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This commit is contained in:
LuiKlee
2025-12-13 16:39:25 +08:00
parent 4fe8e29ba5
commit 8f77465bc3
31 changed files with 339 additions and 322 deletions
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2
src/chat/emoji_system/emoji_manager.py
View File

@@ -4,7 +4,6 @@ import binascii
import hashlib
import io
import json
import json_repair
import os
import random
import re
@@ -12,6 +11,7 @@ import time
import traceback
from typing import Any, Optional, cast
import json_repair
from PIL import Image
from rich.traceback import install
from sqlalchemy import select

2
src/chat/message_receive/storage.py
View File

@@ -3,7 +3,7 @@ import re
import time
import traceback
from collections import deque
from typing import TYPE_CHECKING, Optional, Any, cast
from typing import TYPE_CHECKING, Any, Optional, cast
import orjson
from sqlalchemy import desc, insert, select, update

2
src/chat/replyer/default_generator.py
View File

@@ -1799,7 +1799,7 @@ class DefaultReplyer:
)
if content:
if not global_config.response_splitter.enable or global_config.response_splitter.split_mode != 'llm':
if not global_config.response_splitter.enable or global_config.response_splitter.split_mode != "llm":
# 移除 [SPLIT] 标记，防止消息被分割
content = content.replace("[SPLIT]", "")

17
src/chat/semantic_interest/auto_trainer.py
View File

@@ -10,9 +10,8 @@ from datetime import datetime, timedelta
from pathlib import Path
from typing import Any
from src.common.logger import get_logger
from src.config.config import global_config
from src.chat.semantic_interest.trainer import SemanticInterestTrainer
from src.common.logger import get_logger
logger = get_logger("semantic_interest.auto_trainer")
@@ -78,7 +77,7 @@ class AutoTrainer:
"""加载缓存的人设状态"""
if self.persona_cache_file.exists():
try:
with open(self.persona_cache_file, "r", encoding="utf-8") as f:
with open(self.persona_cache_file, encoding="utf-8") as f:
cache = json.load(f)
self.last_persona_hash = cache.get("persona_hash")
last_train_str = cache.get("last_train_time")
@@ -142,7 +141,7 @@ class AutoTrainer:
return True
if current_hash != self.last_persona_hash:
logger.info(f"[自动训练器] 检测到人设变化")
logger.info("[自动训练器] 检测到人设变化")
logger.info(f" - 旧哈希: {self.last_persona_hash[:8]}")
logger.info(f" - 新哈希: {current_hash[:8]}")
return True
@@ -236,7 +235,7 @@ class AutoTrainer:
# 创建"latest"符号链接
self._create_latest_link(model_path)
logger.info(f"[自动训练器] 训练完成!")
logger.info("[自动训练器] 训练完成!")
logger.info(f" - 模型: {model_path.name}")
logger.info(f" - 准确率: {metrics.get('test_accuracy', 0):.4f}")
@@ -265,7 +264,7 @@ class AutoTrainer:
import shutil
shutil.copy2(model_path, latest_path)
logger.info(f"[自动训练器] 已更新 latest 模型")
logger.info("[自动训练器] 已更新 latest 模型")
except Exception as e:
logger.warning(f"[自动训练器] 创建 latest 链接失败: {e}")
@@ -283,7 +282,7 @@ class AutoTrainer:
"""
# 检查是否已经有任务在运行
if self._scheduled_task_running:
logger.info(f"[自动训练器] 定时任务已在运行，跳过重复启动")
logger.info("[自动训练器] 定时任务已在运行，跳过重复启动")
return
self._scheduled_task_running = True
@@ -330,10 +329,10 @@ class AutoTrainer:
# 没有找到，返回 latest
latest_path = self.model_dir / "semantic_interest_latest.pkl"
if latest_path.exists():
logger.debug(f"[自动训练器] 使用 latest 模型")
logger.debug("[自动训练器] 使用 latest 模型")
return latest_path
logger.warning(f"[自动训练器] 未找到可用模型")
logger.warning("[自动训练器] 未找到可用模型")
return None
def cleanup_old_models(self, keep_count: int = 5):

