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feat: Enhance plugin runtime configuration and hook management

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- Added `inactive_plugins` field to `RunnerReadyPayload` and `ReloadPluginResultPayload` to track plugins that are not activated due to being disabled or unmet dependencies.
- Introduced `InspectPluginConfigPayload` and `InspectPluginConfigResultPayload` for inspecting plugin configuration metadata.
- Implemented `PluginActivationStatus` enum to better represent plugin activation states.
- Updated `_activate_plugin` method to return activation status and handle inactive plugins accordingly.
- Added hooks for send service to allow modification of messages before and after sending.
- Created new runtime routes for listing hook specifications in the WebUI.
- Refactored plugin configuration handling to utilize runtime inspection for better accuracy and flexibility.
- Enhanced error handling and logging for plugin configuration operations.
This commit is contained in:
DrSmoothl
2026-04-02 21:16:31 +08:00
parent 56f7184c4d
commit 7d0d429640
22 changed files with 2698 additions and 1120 deletions
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225
src/maisaka/chat_loop_service.py
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@@ -3,7 +3,7 @@
from dataclasses import dataclass
from datetime import datetime
from time import perf_counter
from typing import List, Optional, Sequence
from typing import Any, List, Optional, Sequence
import asyncio
import json
@@ -26,6 +26,15 @@ from src.llm_models.model_client.base_client import BaseClient
from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
from src.llm_models.payload_content.resp_format import RespFormat, RespFormatType
from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionInput, ToolOption, normalize_tool_options
from src.plugin_runtime.hook_payloads import (
deserialize_prompt_messages,
deserialize_tool_calls,
serialize_prompt_messages,
serialize_tool_calls,
serialize_tool_definitions,
)
from src.plugin_runtime.hook_schema_utils import build_object_schema
from src.plugin_runtime.host.hook_spec_registry import HookSpec, HookSpecRegistry
from src.services.llm_service import LLMServiceClient
from .builtin_tools import get_builtin_tools
@@ -58,6 +67,123 @@ class ToolFilterSelection(BaseModel):
logger = get_logger("maisaka_chat_loop")
def register_maisaka_hook_specs(registry: HookSpecRegistry) -> List[HookSpec]:
"""注册 Maisaka 规划器内置 Hook 规格。
Args:
registry: 目标 Hook 规格注册中心。
Returns:
List[HookSpec]: 实际注册的 Hook 规格列表。
"""
return registry.register_hook_specs(
[
HookSpec(
name="maisaka.planner.before_request",
description="在 Maisaka 向模型发起规划请求前触发，可改写消息窗口与工具定义。",
parameters_schema=build_object_schema(
{
"messages": {
"type": "array",
"description": "即将发给模型的 PromptMessage 列表。",
},
"tool_definitions": {
"type": "array",
"description": "当前候选工具定义列表。",
},
"selected_history_count": {
"type": "integer",
"description": "当前选中的上下文消息数量。",
},
"built_message_count": {
"type": "integer",
"description": "实际发送给模型的消息数量。",
},
"selection_reason": {
"type": "string",
"description": "上下文选择说明。",
},
"session_id": {
"type": "string",
"description": "当前会话 ID。",
},
},
required=[
"messages",
"tool_definitions",
"selected_history_count",
"built_message_count",
"selection_reason",
"session_id",
],
),
default_timeout_ms=6000,
allow_abort=False,
allow_kwargs_mutation=True,
),
HookSpec(
name="maisaka.planner.after_response",
description="在 Maisaka 收到模型响应后触发，可调整文本结果与工具调用列表。",
parameters_schema=build_object_schema(
{
"response": {
"type": "string",
"description": "模型返回的文本内容。",
},
"tool_calls": {
"type": "array",
"description": "模型返回的工具调用列表。",
},
"selected_history_count": {
"type": "integer",
"description": "当前选中的上下文消息数量。",
},
"built_message_count": {
"type": "integer",
"description": "实际发送给模型的消息数量。",
},
"selection_reason": {
"type": "string",
"description": "上下文选择说明。",
},
"session_id": {
"type": "string",
"description": "当前会话 ID。",
},
"prompt_tokens": {
"type": "integer",
"description": "输入 Token 数。",
},
"completion_tokens": {
"type": "integer",
"description": "输出 Token 数。",
},
"total_tokens": {
"type": "integer",
"description": "总 Token 数。",
},
},
required=[
"response",
"tool_calls",
"selected_history_count",
"built_message_count",
"selection_reason",
"session_id",
"prompt_tokens",
"completion_tokens",
"total_tokens",
],
),
default_timeout_ms=6000,
allow_abort=False,
