From 10c3f907209c5c0ff6d7faf18d2434f16f6064d5 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 15:04:23 +0800 Subject: [PATCH 01/18] Update README.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1310d4879..00afd83e8 100644 --- a/README.md +++ b/README.md @@ -75,8 +75,9 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart - 在Napcat的网络设置中添加ws反向代理:ws://localhost:8080/onebot/v11/ws 4. **配置文件设置** - - 将.env文件打开，填上你的apikey（硅基流动或deepseekapi） - - 将bot_config.toml文件打开，并填写相关内容，不然无法正常运行 + - 修改.env的 变量值为 prod + - 将.env.prod文件打开，填上你的apikey（硅基流动或deepseekapi） + - 将bot_config_toml改名为bot_config.toml，打开并填写相关内容，不然无法正常运行 #### .env 文件配置说明 ```ini From b98314da4fca0181a53049c406fb409a72a3d893 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 18:36:36 +0800 Subject: [PATCH 02/18] =?UTF-8?q?v0.3.3=20=E5=BC=82=E6=AD=A5=E5=A4=84?= =?UTF-8?q?=E7=90=86=E8=AE=B0=E5=BF=86=EF=BC=8C=E4=BF=AE=E5=A4=8D=E4=BA=86?= =?UTF-8?q?GUI?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/gui/reasoning_gui.py | 34 ++++++++++++-- src/plugins/chat/__init__.py | 2 +- src/plugins/chat/config.py | 1 + src/plugins/chat/prompt_builder.py | 11 +++-- src/plugins/memory_system/llm_module.py | 42 +++++++++--------- .../memory_system/llm_module_memory_make.py | 40 +++++++++-------- src/plugins/memory_system/memory.py | 44 +++++++++---------- src/plugins/schedule/schedule_llm_module.py | 26 ++++++----- 8 files changed, 116 insertions(+), 84 deletions(-) diff --git a/src/gui/reasoning_gui.py b/src/gui/reasoning_gui.py index f12b59798..61fb34560 100644 --- a/src/gui/reasoning_gui.py +++ b/src/gui/reasoning_gui.py @@ -7,6 +7,23 @@ import threading import queue import sys import os +from dotenv import load_dotenv + +# 获取当前文件的目录 +current_dir = os.path.dirname(os.path.abspath(__file__)) +# 获取项目根目录 +root_dir = os.path.abspath(os.path.join(current_dir, '..', '..')) + +# 加载环境变量 +if os.path.exists(os.path.join(root_dir, '.env.dev')): + load_dotenv(os.path.join(root_dir, '.env.dev')) + print("成功加载开发环境配置") +elif os.path.exists(os.path.join(root_dir, '.env.prod')): + load_dotenv(os.path.join(root_dir, '.env.prod')) + print("成功加载生产环境配置") +else: + print("未找到环境配置文件") + sys.exit(1) from pymongo import MongoClient from typing import Optional @@ -14,14 +31,23 @@ from typing import Optional class Database: _instance: Optional["Database"] = None - def __init__(self, host: str, port: int, db_name: str): - self.client = MongoClient(host, port) + def __init__(self, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None): + if username and password: + self.client = MongoClient( + host=host, + port=port, + username=username, + password=password, + authSource=auth_source or 'admin' + ) + else: + self.client = MongoClient(host, port) self.db = self.client[db_name] @classmethod - def initialize(cls, host: str, port: int, db_name: str) -> "Database": + def initialize(cls, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None) -> "Database": if cls._instance is None: - cls._instance = cls(host, port, db_name) + cls._instance = cls(host, port, db_name, username, password, auth_source) return cls._instance @classmethod diff --git a/src/plugins/chat/__init__.py b/src/plugins/chat/__init__.py index 0f81d30d6..3fdcec922 100644 --- a/src/plugins/chat/__init__.py +++ b/src/plugins/chat/__init__.py @@ -90,7 +90,7 @@ async def monitor_relationships(): async def build_memory_task(): """每30秒执行一次记忆构建""" print("\033[1;32m[记忆构建]\033[0m 开始构建记忆...") - hippocampus.build_memory(chat_size=12) + await hippocampus.build_memory(chat_size=30) print("\033[1;32m[记忆构建]\033[0m 记忆构建完成") diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 0232219f8..dc7c14203 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -136,3 +136,4 @@ llm_config.DEEP_SEEK_BASE_URL = config.deep_seek_base_url if not global_config.enable_advance_output: # logger.remove() pass + diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index ba0e9b4cc..01fe3e82d 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -72,12 +72,15 @@ class PromptBuilder: # print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}") # 使用集合去重 - all_memories = list(set(all_first_layer_items) | set(overlapping_second_layer)) + # 从每个来源随机选择2条记忆（如果有的话） + selected_first_layer = random.sample(all_first_layer_items, min(2, len(all_first_layer_items))) if all_first_layer_items else [] + selected_second_layer = random.sample(list(overlapping_second_layer), min(2, len(overlapping_second_layer))) if overlapping_second_layer else [] + + # 合并并去重 + all_memories = list(set(selected_first_layer + selected_second_layer)) if all_memories: print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆: {all_memories}") - - if all_memories: # 只在列表非空时选择随机项 - random_item = choice(all_memories) + random_item = " ".join(all_memories) memory_prompt = f"看到这些聊天，你想起来{random_item}\n" else: memory_prompt = "" # 如果没有记忆，则返回空字符串 diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py index bd7f60dc3..34e4e4af1 100644 --- a/src/plugins/memory_system/llm_module.py +++ b/src/plugins/memory_system/llm_module.py @@ -3,6 +3,8 @@ import requests from typing import Tuple, Union import time from nonebot import get_driver +import aiohttp +import asyncio driver = get_driver() config = driver.config @@ -15,7 +17,7 @@ class LLMModel: self.api_key = config.siliconflow_key self.base_url = config.siliconflow_base_url - def generate_response(self, prompt: str) -> Tuple[str, str]: + async def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -34,32 +36,32 @@ class LLMModel: api_url = f"{self.base_url.rstrip('/')}/chat/completions" max_retries = 3 - base_wait_time = 15 # 基础等待时间（秒） + base_wait_time = 15 for retry in range(max_retries): try: - response = requests.post(api_url, headers=headers, json=data) + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content - return "没有返回结果", "" - - except requests.exceptions.RequestException as e: + except Exception as e: if retry < max_retries - 1: # 如果还有重试机会 wait_time = base_wait_time * (2 ** retry) print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - time.sleep(wait_time) + await asyncio.sleep(wait_time) else: return f"请求失败: {str(e)}", "" diff --git a/src/plugins/memory_system/llm_module_memory_make.py b/src/plugins/memory_system/llm_module_memory_make.py index 04ab6dbc6..5b01a2917 100644 --- a/src/plugins/memory_system/llm_module_memory_make.py +++ b/src/plugins/memory_system/llm_module_memory_make.py @@ -4,6 +4,8 @@ from typing import Tuple, Union import time from ..chat.config import BotConfig from nonebot import get_driver +import aiohttp +import asyncio driver = get_driver() config = driver.config @@ -21,7 +23,7 @@ class LLMModel: print(f"API URL: {self.base_url}") # 打印 base_url 用于调试 - def generate_response(self, prompt: str) -> Tuple[str, str]: + async def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -44,28 +46,28 @@ class LLMModel: for retry in range(max_retries): try: - response = requests.post(api_url, headers=headers, json=data) + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content - return "没有返回结果", "" - - except requests.exceptions.RequestException as e: + except Exception as e: if retry < max_retries - 1: # 如果还有重试机会 wait_time = base_wait_time * (2 ** retry) print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - time.sleep(wait_time) + await asyncio.sleep(wait_time) else: return f"请求失败: {str(e)}", "" diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index f2b162afb..cab41f903 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -193,7 +193,25 @@ class Hippocampus: chat_text.append(chat_) return chat_text - def build_memory(self,chat_size=12): + async def memory_compress(self, input_text, rate=1): + information_content = calculate_information_content(input_text) + print(f"文本的信息量（熵）: {information_content:.4f} bits") + topic_num = max(1, min(5, int(information_content * rate / 4))) + topic_prompt = find_topic(input_text, topic_num) + topic_response = await self.llm_model.generate_response(topic_prompt) + # 检查 topic_response 是否为元组 + if isinstance(topic_response, tuple): + topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 + else: + topics = topic_response.split(",") + compressed_memory = set() + for topic in topics: + topic_what_prompt = topic_what(input_text,topic) + topic_what_response = await self.llm_model_small.generate_response(topic_what_prompt) + compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储 + return compressed_memory + + async def build_memory(self,chat_size=12): #最近消息获取频率 time_frequency = {'near':1,'mid':2,'far':2} memory_sample = self.get_memory_sample(chat_size,time_frequency) @@ -208,9 +226,7 @@ class Hippocampus: if input_text: # 生成压缩后记忆 first_memory = set() - first_memory = self.memory_compress(input_text, 2.5) - # 延时防止访问超频 - # time.sleep(5) + first_memory = await self.memory_compress(input_text, 2.5) #将记忆加入到图谱中 for topic, memory in first_memory: topics = segment_text(topic) @@ -224,26 +240,6 @@ class Hippocampus: else: print(f"空消息 跳过") self.memory_graph.save_graph_to_db() - - def memory_compress(self, input_text, rate=1): - information_content = calculate_information_content(input_text) - print(f"文本的信息量（熵）: {information_content:.4f} bits") - topic_num = max(1, min(5, int(information_content * rate / 4))) - # print(topic_num) - topic_prompt = find_topic(input_text, topic_num) - topic_response = self.llm_model.generate_response(topic_prompt) - # 检查 topic_response 是否为元组 - if isinstance(topic_response, tuple): - topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 - else: - topics = topic_response.split(",") - # print(topics) - compressed_memory = set() - for topic in topics: - topic_what_prompt = topic_what(input_text,topic) - topic_what_response = self.llm_model_small.generate_response(topic_what_prompt) - compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储 - return compressed_memory def segment_text(text): diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index 408e7d546..cf88a8653 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -1,5 +1,6 @@ import os import requests +import aiohttp from typing import Tuple, Union from nonebot import get_driver @@ -22,7 +23,7 @@ class LLMModel: self.model_name = model_name self.params = kwargs - def generate_response(self, prompt: str) -> Tuple[str, str]: + async def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -41,17 +42,18 @@ class LLMModel: api_url = f"{self.base_url.rstrip('/')}/chat/completions" try: - response = requests.post(api_url, headers=headers, json=data) - response.raise_for_status() # 检查响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content # 返回内容和推理内容 - return "没有返回结果", "" # 返回两个值 - - except requests.exceptions.RequestException as e: + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + response.raise_for_status() # 检查响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content # 返回内容和推理内容 + return "没有返回结果", "" # 返回两个值 + + except Exception as e: return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串 # 示例用法 From a7e6bc14dc9a71ad7eaea0eaae9b950f5d2919cd Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:17:05 +0800 Subject: [PATCH 03/18] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E7=8E=AF=E5=A2=83?= =?UTF-8?q?=E5=8F=98=E9=87=8F=E4=BD=8D=E7=BD=AE=E5=B9=B6=E6=8C=82=E8=BD=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 3 +-- docker-compose.yml | 7 ++++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/Dockerfile b/Dockerfile index 111466ff3..6a7aa09c0 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,8 +2,7 @@ FROM nonebot/nb-cli:latest WORKDIR / COPY . /MaiMBot/ WORKDIR /MaiMBot -RUN mv config/env.example config/.env \ -&& mv config/bot_config_toml config/bot_config.toml +RUN mv config/bot_config_toml config/bot_config.toml RUN pip install --upgrade -r requirements.txt VOLUME [ "/MaiMBot/config" ] EXPOSE 8080 diff --git a/docker-compose.yml