14
src/chat/semantic_interest/dataset.py
View File

@@ -3,7 +3,6 @@
从数据库采样消息并使用 LLM 进行兴趣度标注
"""
import asyncio
import json
import random
from datetime import datetime, timedelta
@@ -11,7 +10,6 @@ from pathlib import Path
from typing import Any
from src.common.logger import get_logger
from src.config.config import global_config
logger = get_logger("semantic_interest.dataset")
@@ -111,16 +109,16 @@ class DatasetGenerator:
async def initialize(self):
"""初始化 LLM 客户端"""
try:
from src.llm_models.utils_model import LLMRequest
from src.config.config import model_config
from src.llm_models.utils_model import LLMRequest
# 使用 utilities 模型配置（标注更偏工具型）
if hasattr(model_config.model_task_config, 'utils'):
if hasattr(model_config.model_task_config, "utils"):
self.model_client = LLMRequest(
model_set=model_config.model_task_config.utils,
request_type="semantic_annotation"
)
logger.info(f"数据集生成器初始化完成，使用 utils 模型")
logger.info("数据集生成器初始化完成，使用 utils 模型")
else:
logger.error("未找到 utils 模型配置")
self.model_client = None
@@ -149,9 +147,9 @@ class DatasetGenerator:
Returns:
消息样本列表
"""
from src.common.database.api.query import QueryBuilder
from src.common.database.core.models import Messages
from sqlalchemy import func, or_
logger.info(f"开始采样消息，时间范围: 最近 {days} 天，目标数量: {max_samples}")
@@ -632,7 +630,7 @@ class DatasetGenerator:
# 提取JSON内容
import re
json_match = re.search(r'```json\s*({.*?})\s*```', response, re.DOTALL)
json_match = re.search(r"```json\s*({.*?})\s*```", response, re.DOTALL)
if json_match:
json_str = json_match.group(1)
else:
@@ -703,7 +701,7 @@ class DatasetGenerator:
Returns:
(文本列表, 标签列表)
"""
with open(path, "r", encoding="utf-8") as f:
with open(path, encoding="utf-8") as f:
data = json.load(f)
texts = [item["message_text"] for item in data]

1
src/chat/semantic_interest/features_tfidf.py
View File

@@ -3,7 +3,6 @@
使用字符级 n-gram 提取中文消息的 TF-IDF 特征
"""
from pathlib import Path
from sklearn.feature_extraction.text import TfidfVectorizer

4
src/chat/semantic_interest/model_lr.py
View File

@@ -4,17 +4,15 @@
"""
import time
from pathlib import Path
from typing import Any
import joblib
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from src.common.logger import get_logger
from src.chat.semantic_interest.features_tfidf import TfidfFeatureExtractor
from src.common.logger import get_logger
logger = get_logger("semantic_interest.model")

10
src/chat/semantic_interest/optimized_scorer.py
View File

@@ -16,7 +16,7 @@ from collections import Counter
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable
from typing import Any
import numpy as np
@@ -110,7 +110,7 @@ class FastScorer:
self.total_time = 0.0
# n-gram 正则（预编译）
self._tokenize_pattern = re.compile(r'\s+')
self._tokenize_pattern = re.compile(r"\s+")
@classmethod
def from_sklearn_model(
@@ -139,13 +139,13 @@ class FastScorer:
将 TF-IDF 的 idf 和 LR 的权重合并为单一的 token→weight 字典
"""
# 获取底层 sklearn 对象
if hasattr(vectorizer, 'vectorizer'):
if hasattr(vectorizer, "vectorizer"):
# TfidfFeatureExtractor 包装类
tfidf = vectorizer.vectorizer
else:
tfidf = vectorizer
if hasattr(model, 'clf'):
if hasattr(model, "clf"):
# SemanticInterestModel 包装类
clf = model.clf
else:
@@ -611,7 +611,7 @@ def convert_sklearn_to_fast(
# 从 vectorizer 配置推断 n-gram range
if config is None:
vconfig = vectorizer.get_config() if hasattr(vectorizer, 'get_config') else {}
vconfig = vectorizer.get_config() if hasattr(vectorizer, "get_config") else {}
config = FastScorerConfig(
ngram_range=vconfig.get("ngram_range", (2, 4)),
weight_prune_threshold=1e-4,

