From 34907fdbf5217c6eeb372c1b603eb0bbf759557b Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Fri, 7 Mar 2025 18:41:41 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E4=BA=86=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E5=91=BD=E5=90=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/chat/config.py | 19 ++++++++++++++---- src/plugins/chat/emoji_manager.py | 30 +++++++++++++++++++++------- src/plugins/chat/topic_identifier.py | 4 ++-- src/plugins/memory_system/memory.py | 10 +++++----- src/plugins/models/utils_model.py | 3 +-- 5 files changed, 46 insertions(+), 20 deletions(-) diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index dfce0cd64..5c3c0b27a 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -43,8 +43,12 @@ class BotConfig: llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {}) llm_normal: Dict[str, str] = field(default_factory=lambda: {}) llm_normal_minor: Dict[str, str] = field(default_factory=lambda: {}) + llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {}) + llm_summary_by_topic: Dict[str, str] = field(default_factory=lambda: {}) + llm_emotion_judge: Dict[str, str] = field(default_factory=lambda: {}) embedding: Dict[str, str] = field(default_factory=lambda: {}) vlm: Dict[str, str] = field(default_factory=lambda: {}) + moderation: Dict[str, str] = field(default_factory=lambda: {}) MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率 MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率 @@ -112,8 +116,6 @@ class BotConfig: config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY) config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY) config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY) - config.API_USING = response_config.get("api_using", config.API_USING) - config.API_PAID = response_config.get("api_paid", config.API_PAID) config.max_response_length = response_config.get("max_response_length", config.max_response_length) # 加载模型配置 @@ -131,6 +133,15 @@ class BotConfig: if "llm_normal_minor" in model_config: config.llm_normal_minor = model_config["llm_normal_minor"] + + if "llm_topic_judge" in model_config: + config.llm_topic_judge = model_config["llm_topic_judge"] + + if "llm_summary_by_topic" in model_config: + config.llm_summary_by_topic = model_config["llm_summary_by_topic"] + + if "llm_emotion_judge" in model_config: + config.llm_emotion_judge = model_config["llm_emotion_judge"] if "vlm" in model_config: config.vlm = model_config["vlm"] @@ -138,8 +149,8 @@ class BotConfig: if "embedding" in model_config: config.embedding = model_config["embedding"] - if "rerank" in model_config: - config.rerank = model_config["rerank"] + if "moderation" in model_config: + config.moderation = model_config["moderation"] # 消息配置 if "message" in toml_dict: diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index 3ba167308..432d11753 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -41,8 +41,8 @@ class EmojiManager: def __init__(self): self.db = Database.get_instance() self._scan_task = None - self.llm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000) - self.lm = LLM_request(model=global_config.llm_normal_minor, max_tokens=1000) + self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000) + self.llm_emotion_judge = LLM_request(model=global_config.llm_normal_minor, max_tokens=60,temperature=0.8) #更高的温度，更少的token（后续可以根据情绪来调整温度） def _ensure_emoji_dir(self): """确保表情存储目录存在""" @@ -69,7 +69,17 @@ class EmojiManager: raise RuntimeError("EmojiManager not initialized") def _ensure_emoji_collection(self): - """确保emoji集合存在并创建索引""" + """确保emoji集合存在并创建索引 + + 这个函数用于确保MongoDB数据库中存在emoji集合,并创建必要的索引。 + + 索引的作用是加快数据库查询速度: + - embedding字段的2dsphere索引: 用于加速向量相似度搜索,帮助快速找到相似的表情包 + - tags字段的普通索引: 加快按标签搜索表情包的速度 + - filename字段的唯一索引: 确保文件名不重复,同时加快按文件名查找的速度 + + 没有索引的话,数据库每次查询都需要扫描全部数据,建立索引后可以大大提高查询效率。 + """ if 'emoji' not in self.db.db.list_collection_names(): self.db.db.create_collection('emoji') self.db.db.emoji.create_index([('embedding', '2dsphere')]) @@ -93,6 +103,11 @@ class EmojiManager: text: 输入文本 Returns: Optional[str]: 表情包文件路径，如果没有找到则返回None + + + 可不可以通过 配置文件中的指令 来自定义使用表情包的逻辑？ + 我觉得可行 + """ try: self._ensure_db() @@ -152,7 +167,8 @@ class EmojiManager: {'$inc': {'usage_count': 1}} ) logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})") - return selected_emoji['path'],"[表情包: %s]" % selected_emoji.get('discription', '无描述') + # 稍微改一下文本描述，不然容易产生幻觉，描述已经包含 表情包 了 + return selected_emoji['path'],"[ %s ]" % selected_emoji.get('discription', '无描述') except Exception as search_error: logger.error(f"搜索表情包失败: {str(search_error)}") @@ -169,7 +185,7 @@ class EmojiManager: try: prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感' - content, _ = await self.llm.generate_response_for_image(prompt, image_base64) + content, _ = await self.vlm.generate_response_for_image(prompt, image_base64) logger.debug(f"输出描述: {content}") return content @@ -181,7 +197,7 @@ class EmojiManager: try: prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容' - content, _ = await self.llm.generate_response_for_image(prompt, image_base64) + content, _ = await self.vlm.generate_response_for_image(prompt, image_base64) logger.debug(f"输出描述: {content}") return content @@ -193,7 +209,7 @@ class EmojiManager: try: prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。' - content, _ = await self.lm.generate_response_async(prompt) + content, _ = await self.llm_emotion_judge.generate_response_async(prompt) logger.info(f"输出描述: {content}") return content diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py index 8e6d41c7d..6579d15ac 100644 --- a/src/plugins/chat/topic_identifier.py +++ b/src/plugins/chat/topic_identifier.py @@ -11,7 +11,7 @@ config = driver.config class TopicIdentifier: def __init__(self): - self.llm_client = LLM_request(model=global_config.llm_topic_extract) + self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge) async def identify_topic_llm(self, text: str) -> Optional[List[str]]: """识别消息主题，返回主题列表""" @@ -23,7 +23,7 @@ class TopicIdentifier: 消息内容：{text}""" # 使用 LLM_request 类进行请求 - topic, _ = await self.llm_client.generate_response(prompt) + topic, _ = await self.llm_topic_judge.generate_response(prompt) if not topic: print(f"\033[1;31m[错误]\033[0m LLM API 返回为空") diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index fd001e791..44f5eb713 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -132,8 +132,8 @@ class Memory_graph: class Hippocampus: def __init__(self,memory_graph:Memory_graph): self.memory_graph = memory_graph - self.llm_model_get_topic = LLM_request(model = global_config.llm_normal_minor,temperature=0.5) - self.llm_model_summary = LLM_request(model = global_config.llm_normal,temperature=0.5) + self.llm_topic_judge = LLM_request(model = global_config.llm_topic_judge,temperature=0.5) + self.llm_summary_by_topic = LLM_request(model = global_config.llm_summary_by_topic,temperature=0.5) def get_all_node_names(self) -> list: """获取记忆图中所有节点的名字列表 @@ -179,7 +179,7 @@ class Hippocampus: #获取topics topic_num = self.calculate_topic_num(input_text, compress_rate) - topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num)) + topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num)) # 修改话题处理逻辑 # 定义需要过滤的关键词 filter_keywords = ['表情包', '图片', '回复', '聊天记录'] @@ -196,7 +196,7 @@ class Hippocampus: for topic in filtered_topics: topic_what_prompt = self.topic_what(input_text, topic) # 创建异步任务 - task = self.llm_model_summary.generate_response_async(topic_what_prompt) + task = self.llm_summary_by_topic.generate_response_async(topic_what_prompt) tasks.append((topic.strip(), task)) # 等待所有任务完成 @@ -506,7 +506,7 @@ class Hippocampus: Returns: list: 识别出的主题列表 """ - topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5)) + topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5)) # print(f"话题: {topics_response[0]}") topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] # print(f"话题: {topics}") diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index b377eac14..7bfc966f6 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -197,13 +197,12 @@ class LLM_request: ) return content, reasoning_content - async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]: + async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple[str, str]]: """异步方式根据输入的提示生成模型的响应""" # 构建请求体 data = { "model": self.model_name, "messages": [{"role": "user", "content": prompt}], - "temperature": 0.5, "max_tokens": global_config.max_response_length, **self.params }