ref：重构maisaka内置工具逻辑，拆分文件
This commit is contained in:
SengokuCola
@@ -688,39 +688,41 @@ class DefaultReplyer:
|
||||
return None
|
||||
|
||||
def get_chat_prompt_for_chat(self, chat_id: str) -> str:
|
||||
"""
|
||||
根据聊天流ID获取匹配的额外prompt（仅匹配group类型）
|
||||
|
||||
Args:
|
||||
chat_id: 聊天流ID（哈希值）
|
||||
|
||||
Returns:
|
||||
str: 匹配的额外prompt内容，如果没有匹配则返回空字符串
|
||||
"""
|
||||
if not global_config.experimental.chat_prompts:
|
||||
"""根据聊天流 ID 获取匹配的额外 prompt。"""
|
||||
if not global_config.chat.chat_prompts:
|
||||
return ""
|
||||
|
||||
for chat_prompt_str in global_config.experimental.chat_prompts:
|
||||
if not isinstance(chat_prompt_str, str):
|
||||
for chat_prompt_item in global_config.chat.chat_prompts:
|
||||
if hasattr(chat_prompt_item, "rule_type") and hasattr(chat_prompt_item, "prompt"):
|
||||
if str(chat_prompt_item.rule_type or "").strip() != "group":
|
||||
continue
|
||||
|
||||
config_chat_id = self._build_chat_uid(
|
||||
str(chat_prompt_item.platform or "").strip(),
|
||||
str(chat_prompt_item.item_id or "").strip(),
|
||||
True,
|
||||
)
|
||||
prompt_content = str(chat_prompt_item.prompt or "").strip()
|
||||
if config_chat_id == chat_id and prompt_content:
|
||||
logger.debug(f"匹配到群聊 prompt 配置，chat_id: {chat_id}, prompt: {prompt_content[:50]}...")
|
||||
return prompt_content
|
||||
continue
|
||||
|
||||
# 解析配置字符串，检查类型是否为group
|
||||
parts = chat_prompt_str.split(":", 3)
|
||||
if len(parts) != 4:
|
||||
if not isinstance(chat_prompt_item, str):
|
||||
continue
|
||||
|
||||
stream_type = parts[2]
|
||||
# 只匹配group类型
|
||||
if stream_type != "group":
|
||||
# 兼容旧格式的 platform:id:type:prompt 配置字符串。
|
||||
parts = chat_prompt_item.split(":", 3)
|
||||
if len(parts) != 4 or parts[2] != "group":
|
||||
continue
|
||||
|
||||
result = self._parse_chat_prompt_config_to_chat_id(chat_prompt_str)
|
||||
result = self._parse_chat_prompt_config_to_chat_id(chat_prompt_item)
|
||||
if result is None:
|
||||
continue
|
||||
|
||||
config_chat_id, prompt_content = result
|
||||
if config_chat_id == chat_id:
|
||||
logger.debug(f"匹配到群聊prompt配置，chat_id: {chat_id}, prompt: {prompt_content[:50]}...")
|
||||
logger.debug(f"匹配到群聊 prompt 配置，chat_id: {chat_id}, prompt: {prompt_content[:50]}...")
|
||||
return prompt_content
|
||||
|
||||
return ""
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -21,7 +21,6 @@ from .official_configs import (
|
||||
DatabaseConfig,
|
||||
DebugConfig,
|
||||
EmojiConfig,
|
||||
ExperimentalConfig,
|
||||
ExpressionConfig,
|
||||
KeywordReactionConfig,
|
||||
LPMMKnowledgeConfig,
|
||||
@@ -56,7 +55,7 @@ CONFIG_DIR: Path = PROJECT_ROOT / "config"
|
||||
BOT_CONFIG_PATH: Path = (CONFIG_DIR / "bot_config.toml").resolve().absolute()
|
||||
MODEL_CONFIG_PATH: Path = (CONFIG_DIR / "model_config.toml").resolve().absolute()
|
||||
MMC_VERSION: str = "1.0.0"
|
||||
CONFIG_VERSION: str = "8.2.1"
|
||||
CONFIG_VERSION: str = "8.3.0"
|
||||
MODEL_CONFIG_VERSION: str = "1.13.1"
|
||||
|
||||
logger = get_logger("config")
|
||||
@@ -113,13 +112,10 @@ class Config(ConfigBase):
|
||||
debug: DebugConfig = Field(default_factory=DebugConfig)
|
||||
"""调试配置类"""
|
||||
|
||||
experimental: ExperimentalConfig = Field(default_factory=ExperimentalConfig)
|
||||
"""实验性功能配置类"""
|
||||
|
||||
maim_message: MaimMessageConfig = Field(default_factory=MaimMessageConfig)
|
||||
"""maim_message配置类"""
|
||||
|
||||
lpmm_knowledge: LPMMKnowledgeConfig = Field(default_factory=LPMMKnowledgeConfig)
|
||||
lpmm_knowledge: LPMMKnowledgeConfig = Field(default_factory=LPMMKnowledgeConfig, repr=False)
|
||||
"""LPMM知识库配置类"""
|
||||
|
||||
webui: WebUIConfig = Field(default_factory=WebUIConfig)
|
||||
|
||||
@@ -253,11 +253,23 @@ def try_migrate_legacy_bot_config_dict(data: dict[str, Any]) -> MigrationResult:
|
||||
migrated_any = True
|
||||
reasons.append("expression.manual_reflect_operator_id")
|
||||
|
||||
chat = _as_dict(data.get("chat"))
|
||||
if chat is None:
|
||||
chat = {}
|
||||
data["chat"] = chat
|
||||
|
||||
mem = _as_dict(data.get("memory"))
|
||||
if mem is not None:
|
||||
if _migrate_target_item_list(mem, "global_memory_blacklist"):
|
||||
migrated_any = True
|
||||
reasons.append("memory.global_memory_blacklist")
|
||||
for removed_key in (
|
||||
"agent_timeout_seconds",
|
||||
"global_memory",
|
||||
"global_memory_blacklist",
|
||||
"max_agent_iterations",
|
||||
):
|
||||
if removed_key in mem:
|
||||
mem.pop(removed_key, None)
|
||||
migrated_any = True
|
||||
reasons.append(f"memory.{removed_key}_removed")
|
||||
|
||||
exp = _as_dict(data.get("experimental"))
|
||||
if exp is not None:
|
||||
@@ -265,7 +277,16 @@ def try_migrate_legacy_bot_config_dict(data: dict[str, Any]) -> MigrationResult:
|
||||
migrated_any = True
|
||||
reasons.append("experimental.chat_prompts")
|
||||
|
||||
chat = _as_dict(data.get("chat"))
|
||||
for key in ("private_plan_style", "group_chat_prompt", "private_chat_prompts", "chat_prompts"):
|
||||
if key in exp and key not in chat:
|
||||
chat[key] = exp[key]
|
||||
migrated_any = True
|
||||
reasons.append(f"experimental.{key}_moved_to_chat")
|
||||
|
||||
data.pop("experimental", None)
|
||||
migrated_any = True
|
||||
reasons.append("experimental_removed")
|
||||
|
||||
if chat is not None and "think_mode" in chat:
|
||||
chat.pop("think_mode", None)
|
||||
migrated_any = True
|
||||
|
||||
@@ -244,15 +244,45 @@ class ChatConfig(ConfigBase):
|
||||
},
|
||||
)
|
||||
"""每个聊天流最大保存的Plan/Reply日志数量，超过此数量时会自动删除最老的日志"""
|
||||
|
||||
llm_quote: bool = Field(
|
||||
default=False,
|
||||
private_plan_style: str = Field(
|
||||
default=(
|
||||
"1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用\n"
|
||||
"2.如果相同的内容已经被执行，请不要重复执行\n"
|
||||
"3.某句话如果已经被回复过，不要重复回复"
|
||||
),
|
||||
json_schema_extra={
|
||||
"x-widget": "switch",
|
||||
"x-icon": "quote",
|
||||
"x-widget": "textarea",
|
||||
"x-icon": "user",
|
||||
},
|
||||
)
|
||||
"""是否在 reply action 中启用 quote 参数，启用后 LLM 可以控制是否引用消息"""
|
||||
"""_wrap_私聊说话规则，行为风格"""
|
||||
|
||||
group_chat_prompt: str = Field(
|
||||
default="不要回复的太频繁！控制回复的频率，不要每个人的消息都回复，只回复你感兴趣的或者主动提及你的。",
|
||||
json_schema_extra={
|
||||
"x-widget": "textarea",
|
||||
"x-icon": "users",
|
||||
},
|
||||
)
|
||||
"""_wrap_群聊通用注意事项"""
|
||||
|
||||
private_chat_prompts: str = Field(
|
||||
default="",
|
||||
json_schema_extra={
|
||||
