From d466ddaec8725eafe4d7ba75ae2c66eafe560bb8 Mon Sep 17 00:00:00 2001 From: tt-P607 <68868379+tt-P607@users.noreply.github.com> Date: Thu, 4 Dec 2025 03:19:52 +0800 Subject: [PATCH 1/4] =?UTF-8?q?refactor(search):=20=E5=B0=86=20Exa=20?= =?UTF-8?q?=E5=BC=95=E6=93=8E=E8=BF=81=E7=A7=BB=E5=88=B0=20search=5Fand=5F?= =?UTF-8?q?contents=20API?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 此提交将 Exa 搜索引擎更新为使用 `search_and_contents` 方法，而不是之前的 `search` 方法。 `search_and_contents` 端点更高效，并且可以直接提供高亮片段。此更改用更具上下文感知的高亮替代了对摘要的依赖，从而显著提高了搜索结果的质量和相关性。 --- .../web_search_tool/engines/exa_engine.py | 42 +++++++++---------- 1 file changed, 20 insertions(+), 22 deletions(-) diff --git a/src/plugins/built_in/web_search_tool/engines/exa_engine.py b/src/plugins/built_in/web_search_tool/engines/exa_engine.py index 20b645dd7..f4dabae6a 100644 --- a/src/plugins/built_in/web_search_tool/engines/exa_engine.py +++ b/src/plugins/built_in/web_search_tool/engines/exa_engine.py @@ -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({ From 22767ce2349fe2c07b575c464d05213847e15f53 Mon Sep 17 00:00:00 2001 From: tt-P607 <68868379+tt-P607@users.noreply.github.com> Date: Thu, 4 Dec 2025 04:12:36 +0800 Subject: [PATCH 2/4] =?UTF-8?q?feat(context):=20=E4=B8=BA=E5=A4=A7?= =?UTF-8?q?=E8=AF=AD=E8=A8=80=E6=A8=A1=E5=9E=8B=E6=8F=90=E4=BE=9B=E8=BF=87?= =?UTF-8?q?=E5=8E=BB=E7=BD=91=E9=A1=B5=E6=90=9C=E7=B4=A2=E7=9A=84=E4=B8=8A?= =?UTF-8?q?=E4=B8=8B=E6=96=87=E8=AE=B0=E5=BF=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 此更改使聊天机器人能够记住并引用过去网页搜索的相关信息，从而显著提高响应质量和连贯性。 系统不再将每个查询视为孤立事件，而是在生成新响应之前，对之前的 `web_search` 结果缓存进行向量相似度搜索。如果发现过去的相关信息，会自动作为“相关历史搜索结果”注入到大语言模型的提示中。 这使模型能够立即访问相关背景信息，避免对已经讨论过的主题重复搜索。 为了支持这一新功能： - 对 `web_search` 工具的提示进行了改写，以通过确保结果被高效缓存和调用，鼓励大语言模型更频繁地使用它。 - 重要工具结果（如网页搜索）的预览长度已增加 --- src/common/cache_manager.py | 120 ++++++++++++++++++ src/plugin_system/core/stream_tool_history.py | 28 +++- .../kokoro_flow_chatter/context_builder.py | 44 +++++-- .../web_search_tool/tools/web_search.py | 7 +- 4 files changed, 186 insertions(+), 13 deletions(-) diff --git a/src/common/cache_manager.py b/src/common/cache_manager.py index 9118adb40..f1abfe9d9 100644 --- a/src/common/cache_manager.py +++ b/src/common/cache_manager.py @@ -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() diff --git a/src/plugin_system/core/stream_tool_history.py b/src/plugin_system/core/stream_tool_history.py index 8e498395b..8f8b2f48b 100644 --- a/src/plugin_system/core/stream_tool_history.py +++ b/src/plugin_system/core/stream_tool_history.py @@ -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] + "..." + 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 = json_str except Exception: - self.result_preview = str(content)[:500] + "..." + str_content = str(content) + if len(str_content) > max_length: + self.result_preview = str_content[:max_length] + "..." + else: + self.result_preview = str_content else: - self.result_preview = str(content)[:500] + "..." + 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: diff --git a/src/plugins/built_in/kokoro_flow_chatter/context_builder.py b/src/plugins/built_in/kokoro_flow_chatter/context_builder.py index cc6c5cfbf..b57f06f42 100644 --- a/src/plugins/built_in/kokoro_flow_chatter/context_builder.py +++ b/src/plugins/built_in/kokoro_flow_chatter/context_builder.py @@ -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"] diff --git a/src/plugins/built_in/web_search_tool/tools/web_search.py b/src/plugins/built_in/web_search_tool/tools/web_search.py index 82ce0325a..d9238fae5 100644 --- a/src/plugins/built_in/web_search_tool/tools/web_search.py +++ b/src/plugins/built_in/web_search_tool/tools/web_search.py @@ -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] = [ From 77a10e54844981d368db358c2685ba6387ef1ed4 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 4 Dec 2025 00:57:07 +0000 Subject: [PATCH 3/4] Initial plan From 9e9a7c62370ce292e0f179038a9a52f74a451714 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 4 Dec 2025 01:04:40 +0000 Subject: [PATCH 4/4] fix(bot): improve code quality by addressing ruff linting warnings Co-authored-by: Windpicker-owo <221029311+Windpicker-owo@users.noreply.github.com> --- bot.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/bot.py b/bot.py index aeec5d0d6..b3c653096 100644 --- a/bot.py +++ b/bot.py @@ -296,7 +296,7 @@ class DatabaseManager: # 使用线程执行器运行潜在的阻塞操作 await initialize_sql_database() elapsed_time = time.time() - start_time - + db_type = global_config.database.database_type if global_config else "unknown" logger.info( f"数据库连接初始化成功，使用 {db_type} 数据库，耗时: {elapsed_time:.2f}秒" @@ -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: