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Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

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This commit is contained in:
SengokuCola
2025-07-16 11:02:43 +08:00
parent e5689117cf c71f2b21c0
commit 26f5bf4d36
8 changed files with 56 additions and 23 deletions
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4
src/chat/knowledge/embedding_store.py
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@@ -26,7 +26,7 @@ from rich.progress import (
TextColumn,
)
from src.manager.local_store_manager import local_storage
from src.chat.utils.utils import get_embedding
from src.chat.utils.utils import get_embedding_sync
from src.config.config import global_config
@@ -99,7 +99,7 @@ class EmbeddingStore:
self.idx2hash = None
def _get_embedding(self, s: str) -> List[float]:
return get_embedding(s)
return get_embedding_sync(s)
def get_test_file_path(self):
return EMBEDDING_TEST_FILE

4
src/chat/knowledge/ie_process.py
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@@ -28,7 +28,7 @@ def _extract_json_from_text(text: str) -> dict:
def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
entity_extract_context = prompt_template.build_entity_extract_context(paragraph)
response, (reasoning_content, model_name) = llm_req.generate_response_async(entity_extract_context)
response, (reasoning_content, model_name) = llm_req.generate_response_sync(entity_extract_context)
entity_extract_result = _extract_json_from_text(response)
# 尝试load JSON数据
@@ -50,7 +50,7 @@ def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) ->
rdf_extract_context = prompt_template.build_rdf_triple_extract_context(
paragraph, entities=json.dumps(entities, ensure_ascii=False)
)
response, (reasoning_content, model_name) = llm_req.generate_response_async(rdf_extract_context)
response, (reasoning_content, model_name) = llm_req.generate_response_sync(rdf_extract_context)
entity_extract_result = _extract_json_from_text(response)
# 尝试load JSON数据

4
src/chat/knowledge/qa_manager.py
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@@ -10,7 +10,7 @@ from .kg_manager import KGManager
# from .lpmmconfig import global_config
from .utils.dyn_topk import dyn_select_top_k
from src.llm_models.utils_model import LLMRequest
from src.chat.utils.utils import get_embedding
from src.chat.utils.utils import get_embedding_sync
from src.config.config import global_config
MAX_KNOWLEDGE_LENGTH = 10000 # 最大知识长度
@@ -36,7 +36,7 @@ class QAManager:
# 生成问题的Embedding
part_start_time = time.perf_counter()
question_embedding = await get_embedding(question)
question_embedding = await get_embedding_sync(question)
if question_embedding is None:
logger.error("生成问题Embedding失败")
return None

24
src/chat/planner_actions/planner.py
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@@ -1,7 +1,7 @@
import json
import time
import traceback
from typing import Dict, Any, Optional
from typing import Dict, Any, Optional, Tuple
from rich.traceback import install
from datetime import datetime
from json_repair import repair_json
@@ -81,13 +81,14 @@ class ActionPlanner:
self.last_obs_time_mark = 0.0
def find_message_by_id(self, message_id: str, message_id_list: list) -> Optional[Dict[str, Any]]:
# sourcery skip: use-next
"""
根据message_id从message_id_list中查找对应的原始消息
Args:
message_id: 要查找的消息ID
message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
Returns:
找到的原始消息字典，如果未找到则返回None
"""
@@ -98,7 +99,7 @@ class ActionPlanner:
async def plan(
self, mode: ChatMode = ChatMode.FOCUS
) -> Dict[str, Dict[str, Any] | str]: # sourcery skip: dict-comprehension
) -> Tuple[Dict[str, Dict[str, Any] | str], Optional[Dict[str, Any]]]: # sourcery skip: dict-comprehension
"""
规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
"""
@@ -107,7 +108,8 @@ class ActionPlanner:
reasoning = "规划器初始化默认"
action_data = {}
current_available_actions: Dict[str, ActionInfo] = {}
target_message = None # 初始化target_message变量
target_message: Optional[Dict[str, Any]] = None # 初始化target_message变量
prompt: str = ""
try:
is_group_chat = True
@@ -128,10 +130,7 @@ class ActionPlanner:
# 如果没有可用动作或只有no_reply动作，直接返回no_reply
if not current_available_actions:
if mode == ChatMode.FOCUS:
action = "no_reply"
else:
action = "no_action"
action = "no_reply" if mode == ChatMode.FOCUS else "no_action"
reasoning = "没有可用的动作"
logger.info(f"{self.log_prefix}{reasoning}")
return {
@@ -140,7 +139,7 @@ class ActionPlanner:
"action_data": action_data,
"reasoning": reasoning,
},
}
}, None
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
prompt, message_id_list = await self.build_planner_prompt(
@@ -196,8 +195,7 @@ class ActionPlanner:
# 在FOCUS模式下，非no_reply动作需要target_message_id
if mode == ChatMode.FOCUS and action != "no_reply":
target_message_id = parsed_json.get("target_message_id")
if target_message_id:
if target_message_id := parsed_json.get("target_message_id"):
# 根据target_message_id查找原始消息
target_message = self.find_message_by_id(target_message_id, message_id_list)
else:
@@ -278,7 +276,7 @@ class ActionPlanner:
if mode == ChatMode.FOCUS:
by_what = "聊天内容"
target_prompt = "\n \"target_message_id\":\"触发action的消息id\""
target_prompt = '\n "target_message_id":"触发action的消息id"'
no_action_block = """重要说明1：
- 'no_reply' 表示只进行不进行回复，等待合适的回复时机
- 当你刚刚发送了消息，没有人回复时，选择no_reply

