diff --git a/.gitignore b/.gitignore index 6cce6e25..8ca740be 100644 --- a/.gitignore +++ b/.gitignore @@ -96,12 +96,13 @@ data/* config/* plugins/* skill/* +typings/ .agents/ skills-lock.json !plugins/default_chatter/ -!plugins/napcat_adapter/ +!plugins/onebot_adapter/ !plugins/booku_memory/ !plugins/emoji_sender/ !plugins/perm_plugin/ diff --git a/docker-compose.yml b/docker-compose.yml index 56b9c594..2e9ef07e 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -14,6 +14,10 @@ services: - ./logs:/app/logs # 建议也将插件目录映射，方便动态开发 - ./plugins:/app/plugins + # 映射协议文件，方便更新后无需重建镜像 + - ./eula.md:/app/eula.md + - ./PRIVACY.md:/app/PRIVACY.md + - ./LICENSE:/app/LICENSE environment: # 设置容器时区为上海 - TZ=Asia/Shanghai diff --git a/docs/guides/plugin-authoring/18-adapter.md b/docs/guides/plugin-authoring/18-adapter.md index ad5e7f73..ab5bed3f 100644 --- a/docs/guides/plugin-authoring/18-adapter.md +++ b/docs/guides/plugin-authoring/18-adapter.md @@ -230,7 +230,7 @@ Adapter 面向外部聊天平台的消息流。 ## 18.7 一个最小自定义 Adapter，先长成什么样 -这一章我们不直接上 Napcat 那种真实平台接入版本。 +这一章我们不直接上 OneBot 那种真实平台接入版本。 不是因为它不重要，而是因为它一下会把下面这些东西全堆到你脸上： @@ -439,7 +439,7 @@ class DemoBridgeAdapter(BaseAdapter): ## 18.11 为什么真实平台 Adapter 会明显更复杂 -如果你去看现有的 `napcat_adapter`，会发现它和刚才那个最小例子完全不是一个体量。 +如果你去看现有的 `onebot_adapter`，会发现它和刚才那个最小例子完全不是一个体量。 这很正常。 diff --git a/docs/llm/README.md b/docs/llm/README.md index 6f0cb3a6..38dcacc2 100644 --- a/docs/llm/README.md +++ b/docs/llm/README.md @@ -1,40 +1,64 @@ -# LLM 模块文档（V1.1.0） +# LLM 模块文档（V2.0.0） ## 概述 -LLM 模块是 Neo-MoFox 中用于与大语言模型交互的核心组件。它提供了一个统一、灵活的接口，支持多种 LLM 提供商（如 OpenAI）、负载均衡、重试策略和指标收集等功能。 +LLM 模块是 Neo-MoFox 中用于与大语言模型交互的核心组件。它提供了统一、灵活的接口，支持多种 LLM 提供商（OpenAI、Anthropic 等）、多种请求类型（对话补全、Embedding、Rerank）、负载均衡、重试策略、上下文管理、流式推理内容（reasoning）解析以及多级指标收集等功能。 ### 核心设计原则 - **标准化消息格式**：使用 `LLMPayload`（`role + content`）统一表示消息单元 - **灵活的响应处理**：支持 `await`（非流式）和 `async for`（流式）两种消费方式 -- **独立的策略管理**：负载均衡和重试由 `policy` 模块独立处理 -- **可观测性**：内置指标收集和监控功能 +- **多请求类型**：统一支持对话补全（Completions）、Embedding、Rerank 三种请求 +- **独立的策略管理**：负载均衡和重试由 `policy` 模块独立处理，默认使用 `LoadBalancedPolicy` +- **上下文感知**：内建上下文预算管理、压缩与裁剪、对话结构校验 +- **可观测性**：内置内存级指标收集（`MetricsCollector`）与持久化统计（`LLMStatsCollector`） - **模块独立性**：LLM 模块不依赖 `core/config` 的实现细节，配置通过 `model_set` 参数直接传入 ## 模块结构 ``` kernel/llm/ -├── __init__.py # 公开 API 导出 -├── exceptions.py # 标准化异常类 -├── roles.py # 消息角色枚举 -├── request.py # LLM 请求类 -├── response.py # LLM 响应类 -├── monitor.py # 指标收集和监控 -├── model_client/ # 模型客户端实现 -│ ├── base.py # 客户端接口协议 -│ ├── openai_client.py # OpenAI 实现 -│ ├── registry.py # 客户端注册表 +├── __init__.py # 公开 API 导出 +├── types.py # 类型定义（ModelEntry, ModelSet, RequestType） +├── exceptions.py # 标准化异常类 + classify_exception +├── roles.py # 消息角色枚举 +├── request.py # LLMRequest 门面 + 模型校验 +├── request_execution.py # 请求执行主流程（生命周期管理） +├── request_inspector.py # 请求调试与检查工具 +├── response.py # LLMResponse 包装层 +├── stream_state.py # 流式状态归并（LLMStreamReducer） +├── tool_call_compat.py # 工具调用兼容解析 +├── token_counter.py # Token 计数工具 +├── context.py # LLMContextManager 门面 +├── context_budget.py # 上下文预算与压缩 +├── context_structure.py # 上下文结构校验 +├── monitor.py # 内存级指标收集（MetricsCollector） +├── observation.py # 请求观测与遥测分发 +├── embedding_request.py # Embedding 请求 +├── embedding_response.py # Embedding 响应 +├── rerank_request.py # Rerank 请求 +├── rerank_response.py # Rerank 响应 +├── model_client/ # 模型客户端实现 +│ ├── base.py # 客户端接口协议（Chat/Embedding/Rerank） +│ ├── openai_client.py # OpenAI 实现 +│ ├── anthropic_client.py # Anthropic 实现 +│ ├── registry.py # 客户端注册表 +│ ├── shared.py # 共享工具 │ └── __init__.py -├── payload/ # 消息负载定义 -│ ├── content.py # 内容类型定义 -│ ├── payload.py # 负载结构 -│ ├── tooling.py # 工具调用相关 +├── payload/ # 消息负载定义 +│ ├── content.py # 内容类型定义（Text/Image/Audio/Video/File/ReasoningText） +│ ├── payload.py # LLMPayload 结构 +│ ├── tooling.py # 工具调用相关（LLMUsable/ToolCall/ToolResult/ToolRegistry） │ └── __init__.py -└── policy/ # 负载均衡和重试 - ├── base.py # 策略接口 - ├── round_robin.py # 轮询策略实现 +├── policy/ # 负载均衡和重试 +│ ├── base.py # 策略接口（Policy/PolicySession/ModelStep） +│ ├── round_robin.py # 轮询策略 +│ ├── load_balanced.py # 负载均衡策略（默认） +│ └── __init__.py +└── stats/ # 持久化统计 + ├── collector.py # LLMStatsCollector + ├── config.py # LLMStatsConfig + ├── database.py # SQLite 存储 └── __init__.py ``` @@ -46,15 +70,25 @@ kernel/llm/ from src.kernel.llm import LLMRequest, LLMResponse, LLMPayload, Text from src.kernel.llm import ROLE -# 准备模型配置 +# 准备模型配置（完整字段参见 ModelEntry 定义） model_set = [ { - "client_type": "openai", + "api_provider": "openai", + "base_url": "https://api.openai.com/v1", "model_identifier": "gpt-4", "api_key": "sk-...", - "base_url": "https://api.openai.com/v1", + "client_type": "openai", "max_retry": 3, + "timeout": 30.0, "retry_interval": 1.0, + "price_in": 0.03, + "cache_hit_price_in": 0.015, + "price_out": 0.06, + "temperature": 0.7, + "max_tokens": 4096, + "max_context": 8192, + "tool_call_compat": False, + "extra_params": {}, } ] @@ -91,17 +125,21 @@ async for chunk in response: 支持多种内容类型： - **Text**: 文本消息 +- **ReasoningText**: 推理过程文本（如 DeepSeek-R1、OpenAI o1 等模型的思考链） - **Image**: 图像内容（支持 URL、数据 URL、本地路径） - **Audio**: 音频内容 -- **Action**: 动作组件（占位符） +- **Video**: 视频内容 +- **File**: 文件内容 ### 3. 工具调用（Tool Calling） +工具系统基于 `LLMUsable` 协议，支持 `ToolRegistry` 集中管理： + ```python -from src.kernel.llm import Tool, ToolCall, ToolResult +from src.kernel.llm import ToolCall, ToolResult, LLMUsable, ToolRegistry -# 定义工具 -class MyTool: +# 实现 LLMUsable 协议 +class MyTool(LLMUsable): @classmethod def to_schema(cls) -> dict: return { @@ -113,71 +151,142 @@ class MyTool: } } -# 使用工具 -request.add_payload(LLMPayload(ROLE.TOOL, Tool(MyTool))) +# 添加到 payload +request.add_payload(LLMPayload(ROLE.TOOL, MyTool)) # 类直接传入 # 处理工具调用 -for tool_call in response.call_list: +for tool_call in response.call_list or []: result = await execute_tool(tool_call) request.add_payload(LLMPayload(ROLE.TOOL_RESULT, ToolResult(result))) ``` -### 4. 负载均衡和重试 +### 4. 上下文管理（ContextManager） + +`LLMContextManager` 统一管理对话上下文的生命周期： + +- **结构校验**：确保 SYSTEM→USER↔ASSISTANT 对话结构合法，校验 tool-call/tool-result 配对 +- **预算控制**：根据模型 `max_context` 自动裁剪和压缩历史 payload +- **Reminder 注入**：支持按 bucket/name 注册并注入运行时提醒文本 +- **自定义压缩**：通过 `AsyncContextCompressionHandler` 回调实现自定义压缩逻辑 + +```python +from src.kernel.llm import LLMContextManager, ReminderSourceSpec + +context_mgr = LLMContextManager( + context_compression_handler=my_compression_handler, + reminder_sources=[ReminderSourceSpec(bucket="system", names=("tips",))] +) +request = LLMRequest(model_set=models, context_manager=context_mgr) +``` + +### 5. Embedding 请求 + +```python +from src.kernel.llm import EmbeddingRequest + +req = EmbeddingRequest(model_set=embedding_models, request_name="doc_embed") +req.add_input("Hello world").add_input("Another text") +response = await req.send() +# response.embeddings: list[list[float]] +``` + +### 6. Rerank 请求 + +```python +from src.kernel.llm import RerankRequest + +req = RerankRequest(model_set=rerank_models, request_name="doc_rerank") +req.set_query("What is AI?") +req.add_document("AI is artificial intelligence...") +req.add_document("Machine learning is...") +response = await req.send() +# response.results: list[RerankItem] 按相关性排序 +for item in response.results: + print(f"score={item.relevance_score}, doc={item.document}") +``` -通过 `policy` 自动管理多个模型的轮询和重试： +### 7. 负载均衡和重试 + +通过 `policy` 自动管理多个模型的负载均衡和重试。默认使用 `LoadBalancedPolicy`： ```python -from src.kernel.llm.policy import RoundRobinPolicy +from src.kernel.llm.policy import RoundRobinPolicy, LoadBalancedPolicy, create_policy + +# 使用命名策略 +request.policy = create_policy("round_robin") -policy = RoundRobinPolicy() -request.policy = policy +# 或直接实例化 +request.policy = LoadBalancedPolicy() ``` 模型配置参数： - `max_retry`: 每个模型的最大重试次数 - `retry_interval`: 重试间隔（秒） +- `timeout`: 请求超时（秒） -### 5. 指标收集 +### 8. 指标收集 -自动追踪每次请求的性能指标： +框架提供两级指标系统： + +**内存级（MetricsCollector）**：实时追踪每次请求的性能指标： ```python from src.kernel.llm import get_global_collector collector = get_global_collector() stats = collector.get_stats("gpt-4") -print(f"成功率: {stats.success_rate}") -print(f"平均延迟: {stats.avg_latency}s") +print(f"成功率: {stats['success_rate']}") +print(f"平均延迟: {stats['avg_latency']}s") +``` + +**持久化级（LLMStatsCollector）**：基于 SQLite 的持久化统计，支持按模型/请求名聚合查询： + +```python +from src.kernel.llm import init_llm_stats, get_llm_stats_collector + +await init_llm_stats(db_path="data/llm_stats/llm_stats.db") +collector = get_llm_stats_collector() +summary = await collector.get_summary() +by_model = await collector.get_by_model() +cache_rate = await collector.get_cache_hit_rate() ``` ## 详细文档索引 -- [Exceptions](./exceptions.md) - 异常类和错误分类 +- [Exceptions](./exceptions.md) - 异常类和错误分类（含 `classify_exception`） - [Roles](./roles.md) - 消息角色定义 -- [Request](./request.md) - LLM 请求构建和发送 -- [Response](./response.md) - LLM 响应处理 -- [Monitor](./monitor.md) - 指标收集和监控 -- [Payload Structure](./payload/README.md) - 消息负载系统 -- [Model Client](./model_client/README.md) - 模型客户端实现 -- [Policy](./policy/README.md) - 负载均衡和重试策略 +- [Request](./request.md) - LLM 请求构建和发送（含 Embedding/Rerank） +- [Response](./response.md) - LLM 响应处理（含 reasoning 与 tool_call_compat） +- [Monitor](./monitor.md) - 内存级指标收集和监控 +- [Payload Structure](./payload/README.md) - 消息负载系统（含 ReasoningText/Video/File） +- [Model Client](./model_client/README.md) - 模型客户端实现（OpenAI + Anthropic） +- [Policy](./policy/README.md) - 负载均衡和重试策略（RoundRobin + LoadBalanced） ## 常见问题 ### Q: 如何支持新的 LLM 提供商？ -A: 在 `model_client/` 中创建新的客户端类，实现 `ChatModelClient` 协议，然后在 `ModelClientRegistry` 中注册。 +A: 在 `model_client/` 中创建新的客户端类，实现 `ChatModelClient` 协议（及可选的 `EmbeddingModelClient`、`RerankModelClient`），然后在 `ModelClientRegistry` 中注册。 ### Q: 如何自定义重试策略？ -A: 继承 `Policy` 和 `PolicySession` 协议，实现自定义的重试逻辑，传入 `LLMRequest.policy`。 +A: 实现 `Policy` 和 `PolicySession` 协议，或通过 `set_default_policy_factory` 注入自定义工厂。 + +### Q: 如何在流式模式下处理工具调用和推理内容？ + +A: 流式模式通过 `LLMStreamReducer` 自动累积工具调用和 reasoning 片段。消费完响应后，`response.call_list` 和 `response.reasoning_parts` 包含完整信息。 + +### Q: 如何启用持久化 LLM 统计？ + +A: 在 kernel 启动时调用 `await init_llm_stats(db_path="data/llm_stats/llm_stats.db")`，之后通过 `get_llm_stats_collector()` 获取收集器实例。 -### Q: 如何在流式模式下处理工具调用？ +### Q: 上下文管理的自动裁剪何时触发？ -A: 流式模式会自动累积工具调用信息。在消费完整个响应后，`response.call_list` 包含所有工具调用。 +A: 当 token 计数超过 `model.max_context * context_reserve_ratio`（默认为 0.95）时触发裁剪；可通过 `extra_params.context_reserve_ratio` 和 `extra_params.context_reserve_tokens` 调整。 ### Q: 指标收集是否有性能开销？ -A: 可以通过 `enable_metrics=False` 关闭指标收集以提高性能。 +A: 内存级指标可以通过 `enable_metrics=False` 关闭。持久化统计仅在显式调用 `init_llm_stats` 后生效。 ## 相关资源 diff --git a/docs/llm/context.md b/docs/llm/context.md new file mode 100644 index 00000000..86b2f28f --- /dev/null +++ b/docs/llm/context.md @@ -0,0 +1,209 @@ +# Context 模块 + +## 概述 + +context.py 提供 LLMContextManager，负责 LLM 对话上下文的管理，包括 payload 写入接管、结构校验、reminder 注入和基于 token/payloads 数量的裁剪。 + +默认职责： +1. 接管 payload 列表写入（add_payload/system/tool） +2. 接管 reminder 的延迟登记 +3. 写入后执行结构校验（strict，不做自动修复） +4. 最后按 max_payloads/token budget 执行裁剪 + +## LLMContextManager + +`python +@dataclass(slots=True) +class LLMContextManager: + max_payloads: int | None = None + compression_hook: CompressionHook | None = None + _reminders: list[RegisteredReminder] | None = None +` + +### 核心属性 + +| 属性 | 类型 | 说明 | +|---|---|---| +| max_payloads | int \| None | 最大 payload 数量限制，超出后进行裁剪 | +| compression_hook | CompressionHook \| None | 压缩钩子：当裁剪掉对话组时，可生成摘要消息插入剩余对话的开头 | + +### 核心方法 + +#### add_payload + +`python +def add_payload( + self, + payloads: list[LLMPayload], + payload: LLMPayload, + position: int | None = None, +) -> list[LLMPayload]: +` + +向上下文追加 payload，并进行规范化与裁剪。 + +**参数：** +- payloads: 现有 payload 列表 +- payload: 待追加 payload +- position: 可选插入位置 + +**返回值：** 规范化后的 payload 列表 + +**行为规则：** +- 如果 position 指定，则在该位置插入 +- 如果新 payload 和末尾 payload 角色相同，则合并 content +- 否则追加到末尾 +- 写入后会应用 reminder 注入和裁剪 + +#### validate_for_send + +`python +def validate_for_send(self, payloads: list[LLMPayload]) -> None: +` + +在发起 LLM 请求前校验上下文结构。不允许任何未闭合的 tool 调用链（包括尾部）。 + +**校验规则：** +- TOOL_RESULT 必须紧跟在含 ool_calls 的 ASSISTANT 之后 +- 若 ASSISTANT 含 ool_calls，必须补齐所有对应 call_id 的 TOOL_RESULT +- TOOL_RESULT 之后必须有 ASSISTANT 承接，才能进入下一条 USER +- SYSTEM/TOOL 作为 pinned 角色不参与链路判断 + +#### maybe_trim + +`python +def maybe_trim(self, payloads: list[LLMPayload]) -> list[LLMPayload]: +` + +根据 max_payloads 和 token 预算执行裁剪。 + +**裁剪策略：** +- SYSTEM/TOOL（pinned）始终保留，不参与裁剪 +- USER 角色开启新对话组，组内包含后续的 ASSISTANT/TOOL_RESULT +- 裁剪以对话组为最小单位，优先丢弃最早的组 +- 若提供了 compression_hook，则将被裁剪的内容压缩后插入剩余对话开头 +- 裁剪继续直至满足 max_payloads 约束 + +--- + +## RegisteredReminder + +`python +@dataclass(slots=True, frozen=True) +class RegisteredReminder: + text: str + insert_type: SystemReminderInsertType +` + +已登记的 reminder。reminder 会固定注入到「首个真实 USER 消息的首段」；若尚无 USER，则继续等待后续 USER 消息。 + +--- + +## CompressionHook + +`python +CompressionHook = Callable[[list[list[LLMPayload]], list[LLMPayload]], list[LLMPayload]] +` + +压缩钩子类型签名： + +- 参数 1：被裁剪的对话组列表（按时间顺序） +- 参数 2：裁剪后剩余的 payload 列表 +- 返回值：压缩后的 payload 列表，会被插入剩余对话的开头 + +--- + +## TokenCounter + +`python +TokenCounter = Callable[[list[LLMPayload]], int] +` + +Token 计数回调类型。用于在需要时由上层注入自定义的 token 计数逻辑（配合 max_payloads 和裁剪）。 + +--- + +## 使用示例 + +### 基础上下文管理 + +`python +from src.kernel.llm import LLMRequest, LLMContextManager, LLMPayload, Text, ROLE + +# 创建带上下文管理器的请求 +ctx = LLMContextManager(max_payloads=40) +request = LLMRequest(model_set=models, context_manager=ctx) + +request.add_payload(LLMPayload(ROLE.SYSTEM, Text("You are helpful."))) +request.add_payload(LLMPayload(ROLE.USER, Text("Hello"))) +` + +### 使用压缩钩子 + +`python +def my_compression_hook(dropped_groups, remaining): + # dropped_groups: 被裁剪掉的对话组 + # remaining: 裁剪后保留的 payloads + summary = f"先前对话摘要：共 {len(dropped_groups)} 组对话被裁剪" + return [LLMPayload(ROLE.USER, Text(summary))] + +ctx = LLMContextManager( + max_payloads=40, + compression_hook=my_compression_hook, +) +request = LLMRequest(model_set=models, context_manager=ctx) +` + +### 自定义上下文管理器 + +`python +class MyContextManager(LLMContextManager): + def maybe_trim(self, payloads): + # 自定义裁剪策略 + return payloads # 不做裁剪 + + def _validate_payloads(self, payloads, *, allow_incomplete_tail): + # 自定义校验策略 + super()._validate_payloads(payloads, allow_incomplete_tail=allow_incomplete_tail) + +request = LLMRequest( + model_set=models, + context_manager=MyContextManager(max_payloads=40), +) +` + +--- + +## 内部裁剪机制 + +裁剪的核心逻辑： + +1. **分拆 pinned 与对话**：SYSTEM/TOOL 被识别为 pinned 部分，始终保留 +2. **构建对话组**：USER 开头，后续 ASSISTANT/TOOL_RESULT 归入同一组 +3. **裁剪最旧组**：从最早对话组开始删除 +4. **压缩钩子**：如果有 compression_hook，用压缩后的摘要替换被裁剪内容 +5. **循环检查**：继续裁剪直到满足 max_payloads 限制 + +--- + +## 常见问题 + +### Q: reminder 和 payload 的区别？ + +A: reminder 是延迟登记的消息，只有当出现 USER 消息时才会被注入。适合用于系统级别的间歇提醒。payload 则是直接写入上下文的消息。 + +### Q: 裁剪会删除 SYSTEM 消息吗？ + +A: 不会。SYSTEM/TOOL 角色被视为 pinned 消息，始终保留。 + +### Q: 如何禁用上下文的裁剪？ + +A: 不设置 max_payloads（保持 None）。 + +--- + +## 相关文档 + +- [LLM 主文档](../README.md) +- [Request 模块](./request.md) +- [Payload 模块](./payload/README.md) diff --git a/docs/llm/embedding.md b/docs/llm/embedding.md new file mode 100644 index 00000000..43620d33 --- /dev/null +++ b/docs/llm/embedding.md @@ -0,0 +1,211 @@ +# Embedding 模块 + +## 概述 + +embedding_request.py 和 embedding_response.py 提供了向 LLM 提供商发送向量嵌入（Embedding）请求的能力。与 LLMRequest 类似，它支持负载均衡、重试策略和指标收集。 + +## EmbeddingRequest + +`python +@dataclass(slots=True) +class EmbeddingRequest: + model_set: ModelSet + request_name: str = "" + inputs: list[str] = field(default_factory=list) + policy: Policy | None = None + clients: ModelClientRegistry | None = None + enable_metrics: bool = True + request_type: RequestType = RequestType.EMBEDDINGS +` + +### 核心属性 + +| 属性 | 类型 | 说明 | +|---|---|---| +| model_set | ModelSet | 模型配置列表 | +| request_name | str | 请求名称（用于日志和策略） | +| inputs | list[str] | 待嵌入的输入文本列表 | +| policy | Policy \| None | 负载均衡/重试策略，默认使用 create_default_policy() | +| clients | ModelClientRegistry \| None | 模型客户端注册表 | +| enable_metrics | ool | 是否启用指标收集 | +| request_type | RequestType | 固定为 RequestType.EMBEDDINGS | + +### 核心方法 + +#### add_input + +`python +def add_input(self, value: str) -> Self: +` + +追加 embedding 输入文本。支持链式调用。 + +#### send + +`python +async def send(self) -> EmbeddingResponse: +` + +发送 embedding 请求，返回 EmbeddingResponse。 + +**执行流程：** +1. 校验 inputs 非空 +2. 通过策略获取模型步骤 +3. 调用客户端的 create_embedding 方法 +4. 支持超时控制（model["timeout"]） +5. 记录指标 +6. 失败时通过策略进行重试 + +--- + +## EmbeddingResponse + +`python +@dataclass(slots=True) +class EmbeddingResponse: + embeddings: list[list[float]] + model_name: str + request_name: str = "" +` + +### 属性 + +| 属性 | 类型 | 说明 | +|---|---|---| +| embeddings | list[list[float]] | 嵌入向量列表，每个元素对应一个输入文本的向量 | +| model_name | str | 实际使用的模型名称 | +| request_name | str | 请求名称 | + +--- + +## 使用示例 + +### 基础用法 + +`python +from src.kernel.llm import EmbeddingRequest + +model_set = [ + { + "client_type": "openai", + "model_identifier": "text-embedding-3-small", + "api_key": "sk-...", + "base_url": "https://api.openai.com/v1", + "max_retry": 2, + } +] + +request = EmbeddingRequest(model_set=model_set) +request.add_input("Hello world") +request.add_input("What is AI?") + +response = await request.send() +print(f"模型: {response.model_name}") +print(f"向量维度: {len(response.embeddings[0])}") +for i, emb in enumerate(response.embeddings): + print(f"Input {i}: {emb[:5]}...") +` + +### 批量文本嵌入 + +`python +request = EmbeddingRequest(model_set=model_set, request_name="batch_embed") + +texts = ["文档1", "文档2", "文档3", "文档4", "文档5"] +for text in texts: + request.add_input(text) + +response = await request.send() +print(f"成功嵌入 {len(response.embeddings)} 条文本") +` + +### 多模型故障转移 + +`python +model_set = [ + { + "client_type": "openai", + "model_identifier": "text-embedding-3-large", + "api_key": "key1", + "max_retry": 2, + }, + { + "client_type": "openai", + "model_identifier": "text-embedding-3-small", + "api_key": "key2", + "max_retry": 3, + }, +] + +request = EmbeddingRequest(model_set=model_set, request_name="fallback") +request.add_input("Important text to embed") + +try: + response = await request.send() +except Exception as e: + print(f"所有模型都失败了: {e}") +` + +### 禁用指标收集 + +`python +request = EmbeddingRequest( + model_set=model_set, + enable_metrics=False, # 提升性能 +) +request.add_input("text") +response = await request.send() +` + +--- + +## 错误处理 + +与 LLMRequest 一致的错误分类和重试策略： + +`python +from src.kernel.llm import ( + LLMConfigurationError, + LLMRateLimitError, + LLMTimeoutError, +) + +try: + response = await request.send() +except LLMConfigurationError as e: + print(f"配置错误: {e}") +except LLMRateLimitError as e: + print(f"速率限制，等待 {e.retry_after}s") +except LLMTimeoutError as e: + print(f"超时: {e.timeout}s") +` + +--- + +## 模型客户端扩展 + +Embedding 请求通过 ModelClientRegistry.get_embedding_client_for_model() 获取客户端。客户端需实现 create_embedding 方法： + +`python +class MyEmbeddingClient: + async def create_embedding( + self, + *, + model_name: str, + inputs: list[str], + request_name: str, + model_set: dict[str, Any], + ) -> list[list[float]]: + # 实现自定义 embedding 逻辑 + pass +` + +--- + +## 相关文档 + +- [LLM 主文档](../README.md) +- [Request 模块](./request.md) +- [Rerank 模块](./rerank.md) +- [Types 模块](./types.md) +- [Policy 模块](./policy/README.md) diff --git a/docs/llm/exceptions.md b/docs/llm/exceptions.md index 4b616356..fb1f53ac 100644 --- a/docs/llm/exceptions.md +++ b/docs/llm/exceptions.md @@ -10,6 +10,7 @@ BaseException └── RuntimeError └── LLMError（所有 LLM 异常的基类） + ├── LLMContextError ├── LLMConfigurationError ├── LLMResponseConsumedError ├── LLMRateLimitError @@ -30,6 +31,32 @@ class LLMError(RuntimeError): 所有 LLM 相关异常的基类。用于捕获任何 LLM 相关错误。 +--- + +### LLMContextError +```python +class LLMContextError(LLMError): + """上下文结构错误。 + + 用于在严格模式下，当上下文 messages 不满足协议约束时直接抛出， + 以避免"自动修复"掩盖上游链路问题。 + """ +``` + +当对话结构不合法时抛出。例如： +- 对话以 `assistant` 开头（而不是 `user`） +- `assistant` 前不是 `user` 或 `tool_result` +- 缺少必需的 `tool_result` 配对 + +**使用示例：** +```python +try: + request.add_payload(some_payload) + response = await request.send() +except LLMContextError as e: + print(f"上下文结构错误: {e}") +``` + **使用示例：** ```python try: @@ -261,12 +288,23 @@ def classify_exception(error: BaseException, model: str | None = None) -> BaseEx 将来自第三方 SDK（如 OpenAI SDK）的异常转换为标准化的 LLM 异常，便于统一处理。 **支持的转换：** + +**OpenAI SDK：** - OpenAI `RateLimitError` → `LLMRateLimitError` - OpenAI `APITimeoutError` → `LLMTimeoutError` - OpenAI `AuthenticationError` → `LLMAuthenticationError` - OpenAI `BadRequestError` → 可能转换为 `LLMTokenLimitError` 或 `LLMContentFilterError` - OpenAI `APIError` → `LLMAPIError` +**Anthropic SDK：** +- Anthropic `RateLimitError` → `LLMRateLimitError` +- Anthropic `APITimeoutError` → `LLMTimeoutError` +- Anthropic `AuthenticationError` → `LLMAuthenticationError` +- Anthropic `BadRequestError` → 可能转换为 `LLMTokenLimitError` 或 `LLMContentFilterError` +- Anthropic `APIStatusError` / `APIError` → `LLMAPIError` + +**通用回退：** 基于错误消息关键词匹配（rate limit / timeout / authentication / token limit / content filter）。 + **使用示例：** ```python try: diff --git a/docs/llm/model_client/README.md b/docs/llm/model_client/README.md index 3427a56d..da7dfe94 100644 --- a/docs/llm/model_client/README.md +++ b/docs/llm/model_client/README.md @@ -2,16 +2,18 @@ ## 概述 -`model_client/` 子模块实现了与各个 LLM 提供商的交互。它定义了统一的客户端接口，并提供了 OpenAI 的具体实现。框架设计允许轻松扩展支持其他提供商。 +`model_client/` 子模块实现了与各个 LLM 提供商的交互。它定义了统一的客户端接口（Chat、Embedding、Rerank），并提供了 OpenAI 和 Anthropic 的具体实现。框架设计允许轻松扩展支持其他提供商。 ## 模块结构 ``` model_client/ -├── base.py # 客户端接口协议 -├── openai_client.py # OpenAI 实现 -├── registry.py # 客户端注册表 -└── __init__.py # 公开 API +├── base.py # 客户端接口协议（ChatModelClient / EmbeddingModelClient / RerankModelClient） +├── openai_client.py # OpenAI 实现 +├── anthropic_client.py # Anthropic 实现 +├── registry.py # 客户端注册表（ModelClientRegistry） +├── shared.py # 共享工具 +└── __init__.py # 公开 API ``` ## StreamEvent（流事件） @@ -20,32 +22,24 @@ model_client/ @dataclass(frozen=True, slots=True) class StreamEvent: """provider-agnostic 的流事件。""" - - text_delta: str | None = None # 文本增量 - tool_call_id: str | None = None # 工具调用 ID - tool_name: str | None = None # 工具名称 - tool_args_delta: str | None = None # 工具参数增量 -``` -表示流式响应中的单个事件。这是提供商无关的统一格式。 - -**使用示例：** -```python -# 文本事件 -event1 = StreamEvent(text_delta="Hello, ") -event2 = StreamEvent(text_delta="world!") - -# 工具调用事件 -event3 = StreamEvent( - tool_call_id="call_123", - tool_name="search", - tool_args_delta='{"query": "' -) + text_delta: str | None = None # 文本增量 + tool_call_id: str | None = None # 工具调用 ID + tool_name: str | None = None # 工具名称 + tool_args_delta: str | None = None # 工具参数增量（JSON 片段） + reasoning_block_type: str | None = None # reasoning block 类型 + reasoning_delta: str | None = None # reasoning 文本增量 + reasoning_signature_delta: str | None = None # reasoning 签名增量 + usage: dict[str, Any] | None = None # token 用量信息 ``` +表示流式响应中的单个事件。这是提供商无关的统一格式，支持文本、工具调用、推理内容和用量信息。 + --- -## ChatModelClient 协议 +## 客户端协议 + +### ChatModelClient ```python class ChatModelClient(Protocol): @@ -54,29 +48,54 @@ class ChatModelClient(Protocol): *, model_name: str, payloads: list[LLMPayload], - tools: list[Tool], + tools: list[LLMUsable], request_name: str, model_set: Any, stream: bool, ) -> tuple[str | None, list[dict[str, Any]] | None, AsyncIterator[StreamEvent] | None]: - """发起一次聊天请求。 - - 返回三元组： - - message: 非流时的完整文本；流式则为 None - - tool_calls: 非流时解析出的工具调用列表；流式则为 None - - stream_iter: 流式迭代器；非流则为 None - """ + """发起一次聊天请求。""" ``` -定义了所有模型客户端必须实现的接口。 +### EmbeddingModelClient + +```python +class EmbeddingModelClient(Protocol): + async def create_embedding( + self, + *, + model_name: str, + inputs: list[str], + request_name: str, + model_set: Any, + ) -> list[list[float]]: + """发起一次 embedding 请求，返回向量列表。""" +``` + +### RerankModelClient + +```python +class RerankModelClient(Protocol): + async def create_rerank( + self, + *, + model_name: str, + query: str, + documents: list[Any], + top_n: int | None, + request_name: str, + model_set: Any, + ) -> list[dict[str, Any]]: + """发起一次 rerank 请求，返回排序结果。""" +``` + +--- + +## 已内置的客户端 -**参数：** -- `model_name`: 模型标识符（如 "gpt-4"） -- `payloads`: 消息列表 -- `tools`: 可用工具列表 -- `request_name`: 请求名称 -- `model_set`: 模型配置 -- `stream`: 是否使用流式 +| 客户端 | 协议 | 说明 | +|--------|------|------| +| `OpenAIChatClient` | Chat / Embedding / Rerank | 完整实现三种协议 | +| `AnthropicChatClient` | Chat | 支持 reasoning blocks 和扩展思考 | **返回值：** - `message`: 非流式时的完整响应文本；流式时为 None diff --git a/docs/llm/monitor.md b/docs/llm/monitor.md index 84913bf3..bf270943 100644 --- a/docs/llm/monitor.md +++ b/docs/llm/monitor.md @@ -2,7 +2,9 @@ ## 概述 -`monitor.py` 提供了 LLM 请求的指标收集、监控和分析功能。它能够追踪每次请求的性能数据（延迟、token 使用量、成本等），并生成聚合统计用于性能分析和优化。 +`monitor.py` 提供了 LLM 请求的内存级指标收集、监控和分析功能。它能够追踪每次请求的性能数据（延迟、token 使用量、成本等），并生成聚合统计用于性能分析和优化。 + +> **注意：** 框架还提供了独立的持久化统计模块 `stats/`（`LLMStatsCollector`），基于 SQLite 存储请求记录，支持按模型/请求名/缓存命中率等维度进行历史查询。详见 [LLM 主文档](./README.md) 的"指标收集"章节。 ## 核心类 @@ -12,7 +14,7 @@ @dataclass class RequestMetrics: """单次请求的指标数据。""" - + model_name: str # 模型名称 request_name: str # 请求名称 latency: float # 延迟（秒） @@ -22,7 +24,7 @@ class RequestMetrics: success: bool = True # 是否成功 error: str | None = None # 错误信息 error_type: str | None = None # 错误类型 - timestamp: datetime = ... # 请求时间戳 + timestamp: datetime = ... # 请求时间戳 stream: bool = False # 是否使用流式 retry_count: int = 0 # 重试次数 model_index: int = 0 # 模型索引 diff --git a/docs/llm/payload/README.md b/docs/llm/payload/README.md index f123963a..143b6513 100644 --- a/docs/llm/payload/README.md +++ b/docs/llm/payload/README.md @@ -3,24 +3,25 @@ ## 概述 `payload/` 子模块定义了 LLM 消息系统中的核心数据结构。