code == "schema_mismatch"
|
||||
assert ei.value.details["validator"] == "required"
|
||||
assert tool.calls == []
|
||||
|
||||
|
||||
# ---- R10 integration: _execute_tool catches and returns structured dict ----
|
||||
|
||||
|
||||
async def test_execute_tool_catches_validation_error_returns_structured_dict():
|
||||
"""ReActEngine._execute_tool 应优先捕获 ToolValidationError 并保留 error_code。"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
tool = _StubTool(schema=_SCHEMA)
|
||||
engine = ReActEngine.__new__(ReActEngine) # 绕过 __init__
|
||||
result = await engine._execute_tool("stub", {"count": "not-int"}, [tool])
|
||||
|
||||
assert "error" in result
|
||||
assert result["error_code"] == "tool_call_invalid"
|
||||
assert "details" in result
|
||||
assert tool.calls == [] # execute 未执行
|
||||
|
||||
|
||||
async def test_execute_tool_catches_missing_required_returns_schema_mismatch():
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
tool = _StubTool(schema=_SCHEMA)
|
||||
engine = ReActEngine.__new__(ReActEngine)
|
||||
result = await engine._execute_tool("stub", {}, [tool])
|
||||
|
||||
assert result["error_code"] == "schema_mismatch"
|
||||
assert result["details"]["validator"] == "required"
|
||||
|
||||
|
||||
# ---- R10 self-check: structured error string survives into tool message ----
|
||||
|
||||
|
||||
async def test_structured_error_dict_str_includes_error_code_for_llm_self_correction():
|
||||
"""_build_tool_result_message 把 dict 转 str(content),LLM 看到文本含 error_code。"""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
tool = _StubTool(schema=_SCHEMA)
|
||||
engine = ReActEngine.__new__(ReActEngine)
|
||||
result = await engine._execute_tool("stub", {"count": "bad"}, [tool])
|
||||
msg = await engine._build_tool_result_message(
|
||||
tool_call_id="t1",
|
||||
result=result,
|
||||
)
|
||||
assert msg["role"] == "tool"
|
||||
assert msg["tool_call_id"] == "t1"
|
||||
assert "tool_call_invalid" in msg["content"]
|
||||
|
||||
|
||||
# ---- W3 fix: 内部 kwargs(_前缀)不参与 schema 校验 ----
|
||||
|
||||
|
||||
async def test_internal_kwargs_underscore_prefixed_skipped_by_validation():
|
||||
"""_skip_dangerous_check 等内部参数不参与 schema 校验。
|
||||
|
||||
防止 additionalProperties:false 的 schema 拒绝内部控制参数(W3)。
|
||||
"""
|
||||
tool = _StubTool(schema=_SCHEMA) # additionalProperties: False
|
||||
# _skip_dangerous_check 是内部参数,不应被 schema 拒绝
|
||||
result = await tool.safe_execute(count=5, _skip_dangerous_check=True)
|
||||
assert result == {"ok": True}
|
||||
# execute 仍然收到完整 kwargs(内部参数透传给工具)
|
||||
assert tool.calls == [{"count": 5, "_skip_dangerous_check": True}]
|
||||
|
|
@ -0,0 +1,394 @@
|
|||
"""U4/G1: Verify 失败回灌 ReAct 测试
|
||||
|
||||
characterization-first: 先覆盖现有 verify 行为(max_reinjections=0 等价于不回灌),
|
||||
再测新回灌行为(reinjection on first fail, break on second fail)。
|
||||
|
||||
R1: verify 首次失败 → errors 注入 conversation → LLM 自纠正 → 二次 verify 通过
|
||||
R2: verify 二次失败 → 中断返回错误附 verify log
|
||||
R3: max_reinjections 可配置(默认 1),=0 等价于不回灌;回灌受 max_steps 约束
|
||||
R13: ServerConfig.verification 配置项
|
||||
R14: max_reinjections 默认值为 1
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
|
||||
from agentkit.core.react import ReActEngine
|
||||
from agentkit.core.verification_loop import VerificationResult
|
||||
from agentkit.llm.gateway import LLMGateway
|
||||
from agentkit.llm.protocol import LLMResponse, TokenUsage
|
||||
|
||||
|
||||
# ── Helpers ──────────────────────────────────────────────
|
||||
|
||||
|
||||
def make_mock_gateway(responses: list[LLMResponse]) -> LLMGateway:
|
||||
"""创建按顺序返回给定响应的 mock LLMGateway。"""
|
||||
gateway = MagicMock(spec=LLMGateway)
|
||||
gateway.chat = AsyncMock(side_effect=responses)
|
||||