12
src/chat/utils/utils.py
View File

@@ -122,6 +122,18 @@ async def get_embedding(text, request_type="embedding"):
return embedding
def get_embedding_sync(text, request_type="embedding"):
"""获取文本的embedding向量（同步版本）"""
# TODO: API-Adapter修改标记
llm = LLMRequest(model=global_config.model.embedding, request_type=request_type)
try:
embedding = llm.get_embedding_sync(text)
except Exception as e:
logger.error(f"获取embedding失败: {str(e)}")
embedding = None
return embedding
def get_recent_group_speaker(chat_stream_id: str, sender, limit: int = 12) -> list:
# 获取当前群聊记录内发言的人
filter_query = {"chat_id": chat_stream_id}

23
src/llm_models/utils_model.py
View File

@@ -827,6 +827,29 @@ class LLMRequest:
)
return embedding
def get_embedding_sync(self, text: str) -> Union[list, None]:
"""同步方法：获取文本的embedding向量
Args:
text: 需要获取embedding的文本
Returns:
list: embedding向量，如果失败则返回None
"""
return asyncio.run(self.get_embedding(text))
def generate_response_sync(self, prompt: str, **kwargs) -> Union[str, Tuple]:
"""同步方式根据输入的提示生成模型的响应
Args:
prompt: 输入的提示文本
**kwargs: 额外的参数
Returns:
Union[str, Tuple]: 模型响应内容，如果有工具调用则返回元组
"""
return asyncio.run(self.generate_response_async(prompt, **kwargs))
def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
"""压缩base64格式的图片到指定大小

4
src/plugins/built_in/core_actions/no_reply.py
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@@ -24,8 +24,8 @@ class NoReplyAction(BaseAction):
2. 累计新消息数量达到随机阈值 (默认5-10条) 则结束等待
"""
focus_activation_type = ActionActivationType.NEVER
normal_activation_type = ActionActivationType.NEVER
focus_activation_type = ActionActivationType.ALWAYS
normal_activation_type = ActionActivationType.ALWAYS
mode_enable = ChatMode.FOCUS
parallel_action = False

4
src/plugins/built_in/core_actions/plugin.py
View File

@@ -36,8 +36,8 @@ class ReplyAction(BaseAction):
"""回复动作 - 参与聊天回复"""
# 激活设置
focus_activation_type = ActionActivationType.NEVER
normal_activation_type = ActionActivationType.NEVER
focus_activation_type = ActionActivationType.ALWAYS
normal_activation_type = ActionActivationType.ALWAYS
mode_enable = ChatMode.FOCUS
parallel_action = False