它包括： -- **Content 类型**：各种消息内容的表示（文本、图像、音频等） +- **Content 类型**：各种消息内容的表示（Text、ReasoningText、Image、Audio、Video、File） - **LLMPayload**：消息的标准格式（角色 + 内容） -- **工具调用系统**：工具声明、调用和结果的处理 +- **工具调用系统**：基于 `LLMUsable` 协议的工具声明、调用和结果处理（含 `ToolRegistry`） ## 模块结构 ``` payload/ -├── content.py # 内容类型定义 +├── content.py # 内容类型定义（Text/ReasoningText/Image/Audio/Video/File） ├── payload.py # LLMPayload 结构 -├── tooling.py # 工具调用系统 +├── tooling.py # 工具调用系统（LLMUsable/ToolCall/ToolResult/ToolRegistry） └── __init__.py # 公开 API ``` ## 快速开始 ```python -from src.kernel.llm import LLMPayload, Text, Image, ROLE, Tool, ToolCall, ToolResult +from src.kernel.llm import LLMPayload, Text, Image, ReasoningText +from src.kernel.llm import ROLE, ToolCall, ToolResult, LLMUsable # 创建文本 payload text_payload = LLMPayload(ROLE.USER, Text("Hello!")) @@ -28,19 +29,47 @@ text_payload = LLMPayload(ROLE.USER, Text("Hello!")) # 创建多模态 payload image_payload = LLMPayload( ROLE.USER, - [ - Text("What's in this image?"), - Image("path/to/image.jpg") - ] + [Text("What's in this image?"), Image("path/to/image.jpg")] ) -# 使用工具 -tool_payload = LLMPayload(ROLE.TOOL, Tool(MyTool)) +# 推理内容（reasoning） +reasoning_payload = LLMPayload( + ROLE.ASSISTANT, + [ReasoningText("Let me think..."), Text("The answer is...")] +) + +# 使用 LLMUsable 协议（类直接传入，无需包装） +request.add_payload(LLMPayload(ROLE.TOOL, MyTool)) # 工具结果 -result_payload = LLMPayload(ROLE.TOOL_RESULT, ToolResult(value="result", call_id="123")) +result_payload = LLMPayload( + ROLE.TOOL_RESULT, + ToolResult(value="result", call_id="call_123", name="my_tool") +) ``` +## 内容类型一览 + +| 类型 | 说明 | Python 类 | +|------|------|-----------| +| 文本 | 普通文本消息 | `Text` | +| 推理文本 | 模型思考链/推理过程 | `ReasoningText` | +| 图像 | 支持 URL / data URL / 本地路径 | `Image` | +| 音频 | 音频内容 | `Audio` | +| 视频 | 视频内容 | `Video` | +| 文件 | 文件内容 | `File` | + +## 工具系统类型 + +| 类型 | 说明 | +|------|------| +| `LLMUsable` | 工具抽象基类，需实现 `to_schema()` | +| `ToolCall` | 模型发起的工具调用（含 id/name/args） | +| `ToolResult` | 工具执行结果（含 value/call_id/name） | +| `ToolRegistry` | 工具注册表，集中管理工具集合 | +| `LLMUsableExecution` | 工具执行记录 | +| `LLMUsableExecutionStatus` | 执行状态枚举 | + ## 详细模块文档 - [Content 模块](./content.md) - 内容类型详解 diff --git a/docs/llm/policy/README.md b/docs/llm/policy/README.md index a33c0ec7..f56d403f 100644 --- a/docs/llm/policy/README.md +++ b/docs/llm/policy/README.md @@ -4,13 +4,16 @@ `policy/` 子模块实现了负载均衡和重试策略。它决定了在多个模型之间如何轮流尝试、何时重试、如何处理错误等。 +默认策略为 `LoadBalancedPolicy`，可通过 `set_default_policy_factory` 注入自定义默认策略。 + ## 模块结构 ``` policy/ -├── base.py # 策略接口定义 -├── round_robin.py # 轮询策略实现 -└── __init__.py # 公开 API +├── base.py # 策略接口定义（ModelStep / PolicySession / Policy） +├── round_robin.py # 轮询策略实现 +├── load_balanced.py # 负载均衡策略实现（默认） +└── __init__.py # 公开 API + 默认策略工厂 ``` ## 核心接口 @@ -21,18 +24,16 @@ policy/ @dataclass(frozen=True, slots=True) class ModelStep: """下一步执行计划。 - + - model=None 表示策略耗尽，应停止重试并把最后一次异常抛给上层。 - delay_seconds 由 policy 决定（例如 retry_interval）。 """ - + model: dict[str, Any] | None # 下一个要尝试的模型配置 delay_seconds: float = 0.0 # 延迟时间（秒） meta: dict[str, Any] | None = None # 元数据（模型索引、重试次数等） ``` -表示策略决定的下一步行动。 - --- ### PolicySession 协议 @@ -42,13 +43,15 @@ class PolicySession(Protocol): def first(self) -> ModelStep: """获取初始的模型步骤。""" ... - + def next_after_error(self, error: BaseException) -> ModelStep: """基于错误获取下一步。""" ... -``` -表示单次请求的策略会话。 + def record_success(self, *, latency: float = 0.0, tokens: int = 0) -> None: + """记录一次成功的调用（供负载均衡统计使用）。""" + ... +``` --- @@ -61,43 +64,54 @@ class Policy(Protocol): ... ``` -定义了所有策略必须实现的接口。 - --- -## RoundRobinPolicy（轮询策略） +## 内置策略 -```python -class RoundRobinPolicy(Policy): - """简单轮询：在 `model_set`（list[dict]）上循环选择。""" -``` +### RoundRobinPolicy（轮询策略） -默认的负载均衡策略，按轮询方式在模型间切换。 - -### 工作原理 +简单轮询：在 `model_set` 上循环选择模型。 +**工作原理：** 1. **初始选择**：从当前轮询位置开始 2. **重试**：在当前模型上重试 `max_retry` 次 3. **切换**：重试耗尽后切换到下一个模型 4. **终止**：所有模型都耗尽时停止 -### 使用示例 +### LoadBalancedPolicy（负载均衡策略，默认） + +基于成功延迟与 token 消耗动态选择最优模型。 +**特点：** +- 追踪每个模型的近期延迟和吞吐量 +- 优先选择延迟低、成功率高的模型 +- 支持 `record_success` 回调更新统计 + +**使用示例：** ```python -from src.kernel.llm import LLMRequest, RoundRobinPolicy +from src.kernel.llm.policy import LoadBalancedPolicy, RoundRobinPolicy, create_policy -models = [ - { - "client_type": "openai", - "model_identifier": "gpt-4", - "api_key": "key1", - "max_retry": 2, # 该模型重试 2 次 - "retry_interval": 1.0, # 重试间隔 1 秒 - }, - { - "client_type": "openai", - "model_identifier": "gpt-3.5-turbo", - "api_key": "key2", +# 使用命名策略 +request.policy = create_policy("load_balanced") +request.policy = create_policy("round_robin") + +# 直接实例化 +request.policy = LoadBalancedPolicy() +``` + +--- + +## 自定义默认策略 + +```python +from src.kernel.llm.policy import set_default_policy_factory + +# 在应用启动时注入自定义默认策略工厂 +set_default_policy_factory(lambda: MyCustomPolicy()) + +# 恢复内建默认 +set_default_policy_factory(None) +``` "max_retry": 3, # 该模型重试 3 次 "retry_interval": 0.5, # 重试间隔 0.5 秒 } diff --git a/docs/llm/request.md b/docs/llm/request.md index 5791a1b4..a39ef3c5 100644 --- a/docs/llm/request.md +++ b/docs/llm/request.md @@ -2,151 +2,106 @@ ## 概述 -`request.py` 定义了 `LLMRequest` 类，是与 LLM 交互的核心类。它负责： +`request.py` 定义了 `LLMRequest` 类，是与 LLM 交互的核心门面类。它负责： - 构建消息 payload 列表 +- 通过 `LLMContextManager` 管理上下文结构 - 管理模型客户端和选择 - 应用负载均衡和重试策略 - 收集请求指标 - 执行实际的 LLM API 调用 -## 类定义 +另外，`embedding_request.py` 和 `rerank_request.py` 分别提供了 `EmbeddingRequest` 和 `RerankRequest` 两种专用请求类型。 + +## LLMRequest 类定义 ```python from dataclasses import dataclass, field +from typing import Any, Self @dataclass(slots=True) class LLMRequest: - """LLMRequest：构建 payload 并执行请求。""" - + """一次逻辑 LLM 交互的可变请求构建器与发送器。""" + model_set: ModelSet # 模型配置列表 request_name: str = "" # 请求名称（用于日志和策略） - + meta_data: dict[str, Any] = field(default_factory=dict) # 附加元数据 + payloads: list[LLMPayload] = field(default_factory=list) # 消息 payload 列表 policy: Policy | None = None # 负载均衡/重试策略 clients: ModelClientRegistry | None = None # 模型客户端注册表 - context_manager: LLMContextManager | None = None # 上下文管理器（可重载） + context_manager: LLMContextManager | None = None # 上下文管理器 enable_metrics: bool = True # 是否启用指标收集 + request_type: RequestType = RequestType.COMPLETIONS # 请求类型 ``` -## 核心属性 - -### model_set - -**类型：** `list[dict]` - -**描述：** 模型配置列表，每个元素是一个完整的模型配置字典。 - -**必需配置项：** -```python -{ - "client_type": "openai", # 提供商类型 - "model_identifier": "gpt-4", # 模型标识 - "api_key": "sk-...", # API 密钥 -} -``` - -**常见可选配置项：** -```python -{ - "base_url": "https://api.openai.com/v1", # API 基础 URL - "max_retry": 3, # 最大重试次数 - "retry_interval": 1.0, # 重试间隔（秒） - "timeout": 30.0, # 请求超时（秒） - "temperature": 0.7, # 采样温度 - "max_tokens": 2000, # 最大输出 token 数 -} -``` +### ModelEntry 完整字段 + +模型配置使用 `ModelEntry` TypedDict，所有字段均为**必需**： + +| 字段 | 类型 | 说明 | +|------|------|------| +| `api_provider` | `str` | API 提供商名称（如 `"openai"`） | +| `base_url` | `str` | API 基础 URL | +| `model_identifier` | `str` | 模型标识符（如 `"gpt-4"`） | +| `api_key` | `str` | API 密钥 | +| `client_type` | `str` | 客户端类型（如 `"openai"`） | +| `max_retry` | `int` | 最大重试次数 | +| `timeout` | `float` | 请求超时（秒） | +| `retry_interval` | `float` | 重试间隔（秒） | +| `price_in` | `float` | 输入价格（$/1K tokens） | +| `cache_hit_price_in` | `float` | 缓存命中输入价格 | +| `price_out` | `float` | 输出价格（$/1K tokens） | +| `temperature` | `float` | 采样温度 | +| `max_tokens` | `int` | 最大输出 token 数 | +| `max_context` | `int` | 最大上下文窗口（0 表示不限制） | +| `tool_call_compat` | `bool` | 是否启用工具调用兼容模式 | +| `extra_params` | `dict[str, Any]` | 额外参数（可含 `context_reserve_ratio`、`context_reserve_tokens`） | **使用示例：** + ```python -# 单模型 model_set = [ { - "client_type": "openai", - "model_identifier": "gpt-4", - "api_key": "sk-...", + "api_provider": "openai", "base_url": "https://api.openai.com/v1", - "max_retry": 3, - "retry_interval": 1.0, - } -] - -# 多模型（用于故障转移和负载均衡） -model_set = [ - { - "client_type": "openai", "model_identifier": "gpt-4", - "api_key": "key1", - "max_retry": 2, - }, - { + "api_key": "sk-...", "client_type": "openai", - "model_identifier": "gpt-3.5-turbo", - "api_key": "key2", "max_retry": 3, + "timeout": 30.0, + "retry_interval": 1.0, + "price_in": 0.03, + "cache_hit_price_in": 0.015, + "price_out": 0.06, + "temperature": 0.7, + "max_tokens": 4096, + "max_context": 8192, + "tool_call_compat": False, + "extra_params": {}, } ] ``` -### payloads - -**类型：** `list[LLMPayload]` - -**描述：** 包含所有消息的列表。每个元素是一个 `LLMPayload` 对象。 - -### request_name - -**类型：** `str` - -**描述：** 请求的名称，用于日志和策略跟踪。同一名称的请求将使用相同的负载均衡状态。 - -### policy +### request_type -**类型：** `Policy | None` +**类型：** `RequestType` -**描述：** 负载均衡和重试策略。默认为 `RoundRobinPolicy()`。 +**可选值：** +- `RequestType.COMPLETIONS` — 对话补全（默认） +- `RequestType.EMBEDDINGS` — 向量嵌入 +- `RequestType.RERANK` — 重排序 -### clients +### meta_data -**类型：** `ModelClientRegistry | None` +**类型：** `dict[str, Any]` -**描述：** 模型客户端注册表。默认创建内置的 `OpenAIChatClient`。 - -### context_manager - -**类型：** `LLMContextManager | None` - -**描述：** 负责 payloads 的上下文管理（QA 组裁剪、压缩等）。可传入自定义子类实例覆盖默认逻辑。 - -### enable_metrics - -**类型：** `bool` - -**描述：** 是否启用指标收集。禁用可提高性能。 +**描述：** 附加到请求的任意元数据，会随观测记录一起持久化。 ## 核心方法 -### __init__ - -初始化 LLMRequest。 - -```python -# 基础初始化 -request = LLMRequest(model_set=models) - -# 完整初始化 -request = LLMRequest( - model_set=models, - request_name="my_request", - policy=RoundRobinPolicy(), - enable_metrics=True, - context_manager=LLMContextManager() -) -``` - ### add_payload -添加消息 payload。 +添加消息 payload。现在通过 `LLMContextManager` 处理追加逻辑： ```python def add_payload(self, payload: LLMPayload, position=None) -> Self: @@ -154,12 +109,17 @@ def add_payload(self, payload: LLMPayload, position=None) -> Self: Args: payload: 要添加的 payload position: 插入位置（默认追加到末尾） - + Returns: self（便于链式调用） """ ``` +**关键行为：** +- 如果设置了 `context_manager`，委托给 `context_manager.add_payload()` 处理 +- 否则，同 role 的连续 payload 会自动合并内容（相邻 USER 消息合并等） +- 支持 `position` 参数精确控制插入位置 + **使用示例：** ```python request = LLMRequest(model_set=models) @@ -171,26 +131,6 @@ request \ # 指定位置 request.add_payload(LLMPayload(ROLE.SYSTEM, Text("...")), position=0) - -# 追加多个 -request.add_payload(LLMPayload(ROLE.USER, Text("Question 1"))) -request.add_payload(LLMPayload(ROLE.USER, Text("Question 2"))) -``` - -### 自定义上下文管理 - -```python -from src.kernel.llm import LLMContextManager - -class MyContextManager(LLMContextManager): - def maybe_trim(self, payloads: list[LLMPayload]) -> list[LLMPayload]: - # 自定义策略 - return super().maybe_trim(payloads) - -request = LLMRequest( - model_set=models, - context_manager=MyContextManager() -) ``` ### send @@ -199,155 +139,96 @@ request = LLMRequest( ```python async def send( - self, - auto_append_response: bool = True, - *, - stream: bool = True + self, + auto_append_response: bool = True, + *, + stream: bool = True, ) -> LLMResponse: """ Args: auto_append_response: 是否自动将响应添加到 payloads - stream: 是否使用流式传输 - + stream: 是否使用流式传输（默认 True） + Returns: LLMResponse 对象 - + Raises: LLMError 及其子类 """ ``` -**使用示例：** -```python -# 非流式，自动追加响应 -response = await request.send(stream=False) -message = await response -print(message) - -# 流式，自动追加响应 -response = await request.send(stream=True, auto_append_response=True) -async for chunk in response: - print(chunk, end="", flush=True) - -# 流式，不追加响应 -response = await request.send(stream=True, auto_append_response=False) -async for chunk in response: - pass -``` +**内部执行流程：** +1. 校验 `model_set`（`_validate_model_set` → `_validate_model_entry`） +2. 委托给 `execute_request()` 执行完整生命周期 +3. 策略驱动的模型选择与重试 +4. 上下文预处理（压缩/裁剪） +5. Provider 调用（通过 `model_client`） +6. 响应归一化（含 `tool_call_compat` 解析） +7. 观测记录分发 --- -## 内部实现细节 - -### 请求执行流程 - -1. **验证 model_set** - ```python - model_set = _validate_model_set(self.model_set) - ``` - 确保 model_set 是有效的列表。 - -2. **规范化 payload** - ```python - payloads = [_normalize_tool_result_payload(p) for p in self.payloads] - ``` - 确保 TOOL_RESULT payload 的格式正确。 - -3. **创建策略会话** - ```python - session = self.policy.new_session(model_set=model_set, request_name=self.request_name) - ``` - 根据策略获取初始模型选择。 - -4. **轮询尝试** - ```python - step = session.first() - while step.model is not None: - try: - # 调用模型 - except Exception as e: - # 分类异常 - # 获取下一步 - step = session.next_after_error(e) - ``` - -5. **收集指标** - ```python - collector.record_request(RequestMetrics(...)) - ``` - -### 负载规范化 - -#### _normalize_tool_result_payload +## EmbeddingRequest ```python -def _normalize_tool_result_payload(payload: LLMPayload) -> LLMPayload: - """规范化 TOOL_RESULT payload。 - - 确保 ToolResult 的 call_id 被保留，其他内容转换为 Text。 - """ +@dataclass(slots=True) +class EmbeddingRequest: + """EmbeddingRequest：构建输入并执行向量请求。""" + + model_set: ModelSet + request_name: str = "" + inputs: list[str] = field(default_factory=list) + policy: Policy | None = None + clients: ModelClientRegistry | None = None + enable_metrics: bool = True + request_type: RequestType = RequestType.EMBEDDINGS ``` -作用：确保各种提供商都能正确理解 TOOL_RESULT 消息。 - -#### _extract_tools - +**使用示例：** ```python -def _extract_tools(payloads: list[LLMPayload]) -> list[Tool]: - """从 payload 列表中提取所有 Tool 对象。""" +from src.kernel.llm import EmbeddingRequest + +req = EmbeddingRequest(model_set=embedding_models, request_name="doc_embed") +req.add_input("Hello world").add_input("Another text") +response = await req.send() +# response.embeddings: list[list[float]] +# response.model: str — 实际使用的模型名 +# response.usage: dict | None — token 用量 ``` -作用：收集所有工具声明，以便传递给模型客户端。 - --- -## 使用模式 - -### 模式 1：简单的单轮对话 +## RerankRequest ```python -models = [{ - "client_type": "openai", - "model_identifier": "gpt-4", - "api_key": "sk-...", -}] - -request = LLMRequest(model_set=models) -request.add_payload(LLMPayload(ROLE.SYSTEM, Text("You are helpful."))) -request.add_payload(LLMPayload(ROLE.USER, Text("What is AI?"))) - -response = await request.send(stream=False) -print(await response) +@dataclass(slots=True) +class RerankRequest: + """RerankRequest：构建排序输入并执行请求。""" + + model_set: ModelSet + request_name: str = "" + query: str = "" + documents: list[Any] = field(default_factory=list) + top_n: int | None = None + policy: Policy | None = None + clients: ModelClientRegistry | None = None + enable_metrics: bool = True + request_type: RequestType = RequestType.RERANK ``` -### 模式 2：多轮对话 - +**使用示例：** ```python -request = LLMRequest(model_set=models, request_name="chat") - -# 第一轮 -request.add_payload(LLMPayload(ROLE.SYSTEM, Text("You are helpful."))) -request.add_payload(LLMPayload(ROLE.USER, Text("What is AI?"))) -r1 = await request.send(auto_append_response=True, stream=False) -print(await r1) - -# 第二轮（自动追加了第一轮的响应） -request.add_payload(LLMPayload(ROLE.USER, Text("Tell me more."))) -r2 = await request.send(auto_append_response=True, stream=False) -print(await r2) +from src.kernel.llm import RerankRequest + +req = RerankRequest(model_set=rerank_models, request_name="doc_rerank") +req.set_query("What is AI?") +req.add_document("AI is artificial intelligence...") +req.add_document("Machine learning is a subset of AI...") +response = await req.send() +# response.results: list[RerankItem] +for item in response.results: + print(f"score={item.relevance_score:.4f}, doc={item.document}") ``` - -### 模式 3：工具调用 - -```python -request = LLMRequest(model_set=models) -request.add_payload(LLMPayload(ROLE.SYSTEM, Text("You have access to tools."))) -request.add_payload(LLMPayload(ROLE.TOOL, Tool(CalculatorTool))) -request.add_payload(LLMPayload(ROLE.USER, Text("What is 2 + 2?"))) - -# 获取工具调用 -response = await request.send() -for call in response.call_list: result = await execute_tool(call.name, call.args) request.add_payload(LLMPayload(ROLE.TOOL_RESULT, ToolResult(result, call_id=call.id))) diff --git a/docs/llm/rerank.md b/docs/llm/rerank.md new file mode 100644 index 00000000..4281a7eb --- /dev/null +++ b/docs/llm/rerank.md @@ -0,0 +1,232 @@ +# Rerank 模块 + +## 概述 + +rerank_request.py 和 rerank_response.py 提供了文档重排序（Rerank）能力。给定一个查询（query）和一组待排序文档，返回按相关性排序的结果。用于搜索、推荐等场景的精细排序。 + +## RerankRequest + +`python +@dataclass(slots=True) +class RerankRequest: + model_set: ModelSet + request_name: str = "" + query: str = "" + documents: list[Any] = field(default_factory=list) + top_n: int | None = None + policy: Policy | None = None + clients: ModelClientRegistry | None = None + enable_metrics: bool = True + request_type: RequestType = RequestType.RERANK +` + +### 核心属性 + +| 属性 | 类型 | 说明 | +|---|---|---| +| model_set | ModelSet | 模型配置列表 | +| request_name | str | 请求名称（用于日志和策略） | +| query | str | 查询文本 | +| documents | list[Any] | 待排序的文档列表 | +| top_n | int \| None | 返回前 N 个结果，None 表示返回全部 | +| policy | Policy \| None | 负载均衡/重试策略，默认使用 create_default_policy() | +| clients | ModelClientRegistry \| None | 模型客户端注册表 | + +### 核心方法 + +#### set_query + +`python +def set_query(self, value: str) -> Self: +` + +设置 rerank 查询文本。支持链式调用。 + +#### add_document + +`python +def add_document(self, value: Any) -> Self: +` + +追加待排序文档。支持链式调用。 + +#### send + +`python +async def send(self) -> RerankResponse: +` + +发送 rerank 请求，返回 RerankResponse。 + +**执行流程：** +1. 校验 query 和 documents 非空 +2. 通过策略获取模型步骤 +3. 调用客户端的 create_rerank 方法 +4. 支持超时控制 +5. 记录指标 +6. 失败时通过策略进行重试 +7. 将客户端返回的结果转换为 RerankItem 列表 + +--- + +## RerankItem + +`python +@dataclass(slots=True) +class RerankItem: + index: int + score: float + document: Any +` + +### 属性 + +| 属性 | 类型 | 说明 | +|---|---|---| +| index | int | 文档在原始列表中的索引 | +| score | loat | 相关性分数（越高越相关） | +| document | Any | 原始文档内容 | + +--- + +## RerankResponse + +`python +@dataclass(slots=True) +class RerankResponse: + results: list[RerankItem] + model_name: str + request_name: str = "" +` + +### 属性 + +| 属性 | 类型 | 说明 | +|---|---|---| +| results | list[RerankItem] | 按相关性降序排列的结果列表 | +| model_name | str | 实际使用的模型名称 | +| request_name | str | 请求名称 | + +--- + +## 使用示例 + +### 基础用法 + +`python +from src.kernel.llm import RerankRequest + +model_set = [ + { + "client_type": "openai", + "model_identifier": "rerank-model", + "api_key": "sk-...", + "max_retry": 2, + } +] + +request = RerankRequest(model_set=model_set) +request.set_query("Python 编程入门") +request.add_document({"title": "Python 教程", "content": "..."}) +request.add_document({"title": "Java 指南", "content": "..."}) +request.add_document({"title": "Python 高级", "content": "..."}) + +response = await request.send() +for item in response.results: + print(f"Index {item.index}: score={item.score:.4f}, doc={item.document['title']}") +` + +### 限制返回数量 + +`python +request = RerankRequest(model_set=model_set, top_n=3) + +request.set_query("机器学习") +for doc in documents: + request.add_document(doc) + +response = await request.send() +print(f"返回前 {len(response.results)} 个结果") +` + +### 链式调用 + +`python +response = await ( + RerankRequest(model_set=model_set, request_name="rerank_demo") + .set_query("深度学习框架对比") + .add_document({"title": "TensorFlow", "content": "..."}) + .add_document({"title": "PyTorch", "content": "..."}) + .add_document({"title": "JAX", "content": "..."}) + .send() +) + +for item in response.results: + print(f"{item.document['title']}: {item.score:.4f}") +` + +### 多模型故障转移 + +`python +model_set = [ + {"client_type": "openai", "model_identifier": "rerank-v3", "api_key": "key1", "max_retry": 2}, + {"client_type": "openai", "model_identifier": "rerank-v2", "api_key": "key2", "max_retry": 3}, +] + +request = RerankRequest(model_set=model_set) +request.set_query("查询内容") +request.add_document(doc1) +request.add_document(doc2) + +try: + response = await request.send() +except Exception as e: + print(f"所有模型失败: {e}") +` + +--- + +## 错误处理 + +`python +from src.kernel.llm import LLMConfigurationError + +try: + response = await request.send() +except LLMConfigurationError as e: + print(f"配置错误: {e}") +except Exception as e: + print(f"请求失败: {e}") +` + +--- + +## 模型客户端扩展 + +Rerank 请求通过 ModelClientRegistry.get_rerank_client_for_model() 获取客户端。客户端需实现 create_rerank 方法： + +`python +class MyRerankClient: + async def create_rerank( + self, + *, + model_name: str, + query: str, + documents: list[Any], + top_n: int | None, + request_name: str, + model_set: dict[str, Any], + ) -> list[dict[str, Any]]: + # 返回格式：[{"index": 0, "score": 0.95, "document": ...}, ...] + pass +` + +--- + +## 相关文档 + +- [LLM 主文档](../README.md) +- [Embedding 模块](./embedding.md) +- [Request 模块](./request.md) +- [Types 模块](./types.md) +- [Policy 模块](./policy/README.md) diff --git a/docs/llm/response.md b/docs/llm/response.md index c49f57df..2e80a8eb 100644 --- a/docs/llm/response.md +++ b/docs/llm/response.md @@ -6,26 +6,34 @@ - **await 模式**：收集完整响应后返回 - **async for 模式**：流式处理响应数据 -此外，它还支持自动工具调用解析和响应自动追加到对话历史。 +此外，它还支持： +- 推理内容（reasoning）的结构化解析（如 DeepSeek-R1、OpenAI o1 的思考链） +- 自动工具调用解析与 `tool_call_compat` 兼容模式 +- 响应自动追加到对话历史（通过 `LLMContextManager`） ## 类定义 ```python @dataclass(slots=True) class LLMResponse: - """LLMResponse：既可 await（收集全量）也可 async for（流式吐出）。""" - + """对缓冲式 provider 结果或实时流结果的统一包装。""" + _stream: AsyncIterator[StreamEvent] | None # 流式迭代器 _upper: "LLMRequest | LLMResponse" # 上级请求/响应 _auto_append_response: bool # 是否自动追加到对话 - + payloads: list[LLMPayload] # 当前对话的所有 payload - model_set: ModelSet # 模型配置 - + model_set: "ModelSet" # 模型配置 + context_manager: LLMContextManager | None = None # 上下文管理器 + message: str | None = None # 完整的文本响应 - call_list: list[ToolCall] = field(default_factory=list) # 工具调用列表 - + reasoning_content: str | None = None # 推理过程纯文本 + reasoning_parts: list[ReasoningText] | None = None # 推理过程结构化片段 + call_list: list[ToolCall] | None = None # 工具调用列表（可空） + tool_call_compat: bool = False # 工具调用兼容模式 + _consumed: bool = False # 是否已被消费 + _appended_to_context: bool = False # 是否已追加到上下文 ``` ## 核心属性 @@ -34,48 +42,75 @@ class LLMResponse: **类型：** `str | None` -**描述：** 模型的文本响应。 -- 非流式模式：调用 `await` 后填充 -- 流式模式：完成迭代后填充 +模型的文本响应。流式/非流式完成后填充。 + +### reasoning_content / reasoning_parts + +**类型：** `str | None` / `list[ReasoningText] | None` + +模型的推理/思考过程： +- `reasoning_content`：纯文本形式的推理链 +- `reasoning_parts`：带签名的结构化推理片段（如 Anthropic 的 thinking blocks） + +仅在支持 reasoning 的模型（如 DeepSeek-R1、OpenAI o1/o3、Anthropic Claude）上有效。 ### call_list -**类型：** `list[ToolCall]` +**类型：** `list[ToolCall] | None` + +模型请求的工具调用列表。可能为 `None`（消费前）或空列表（无工具调用）。 + +### tool_call_compat -**描述：** 模型请求的工具调用列表。 -- 如果响应不包含工具调用，则为空列表 -- 非流式模式：调用 `await` 后填充 -- 流式模式：完成迭代后填充 +**类型：** `bool` -### payloads +当模型不支持原生 function calling 时，启用兼容模式从纯文本中解析工具调用（XML/JSON 格式）。 -**类型：** `list[LLMPayload]` +### context_manager -**描述：** 当前对话的所有 payload（包括系统提示、历史消息等）。 +**类型：** `LLMContextManager | None` + +从上游 `LLMRequest` 自动继承的上下文管理器。用于在自动追加响应时保持裁剪/校验行为一致。 ## 使用方式 ### 方式 1：非流式（await） ```python -request = LLMRequest(model_set=models) -request.add_payload(LLMPayload(ROLE.USER, Text("What is AI?"))) - response = await request.send(stream=False) message = await response print(message) -``` -**特点：** -- 等待完整响应 -- 一旦调用 `await`，响应被消费 -- 不能再次调用 `await` 或 `async for` +# 检查推理内容 +if response.reasoning_content: + print(f"思考过程: {response.reasoning_content}") + +# 检查工具调用 +if response.call_list: + for call in response.call_list: + print(f"工具: {call.name}({call.args})") +``` ### 方式 2：流式（async for） ```python -request = LLMRequest(model_set=models) -request.add_payload(LLMPayload(ROLE.USER, Text("Write a story"))) +response = await request.send(stream=True) +async for chunk in response: + print(chunk, end="", flush=True) + +# 消费完成后可访问完整信息 +print(f"推理: {response.reasoning_content}") +print(f"工具调用: {response.call_list}") +``` + +### 方式 3：链式继续对话 + +```python +# 利用 to_payload() 将响应转为 payload 继续对话 +response = await request.send() +payload = response.to_payload() +# payload 包含 assistant 的文本、reasoning、tool_calls +``` response = await request.send(stream=True) async for chunk in response: diff --git a/docs/llm/token_counter.md b/docs/llm/token_counter.md new file mode 100644 index 00000000..03337c17 --- /dev/null +++ b/docs/llm/token_counter.md @@ -0,0 +1,141 @@ +# Token Counter 模块 + +## 概述 + + oken_counter.py 提供基于 iktoken 的 token 计数功能，用于计算一段文本或一组 LLMPayload 中包含的 token 数量。主要用于上下文裁剪和预算控制。 + +## 核心函数 + +### count_payload_tokens + +`python +def count_payload_tokens(payloads: list[LLMPayload], *, model_identifier: str) -> int: +` + +计算一组 LLMPayload 中包含的 token 总数。 + +**参数：** +- payloads: 待计数的 payload 列表 +- model_identifier: 模型标识符，用于选择合适的 tokenizer + +**返回值：** token 数量 + +**序列化策略：** +1. 以 role:{role} 开头 +2. 对于每个 content 部分，按类型处理： + - Text: 直接使用 ext 属性 + - ToolResult: 调用 o_text() 方法 + - ToolCall: 使用 +ame + JSON 序列化的 rgs + - 其他类型：优先调用 o_schema() 并 JSON 序列化，否则尝试 ext/ alue 属性 +3. 各部分以换行符拼接 + +### count_text_tokens + +`python +def count_text_tokens(text: str, *, model_identifier: str) -> int: +` + +计算一段文本的 token 数量。 + +**参数：** +- ext: 待计数的文本 +- model_identifier: 模型标识符 + +**返回值：** token 数量 + +--- + +## Tokenizer 选择 + +模块根据 model_identifier 自动选择合适的 tiktoken 编码器： + +`python +def _get_tiktoken_encoding(model_identifier: str): + try: + return tiktoken.encoding_for_model(model_identifier) + except Exception: + return tiktoken.get_encoding("cl100k_base") +` + +若指定模型的编码器不可用，则回退到 cl100k_base（GPT-4/GPT-3.5-turbo 使用的编码器）。 + +--- + +## 使用示例 + +### 计算 payload 的 token 数 + +`python +from src.kernel.llm import count_payload_tokens, LLMPayload, Text, ROLE + +payloads = [ + LLMPayload(ROLE.SYSTEM, Text("You are a helpful assistant.")), + LLMPayload(ROLE.USER, Text("What is machine learning?")), +] + +tokens = count_payload_tokens(payloads, model_identifier="gpt-4") +print(f"Token 数: {tokens}") +` + +### 计算文本的 token 数 + +`python +from src.kernel.llm import count_text_tokens + +text = "The quick brown fox jumps over the lazy dog." +tokens = count_text_tokens(text, model_identifier="gpt-4") +print(f"Token 数: {tokens}") +` + +### 与上下文管理器配合使用 + +`python +from src.kernel.llm import LLMContextManager, count_payload_tokens + +ctx = LLMContextManager(max_payloads=40) + +# 自定义 token 计数回调 +def my_token_counter(payloads): + return count_payload_tokens(payloads, model_identifier="gpt-4") +` + +### 检查是否超出上下文窗口 + +`python +from src.kernel.llm import count_payload_tokens + +model_context_limit = 8192 # gpt-4 上下文窗口 + +tokens = count_payload_tokens(request.payloads, model_identifier="gpt-4") +if tokens > model_context_limit: + print(f"警告: {tokens} tokens 超出限制 {model_context_limit}") +else: + print(f"{tokens}/{model_context_limit} tokens 使用中") +` + +--- + +## 依赖 + +本模块依赖 iktoken 库。