allow_kwargs_mutation=True,
),
]
)
class MaisakaChatLoopService:
"""负责 Maisaka 主对话循环、系统提示词和终端渲染。"""
@@ -105,6 +231,35 @@ class MaisakaChatLoopService:
return self._personality_prompt
@staticmethod
def _get_runtime_manager() -> Any:
"""获取插件运行时管理器。
Returns:
Any: 插件运行时管理器单例。
"""
from src.plugin_runtime.integration import get_plugin_runtime_manager
return get_plugin_runtime_manager()
@staticmethod
def _coerce_int(value: Any, default: int) -> int:
"""将任意值安全转换为整数。
Args:
value: 待转换的输入值。
default: 转换失败时的默认值。
Returns:
int: 转换后的整数结果。
"""
try:
return int(value)
except (TypeError, ValueError):
return default
def _build_personality_prompt(self) -> str:
"""构造人格提示词。"""
@@ -580,6 +735,26 @@ class MaisakaChatLoopService:
else:
all_tools = [*get_builtin_tools(), *self._extra_tools]
before_request_result = await self._get_runtime_manager().invoke_hook(
"maisaka.planner.before_request",
messages=serialize_prompt_messages(built_messages),
tool_definitions=serialize_tool_definitions(all_tools),
selected_history_count=len(selected_history),
built_message_count=len(built_messages),
selection_reason=selection_reason,
session_id=self._session_id,
)
before_request_kwargs = before_request_result.kwargs
raw_messages = before_request_kwargs.get("messages")
if isinstance(raw_messages, list):
try:
built_messages = deserialize_prompt_messages(raw_messages)
except Exception as exc:
logger.warning(f"Hook maisaka.planner.before_request 返回的 messages 无法反序列化，已忽略: {exc}")
raw_tool_definitions = before_request_kwargs.get("tool_definitions")
if isinstance(raw_tool_definitions, list):
all_tools = [item for item in raw_tool_definitions if isinstance(item, dict)]
ordered_panels = PromptCLIVisualizer.build_prompt_panels(
built_messages,
image_display_mode=global_config.maisaka.terminal_image_display_mode,
@@ -625,33 +800,63 @@ class MaisakaChatLoopService:
)
logger.info(f"本轮Prompt统计: {prompt_stats_text}")
final_response = generation_result.response or ""
final_tool_calls = list(generation_result.tool_calls or [])
after_response_result = await self._get_runtime_manager().invoke_hook(
"maisaka.planner.after_response",
response=final_response,
tool_calls=serialize_tool_calls(final_tool_calls),
selected_history_count=len(selected_history),
built_message_count=len(built_messages),
selection_reason=selection_reason,
session_id=self._session_id,
prompt_tokens=generation_result.prompt_tokens,
completion_tokens=generation_result.completion_tokens,
total_tokens=generation_result.total_tokens,
)
after_response_kwargs = after_response_result.kwargs
if "response" in after_response_kwargs:
final_response = str(after_response_kwargs.get("response") or "")
raw_tool_calls = after_response_kwargs.get("tool_calls")
if isinstance(raw_tool_calls, list):
try:
final_tool_calls = deserialize_tool_calls(raw_tool_calls)
except Exception as exc:
logger.warning(f"Hook maisaka.planner.after_response 返回的 tool_calls 无法反序列化，已忽略: {exc}")
prompt_tokens = self._coerce_int(after_response_kwargs.get("prompt_tokens"), generation_result.prompt_tokens)
completion_tokens = self._coerce_int(
after_response_kwargs.get("completion_tokens"),
generation_result.completion_tokens,
)
total_tokens = self._coerce_int(after_response_kwargs.get("total_tokens"), generation_result.total_tokens)
tool_call_summaries = [
{
"调用编号": getattr(tool_call, "call_id", getattr(tool_call, "id", None)),
"工具名": getattr(tool_call, "func_name", getattr(tool_call, "name", None)),
"参数": getattr(tool_call, "args", getattr(tool_call, "arguments", None)),
}
for tool_call in (generation_result.tool_calls or [])
for tool_call in final_tool_calls
]
logger.info(
f"Maisaka 规划器返回结果: 内容={generation_result.response or ''!r} "
f"Maisaka 规划器返回结果: 内容={final_response!r} "
f"工具调用={tool_call_summaries}"
)
raw_message = AssistantMessage(
content=generation_result.response or "",
content=final_response,
timestamp=datetime.now(),
tool_calls=generation_result.tool_calls or [],
tool_calls=final_tool_calls,
)
return ChatResponse(
content=generation_result.response,
tool_calls=generation_result.tool_calls or [],
content=final_response or None,
tool_calls=final_tool_calls,
raw_message=raw_message,
selected_history_count=len(selected_history),
prompt_tokens=generation_result.prompt_tokens,
prompt_tokens=prompt_tokens,
built_message_count=len(built_messages),
completion_tokens=generation_result.completion_tokens,
total_tokens=generation_result.total_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
)
@staticmethod