b/docker-compose.yml index 4c136b284..cfe787c04 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -27,7 +27,7 @@ services: - mongodb:/data/db - mongodbCONFIG:/data/configdb image: mongo:latest - + maimbot: container_name: maimbot environment: @@ -41,8 +41,8 @@ services: volumes: - maimbotCONFIG:/MaiMBot/config - maimbotDATA:/MaiMBot/data + - ./.env.prod:/MaiMBot/.env.prod image: sengokucola/maimbot:latest - volumes: maimbotCONFIG: @@ -51,4 +51,5 @@ volumes: napcatCONFIG: mongodb: mongodbCONFIG: - + + From 352a10d4ee53663b339cfdd607cb60b80f36e0d0 Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:27:53 +0800 Subject: [PATCH 04/18] add colorama --- requirements.txt | Bin 520 -> 540 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/requirements.txt b/requirements.txt index 3f0fe05ddd8064040ea79e51e0a7998bbc477374..11536d12ac13cd06045bcb620cb223d5e19ba7ec 100644 GIT binary patch delta 28 gcmeBRnZvSygGq#!fr}xTA)g@!h>I8!8FGOb08&^5U;qFB delta 7 OcmbQk(!sKUg9!i$Jptzc From c1f2b6a0eb942b0ae1f659dd4fb352e8fbae04f6 Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:36:04 +0800 Subject: [PATCH 05/18] =?UTF-8?q?#=20=E6=82=A8=E4=B8=8D=E5=BA=94=E8=AF=A5?= =?UTF-8?q?=E4=BF=AE=E6=94=B9=E9=BB=98=E8=AE=A4=E5=80=BC=EF=BC=8C=E8=BF=99?= =?UTF-8?q?=E4=B8=AA=E6=96=87=E4=BB=B6=E8=A2=AB=E4=BB=93=E5=BA=93=E7=B4=A2?= =?UTF-8?q?=E5=BC=95=EF=BC=8C=E8=AF=B7=E4=BF=AE=E6=94=B9.env.prod?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .env | 25 +++++++++++++++++++++++++ .gitignore | 3 +-- 2 files changed, 26 insertions(+), 2 deletions(-) create mode 100644 .env diff --git a/.env b/.env new file mode 100644 index 000000000..2c8e76c18 --- /dev/null +++ b/.env @@ -0,0 +1,25 @@ +# 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod +HOST=127.0.0.1 +PORT=8080 + +COMMAND_START=["/"] + +# 插件配置 +PLUGINS=["src2.plugins.chat"] + +# 默认配置 +MONGODB_HOST=127.0.0.1 +MONGODB_PORT=27017 +DATABASE_NAME=MegBot + +MONGODB_USERNAME = "" # 默认空值 +MONGODB_PASSWORD = "" # 默认空值 +MONGODB_AUTH_SOURCE = "" # 默认空值 + +#key and url +CHAT_ANY_WHERE_KEY= +SILICONFLOW_KEY= +CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 +SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +DEEP_SEEK_KEY= +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file diff --git a/.gitignore b/.gitignore index c19b9ce33..097126da9 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,7 @@ reasoning_content.bat reasoning_window.bat queue_update.txt memory_graph.gml -.env.dev +.env.* # Byte-compiled / optimized / DLL files @@ -147,7 +147,6 @@ celerybeat.pid *.sage.py # Environments -.env .venv env/ venv/ From 3db73fe03c6ea4874503defdcd7b7a4ed7e398ab Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:37:32 +0800 Subject: [PATCH 06/18] ENVIRONMENT=prod --- .env | 1 + 1 file changed, 1 insertion(+) diff --git a/.env b/.env index 2c8e76c18..6e242e1f5 100644 --- a/.env +++ b/.env @@ -1,4 +1,5 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod +ENVIRONMENT=prod HOST=127.0.0.1 PORT=8080 From 304562d93a7831bf1f63e4e6d096d011136db714 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 19:43:14 +0800 Subject: [PATCH 07/18] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86=E6=97=A5?= =?UTF-8?q?=E7=A8=8B=E5=BC=82=E6=AD=A5bug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/schedule/schedule_llm_module.py | 23 ++++++++++----------- 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index cf88a8653..1fcad52d2 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -23,7 +23,7 @@ class LLMModel: self.model_name = model_name self.params = kwargs - async def generate_response(self, prompt: str) -> Tuple[str, str]: + def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -42,17 +42,16 @@ class LLMModel: api_url = f"{self.base_url.rstrip('/')}/chat/completions" try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - response.raise_for_status() # 检查响应状态 - - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content # 返回内容和推理内容 - return "没有返回结果", "" # 返回两个值 - + response = requests.post(api_url, headers=headers, json=data) + response.raise_for_status() # 检查响应状态 + + result = response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content # 返回内容和推理内容 + return "没有返回结果", "" # 返回两个值 + except Exception as e: return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串 From f48f33a999904c5fe2ce6ec0aec07ee5af4b6821 Mon Sep 17 00:00:00 2001 From: HYY1116 Date: Sun, 2 Mar 2025 19:48:25 +0800 Subject: [PATCH 08/18] =?UTF-8?q?fix:=20=E6=A8=A1=E5=9E=8B=E4=BC=98?