gateway.get_provider_name_for_model = MagicMock(return_value=None)
|
||||
return gateway
|
||||
|
||||
|
||||
def make_response(content: str = "") -> LLMResponse:
|
||||
return LLMResponse(
|
||||
content=content,
|
||||
model="test-model",
|
||||
usage=TokenUsage(prompt_tokens=10, completion_tokens=20),
|
||||
tool_calls=[],
|
||||
)
|
||||
|
||||
|
||||
def make_verify_result(passed: bool, errors: list[str] | None = None) -> VerificationResult:
|
||||
return VerificationResult(
|
||||
passed=passed,
|
||||
attempts=1,
|
||||
test_output="$ pytest\nFAILED test_x.py" if not passed else "$ pytest\nOK",
|
||||
errors=errors or ([] if passed else ["test_x.py::test_failed"]),
|
||||
)
|
||||
|
||||
|
||||
def make_mock_vloop(verify_results: list[VerificationResult]) -> MagicMock:
|
||||
"""创建一个 mock VerificationLoop,verify() 按顺序返回给定结果。"""
|
||||
vloop = MagicMock()
|
||||
vloop.verify = AsyncMock(side_effect=verify_results)
|
||||
return vloop
|
||||
|
||||
|
||||
# ── Characterization: max_reinjections=0 等价于当前行为 ──────────
|
||||
|
||||
|
||||
class TestVerifyCharacterization:
|
||||
"""现有 verify 行为(max_reinjections=0):失败仅记录 trajectory,不回灌。"""
|
||||
|
||||
async def test_verify_disabled_no_verify_step(self):
|
||||
"""verification_enabled=False → 不运行 verify,trajectory 无 verification step。"""
|
||||
gateway = make_mock_gateway([make_response("final answer")])
|
||||
engine = ReActEngine(llm_gateway=gateway, max_steps=3)
|
||||
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "do something"}],
|
||||
)
|
||||
|
||||
assert result.output == "final answer"
|
||||
assert all(s.tool_name != "verification" for s in result.trajectory)
|
||||
|
||||
async def test_verify_pass_no_extra_step(self):
|
||||
"""verify 通过 → 不追加 verification step。"""
|
||||
gateway = make_mock_gateway([make_response("answer")])
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=3,
|
||||
verification_enabled=True,
|
||||
verification_commands=["echo ok"],
|
||||
max_reinjections=0,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop([make_verify_result(passed=True)]),
|
||||
):
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "do something"}],
|
||||
)
|
||||
|
||||
assert result.output == "answer"
|
||||
assert all(s.tool_name != "verification" for s in result.trajectory)
|
||||
|
||||
async def test_verify_fail_max_zero_no_reinjection(self):
|
||||
"""max_reinjections=0 + verify 失败 → 仅记录 trajectory,不回灌 LLM。
|
||||
|
||||
这是当前行为的 characterization:gateway.chat 只被调用一次。
|
||||
"""
|
||||
gateway = make_mock_gateway([make_response("bad answer")])
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=3,
|
||||
verification_enabled=True,
|
||||
verification_commands=["false"],
|
||||
max_reinjections=0,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop([make_verify_result(passed=False)]),
|
||||
):
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "do something"}],
|
||||
)
|
||||
|
||||
# LLM 只被调用一次(无回灌)
|
||||
assert gateway.chat.await_count == 1
|
||||
# 输出仍保留
|
||||
assert result.output == "bad answer"
|
||||
# trajectory 包含 verification step
|
||||
verify_steps = [s for s in result.trajectory if s.tool_name == "verification"]
|
||||
assert len(verify_steps) == 1
|
||||
assert verify_steps[0].result["passed"] is False
|
||||
|
||||
|
||||
# ── R1: 回灌后 LLM 自纠正 → 二次 verify 通过 ──────────
|
||||
|
||||
|
||||
class TestVerifyReinjection:
|
||||
"""verify 失败回灌 conversation,LLM 自纠正后二次 verify 通过。"""
|
||||
|
||||
async def test_first_fail_reinject_second_pass(self):
|
||||
"""R1: verify 首次失败 → errors 注入 conversation → LLM 修正 → 二次 verify 通过。"""
|
||||
gateway = make_mock_gateway(
|
||||
[
|
||||
make_response("bad code"), # 第一次:错误答案
|
||||
make_response("fixed code"), # 第二次:修正后答案