"x-widget": "textarea",
|
||||
"x-icon": "user",
|
||||
},
|
||||
)
|
||||
"""_wrap_私聊通用注意事项"""
|
||||
|
||||
chat_prompts: list["ExtraPromptItem"] = Field(
|
||||
default_factory=lambda: [],
|
||||
json_schema_extra={
|
||||
"x-widget": "custom",
|
||||
"x-icon": "list",
|
||||
},
|
||||
)
|
||||
"""_wrap_为指定聊天添加额外的 prompt 配置列表"""
|
||||
|
||||
enable_talk_value_rules: bool = Field(
|
||||
default=True,
|
||||
@@ -356,43 +386,6 @@ class MemoryConfig(ConfigBase):
|
||||
|
||||
__ui_parent__ = "emoji"
|
||||
|
||||
max_agent_iterations: int = Field(
|
||||
default=5,
|
||||
ge=1,
|
||||
json_schema_extra={
|
||||
"x-widget": "input",
|
||||
"x-icon": "layers",
|
||||
},
|
||||
)
|
||||
"""记忆思考深度（最低为1）"""
|
||||
|
||||
agent_timeout_seconds: float = Field(
|
||||
default=120.0,
|
||||
json_schema_extra={
|
||||
"x-widget": "input",
|
||||
"x-icon": "clock",
|
||||
},
|
||||
)
|
||||
"""最长回忆时间（秒）"""
|
||||
|
||||
global_memory: bool = Field(
|
||||
default=False,
|
||||
json_schema_extra={
|
||||
"x-widget": "switch",
|
||||
"x-icon": "globe",
|
||||
},
|
||||
)
|
||||
"""是否允许记忆检索在聊天记录中进行全局查询（忽略当前chat_id，仅对 search_chat_history 等工具生效）"""
|
||||
|
||||
global_memory_blacklist: list[TargetItem] = Field(
|
||||
default_factory=lambda: [],
|
||||
json_schema_extra={
|
||||
"x-widget": "custom",
|
||||
"x-icon": "shield-off",
|
||||
},
|
||||
)
|
||||
"""_wrap_全局记忆黑名单，当启用全局记忆时，不将特定聊天流纳入检索"""
|
||||
|
||||
chat_history_topic_check_message_threshold: int = Field(
|
||||
default=80,
|
||||
ge=1,
|
||||
@@ -444,10 +437,6 @@ class MemoryConfig(ConfigBase):
|
||||
|
||||
def model_post_init(self, context: Optional[dict] = None) -> None:
|
||||
"""验证配置值"""
|
||||
if self.max_agent_iterations < 1:
|
||||
raise ValueError(f"max_agent_iterations 必须至少为1，当前值: {self.max_agent_iterations}")
|
||||
if self.agent_timeout_seconds <= 0:
|
||||
raise ValueError(f"agent_timeout_seconds 必须大于0，当前值: {self.agent_timeout_seconds}")
|
||||
if self.chat_history_topic_check_message_threshold < 1:
|
||||
raise ValueError(
|
||||
f"chat_history_topic_check_message_threshold 必须至少为1，当前值: {self.chat_history_topic_check_message_threshold}"
|
||||
@@ -1052,57 +1041,13 @@ class ExtraPromptItem(ConfigBase):
|
||||
"""额外的prompt内容"""
|
||||
|
||||
def model_post_init(self, context: Optional[dict] = None) -> None:
|
||||
if not self.platform and not self.item_id and not self.prompt:
|
||||
return super().model_post_init(context)
|
||||
if not self.platform or not self.item_id or not self.prompt:
|
||||
raise ValueError("ExtraPromptItem 中 platform, id 和 prompt 不能为空")
|
||||
return super().model_post_init(context)
|
||||
|
||||
|
||||
class ExperimentalConfig(ConfigBase):
|
||||
"""实验功能配置类"""
|
||||
|
||||
__ui_parent__ = "debug"
|
||||
|
||||
private_plan_style: str = Field(
|
||||
default=(
|
||||
"1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用"
|
||||
"2.如果相同的内容已经被执行，请不要重复执行"
|
||||
"3.某句话如果已经被回复过，不要重复回复"
|
||||
),
|
||||
json_schema_extra={
|
||||
"x-widget": "textarea",
|
||||
"x-icon": "user",
|
||||
},
|
||||
)
|
||||
"""_wrap_私聊说话规则，行为风格（实验性功能）"""
|
||||
|
||||
group_chat_prompt: str = Field(
|
||||
default="",
|
||||
json_schema_extra={
|
||||
"x-widget": "textarea",
|
||||
"x-icon": "users",
|
||||
},
|
||||
)
|
||||
"""_wrap_群聊通用注意事项（实验性功能）"""
|
||||
|
||||
private_chat_prompts: str = Field(
|
||||
default="",
|
||||
json_schema_extra={
|
||||
"x-widget": "textarea",
|
||||
"x-icon": "user",
|
||||
},
|
||||
)
|
||||
"""_wrap_私聊通用注意事项（实验性功能）"""
|
||||
|
||||
chat_prompts: list[ExtraPromptItem] = Field(
|
||||
default_factory=lambda: [],
|
||||
json_schema_extra={
|
||||
"x-widget": "custom",
|
||||
"x-icon": "list",
|
||||
},
|
||||
)
|
||||
"""_wrap_为指定聊天添加额外的prompt配置列表"""
|
||||
|
||||
|
||||
class MaimMessageConfig(ConfigBase):
|
||||
"""maim_message配置类"""
|
||||
|
||||
@@ -1482,16 +1427,6 @@ class MaiSakaConfig(ConfigBase):
|
||||
},
|
||||
)
|
||||
"""启用知识库模块"""
|
||||
|
||||
show_analyze_cognition_prompt: bool = Field(
|
||||
default=False,
|
||||
json_schema_extra={
|
||||
"x-widget": "switch",
|
||||
"x-icon": "terminal",
|
||||
},
|
||||
)
|
||||
"""是否在 CLI 中显示 analyze_cognition 的 Prompt"""
|
||||
|
||||
show_thinking: bool = Field(
|
||||
default=True,
|
||||
json_schema_extra={
|
||||
|
||||
@@ -843,12 +843,6 @@ class LLMOrchestrator:
|
||||
|
||||
for _ in range(max_attempts):
|
||||
model_info, api_provider, client = self._select_model(exclude_models=failed_models_this_request)
|
||||
if self.request_type.startswith("maisaka_"):
|
||||
logger.info(
|
||||
f"LLMOrchestrator[{self.request_type}] 已选择模型 model={model_info.name} "
|
||||
f"provider={api_provider.name} request_type={request_type.value}"
|
||||
)
|
||||
|
||||
message_list = []
|
||||
if message_factory:
|
||||
message_list = message_factory(client)
|
||||
|
||||
71
src/maisaka/builtin_tool/__init__.py
Normal file
71
src/maisaka/builtin_tool/__init__.py
Normal file
@@ -0,0 +1,71 @@
|
||||
"""Maisaka 内置工具聚合入口。"""
|
||||
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
from src.llm_models.payload_content.tool_option import ToolDefinitionInput
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
from .no_reply import get_tool_spec as get_no_reply_tool_spec
|
||||
from .no_reply import handle_tool as handle_no_reply_tool
|
||||
from .query_jargon import get_tool_spec as get_query_jargon_tool_spec
|
||||
from .query_jargon import handle_tool as handle_query_jargon_tool
|
||||
from .query_person_info import get_tool_spec as get_query_person_info_tool_spec
|
||||
from .query_person_info import handle_tool as handle_query_person_info_tool
|
||||
from .reply import get_tool_spec as get_reply_tool_spec
|
||||
from .reply import handle_tool as handle_reply_tool
|
||||
from .send_emoji import get_tool_spec as get_send_emoji_tool_spec
|
||||
from .send_emoji import handle_tool as handle_send_emoji_tool
|
||||
from .wait import get_tool_spec as get_wait_tool_spec
|
||||
from .wait import handle_tool as handle_wait_tool
|
||||
|
||||
BuiltinToolHandler = Callable[[ToolInvocation, Optional[ToolExecutionContext]], Awaitable[ToolExecutionResult]]
|
||||
|
||||
|
||||
def get_builtin_tool_specs() -> List[ToolSpec]:
|
||||
"""获取默认启用的内置工具声明列表。"""
|
||||
|
||||
return [
|
||||
get_wait_tool_spec(),
|
||||
get_reply_tool_spec(),
|
||||
get_query_jargon_tool_spec(),
|
||||
get_no_reply_tool_spec(),