5
src/chat/semantic_interest/runtime_scorer.py
View File

@@ -16,11 +16,10 @@ from pathlib import Path
from typing import Any
import joblib
import numpy as np
from src.common.logger import get_logger
from src.chat.semantic_interest.features_tfidf import TfidfFeatureExtractor
from src.chat.semantic_interest.model_lr import SemanticInterestModel
from src.common.logger import get_logger
logger = get_logger("semantic_interest.scorer")
@@ -611,7 +610,7 @@ class ModelManager:
async with self._lock:
# 检查是否已经启动
if self._auto_training_started:
logger.debug(f"[模型管理器] 自动训练任务已启动，跳过")
logger.debug("[模型管理器] 自动训练任务已启动，跳过")
return
try:

4
src/chat/semantic_interest/trainer.py
View File

@@ -3,16 +3,15 @@
统一的训练流程入口，包含数据采样、标注、训练、评估
"""
import asyncio
from datetime import datetime
from pathlib import Path
from typing import Any
import joblib
from src.common.logger import get_logger
from src.chat.semantic_interest.dataset import DatasetGenerator, generate_training_dataset
from src.chat.semantic_interest.model_lr import train_semantic_model
from src.common.logger import get_logger
logger = get_logger("semantic_interest.trainer")
@@ -110,7 +109,6 @@ class SemanticInterestTrainer:
logger.info(f"开始训练模型，数据集: {dataset_path}")
# 加载数据集
from src.chat.semantic_interest.dataset import DatasetGenerator
texts, labels = DatasetGenerator.load_dataset(dataset_path)
# 训练模型

2
src/chat/utils/utils.py
View File

@@ -13,7 +13,7 @@ from src.common.data_models.database_data_model import DatabaseUserInfo
# MessageRecv 已被移除，现在使用 DatabaseMessages
from src.common.logger import get_logger
from src.common.message_repository import count_and_length_messages, count_messages, find_messages
from src.common.message_repository import count_and_length_messages, find_messages
from src.config.config import global_config, model_config
from src.llm_models.utils_model import LLMRequest
from src.person_info.person_info import PersonInfoManager, get_person_info_manager

1
src/common/data_models/bot_interest_data_model.py
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@@ -10,6 +10,7 @@ from typing import Any
import numpy as np
from src.config.config import model_config
from . import BaseDataModel

3
src/common/database/optimization/preloader.py
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@@ -9,11 +9,10 @@
import asyncio
import time
from collections import defaultdict
from collections import OrderedDict, defaultdict
from collections.abc import Awaitable, Callable
from dataclasses import dataclass, field
from typing import Any
from collections import OrderedDict
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

1
src/llm_models/utils_model.py
View File

@@ -29,7 +29,6 @@ from enum import Enum
from typing import Any, ClassVar, Literal
import numpy as np
from rich.traceback import install
from src.common.logger import get_logger

2
src/main.py
View File

@@ -7,7 +7,7 @@ import time
import traceback
from collections.abc import Callable, Coroutine
from random import choices
from typing import Any, cast
from typing import Any
from rich.traceback import install

3
src/memory_graph/short_term_manager.py
View File

@@ -11,11 +11,10 @@ import asyncio
import json
import re
import uuid
import json_repair
from pathlib import Path
from typing import Any
from collections import defaultdict
import json_repair
import numpy as np
from src.common.logger import get_logger