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=5,
|
||||
verification_enabled=True,
|
||||
verification_commands=["pytest"],
|
||||
max_reinjections=1, # 默认值,允许 1 次回灌
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop(
|
||||
[
|
||||
make_verify_result(passed=False), # 第一次 verify 失败
|
||||
make_verify_result(passed=True), # 第二次 verify 通过
|
||||
]
|
||||
),
|
||||
):
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "write code"}],
|
||||
)
|
||||
|
||||
# LLM 被调用两次
|
||||
assert gateway.chat.await_count == 2
|
||||
# 最终输出是修正后的答案
|
||||
assert result.output == "fixed code"
|
||||
# 二次 verify 通过,不追加 verification step
|
||||
verify_steps = [s for s in result.trajectory if s.tool_name == "verification"]
|
||||
assert len(verify_steps) == 0
|
||||
|
||||
async def test_reinjected_user_message_appears_in_conversation(self):
|
||||
"""R1 集成:回灌的 user 消息出现在 conversation,含 errors 文本。"""
|
||||
gateway = make_mock_gateway(
|
||||
[
|
||||
make_response("bad"),
|
||||
make_response("good"),
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=5,
|
||||
verification_enabled=True,
|
||||
verification_commands=["pytest"],
|
||||
max_reinjections=1,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop(
|
||||
[
|
||||
make_verify_result(passed=False, errors=["AssertionError: x != y"]),
|
||||
make_verify_result(passed=True),
|
||||
]
|
||||
),
|
||||
):
|
||||
await engine.execute(
|
||||
messages=[{"role": "user", "content": "write code"}],
|
||||
)
|
||||
|
||||
# 第二次 LLM 调用时,conversation 应包含回灌的 user 消息
|
||||
second_call = gateway.chat.await_args_list[1]
|
||||
msgs_sent = second_call.kwargs.get("messages") or second_call[1].get("messages")
|
||||
reinjected = [
|
||||
m for m in msgs_sent if m.get("role") == "user" and "验证失败" in m.get("content", "")
|
||||
]
|
||||
assert len(reinjected) >= 1
|
||||
assert "AssertionError: x != y" in reinjected[-1]["content"]
|
||||
|
||||
|
||||
# ── R2: 二次 verify 失败 → 中断返回错误 ──────────
|
||||
|
||||
|
||||
class TestVerifyDoubleFailure:
|
||||
"""verify 二次失败 → 中断,返回错误附 verify log。"""
|
||||
|
||||
async def test_second_fail_breaks_with_verify_log(self):
|
||||
"""R2: 二次 verify 失败 → 中断,trajectory 含 verify log + errors。"""
|
||||
gateway = make_mock_gateway(
|
||||
[
|
||||
make_response("bad v1"),
|
||||
make_response("bad v2"),
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=5,
|
||||
verification_enabled=True,
|
||||
verification_commands=["pytest"],
|
||||
max_reinjections=1,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop(
|
||||
[
|
||||
make_verify_result(passed=False, errors=["err1"]),
|
||||
make_verify_result(passed=False, errors=["err2"]),
|
||||
]
|
||||
),
|
||||
):
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "write code"}],
|
||||
)
|
||||
|
||||
# LLM 被调用两次(initial + 1 reinjection)
|
||||
assert gateway.chat.await_count == 2
|
||||
# 状态标记 verify 失败
|
||||
assert result.status == "verify_failed"
|
||||
# 输出保留(LLM 最后的答案)
|
||||
assert result.output == "bad v2"
|
||||
# trajectory 含 verification step with errors
|
||||
verify_steps = [s for s in result.trajectory if s.tool_name == "verification"]
|
||||
assert len(verify_steps) == 1
|
||||
assert verify_steps[0].result["passed"] is False
|
||||
assert "err2" in verify_steps[0].result["errors"]
|
||||
|
||||
|
||||
# ── R3: 配置 + 边界 ──────────
|
||||
|
||||
|
||||
class TestVerifyReinjectionConfig:
|
||||
"""max_reinjections 配置测试。"""
|
||||
|
||||
def test_default_max_reinjections_is_one(self):
|
||||
"""R14 self-check: max_reinjections 默认值为 1。"""
|
||||
gateway = make_mock_gateway([])
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
assert engine._max_reinjections == 1
|
||||
|
||||
