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Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox-Core into dev

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This commit is contained in:
Windpicker-owo
2025-12-04 09:09:14 +08:00
parent d12a5745d5 3293e2601f
commit 9084a123ce
6 changed files with 211 additions and 40 deletions
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8
bot.py
View File

@@ -610,9 +610,9 @@ async def wait_for_user_input():
try:
if os.getenv("ENVIRONMENT") != "production":
logger.info("程序执行完成，按 Ctrl+C 退出...")
# 使用非阻塞循环
while True:
await asyncio.sleep(0.1)
# 使用 asyncio.Event 而不是 sleep 循环
shutdown_event = asyncio.Event()
await shutdown_event.wait()
except KeyboardInterrupt:
logger.info("用户中断程序")
return True
@@ -652,7 +652,7 @@ async def main_async():
user_input_done = asyncio.create_task(wait_for_user_input())
# 使用wait等待任意一个任务完成
done, pending = await asyncio.wait([main_task, user_input_done], return_when=asyncio.FIRST_COMPLETED)
done, _pending = await asyncio.wait([main_task, user_input_done], return_when=asyncio.FIRST_COMPLETED)
# 如果用户输入任务完成（用户按了Ctrl+C），取消主任务
if user_input_done in done and main_task not in done:

120
src/common/cache_manager.py
View File

@@ -574,6 +574,126 @@ class CacheManager:
}
}
async def recall_relevant_cache(
self,
query_text: str,
tool_name: str | None = None,
top_k: int = 3,
similarity_threshold: float = 0.70,
) -> list[dict[str, Any]]:
"""
根据语义相似度主动召回相关的缓存条目
用于在回复前扫描缓存，找到与当前对话相关的历史搜索结果
Args:
query_text: 用于语义匹配的查询文本（通常是最近几条聊天内容）
tool_name: 可选，限制只召回特定工具的缓存（如 "web_search"）
top_k: 返回的最大结果数
similarity_threshold: 相似度阈值（L2距离，越小越相似）
Returns:
相关缓存条目列表，每个条目包含 {tool_name, query, content, similarity}
"""
if not query_text or not self.embedding_model:
return []
try:
# 生成查询向量
embedding_result = await self.embedding_model.get_embedding(query_text)
if not embedding_result:
return []
embedding_vector = embedding_result[0] if isinstance(embedding_result, tuple) else embedding_result
validated_embedding = self._validate_embedding(embedding_vector)
if validated_embedding is None:
return []
query_embedding = np.array([validated_embedding], dtype="float32")
# 从 L2 向量数据库查询
results = vector_db_service.query(
collection_name=self.semantic_cache_collection_name,
query_embeddings=query_embedding.tolist(),
n_results=top_k * 2, # 多取一些，后面会过滤
)
if not results or not results.get("ids") or not results["ids"][0]:
logger.debug("[缓存召回] 未找到相关缓存")
return []
recalled_items = []
ids = results["ids"][0] if isinstance(results["ids"][0], list) else [results["ids"][0]]
distances = results.get("distances", [[]])[0] if results.get("distances") else []
for i, cache_key in enumerate(ids):
distance = distances[i] if i < len(distances) else 1.0
# 过滤相似度不够的
if distance > similarity_threshold:
continue
# 从数据库获取缓存数据
cache_obj = await db_query(
model_class=CacheEntries,
query_type="get",
filters={"cache_key": cache_key},
single_result=True,
)
if not cache_obj:
continue
# 检查是否过期
expires_at = getattr(cache_obj, "expires_at", 0)
if time.time() >= expires_at:
continue
# 获取工具名称并过滤
cached_tool_name = getattr(cache_obj, "tool_name", "")
if tool_name and cached_tool_name != tool_name:
continue
# 解析缓存内容
try:
cache_value = getattr(cache_obj, "cache_value", "{}")
data = orjson.loads(cache_value)
content = data.get("content", "") if isinstance(data, dict) else str(data)
# 从 cache_key 中提取原始查询（格式: tool_name::{"query": "xxx", ...}::file_hash）
original_query = ""
try:
key_parts = cache_key.split("::")
if len(key_parts) >= 2:
args_json = key_parts[1]
args = orjson.loads(args_json)
original_query = args.get("query", "")
except Exception:
pass
recalled_items.append({
"tool_name": cached_tool_name,
"query": original_query,
"content": content,
"similarity": 1.0 - distance, # 转换为相似度分数
})
except Exception as e:
logger.warning(f"解析缓存内容失败: {e}")
continue
if len(recalled_items) >= top_k:
break
if recalled_items:
logger.info(f"[缓存召回] 找到 {len(recalled_items)} 条相关缓存")
return recalled_items
except Exception as e:
logger.error(f"[缓存召回] 语义召回失败: {e}")
return []
# 全局实例
tool_cache = CacheManager()

30
src/plugin_system/core/stream_tool_history.py
View File

@@ -32,15 +32,35 @@ class ToolCallRecord:
"""后处理：生成结果预览"""
if self.result and not self.result_preview:
content = self.result.get("content", "")
# 联网搜索等重要工具不截断结果
no_truncate_tools = {"web_search", "web_surfing", "knowledge_search"}
should_truncate = self.tool_name not in no_truncate_tools
max_length = 500 if should_truncate else 10000 # 联网搜索给更大的限制
if isinstance(content, str):
self.result_preview = content[:500] + ("..." if len(content) > 500 else "")
if len(content) > max_length:
self.result_preview = content[:max_length] + "..."
else:
self.result_preview = content
elif isinstance(content, list | dict):
try:
self.result_preview = orjson.dumps(content, option=orjson.OPT_NON_STR_KEYS).decode("utf-8")[:500] + "..."
except Exception:
self.result_preview = str(content)[:500] + "..."
json_str = orjson.dumps(content, option=orjson.OPT_NON_STR_KEYS).decode("utf-8")
if len(json_str) > max_length:
self.result_preview = json_str[:max_length] + "..."
else:
self.result_preview = str(content)[:500] + "..."
self.result_preview = json_str
except Exception:
str_content = str(content)
if len(str_content) > max_length:
self.result_preview = str_content[:max_length] + "..."
else:
self.result_preview = str_content
else:
str_content = str(content)
if len(str_content) > max_length:
self.result_preview = str_content[:max_length] + "..."
else:
self.result_preview = str_content
class StreamToolHistoryManager:

44
src/plugins/built_in/kokoro_flow_chatter/context_builder.py
View File

@@ -334,12 +334,46 @@ class KFCContextBuilder:
tool_executor = ToolExecutor(chat_id=self.chat_id)
# 首先获取当前的历史记录（在执行新工具调用之前）
info_parts = []
# ========== 1. 主动召回联网搜索缓存 ==========
try:
from src.common.cache_manager import tool_cache
# 使用聊天历史作为语义查询
query_text = chat_history if chat_history else target_message
recalled_caches = await tool_cache.recall_relevant_cache(
query_text=query_text,
tool_name="web_search", # 只召回联网搜索的缓存
top_k=2,
similarity_threshold=0.65, # 相似度阈值
)
if recalled_caches:
recall_parts = ["### 🔍 相关的历史搜索结果"]
for item in recalled_caches:
original_query = item.get("query", "")
content = item.get("content", "")
similarity = item.get("similarity", 0)
if content:
# 截断过长的内容
if len(content) > 500:
content = content[:500] + "..."
recall_parts.append(f"**搜索「{original_query}」** (相关度:{similarity:.0%})\n{content}")
info_parts.append("\n\n".join(recall_parts))
logger.info(f"[缓存召回] 召回了 {len(recalled_caches)} 条相关搜索缓存")
except Exception as e:
logger.debug(f"[缓存召回] 召回失败（非关键）: {e}")
# ========== 2. 获取工具调用历史 ==========
tool_history_str = tool_executor.history_manager.format_for_prompt(
max_records=3, include_results=True
)
if tool_history_str:
info_parts.append(tool_history_str)
# 然后执行工具调用
# ========== 3. 执行工具调用 ==========
tool_results, _, _ = await tool_executor.execute_from_chat_message(
sender=sender_name,
target_message=target_message,
@@ -347,12 +381,6 @@ class KFCContextBuilder:
return_details=False,
)
info_parts = []
# 显示之前的工具调用历史（不包括当前这次调用）
if tool_history_str:
info_parts.append(tool_history_str)
# 显示当前工具调用的结果（简要信息）
if tool_results:
current_results_parts = ["### 🔧 刚获取的工具信息"]