|
||||
get_send_emoji_tool_spec(),
|
||||
]
|
||||
|
||||
|
||||
def get_all_builtin_tool_specs() -> List[ToolSpec]:
|
||||
"""获取全部内置工具声明列表。"""
|
||||
|
||||
return [
|
||||
get_wait_tool_spec(),
|
||||
get_reply_tool_spec(),
|
||||
get_query_jargon_tool_spec(),
|
||||
get_query_person_info_tool_spec(),
|
||||
get_no_reply_tool_spec(),
|
||||
get_send_emoji_tool_spec(),
|
||||
]
|
||||
|
||||
|
||||
def get_builtin_tools() -> List[ToolDefinitionInput]:
|
||||
"""获取兼容旧模型层的内置工具定义。"""
|
||||
|
||||
return [tool_spec.to_llm_definition() for tool_spec in get_builtin_tool_specs()]
|
||||
|
||||
|
||||
def build_builtin_tool_handlers(tool_ctx: BuiltinToolRuntimeContext) -> Dict[str, BuiltinToolHandler]:
|
||||
"""构建内置工具处理器映射。"""
|
||||
|
||||
return {
|
||||
"reply": lambda invocation, context=None: handle_reply_tool(tool_ctx, invocation, context),
|
||||
"no_reply": lambda invocation, context=None: handle_no_reply_tool(tool_ctx, invocation, context),
|
||||
"query_jargon": lambda invocation, context=None: handle_query_jargon_tool(tool_ctx, invocation, context),
|
||||
"query_person_info": lambda invocation, context=None: handle_query_person_info_tool(
|
||||
tool_ctx,
|
||||
invocation,
|
||||
context,
|
||||
),
|
||||
"wait": lambda invocation, context=None: handle_wait_tool(tool_ctx, invocation, context),
|
||||
"send_emoji": lambda invocation, context=None: handle_send_emoji_tool(tool_ctx, invocation, context),
|
||||
}
|
||||
199
src/maisaka/builtin_tool/context.py
Normal file
199
src/maisaka/builtin_tool/context.py
Normal file
@@ -0,0 +1,199 @@
|
||||
"""Maisaka 内置工具执行上下文。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from base64 import b64decode
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.utils.utils import process_llm_response
|
||||
from src.common.data_models.message_component_data_model import EmojiComponent, MessageSequence, TextComponent
|
||||
from src.config.config import global_config
|
||||
from src.core.tooling import ToolExecutionResult
|
||||
|
||||
from ..context_messages import SessionBackedMessage
|
||||
from ..message_adapter import format_speaker_content
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..reasoning_engine import MaisakaReasoningEngine
|
||||
from ..runtime import MaisakaHeartFlowChatting
|
||||
|
||||
|
||||
class BuiltinToolRuntimeContext:
|
||||
"""为拆分后的内置工具提供统一运行时能力。"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
engine: "MaisakaReasoningEngine",
|
||||
runtime: "MaisakaHeartFlowChatting",
|
||||
) -> None:
|
||||
self.engine = engine
|
||||
self.runtime = runtime
|
||||
|
||||
@staticmethod
|
||||
def build_success_result(
|
||||
tool_name: str,
|
||||
content: str = "",
|
||||
structured_content: Any = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""构造统一工具成功结果。"""
|
||||
|
||||
return ToolExecutionResult(
|
||||
tool_name=tool_name,
|
||||
success=True,
|
||||
content=content,
|
||||
structured_content=structured_content,
|
||||
metadata=dict(metadata or {}),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def build_failure_result(
|
||||
tool_name: str,
|
||||
error_message: str,
|
||||
structured_content: Any = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""构造统一工具失败结果。"""
|
||||
|
||||
return ToolExecutionResult(
|
||||
tool_name=tool_name,
|
||||
success=False,
|
||||
error_message=error_message,
|
||||
structured_content=structured_content,
|
||||
metadata=dict(metadata or {}),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def normalize_words(raw_words: Any) -> List[str]:
|
||||
"""清洗黑话查询词条列表。"""
|
||||
|
||||
if not isinstance(raw_words, list):
|
||||
return []
|
||||
|
||||
normalized_words: List[str] = []
|
||||
seen_words: set[str] = set()
|
||||
for item in raw_words:
|
||||
if not isinstance(item, str):
|
||||
continue
|
||||
word = item.strip()
|
||||
if not word or word in seen_words:
|
||||
continue
|
||||
seen_words.add(word)
|
||||
normalized_words.append(word)
|
||||
return normalized_words
|
||||
|
||||
@staticmethod
|
||||
def normalize_jargon_query_results(raw_results: Any) -> List[Dict[str, object]]:
|
||||
"""规范化黑话查询结果列表。"""
|
||||
|
||||
if not isinstance(raw_results, list):
|
||||
return []
|
||||
|
||||
normalized_results: List[Dict[str, object]] = []
|
||||
for raw_item in raw_results:
|
||||
if not isinstance(raw_item, dict):
|
||||
continue
|
||||
word = str(raw_item.get("word") or "").strip()
|
||||
matches = raw_item.get("matches")
|
||||
normalized_matches: List[Dict[str, str]] = []
|
||||
if isinstance(matches, list):
|
||||
for match in matches:
|
||||
if not isinstance(match, dict):
|
||||
continue
|
||||
content = str(match.get("content") or "").strip()
|
||||
meaning = str(match.get("meaning") or "").strip()
|
||||
if not content or not meaning:
|
||||
continue
|
||||
normalized_matches.append({"content": content, "meaning": meaning})
|
||||
|
||||
normalized_results.append(
|
||||
{
|
||||
"word": word,
|
||||
"found": bool(raw_item.get("found", bool(normalized_matches))),
|
||||
"matches": normalized_matches,
|
||||
}
|
||||
)
|
||||
return normalized_results
|
||||
|
||||
@staticmethod
|
||||
def post_process_reply_text(reply_text: str) -> List[str]:
|
||||
"""沿用旧回复链的文本后处理，执行分段与错别字注入。"""
|
||||
|
||||
processed_segments: List[str] = []
|
||||
for segment in process_llm_response(reply_text):
|
||||
normalized_segment = segment.strip()
|
||||
if normalized_segment:
|
||||
processed_segments.append(normalized_segment)
|
||||
|
||||
if processed_segments:
|
||||
return processed_segments
|
||||
return [reply_text.strip()]
|
||||
|
||||
def get_runtime_manager(self) -> Any:
|
||||
"""获取插件运行时管理器。"""
|
||||
|
||||
return self.engine._get_runtime_manager()
|
||||
|
||||
def append_guided_reply_to_chat_history(self, reply_text: str) -> None:
|
||||
"""将引导回复写回 Maisaka 历史。"""
|
||||
|
||||
bot_name = global_config.bot.nickname.strip() or "MaiSaka"
|
||||
reply_timestamp = datetime.now()
|
||||
planner_prefix = (
|
||||
f"[时间]{reply_timestamp.strftime('%H:%M:%S')}\n"
|
||||
f"[用户]{bot_name}\n"
|
||||
"[用户群昵称]\n"
|
||||
"[msg_id]\n"
|
||||
"[发言内容]"
|
||||
)
|
||||
history_message = SessionBackedMessage(
|
||||
raw_message=MessageSequence([TextComponent(f"{planner_prefix}{reply_text}")]),
|
||||
visible_text=format_speaker_content(
|
||||
bot_name,
|
||||
reply_text,
|
||||
reply_timestamp,
|
||||