2
src/person_info/relationship_fetcher.py
View File

@@ -182,7 +182,7 @@ class RelationshipFetcher:
kw_lower = kw.lower()
# 排除聊天互动、情感需求等不是真实兴趣的词汇
if not any(excluded in kw_lower for excluded in [
'亲亲', '撒娇', '被宠', '被夸', '聊天', '互动', '关心', '专注', '需要'
"亲亲", "撒娇", "被宠", "被夸", "聊天", "互动", "关心", "专注", "需要"
]):
filtered_keywords.append(kw)

1
src/plugin_system/utils/permission_decorators.py
View File

@@ -11,7 +11,6 @@ from inspect import iscoroutinefunction
from src.chat.message_receive.chat_stream import ChatStream
from src.plugin_system.apis.logging_api import get_logger
from src.plugin_system.apis.permission_api import permission_api
from src.plugin_system.apis.send_api import text_to_stream
logger = get_logger(__name__)

6
src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py
View File

@@ -292,7 +292,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
return
# 防止并发初始化（使用锁）
if not hasattr(self, '_init_lock'):
if not hasattr(self, "_init_lock"):
self._init_lock = asyncio.Lock()
async with self._init_lock:
@@ -354,7 +354,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
logger.debug("[语义评分] 已有模型，跳过自动训练启动")
except FileNotFoundError:
logger.warning(f"[语义评分] 未找到训练模型，将自动训练...")
logger.warning("[语义评分] 未找到训练模型，将自动训练...")
# 触发首次训练
trained, model_path = await auto_trainer.auto_train_if_needed(
persona_info=persona_info,
@@ -464,7 +464,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
logger.info("[语义评分] 开始重新加载模型...")
# 检查人设是否变化
if hasattr(self, 'model_manager') and self.model_manager:
if hasattr(self, "model_manager") and self.model_manager:
persona_info = self._get_current_persona_info()
reloaded = await self.model_manager.check_and_reload_for_persona(persona_info)
if reloaded:

2
src/plugins/built_in/kokoro_flow_chatter/prompt_modules_unified.py
View File

@@ -117,7 +117,7 @@ def build_custom_decision_module() -> str:
custom_prompt = getattr(kfc_config, "custom_decision_prompt", "")
# 调试输出
logger.debug(f"[自定义决策提示词] 原始值: {repr(custom_prompt)}, 类型: {type(custom_prompt)}")
logger.debug(f"[自定义决策提示词] 原始值: {custom_prompt!r}, 类型: {type(custom_prompt)}")
if not custom_prompt or not custom_prompt.strip():
logger.debug("[自定义决策提示词] 为空或仅含空白字符，跳过")

20
src/plugins/built_in/napcat_adapter/src/handlers/to_core/message_handler.py
View File

@@ -2,21 +2,28 @@
from __future__ import annotations
import asyncio
import base64
import time
from pathlib import Path
from typing import TYPE_CHECKING, Any
from mofox_wire import (
MessageBuilder,
SegPayload,
)
import orjson
from mofox_wire import MessageBuilder, SegPayload
from src.common.logger import get_logger
from src.plugin_system.apis import config_api
from ...event_models import ACCEPT_FORMAT, QQ_FACE, RealMessageType
from ..utils import *
from ..utils import (
get_forward_message,
get_group_info,
get_image_base64,
get_member_info,
get_message_detail,
get_record_detail,
get_self_info,
)
if TYPE_CHECKING:
from ....plugin import NapcatAdapter
@@ -300,8 +307,7 @@ class MessageHandler:
try:
if file_path and Path(file_path).exists():
# 本地文件处理
with open(file_path, "rb") as f:
video_data = f.read()
video_data = await asyncio.to_thread(Path(file_path).read_bytes)
video_base64 = base64.b64encode(video_data).decode("utf-8")
logger.debug(f"视频文件大小: {len(video_data) / (1024 * 1024):.2f} MB")

3
src/plugins/built_in/napcat_adapter/src/handlers/to_core/meta_event_handler.py
View File