async def test_max_reinjections_zero_skips_reinjection(self):
|
||||
"""R3: max_reinjections=0 → 等价于不回灌(当前行为)。"""
|
||||
gateway = make_mock_gateway([make_response("only answer")])
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=5,
|
||||
verification_enabled=True,
|
||||
verification_commands=["false"],
|
||||
max_reinjections=0,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop([make_verify_result(passed=False)]),
|
||||
):
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "do something"}],
|
||||
)
|
||||
|
||||
assert gateway.chat.await_count == 1 # 无回灌
|
||||
assert result.output == "only answer"
|
||||
|
||||
async def test_reinjection_hits_max_steps_interrupts(self):
|
||||
"""R3 edge: 回灌期间达到 max_steps → 中断(不无限循环)。"""
|
||||
# max_steps=2, max_reinjections=5(max_steps 先到)
|
||||
# LLM 调用 1:final answer → verify 失败 → reinject
|
||||
# LLM 调用 2:final answer → verify 失败 → step=2=max_steps → 中断
|
||||
gateway = make_mock_gateway(
|
||||
[
|
||||
make_response("ans1"),
|
||||
make_response("ans2"),
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=2,
|
||||
verification_enabled=True,
|
||||
verification_commands=["false"],
|
||||
max_reinjections=5, # 远大于 max_steps,验证 max_steps 优先
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop(
|
||||
[
|
||||
make_verify_result(passed=False),
|
||||
make_verify_result(passed=False),
|
||||
]
|
||||
),
|
||||
):
|
||||
result = await engine.execute(
|
||||
messages=[{"role": "user", "content": "do something"}],
|
||||
)
|
||||
|
||||
# LLM 被调用 2 次(受 max_steps=2 限制)
|
||||
assert gateway.chat.await_count == 2
|
||||
# 第二次 verify 失败,因 max_reinjections=5 未到但 max_steps 到了
|
||||
# → 应中断(verify_failed 或 partial)
|
||||
assert result.status in ("verify_failed", "partial")
|
||||
|
||||
|
||||
# ── execute_stream 回灌 ──────────
|
||||
|
||||
|
||||
class TestVerifyReinjectionStream:
|
||||
"""execute_stream 模式下的回灌行为。"""
|
||||
|
||||
async def test_stream_reinjection_first_fail_second_pass(self):
|
||||
"""R1 stream: verify 首次失败 → 回灌 → 二次通过。"""
|
||||
from agentkit.llm.protocol import StreamChunk
|
||||
|
||||
def make_stream_chunks(content: str):
|
||||
"""返回一个返回 chunk 列表的 async generator factory。"""
|
||||
|
||||
async def _stream(**kwargs):
|
||||
# Simulate streaming: yield content in 2 chunks
|
||||
mid = len(content) // 2
|
||||
yield StreamChunk(content=content[:mid], model="test-model")
|
||||
yield StreamChunk(content=content[mid:], model="test-model")
|
||||
|
||||
return _stream
|
||||
|
||||
gateway = MagicMock(spec=LLMGateway)
|
||||
gateway.chat_stream = MagicMock(
|
||||
side_effect=[
|
||||
make_stream_chunks("bad code")(),
|
||||
make_stream_chunks("fixed code")(),
|
||||
]
|
||||
)
|
||||
gateway.get_provider_name_for_model = MagicMock(return_value=None)
|
||||
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
max_steps=5,
|
||||
verification_enabled=True,
|
||||
verification_commands=["pytest"],
|
||||
max_reinjections=1,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"agentkit.core.verification_loop.VerificationLoop",
|
||||
return_value=make_mock_vloop(
|
||||
[
|
||||
make_verify_result(passed=False),
|
||||
make_verify_result(passed=True),
|
||||
]
|
||||
),
|
||||
):
|
||||
events = []
|
||||
async for event in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "write code"}],
|
||||
):
|
||||
events.append(event)
|
||||
|
||||
# chat_stream 被调用两次
|
||||
assert gateway.chat_stream.call_count == 2
|
||||
# 有 final_answer 事件
|
||||
final_events = [e for e in events if e.event_type == "final_answer"]
|
||||
assert len(final_events) >= 1
|
||||
# 最终输出是修正后的答案
|
||||
assert "fixed code" in final_events[-1].data.get("output", "")
|
||||
Loading…
Reference in New Issue