),
|
||||
timestamp=reply_timestamp,
|
||||
source_kind="guided_reply",
|
||||
)
|
||||
self.runtime._chat_history.append(history_message)
|
||||
|
||||
def append_sent_emoji_to_chat_history(
|
||||
self,
|
||||
*,
|
||||
emoji_base64: str,
|
||||
success_message: str,
|
||||
) -> None:
|
||||
"""将 bot 主动发送的表情包同步到 Maisaka 历史。"""
|
||||
|
||||
bot_name = global_config.bot.nickname.strip() or "MaiSaka"
|
||||
reply_timestamp = datetime.now()
|
||||
planner_prefix = (
|
||||
f"[时间]{reply_timestamp.strftime('%H:%M:%S')}\n"
|
||||
f"[用户]{bot_name}\n"
|
||||
"[用户群昵称]\n"
|
||||
"[msg_id]\n"
|
||||
"[发言内容]"
|
||||
)
|
||||
history_message = SessionBackedMessage(
|
||||
raw_message=MessageSequence(
|
||||
[
|
||||
TextComponent(planner_prefix),
|
||||
EmojiComponent(
|
||||
binary_hash="",
|
||||
content=success_message,
|
||||
binary_data=b64decode(emoji_base64),
|
||||
),
|
||||
]
|
||||
),
|
||||
visible_text=format_speaker_content(
|
||||
bot_name,
|
||||
"[表情包]",
|
||||
reply_timestamp,
|
||||
),
|
||||
timestamp=reply_timestamp,
|
||||
source_kind="guided_reply",
|
||||
)
|
||||
self.runtime._chat_history.append(history_message)
|
||||
34
src/maisaka/builtin_tool/no_reply.py
Normal file
34
src/maisaka/builtin_tool/no_reply.py
Normal file
@@ -0,0 +1,34 @@
|
||||
"""no_reply 内置工具。"""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
|
||||
|
||||
def get_tool_spec() -> ToolSpec:
|
||||
"""获取 no_reply 工具声明。"""
|
||||
|
||||
return ToolSpec(
|
||||
name="no_reply",
|
||||
brief_description="本轮不进行回复，等待其他用户的新消息。",
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
)
|
||||
|
||||
|
||||
async def handle_tool(
|
||||
tool_ctx: BuiltinToolRuntimeContext,
|
||||
invocation: ToolInvocation,
|
||||
context: Optional[ToolExecutionContext] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""执行 no_reply 内置工具。"""
|
||||
|
||||
del context
|
||||
tool_ctx.runtime._enter_stop_state()
|
||||
return tool_ctx.build_success_result(
|
||||
invocation.tool_name,
|
||||
"当前对话循环已暂停，等待新消息到来。",
|
||||
metadata={"pause_execution": True},
|
||||
)
|
||||
143
src/maisaka/builtin_tool/query_jargon.py
Normal file
143
src/maisaka/builtin_tool/query_jargon.py
Normal file
@@ -0,0 +1,143 @@
|
||||
"""query_jargon 内置工具。"""
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import json
|
||||
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
from src.learners.jargon_explainer import search_jargon
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
|
||||
|
||||
def get_tool_spec() -> ToolSpec:
|
||||
"""获取 query_jargon 工具声明。"""
|
||||
|
||||
return ToolSpec(
|
||||
name="query_jargon",
|
||||
brief_description="查询当前聊天上下文中的黑话或词条含义。",
|
||||
detailed_description="参数说明：\n- words：array，必填。要查询的词条列表。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"words": {
|
||||
"type": "array",
|
||||
"description": "要查询的词条列表。",
|
||||
"items": {"type": "string"},
|
||||
},
|
||||
},
|
||||
"required": ["words"],
|
||||
},
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
)
|
||||
|
||||
|
||||
async def handle_tool(
|
||||
tool_ctx: BuiltinToolRuntimeContext,
|
||||
invocation: ToolInvocation,
|
||||
context: Optional[ToolExecutionContext] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""执行 query_jargon 内置工具。"""
|
||||
|
||||
del context
|
||||
raw_words = invocation.arguments.get("words")
|
||||
|
||||
if not isinstance(raw_words, list):
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"查询黑话工具需要提供 `words` 数组参数。",
|
||||
)
|
||||
|
||||
words = tool_ctx.normalize_words(raw_words)
|
||||
if not words:
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"查询黑话工具至少需要一个非空词条。",
|
||||
)
|
||||
|
||||
limit = 5
|
||||
case_sensitive = False
|
||||
enable_fuzzy_fallback = True
|
||||
before_search_result = await tool_ctx.get_runtime_manager().invoke_hook(
|
||||
"jargon.query.before_search",
|
||||
words=list(words),
|
||||
session_id=tool_ctx.runtime.session_id,
|
||||
limit=limit,
|
||||
case_sensitive=case_sensitive,
|
||||
enable_fuzzy_fallback=enable_fuzzy_fallback,
|
||||
abort_message="黑话查询已被 Hook 中止。",
|
||||
)
|
||||
if before_search_result.aborted:
|
||||
abort_message = str(before_search_result.kwargs.get("abort_message") or "黑话查询已被 Hook 中止。").strip()
|
||||
return tool_ctx.build_failure_result(invocation.tool_name, abort_message or "黑话查询已被 Hook 中止。")
|
||||
|
||||
before_search_kwargs = before_search_result.kwargs
|
||||
if before_search_kwargs.get("words") is not None:
|
||||
words = tool_ctx.normalize_words(before_search_kwargs.get("words"))
|
||||
|
||||
if not words:
|
||||
return tool_ctx.build_failure_result(invocation.tool_name, "Hook 过滤后没有可查询的黑话词条。")
|
||||
|
||||
try:
|
||||
limit = int(before_search_kwargs.get("limit", limit))
|
||||
except (TypeError, ValueError):
|
||||
limit = 5
|
||||
limit = max(limit, 1)
|
||||
case_sensitive = bool(before_search_kwargs.get("case_sensitive", case_sensitive))
|
||||
enable_fuzzy_fallback = bool(before_search_kwargs.get("enable_fuzzy_fallback", enable_fuzzy_fallback))
|
||||
|
||||
results: List[Dict[str, object]] = []
|
||||
for word in words:
|
||||
exact_matches = search_jargon(
|
||||
keyword=word,
|
||||
chat_id=tool_ctx.runtime.session_id,
|
||||
limit=limit,
|
||||
case_sensitive=case_sensitive,
|
||||
fuzzy=False,
|
||||
)
|
||||
matched_entries = exact_matches
|
||||
if not matched_entries and enable_fuzzy_fallback:
|
||||
matched_entries = search_jargon(
|
||||
keyword=word,
|
||||
chat_id=tool_ctx.runtime.session_id,
|
||||
limit=limit,
|
||||
case_sensitive=case_sensitive,
|
||||
fuzzy=True,
|
||||
)
|
||||
|
||||
results.append(
|
||||
{
|
||||
"word": word,
|
||||
"found": bool(matched_entries),
|
||||
"matches": matched_entries,
|
||||
}
|
||||
)
|
||||
|
||||
after_search_result = await tool_ctx.get_runtime_manager().invoke_hook(
|
||||
"jargon.query.after_search",
|
||||
words=list(words),
|
||||
session_id=tool_ctx.runtime.session_id,
|
||||
limit=limit,
|
||||
case_sensitive=case_sensitive,
|
||||
enable_fuzzy_fallback=enable_fuzzy_fallback,
|
||||
results=list(results),
|
||||
abort_message="黑话查询结果已被 Hook 中止。",
|
||||
)
|
||||
if after_search_result.aborted:
|
||||
abort_message = str(after_search_result.kwargs.get("abort_message") or "黑话查询结果已被 Hook 中止。").strip()
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
abort_message or "黑话查询结果已被 Hook 中止。",
|
||||
)