@@ -22,6 +22,7 @@ class MetaEventHandler:
self.adapter = adapter
self.plugin_config: dict[str, Any] | None = None
self._interval_checking = False
self._heartbeat_task: asyncio.Task | None = None
def set_plugin_config(self, config: dict[str, Any]) -> None:
"""设置插件配置"""
@@ -41,7 +42,7 @@ class MetaEventHandler:
self_id = raw.get("self_id")
if not self._interval_checking and self_id:
# 第一次收到心跳包时才启动心跳检查
asyncio.create_task(self.check_heartbeat(self_id))
self._heartbeat_task = asyncio.create_task(self.check_heartbeat(self_id))
self.last_heart_beat = time.time()
interval = raw.get("interval")
if interval:

36
src/plugins/built_in/siliconflow_api_index_tts/plugin.py
View File

@@ -7,6 +7,7 @@ import asyncio
import base64
import hashlib
from pathlib import Path
from typing import ClassVar
import aiohttp
import toml
@@ -139,25 +140,34 @@ class SiliconFlowIndexTTSAction(BaseAction):
action_description = "使用SiliconFlow API进行高质量的IndexTTS语音合成，支持零样本语音克隆"
# 关键词配置
activation_keywords = ["克隆语音", "模仿声音", "语音合成", "indextts", "声音克隆", "语音生成", "仿声", "变声"]
activation_keywords: ClassVar[list[str]] = [
"克隆语音",
"模仿声音",
"语音合成",
"indextts",
"声音克隆",
"语音生成",
"仿声",
"变声",
]
keyword_case_sensitive = False
# 动作参数定义
action_parameters = {
action_parameters: ClassVar[dict[str, str]] = {
"text": "需要合成语音的文本内容，必填，应当清晰流畅",
"speed": "语速（可选），范围0.1-3.0，默认1.0"
"speed": "语速（可选），范围0.1-3.0，默认1.0",
}
# 动作使用场景
action_require = [
action_require: ClassVar[list[str]] = [
"当用户要求语音克隆或模仿某个声音时使用",
"当用户明确要求进行语音合成时使用",
"当需要高质量语音输出时使用",
"当用户要求变声或仿声时使用"
"当用户要求变声或仿声时使用",
]
# 关联类型 - 支持语音消息
associated_types = ["voice"]
associated_types: ClassVar[list[str]] = ["voice"]
async def execute(self) -> tuple[bool, str]:
"""执行SiliconFlow IndexTTS语音合成"""
@@ -258,11 +268,11 @@ class SiliconFlowTTSCommand(BaseCommand):
command_name = "sf_tts"
command_description = "使用SiliconFlow IndexTTS进行语音合成"
command_aliases = ["sftts", "sf语音", "硅基语音"]
command_aliases: ClassVar[list[str]] = ["sftts", "sf语音", "硅基语音"]
command_parameters = {
command_parameters: ClassVar[dict[str, dict[str, object]]] = {
"text": {"type": str, "required": True, "description": "要合成的文本"},
"speed": {"type": float, "required": False, "description": "语速 (0.1-3.0)"}
"speed": {"type": float, "required": False, "description": "语速 (0.1-3.0)"},
}
async def execute(self, text: str, speed: float = 1.0) -> tuple[bool, str]:
@@ -341,14 +351,14 @@ class SiliconFlowIndexTTSPlugin(BasePlugin):
# 必需的抽象属性
enable_plugin: bool = True
dependencies: list[str] = []
dependencies: ClassVar[list[str]] = []
config_file_name: str = "config.toml"
# Python依赖
python_dependencies = ["aiohttp>=3.8.0"]
python_dependencies: ClassVar[list[str]] = ["aiohttp>=3.8.0"]
# 配置描述
config_section_descriptions = {
config_section_descriptions: ClassVar[dict[str, str]] = {
"plugin": "插件基本配置",
"components": "组件启用配置",
"api": "SiliconFlow API配置",
@@ -356,7 +366,7 @@ class SiliconFlowIndexTTSPlugin(BasePlugin):
}
# 配置schema
config_schema = {
config_schema: ClassVar[dict[str, dict[str, ConfigField]]] = {
"plugin": {
"enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
"config_version": ConfigField(type=str, default="2.0.0", description="配置文件版本"),