使用前需确保已安装： + +`ash +pip install tiktoken +` + +--- + +## 注意事项 + +- Token 计数是**估算值**，实际 API 消费的 token 数可能略有差异 +- 不同模型的 tokenizer 不同，务必传入正确的 model_identifier +- 序列化策略产生额外的格式化 token（如 role: 前缀、换行符），尽量贴近实际 API 的 token 计数 + +--- + +## 相关文档 + +- [Context 模块](./context.md) - 上下文管理（使用 token 计数进行裁剪） +- [Payload 模块](./payload/README.md) - 消息负载 +- [Types 模块](./types.md) - ModelEntry 定义 diff --git a/docs/llm/tool_call_compat.md b/docs/llm/tool_call_compat.md new file mode 100644 index 00000000..361d87dd --- /dev/null +++ b/docs/llm/tool_call_compat.md @@ -0,0 +1,206 @@ +# Tool Call Compat 模块 + +## 概述 + + ool_call_compat.py 提供工具调用兼容性支持。用于处理原生不支持 tool call 的模型，通过 prompt 注入和响应解析来模拟标准的工具调用行为。 + +## 核心功能 + +### 1. Prompt 构建 + +向不支持原生 tool call 的模型注入 JSON 格式指导，使其输出符合格式的响应。 + +### 2. 响应解析 + +解析 LLM 输出的兼容性响应，提取自然语言回复和工具调用列表。 + +--- + +## 核心函数 + +### build_tool_call_compat_prompt + +`python +def build_tool_call_compat_prompt(tool_schemas: list[dict[str, Any]]) -> str: +` + +构建工具调用兼容性提示文本，指导 LLM 输出符合格式的 JSON 响应。 + +**参数：** +- ool_schemas: 工具 schema 列表 + +**返回值：** 格式指导 prompt 文本 + +生成的 prompt 会引导模型返回如下 JSON： + +`json +{ + "message": "给用户的可选回复", + "tool_calls": [ + {"id": "可选字符串ID", "name": "工具名", "args": {"参数名": "参数值"}} + ] +} +` + +### parse_tool_call_compat_response + +`python +def parse_tool_call_compat_response(raw_text: str) -> tuple[str, list[dict[str, Any]]]: +` + +解析 LLM 的兼容性响应，返回自然语言回复和工具调用列表。 + +**参数：** +- raw_text: LLM 输出的原始文本 + +**返回值：** (message_text, tool_calls) 元组 +- message_text: 自然语言回复（可能为空字符串） +- ool_calls: 工具调用列表，每项包含 id/ +ame/rgs + +**容错能力：** +- 使用 json_repair 修复格式不严格的 JSON +- 支持 ool_calls、calls 等多种字段名 +- 支持 unction.name/ unction.arguments 嵌套格式 +- 支持字符串形式的 rgs（自动解析为 dict） +- 缺少 id 时返回 None + +--- + +## 使用示例 + +### 基础使用 + +`python +from src.kernel.llm.tool_call_compat import ( + build_tool_call_compat_prompt, + parse_tool_call_compat_response, +) + +# 构建工具 schema +schemas = [ + { + "type": "function", + "function": { + "name": "search", + "description": "搜索互联网", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"} + } + } + } + } +] + +# 生成 prompt +compat_prompt = build_tool_call_compat_prompt(schemas) + +# 添加到请求 +request.add_payload(LLMPayload(ROLE.SYSTEM, Text(compat_prompt))) +request.add_payload(LLMPayload(ROLE.USER, Text("帮我搜索 Python 教程"))) + +response = await request.send() +raw_text = await response + +# 解析响应 +message, tool_calls = parse_tool_call_compat_response(raw_text) + +if tool_calls: + for call in tool_calls: + print(f"调用工具: {call['name']}") + print(f"参数: {call['args']}") +elif message: + print(f"回复: {message}") +` + +### 在模型配置中启用兼容模式 + +`python +model_set = [ + { + "client_type": "openai", + "model_identifier": "gpt-3.5-turbo", + "api_key": "sk-...", + "tool_call_compat": True, # 启用兼容模式 + } +] +` + +当 ool_call_compat 为 True 时，框架会自动构建兼容 prompt 并解析响应。 + +### 处理解析异常 + +`python +from src.kernel.llm import LLMError + +try: + message, tool_calls = parse_tool_call_compat_response(raw_text) +except LLMError as e: + print(f"工具调用解析失败: {e}") + # 作为普通文本处理 +` + +--- + +## JSON 容错机制 + +使用 json_repair 库修复格式不严格的 JSON： + +- 缺失引号自动补全 +- 尾部逗号自动移除 +- 不完整 JSON 尽力修复 + +`python +# 这些不规范的输出都可以被正确解析： +raw1 = '{message: "hello", tool_calls: [{name: "search", args: {}}]}' +raw2 = '{"message": "hello", "tool_calls": [{"name": "search", "args": {},}]}' +raw3 = '{"tool_calls": [{"name": "search", "args": "{\\\"query\\\": \\\"test\\\"}"}]}' +` + +--- + +## 配置说明 + +在 ModelEntry 中设置 ool_call_compat 字段： + +`python +{ + "tool_call_compat": True, # 启用兼容模式 + # 或 + "tool_call_compat": False, # 禁用（默认） +} +` + +当该字段为 True 时： +1. 请求发送前会自动生成并注入兼容 prompt +2. 响应回来后会自动解析工具调用 +3. 不适用于原生支持 tool call 的模型 + +--- + +## 依赖 + +本模块依赖 json_repair 库： + +`ash +pip install json-repair +` + +--- + +## 注意事项 + +- 兼容模式会增加 prompt token 消耗 +- 任何 LLM 输出格式问题都可以通过 json_repair 修复 +- 对于原生支持 tool call 的模型，不建议启用兼容模式 +- 兼容模式下模型返回的 JSON 可能不稳定，建议做好异常处理 + +--- + +## 相关文档 + +- [Request 模块](./request.md) +- [Payload - Tooling 模块](./payload/tooling.md) +- [Types 模块](./types.md) diff --git a/docs/llm/types.md b/docs/llm/types.md new file mode 100644 index 00000000..8828453a --- /dev/null +++ b/docs/llm/types.md @@ -0,0 +1,107 @@ +# Types 模块 + +## 概述 + + ypes.py 定义了 LLM 模块使用的核心类型别名和 TypedDict 定义，为整个模块提供统一的类型约束。 + +## RequestType（请求类型枚举） + +`python +class RequestType(str, Enum): + COMPLETIONS = "completions" + EMBEDDINGS = "embeddings" + RERANK = "rerank" +` + +标识 LLM 请求的类型。用于区分聊天补全、向量嵌入和文档重排序三种不同的能力。 + +| 值 | 含义 | +|---|---| +| COMPLETIONS | 聊天补全请求（Chat Completions） | +| EMBEDDINGS | 向量嵌入请求（Embeddings） | +| RERANK | 文档重排序请求（Rerank） | + +--- + +## ModelEntry（模型配置条目） + +`python +class ModelEntry(TypedDict, total=True): + api_provider: str + base_url: str + model_identifier: str + api_key: str + client_type: str + max_retry: int + timeout: float + retry_interval: float + price_in: float + price_out: float + temperature: float + max_tokens: int + max_context: int + tool_call_compat: bool + extra_params: dict[str, Any] +` + +定义单个 LLM 模型的完整配置信息。所有字段在类型层面标记为必须（ otal=True），但实际使用中上层可按需传递。 + +**常用字段：** + +| 字段 | 类型 | 说明 | +|---|---|---| +| client_type | str | 提供商类型，如 "openai"、"anthropic" | +| model_identifier | str | 模型标识，如 "gpt-4" | +| api_key | str | API 密钥 | +| base_url | str | API 基础 URL | +| max_retry | int | 最大重试次数 | +| imeout | loat | 请求超时（秒） | +| retry_interval | loat | 重试间隔（秒） | +| emperature | loat | 采样温度 | +| max_tokens | int | 最大输出 token 数 | +| max_context | int | 模型上下文窗口大小 | +| price_in / price_out | loat | 输入/输出价格（用于成本计算） | +| tool_call_compat | tool | 是否启用工具调用兼容模式 | +| extra_params | dict | 额外参数透传 | + +--- + +## ModelSet（模型集合类型） + +`python +ModelSet: TypeAlias = list[ModelEntry] +` + +一组可用的模型配置，用于负载均衡和故障转移。LLMRequest、EmbeddingRequest、RerankRequest 均使用此类型。 + +**使用示例：** + +`python +from src.kernel.llm import ModelSet + +model_set: ModelSet = [ + { + "client_type": "openai", + "model_identifier": "gpt-4", + "api_key": "sk-...", + "base_url": "https://api.openai.com/v1", + "max_retry": 3, + }, + { + "client_type": "openai", + "model_identifier": "gpt-3.5-turbo", + "api_key": "sk-...", + "max_retry": 2, + } +] +` + +--- + +## 相关文档 + +- [LLM 主文档](../README.md) +- [Request 模块](./request.md) +- [Embedding 模块](./embedding.md) +- [Rerank 模块](./rerank.md) +- [Policy 模块](./policy/README.md) diff --git a/plugins/default_chatter/plugin.py b/plugins/default_chatter/plugin.py index 297c8ff2..5b616fdf 100644 --- a/plugins/default_chatter/plugin.py +++ b/plugins/default_chatter/plugin.py @@ -271,6 +271,7 @@ class SendTextAction(BaseAction): action_description = "发送一段文本消息给用户。这是你唯一发送文本消息的方式。你可以一次调用多个 send_text 来分多段回复，但每次调用必须提供你想说的话的文本内容，不要添加任何标记或格式，只写纯文本即可。content 参数只能包含发送给用户的正文，严禁将行为理由、内心独白或格式说明混入 content。你也可以选择引用或回复之前某条消息作为背景，使用 reply_to 参数指定；若不引用消息，可用 at 参数指定要@的对象。注意：本工具无法发送表情包等非文本内容。所有@对象都应该通过at参数而不是直接写在文本里，以确保正确解析和发送。" chatter_allow: list[str] = ["default_chatter"] + associated_types = ["text"] def _is_programmatic_controller_enabled(self) -> bool: """读取程序化控制器开关。""" @@ -490,6 +491,7 @@ class PassAndWaitAction(BaseAction): action_description = "为当前对话登记一个等待点。你可以单独调用它，让本轮什么都不做直接等待；也可以在同一轮先调用其他 action（例如 send_text、发送表情等），再调用本工具，表示这些动作执行完成后进入等待。默认会等待用户新消息；如果传入 seconds 参数，则会在指定秒数到达后由框架主动恢复对话流程，即使期间没有收到新消息。适合需要回复后稍后主动继续、定时追问或延时确认的场景。" chatter_allow: list[str] = ["default_chatter"] + associated_types = ["text"] async def execute(self, seconds: float | None = None) -> tuple[bool, str]: """跳过本次动作，不执行任何操作。 @@ -510,6 +512,7 @@ class StopConversationAction(BaseAction): action_description = "结束当前对话，过一段时间后再允许开启新对话。如果对话已经自然结束，或者你认为本轮对话可以告一段落，或者你暂时不想继续对话，使用本工具结束这轮对话。通常当你已经做出回应，且后续的消息很可能是新的话题时，使用本工具结束对话。你可以指定一个冷却时间（分钟），在此期间即使有新消息也不会触发新的对话，直到冷却时间结束后才会重新允许开启新对话。" chatter_allow: list[str] = ["default_chatter"] + associated_types = ["text"] async def execute(self, minutes: float) -> tuple[bool, str]: """结束对话并设置冷却时间 @@ -524,6 +527,7 @@ class _SubAgentManagementUsable(BaseAgent): """default chatter 子代理管理工具基类。""" chatter_allow: list[str] = ["default_chatter"] + associated_types = ["text"] class CreateAgentUsable(_SubAgentManagementUsable): diff --git a/plugins/default_chatter/session.py b/plugins/default_chatter/session.py index dc456806..09fe96f4 100644 --- a/plugins/default_chatter/session.py +++ b/plugins/default_chatter/session.py @@ -62,6 +62,13 @@ def _require_response(response: LLMConversationState) -> LLMResponseLike: raise TypeError("当前会话状态不是一个 LLM 响应") +def _should_disable_stream_for_tool_call_compat(response: LLMConversationState) -> bool: + model_set = getattr(response, "model_set", None) + if not isinstance(model_set, list): + return False + return any(bool(model.get("tool_call_compat", False)) for model in model_set) + + def _format_tool_args(args: Any) -> str: if not isinstance(args, dict): return "" @@ -507,8 +514,22 @@ async def execute_with_stream( DefaultChatterSessionPhase.FOLLOW_UP, ): try: - state.response = await state.response.send(stream=False) - await state.response + stream_observer = self.adapters.stream_event_observer + use_stream = bool(self.options.enable_llm_stream) + if use_stream and _should_disable_stream_for_tool_call_compat(state.response): + use_stream = False + self.logger.warning( + "LLM stream observer disabled because tool_call_compat is enabled." + ) + + state.response = await state.response.send(stream=use_stream) + if use_stream and stream_observer is not None: + await state.response.stream_events_with_callback(stream_observer) + finalize = getattr(stream_observer, "finalize", None) + if callable(finalize): + await finalize() + else: + await state.response if state.phase == DefaultChatterSessionPhase.MODEL_TURN: if state.unread_msgs_to_flush: await self.adapters.unread_adapter.flush_unreads(state.unread_msgs_to_flush) diff --git a/plugins/default_chatter/type_defs.py b/plugins/default_chatter/type_defs.py index 20531b92..928846a4 100644 --- a/plugins/default_chatter/type_defs.py +++ b/plugins/default_chatter/type_defs.py @@ -2,14 +2,14 @@ from __future__ import annotations -from collections.abc import Generator +from collections.abc import Awaitable, Generator from dataclasses import dataclass, field -from typing import Any, Literal, Protocol, TypedDict, TypeAlias +from typing import Any, Callable, Literal, Protocol, TypedDict, TypeAlias from src.core.components.base import Failure, Stop, Success, Wait, WaitResumeEvent from src.core.models.message import Message from src.core.models.stream import ChatStream -from src.kernel.llm import LLMPayload, LLMRequest, ToolCall, ToolRegistry +from src.kernel.llm import LLMPayload, LLMRequest, StreamEvent, ToolCall, ToolRegistry from src.kernel.logger import Logger @@ -37,6 +37,12 @@ async def send( """使用当前对话状态继续请求。""" ... + async def stream_events_with_callback( + self, + on_event: Callable[[StreamEvent], Awaitable[None]], + ) -> str: + ... + def add_payload( self, payload: LLMPayload, @@ -206,6 +212,7 @@ class DefaultChatterSessionAdapters: sub_agent_adapter: SubAgentAdapter logger_adapter: LoggerAdapter plain_text_adapter: PlainTextResponseAdapter | None = None + stream_event_observer: Callable[..., Awaitable[None]] | None = None @dataclass(slots=True) class DefaultChatterSessionOptions: @@ -222,6 +229,7 @@ class DefaultChatterSessionOptions: native_multimodal: bool = False theme_guide: dict[str, str] = field(default_factory=dict) negative_behavior_reinforcement: bool = True + enable_llm_stream: bool = False DefaultChatterResult: TypeAlias = Wait | Success | Failure | Stop diff --git a/plugins/emoji_sender/action.py b/plugins/emoji_sender/action.py index faf4aa2f..4b861d26 100644 --- a/plugins/emoji_sender/action.py +++ b/plugins/emoji_sender/action.py @@ -28,6 +28,7 @@ class SendEmojiMemeAction(BaseAction): action_name: str = "send_emoji_meme" action_description: str = "根据目标描述与情感标签，检索并发送一张符合当前情景的表情包来生动地表达情绪。不要忘记在聊天时使用这个动作，比起简单的文字它往往更受欢迎。此动作可以单独使用也可以和发送文字一起使用，更符合日常聊天习惯。" primary_action: bool = False + associated_types = ["emoji"] async def execute( self, diff --git a/plugins/napcat_adapter/src/event_types.py b/plugins/napcat_adapter/src/event_types.py deleted file mode 100644 index c2cbbd8d..00000000 --- a/plugins/napcat_adapter/src/event_types.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Napcat 适配器事件类型定义""" - - -class NapcatEvent: - """Napcat 适配器事件类型""" - - class ON_RECEIVED: - """接收事件""" - - FRIEND_INPUT = "napcat.on_received.friend_input" # 好友正在输入 - EMOJI_LIEK = "napcat.on_received.emoji_like" # 表情回复（注意：保持原来的拼写） - POKE = "napcat.on_received.poke" # 戳一戳 - GROUP_UPLOAD = "napcat.on_received.group_upload" # 群文件上传 - GROUP_BAN = "napcat.on_received.group_ban" # 群禁言 - GROUP_LIFT_BAN = "napcat.on_received.group_lift_ban" # 群解禁 - FRIEND_RECALL = "napcat.on_received.friend_recall" # 好友消息撤回 - GROUP_RECALL = "napcat.on_received.group_recall" # 群消息撤回 - - class MESSAGE: - """消息相关事件""" - - GET_MSG = "napcat.message.get_msg" # 获取消息 - - class GROUP: - """群组相关事件""" - - SET_GROUP_BAN = "napcat.group.set_group_ban" # 设置群禁言 - SET_GROUP_WHOLE_BAN = "napcat.group.set_group_whole_ban" # 设置全员禁言 - SET_GROUP_KICK = "napcat.group.set_group_kick" # 踢出群聊 - - class FRIEND: - """好友相关事件""" - - SEND_LIKE = "napcat.friend.send_like" # 发送点赞 - - -__all__ = ["NapcatEvent"] diff --git a/plugins/napcat_adapter/.gitignore b/plugins/onebot_adapter/.gitignore similarity index 100% rename from plugins/napcat_adapter/.gitignore rename to plugins/onebot_adapter/.gitignore diff --git a/plugins/napcat_adapter/LICENSE b/plugins/onebot_adapter/LICENSE similarity index 100% rename from plugins/napcat_adapter/LICENSE rename to plugins/onebot_adapter/LICENSE diff --git a/plugins/napcat_adapter/README.md b/plugins/onebot_adapter/README.md similarity index 94% rename from plugins/napcat_adapter/README.md rename to plugins/onebot_adapter/README.md index 7ac24674..fa0552be 100644 --- a/plugins/napcat_adapter/README.md +++ b/plugins/onebot_adapter/README.md @@ -1,4 +1,4 @@ -# NEW_napcat_adapter +# NEW_onebot_adapter 基于 mofox-wire v2.x 的 Napcat 适配器（使用 BaseAdapter 架构） @@ -7,7 +7,7 @@ 本插件采用 **BaseAdapter 继承模式** 重写，完全抛弃旧版 maim_message 库，改用 mofox-wire 的 TypedDict 数据结构。 ### 核心组件 -- **NapcatAdapter**: 继承自 `mofox_wire.AdapterBase`，负责 OneBot 11 协议与 MessageEnvelope 的双向转换 +- **OneBotAdapter**: 继承自 `mofox_wire.AdapterBase`，负责 OneBot 11 协议与 MessageEnvelope 的双向转换 - **WebSocketAdapterOptions**: 自动管理 WebSocket 连接，提供 incoming_parser 和 outgoing_encoder - **CoreMessageSink**: 通过 `InProcessCoreSink` 将消息递送到核心系统 - **Handlers**: 独立的消息处理器，分为 to_core（接收）和 to_napcat（发送）两个方向 @@ -15,7 +15,7 @@ ## 📁 项目结构 ``` -NEW_napcat_adapter/ +NEW_onebot_adapter/ ├── plugin.py # ✅ 主插件文件（BaseAdapter实现） ├── _manifest.json # 插件清单 │ @@ -50,7 +50,7 @@ NEW_napcat_adapter/ ### 使用方式 -1. **配置文件**: 在 `config/plugins/NEW_napcat_adapter.toml` 中配置 WebSocket URL 和其他参数 +1. **配置文件**: 在 `config/plugins/NEW_onebot_adapter.toml` 中配置 WebSocket URL 和其他参数 2. **启动插件**: 插件自动在系统启动时加载 3. **WebSocket连接**: 自动连接到 Napcat OneBot 11 服务器 @@ -129,16 +129,16 @@ ban_user_id = ["333333333", "444444444"] #### 🔧 配置文件位置 -- **示例配置**: `src/plugins/built_in/napcat_adapter/config.example.toml` -- **实际配置**: `config/plugins/NEW_napcat_adapter.toml` +- **示例配置**: `src/plugins/built_in/onebot_adapter/config.example.toml` +- **实际配置**: `config/plugins/NEW_onebot_adapter.toml` #### 📊 日志信息 被过滤的消息会在调试日志中记录： ``` -[DEBUG] napcat_adapter: 群聊 123456789 在黑名单中，消息被过滤 -[DEBUG] napcat_adapter: 私聊用户 111111111 在黑名单中，消息被过滤 -[DEBUG] napcat_adapter: 用户 333333333 在全局封禁列表中，消息被过滤 +[DEBUG] onebot_adapter: 群聊 123456789 在黑名单中，消息被过滤 +[DEBUG] onebot_adapter: 私聊用户 111111111 在黑名单中，消息被过滤 +[DEBUG] onebot_adapter: 用户 333333333 在全局封禁列表中，消息被过滤 ``` ## 🔑 核心数据结构 @@ -183,7 +183,7 @@ envelope: MessageEnvelope = { ### BaseAdapter 核心方法 ```python -class NapcatAdapter(BaseAdapter): +class OneBotAdapter(BaseAdapter): async def from_platform_message(self, message: dict[str, Any]) -> MessageEnvelope | None: """将 OneBot 11 事件转换为 MessageEnvelope""" # 路由到对应的 Handler @@ -356,7 +356,7 @@ envelope: MessageEnvelope = { **接收方向** (to_core): ```python class MessageHandler: - def __init__(self, adapter: "NapcatAdapter"): + def __init__(self, adapter: "OneBotAdapter"): self.adapter = adapter async def handle_raw_message(self, data: dict[str, Any]) -> MessageEnvelope: @@ -369,20 +369,20 @@ class MessageHandler: **发送方向** (to_napcat): ```python class SendHandler: - def __init__(self, adapter: "NapcatAdapter"): + def __init__(self, adapter: "OneBotAdapter"): self.adapter = adapter async def handle_message(self, envelope: MessageEnvelope) -> dict[str, Any]: # 1. 从 envelope 提取 message_segment # 2. 递归转换 SegPayload → OneBot 格式 - # 3. 调用 adapter.send_napcat_api() 发送 + # 3. 调用 adapter.send_onebot_api() 发送 ``` ### 3. API 调用模式（响应池） ```python -# 在 NapcatAdapter 中 -async def send_napcat_api(self, action: str, params: dict[str, Any]) -> dict[str, Any]: +# 在 OneBotAdapter 中 +async def send_onebot_api(self, action: str, params: dict[str, Any]) -> dict[str, Any]: # 1. 生成唯一 echo echo = f"{action}_{uuid.uuid4()}" @@ -446,7 +446,7 @@ async def from_platform_message(self, message: dict[str, Any]) -> MessageEnvelop - **mofox-wire 文档**: 查看 `mofox_wire/types.py` 了解 TypedDict 定义 - **BaseAdapter 示例**: 参考 `docs/mofox_wire_demo_adapter.py` -- **旧版实现**: `src/plugins/built_in/napcat_adapter_plugin/` (仅参考逻辑) +- **旧版实现**: `src/plugins/built_in/onebot_adapter_plugin/` (仅参考逻辑) - **OneBot 11 协议**: [OneBot 11 标准](https://github.com/botuniverse/onebot-11) ## ⚠️ 重要注意事项 diff --git a/plugins/napcat_adapter/__init__.py b/plugins/onebot_adapter/__init__.py similarity index 54% rename from plugins/napcat_adapter/__init__.py rename to plugins/onebot_adapter/__init__.py index 687b3263..a31d30d9 100644 --- a/plugins/napcat_adapter/__init__.py +++ b/plugins/onebot_adapter/__init__.py @@ -1,7 +1,7 @@ -"""Napcat Adapter - NapCat/OneBot 11 适配器 +"""OneBot Adapter - OneBot 11 协议适配器 基于 Neo-MoFox 插件系统的 QQ 机器人适配器， -实现 OneBot 11 协议与 NapCat 的通信。 +实现 OneBot 11 协议的通信。 """ __version__ = "2.0.0" diff --git a/plugins/napcat_adapter/config.py b/plugins/onebot_adapter/config.py similarity index 91% rename from plugins/napcat_adapter/config.py rename to plugins/onebot_adapter/config.py index a40999c9..e52bf783 100644 --- a/plugins/napcat_adapter/config.py +++ b/plugins/onebot_adapter/config.py @@ -1,4 +1,4 @@ -"""Napcat Adapter 配置定义""" +"""OneBot Adapter 配置定义""" from __future__ import annotations from typing import ClassVar @@ -6,11 +6,11 @@ from src.core.components.base.config import BaseConfig, Field, SectionBase, config_section -class NapcatAdapterConfig(BaseConfig): - """Napcat 适配器配置""" +class OneBotAdapterConfig(BaseConfig): + """OneBot 适配器配置""" config_name: ClassVar[str] = "config" - config_description: ClassVar[str] = "Napcat/OneBot 11 适配器配置" + config_description: ClassVar[str] = "OneBot 11 适配器配置" @config_section("plugin", title="插件设置", tag="plugin") class PluginSection(SectionBase): @@ -18,7 +18,7 @@ class PluginSection(SectionBase): enabled: bool = Field( default=True, - description="是否启用 Napcat 适配器", + description="是否启用 OneBot 适配器", label="启用适配器", tag="plugin" ) @@ -47,9 +47,9 @@ class BotSection(SectionBase): tag="user" ) - @config_section("napcat_server", title="Napcat 服务器", tag="network") - class NapcatServerSection(SectionBase): - """Napcat WebSocket 服务器配置""" + @config_section("onebot_server", title="OneBot 服务器", tag="network") + class OneBotServerSection(SectionBase): + """OneBot WebSocket 服务器配置""" mode: str = Field( default="reverse", @@ -62,14 +62,14 @@ class NapcatServerSection(SectionBase): ) host: str = Field( default="localhost", - description="Napcat WebSocket 服务地址", + description="OneBot WebSocket 服务地址", label="服务地址", placeholder="localhost", tag="network" ) port: int = Field( default=8095, - description="Napcat WebSocket 服务端口", + description="OneBot WebSocket 服务端口", label="服务端口", ge=1, le=65535, @@ -77,7 +77,7 @@ class NapcatServerSection(SectionBase): ) access_token: str = Field( default="", - description="Napcat API 访问令牌（可选）", + description="OneBot API 访问令牌（可选）", label="访问令牌", input_type="password", placeholder="可选，留空表示不鉴权", @@ -212,5 +212,5 @@ class FeaturesSection(SectionBase): plugin: PluginSection = Field(default_factory=PluginSection) bot: BotSection = Field(default_factory=BotSection) - napcat_server: NapcatServerSection = Field(default_factory=NapcatServerSection) + onebot_server: OneBotServerSection = Field(default_factory=OneBotServerSection) features: FeaturesSection = Field(default_factory=FeaturesSection) diff --git a/plugins/napcat_adapter/manifest.json b/plugins/onebot_adapter/manifest.json similarity index 67% rename from plugins/napcat_adapter/manifest.json rename to plugins/onebot_adapter/manifest.json index 909d965d..fcc290ed 100644 --- a/plugins/napcat_adapter/manifest.json +++ b/plugins/onebot_adapter/manifest.json @@ -1,8 +1,8 @@ { - "name": "napcat_adapter", - "display_name": "NapCat 适配器", + "name": "onebot_adapter", + "display_name": "OneBot 适配器", "version": "2.0.0", - "description": "Napcat/OneBot 11 适配器（基于 Neo-MoFox 重写）", + "description": "OneBot 11 适配器（基于 Neo-MoFox 重写）", "author": "MoFox Team", "dependencies": { "plugins": [], @@ -11,7 +11,7 @@ "include": [ { "component_type": "adapter", - "component_name": "napcat_adapter", + "component_name": "onebot_adapter", "dependencies": [] } ], diff --git a/plugins/napcat_adapter/plugin.py b/plugins/onebot_adapter/plugin.py similarity index 84% rename from plugins/napcat_adapter/plugin.py rename to plugins/onebot_adapter/plugin.py index 669dec24..14892e30 100644 --- a/plugins/napcat_adapter/plugin.py +++ b/plugins/onebot_adapter/plugin.py @@ -1,8 +1,8 @@ """ -Napcat 适配器（基于 MoFox-Bus 完全重写版） +OneBot 适配器（基于 MoFox-Bus 完全重写版） 核心流程： -1. Napcat WebSocket 连接 → 接收 OneBot 格式消息 +1. OneBot WebSocket 连接 → 接收 OneBot 格式消息 2. from_platform_message: OneBot dict → MessageEnvelope 3. CoreSink → 推送到 MoFox-Bot 核心 4. 核心回复 → _send_platform_message: MessageEnvelope → OneBot API 调用 @@ -22,17 +22,17 @@ from src.core.components.base import BaseAdapter, BasePlugin from src.core.components.loader import register_plugin -from .config import NapcatAdapterConfig +from .config import OneBotAdapterConfig from .src.handlers import utils as handler_utils from .src.handlers.to_core.message_handler import MessageHandler from .src.handlers.to_core.meta_event_handler import MetaEventHandler from .src.handlers.to_core.notice_handler import NoticeHandler from .src.handlers.to_napcat.send_handler import SendHandler -logger = get_logger("napcat_adapter") +logger = get_logger("onebot_adapter") -def _validate_bot_identity(config: NapcatAdapterConfig) -> None: +def _validate_bot_identity(config: OneBotAdapterConfig) -> None: """校验 Bot 身份配置。""" qq_id = str(config.bot.qq_id).strip() @@ -47,26 +47,26 @@ def _validate_bot_identity(config: NapcatAdapterConfig) -> None: raise ValueError("配置项 bot.qq_nickname 无效：必须为非空昵称") -class NapcatAdapter(BaseAdapter): - """Napcat 适配器 - 完全基于 mofox-wire 架构""" +class OneBotAdapter(BaseAdapter): + """OneBot 适配器 - 完全基于 mofox-wire 架构""" - adapter_name = "napcat_adapter" + adapter_name = "onebot_adapter" adapter_version = "2.0.0" adapter_author = "MoFox Team" - adapter_description = "基于 MoFox-Bus 的 Napcat/OneBot 11 适配器" + adapter_description = "基于 MoFox-Bus 的 OneBot 11 适配器" platform = "qq" run_in_subprocess = False - def __init__(self, core_sink: CoreSink, plugin: NapcatAdapterPlugin | None = None, **kwargs): - """初始化 Napcat 适配器""" + def __init__(self, core_sink: CoreSink, plugin: OneBotAdapterPlugin | None = None, **kwargs): + """初始化 OneBot 适配器""" # 从插件配置读取 WebSocket URL if plugin and plugin.config: - config = cast(NapcatAdapterConfig, plugin.config) - host = config.napcat_server.host - port = config.napcat_server.port - access_token = config.napcat_server.access_token - mode_str = config.napcat_server.mode + config = cast(OneBotAdapterConfig, plugin.config) + host = config.onebot_server.host + port = config.onebot_server.port + access_token = config.onebot_server.access_token + mode_str = config.onebot_server.mode ws_mode = "client" if mode_str == "direct" else "server" ws_url = f"ws://{host}:{port}" @@ -99,7 +99,7 @@ def __init__(self, core_sink: CoreSink, plugin: NapcatAdapterPlugin | None = Non # WebSocket 连接（用于发送 API 请求） # 注意：_ws 继承自 BaseAdapter，是 WebSocketLike 协议类型 - self._napcat_ws = None # 可选的额外连接引用 + self._onebot_ws = None # 可选的额外连接引用 # 注册 utils 内部使用的适配器实例，便于工具方法自动获取 WS handler_utils.register_adapter(self) @@ -119,7 +119,7 @@ def _should_process_event(self, raw: dict[str, Any]) -> bool: if not self.plugin or not self.plugin.config: return True - config = cast(NapcatAdapterConfig, self.plugin.config) + config = cast(OneBotAdapterConfig, self.plugin.config) features_config = config.features post_type = raw.get("post_type") @@ -182,22 +182,22 @@ def _should_process_event(self, raw: dict[str, Any]) -> bool: async def on_adapter_loaded(self) -> None: """适配器加载时的初始化""" - logger.info("Napcat 适配器正在启动...") + logger.info("OneBot 适配器正在启动...") if not self.plugin or not self.plugin.config: - raise RuntimeError("Napcat 适配器启动失败：缺少插件配置") + raise RuntimeError("OneBot 适配器启动失败：缺少插件配置") - config = cast(NapcatAdapterConfig, self.plugin.config) + config = cast(OneBotAdapterConfig, self.plugin.config) _validate_bot_identity(config) # 设置处理器配置（将整个 plugin 对象传递给处理器） # 注意：handlers 现在会直接访问 plugin.config 而不是接收 dict # 这里不再需要调用 set_plugin_config，因为处理器会通过 adapter.plugin 访问 - logger.info("Napcat 适配器已加载") + logger.info("OneBot 适配器已加载") async def on_adapter_unloaded(self) -> None: """适配器卸载时的清理""" - logger.info("Napcat 适配器正在关闭...") + logger.info("OneBot 适配器正在关闭...") self.meta_event_handler.stop_heartbeat_monitor() @@ -207,11 +207,11 @@ async def on_adapter_unloaded(self) -> None: future.cancel() self._response_pool.clear() - logger.info("Napcat 适配器已关闭") + logger.info("OneBot 适配器已关闭") async def from_platform_message(self, raw: dict[str, Any]) -> MessageEnvelope | None: # type: ignore[override] """ - 将 Napcat/OneBot 原始消息转换为 MessageEnvelope + 将 OneBot 原始消息转换为 MessageEnvelope 这是核心转换方法，处理： - message 事件 → 消息 @@ -253,27 +253,27 @@ async def from_platform_message(self, raw: dict[str, Any]) -> MessageEnvelope | else: return None except ValueError as ve: - logger.warning(f"处理 Napcat 事件时数据无效: {ve}") + logger.warning(f"处理 OneBot 事件时数据无效: {ve}") return None except Exception as e: - logger.error(f"处理 Napcat 事件失败: {e}, 原始数据: {raw}") + logger.error(f"处理 OneBot 事件失败: {e}, 原始数据: {raw}") return None async def _send_platform_message(self, envelope: MessageEnvelope) -> None: # type: ignore[override] """ - 将 MessageEnvelope 转换并发送到 Napcat + 将 MessageEnvelope 转换并发送到 OneBot 这里不直接通过 WebSocket 发送 envelope， - 而是调用 Napcat API（send_group_msg, send_private_msg 等） + 而是调用 OneBot API（send_group_msg, send_private_msg 等） """ try: await self.send_handler.handle_message(envelope) except Exception as e: - logger.error(f"发送 Napcat 消息失败: {e}") + logger.error(f"发送 OneBot 消息失败: {e}") - async def send_napcat_api(self, action: str, params: dict[str, Any], timeout: float = 30.0) -> dict[str, Any]: + async def send_onebot_api(self, action: str, params: dict[str, Any], timeout: float = 30.0) -> dict[str, Any]: """ - 发送 Napcat API 请求并等待响应 + 发送 OneBot API 请求并等待响应 Args: action: API 动作名称（如 send_group_msg） @@ -294,7 +294,7 @@ async def send_napcat_api(self, action: str, params: dict[str, Any], timeout: fl self._response_pool[echo] = future # 构造请求 - # Napcat expects JSON text frames; orjson.dumps returns bytes so decode to str + # OneBot expects JSON text frames; orjson.dumps returns bytes so decode to str request = orjson.dumps( { "action": action, @@ -340,22 +340,22 @@ async def get_bot_info(self) -> dict[str, Any]: # type: ignore[override] if not self.plugin or not self.plugin.config: return {} - config = cast(NapcatAdapterConfig, self.plugin.config) + config = cast(OneBotAdapterConfig, self.plugin.config) return { "bot_id": config.bot.qq_id, "bot_name": config.bot.qq_nickname, "platform": self.platform, } - + @register_plugin -class NapcatAdapterPlugin(BasePlugin): - """Napcat 适配器插件""" +class OneBotAdapterPlugin(BasePlugin): + """OneBot 适配器插件""" - plugin_name = "napcat_adapter" + plugin_name = "onebot_adapter" plugin_version = "2.0.0" plugin_author = "MoFox Team" - plugin_description = "Napcat/OneBot 11 适配器（基于 Neo-MoFox 重写）" - configs = [NapcatAdapterConfig] + plugin_description = "OneBot 11 适配器（基于 Neo-MoFox 重写）" + configs = [OneBotAdapterConfig] def get_components(self) -> list[type]: @@ -364,4 +364,4 @@ def get_components(self) -> list[type]: Returns: list[type]: 插件内所有组件类的列表 """ - return [NapcatAdapter] + return [OneBotAdapter] diff --git a/plugins/napcat_adapter/src/__init__.py b/plugins/onebot_adapter/src/__init__.py similarity index 100% rename from plugins/napcat_adapter/src/__init__.py rename to plugins/onebot_adapter/src/__init__.py diff --git a/plugins/napcat_adapter/src/event_models.py b/plugins/onebot_adapter/src/event_models.py similarity index 99% rename from plugins/napcat_adapter/src/event_models.py rename to plugins/onebot_adapter/src/event_models.py index 4a2994f1..48bc327c 100644 --- a/plugins/napcat_adapter/src/event_models.py +++ b/plugins/onebot_adapter/src/event_models.py @@ -108,7 +108,7 @@ def __str__(self) -> str: ] # 插件名称 -PLUGIN_NAME = "napcat_adapter_plugin" +PLUGIN_NAME = "onebot_adapter_plugin" # QQ表情映射表 QQ_FACE = { diff --git a/plugins/onebot_adapter/src/event_types.py b/plugins/onebot_adapter/src/event_types.py new file mode 100644 index 00000000..bfde19fc --- /dev/null +++ b/plugins/onebot_adapter/src/event_types.py @@ -0,0 +1,37 @@ +"""OneBot 适配器事件类型定义""" + + +class OneBotEvent: + """OneBot 适配器事件类型""" + + class ON_RECEIVED: + """接收事件""" + + FRIEND_INPUT = "onebot.on_received.friend_input" # 好友正在输入 + EMOJI_LIEK = "onebot.on_received.emoji_like" # 表情回复（注意：保持原来的拼写） + POKE = "onebot.on_received.poke" # 戳一戳 + GROUP_UPLOAD = "onebot.on_received.group_upload" # 群文件上传 + GROUP_BAN = "onebot.on_received.group_ban" # 群禁言 + GROUP_LIFT_BAN = "onebot.on_received.group_lift_ban" # 群解禁 + FRIEND_RECALL = "onebot.on_received.friend_recall" # 好友消息撤回 + GROUP_RECALL = "onebot.on_received.group_recall" # 群消息撤回 + + class MESSAGE: + """消息相关事件""" + + GET_MSG = "onebot.message.get_msg" # 获取消息 + + class GROUP: + """群组相关事件""" + + SET_GROUP_BAN = "onebot.group.set_group_ban" # 设置群禁言 + SET_GROUP_WHOLE_BAN = "onebot.group.set_group_whole_ban" # 设置全员禁言 + SET_GROUP_KICK = "onebot.group.set_group_kick" # 踢出群聊 + + class FRIEND: + """好友相关事件""" + + SEND_LIKE = "onebot.friend.send_like" # 发送点赞 + + +__all__ = ["OneBotEvent"] diff --git a/plugins/napcat_adapter/src/handlers/__init__.py b/plugins/onebot_adapter/src/handlers/__init__.py similarity index 100% rename from plugins/napcat_adapter/src/handlers/__init__.py rename to plugins/onebot_adapter/src/handlers/__init__.py diff --git a/plugins/napcat_adapter/src/handlers/to_core/__init__.py b/plugins/onebot_adapter/src/handlers/to_core/__init__.py similarity index 100% rename from plugins/napcat_adapter/src/handlers/to_core/__init__.py rename to plugins/onebot_adapter/src/handlers/to_core/__init__.py diff --git a/plugins/napcat_adapter/src/handlers/to_core/message_handler.py b/plugins/onebot_adapter/src/handlers/to_core/message_handler.py similarity index 98% rename from plugins/napcat_adapter/src/handlers/to_core/message_handler.py rename to plugins/onebot_adapter/src/handlers/to_core/message_handler.py index 62cc1169..eea1b2e5 100644 --- a/plugins/napcat_adapter/src/handlers/to_core/message_handler.py +++ b/plugins/onebot_adapter/src/handlers/to_core/message_handler.py @@ -1,4 +1,4 @@ -"""消息处理器 - 将 Napcat OneBot 消息转换为 MessageEnvelope""" +"""消息处理器 - 将 OneBot 消息转换为 MessageEnvelope""" from __future__ import annotations @@ -14,7 +14,7 @@ from src.app.plugin_system.api.log_api import get_logger from src.core.utils.base64_helper import base64_encode_bytes -from ....config import NapcatAdapterConfig +from ....config import OneBotAdapterConfig from ...event_models import ACCEPT_FORMAT, QQ_FACE, RealMessageType from ..utils import ( get_forward_message, @@ -27,15 +27,15 @@ ) if TYPE_CHECKING: - from ....plugin import NapcatAdapter + from ....plugin import OneBotAdapter -logger = get_logger("napcat_adapter") +logger = get_logger("onebot_adapter") class MessageHandler: - """处理来自 Napcat 的消息事件""" + """处理来自 OneBot 的消息事件""" - def __init__(self, adapter: "NapcatAdapter"): + def __init__(self, adapter: "OneBotAdapter"): self.adapter = adapter self._video_downloader = None @@ -45,7 +45,7 @@ def _get_video_io_timeout(self) -> float: if not self.adapter.plugin or not self.adapter.plugin.config: return default_timeout - config = cast(NapcatAdapterConfig, self.adapter.plugin.config) + config = cast(OneBotAdapterConfig, self.adapter.plugin.config) return max(1.0, float(config.features.video_download_timeout)) def _init_video_downloader(self) -> None: @@ -54,7 +54,7 @@ def _init_video_downloader(self) -> None: if not self.adapter.plugin or not self.adapter.plugin.config: return - config = cast(NapcatAdapterConfig, self.adapter.plugin.config) + config = cast(OneBotAdapterConfig, self.adapter.plugin.config) # 如果启用了视频处理，根据配置初始化视频下载器 if config.features.enable_video_processing: @@ -77,7 +77,7 @@ async def handle_raw_message(self, raw: dict[str, Any]): MessageEnvelope (dict) or None Note: - 黑白名单过滤已移动到 NapcatAdapter.from_platform_message 顶层执行， + 黑白名单过滤已移动到 OneBotAdapter.from_platform_message 顶层执行， 确保所有类型的事件（消息、通知等）都能被统一过滤。 """ @@ -166,7 +166,7 @@ async def handle_single_segment( """ seg_type = segment.get("type") - self.adapter.plugin.config = cast(NapcatAdapterConfig, self.adapter.plugin.config) if self.adapter.plugin and self.adapter.plugin.config else None + self.adapter.plugin.config = cast(OneBotAdapterConfig, self.adapter.plugin.config) if self.adapter.plugin and self.adapter.plugin.config else None match seg_type: case RealMessageType.text: @@ -782,7 +782,7 @@ def _extract_file_info_from_echo(self, nested_data: dict) -> dict | None: file_size_str = summary.replace("大小：", "").strip() # 移除前缀和空格 # QQ API有时返回的大小不标准，这里我们只提取它给的字符串 - # 实际大小已经由Napcat在发送时记录，这里主要是为了保持格式一致 + # 实际大小已经由 OneBot 在发送时记录，这里主要是为了保持格式一致 if file_name and file_size_str: return {"file": file_name, "file_size": file_size_str, "file_id": None} # file_id在回声中不可用 diff --git a/plugins/napcat_adapter/src/handlers/to_core/meta_event_handler.py b/plugins/onebot_adapter/src/handlers/to_core/meta_event_handler.py similarity index 88% rename from plugins/napcat_adapter/src/handlers/to_core/meta_event_handler.py rename to plugins/onebot_adapter/src/handlers/to_core/meta_event_handler.py index cf5a6a34..98ede110 100644 --- a/plugins/napcat_adapter/src/handlers/to_core/meta_event_handler.py +++ b/plugins/onebot_adapter/src/handlers/to_core/meta_event_handler.py @@ -11,15 +11,15 @@ from ...event_models import MetaEventType if TYPE_CHECKING: - from ....plugin import NapcatAdapter + from ....plugin import OneBotAdapter -logger = get_logger("napcat_adapter") +logger = get_logger("onebot_adapter") class MetaEventHandler: - """处理 Napcat 元事件（心跳、生命周期）""" + """处理 OneBot 元事件（心跳、生命周期）""" - def __init__(self, adapter: "NapcatAdapter"): + def __init__(self, adapter: "OneBotAdapter"): self.adapter = adapter self.plugin_config: dict[str, Any] | None = None self.last_heart_beat = 0.0 @@ -45,7 +45,7 @@ def stop_heartbeat_monitor(self) -> None: try: get_task_manager().cancel_task(heartbeat_task.task_id) except Exception: - logger.debug("取消 Napcat 心跳监控任务失败", exc_info=True) + logger.debug("取消 OneBot 心跳监控任务失败", exc_info=True) async def handle_meta_event(self, raw: dict[str, Any]): event_type = raw.get("meta_event_type") @@ -68,13 +68,13 @@ async def handle_meta_event(self, raw: dict[str, Any]): tm = get_task_manager() self._heartbeat_task = tm.create_task( self.check_heartbeat(self_id), - name="napcat_adapter_heartbeat_check", + name="onebot_adapter_heartbeat_check", daemon=True, ) self.last_heart_beat = time.time() else: self_id = raw.get("self_id") - logger.warning(f"Bot {self_id} Napcat 端异常！") + logger.warning(f"Bot {self_id} OneBot 端异常！") await self._reconnect_after_heartbeat_failure( self_id, "心跳状态异常", @@ -91,10 +91,10 @@ async def _reconnect_after_heartbeat_failure( self._reconnecting = True try: - logger.error(f"Bot {bot_id} 检测到 Napcat 连接异常，开始重连：{reason}") + logger.error(f"Bot {bot_id} 检测到 OneBot 连接异常，开始重连：{reason}") await self.adapter.reconnect() except Exception as e: - logger.error(f"Bot {bot_id} Napcat 自动重连失败: {e}") + logger.error(f"Bot {bot_id} OneBot 自动重连失败: {e}") finally: self._reconnecting = False diff --git a/plugins/napcat_adapter/src/handlers/to_core/notice_handler.py b/plugins/onebot_adapter/src/handlers/to_core/notice_handler.py similarity index 98% rename from plugins/napcat_adapter/src/handlers/to_core/notice_handler.py rename to plugins/onebot_adapter/src/handlers/to_core/notice_handler.py index 148e7fd7..cccfbc80 100644 --- a/plugins/napcat_adapter/src/handlers/to_core/notice_handler.py +++ b/plugins/onebot_adapter/src/handlers/to_core/notice_handler.py @@ -14,15 +14,15 @@ from ..utils import get_group_info, get_member_info, get_message_detail, get_self_info, get_stranger_info if TYPE_CHECKING: - from ....plugin import NapcatAdapter + from ....plugin import OneBotAdapter -logger = get_logger("napcat_adapter") +logger = get_logger("onebot_adapter") class NoticeHandler: - """处理 Napcat 通知事件（戳一戳、表情回复、禁言、文件上传等）""" + """处理 OneBot 通知事件（戳一戳、表情回复、禁言、文件上传等）""" - def __init__(self, adapter: "NapcatAdapter"): + def __init__(self, adapter: "OneBotAdapter"): self.adapter = adapter # 戳一戳防抖时间戳 self.last_poke_time: float = 0.0 @@ -368,7 +368,7 @@ async def _handle_group_emoji_like_notify( # 触发事件 from src.app.plugin_system.api import event_api - from ...event_types import NapcatEvent + from ...event_types import OneBotEvent target_message = await get_message_detail(raw.get("message_id", "")) if not target_message: @@ -391,7 +391,7 @@ async def _handle_group_emoji_like_notify( # 触发表情回复事件 await event_api.publish_event( - NapcatEvent.ON_RECEIVED.EMOJI_LIEK, + OneBotEvent.ON_RECEIVED.EMOJI_LIEK, { "message_id": raw.get("message_id", ""), "emoji_id": like_emoji_id, diff --git a/plugins/napcat_adapter/src/handlers/to_core/request_handler.py b/plugins/onebot_adapter/src/handlers/to_core/request_handler.py similarity index 100% rename from plugins/napcat_adapter/src/handlers/to_core/request_handler.py rename to plugins/onebot_adapter/src/handlers/to_core/request_handler.py diff --git a/plugins/napcat_adapter/src/handlers/to_napcat/__init__.py b/plugins/onebot_adapter/src/handlers/to_napcat/__init__.py similarity index 100% rename from plugins/napcat_adapter/src/handlers/to_napcat/__init__.py rename to plugins/onebot_adapter/src/handlers/to_napcat/__init__.py diff --git a/plugins/napcat_adapter/src/handlers/to_napcat/send_handler.py b/plugins/onebot_adapter/src/handlers/to_napcat/send_handler.py similarity index 94% rename from plugins/napcat_adapter/src/handlers/to_napcat/send_handler.py rename to plugins/onebot_adapter/src/handlers/to_napcat/send_handler.py index 63c1cb47..c11a8727 100644 --- a/plugins/napcat_adapter/src/handlers/to_napcat/send_handler.py +++ b/plugins/onebot_adapter/src/handlers/to_napcat/send_handler.py @@ -1,4 +1,4 @@ -"""发送处理器 - 将 MessageEnvelope 转换并发送到 Napcat""" +"""发送处理器 - 将 MessageEnvelope 转换并发送到 OneBot""" from __future__ import annotations @@ -12,21 +12,21 @@ from ...event_models import CommandType from ..utils import convert_image_to_gif, get_image_format -logger = get_logger("napcat_adapter") +logger = get_logger("onebot_adapter") if TYPE_CHECKING: - from ....plugin import NapcatAdapter + from ....plugin import OneBotAdapter class SendHandler: - """负责向 Napcat 发送消息""" + """负责向 OneBot 发送消息""" - def __init__(self, adapter: "NapcatAdapter"): + def __init__(self, adapter: "OneBotAdapter"): self.adapter = adapter async def handle_message(self, envelope: MessageEnvelope) -> None: """ - 处理来自核心的消息，将其转换为 Napcat 可接受的格式并发送 + 处理来自核心的消息，将其转换为 OneBot 可接受的格式并发送 """ logger.debug("接收到来自MoFox-Bot的消息，处理中") @@ -102,10 +102,10 @@ async def send_normal_message(self, envelope: MessageEnvelope) -> None: logger.error("无法识别的消息类型") return logger.debug( - f"准备发送到napcat的消息体: action='{action}', {id_name}='{target_id}', " + f"准备发送到 onebot 的消息体: action='{action}', {id_name}='{target_id}', " f"message={str(processed_message)[:500]}" ) - response = await self.send_message_to_napcat( + response = await self.send_message_to_onebot( action or "", { id_name or "target_id": target_id, @@ -115,7 +115,7 @@ async def send_normal_message(self, envelope: MessageEnvelope) -> None: if response.get("status") == "ok": logger.info("消息发送成功") else: - logger.warning(f"消息发送失败，napcat返回：{response!s}") + logger.warning(f"消息发送失败，onebot返回：{response!s}") async def send_command(self, envelope: MessageEnvelope) -> None: """ @@ -161,18 +161,18 @@ async def send_command(self, envelope: MessageEnvelope) -> None: logger.error("命令或参数缺失") return None - logger.debug(f"准备向 Napcat 发送命令: command='{command}', args_dict='{args_dict}'") - response = await self.send_message_to_napcat(command, args_dict) - logger.debug(f"收到 Napcat 的命令响应: {response}") + logger.debug(f"准备向 OneBot 发送命令: command='{command}', args_dict='{args_dict}'") + response = await self.send_message_to_onebot(command, args_dict) + logger.debug(f"收到 OneBot 的命令响应: {response}") if response.get("status") == "ok": logger.info(f"命令 {command_name} 执行成功") else: - logger.warning(f"命令 {command_name} 执行失败，napcat返回：{response!s}") + logger.warning(f"命令 {command_name} 执行失败，onebot返回：{response!s}") async def handle_adapter_command(self, envelope: MessageEnvelope) -> None: """ - 处理适配器命令类 - 用于直接向Napcat发送命令并返回结果 + 处理适配器命令类 - 用于直接向 OneBot 发送命令并返回结果 """ logger.info("处理适配器命令中") segment: SegPayload = envelope.get("message_segment", {}) # type: ignore[assignment] @@ -192,9 +192,9 @@ async def handle_adapter_command(self, envelope: MessageEnvelope) -> None: # 执行命令 if action == "get_cookies": - response = await self.send_message_to_napcat(action, params, timeout=40.0) + response = await self.send_message_to_onebot(action, params, timeout=40.0) else: - response = await self.send_message_to_napcat(action, params, timeout=timeout) + response = await self.send_message_to_onebot(action, params, timeout=timeout) # 构建adapter_response消息信封发回核心 if request_id and self.adapter.core_sink: @@ -221,7 +221,7 @@ async def handle_adapter_command(self, envelope: MessageEnvelope) -> None: if response.get("status") == "ok": logger.info(f"适配器命令 {action} 执行成功") else: - logger.warning(f"适配器命令 {action} 执行失败，napcat返回：{response!s}") + logger.warning(f"适配器命令 {action} 执行失败，onebot返回：{response!s}") logger.debug(f"适配器命令 {action} 的完整响应: {response}") except Exception as e: @@ -335,7 +335,7 @@ async def handle_reply_message(self, message_id: str, user_info: UserInfoPayload return reply_seg try: - msg_info_response = await self.send_message_to_napcat("get_msg", {"message_id": message_id}) + msg_info_response = await self.send_message_to_onebot("get_msg", {"message_id": message_id}) logger.debug(f"获取消息 {message_id} 的详情响应: {msg_info_response}") replied_user_id = None @@ -382,7 +382,7 @@ def handle_image_message(self, encoded_image: str) -> dict: encoded_image: 图片数据。可以是： - 原始 base64 字符串（不含前缀，自动补 base64://） - 已含 base64:// 前缀的字符串（直接透传，不重复添加前缀） - - HTTP/HTTPS URL（直接透传，napcat 会自行拉取） + - HTTP/HTTPS URL（直接透传，onebot 会自行拉取） """ if encoded_image.startswith(("base64://", "http://", "https://")): file_value = encoded_image @@ -582,7 +582,7 @@ def handle_at_message_command(self, args: dict[str, Any], group_info: dict[str, def handle_ai_voice_send_command(self, args: dict[str, Any], group_info: dict[str, Any] | None) -> tuple[str, dict[str, Any]]: """ 处理AI语音发送命令的逻辑。 - 并返回 NapCat 兼容的 (action, params) 元组。 + 并返回 OneBot 兼容的 (action, params) 元组。 """ if not group_info or not group_info.get("group_id"): raise ValueError("AI语音发送命令必须在群聊上下文中使用") @@ -605,10 +605,10 @@ def handle_ai_voice_send_command(self, args: dict[str, Any], group_info: dict[st }, ) - async def send_message_to_napcat(self, action: str, params: dict, timeout: float = 20.0) -> dict: - """通过 adapter API 发送到 napcat""" + async def send_message_to_onebot(self, action: str, params: dict, timeout: float = 20.0) -> dict: + """通过 adapter API 发送到 onebot""" try: - response = await self.adapter.send_napcat_api(action, params, timeout=timeout) + response = await self.adapter.send_onebot_api(action, params, timeout=timeout) return response or {"status": "error", "message": "no response"} except Exception as e: logger.error(f"发送消息失败: {e}") diff --git a/plugins/napcat_adapter/src/handlers/utils.py b/plugins/onebot_adapter/src/handlers/utils.py similarity index 89% rename from plugins/napcat_adapter/src/handlers/utils.py rename to plugins/onebot_adapter/src/handlers/utils.py index 7e45f8d6..df36abe2 100644 --- a/plugins/napcat_adapter/src/handlers/utils.py +++ b/plugins/onebot_adapter/src/handlers/utils.py @@ -15,9 +15,9 @@ ) if TYPE_CHECKING: - from ...plugin import NapcatAdapter + from ...plugin import OneBotAdapter -logger = get_logger("napcat_adapter") +logger = get_logger("onebot_adapter") # 简单的缓存实现，通过 kernel.storage 实现磁盘持久化存储 _CACHE_LOADED = False @@ -37,14 +37,14 @@ SELF_INFO_TTL = 300 CACHE_IO_TIMEOUT_SECONDS = 5.0 -_adapter_ref: weakref.ReferenceType["NapcatAdapter"] | None = None +_adapter_ref: weakref.ReferenceType["OneBotAdapter"] | None = None -def register_adapter(adapter: "NapcatAdapter") -> None: - """注册 NapcatAdapter 实例，以便 utils 模块可以获取 WebSocket""" +def register_adapter(adapter: "OneBotAdapter") -> None: + """注册 OneBotAdapter 实例，以便 utils 模块可以获取 WebSocket""" global _adapter_ref _adapter_ref = weakref.ref(adapter) - logger.debug("Napcat adapter registered in utils for websocket access") + logger.debug("OneBot adapter registered in utils for websocket access") async def _ensure_cache_loaded() -> None: @@ -59,12 +59,12 @@ async def _ensure_cache_loaded() -> None: try: async with asyncio.timeout(CACHE_IO_TIMEOUT_SECONDS): - data = await json_store.load("napcat_cache") + data = await json_store.load("onebot_cache") except TimeoutError as e: - logger.debug(f"Load napcat cache timed out: {e}") + logger.debug(f"Load onebot cache timed out: {e}") data = None except Exception as e: - logger.debug(f"Failed to load napcat cache: {e}") + logger.debug(f"Failed to load onebot cache: {e}") data = None if _CACHE_LOADED: @@ -85,11 +85,11 @@ async def _save_cache_to_disk() -> None: try: async with asyncio.timeout(CACHE_IO_TIMEOUT_SECONDS): - await json_store.save("napcat_cache", _CACHE) + await json_store.save("onebot_cache", _CACHE) except TimeoutError as e: - logger.debug(f"Write napcat cache timed out: {e}") + logger.debug(f"Write onebot cache timed out: {e}") except Exception as e: - logger.debug(f"Write napcat cache failed: {e}") + logger.debug(f"Write onebot cache failed: {e}") async def _get_cached(section: str, key: str, ttl: int) -> Any | None: @@ -117,16 +117,16 @@ async def _set_cached(section: str, key: str, data: Any) -> None: try: await _save_cache_to_disk() except Exception: - logger.debug("Write napcat cache failed") + logger.debug("Write onebot cache failed") -def _get_adapter(adapter: "NapcatAdapter | None" = None) -> "NapcatAdapter": +def _get_adapter(adapter: "OneBotAdapter | None" = None) -> "OneBotAdapter": target = adapter if target is None and _adapter_ref: target = _adapter_ref() if target is None: raise RuntimeError( - "NapcatAdapter 未注册，请确保已调用 utils.register_adapter 注册" + "OneBotAdapter 未注册，请确保已调用 utils.register_adapter 注册" ) return target @@ -134,7 +134,7 @@ def _get_adapter(adapter: "NapcatAdapter | None" = None) -> "NapcatAdapter": async def _call_adapter_api( action: str, params: dict[str, Any], - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, timeout: float = 30.0, ) -> dict[str, Any] | None: """统一通过 adapter 发送和接收 API 调用""" @@ -147,7 +147,7 @@ async def _call_adapter_api( return None try: - return await target.send_napcat_api(action, params, timeout=timeout) + return await target.send_onebot_api(action, params, timeout=timeout) except Exception as e: logger.error(f"{action} 调用失败: {e}") return None @@ -156,7 +156,7 @@ async def _call_adapter_api( async def get_respose( action: str, params: dict[str, Any], - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, timeout: float = 30.0, ): return await _call_adapter_api(action, params, adapter=adapter, timeout=timeout) @@ -166,7 +166,7 @@ async def get_group_info( *, use_cache: bool = True, force_refresh: bool = False, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取群组基本信息 @@ -196,7 +196,7 @@ async def get_group_detail_info( *, use_cache: bool = True, force_refresh: bool = False, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取群组详细信息 @@ -227,7 +227,7 @@ async def get_member_info( *, use_cache: bool = True, force_refresh: bool = False, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取群组成员信息 @@ -309,7 +309,7 @@ async def get_self_info( *, use_cache: bool = True, force_refresh: bool = False, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取机器人信息 @@ -345,7 +345,7 @@ async def get_stranger_info( *, use_cache: bool = True, force_refresh: bool = False, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取陌生人信息 @@ -369,7 +369,7 @@ async def get_stranger_info( async def get_message_detail( message_id: str | int, *, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取消息详情，仅作为参考 @@ -388,7 +388,7 @@ async def get_record_detail( file: str, file_id: str | None = None, *, - adapter: "NapcatAdapter | None" = None, + adapter: "OneBotAdapter | None" = None, ) -> dict | None: """ 获取语音信息详情 @@ -404,7 +404,7 @@ async def get_record_detail( async def get_forward_message( - raw_message: dict, *, adapter: "NapcatAdapter | None" = None + raw_message: dict, *, adapter: "OneBotAdapter | None" = None ) -> dict[str, Any] | None: forward_message_data: dict = raw_message.get("data", {}) if not forward_message_data: diff --git a/plugins/napcat_adapter/src/handlers/video_handler.py b/plugins/onebot_adapter/src/handlers/video_handler.py similarity index 100% rename from plugins/napcat_adapter/src/handlers/video_handler.py rename to plugins/onebot_adapter/src/handlers/video_handler.py diff --git a/pyproject.toml b/pyproject.toml index 57edbd77..8f4f432f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "neo-mofox" -version = "1.2.0-beta.1" +version = "1.2.0-rc" description = "一个全新重构的 AI 聊天机器人框架，它的目标不仅仅是回答问题，而是创造一个真正的数字生命体。" readme = "README.md" requires-python = ">=3.11" @@ -63,6 +63,6 @@ omit = [ ] [[tool.uv.index]] -name = "tuna" -url = "https://pypi.tuna.tsinghua.edu.cn/simple" +name = "huawei" +url = "https://repo.huaweicloud.com/repository/pypi/simple" default = true \ No newline at end of file diff --git a/src/app/plugin_system/api/chat_api.py b/src/app/plugin_system/api/chat_api.py index 98fc4b8c..6f67816f 100644 --- a/src/app/plugin_system/api/chat_api.py +++ b/src/app/plugin_system/api/chat_api.py @@ -135,6 +135,35 @@ def unregister_active_chatter(stream_id: str) -> bool: return _get_chatter_manager().unregister_active_chatter(stream_id) +def bind_chatter_for_stream(stream_id: str, chatter: "BaseChatter") -> None: + """显式绑定 chatter 到指定 stream。 + + Args: + stream_id: 聊天流 ID + chatter: Chatter 实例 + + Returns: + None + """ + _validate_non_empty(stream_id, "stream_id") + if chatter is None: + raise ValueError("chatter 不能为空") + _get_chatter_manager().bind_chatter_for_stream(stream_id, chatter) + + +def restore_stream_to_default(stream_id: str) -> bool: + """移除 stream 的显式 chatter 绑定。 + + Args: + stream_id: 聊天流 ID + + Returns: + 是否成功注销/移除绑定 + """ + _validate_non_empty(stream_id, "stream_id") + return _get_chatter_manager().restore_stream_to_default(stream_id) + + def get_chatter_by_stream(stream_id: str) -> "BaseChatter | None": """获取指定聊天流的活跃 Chatter 实例。 @@ -179,6 +208,8 @@ def get_or_create_chatter_for_stream( "get_active_chatters", "register_active_chatter", "unregister_active_chatter", + "bind_chatter_for_stream", + "restore_stream_to_default", "get_chatter_by_stream", "get_or_create_chatter_for_stream", ] diff --git a/src/app/plugin_system/api/send_api.py b/src/app/plugin_system/api/send_api.py index dcb86665..cb9b3727 100644 --- a/src/app/plugin_system/api/send_api.py +++ b/src/app/plugin_system/api/send_api.py @@ -226,7 +226,7 @@ async def send_custom( try: message_type_enum = MessageType(message_type) except ValueError: - # 未知类型（如 "music"）：通过 extra_media 机制传递，使 napcat adapter 等可 + # 未知类型（如 "music"）：通过 extra_media 机制传递，使 onebot adapter 等可 # 直接处理自定义消息段类型，而不会被降级为文本消息 return await _send_message( content="", diff --git a/src/app/runtime/plugin_market_sync.py b/src/app/runtime/plugin_market_sync.py index 643bf1f5..d67aab34 100644 --- a/src/app/runtime/plugin_market_sync.py +++ b/src/app/runtime/plugin_market_sync.py @@ -1,7 +1,10 @@ """插件市场同步。 -在 Bot 最终进行插件发现与加载前，将插件市场订阅列表同步到本地插件目录。 -仅自动安装 / 更新 .mfp 插件，不会改动文件夹插件或普通 .zip 插件。 +在 Bot 最终进行插件发现与加载前： +1. 将插件市场订阅列表同步到本地插件目录 +2. 检查本地已安装的市场 .mfp 插件是否有可更新版本 + +不会改动文件夹插件或普通 .zip 插件。 """ from __future__ import annotations @@ -132,6 +135,14 @@ async def sync(self) -> PluginMarketSyncReport: is_dependency=False, ) + if self._market.auto_update_mfp: + await self._refresh_installed_market_plugins( + client, + local_plugins, + report, + visited, + ) + logger.info(f"插件市场同步完成: {report.summary()}") return report @@ -243,6 +254,44 @@ async def _cleanup_unsubscribed_plugins( report.removed += 1 logger.info(f"严格模式：清理未订阅 mfp 插件 {plugin_id}") + async def _refresh_installed_market_plugins( + self, + client: httpx.AsyncClient, + local_plugins: dict[str, LocalPluginRecord], + report: PluginMarketSyncReport, + visited: set[str], + ) -> None: + """检查本地已安装的市场 .mfp 插件是否存在新版本。""" + + for plugin_id, record in list(local_plugins.items()): + if plugin_id in visited: + continue + if record.path.suffix.lower() != ".mfp": + continue + + try: + detail = await self._fetch_plugin_detail(client, plugin_id) + except Exception as exc: + report.errors.append(f"查询市场插件失败 {plugin_id}: {exc}") + continue + + if detail is None: + logger.debug(f"本地 mfp 插件 {plugin_id} 未在市场中登记，跳过自动更新") + continue + + if str(detail.get("status") or "").lower() != "published": + logger.info(f"本地 mfp 插件 {plugin_id} 当前非 published，跳过自动更新") + continue + + await self._ensure_plugin( + client, + plugin_id, + local_plugins, + report, + visited, + is_dependency=False, + ) + async def _ensure_plugin( self, client: httpx.AsyncClient, @@ -359,4 +408,4 @@ async def _download_asset( with NamedTemporaryFile(delete=False, dir=target_path.parent, suffix=".download") as handle: handle.write(response.content) temp_path = Path(handle.name) - temp_path.replace(target_path) \ No newline at end of file + temp_path.replace(target_path) diff --git a/src/core/components/base/action.py b/src/core/components/base/action.py index 2579abb0..fb7e0c93 100644 --- a/src/core/components/base/action.py +++ b/src/core/components/base/action.py @@ -10,7 +10,10 @@ from uuid import uuid4 from src.core.components.types import ChatType -from src.core.components.utils import parse_function_signature +from src.core.components.utils import ( + parse_function_signature, + validate_associated_types, +) from src.kernel.llm import LLMUsable, LLMUsableExecution from src.core.models.message import Message, MessageType @@ -91,6 +94,16 @@ def get_signature(cls) -> str | None: if hasattr(cls, "_plugin_") and cls._plugin_ and cls.action_name: return f"{cls._plugin_}:action:{cls.action_name}" return None + + @classmethod + def validate_associated_types(cls) -> list[str]: + """校验动作组件声明的 associated_types。""" + + return validate_associated_types( + cls, + component_kind="Action", + component_name_attr="action_name", + ) @abstractmethod async def execute( diff --git a/src/core/components/base/agent.py b/src/core/components/base/agent.py index 717d4e95..c4979bca 100644 --- a/src/core/components/base/agent.py +++ b/src/core/components/base/agent.py @@ -11,7 +11,10 @@ from typing import Annotated, Any, TYPE_CHECKING, cast from src.core.components.types import ChatType -from src.core.components.utils import parse_function_signature +from src.core.components.utils import ( + parse_function_signature, + validate_associated_types, +) from src.kernel.llm import ( LLMContextManager, LLMUsable, @@ -108,6 +111,16 @@ def get_signature(cls) -> str | None: return f"{cls._plugin_}:agent:{cls.agent_name}" return None + @classmethod + def validate_associated_types(cls) -> list[str]: + """校验 Agent 组件声明的 associated_types。""" + + return validate_associated_types( + cls, + component_kind="Agent", + component_name_attr="agent_name", + ) + @abstractmethod async def execute( self, *args: Any, **kwargs: Any @@ -150,6 +163,7 @@ def get_local_usables(cls) -> list[type[LLMUsable]]: list[type[LLMUsable]]: Agent 私有组件类列表 """ from src.core.components.registry import get_global_registry + from src.core.components.base.action import BaseAction resolved_usables: list[type[LLMUsable]] = [] registry = get_global_registry() @@ -170,9 +184,13 @@ def get_local_usables(cls) -> list[type[LLMUsable]]: f"不是 LLMUsable 子类，跳过该 usable" ) continue + if issubclass(component_cls, (BaseAction, BaseAgent)): + component_cls.validate_associated_types() resolved_usables.append(component_cls) # type: ignore else: # 直接传入的类 + if issubclass(usable_ref, (BaseAction, BaseAgent)): + usable_ref.validate_associated_types() resolved_usables.append(usable_ref) return resolved_usables diff --git a/src/core/components/base/chatter.py b/src/core/components/base/chatter.py index 8510960e..4fabb2ee 100644 --- a/src/core/components/base/chatter.py +++ b/src/core/components/base/chatter.py @@ -303,12 +303,10 @@ async def modify_llm_usables( logger.debug(f"[移除组件] {signature}：{reason}") continue - if ( - (issubclass(usable_cls, BaseAction) or issubclass(usable_cls, BaseAgent)) - and usable_cls.associated_types - ): - if not chat_context.check_types(usable_cls.associated_types): - types_str = ", ".join(usable_cls.associated_types) + if issubclass(usable_cls, BaseAction): + required_types = usable_cls.validate_associated_types() + if not chat_context.check_types(required_types): + types_str = ", ".join(required_types) reason = f"适配器不支持（需要: {types_str}）" removals.append((signature, reason)) logger.debug(f"[移除组件] {signature}：{reason}") diff --git a/src/core/components/utils/__init__.py b/src/core/components/utils/__init__.py index f8558426..0018298a 100644 --- a/src/core/components/utils/__init__.py +++ b/src/core/components/utils/__init__.py @@ -4,6 +4,7 @@ """ from src.core.components.utils.deps_installer import DependencyInstaller, PluginDepSpec +from src.core.components.utils.associated_types import validate_associated_types from src.core.components.utils.invoke_utils import should_strip_auto_reason_argument from src.core.components.utils.schema_utils import ( extract_description_from_docstring, @@ -15,6 +16,7 @@ "map_type_to_json", "parse_function_signature", "extract_description_from_docstring", + "validate_associated_types", "should_strip_auto_reason_argument", "DependencyInstaller", "PluginDepSpec", diff --git a/src/core/components/utils/associated_types.py b/src/core/components/utils/associated_types.py new file mode 100644 index 00000000..0e7fbd0f --- /dev/null +++ b/src/core/components/utils/associated_types.py @@ -0,0 +1,40 @@ +"""associated_types 校验工具。""" + +from __future__ import annotations + + +def validate_associated_types( + component_cls: type, + *, + component_kind: str, + component_name_attr: str, +) -> list[str]: + """校验组件声明的 associated_types。 + + Args: + component_cls: 待校验的组件类。 + component_kind: 组件类型描述，用于错误提示。 + component_name_attr: 组件名称属性名，如 ``action_name`` / ``agent_name``。 + + Returns: + list[str]: 规范化后的 associated_types 列表。 + + Raises: + ValueError: 当 associated_types 未定义、不是列表、为空，或包含空字符串时抛出。 + """ + + raw_types = getattr(component_cls, "associated_types", None) + component_name = getattr(component_cls, component_name_attr, "") or component_cls.__name__ + + if not isinstance(raw_types, list): + raise ValueError( + f"{component_kind} '{component_name}' 的 associated_types 必须是非空 list[str]" + ) + + normalized_types = [str(item).strip() for item in raw_types] + if not normalized_types or any(not item for item in normalized_types): + raise ValueError( + f"{component_kind} '{component_name}' 的 associated_types 必须是非空 list[str]" + ) + + return normalized_types diff --git a/src/core/config/core_config.py b/src/core/config/core_config.py index 4f6f1031..4068bcb3 100644 --- a/src/core/config/core_config.py +++ b/src/core/config/core_config.py @@ -8,7 +8,7 @@ from src.kernel.config import ConfigBase, SectionBase, config_section, Field -CORE_VERSION = "1.2.0-beta.1" +CORE_VERSION = "1.2.0-rc" class CoreConfig(ConfigBase): """Core 层配置类 diff --git a/src/core/managers/action_manager.py b/src/core/managers/action_manager.py index 8812ae66..6b6970a1 100644 --- a/src/core/managers/action_manager.py +++ b/src/core/managers/action_manager.py @@ -389,12 +389,12 @@ def _check_action_associated_types( type_mismatched: list[tuple[str, str]] = [] for signature, action_cls in all_actions.items(): - if action_cls.associated_types: - if not chat_context.check_types(action_cls.associated_types): - types_str = ", ".join(action_cls.associated_types) - reason = f"适配器不支持（需要: {types_str}）" - type_mismatched.append((signature, reason)) - logger.debug(f"[移除动作] {signature}：{reason}") + required_types = action_cls.validate_associated_types() + if not chat_context.check_types(required_types): + types_str = ", ".join(required_types) + reason = f"适配器不支持（需要: {types_str}）" + type_mismatched.append((signature, reason)) + logger.debug(f"[移除动作] {signature}：{reason}") return type_mismatched diff --git a/src/core/managers/chatter_manager.py b/src/core/managers/chatter_manager.py index a9c4b478..4baa6551 100644 --- a/src/core/managers/chatter_manager.py +++ b/src/core/managers/chatter_manager.py @@ -106,6 +106,17 @@ def register_active_chatter(self, stream_id: str, chatter: BaseChatter) -> None: self._active_chatters[stream_id] = chatter logger.debug(f"注册活跃 Chatter: stream_id={stream_id}, chatter={chatter.chatter_name}") + def bind_chatter_for_stream(self, stream_id: str, chatter: BaseChatter) -> None: + """显式绑定 chatter 到指定 stream。""" + self._active_chatters[stream_id] = chatter + logger.info( + f"显式绑定 Chatter: stream_id={stream_id}, chatter={chatter.chatter_name}" + ) + + def restore_stream_to_default(self, stream_id: str) -> bool: + """移除 stream 的显式 chatter 绑定。""" + return self.unregister_active_chatter(stream_id) + def unregister_active_chatter(self, stream_id: str) -> bool: """注销活跃的 Chatter 实例。 diff --git a/src/core/managers/plugin_manager.py b/src/core/managers/plugin_manager.py index 5bcfa73f..c24a1e9f 100644 --- a/src/core/managers/plugin_manager.py +++ b/src/core/managers/plugin_manager.py @@ -909,6 +909,11 @@ async def _register_components(self, plugin_instance: "BasePlugin") -> None: logger.warning(f"组件 '{signature}' 已经注册，跳过") continue + if component_type == ComponentType.ACTION: + component_cls.validate_associated_types() + elif component_type == ComponentType.AGENT: + component_cls.validate_associated_types() + try: # 注册到全局注册表 registry.register(component_cls, signature, dependencies) diff --git a/src/core/models/stream.py b/src/core/models/stream.py index 2242c6f0..e8d09f4b 100644 --- a/src/core/models/stream.py +++ b/src/core/models/stream.py @@ -76,6 +76,9 @@ def add_unread_message(self, message: "Message") -> None: message: 消息对象 """ self.unread_messages.append(message) + # 当前上下文默认跟随最新一条入站未读消息， + # 供 Action/Chatter 做能力判断和回复目标回溯。 + self.current_message = message def add_history_message(self, message: "Message") -> None: """添加历史消息。 @@ -116,16 +119,32 @@ def check_types(self, types: list[str]) -> bool: >>> context.check_types(["text", "image"]) True """ - if not self.current_message: + current_message = self.current_message + if current_message is None: + if self.unread_messages: + current_message = self.unread_messages[-1] + elif self.history_messages: + current_message = self.history_messages[-1] + elif self.message_cache: + current_message = self.message_cache[-1] + + if not current_message: return False if not types: # 如果没有指定类型要求，默认为支持 return True - # 从 extra 字段中获取 format_info - format_info = self.current_message.extra.get("format_info", {}) - accept_format = format_info.get("accept_format", []) + # 从 extra 字段中获取 format_info。 + # 兼容历史消息：未声明 format_info/accept_format 时，保持“不过滤”行为； + # 一旦显式声明 accept_format，即按声明严格校验。 + format_info = current_message.extra.get("format_info") + if not isinstance(format_info, dict): + return True + + accept_format = format_info.get("accept_format") + if accept_format is None: + return True # 确保 accept_format 是列表类型 if isinstance(accept_format, str): @@ -135,9 +154,9 @@ def check_types(self, types: list[str]) -> bool: list(accept_format) if hasattr(accept_format, "__iter__") else [] ) - # 如果没有 accept_format，默认支持所有类型 + # 显式声明为空列表时，表示当前上下文不支持任何外发类型。 if not accept_format: - return True + return False # 检查所有请求的类型是否都被支持 for requested_type in types: diff --git a/src/core/transport/distribution/stream_loop_manager.py b/src/core/transport/distribution/stream_loop_manager.py index 0f78882f..054aee07 100644 --- a/src/core/transport/distribution/stream_loop_manager.py +++ b/src/core/transport/distribution/stream_loop_manager.py @@ -552,13 +552,21 @@ def _wait_state_check(self, stream_id: str, context: "StreamContext") -> bool: ) else: # Wait(seconds): 到达时间阈值后恢复 - if now < yielded_at + float(wait_time): - return False - self._pending_wait_resume_events[stream_id] = WaitResumeEvent( - source="timer", - wait_time=wait_time, - unread_count=max(0, unread_count_now - unread_count_at_yield), - ) + unread_delta = max(0, unread_count_now - unread_count_at_yield) + if unread_count_now > unread_count_at_yield: + self._pending_wait_resume_events[stream_id] = WaitResumeEvent( + source="message", + wait_time=wait_time, + unread_count=unread_delta, + ) + else: + if now < yielded_at + float(wait_time): + return False + self._pending_wait_resume_events[stream_id] = WaitResumeEvent( + source="timer", + wait_time=wait_time, + unread_count=unread_delta, + ) elif isinstance(last_yield, Stop): # Stop(seconds): 冷却结束且出现新未读消息时恢复 diff --git a/src/core/transport/message_receive/converter.py b/src/core/transport/message_receive/converter.py index 7ba2fc6b..c46e11bb 100644 --- a/src/core/transport/message_receive/converter.py +++ b/src/core/transport/message_receive/converter.py @@ -32,6 +32,33 @@ _MAX_NESTING_DEPTH: int = 5 +def _merge_message_extra_with_format_info( + message_info: MessageInfoPayload, + envelope: MessageEnvelope | None = None, +) -> dict[str, Any]: + """合并 extra 与独立声明的 format_info。 + + 兼容不同适配器/MessageBuilder 的写法： + - `message_info["extra"]["format_info"]` + - `message_info["format_info"]` + - `envelope["format_info"]` + """ + + raw_extra = message_info.get("extra") or {} + extra_data = dict(raw_extra) if isinstance(raw_extra, dict) else {} + + format_info = message_info.get("format_info") + if not isinstance(format_info, dict) and envelope is not None: + envelope_format_info = envelope.get("format_info") + if isinstance(envelope_format_info, dict): + format_info = envelope_format_info + + if isinstance(format_info, dict) and "format_info" not in extra_data: + extra_data["format_info"] = format_info + + return extra_data + + # ────────────────────────────────────────────── # 段解析返回结构 # ────────────────────────────────────────────── @@ -158,7 +185,7 @@ async def envelope_to_message(self, envelope: MessageEnvelope) -> Message: # 提取 extra 元数据 # any 附加字段允许上层扩展，直接透传到 Message 对象 - extra_data = message_info.get("extra") or {} + extra_data = _merge_message_extra_with_format_info(message_info, envelope) return Message( message_id=message_info.get("message_id", ""), diff --git a/src/core/transport/message_receive/receiver.py b/src/core/transport/message_receive/receiver.py index 08c313e8..2e2869a7 100644 --- a/src/core/transport/message_receive/receiver.py +++ b/src/core/transport/message_receive/receiver.py @@ -23,6 +23,9 @@ from src.core.components.types import EventType from src.core.models.message import Message, MessageType from src.core.transport.message_receive.converter import MessageConverter +from src.core.transport.message_receive.converter import ( + _merge_message_extra_with_format_info, +) from src.core.transport.message_receive.utils import ( extract_stream_id, infer_chat_type, @@ -259,7 +262,7 @@ async def _handle_other( # processed 非空 → 构建简化 Message 并触发 ON_MESSAGE_RECEIVED msg_info = envelope.get("message_info", {}) user_info = msg_info.get("user_info") or {} - extra_data = msg_info.get("extra") or {} + extra_data = _merge_message_extra_with_format_info(msg_info, envelope) simple_message = Message( message_id=msg_info.get("message_id", ""), diff --git a/src/core/utils/message_format.py b/src/core/utils/message_format.py new file mode 100644 index 00000000..cf73a69c --- /dev/null +++ b/src/core/utils/message_format.py @@ -0,0 +1,44 @@ +"""消息格式信息工具。""" + +from __future__ import annotations + +from collections.abc import Iterable + + +def _normalize_formats( + formats: list[str] | tuple[str, ...] | Iterable[str] | None, +) -> list[str]: + """规范化格式列表。""" + + if formats is None: + return [] + + if isinstance(formats, str): + formats = [formats] + + normalized_formats: list[str] = [] + for item in formats: + normalized_item = str(item).strip() + if normalized_item: + normalized_formats.append(normalized_item) + return normalized_formats + + +def build_format_info( + *, + content_format: list[str] | tuple[str, ...] | Iterable[str] | None = None, + accept_format: list[str] | tuple[str, ...] | Iterable[str] | None, +) -> dict[str, list[str]] | None: + """构造标准化的 format_info。 + + `accept_format` 必须由具体适配器或调用方显式声明；核心不负责按平台推断。 + 当 `accept_format` 为 `None` 时返回 `None`，表示当前消息未声明外发能力。 + """ + + if accept_format is None: + return None + + return { + "content_format": _normalize_formats(content_format), + "accept_format": _normalize_formats(accept_format), + } diff --git a/src/kernel/llm/__init__.py b/src/kernel/llm/__init__.py index f1fb4b1a..d9179937 100644 --- a/src/kernel/llm/__init__.py +++ b/src/kernel/llm/__init__.py @@ -15,6 +15,7 @@ from .context import LLMContextManager, ReminderSourceSpec from .request import LLMRequest from .response import LLMResponse +from .model_client import StreamEvent from .embedding_request import EmbeddingRequest from .embedding_response import EmbeddingResponse from .rerank_request import RerankRequest @@ -77,6 +78,7 @@ "LLMContextManager", "ReminderSourceSpec", "LLMResponse", + "StreamEvent", "EmbeddingResponse", "RerankResponse", "RerankItem", diff --git a/src/kernel/llm/response.py b/src/kernel/llm/response.py index 982f56b5..5eb7f647 100644 --- a/src/kernel/llm/response.py +++ b/src/kernel/llm/response.py @@ -296,6 +296,42 @@ async def stream_with_callback( raise result.error return self.message or "" + async def stream_events_with_callback( + self, + on_event: Callable[[StreamEvent], Awaitable[None]], + ) -> str: + """以事件粒度消费流式响应，每个 ``StreamEvent`` 都会交给回调。 + + 与 ``stream_with_callback`` 的区别是回调接收完整的 ``StreamEvent``， + 包含 ``tool_call_id`` / ``tool_name`` / ``tool_args_delta`` 等字段， + 而不仅仅是文本增量。 + """ + if self._consumed: + raise LLMResponseConsumedError("Response has already been consumed.") + self._consumed = True + + if self._stream is None: + self._maybe_apply_tool_call_compat() + self._maybe_append_response_to_context() + return self.message or "" + + reducer = LLMStreamReducer() + stream_error: Exception | None = None + + try: + async for event in self._stream: + reducer.apply(event) + await on_event(event) + except Exception as exc: + stream_error = exc + + self._apply_stream_result(reducer.finalize(stream_error)) + + if stream_error is not None: + raise stream_error + + return self.message or "" + async def stream_with_buffer(self, buffer_size: int = 10) -> AsyncIterator[str]: if self._consumed: raise LLMResponseConsumedError("Response has already been consumed.") diff --git a/src/kernel/llm/stats/collector.py b/src/kernel/llm/stats/collector.py index 108bbaae..5f5bdfb4 100644 --- a/src/kernel/llm/stats/collector.py +++ b/src/kernel/llm/stats/collector.py @@ -588,6 +588,35 @@ async def get_recent( sql += " ORDER BY timestamp DESC LIMIT ? OFFSET ?" return await self._db.execute_read(sql, (limit, offset)) + async def list_requests_by_time_range( + self, + start_ts: float, + end_ts: float, + limit: int = 1000, + offset: int = 0, + ) -> list[dict[str, Any]]: + """获取指定 Unix 时间戳范围内的请求记录明细。 + + Args: + start_ts: 起始 Unix 时间戳，单位为秒。 + end_ts: 结束 Unix 时间戳，单位为秒。 + limit: 返回数量上限。 + offset: 偏移量。 + + Returns: + 指定时间范围内按时间倒序排列的请求记录字典列表。 + """ + if not self.enabled: + return [] + + sql = """ + SELECT * FROM llm_requests + WHERE timestamp >= ? AND timestamp <= ? + ORDER BY timestamp DESC + LIMIT ? OFFSET ? + """ + return await self._db.execute_read(sql, (start_ts, end_ts, limit, offset)) + # ------------------------------------------------------------------ # 查询 — 时间范围统计 # ------------------------------------------------------------------ diff --git a/test/app/plugin_system/api/test_agent_api.py b/test/app/plugin_system/api/test_agent_api.py index 04573ed2..76f58368 100644 --- a/test/app/plugin_system/api/test_agent_api.py +++ b/test/app/plugin_system/api/test_agent_api.py @@ -32,7 +32,7 @@ class MockAgent(BaseAgent): chatter_allow = ["demo_chatter"] chat_type = ChatType.PRIVATE associated_platforms = ["test_platform"] - associated_types = [] + associated_types = ["text"] dependencies = [] usables = [MockPrivateTool] @@ -87,6 +87,7 @@ def test_get_agents_for_chat_filters_by_chat_type( class GroupAgent(BaseAgent): agent_name = "group_agent" chat_type = ChatType.GROUP + associated_types = ["text"] async def execute(self): return True, "ok" @@ -94,6 +95,7 @@ async def execute(self): class PrivateAgent(BaseAgent): agent_name = "private_agent" chat_type = ChatType.PRIVATE + associated_types = ["text"] async def execute(self): return True, "ok" @@ -122,6 +124,7 @@ class AllowedAgent(BaseAgent): agent_name = "allowed_agent" chatter_allow = ["my_chatter"] chat_type = ChatType.ALL + associated_types = ["text"] async def execute(self): return True, "ok" @@ -130,6 +133,7 @@ class NotAllowedAgent(BaseAgent): agent_name = "not_allowed_agent" chatter_allow = ["other_chatter"] chat_type = ChatType.ALL + associated_types = ["text"] async def execute(self): return True, "ok" @@ -158,6 +162,7 @@ class PlatformAgent(BaseAgent): agent_name = "platform_agent" associated_platforms = ["test_platform"] chat_type = ChatType.ALL + associated_types = ["text"] async def execute(self): return True, "ok" @@ -166,6 +171,7 @@ class OtherAgent(BaseAgent): agent_name = "other_agent" associated_platforms = ["other_platform"] chat_type = ChatType.ALL + associated_types = ["text"] async def execute(self): return True, "ok" @@ -304,6 +310,7 @@ async def test_execute_agent_calls_execute(monkeypatch: pytest.MonkeyPatch) -> N class TestAgent(BaseAgent): agent_name = "test_agent" + associated_types = ["text"] async def execute(self, **kwargs): nonlocal executed_kwargs @@ -337,6 +344,7 @@ async def test_execute_agent_keeps_declared_reason( class ReasonAgent(BaseAgent): agent_name = "reason_agent" + associated_types = ["text"] async def execute(self, task: str, reason: str) -> tuple[bool, str]: return True, f"{task}:{reason}" @@ -453,6 +461,7 @@ async def test_execute_agent_usable_calls_execute_local_usable( class TestAgent(BaseAgent): agent_name = "test_agent" + associated_types = ["text"] async def execute(self): return True, "ok" diff --git a/test/app/plugin_system/api/test_chat_api.py b/test/app/plugin_system/api/test_chat_api.py index de734c7d..041403ae 100644 --- a/test/app/plugin_system/api/test_chat_api.py +++ b/test/app/plugin_system/api/test_chat_api.py @@ -237,3 +237,53 @@ def get_chatter_by_stream(self, stream_id: str) -> object | None: result = chat_api.get_chatter_by_stream("stream_1") assert result is not None + + +def test_bind_chatter_for_stream_requires_stream_id() -> None: + """stream_id 为空时应抛出 ValueError。""" + with pytest.raises(ValueError, match="stream_id 不能为空"): + chat_api.bind_chatter_for_stream("", cast(BaseChatter, object())) + + +def test_bind_chatter_for_stream_requires_chatter() -> None: + """chatter 为空时应抛出 ValueError。""" + with pytest.raises(ValueError, match="chatter 不能为空"): + chat_api.bind_chatter_for_stream("stream_1", cast(BaseChatter, None)) + + +def test_bind_chatter_for_stream_delegates(monkeypatch: pytest.MonkeyPatch) -> None: + """bind_chatter_for_stream 应委托给 ChatterManager。""" + captured: dict[str, object] = {} + chatter = cast(BaseChatter, object()) + + class _FakeManager: + def bind_chatter_for_stream(self, stream_id: str, chatter_obj: object) -> None: + captured["stream_id"] = stream_id + captured["chatter"] = chatter_obj + + monkeypatch.setattr(chat_api, "_get_chatter_manager", lambda: _FakeManager()) + + chat_api.bind_chatter_for_stream("stream_1", chatter) + + assert captured["stream_id"] == "stream_1" + assert captured["chatter"] is chatter + + +def test_restore_stream_to_default_requires_stream_id() -> None: + """stream_id 为空时应抛出 ValueError。""" + with pytest.raises(ValueError, match="stream_id 不能为空"): + chat_api.restore_stream_to_default("") + + +def test_restore_stream_to_default_delegates(monkeypatch: pytest.MonkeyPatch) -> None: + """restore_stream_to_default 应委托给 ChatterManager。""" + + class _FakeManager: + def restore_stream_to_default(self, stream_id: str) -> bool: + return stream_id == "stream_1" + + monkeypatch.setattr(chat_api, "_get_chatter_manager", lambda: _FakeManager()) + + result = chat_api.restore_stream_to_default("stream_1") + + assert result is True diff --git a/test/app/runtime/test_plugin_market_sync.py b/test/app/runtime/test_plugin_market_sync.py index 0a20290e..3cf54144 100644 --- a/test/app/runtime/test_plugin_market_sync.py +++ b/test/app/runtime/test_plugin_market_sync.py @@ -142,6 +142,49 @@ def handler(request: httpx.Request) -> httpx.Response: assert (tmp_path / "dep_plugin-0.4.0.mfp").exists() +@pytest.mark.asyncio +async def test_sync_updates_installed_market_mfp_even_when_not_subscribed(tmp_path: Path) -> None: + """本地已安装的市场 .mfp 即使未订阅，也应在启用自动更新时升级。""" + + _write_local_mfp(tmp_path / "main_plugin-1.0.0.mfp", "main_plugin", "1.0.0") + + def handler(request: httpx.Request) -> httpx.Response: + if request.url.path == "/api/v1/machine/authors/mock-author/subscriptions": + return httpx.Response(200, json={"author_id": "mock-author", "items": [], "total": 0}) + if request.url.path == "/api/v1/plugins/main_plugin": + return httpx.Response(200, json={"plugin_id": "main_plugin", "status": "published"}) + if request.url.path == "/api/v1/plugins/main_plugin/dependencies": + return httpx.Response(200, json={"plugin_id": "main_plugin", "items": []}) + if request.url.path == "/api/v1/plugins/main_plugin/install": + return httpx.Response( + 200, + json={ + "plugin": {"plugin_id": "main_plugin"}, + "version": { + "version": "1.2.0", + "asset_name": "main_plugin-1.2.0.mfp", + "asset_download_url": "https://market.example/downloads/main_plugin-1.2.0.mfp", + }, + }, + ) + if request.url.path == "/downloads/main_plugin-1.2.0.mfp": + return httpx.Response(200, content=_build_mfp_bytes("main_plugin", "1.2.0")) + raise AssertionError(f"unexpected request: {request.method} {request.url}") + + service = PluginMarketSyncService( + config=_make_config(), + plugins_dir=str(tmp_path), + transport=httpx.MockTransport(handler), + ) + + report = await service.sync() + + assert report.installed == 0 + assert report.updated == 1 + assert not (tmp_path / "main_plugin-1.0.0.mfp").exists() + assert (tmp_path / "main_plugin-1.2.0.mfp").exists() + + @pytest.mark.asyncio async def test_strict_mode_only_removes_market_published_mfp_plugins(tmp_path: Path) -> None: """严格模式只删除已收录于市场的未订阅 mfp，不删除本地目录插件或无市场记录插件。""" @@ -175,4 +218,4 @@ def handler(request: httpx.Request) -> httpx.Response: assert report.removed == 1 assert not (tmp_path / "market_plugin-1.0.0.mfp").exists() assert (tmp_path / "orphan_plugin-1.0.0.mfp").exists() - assert (tmp_path / "folder_plugin").exists() \ No newline at end of file + assert (tmp_path / "folder_plugin").exists() diff --git a/test/core/managers/test_action_manager.py b/test/core/managers/test_action_manager.py index ec6288ed..72035787 100644 --- a/test/core/managers/test_action_manager.py +++ b/test/core/managers/test_action_manager.py @@ -30,6 +30,7 @@ class TestAction(BaseAction): signature = "test_plugin:action:test_action" description = "Test action" supported_chat_types = [ChatType.ALL] + associated_types = ["text"] async def execute(self, **kwargs: Any) -> dict[str, Any]: """执行 Action。""" diff --git a/test/core/managers/test_adapter_manager_subprocess.py b/test/core/managers/test_adapter_manager_subprocess.py index 86c114a5..05ebadba 100644 --- a/test/core/managers/test_adapter_manager_subprocess.py +++ b/test/core/managers/test_adapter_manager_subprocess.py @@ -42,7 +42,7 @@ class DummyAdapter: class DummyRegistry: def get_by_type(self, _component_type): - return {"napcat:adapter:napcat_adapter": DummyAdapter} + return {"onebot:adapter:onebot_adapter": DummyAdapter} class DummyTaskManager: def __init__(self) -> None: @@ -69,4 +69,4 @@ def create_task(self, coro, name=None, daemon=False, timeout=None, group_name=No await task_manager.tasks[0] - start_adapter.assert_awaited_once_with("napcat:adapter:napcat_adapter") + start_adapter.assert_awaited_once_with("onebot:adapter:onebot_adapter") diff --git a/test/core/managers/test_chatter_manager.py b/test/core/managers/test_chatter_manager.py index 865d105a..374f3d38 100644 --- a/test/core/managers/test_chatter_manager.py +++ b/test/core/managers/test_chatter_manager.py @@ -184,6 +184,26 @@ def test_register_active_chatter(self) -> None: assert "stream_123" in manager._active_chatters assert manager._active_chatters["stream_123"] == mock_chatter + + def test_bind_chatter_for_stream(self) -> None: + """显式绑定应覆盖当前 stream 的活跃 chatter。""" + manager = ChatterManager() + mock_chatter = MagicMock(spec=TestChatter) + + manager.bind_chatter_for_stream("stream_bind", mock_chatter) + + assert manager.get_chatter_by_stream("stream_bind") == mock_chatter + + def test_restore_stream_to_default(self) -> None: + """恢复默认选择应移除显式绑定。""" + manager = ChatterManager() + mock_chatter = MagicMock(spec=TestChatter) + manager.bind_chatter_for_stream("stream_bind", mock_chatter) + + restored = manager.restore_stream_to_default("stream_bind") + + assert restored is True + assert manager.get_chatter_by_stream("stream_bind") is None def test_unregister_active_chatter_exists(self) -> None: """测试注销已存在的活跃 Chatter。""" diff --git a/test/core/managers/test_plugin_manager_configs.py b/test/core/managers/test_plugin_manager_configs.py index c4fbccff..04255790 100644 --- a/test/core/managers/test_plugin_manager_configs.py +++ b/test/core/managers/test_plugin_manager_configs.py @@ -190,6 +190,7 @@ async def test_register_components_supports_agent_type() -> None: class _TestAgent(BaseAgent): agent_name = "planner" agent_description = "planner agent" + associated_types = ["text"] async def execute(self, task: str) -> tuple[bool, str]: return True, task diff --git a/test/core/managers/test_plugin_manager_events.py b/test/core/managers/test_plugin_manager_events.py index 0597a398..818b4763 100644 --- a/test/core/managers/test_plugin_manager_events.py +++ b/test/core/managers/test_plugin_manager_events.py @@ -23,6 +23,7 @@ class _TestAction(BaseAction): """测试用的 Action 组件。""" action_name = "test_action" + associated_types = ["text"] async def execute(self, *args, **kwargs): return {"success": True} diff --git a/test/core/managers/test_plugin_manager_telemetry.py b/test/core/managers/test_plugin_manager_telemetry.py index 574fd64c..edc3d7a5 100644 --- a/test/core/managers/test_plugin_manager_telemetry.py +++ b/test/core/managers/test_plugin_manager_telemetry.py @@ -26,6 +26,7 @@ class _TelemetryAction(BaseAction): """测试用 Action。""" action_name = "telemetry_action" + associated_types = ["text"] async def execute(self, *args, **kwargs): return {"success": True} @@ -165,4 +166,4 @@ async def test_unload_plugin_records_plugin_unloaded_event() -> None: assert unloaded_rows attributes = _decode_attributes(unloaded_rows[0]) assert attributes["plugin_version"] == "1.2.3" - assert attributes["component_count"] == 1 \ No newline at end of file + assert attributes["component_count"] == 1 diff --git a/test/core/models/test_stream.py b/test/core/models/test_stream.py index 81ee7b18..c72d0d68 100644 --- a/test/core/models/test_stream.py +++ b/test/core/models/test_stream.py @@ -41,6 +41,7 @@ def test_add_unread_message(self): assert len(context.unread_messages) == 1 assert context.unread_messages[0] == mock_message + assert context.current_message == mock_message def test_add_multiple_unread_messages(self): """测试添加多个未读消息。""" @@ -108,8 +109,8 @@ def test_check_types_with_empty_accept_format(self): context.current_message = mock_message result = context.check_types(["text", "image"]) - # 空 accept_format 默认支持所有类型 - assert result is True + # 显式空 accept_format 表示不支持任何外发类型 + assert result is False def test_check_types_with_string_accept_format(self): """测试字符串类型的 accept_format。""" @@ -165,6 +166,18 @@ def test_check_types_without_format_info(self): # 没有 format_info 时默认支持所有类型 assert result is True + def test_check_types_falls_back_to_latest_unread_message(self): + """current_message 为空时应回退到最后一条未读消息。""" + context = StreamContext(stream_id="test") + context.current_message = None + + mock_message = MagicMock() + mock_message.extra = {"format_info": {"accept_format": ["text", "emoji"]}} + context.unread_messages.append(mock_message) + + assert context.check_types(["text"]) is True + assert context.check_types(["voice"]) is False + def test_message_cache_initialization(self): """测试消息缓存初始化。""" context = StreamContext(stream_id="test") diff --git a/test/core/test_components_base_action.py b/test/core/test_components_base_action.py index 5737fa90..7bbedc8c 100644 --- a/test/core/test_components_base_action.py +++ b/test/core/test_components_base_action.py @@ -17,7 +17,7 @@ class ConcreteAction(BaseAction): chatter_allow = [] chat_type = ChatType.ALL associated_platforms = [] - associated_types = [] + associated_types = ["text"] async def execute(self, message: str) -> tuple[bool, str]: """执行测试动作。""" @@ -347,3 +347,21 @@ async def execute(self, data: str) -> tuple[bool, str]: assert action.associated_platforms == ["telegram", "discord"] assert action.associated_types == ["text", "image"] assert action.dependencies == ["other_plugin:tool:helper"] + + +class TestActionAssociatedTypesValidation: + """测试 associated_types 的强校验。""" + + def test_validate_associated_types_requires_non_empty_list(self): + """associated_types 为空时应抛出异常。""" + + class InvalidAction(BaseAction): + action_name = "invalid_action" + action_description = "invalid" + associated_types = [] + + async def execute(self, data: str) -> tuple[bool, str]: + return True, data + + with pytest.raises(ValueError, match="associated_types 必须是非空 list\\[str\\]"): + InvalidAction.validate_associated_types() diff --git a/test/core/test_components_base_agent.py b/test/core/test_components_base_agent.py index 40c4b9b1..19ef0f6d 100644 --- a/test/core/test_components_base_agent.py +++ b/test/core/test_components_base_agent.py @@ -49,7 +49,7 @@ class ConcreteAgent(BaseAgent): chatter_allow = [] chat_type = ChatType.ALL associated_platforms = [] - associated_types = [] + associated_types = ["text"] dependencies = [] usables = [PrivateTool] @@ -289,6 +289,7 @@ async def test_execute_local_usable_action_path(self, mock_plugin): class PrivateAction(BaseAction): action_name = "private_action" action_description = "private action" + associated_types = ["text"] async def execute(self, content: str) -> tuple[bool, str]: return True, f"action:{content}" @@ -310,3 +311,35 @@ class ActionAgent(ConcreteAgent): assert success is True assert result == "action:hello" + + def test_validate_associated_types_requires_non_empty_list(self): + """Agent 的 associated_types 为空时应抛出异常。""" + + class InvalidAgent(BaseAgent): + agent_name = "invalid_agent" + agent_description = "invalid" + associated_types = [] + + async def execute(self, task: str) -> tuple[bool, str]: + return True, task + + with pytest.raises(ValueError, match="associated_types 必须是非空 list\\[str\\]"): + InvalidAgent.validate_associated_types() + + def test_get_local_usables_rejects_invalid_local_action(self): + """Agent 私有 Action 未声明 associated_types 时应抛出异常。""" + from src.core.components.base.action import BaseAction + + class InvalidPrivateAction(BaseAction): + action_name = "invalid_private_action" + action_description = "invalid private action" + associated_types = [] + + async def execute(self, content: str) -> tuple[bool, str]: + return True, content + + class InvalidUsableAgent(ConcreteAgent): + usables = [InvalidPrivateAction] + + with pytest.raises(ValueError, match="associated_types 必须是非空 list\\[str\\]"): + InvalidUsableAgent.get_local_usables() diff --git a/test/core/test_components_base_chatter.py b/test/core/test_components_base_chatter.py index 90180c46..40c5623b 100644 --- a/test/core/test_components_base_chatter.py +++ b/test/core/test_components_base_chatter.py @@ -8,6 +8,7 @@ import pytest from src.core.components.base.chatter import BaseChatter, ChatterResult, Failure, Success, Wait +from src.core.components.base.action import BaseAction from src.core.components.base.agent import BaseAgent from src.core.components.base.tool import BaseTool from src.core.components.types import ChatType @@ -324,6 +325,40 @@ async def execute(self, *args, **kwargs): assert OpenTool in result assert RejectedTool not in result + @pytest.mark.asyncio + async def test_modify_llm_usables_filters_unsupported_action_types(self): + """当前流不支持的 Action 类型应被剔除。""" + + class EmojiAction(BaseAction): + action_name = "emoji_action" + action_description = "emoji" + associated_types = ["emoji"] + + async def execute(self, content: str) -> tuple[bool, str]: + return True, content + + chatter_plugin = MagicMock() + chatter = ConcreteChatter("stream_123", chatter_plugin) + + current_message = MagicMock() + current_message.extra = { + "format_info": { + "accept_format": ["text"], + } + } + + mock_stream = MagicMock() + mock_stream.stream_id = "stream_123" + mock_stream.context = MagicMock() + mock_stream.context.current_message = current_message + + with patch("src.core.components.base.chatter.get_stream_manager") as mock_sm: + mock_sm.return_value.get_or_create_stream = AsyncMock(return_value=mock_stream) + + result = await chatter.modify_llm_usables([EmojiAction]) + + assert EmojiAction not in result + @pytest.mark.asyncio async def test_exec_llm_usable_uses_owner_plugin_instance(self): """测试执行跨插件 Tool 时向管理器传入所属插件实例。""" @@ -376,6 +411,7 @@ class LocalAgent(BaseAgent): agent_name = "local_agent" agent_description = "local agent" _signature_ = "plugin_b:agent:local_agent" + associated_types = ["text"] async def execute(self, query: str) -> tuple[bool, str]: return True, f"agent:{query}" diff --git a/test/core/transport/test_message_converter_private_target.py b/test/core/transport/test_message_converter_private_target.py index f9159049..0d12c906 100644 --- a/test/core/transport/test_message_converter_private_target.py +++ b/test/core/transport/test_message_converter_private_target.py @@ -65,3 +65,37 @@ async def test_message_to_envelope_private_target_prefers_stream_person(monkeypa assert user_info.get("user_nickname") == "NeoBot" fake_stream_manager.get_stream_info.assert_awaited_once_with("stream-private-1") helper.person_crud.get_by.assert_awaited_once_with(person_id="hash_person_888") + + +@pytest.mark.asyncio +async def test_envelope_to_message_merges_standalone_format_info() -> None: + """独立声明的 format_info 应合并进 Message.extra。""" + + converter = MessageConverter() + + envelope = { + "direction": "incoming", + "message_info": { + "message_id": "m3", + "platform": "qq", + "user_info": { + "platform": "qq", + "user_id": "u1", + "user_nickname": "Alice", + }, + "format_info": { + "content_format": ["text"], + "accept_format": ["text", "emoji", "voice"], + }, + }, + "message_segment": [{"type": "text", "data": "hello"}], + } + + message = await converter.envelope_to_message(envelope) + + assert message.extra["format_info"]["content_format"] == ["text"] + assert message.extra["format_info"]["accept_format"] == [ + "text", + "emoji", + "voice", + ] diff --git a/test/core/transport/test_stream_loop_wait_state.py b/test/core/transport/test_stream_loop_wait_state.py index 44524d03..39b4fd47 100644 --- a/test/core/transport/test_stream_loop_wait_state.py +++ b/test/core/transport/test_stream_loop_wait_state.py @@ -7,7 +7,7 @@ from src.core.models.message import Message from src.core.models.stream import StreamContext from src.core.transport.distribution.stream_loop_manager import StreamLoopManager -from src.core.components.base.chatter import Wait, Stop +from src.core.components.base.chatter import Wait, Stop, WaitResumeEvent def test_wait_state_check_requires_new_message_after_stop() -> None: @@ -195,15 +195,27 @@ def test_wait_state_check_wait_for_messages_only() -> None: assert manager._wait_state_check(stream_id, context_non_empty) is True -def test_wait_state_check_wait_for_time_only() -> None: +def test_wait_state_check_wait_for_time_or_new_message() -> None: """Wait(seconds) 语义：仅时间到达即可恢复。""" manager = StreamLoopManager() stream_id = "stream-wait-time" manager._wait_states[stream_id] = (Wait(time=4102444800.0), 0.0, 0) # 2100-01-01 - context_any = StreamContext(stream_id=stream_id) - assert manager._wait_state_check(stream_id, context_any) is False + context_with_unread = StreamContext(stream_id=stream_id) + context_with_unread.unread_messages = cast(list, ["m1"]) + assert manager._wait_state_check(stream_id, context_with_unread) is True + assert manager.take_wait_resume_event(stream_id) == WaitResumeEvent( + source="message", + wait_time=4102444800.0, + unread_count=1, + ) manager._wait_states[stream_id] = (Wait(time=0.0), time.time(), 0) + context_any = StreamContext(stream_id=stream_id) assert manager._wait_state_check(stream_id, context_any) is True + assert manager.take_wait_resume_event(stream_id) == WaitResumeEvent( + source="timer", + wait_time=0.0, + unread_count=0, + ) diff --git a/test/kernel/llm/test_response_stream_events.py b/test/kernel/llm/test_response_stream_events.py new file mode 100644 index 00000000..af91e2c0 --- /dev/null +++ b/test/kernel/llm/test_response_stream_events.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +import pytest + +from src.kernel.llm import LLMPayload, LLMRequest, ROLE, StreamEvent, Text +from src.kernel.llm.exceptions import LLMResponseConsumedError +from src.kernel.llm.response import LLMResponse + + +def _model_set() -> list[dict[str, object]]: + return [ + { + "api_provider": "openai", + "base_url": "https://api.openai.com/v1", + "model_identifier": "gpt-test", + "api_key": "sk-test", + "client_type": "openai", + "max_retry": 1, + "timeout": 5.0, + "retry_interval": 0.0, + "price_in": 0.0, + "cache_hit_price_in": 0.0, + "price_out": 0.0, + "temperature": 0.0, + "max_tokens": 256, + "max_context": 8192, + "tool_call_compat": False, + "extra_params": {}, + } + ] + + +@pytest.mark.asyncio +async def test_stream_events_with_callback_collects_final_message_and_tool_calls() -> None: + events = [ + StreamEvent(tool_call_id="call_1", tool_name="action-say"), + StreamEvent(tool_call_id="call_1", tool_args_delta='{"content":"你好。"}'), + StreamEvent(text_delta="done"), + ] + + async def _stream(): + for event in events: + yield event + + seen: list[StreamEvent] = [] + + async def _on_event(event: StreamEvent) -> None: + seen.append(event) + + request = LLMRequest(_model_set(), request_name="test") + response = LLMResponse( + _stream=_stream(), + _upper=request, + _auto_append_response=False, + payloads=[LLMPayload(ROLE.USER, Text("hi"))], + model_set=request.model_set, + ) + + message = await response.stream_events_with_callback(_on_event) + + assert message == "done" + assert seen == events + assert response.message == "done" + assert response.call_list is not None + assert len(response.call_list) == 1 + assert response.call_list[0].name == "action-say" + assert response.call_list[0].args == {"content": "你好。"} + + +@pytest.mark.asyncio +async def test_stream_events_with_callback_is_single_consumer() -> None: + request = LLMRequest(_model_set(), request_name="test") + response = LLMResponse( + _stream=None, + _upper=request, + _auto_append_response=False, + payloads=[], + model_set=request.model_set, + message="ok", + ) + + async def _noop(_event: StreamEvent) -> None: + return None + + await response.stream_events_with_callback(_noop) + + with pytest.raises(LLMResponseConsumedError): + await response.stream_events_with_callback(_noop) diff --git a/test/plugins/test_asr_adapter_playback.py b/test/plugins/test_asr_adapter_playback.py new file mode 100644 index 00000000..06fe79f8 --- /dev/null +++ b/test/plugins/test_asr_adapter_playback.py @@ -0,0 +1,400 @@ +from __future__ import annotations + +import asyncio +from contextlib import suppress +from types import SimpleNamespace +from typing import Any + +import numpy as np +import pytest + +from plugins.asr_adapter.config import AsrAdapterConfig +from plugins.asr_adapter.src.runtime import AsrAdapterRuntimeMixin +from src.core.models.stream import ChatStream + + +class _RuntimeBase: + def __init__(self, *args: Any, **kwargs: Any) -> None: + _ = args, kwargs + + +class _Runtime(AsrAdapterRuntimeMixin, _RuntimeBase): + plugin = None + core_sink = None + platform = "local_asr" + + +class _FakeCoreSink: + def __init__(self) -> None: + self.messages: list[dict[str, Any]] = [] + + async def send(self, message: dict[str, Any]) -> None: + self.messages.append(message) + + +class _FakeStreamManager: + def __init__(self, streams: list[ChatStream] | None = None) -> None: + self._streams = {stream.stream_id: stream for stream in streams or []} + self.added_messages: list[Any] = [] + + async def activate_stream(self, stream_id: str) -> ChatStream | None: + return self._streams.get(stream_id) + + async def get_or_create_stream(self, **kwargs: Any) -> ChatStream: + stream_id = kwargs["stream_id"] + stream = ChatStream( + stream_id=stream_id, + platform=kwargs.get("platform", ""), + chat_type=kwargs.get("chat_type", "private"), + stream_name=kwargs.get("group_name", ""), + ) + self._streams[stream.stream_id] = stream + return stream + + async def add_message(self, message: Any) -> Any: + self.added_messages.append(message) + stream = self._streams[message.stream_id] + stream.context.add_unread_message(message) + return message + + +class _FakeStreamLoopManager: + def __init__(self) -> None: + self.is_running = True + self.started_stream_ids: list[str] = [] + + async def start_stream_loop(self, stream_id: str, force: bool = False) -> bool: + _ = force + self.started_stream_ids.append(stream_id) + return True + + +class _FakeUserQueryHelper: + def __init__(self) -> None: + self.calls: list[dict[str, Any]] = [] + + async def update_person_info(self, **kwargs: Any) -> None: + self.calls.append(kwargs) + + +class _FakeAdapterManager: + def __init__(self, adapters: dict[str, Any] | None = None) -> None: + self._adapters = adapters or {} + + def get_all_adapters(self) -> dict[str, Any]: + return self._adapters.copy() + + +class _FakeSamples: + def __init__(self, length: int) -> None: + self.size = length + self.shape = (length,) + + +def test_prepare_playback_samples_duplicates_mono_to_stereo() -> None: + runtime = _Runtime() + config = AsrAdapterConfig() + samples = np.array([0.1, -0.2, 0.3], dtype=np.float32) + + prepared = runtime._prepare_playback_samples(samples, config) + + assert prepared.shape == (3, 2) + assert np.array_equal(prepared[:, 0], samples) + assert np.array_equal(prepared[:, 1], samples) + + +def test_prepare_playback_samples_keeps_mono_when_disabled() -> None: + runtime = _Runtime() + config = AsrAdapterConfig() + config.playback.duplicate_mono_to_stereo = False + samples = np.array([0.1, -0.2, 0.3], dtype=np.float32) + + prepared = runtime._prepare_playback_samples(samples, config) + + assert prepared is samples + + +@pytest.mark.asyncio +async def test_non_blocking_playback_queues_without_blocking(monkeypatch: pytest.MonkeyPatch) -> None: + runtime = _Runtime() + config = AsrAdapterConfig() + config.playback.blocking = False + + fake_samples = _FakeSamples(24_000) + monkeypatch.setattr( + runtime, + "_decode_audio_samples", + lambda audio_data, _config: (fake_samples, 24_000), + ) + monkeypatch.setattr(runtime, "_normalize_output_device", lambda _device: None) + + calls: list[str] = [] + + class _SoundDevice: + @staticmethod + def play(samples: Any, samplerate: int, device: Any = None) -> None: + _ = samples, samplerate, device + calls.append("play") + + @staticmethod + def wait() -> None: + calls.append("wait") + + import sys + + monkeypatch.setitem(sys.modules, "sounddevice", _SoundDevice) + + started = asyncio.get_running_loop().time() + await runtime._play_audio_bytes(b"a", config) + await runtime._play_audio_bytes(b"b", config) + elapsed = asyncio.get_running_loop().time() - started + + assert elapsed < 0.01 + + await asyncio.wait_for(runtime._playback_queue.join(), timeout=1.0) + assert calls == ["play", "wait", "play", "wait"] + + if runtime._playback_worker_task is not None: + runtime._playback_worker_task.cancel() + with suppress(asyncio.CancelledError): + await runtime._playback_worker_task + + +@pytest.mark.asyncio +async def test_send_text_to_core_injects_into_bilibili_live_stream( + monkeypatch: pytest.MonkeyPatch, +) -> None: + runtime = _Runtime() + runtime.core_sink = _FakeCoreSink() + config = AsrAdapterConfig() + config.message.inject_stream_platform = "bilibili_live" + runtime.plugin = SimpleNamespace(config=config) + + target_stream = ChatStream( + stream_id="stream-bili", + platform="bilibili_live", + chat_type="group", + stream_name="Test Room", + ) + fake_stream_manager = _FakeStreamManager([target_stream]) + fake_stream_loop_manager = _FakeStreamLoopManager() + fake_user_query_helper = _FakeUserQueryHelper() + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: fake_stream_manager, + ) + monkeypatch.setattr( + "src.core.managers.adapter_manager.get_adapter_manager", + lambda: _FakeAdapterManager(), + ) + monkeypatch.setattr( + "src.core.transport.distribution.stream_loop_manager.get_stream_loop_manager", + lambda: fake_stream_loop_manager, + ) + monkeypatch.setattr( + "src.core.utils.user_query_helper.get_user_query_helper", + lambda: fake_user_query_helper, + ) + + await runtime._send_text_to_core("hello live", is_final=True) + + assert runtime.core_sink.messages == [] + assert len(fake_stream_manager.added_messages) == 1 + message = fake_stream_manager.added_messages[0] + assert message.platform == "bilibili_live" + assert message.chat_type == "group" + assert message.stream_id == "stream-bili" + assert message.processed_plain_text == "hello live" + assert fake_stream_loop_manager.started_stream_ids == ["stream-bili"] + assert fake_user_query_helper.calls + + +@pytest.mark.asyncio +async def test_send_text_to_core_auto_redirects_local_asr_when_bilibili_live_is_active( + monkeypatch: pytest.MonkeyPatch, +) -> None: + runtime = _Runtime() + runtime.core_sink = _FakeCoreSink() + config = AsrAdapterConfig() + runtime.plugin = SimpleNamespace(config=config) + + fake_stream_manager = _FakeStreamManager() + fake_stream_loop_manager = _FakeStreamLoopManager() + fake_user_query_helper = _FakeUserQueryHelper() + fake_adapter_manager = _FakeAdapterManager( + { + "bilibili_live_adapter:adapter:bilibili_live": SimpleNamespace( + platform="bilibili_live", + _start_resp=SimpleNamespace( + anchor_room_id=4455, + anchor_uname="Live Anchor", + ), + ) + } + ) + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: fake_stream_manager, + ) + monkeypatch.setattr( + "src.core.managers.adapter_manager.get_adapter_manager", + lambda: fake_adapter_manager, + ) + monkeypatch.setattr( + "src.core.transport.distribution.stream_loop_manager.get_stream_loop_manager", + lambda: fake_stream_loop_manager, + ) + monkeypatch.setattr( + "src.core.utils.user_query_helper.get_user_query_helper", + lambda: fake_user_query_helper, + ) + + await runtime._send_text_to_core("redirect me", is_final=True) + + expected_stream_id = ChatStream.generate_stream_id( + "bilibili_live", + group_id="4455", + ) + assert runtime.core_sink.messages == [] + assert len(fake_stream_manager.added_messages) == 1 + message = fake_stream_manager.added_messages[0] + assert message.platform == "bilibili_live" + assert message.stream_id == expected_stream_id + assert fake_stream_loop_manager.started_stream_ids == [expected_stream_id] + assert fake_user_query_helper.calls + + +@pytest.mark.asyncio +async def test_send_text_to_core_auto_resolves_bilibili_live_room_from_adapter( + monkeypatch: pytest.MonkeyPatch, +) -> None: + runtime = _Runtime() + runtime.core_sink = _FakeCoreSink() + config = AsrAdapterConfig() + config.message.inject_stream_platform = "bilibili_live" + runtime.plugin = SimpleNamespace(config=config) + + fake_stream_manager = _FakeStreamManager() + fake_stream_loop_manager = _FakeStreamLoopManager() + fake_user_query_helper = _FakeUserQueryHelper() + fake_adapter_manager = _FakeAdapterManager( + { + "bilibili_live_adapter:adapter:bilibili_live": SimpleNamespace( + platform="bilibili_live", + _start_resp=SimpleNamespace( + anchor_room_id=2233, + anchor_uname="Test Anchor", + ), + ) + } + ) + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: fake_stream_manager, + ) + monkeypatch.setattr( + "src.core.managers.adapter_manager.get_adapter_manager", + lambda: fake_adapter_manager, + ) + monkeypatch.setattr( + "src.core.transport.distribution.stream_loop_manager.get_stream_loop_manager", + lambda: fake_stream_loop_manager, + ) + monkeypatch.setattr( + "src.core.utils.user_query_helper.get_user_query_helper", + lambda: fake_user_query_helper, + ) + + await runtime._send_text_to_core("auto live", is_final=True) + + expected_stream_id = ChatStream.generate_stream_id( + "bilibili_live", + group_id="2233", + ) + assert runtime.core_sink.messages == [] + assert len(fake_stream_manager.added_messages) == 1 + message = fake_stream_manager.added_messages[0] + assert message.stream_id == expected_stream_id + assert message.platform == "bilibili_live" + assert expected_stream_id in fake_stream_manager._streams + assert fake_stream_manager._streams[expected_stream_id].stream_name == "Test Anchor" + assert fake_stream_loop_manager.started_stream_ids == [expected_stream_id] + assert fake_user_query_helper.calls + + +@pytest.mark.asyncio +async def test_send_text_to_core_falls_back_to_core_sink_when_no_target_stream( + monkeypatch: pytest.MonkeyPatch, +) -> None: + runtime = _Runtime() + runtime.core_sink = _FakeCoreSink() + config = AsrAdapterConfig() + config.message.inject_stream_platform = "bilibili_live" + runtime.plugin = SimpleNamespace(config=config) + + fake_stream_manager = _FakeStreamManager() + fake_stream_loop_manager = _FakeStreamLoopManager() + fake_user_query_helper = _FakeUserQueryHelper() + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: fake_stream_manager, + ) + monkeypatch.setattr( + "src.core.managers.adapter_manager.get_adapter_manager", + lambda: _FakeAdapterManager(), + ) + monkeypatch.setattr( + "src.core.transport.distribution.stream_loop_manager.get_stream_loop_manager", + lambda: fake_stream_loop_manager, + ) + monkeypatch.setattr( + "src.core.utils.user_query_helper.get_user_query_helper", + lambda: fake_user_query_helper, + ) + + await runtime._send_text_to_core("fallback", is_final=False) + + assert fake_stream_manager.added_messages == [] + assert len(runtime.core_sink.messages) == 1 + assert runtime.core_sink.messages[0]["message_info"]["platform"] == "local_asr" + + +@pytest.mark.asyncio +async def test_send_text_to_core_keeps_local_asr_when_bilibili_live_is_not_active( + monkeypatch: pytest.MonkeyPatch, +) -> None: + runtime = _Runtime() + runtime.core_sink = _FakeCoreSink() + config = AsrAdapterConfig() + runtime.plugin = SimpleNamespace(config=config) + + fake_stream_manager = _FakeStreamManager() + fake_stream_loop_manager = _FakeStreamLoopManager() + fake_user_query_helper = _FakeUserQueryHelper() + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: fake_stream_manager, + ) + monkeypatch.setattr( + "src.core.managers.adapter_manager.get_adapter_manager", + lambda: _FakeAdapterManager(), + ) + monkeypatch.setattr( + "src.core.transport.distribution.stream_loop_manager.get_stream_loop_manager", + lambda: fake_stream_loop_manager, + ) + monkeypatch.setattr( + "src.core.utils.user_query_helper.get_user_query_helper", + lambda: fake_user_query_helper, + ) + + await runtime._send_text_to_core("stay local", is_final=True) + + assert fake_stream_manager.added_messages == [] + assert len(runtime.core_sink.messages) == 1 + assert runtime.core_sink.messages[0]["message_info"]["platform"] == "local_asr" diff --git a/test/plugins/test_default_chatter_session.py b/test/plugins/test_default_chatter_session.py index d47f07bc..e027123e 100644 --- a/test/plugins/test_default_chatter_session.py +++ b/test/plugins/test_default_chatter_session.py @@ -67,6 +67,10 @@ async def _done() -> "_FakeResponse": return _done().__await__() + async def stream_events_with_callback(self, on_event: Any) -> str: + await on_event(SimpleNamespace(text_delta="hello")) + return self.message + class _FakeToolRegistry: def get_all(self) -> list[Any]: @@ -677,6 +681,72 @@ async def _send(*, stream: bool = False) -> _FakeResponse: assert response.send_count == 2 +@pytest.mark.asyncio +async def test_session_execute_with_stream_uses_stream_observer_when_enabled() -> None: + response = _FakeResponse(payload_roles=[ROLE.USER], message="") + send_stream_args: list[bool] = [] + observed_events: list[Any] = [] + finalize_calls: list[str] = [] + + async def _send(*, stream: bool = False) -> _FakeResponse: + send_stream_args.append(stream) + response.send_count += 1 + response.call_list = [SimpleNamespace(name="action-send_text", args={}, id="1")] + return response + + class _Observer: + async def __call__(self, event: Any) -> None: + observed_events.append(event) + + async def finalize(self) -> None: + finalize_calls.append("done") + + response.send = _send # type: ignore[method-assign] + runtime = _FakeRuntimeAllowUser(response) + session = _build_session( + runtime, + options=DefaultChatterSessionOptions(enable_llm_stream=True), + ) + session.adapters.stream_event_observer = _Observer() + + first = await anext(session.execute_with_stream(_make_chat_stream(), apply_stop_wake_config=False)) + + assert isinstance(first, Wait) + assert send_stream_args == [True] + assert len(observed_events) == 1 + assert finalize_calls == ["done"] + + +@pytest.mark.asyncio +async def test_session_execute_with_stream_disables_stream_for_tool_call_compat() -> None: + response = _FakeResponse( + payload_roles=[ROLE.USER], + message="", + model_set=[{"tool_call_compat": True}], + ) + send_stream_args: list[bool] = [] + + async def _send(*, stream: bool = False) -> _FakeResponse: + send_stream_args.append(stream) + response.send_count += 1 + response.call_list = [SimpleNamespace(name="action-send_text", args={}, id="1")] + return response + + response.send = _send # type: ignore[method-assign] + runtime = _FakeRuntimeAllowUser(response) + session = _build_session( + runtime, + options=DefaultChatterSessionOptions(enable_llm_stream=True), + ) + session.adapters.stream_event_observer = AsyncMock() + + first = await anext(session.execute_with_stream(_make_chat_stream(), apply_stop_wake_config=False)) + + assert isinstance(first, Wait) + assert send_stream_args == [False] + session.adapters.stream_event_observer.assert_not_awaited() + + @pytest.mark.asyncio async def test_session_execute_with_stream_uses_synthetic_trigger_message_for_timer_resume_tool_calls() -> None: response = _FakeResponse(payload_roles=[ROLE.USER], message="") diff --git a/test/plugins/test_napcat_adapter_startup_validation.py b/test/plugins/test_onebot_adapter_startup_validation.py similarity index 84% rename from test/plugins/test_napcat_adapter_startup_validation.py rename to test/plugins/test_onebot_adapter_startup_validation.py index 9a877195..0b60468b 100644 --- a/test/plugins/test_napcat_adapter_startup_validation.py +++ b/test/plugins/test_onebot_adapter_startup_validation.py @@ -1,4 +1,4 @@ -"""测试 napcat_adapter 启动时的身份配置校验。""" +"""测试 onebot_adapter 启动时的身份配置校验。""" from __future__ import annotations @@ -10,9 +10,9 @@ import src.kernel.storage as kernel_storage from src.kernel.concurrency import get_task_manager -from plugins.napcat_adapter.config import NapcatAdapterConfig -from plugins.napcat_adapter.plugin import NapcatAdapter, NapcatAdapterPlugin, _validate_bot_identity -from plugins.napcat_adapter.src.handlers import utils as napcat_utils +from plugins.onebot_adapter.config import OneBotAdapterConfig +from plugins.onebot_adapter.plugin import OneBotAdapter, OneBotAdapterPlugin, _validate_bot_identity +from plugins.onebot_adapter.src.handlers import utils as onebot_utils class _FakeCoreSink: @@ -46,16 +46,16 @@ async def send(self, _request: str) -> None: await asyncio.sleep(1) -class TestNapcatAdapterStartupValidation: +class TestOneBotAdapterStartupValidation: """测试 Napcat 适配器启动校验。""" def test_validate_bot_identity_accepts_valid_values(self) -> None: """有效配置应通过校验。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -84,11 +84,11 @@ def test_validate_bot_identity_accepts_valid_values(self) -> None: def test_validate_bot_identity_rejects_empty_qq_id(self) -> None: """空 qq_id 应被拒绝。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -118,11 +118,11 @@ def test_validate_bot_identity_rejects_empty_qq_id(self) -> None: def test_validate_bot_identity_rejects_non_digit_qq_id(self) -> None: """非数字 qq_id 应被拒绝。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "abc123", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -152,11 +152,11 @@ def test_validate_bot_identity_rejects_non_digit_qq_id(self) -> None: def test_validate_bot_identity_rejects_empty_nickname(self) -> None: """空 qq_nickname 应被拒绝。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": " "}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -187,12 +187,12 @@ def test_validate_bot_identity_rejects_empty_nickname(self) -> None: @pytest.mark.asyncio async def test_get_bot_info_returns_standard_bot_name_field() -> None: - """NapcatAdapter 应按统一契约返回 bot_name。""" - config = NapcatAdapterConfig.from_dict( + """OneBotAdapter 应按统一契约返回 bot_name。""" + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -216,8 +216,8 @@ async def test_get_bot_info_returns_standard_bot_name_field() -> None: }, } ) - plugin = NapcatAdapterPlugin(config=config) - adapter = NapcatAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) + plugin = OneBotAdapterPlugin(config=config) + adapter = OneBotAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) bot_info = await adapter.get_bot_info() @@ -229,14 +229,14 @@ async def test_get_bot_info_returns_standard_bot_name_field() -> None: @pytest.mark.asyncio -async def test_send_napcat_api_times_out_when_websocket_send_blocks() -> None: - """send_napcat_api 应对 WebSocket 发送阻塞施加总超时。""" +async def test_send_onebot_api_times_out_when_websocket_send_blocks() -> None: + """send_onebot_api 应对 WebSocket 发送阻塞施加总超时。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -260,12 +260,12 @@ async def test_send_napcat_api_times_out_when_websocket_send_blocks() -> None: }, } ) - plugin = NapcatAdapterPlugin(config=config) - adapter = NapcatAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) + plugin = OneBotAdapterPlugin(config=config) + adapter = OneBotAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) adapter._ws = cast(Any, _HangingWebSocket()) with pytest.raises(asyncio.TimeoutError): - await adapter.send_napcat_api("send_group_msg", {"group_id": 1}, timeout=0.01) + await adapter.send_onebot_api("send_group_msg", {"group_id": 1}, timeout=0.01) assert adapter._response_pool == {} @@ -274,11 +274,11 @@ async def test_send_napcat_api_times_out_when_websocket_send_blocks() -> None: async def test_handle_video_message_times_out_on_blocked_local_file_read(monkeypatch, tmp_path) -> None: """本地视频读取阻塞时应返回超时占位文本。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -302,8 +302,8 @@ async def test_handle_video_message_times_out_on_blocked_local_file_read(monkeyp }, } ) - plugin = NapcatAdapterPlugin(config=config) - adapter = NapcatAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) + plugin = OneBotAdapterPlugin(config=config) + adapter = OneBotAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) video_file = tmp_path / "video.mp4" video_file.write_bytes(b"test") @@ -314,7 +314,7 @@ async def _blocked_to_thread(_func, *_args, **_kwargs): return b"late" monkeypatch.setattr( - "plugins.napcat_adapter.src.handlers.to_core.message_handler.asyncio.to_thread", + "plugins.onebot_adapter.src.handlers.to_core.message_handler.asyncio.to_thread", _blocked_to_thread, ) @@ -326,44 +326,44 @@ async def _blocked_to_thread(_func, *_args, **_kwargs): @pytest.mark.asyncio -async def test_napcat_cache_load_timeout_does_not_block(monkeypatch) -> None: +async def test_onebot_cache_load_timeout_does_not_block(monkeypatch) -> None: """慢缓存读取应在超时后快速降级。""" original_cache = { - section: values.copy() for section, values in napcat_utils._CACHE.items() + section: values.copy() for section, values in onebot_utils._CACHE.items() } - original_loaded = napcat_utils._CACHE_LOADED + original_loaded = onebot_utils._CACHE_LOADED async def _slow_load(_name: str) -> dict[str, Any] | None: await asyncio.sleep(0.05) return {"group_info": {"1": {"data": {"group_id": 1}, "ts": 1.0}}} - monkeypatch.setattr(napcat_utils, "CACHE_IO_TIMEOUT_SECONDS", 0.01) + monkeypatch.setattr(onebot_utils, "CACHE_IO_TIMEOUT_SECONDS", 0.01) monkeypatch.setattr(kernel_storage.json_store, "load", _slow_load) - napcat_utils._CACHE_LOADED = False - for section in napcat_utils._CACHE.values(): + onebot_utils._CACHE_LOADED = False + for section in onebot_utils._CACHE.values(): section.clear() try: - await napcat_utils._ensure_cache_loaded() - assert napcat_utils._CACHE_LOADED is True - assert napcat_utils._CACHE["group_info"] == {} + await onebot_utils._ensure_cache_loaded() + assert onebot_utils._CACHE_LOADED is True + assert onebot_utils._CACHE["group_info"] == {} finally: - napcat_utils._CACHE_LOADED = original_loaded + onebot_utils._CACHE_LOADED = original_loaded for section_name, values in original_cache.items(): - napcat_utils._CACHE[section_name].clear() - napcat_utils._CACHE[section_name].update(values) + onebot_utils._CACHE[section_name].clear() + onebot_utils._CACHE[section_name].update(values) @pytest.mark.asyncio async def test_meta_event_handler_reconnects_when_heartbeat_times_out(monkeypatch) -> None: """心跳超时时应触发适配器自动重连。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -387,8 +387,8 @@ async def test_meta_event_handler_reconnects_when_heartbeat_times_out(monkeypatc }, } ) - plugin = NapcatAdapterPlugin(config=config) - adapter = NapcatAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) + plugin = OneBotAdapterPlugin(config=config) + adapter = OneBotAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) adapter.reconnect = AsyncMock() handler = adapter.meta_event_handler @@ -396,7 +396,7 @@ async def test_meta_event_handler_reconnects_when_heartbeat_times_out(monkeypatc handler.interval = 0.01 monkeypatch.setattr( - "plugins.napcat_adapter.src.handlers.to_core.meta_event_handler.time.time", + "plugins.onebot_adapter.src.handlers.to_core.meta_event_handler.time.time", lambda: 0.03, ) @@ -410,11 +410,11 @@ async def test_meta_event_handler_reconnects_when_heartbeat_times_out(monkeypatc async def test_on_adapter_unloaded_stops_stale_heartbeat_monitor() -> None: """适配器卸载时应停止残留的心跳监控任务。""" - config = NapcatAdapterConfig.from_dict( + config = OneBotAdapterConfig.from_dict( { "plugin": {"enabled": True, "config_version": "2.0.0"}, "bot": {"qq_id": "123456789", "qq_nickname": "MoFoxBot"}, - "napcat_server": { + "onebot_server": { "mode": "reverse", "host": "localhost", "port": 8095, @@ -438,8 +438,8 @@ async def test_on_adapter_unloaded_stops_stale_heartbeat_monitor() -> None: }, } ) - plugin = NapcatAdapterPlugin(config=config) - adapter = NapcatAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) + plugin = OneBotAdapterPlugin(config=config) + adapter = OneBotAdapter(core_sink=cast(Any, _FakeCoreSink()), plugin=plugin) handler = adapter.meta_event_handler handler.last_heart_beat = 1.0 @@ -447,7 +447,7 @@ async def test_on_adapter_unloaded_stops_stale_heartbeat_monitor() -> None: heartbeat_task = get_task_manager().create_task( asyncio.sleep(60), - name="test_napcat_adapter_heartbeat_check", + name="test_onebot_adapter_heartbeat_check", daemon=True, ) handler._heartbeat_task = heartbeat_task diff --git a/test/plugins/test_voice_chatter.py b/test/plugins/test_voice_chatter.py index 7d74d32e..790250f4 100644 --- a/test/plugins/test_voice_chatter.py +++ b/test/plugins/test_voice_chatter.py @@ -5,8 +5,11 @@ import pytest -from plugins.voice_chatter.config import SherpaOnnxVoiceChatterConfig -from plugins.voice_chatter.plugin import SherpaOnnxVoiceChatter +from plugins.voxcpm_tts_provider.config import VoxCPMTTSProviderConfig +from plugins.voice_chatter.config import VoiceChatterConfig +from plugins.voice_chatter.markers import parse_speech_segments +from plugins.voice_chatter.plugin import SayAction, VoiceChatter +from plugins.voice_chatter.tts import TTSArtifact from src.core.components.base import Failure, Wait @@ -16,6 +19,7 @@ def __init__(self) -> None: self.stop_call_name = "" self.suspend_text = "" self.calls: list[tuple[Any, bool]] = [] + self.adapters = SimpleNamespace(stream_event_observer=None) async def execute_with_stream( self, @@ -60,17 +64,26 @@ async def activate_stream(self, stream_id: str) -> Any | None: return self.chat_stream +class _FakeEmitBackend: + def __init__(self) -> None: + self.emitted: list[tuple[str, Any]] = [] + + async def emit(self, artifact: Any, chat_stream: Any) -> bool: + self.emitted.append((artifact.text, chat_stream)) + return True + + @pytest.mark.asyncio async def test_voice_chatter_execute_uses_default_chatter_service( monkeypatch: pytest.MonkeyPatch, ) -> None: - config = SherpaOnnxVoiceChatterConfig() + config = VoiceChatterConfig() config.plugin.tick_interval = 0.25 config.plugin.allow_message_buffer = False config.plugin.enable_action_suspend = False plugin = SimpleNamespace(config=config) - chatter = SherpaOnnxVoiceChatter(stream_id="stream-1", plugin=plugin) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) chat_stream = SimpleNamespace( stream_id="stream-1", @@ -104,20 +117,107 @@ async def test_voice_chatter_execute_uses_default_chatter_service( assert getattr(options, "enable_cooldown") is False assert getattr(options, "enable_stop_direct_message_wake") is False assert getattr(options, "enable_action_suspend") is False + assert getattr(options, "enable_llm_stream") is False assert session.pass_call_name == "action-pass_and_wait" assert session.stop_call_name == "__voice_chatter_stop_disabled__" assert session.suspend_text == "（语音回合已挂起，等待用户继续说话。）" assert session.calls == [(chat_stream, False)] assert chat_stream.context.tick_interval_override == 0.25 assert chat_stream.context.allow_message_buffer is False + assert session.adapters.stream_event_observer is None + + +@pytest.mark.asyncio +async def test_voice_chatter_execute_enables_streaming_observer_when_configured( + monkeypatch: pytest.MonkeyPatch, +) -> None: + config = VoiceChatterConfig() + config.low_latency_streaming.enabled = True + config.tts.endpoint = "logging" + + plugin = SimpleNamespace(config=config) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) + + chat_stream = SimpleNamespace( + stream_id="stream-1", + platform="test", + chat_type="private", + context=SimpleNamespace( + tick_interval_override=None, + allow_message_buffer=None, + ), + ) + session = _FakeSession() + service = _FakeService(session) + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: _FakeStreamManager(chat_stream), + ) + monkeypatch.setattr( + "plugins.voice_chatter.plugin.get_service", + lambda signature: service if signature == "default_chatter:service:chat_core" else None, + ) + + first = await anext(chatter.execute()) + + assert isinstance(first, Wait) + create_call = service.create_default_session_calls[0] + options = create_call["options"] + assert getattr(options, "enable_llm_stream") is True + assert session.adapters.stream_event_observer is not None + + +@pytest.mark.asyncio +async def test_voice_chatter_execute_disables_streaming_when_observer_init_fails( + monkeypatch: pytest.MonkeyPatch, +) -> None: + config = VoiceChatterConfig() + config.low_latency_streaming.enabled = True + plugin = SimpleNamespace(config=config) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) + + chat_stream = SimpleNamespace( + stream_id="stream-1", + platform="test", + chat_type="private", + context=SimpleNamespace( + tick_interval_override=None, + allow_message_buffer=None, + ), + ) + session = _FakeSession() + service = _FakeService(session) + + monkeypatch.setattr( + "src.core.managers.stream_manager.get_stream_manager", + lambda: _FakeStreamManager(chat_stream), + ) + monkeypatch.setattr( + "plugins.voice_chatter.plugin.get_service", + lambda signature: service if signature == "default_chatter:service:chat_core" else None, + ) + monkeypatch.setattr( + VoiceChatter, + "_build_stream_observer", + lambda self, _chat_stream: None, + ) + + first = await anext(chatter.execute()) + + assert isinstance(first, Wait) + create_call = service.create_default_session_calls[0] + options = create_call["options"] + assert getattr(options, "enable_llm_stream") is False + assert session.adapters.stream_event_observer is None @pytest.mark.asyncio async def test_voice_chatter_execute_fails_when_chat_core_service_is_missing( monkeypatch: pytest.MonkeyPatch, ) -> None: - plugin = SimpleNamespace(config=SherpaOnnxVoiceChatterConfig()) - chatter = SherpaOnnxVoiceChatter(stream_id="stream-1", plugin=plugin) + plugin = SimpleNamespace(config=VoiceChatterConfig()) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) chat_stream = SimpleNamespace( stream_id="stream-1", context=SimpleNamespace( @@ -139,10 +239,10 @@ async def test_voice_chatter_execute_fails_when_chat_core_service_is_missing( def test_voice_chatter_handle_plain_text_response_retries_then_waits() -> None: - config = SherpaOnnxVoiceChatterConfig() + config = VoiceChatterConfig() config.plugin.plain_text_retry_limit = 1 plugin = SimpleNamespace(config=config) - chatter = SherpaOnnxVoiceChatter(stream_id="stream-1", plugin=plugin) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) first = chatter.handle_plain_text_response( message="hello", @@ -166,3 +266,135 @@ def test_voice_chatter_handle_plain_text_response_retries_then_waits() -> None: "action": "wait", "reminder_text": "", } + + +def test_voice_chatter_builds_provider_voice_guide_from_explicit_provider( + monkeypatch: pytest.MonkeyPatch, +) -> None: + config = VoiceChatterConfig() + config.tts.provider = "voxcpm" + plugin = SimpleNamespace(config=config) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) + + provider_config = VoxCPMTTSProviderConfig() + provider_config.prompt.inject_into_voice_chatter = True + provider_config.prompt.voice_chatter_guide = "Use [laughing] when appropriate." + provider_plugin = SimpleNamespace(config=provider_config) + + monkeypatch.setattr( + "plugins.voice_chatter.plugin.get_all_plugins", + lambda: {"voxcpm_tts_provider": provider_plugin}, + ) + + guide = chatter._build_tts_provider_voice_guide() + + assert "Use [laughing]" in guide + + +def test_voice_chatter_builds_provider_voice_guide_from_default_provider( + monkeypatch: pytest.MonkeyPatch, +) -> None: + config = VoiceChatterConfig() + config.tts.provider = "" + plugin = SimpleNamespace(config=config) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) + + provider_config = VoxCPMTTSProviderConfig() + provider_config.plugin.register_as_default = True + provider_config.prompt.inject_into_voice_chatter = True + provider_config.prompt.voice_chatter_guide = "Default provider guide." + provider_plugin = SimpleNamespace(config=provider_config) + + monkeypatch.setattr( + "plugins.voice_chatter.plugin.get_all_plugins", + lambda: {"voxcpm_tts_provider": provider_plugin}, + ) + + guide = chatter._build_tts_provider_voice_guide() + + assert guide == "Default provider guide." + + +def test_parse_speech_segments_preserves_provider_tags() -> None: + segments = parse_speech_segments( + "[laughing]你好。 [Question-ah]真的好久不见。", + default_emotion="calm", + ) + + assert [segment.text for segment in segments] == [ + "[laughing]你好。", + "[Question-ah]真的好久不见。", + ] + assert [segment.emotion for segment in segments] == ["calm", "calm"] + assert [segment.wait_before for segment in segments] == [0.0, 0.0] + + +def test_parse_speech_segments_attaches_tag_only_sentence_to_previous_text() -> None: + segments = parse_speech_segments("这是一句话！[laughing]") + + assert [segment.text for segment in segments] == ["这是一句话！[laughing]"] + + +def test_parse_speech_segments_attaches_leading_tag_only_sentence_to_next_text() -> None: + segments = parse_speech_segments("[laughing]\n这是一句话！") + + assert [segment.text for segment in segments] == ["[laughing]这是一句话！"] + + +def test_parse_speech_segments_filters_special_symbols_but_keeps_tags() -> None: + segments = parse_speech_segments(r"Hello_ \world![laughing]") + + assert [segment.text for segment in segments] == ["Hello world![laughing]"] + + +def test_parse_speech_segments_merges_symbol_prefixed_sentence_into_previous() -> None: + segments = parse_speech_segments("Hello?!Really?!") + + assert [segment.text for segment in segments] == ["Hello?!Really?!"] + + +@pytest.mark.asyncio +async def test_say_action_passes_emotion_and_provider_to_tts(monkeypatch: pytest.MonkeyPatch) -> None: + config = VoiceChatterConfig() + plugin = SimpleNamespace(config=config) + chat_stream = SimpleNamespace(stream_id="stream-1") + action = SayAction(chat_stream=chat_stream, plugin=plugin) + backend = _FakeEmitBackend() + captured: dict[str, Any] = {} + + monkeypatch.setattr( + "plugins.voice_chatter.plugin.build_tts_backend", + lambda _config, _logger: backend, + ) + + async def _fake_synthesize_segments(**kwargs: Any) -> list[TTSArtifact]: + captured.update(kwargs) + return [ + TTSArtifact(text=segment.text, audio=b"wav", emotion=segment.emotion) + for segment in kwargs["segments"] + ] + + monkeypatch.setattr( + "plugins.voice_chatter.plugin.synthesize_segments", + _fake_synthesize_segments, + ) + + ok, detail = await action.execute( + "[laughing]你好。 [Question-ah]稍等一下。", + emotion="happy", + provider="voxcpm", + ) + + assert ok is True + assert "2/2" in detail + assert captured["provider"] == "voxcpm" + assert [segment.emotion for segment in captured["segments"]] == ["happy", "happy"] + assert [segment.wait_before for segment in captured["segments"]] == [0.0, 0.0] + assert [segment.text for segment in captured["segments"]] == [ + "[laughing]你好。", + "[Question-ah]稍等一下。", + ] + assert [text for text, _stream in backend.emitted] == [ + "[laughing]你好。", + "[Question-ah]稍等一下。", + ] diff --git a/test/plugins/test_voice_chatter_streaming.py b/test/plugins/test_voice_chatter_streaming.py new file mode 100644 index 00000000..cca76152 --- /dev/null +++ b/test/plugins/test_voice_chatter_streaming.py @@ -0,0 +1,783 @@ +from __future__ import annotations + +import asyncio +from types import SimpleNamespace +from typing import Any + +import pytest + +from plugins.voice_chatter.config import VoiceChatterConfig +from plugins.voice_chatter.plugin import VoiceChatter +from plugins.voice_chatter.streaming_args import PartialJsonStringFieldExtractor +from plugins.voice_chatter.streaming_assembler import SpeechAssemblerConfig, StreamingSpeechAssembler +from plugins.voice_chatter.streaming_observer import VoiceSayStreamObserver +from plugins.voice_chatter.streaming_tts import StreamingTTSPipeline +from plugins.voice_chatter.tts import TTSArtifact, TTSRequest +from src.core.components.base.chatter import BaseChatter +from src.kernel.llm import LLMPayload, ROLE, StreamEvent + + +class _FakeBackend: + def __init__(self, delays: dict[str, float] | None = None) -> None: + self.delays = delays or {} + self.emitted: list[str] = [] + self.requests: list[TTSRequest] = [] + + async def synthesize(self, request: TTSRequest) -> TTSArtifact: + self.requests.append(request) + await asyncio.sleep(self.delays.get(request.text, 0.0)) + return TTSArtifact(text=request.text, audio=request.text.encode("utf-8")) + + async def emit(self, artifact: TTSArtifact, chat_stream: Any) -> bool: + _ = chat_stream + self.emitted.append(artifact.text) + return True + + +class _FakeLogger: + def __init__(self) -> None: + self.warnings: list[str] = [] + + def info(self, *args: Any, **kwargs: Any) -> None: + _ = args, kwargs + + def error(self, *args: Any, **kwargs: Any) -> None: + _ = args, kwargs + + def debug(self, *args: Any, **kwargs: Any) -> None: + _ = args, kwargs + + def warning(self, message: str, *args: Any, **kwargs: Any) -> None: + _ = args, kwargs + self.warnings.append(message) + + +def test_partial_json_string_field_extractor_supports_fragmented_content() -> None: + extractor = PartialJsonStringFieldExtractor("content") + + assert extractor.feed('{"cont') == "" + assert extractor.feed('ent":"你好') == "你好" + assert extractor.feed('。今天') == "。今天" + assert extractor.feed('天气不错。"}') == "天气不错。" + + +def test_partial_json_string_field_extractor_supports_escapes() -> None: + extractor = PartialJsonStringFieldExtractor("content") + + result = extractor.feed('{"emotion":"happy","content":"他说：\\"你好\\"。\\n第二句。"}') + + assert result == '他说："你好"。\n第二句。' + + + + +@pytest.mark.asyncio +async def test_streaming_tts_pipeline_emits_in_submit_order() -> None: + backend = _FakeBackend(delays={"第一句。": 0.03, "第二句。": 0.0}) + pipeline = StreamingTTSPipeline( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel=2, + logger=None, + ) + + await pipeline.submit_text("第一句。") + await pipeline.submit_text("第二句。") + await pipeline.close() + + assert backend.emitted == ["第一句。", "第二句。"] + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_preplays_completed_sentences() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_1", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_1", tool_args_delta='{"content":"你好。今')) + await observer(StreamEvent(tool_call_id="call_1", tool_args_delta='天天气不错。"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.emitted == ["你好。", "今天天气不错。"] + assert "call_1" in observer.preplayed_say_call_ids + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_passes_emotion_and_provider() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_emo", tool_name="action-say")) + await observer( + StreamEvent( + tool_call_id="call_emo", + tool_args_delta='{"emotion":"happy","provider":"voxcpm","content":"你好。"}', + ) + ) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.requests + assert backend.requests[0].emotion == "happy" + assert backend.requests[0].provider == "voxcpm" + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_flushes_tail_without_punctuation() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_2", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_2", tool_args_delta='{"content":"我明白你的意思"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.emitted == ["我明白你的意思"] + assert "call_2" in observer.preplayed_say_call_ids + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_filters_special_symbols_but_keeps_tags() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_3", tool_name="action-say")) + await observer( + StreamEvent( + tool_call_id="call_3", + tool_args_delta=r'{"content":"Hello_ \\world![laughing]"}', + ) + ) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.requests + assert "".join(request.text for request in backend.requests) == "Hello world![laughing]" + assert all("_" not in request.text and "\\" not in request.text for request in backend.requests) + assert "".join(backend.emitted) == "Hello world![laughing]" + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_merges_continuation_prefixed_sentence() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_4", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_4", tool_args_delta='{"content":"Hello?')) + await observer(StreamEvent(tool_call_id="call_4", tool_args_delta='!Again!"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.requests + assert backend.requests[0].text == "Hello?!Again!" + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_flushes_safe_sentence_before_next_sentence_completes() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_5", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_5", tool_args_delta='{"content":"Hello!First')) + await asyncio.sleep(0) + + assert backend.requests + assert backend.requests[0].text == "Hello!" + + await observer(StreamEvent(tool_call_id="call_5", tool_args_delta=' sentence!"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert [request.text for request in backend.requests] == ["Hello!", "First sentence!"] + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_delays_then_flushes_without_continuation() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=30, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_6", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_6", tool_args_delta='{"content":"Hello!"}')) + + assert backend.requests == [] + + await asyncio.sleep(0.05) + await asyncio.sleep(0) + + assert [request.text for request in backend.requests] == ["Hello!"] + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_attaches_tag_only_tail_to_previous_sentence() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=30, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_7", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_7", tool_args_delta='{"content":"Hello!')) + await observer(StreamEvent(tool_call_id="call_7", tool_args_delta='[laughing]"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert [request.text for request in backend.requests] == ["Hello![laughing]"] + assert backend.emitted == ["Hello![laughing]"] + + +@pytest.mark.asyncio +async def test_voice_say_stream_observer_drops_tag_only_output_without_text() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_8", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_8", tool_args_delta='{"content":"[laughing]"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.requests == [] + assert backend.emitted == [] + + +@pytest.mark.asyncio +async def test_voice_chatter_run_tool_call_skips_preplayed_action_say(monkeypatch: pytest.MonkeyPatch) -> None: + config = VoiceChatterConfig() + plugin = SimpleNamespace(config=config) + chatter = VoiceChatter(stream_id="stream-1", plugin=plugin) + chatter._active_stream_observer = SimpleNamespace(preplayed_say_call_ids={"call_say"}) + + captured_calls: list[list[Any]] = [] + + async def _fake_super( + self: Any, + calls: list[Any], + response: Any, + usable_map: Any, + trigger_msg: Any, + ) -> list[tuple[bool, bool]]: + _ = response, usable_map, trigger_msg + captured_calls.append(calls) + return [(True, True) for _ in calls] + + monkeypatch.setattr(BaseChatter, "run_tool_call", _fake_super) + + added_payloads: list[LLMPayload] = [] + response = SimpleNamespace(add_payload=added_payloads.append) + calls = [ + SimpleNamespace(name="action-say", id="call_say"), + SimpleNamespace(name="action-search_memory", id="call_search"), + ] + + results = await chatter.run_tool_call(calls, response, SimpleNamespace(), None) + + assert results == [(True, True), (True, True)] + assert len(captured_calls) == 1 + assert [call.name for call in captured_calls[0]] == ["action-search_memory"] + assert len(added_payloads) == 1 + assert added_payloads[0].role == ROLE.TOOL_RESULT + + +def test_assembler_chinese_closing_quote_adsorbed() -> None: + """P0: Chinese RIGHT DOUBLE QUOTATION MARK must be adsorbed into the candidate.""" + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("他说：“你好。") == [] + assert asm.feed("”然后继续。") == ["他说：“你好。”"] + assert asm.flush_all() == ["然后继续。"] + + +def test_assembler_leading_punctuation_preserved() -> None: + """P0: Leading ! or ? must not be dropped; they stay as pending_prefix.""" + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + # Feed just a leading punctuation + assert asm.feed("！真的吗。") == [] + result = asm.flush_all() + assert "！" in result[0] + assert "真的吗" in result[0] + + +def test_assembler_cross_delta_double_newline() -> None: + """P1: \\n\\n split across two deltas should still be a paragraph boundary.""" + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("第一段。\n") == [] + assert asm.feed("\n第二段。") == ["第一段。"] + assert asm.flush_all() == ["第二段。"] + + +def test_assembler_force_short_flush() -> None: + """P1: flush_candidate(force_short=True) bypasses the min-length gate.""" + asm = StreamingSpeechAssembler(SpeechAssemblerConfig( + min_sentence_chars=4, merge_short_sentences=True, + )) + assert asm.feed("嗯。") == [] + # Without force_short the short sentence stays held + assert asm.flush_candidate(force_short=False) == [] + # Feed again + asm2 = StreamingSpeechAssembler(SpeechAssemblerConfig( + min_sentence_chars=4, merge_short_sentences=True, + )) + assert asm2.feed("嗯。") == [] + # With force_short the short sentence is flushed + assert asm2.flush_candidate(force_short=True) == ["嗯。"] + + +# --------------------------------------------------------------------------- +# StreamingSpeechAssembler unit tests +# --------------------------------------------------------------------------- + + +def test_assembler_basic_sentence_split() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("你好。今") == ["你好。"] + assert asm.feed("天天气不错。") == [] + assert asm.flush_candidate() == ["今天天气不错。"] + + +def test_assembler_adsorbs_closing_quotes() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("他说：") == [] + assert asm.feed('"你好。') == [] + assert asm.feed('"然后') == ['他说："你好。"'] + + +def test_assembler_adsorbs_tag_suffix() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("你好。") == [] + assert asm.feed("[laugh]今天") == ["你好。[laugh]"] + assert asm.feed("天气不错。") == [] + assert asm.flush_candidate() == ["今天天气不错。"] + + +def test_assembler_no_split_inside_tag() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + result = asm.feed("[style:happy!]你好。") + # Candidate formed but held pending (no follow-up text yet) + assert result == [] + # flush_candidate submits the held candidate + assert asm.flush_candidate() == ["[style:happy!]你好。"] + + +def test_assembler_single_newline_becomes_space() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("你好\n我在听。") == [] + text = asm.flush_all() + assert len(text) == 1 + assert "你好" in text[0] and "我在听" in text[0] + + +def test_assembler_double_newline_boundary() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("第一段。\n\n第二段。") == ["第一段。"] + assert asm.flush_all() == ["第二段。"] + + +def test_assembler_short_sentence_merge() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig( + min_sentence_chars=4, + merge_short_sentences=True, + )) + assert asm.feed("嗯。这个可以。") == [] + assert asm.flush_all() == ["嗯。这个可以。"] + + +def test_assembler_short_sentence_disabled() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig( + min_sentence_chars=1, + merge_short_sentences=False, + )) + assert asm.feed("嗯。这个可以。") == ["嗯。"] + assert asm.flush_all() == ["这个可以。"] + + +def test_assembler_flush_all_includes_tail() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("没有标点符号的文本") == [] + assert asm.flush_all() == ["没有标点符号的文本"] + + +def test_assembler_flush_all_includes_short_prefix() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig( + min_sentence_chars=10, + merge_short_sentences=True, + )) + assert asm.feed("短。") == [] + assert asm.flush_all() == ["短。"] + + +def test_assembler_empty_input() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + assert asm.feed("") == [] + assert asm.flush_candidate() == [] + assert asm.flush_all() == [] + + +def test_assembler_multiple_sentences_in_one_delta() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + result = asm.feed("你好。今天天气不错。明天") + assert result == ["你好。", "今天天气不错。"] + + +def test_assembler_consecutive_terminators() -> None: + asm = StreamingSpeechAssembler(SpeechAssemblerConfig(min_sentence_chars=1)) + result = asm.feed("什么？！真的吗？") + assert result == ["什么？！"] + assert asm.flush_all() == ["真的吗？"] + + +# --------------------------------------------------------------------------- +# Observer-level pipeline & multi-call ordering tests +# --------------------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_observer_uses_global_pipeline_for_multiple_calls() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="call_a", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_b", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="call_a", tool_args_delta='{"content":"第一句。"}')) + await observer(StreamEvent(tool_call_id="call_b", tool_args_delta='{"content":"第二句。"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert backend.emitted == ["第一句。", "第二句。"] + + +@pytest.mark.asyncio +async def test_observer_multiple_calls_interleaved() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="c1", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="c1", tool_args_delta='{"content":"A1!')) + await observer(StreamEvent(tool_call_id="c2", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="c2", tool_args_delta='{"content":"B1!"}')) + await observer(StreamEvent(tool_call_id="c1", tool_args_delta='A2!"}')) + await observer.finalize() + await asyncio.sleep(0) + + # Order: A1! submitted when A2! text arrives (starts new sentence), + # then B1! and A2! flushed in finalize by call state order + assert len(backend.emitted) == 3 + assert "A1!" in backend.emitted + assert "B1!" in backend.emitted + assert "A2!" in backend.emitted + + +@pytest.mark.asyncio +async def test_observer_disabled_still_finalizes_pipeline() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="c1", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="c1", tool_args_delta='{"content":"Hello!"}')) + + # Force disable by setting flag + observer._disabled = True + + # Should not raise; pipeline and states are cleaned up + await observer.finalize() + await asyncio.sleep(0) + + assert len(observer._states) == 0 + + +@pytest.mark.asyncio +async def test_observer_finalize_clears_states() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="c1", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="c1", tool_args_delta='{"content":"Hello."}')) + await observer.finalize() + await asyncio.sleep(0) + + assert len(observer._states) == 0 + + +# --------------------------------------------------------------------------- +# StreamingTTSPipeline edge-case tests +# --------------------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_pipeline_skip_failed_synthesis_continues_emitting() -> None: + emitted: list[str] = [] + + class _SelectiveFailBackend: + def __init__(self) -> None: + self.emitted = emitted + self.requests: list[TTSRequest] = [] + + async def synthesize(self, request: TTSRequest) -> TTSArtifact: + self.requests.append(request) + if "fail" in request.text: + raise RuntimeError("simulated failure") + return TTSArtifact(text=request.text, audio=request.text.encode("utf-8")) + + async def emit(self, artifact: TTSArtifact, chat_stream: Any) -> bool: + _ = chat_stream + self.emitted.append(artifact.text) + return True + + backend = _SelectiveFailBackend() + pipeline = StreamingTTSPipeline( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel=2, + logger=_FakeLogger(), + ) + + await pipeline.submit_text("seq0.") + await pipeline.submit_text("fail.") + await pipeline.submit_text("seq2.") + await pipeline.close() + + assert emitted == ["seq0.", "seq2."] + + +@pytest.mark.asyncio +async def test_pipeline_close_waits_for_pending_tasks() -> None: + backend = _FakeBackend(delays={"slow.": 0.05, "fast.": 0.0}) + pipeline = StreamingTTSPipeline( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel=2, + logger=None, + ) + + await pipeline.submit_text("slow.") + await pipeline.submit_text("fast.") + await pipeline.close() + + assert len(backend.emitted) == 2 + assert "slow." in backend.emitted + assert "fast." in backend.emitted + + +@pytest.mark.asyncio +async def test_pipeline_empty_text_is_ignored() -> None: + backend = _FakeBackend() + pipeline = StreamingTTSPipeline( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel=2, + logger=None, + ) + + await pipeline.submit_text(" ") + await pipeline.submit_text("real.") + await pipeline.close() + + assert len(backend.emitted) == 1 + assert backend.emitted[0] == "real." + + +@pytest.mark.asyncio +async def test_pipeline_submit_after_close_raises() -> None: + backend = _FakeBackend() + pipeline = StreamingTTSPipeline( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel=2, + logger=None, + ) + + await pipeline.close() + with pytest.raises(RuntimeError, match="already closed"): + await pipeline.submit_text("test.") + + +@pytest.mark.asyncio +async def test_observer_continuation_grace_timer_fires() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=30, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="cg", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="cg", tool_args_delta='{"content":"Hello!"}')) + + # Nothing submitted yet (candidate pending, grace timer waiting) + assert len(backend.requests) == 0 + + # Wait for grace timer + await asyncio.sleep(0.05) + await asyncio.sleep(0) + + assert len(backend.requests) == 1 + assert backend.requests[0].text == "Hello!" + + +@pytest.mark.asyncio +async def test_observer_non_action_say_ignored() -> None: + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="c1", tool_name="action-search_memory")) + await observer(StreamEvent(tool_call_id="c1", tool_args_delta='{"query":"test"}')) + await observer.finalize() + await asyncio.sleep(0) + + assert len(backend.requests) == 0 + assert len(observer.preplayed_say_call_ids) == 0 + + +@pytest.mark.asyncio +async def test_observer_disabled_does_not_skip_say_action() -> None: + """P1: When the observer degrades mid-stream, preplayed_say_call_ids + must NOT include the call_id, so the full SayAction still runs.""" + backend = _FakeBackend() + observer = VoiceSayStreamObserver( + backend=backend, + chat_stream=SimpleNamespace(stream_id="s1"), + max_parallel_tts=2, + min_sentence_chars=1, + continuation_grace_ms=0, + flush_tail_on_done=True, + empty_audio_retry_count=0, + logger=_FakeLogger(), + ) + + await observer(StreamEvent(tool_call_id="c1", tool_name="action-say")) + await observer(StreamEvent(tool_call_id="c1", tool_args_delta='{"content":"Hello!"}')) + + # Simulate mid-stream degradation + observer._disabled = True + + await observer.finalize() + await asyncio.sleep(0) + + # Even though preplayed=True and emitted_count > 0, disabled must block skip + assert "c1" not in observer.preplayed_say_call_ids diff --git a/test/plugins/test_voice_chatter_tts_emit.py b/test/plugins/test_voice_chatter_tts_emit.py new file mode 100644 index 00000000..4a3cb08e --- /dev/null +++ b/test/plugins/test_voice_chatter_tts_emit.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from types import SimpleNamespace + +import pytest + +from plugins.voice_chatter.tts import ASR_ADAPTER_SIGNATURE, HttpTTSBackend, TTSArtifact + + +class _FakeSender: + def __init__(self) -> None: + self.calls: list[tuple[object, str | None]] = [] + + async def send_message(self, message: object, adapter_signature: str | None = None) -> bool: + self.calls.append((message, adapter_signature)) + return True + + +@pytest.mark.asyncio +async def test_http_tts_backend_routes_bilibili_live_to_asr_adapter( + monkeypatch: pytest.MonkeyPatch, +) -> None: + sender = _FakeSender() + monkeypatch.setattr( + "src.core.transport.message_send.get_message_sender", + lambda: sender, + ) + + backend = HttpTTSBackend(endpoint="http://example.invalid") + artifact = TTSArtifact(text="hello", audio=b"wav-bytes") + chat_stream = SimpleNamespace( + stream_id="stream-1", + platform="bilibili_live", + chat_type="group", + ) + + ok = await backend.emit(artifact, chat_stream) + + assert ok is True + assert len(sender.calls) == 1 + message, adapter_signature = sender.calls[0] + assert adapter_signature == ASR_ADAPTER_SIGNATURE + assert getattr(message, "platform") == "bilibili_live" + + +@pytest.mark.asyncio +async def test_http_tts_backend_keeps_default_routing_for_local_asr( + monkeypatch: pytest.MonkeyPatch, +) -> None: + sender = _FakeSender() + monkeypatch.setattr( + "src.core.transport.message_send.get_message_sender", + lambda: sender, + ) + + backend = HttpTTSBackend(endpoint="http://example.invalid") + artifact = TTSArtifact(text="hello", audio=b"wav-bytes") + chat_stream = SimpleNamespace( + stream_id="stream-1", + platform="local_asr", + chat_type="private", + ) + + ok = await backend.emit(artifact, chat_stream) + + assert ok is True + assert len(sender.calls) == 1 + _message, adapter_signature = sender.calls[0] + assert adapter_signature is None diff --git a/test/plugins/test_voxcpm_tts_provider.py b/test/plugins/test_voxcpm_tts_provider.py index 728437fd..a148afb0 100644 --- a/test/plugins/test_voxcpm_tts_provider.py +++ b/test/plugins/test_voxcpm_tts_provider.py @@ -54,6 +54,13 @@ def _wav_base64() -> str: return base64.b64encode(buffer.getvalue()).decode("ascii") +def test_voxcpm_provider_config_exposes_prompt_injection_section() -> None: + config = VoxCPMTTSProviderConfig() + + assert config.prompt.inject_into_voice_chatter is True + assert config.prompt.voice_chatter_guide == "" + + @pytest.mark.asyncio async def test_voxcpm_provider_translates_voice_design_request() -> None: fake = _FakeModel() @@ -90,6 +97,30 @@ def factory(model_id: str, **kwargs: Any) -> _FakeModel: assert wav_file.getnchannels() == 1 +@pytest.mark.asyncio +async def test_voxcpm_provider_appends_emotion_to_control_suffix() -> None: + fake = _FakeModel() + config = VoxCPMTTSProviderConfig() + config.voice.control = "warm" + provider = VoxCPMTTSProvider( + config=config, + logger=_Logger(), + model_factory=lambda _model_id, **_kwargs: fake, + ) + + response = await provider.synthesize( + _Request( + stream_id="demo", + text="hello", + emotion="happy", + ) + ) + + assert fake.calls[0]["text"] == "(warm happy)hello" + assert response.metadata["control"] == "warm happy" + assert response.metadata["mode"] == "voice_design" + + @pytest.mark.asyncio async def test_voxcpm_provider_persists_base64_reference_audio(tmp_path) -> None: fake = _FakeModel() diff --git a/uv.lock b/uv.lock index 0e425fe0..98be2734 100644 --- a/uv.lock +++ 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