|
||||
|
||||
raw_results = after_search_result.kwargs.get("results")
|
||||
if raw_results is not None:
|
||||
results = tool_ctx.normalize_jargon_query_results(raw_results)
|
||||
|
||||
structured_content: Dict[str, Any] = {"results": results}
|
||||
return tool_ctx.build_success_result(
|
||||
invocation.tool_name,
|
||||
json.dumps(structured_content, ensure_ascii=False),
|
||||
structured_content=structured_content,
|
||||
)
|
||||
183
src/maisaka/builtin_tool/query_person_info.py
Normal file
183
src/maisaka/builtin_tool/query_person_info.py
Normal file
@@ -0,0 +1,183 @@
|
||||
"""query_person_info 内置工具。"""
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import json
|
||||
|
||||
from sqlmodel import col, select
|
||||
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.database.database_model import PersonInfo
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
from src.know_u.knowledge_store import get_knowledge_store
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
|
||||
|
||||
def get_tool_spec(*, enabled: bool = False) -> ToolSpec:
|
||||
"""获取 query_person_info 工具声明。"""
|
||||
|
||||
return ToolSpec(
|
||||
name="query_person_info",
|
||||
brief_description="查询某个人的档案和相关记忆信息。",
|
||||
detailed_description=(
|
||||
"参数说明：\n"
|
||||
"- person_name：string，必填。人物名称、昵称或用户 ID。\n"
|
||||
"- limit：integer，可选。最多返回多少条匹配记录，默认 3。"
|
||||
),
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"person_name": {
|
||||
"type": "string",
|
||||
"description": "人物名称、昵称或用户 ID。",
|
||||
},
|
||||
"limit": {
|
||||
"type": "integer",
|
||||
"description": "最多返回多少条匹配记录。",
|
||||
"default": 3,
|
||||
},
|
||||
},
|
||||
"required": ["person_name"],
|
||||
},
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
enabled=enabled,
|
||||
)
|
||||
|
||||
|
||||
async def handle_tool(
|
||||
tool_ctx: BuiltinToolRuntimeContext,
|
||||
invocation: ToolInvocation,
|
||||
context: Optional[ToolExecutionContext] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""执行 query_person_info 内置工具。"""
|
||||
|
||||
del context
|
||||
raw_person_name = invocation.arguments.get("person_name")
|
||||
raw_limit = invocation.arguments.get("limit", 3)
|
||||
|
||||
if not isinstance(raw_person_name, str):
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"查询人物信息工具需要提供字符串类型的 `person_name` 参数。",
|
||||
)
|
||||
|
||||
person_name = raw_person_name.strip()
|
||||
if not person_name:
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"查询人物信息工具需要提供非空的 `person_name` 参数。",
|
||||
)
|
||||
|
||||
try:
|
||||
limit = max(1, min(int(raw_limit), 10))
|
||||
except (TypeError, ValueError):
|
||||
limit = 3
|
||||
|
||||
persons = _query_person_records(person_name, limit)
|
||||
result: Dict[str, Any] = {
|
||||
"query": person_name,
|
||||
"persons": persons,
|
||||
"related_knowledge": _query_related_knowledge(person_name, persons, limit),
|
||||
}
|
||||
return tool_ctx.build_success_result(
|
||||
invocation.tool_name,
|
||||
json.dumps(result, ensure_ascii=False),
|
||||
structured_content=result,
|
||||
)
|
||||
|
||||
|
||||
def _query_person_records(person_name: str, limit: int) -> List[Dict[str, Any]]:
|
||||
"""按名称、昵称或用户 ID 查询人物档案。"""
|
||||
|
||||
with get_db_session() as session:
|
||||
records = session.exec(
|
||||
select(PersonInfo)
|
||||
.where(
|
||||
col(PersonInfo.person_name).contains(person_name)
|
||||
| col(PersonInfo.user_nickname).contains(person_name)
|
||||
| col(PersonInfo.user_id).contains(person_name)
|
||||
)
|
||||
.order_by(col(PersonInfo.last_known_time).desc(), col(PersonInfo.id).desc())
|
||||
.limit(limit)
|
||||
).all()
|
||||
persons: List[Dict[str, Any]] = []
|
||||
for record in records:
|
||||
memory_points: List[str] = []
|
||||
if record.memory_points:
|
||||
try:
|
||||
parsed_points = json.loads(record.memory_points)
|
||||
if isinstance(parsed_points, list):
|
||||
memory_points = [str(point).strip() for point in parsed_points if str(point).strip()]
|
||||
except (json.JSONDecodeError, TypeError, ValueError):
|
||||
memory_points = []
|
||||
|
||||
persons.append(
|
||||
{
|
||||
"person_id": record.person_id,
|
||||
"person_name": record.person_name or "",
|
||||
"user_nickname": record.user_nickname,
|
||||
"user_id": record.user_id,
|
||||
"platform": record.platform,
|
||||
"name_reason": record.name_reason or "",
|
||||
"is_known": record.is_known,
|
||||
"know_counts": record.know_counts,
|
||||
"memory_points": memory_points[:20],
|
||||
"last_known_time": record.last_known_time.isoformat() if record.last_known_time is not None else None,
|
||||
}
|
||||
)
|
||||
|
||||
return persons
|
||||
|
||||
|
||||
def _query_related_knowledge(
|
||||
person_name: str,
|
||||
persons: List[Dict[str, Any]],
|
||||
limit: int,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""从 Maisaka knowledge 中补充检索与该人物相关的条目。"""
|
||||
|
||||
store = get_knowledge_store()
|
||||
knowledge_items: List[Dict[str, Any]] = []
|
||||
seen_ids: set[str] = set()
|
||||
|
||||
for person in persons:
|
||||
matched_items = store.get_knowledge_by_user(
|
||||
platform=str(person.get("platform", "")).strip(),
|
||||
user_id=str(person.get("user_id", "")).strip(),
|
||||
user_nickname=str(person.get("user_nickname", "")).strip(),
|
||||
person_name=str(person.get("person_name", "")).strip(),
|
||||
limit=max(limit, 5),
|
||||
)
|
||||
for item in matched_items:
|
||||
item_id = str(item.get("id", "")).strip()
|
||||
if item_id and item_id in seen_ids:
|
||||
continue
|
||||
if item_id:
|
||||
seen_ids.add(item_id)
|
||||
knowledge_items.append(item)
|
||||
|
||||
if not knowledge_items:
|
||||
fallback_items = store.search_knowledge(person_name, limit=max(limit, 5))
|
||||
for item in fallback_items:
|
||||
item_id = str(item.get("id", "")).strip()
|
||||
if item_id and item_id in seen_ids:
|
||||
continue
|
||||
if item_id:
|
||||
seen_ids.add(item_id)
|
||||
knowledge_items.append(item)
|
||||
|
||||
results: List[Dict[str, Any]] = []
|
||||
for item in knowledge_items:
|
||||
results.append(
|
||||
{
|
||||
"id": str(item.get("id", "")).strip(),
|
||||
"category_id": str(item.get("category_id", "")).strip(),
|
||||
"category_name": str(item.get("category_name", "")).strip(),
|
||||
"content": str(item.get("content", "")).strip(),
|
||||
"metadata": item.get("metadata", {}),
|
||||
"created_at": item.get("created_at"),