3
src/plugins/built_in/siliconflow_api_index_tts/upload_voice.py
View File

@@ -43,8 +43,7 @@ class VoiceUploader:
raise FileNotFoundError(f"音频文件不存在: {audio_path}")
# 读取音频文件并转换为base64
with open(audio_path, "rb") as f:
audio_data = f.read()
audio_data = await asyncio.to_thread(audio_path.read_bytes)
audio_base64 = base64.b64encode(audio_data).decode("utf-8")

12
src/plugins/built_in/system_management/plugin.py
View File

@@ -347,8 +347,10 @@ class SystemCommand(PlusCommand):
return
response_parts = [f"🧩 已注册的提示词组件 (共 {len(components)} 个):"]
for comp in components:
response_parts.append(f"• `{comp.name}` (来自: `{comp.plugin_name}`)")
response_parts.extend(
[f"• `{comp.name}` (来自: `{comp.plugin_name}`)" for comp in components]
)
await self._send_long_message("\n".join(response_parts))
@@ -586,8 +588,10 @@ class SystemCommand(PlusCommand):
for plugin_name, comps in by_plugin.items():
response_parts.append(f"🔌 **{plugin_name}**:")
for comp in comps:
response_parts.append(f" ❌ `{comp.name}` ({comp.component_type.value})")
response_parts.extend(
[f" ❌ `{comp.name}` ({comp.component_type.value})" for comp in comps]
)
await self._send_long_message("\n".join(response_parts))

10
src/plugins/built_in/web_search_tool/engines/serper_engine.py
View File

@@ -121,13 +121,17 @@ class SerperSearchEngine(BaseSearchEngine):
# 添加有机搜索结果
if "organic" in data:
for result in data["organic"][:num_results]:
results.append({
results.extend(
[
{
"title": result.get("title", "无标题"),
"url": result.get("link", ""),
"snippet": result.get("snippet", ""),
"provider": "Serper",
})
}
for result in data["organic"][:num_results]
]
)
logger.info(f"Serper搜索成功: 查询='{query}', 结果数={len(results)}")
return results

11
src/plugins/built_in/web_search_tool/plugin.py
View File

@@ -4,6 +4,8 @@ Web Search Tool Plugin
一个功能强大的网络搜索和URL解析插件，支持多种搜索引擎和解析策略。
"""
from typing import ClassVar
from src.common.logger import get_logger
from src.plugin_system import BasePlugin, ComponentInfo, ConfigField, register_plugin
from src.plugin_system.apis import config_api
@@ -30,7 +32,7 @@ class WEBSEARCHPLUGIN(BasePlugin):
# 插件基本信息
plugin_name: str = "web_search_tool" # 内部标识符
enable_plugin: bool = True
dependencies: list[str] = [] # 插件依赖列表
dependencies: ClassVar[list[str]] = [] # 插件依赖列表
def __init__(self, *args, **kwargs):
"""初始化插件，立即加载所有搜索引擎"""
@@ -80,11 +82,14 @@ class WEBSEARCHPLUGIN(BasePlugin):
config_file_name: str = "config.toml" # 配置文件名
# 配置节描述
config_section_descriptions = {"plugin": "插件基本信息", "proxy": "链接本地解析代理配置"}
config_section_descriptions: ClassVar[dict[str, str]] = {
"plugin": "插件基本信息",
"proxy": "链接本地解析代理配置",
}
# 配置Schema定义
# 注意：EXA配置和组件设置已迁移到主配置文件(bot_config.toml)的[exa]和[web_search]部分
config_schema: dict = {
config_schema: ClassVar[dict[str, dict[str, ConfigField]]] = {
"plugin": {
"name": ConfigField(type=str, default="WEB_SEARCH_PLUGIN", description="插件名称"),
"version": ConfigField(type=str, default="1.0.0", description="插件版本"),