42
src/plugins/built_in/web_search_tool/engines/exa_engine.py
View File

@@ -39,7 +39,7 @@ class ExaSearchEngine(BaseSearchEngine):
return self.api_manager.is_available()
async def search(self, args: dict[str, Any]) -> list[dict[str, Any]]:
"""执行优化的Exa搜索（使用新的search API）"""
"""执行优化的Exa搜索（使用 search_and_contents API）"""
if not self.is_available():
return []
@@ -47,13 +47,12 @@ class ExaSearchEngine(BaseSearchEngine):
num_results = min(args.get("num_results", 5), 5) # 默认5个结果，但限制最多5个
time_range = args.get("time_range", "any")
# 使用新的搜索参数格式
# 使用 search_and_contents 的参数格式
exa_args = {
"query": query,
"num_results": num_results,
"contents": {
"text": True,
"summary": True, # 启用自动摘要
},
"type": "auto",
"highlights": True, # 获取高亮片段
}
# 时间范围过滤
@@ -70,20 +69,20 @@ class ExaSearchEngine(BaseSearchEngine):
return []
loop = asyncio.get_running_loop()
# 使用新的search方法
func = functools.partial(exa_client.search, query, **exa_args)
# 使用 search_and_contents 方法
func = functools.partial(exa_client.search_and_contents, **exa_args)
search_response = await loop.run_in_executor(None, func)
# 优化结果处理 - 更注重答案质量
results = []
for res in search_response.results:
# 获取最佳内容片段
summary = getattr(res, "summary", "")
# 获取高亮内容或文本
highlights = getattr(res, "highlights", [])
text = getattr(res, "text", "")
# 智能内容选择：摘要 > 文本开头
if summary and len(summary) > 50:
snippet = summary.strip()
# 智能内容选择：高亮 > 文本开头
if highlights and len(highlights) > 0:
snippet = " ".join(highlights[:3]).strip()
elif text:
snippet = text[:300] + "..." if len(text) > 300 else text
else:
@@ -114,13 +113,12 @@ class ExaSearchEngine(BaseSearchEngine):
query = args["query"]
num_results = min(args.get("num_results", 3), 3) # answer模式默认3个结果，专注质量
# 精简的搜索参数 - 专注快速答案
# 精简的搜索参数 - 使用 search_and_contents
exa_args = {
"query": query,
"num_results": num_results,
"contents": {
"text": False, # 不需要全文
"summary": True, # 优先摘要
},
"type": "auto",
"highlights": True,
}
try:
@@ -129,16 +127,16 @@ class ExaSearchEngine(BaseSearchEngine):
return []
loop = asyncio.get_running_loop()
func = functools.partial(exa_client.search, query, **exa_args)
func = functools.partial(exa_client.search_and_contents, **exa_args)
search_response = await loop.run_in_executor(None, func)
# 极简结果处理 - 只保留最核心信息
results = []
for res in search_response.results:
summary = getattr(res, "summary", "")
highlights = getattr(res, "highlights", [])
# 使用摘要作为答案
answer_text = summary.strip() if summary else ""
# 使用高亮作为答案
answer_text = " ".join(highlights[:2]).strip() if highlights else ""
if answer_text and len(answer_text) > 20:
results.append({

7
src/plugins/built_in/web_search_tool/tools/web_search.py
View File

@@ -28,7 +28,12 @@ class WebSurfingTool(BaseTool):
name: str = "web_search"
description: str = (
"用于执行网络搜索。当用户明确要求搜索，或者需要获取关于公司、产品、事件的最新信息、新闻或动态时，必须使用此工具"
"联网搜索工具。使用场景：\n"
"1. 用户问的问题你不确定答案、需要验证\n"
"2. 涉及最新信息（新闻、产品、事件、时效性内容）\n"
"3. 需要查找具体数据、事实、定义\n"
"4. 用户明确要求搜索\n"
"不要担心调用频率，搜索结果会被缓存。"
)
available_for_llm: bool = True
parameters: ClassVar[list] = [