|
||||
}
|
||||
)
|
||||
return results
|
||||
182
src/maisaka/builtin_tool/reply.py
Normal file
182
src/maisaka/builtin_tool/reply.py
Normal file
@@ -0,0 +1,182 @@
|
||||
"""reply 内置工具。"""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from src.chat.replyer.replyer_manager import replyer_manager
|
||||
from src.common.logger import get_logger
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
from src.services import send_service
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
|
||||
logger = get_logger("maisaka_builtin_reply")
|
||||
|
||||
|
||||
def get_tool_spec() -> ToolSpec:
|
||||
"""获取 reply 工具声明。"""
|
||||
|
||||
return ToolSpec(
|
||||
name="reply",
|
||||
brief_description="根据当前思考生成并发送一条可见回复。",
|
||||
detailed_description=(
|
||||
"参数说明：\n"
|
||||
"- msg_id：string，必填。要回复的目标用户消息编号。\n"
|
||||
"- quote：boolean，可选。当有非常明确的回复目标时，以引用回复的方式发送，默认 true。\n"
|
||||
"- unknown_words：array，可选。回复前可能需要查询的黑话或词条列表。"
|
||||
),
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"msg_id": {
|
||||
"type": "string",
|
||||
"description": "要回复的目标用户消息编号。",
|
||||
},
|
||||
"quote": {
|
||||
"type": "boolean",
|
||||
"description": "当有非常明确的回复目标时，以引用回复的方式发送。",
|
||||
"default": True,
|
||||
},
|
||||
"unknown_words": {
|
||||
"type": "array",
|
||||
"description": "回复前可能需要查询的黑话或词条列表。",
|
||||
"items": {"type": "string"},
|
||||
},
|
||||
},
|
||||
"required": ["msg_id"],
|
||||
},
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
)
|
||||
|
||||
|
||||
async def handle_tool(
|
||||
tool_ctx: BuiltinToolRuntimeContext,
|
||||
invocation: ToolInvocation,
|
||||
context: Optional[ToolExecutionContext] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""执行 reply 内置工具。"""
|
||||
|
||||
latest_thought = context.reasoning if context is not None else invocation.reasoning
|
||||
target_message_id = str(invocation.arguments.get("msg_id") or "").strip()
|
||||
quote_reply = bool(invocation.arguments.get("quote", True))
|
||||
raw_unknown_words = invocation.arguments.get("unknown_words")
|
||||
unknown_words = raw_unknown_words if isinstance(raw_unknown_words, list) else None
|
||||
|
||||
if not target_message_id:
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"回复工具需要提供有效的 `msg_id` 参数。",
|
||||
)
|
||||
|
||||
target_message = tool_ctx.runtime._source_messages_by_id.get(target_message_id)
|
||||
if target_message is None:
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
f"未找到要回复的目标消息，msg_id={target_message_id}",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"{tool_ctx.runtime.log_prefix} 已触发回复工具 "
|
||||
f"目标消息编号={target_message_id} 引用回复={quote_reply} 最新思考={latest_thought!r}"
|
||||
)
|
||||
try:
|
||||
replyer = replyer_manager.get_replyer(
|
||||
chat_stream=tool_ctx.runtime.chat_stream,
|
||||
request_type="maisaka_replyer",
|
||||
replyer_type="maisaka",
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
f"{tool_ctx.runtime.log_prefix} 获取回复生成器时发生异常: 目标消息编号={target_message_id}"
|
||||
)
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"获取 Maisaka 回复生成器时发生异常。",
|
||||
)
|
||||
|
||||
if replyer is None:
|
||||
logger.error(f"{tool_ctx.runtime.log_prefix} 获取 Maisaka 回复生成器失败")
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"Maisaka 回复生成器当前不可用。",
|
||||
)
|
||||
|
||||
try:
|
||||
success, reply_result = await replyer.generate_reply_with_context(
|
||||
reply_reason=latest_thought,
|
||||
stream_id=tool_ctx.runtime.session_id,
|
||||
reply_message=target_message,
|
||||
chat_history=tool_ctx.runtime._chat_history,
|
||||
unknown_words=unknown_words,
|
||||
log_reply=False,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.exception(
|
||||
f"{tool_ctx.runtime.log_prefix} 回复生成器执行异常: 目标消息编号={target_message_id} 异常={exc}"
|
||||
)
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"生成可见回复时发生异常。",
|
||||
)
|
||||
|
||||
reply_text = reply_result.completion.response_text.strip() if success else ""
|
||||
if not reply_text:
|
||||
logger.warning(
|
||||
f"{tool_ctx.runtime.log_prefix} 回复生成器返回空文本: "
|
||||
f"目标消息编号={target_message_id} 错误信息={reply_result.error_message!r}"
|
||||
)
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"生成可见回复失败。",
|
||||
)
|
||||
|
||||
reply_segments = tool_ctx.post_process_reply_text(reply_text)
|
||||
combined_reply_text = "".join(reply_segments)
|
||||
try:
|
||||
sent = False
|
||||
for index, segment in enumerate(reply_segments):
|
||||
sent = await send_service.text_to_stream(
|
||||
text=segment,
|
||||
stream_id=tool_ctx.runtime.session_id,
|
||||
set_reply=quote_reply if index == 0 else False,
|
||||
reply_message=target_message if quote_reply and index == 0 else None,
|
||||
selected_expressions=reply_result.selected_expression_ids or None,
|
||||
typing=index > 0,
|
||||
)
|
||||
if not sent:
|
||||
break
|
||||
except Exception:
|
||||
logger.exception(
|
||||
f"{tool_ctx.runtime.log_prefix} 发送文字消息时发生异常，目标消息编号={target_message_id}"
|
||||
)
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"发送可见回复时发生异常。",
|
||||
)
|
||||
|
||||
if not sent:
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
"可见回复生成成功，但发送失败。",
|
||||
structured_content={
|
||||
"msg_id": target_message_id,
|
||||
"quote": quote_reply,
|
||||
"reply_segments": reply_segments,
|
||||
},
|
||||
)
|
||||
|
||||
target_user_info = target_message.message_info.user_info
|
||||
target_user_name = target_user_info.user_cardname or target_user_info.user_nickname or target_user_info.user_id
|
||||
|
||||
tool_ctx.append_guided_reply_to_chat_history(combined_reply_text)
|
||||
return tool_ctx.build_success_result(
|
||||
invocation.tool_name,
|
||||
"回复已生成并发送。",
|
||||
structured_content={
|
||||
"msg_id": target_message_id,
|
||||
"quote": quote_reply,
|
||||
"reply_text": combined_reply_text,
|
||||
"reply_segments": reply_segments,
|
||||
"target_user_name": target_user_name,
|
||||
},
|
||||
)
|
||||
106
src/maisaka/builtin_tool/send_emoji.py
Normal file
106
src/maisaka/builtin_tool/send_emoji.py
Normal file
@@ -0,0 +1,106 @@
|
||||
"""send_emoji 内置工具。"""
|
||||
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from src.chat.emoji_system.maisaka_tool import send_emoji_for_maisaka
|
||||
from src.common.logger import get_logger
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
|
||||
logger = get_logger("maisaka_builtin_send_emoji")
|
||||
|
||||
|
||||
def get_tool_spec() -> ToolSpec:
|
||||
"""获取 send_emoji 工具声明。"""
|
||||
|
||||
return ToolSpec(
|
||||
name="send_emoji",
|
||||
brief_description="发送一个合适的表情包来辅助表达情绪。",
|
||||
detailed_description="参数说明：\n- emotion：string，可选。希望表达的情绪，例如 happy、sad、angry 等。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"emotion": {
|
||||
"type": "string",
|
||||
"description": "希望表达的情绪，例如 happy、sad、angry 等。",
|
||||
},
|
||||
},
|
||||
},
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
)
|
||||
|
||||
|
||||
async def handle_tool(
|
||||
tool_ctx: BuiltinToolRuntimeContext,
|
||||
invocation: ToolInvocation,
|
||||
context: Optional[ToolExecutionContext] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""执行 send_emoji 内置工具。"""
|
||||
|
||||
del context
|
||||
emotion = str(invocation.arguments.get("emotion") or "").strip()
|
||||
context_texts = [
|
||||
message.get_history_text()
|
||||
for message in tool_ctx.runtime._chat_history[-5:]
|
||||
if message.get_history_text().strip()
|
||||
]
|
||||
structured_result: Dict[str, Any] = {
|
||||
"success": False,
|
||||
"message": "",
|
||||
"description": "",
|
||||
"emotion": [],
|
||||
"requested_emotion": emotion,
|
||||
"matched_emotion": "",
|
||||
}
|
||||
|
||||
logger.info(f"{tool_ctx.runtime.log_prefix} 触发表情包发送工具，请求情绪={emotion!r}")
|
||||
|
||||
try:
|
||||
send_result = await send_emoji_for_maisaka(
|
||||
stream_id=tool_ctx.runtime.session_id,
|
||||
requested_emotion=emotion,
|
||||
reasoning=tool_ctx.engine.last_reasoning_content,
|
||||
context_texts=context_texts,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.exception(f"{tool_ctx.runtime.log_prefix} 发送表情包时发生异常: {exc}")
|
||||
structured_result["message"] = f"发送表情包时发生异常：{exc}"
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
structured_result["message"],
|
||||
structured_content=structured_result,
|
||||
)
|
||||
|
||||
structured_result["description"] = send_result.description
|
||||
structured_result["emotion"] = list(send_result.emotions)
|
||||
structured_result["matched_emotion"] = send_result.matched_emotion
|
||||
structured_result["message"] = send_result.message
|
||||
|
||||
if send_result.success:
|
||||
logger.info(
|
||||
f"{tool_ctx.runtime.log_prefix} 表情包发送成功 "
|
||||
f"描述={send_result.description!r} 情绪标签={send_result.emotions} "
|
||||
f"请求情绪={emotion!r} 命中情绪={send_result.matched_emotion!r}"
|
||||
)
|
||||
tool_ctx.append_sent_emoji_to_chat_history(
|
||||
emoji_base64=send_result.emoji_base64,
|
||||
success_message=send_result.message,
|
||||
)
|
||||
structured_result["success"] = True
|
||||
return tool_ctx.build_success_result(
|
||||
invocation.tool_name,
|
||||
send_result.message,
|
||||
structured_content=structured_result,
|
||||
)
|
||||
|
||||
logger.warning(
|
||||
f"{tool_ctx.runtime.log_prefix} 表情包发送失败 "
|
||||
f"请求情绪={emotion!r} 错误信息={send_result.message}"
|
||||
)
|
||||
return tool_ctx.build_failure_result(
|
||||
invocation.tool_name,
|
||||
structured_result["message"],
|
||||
structured_content=structured_result,
|
||||
)
|
||||
51
src/maisaka/builtin_tool/wait.py
Normal file
51
src/maisaka/builtin_tool/wait.py
Normal file
@@ -0,0 +1,51 @@
|
||||
"""wait 内置工具。"""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
|
||||
|
||||
from .context import BuiltinToolRuntimeContext
|
||||
|
||||
|
||||
def get_tool_spec() -> ToolSpec:
|
||||
"""获取 wait 工具声明。"""
|
||||
|
||||
return ToolSpec(
|
||||
name="wait",
|
||||
brief_description="暂停当前对话并等待用户新的输入。",
|
||||
detailed_description="参数说明：\n- seconds：integer，必填。等待的秒数。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"seconds": {
|
||||
"type": "integer",
|
||||
"description": "等待的秒数。",
|
||||
},
|
||||
},
|
||||
"required": ["seconds"],
|
||||
},
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
)
|
||||
|
||||
|
||||
async def handle_tool(
|
||||
tool_ctx: BuiltinToolRuntimeContext,
|
||||
invocation: ToolInvocation,
|
||||
context: Optional[ToolExecutionContext] = None,
|
||||
) -> ToolExecutionResult:
|
||||
"""执行 wait 内置工具。"""
|
||||
|
||||
del context
|
||||
seconds = invocation.arguments.get("seconds", 30)
|
||||
try:
|
||||
wait_seconds = int(seconds)
|
||||
except (TypeError, ValueError):
|
||||
wait_seconds = 30
|
||||
wait_seconds = max(0, wait_seconds)
|
||||
tool_ctx.runtime._enter_wait_state(seconds=wait_seconds, tool_call_id=invocation.call_id)
|
||||
return tool_ctx.build_success_result(
|
||||
invocation.tool_name,
|
||||
f"当前对话循环进入等待状态，最长等待 {wait_seconds} 秒。",
|
||||
metadata={"pause_execution": True},
|
||||
)
|
||||
@@ -1,159 +0,0 @@
|
||||
"""Maisaka 内置工具声明。"""
|
||||
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from src.core.tooling import ToolSpec, build_tool_detailed_description
|
||||
from src.llm_models.payload_content.tool_option import ToolDefinitionInput
|
||||
|
||||
|
||||
def _build_tool_spec(
|
||||
name: str,
|
||||
brief_description: str,
|
||||
parameters_schema: Dict[str, Any] | None = None,
|
||||
detailed_description: str = "",
|
||||
) -> ToolSpec:
|
||||
"""构建单个内置工具声明。
|
||||
|
||||
Args:
|
||||
name: 工具名称。
|
||||
brief_description: 简要描述。
|
||||
parameters_schema: 参数 Schema。
|
||||
detailed_description: 详细描述；为空时自动根据参数生成。
|
||||
|
||||
Returns:
|
||||
ToolSpec: 构建完成的工具声明。
|
||||
"""
|
||||
|
||||
normalized_schema = deepcopy(parameters_schema) if parameters_schema is not None else None
|
||||
return ToolSpec(
|
||||
name=name,
|
||||
brief_description=brief_description,
|
||||
detailed_description=(
|
||||
detailed_description.strip()
|
||||
or build_tool_detailed_description(normalized_schema)
|
||||
),
|
||||
parameters_schema=normalized_schema,
|
||||
provider_name="maisaka_builtin",
|
||||
provider_type="builtin",
|
||||
)
|
||||
|
||||
|
||||
def create_builtin_tool_specs() -> List[ToolSpec]:
|
||||
"""创建 Maisaka 内置工具声明列表。
|
||||
|
||||
Returns:
|
||||
List[ToolSpec]: 内置工具声明列表。
|
||||
"""
|
||||
|
||||
return [
|
||||
_build_tool_spec(
|
||||
name="wait",
|
||||
brief_description="暂停当前对话并等待用户新的输入。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"seconds": {
|
||||
"type": "integer",
|
||||
"description": "等待的秒数。",
|
||||
},
|
||||
},
|
||||
"required": ["seconds"],
|
||||
},
|
||||
),
|
||||
_build_tool_spec(
|
||||
name="reply",
|
||||
brief_description="根据当前思考生成并发送一条可见回复。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"msg_id": {
|
||||
"type": "string",
|
||||
"description": "要回复的目标用户消息编号。",
|
||||
},
|
||||
"quote": {
|
||||
"type": "boolean",
|
||||
"description": "当有非常明确的回复目标时，以引用回复的方式发送。",
|
||||
"default": True,
|
||||
},
|
||||
"unknown_words": {
|
||||
"type": "array",
|
||||
"description": "回复前可能需要查询的黑话或词条列表。",
|
||||
"items": {"type": "string"},
|
||||
},
|
||||
},
|
||||
"required": ["msg_id"],
|
||||
},
|
||||
),
|
||||
_build_tool_spec(
|
||||
name="query_jargon",
|
||||
brief_description="查询当前聊天上下文中的黑话或词条含义。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"words": {
|
||||
"type": "array",
|
||||
"description": "要查询的词条列表。",
|
||||
"items": {"type": "string"},
|
||||
},
|
||||
},
|
||||
"required": ["words"],
|
||||
},
|
||||
),
|
||||
# _build_tool_spec(
|
||||
# name="query_person_info",
|
||||
# brief_description="查询某个人的档案和相关记忆信息。",
|
||||
# parameters_schema={
|
||||
# "type": "object",
|
||||
# "properties": {
|
||||
# "person_name": {
|
||||
# "type": "string",
|
||||
# "description": "人物名称、昵称或用户 ID。",
|
||||
# },
|
||||
# "limit": {
|
||||
# "type": "integer",
|
||||
# "description": "最多返回多少条匹配记录。",
|
||||
# "default": 3,
|
||||
# },
|
||||
# },
|
||||
# "required": ["person_name"],
|
||||
# },
|
||||
# ),
|
||||
_build_tool_spec(
|
||||
name="no_reply",
|
||||
brief_description="本轮不进行回复，等待其他用户的新消息。",
|
||||
),
|
||||
_build_tool_spec(
|
||||
name="send_emoji",
|
||||
brief_description="发送一个合适的表情包来辅助表达情绪。",
|
||||
parameters_schema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"emotion": {
|
||||
"type": "string",
|
||||
"description": "希望表达的情绪，例如 happy、sad、angry 等。",
|
||||
},
|
||||
},
|
||||
},
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
def get_builtin_tool_specs() -> List[ToolSpec]:
|
||||
"""获取 Maisaka 内置工具声明。
|
||||
|
||||
Returns:
|
||||
List[ToolSpec]: 内置工具声明列表。
|
||||
"""
|
||||
|
||||
return create_builtin_tool_specs()
|
||||
|
||||
|
||||
def get_builtin_tools() -> List[ToolDefinitionInput]:
|
||||
"""获取兼容旧模型层的内置工具定义。
|
||||
|
||||
Returns:
|
||||
List[ToolDefinitionInput]: 可直接传给模型层的工具定义。
|
||||
"""
|
||||
|
||||
return [tool_spec.to_llm_definition() for tool_spec in create_builtin_tool_specs()]
|
||||
@@ -37,7 +37,7 @@ 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
|
||||
from .builtin_tool import get_builtin_tools
|
||||
from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage, ToolResultMessage
|
||||
from .message_adapter import format_speaker_content
|
||||
from .prompt_cli_renderer import PromptCLIVisualizer
|
||||
@@ -290,14 +290,7 @@ class MaisakaChatLoopService:
|
||||
Args:
|
||||
tools_section: 额外注入到提示词中的工具说明片段。
|
||||
"""
|
||||
|
||||
if self._prompts_loaded:
|
||||
return
|
||||
|
||||
async with self._prompt_load_lock:
|
||||
if self._prompts_loaded:
|
||||
return
|
||||
|
||||
try:
|
||||
self._chat_system_prompt = load_prompt(
|
||||
"maisaka_chat",
|
||||
@@ -317,29 +310,29 @@ class MaisakaChatLoopService:
|
||||
prompt_lines: List[str] = []
|
||||
|
||||
if self._is_group_chat is True:
|
||||
if group_chat_prompt := str(global_config.experimental.group_chat_prompt or "").strip():
|
||||
prompt_lines.append(group_chat_prompt)
|
||||
if group_chat_prompt := str(global_config.chat.group_chat_prompt or "").strip():
|
||||
prompt_lines.append(f"通用注意事项：\n{group_chat_prompt}")
|
||||
elif self._is_group_chat is False:
|
||||
if private_chat_prompt := str(global_config.experimental.private_chat_prompts or "").strip():
|
||||
prompt_lines.append(private_chat_prompt)
|
||||
if private_chat_prompt := str(global_config.chat.private_chat_prompts or "").strip():
|
||||
prompt_lines.append(f"通用注意事项：\n{private_chat_prompt}")
|
||||
|
||||
if self._session_id:
|
||||
if chat_prompt := self._get_chat_prompt_for_chat(self._session_id, self._is_group_chat).strip():
|
||||
prompt_lines.append(chat_prompt)
|
||||
prompt_lines.append(f"当前聊天额外注意事项：\n{chat_prompt}")
|
||||
|
||||
if not prompt_lines:
|
||||
return ""
|
||||
|
||||
return f"在该聊天中的注意事项：\n" + "\n".join(prompt_lines) + "\n"
|
||||
return f"在该聊天中的注意事项：\n" + "\n\n".join(prompt_lines) + "\n"
|
||||
|
||||
@staticmethod
|
||||
def _get_chat_prompt_for_chat(chat_id: str, is_group_chat: Optional[bool]) -> str:
|
||||
"""根据聊天流 ID 获取匹配的额外提示。"""
|
||||
|
||||
if not global_config.experimental.chat_prompts:
|
||||
if not global_config.chat.chat_prompts:
|
||||
return ""
|
||||
|
||||
for chat_prompt_item in global_config.experimental.chat_prompts:
|
||||
for chat_prompt_item in global_config.chat.chat_prompts:
|
||||
if hasattr(chat_prompt_item, "platform"):
|
||||
platform = str(chat_prompt_item.platform or "").strip()
|
||||
item_id = str(chat_prompt_item.item_id or "").strip()
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -7,7 +7,7 @@ from typing import Dict, Optional
|
||||
|
||||
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolProvider, ToolSpec
|
||||
|
||||
from .builtin_tools import get_builtin_tool_specs
|
||||
from .builtin_tool import get_builtin_tool_specs
|
||||
|
||||
BuiltinToolHandler = Callable[[ToolInvocation, Optional[ToolExecutionContext]], Awaitable[ToolExecutionResult]]
|
||||
|
||||
|
||||
@@ -24,10 +24,8 @@ from src.config.official_configs import (
|
||||
ChineseTypoConfig,
|
||||
DebugConfig,
|
||||
EmojiConfig,
|
||||
ExperimentalConfig,
|
||||
ExpressionConfig,
|
||||
KeywordReactionConfig,
|
||||
LPMMKnowledgeConfig,
|
||||
MaimMessageConfig,
|
||||
MemoryConfig,
|
||||
MessageReceiveConfig,
|
||||
@@ -109,9 +107,7 @@ async def get_config_section_schema(section_name: str):
|
||||
- response_post_process: ResponsePostProcessConfig
|
||||
- response_splitter: ResponseSplitterConfig
|
||||
- telemetry: TelemetryConfig
|
||||
- experimental: ExperimentalConfig
|
||||
- maim_message: MaimMessageConfig
|
||||
- lpmm_knowledge: LPMMKnowledgeConfig
|
||||
- memory: MemoryConfig
|
||||
- debug: DebugConfig
|
||||
- voice: VoiceConfig
|
||||
@@ -133,9 +129,7 @@ async def get_config_section_schema(section_name: str):
|
||||
"response_post_process": ResponsePostProcessConfig,
|
||||
"response_splitter": ResponseSplitterConfig,
|
||||
"telemetry": TelemetryConfig,
|
||||
"experimental": ExperimentalConfig,
|
||||
"maim_message": MaimMessageConfig,
|
||||
"lpmm_knowledge": LPMMKnowledgeConfig,
|
||||
"memory": MemoryConfig,
|
||||
"debug": DebugConfig,
|
||||
"voice": VoiceConfig,
|
||||
|
||||
Reference in New Issue
Block a user