= =?UTF-8?q?=E5=85=88=E4=BD=BF=E7=94=A8=E6=97=A0=E9=9C=80=E4=BB=98=E8=B4=B9?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B=EF=BC=8C=E5=B9=B6=E6=A8=A1=E5=9E=8B=E5=90=8D?= =?UTF-8?q?=E7=A7=B0=E5=85=A8=E5=B1=80=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config/bot_config_toml | 45 ------------------- src/plugins/chat/config.py | 8 ++++ src/plugins/chat/llm_generator.py | 14 +++--- src/plugins/chat/topic_identifier.py | 3 +- src/plugins/memory_system/llm_module.py | 4 +- .../memory_system/llm_module_memory_make.py | 4 +- src/plugins/schedule/schedule_generator.py | 6 +-- src/plugins/schedule/schedule_llm_module.py | 9 ++-- 8 files changed, 27 insertions(+), 66 deletions(-) delete mode 100644 config/bot_config_toml diff --git a/config/bot_config_toml b/config/bot_config_toml deleted file mode 100644 index 83a3c4971..000000000 --- a/config/bot_config_toml +++ /dev/null @@ -1,45 +0,0 @@ -[bot] -qq = 123456 #填入你的机器人QQ -nickname = "麦麦" #你希望bot被称呼的名字 - -[message] -min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 -max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 -emoji_chance = 0.2 # 麦麦使用表情包的概率 - -[emoji] -check_interval = 120 -register_interval = 10 - -[cq_code] -enable_pic_translate = false - - -[response] -api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 -model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 -model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 -model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 - -[memory] -build_memory_interval = 300 # 记忆构建间隔 - - - -[others] -enable_advance_output = true # 开启后输出更多日志,false关闭true开启 - - -[groups] - -talk_allowed = [ - 123456,12345678 -] #可以回复消息的群 - -talk_frequency_down = [ - 123456,12345678 -] #降低回复频率的群 - -ban_user_id = [ - 123456,12345678 -] #禁止回复消息的QQ号 diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index dc7c14203..68ec6784b 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -33,6 +33,11 @@ class BotConfig: EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） API_USING: str = "siliconflow" # 使用的API + DEEPSEEK_MODEL_R1: str = "deepseek-reasoner" # deepseek-R1模型 + DEEPSEEK_MODEL_V3: str = "deepseek-chat" # deepseek-V3模型 + SILICONFLOW_MODEL_R1: str = "deepseek-ai/DeepSeek-R1" # siliconflow-R1模型 + SILICONFLOW_MODEL_R1_DISTILL: str = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" # siliconflow-R1蒸馏模型 + SILICONFLOW_MODEL_V3: str = "deepseek-ai/DeepSeek-V3" # siliconflow-V3模型 MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率 MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率 MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率 @@ -82,6 +87,9 @@ class BotConfig: 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) + if response_config.get("api_using", config.API_PAID): + config.SILICONFLOW_MODEL_R1 = "Pro/deepseek-ai/DeepSeek-R1" + config.SILICONFLOW_MODEL_V3 = "Pro/deepseek-ai/DeepSeek-V3" # 消息配置 if "message" in toml_dict: diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index bfff1d474..682be8a87 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -206,13 +206,13 @@ class LLMResponseGenerator: if global_config.API_USING == "deepseek": return await self._generate_base_response( message, - "deepseek-reasoner", + global_config.DEEPSEEK_MODEL_R1, {"temperature": 0.7, "max_tokens": 1024} ) else: return await self._generate_base_response( message, - "Pro/deepseek-ai/DeepSeek-R1", + global_config.SILICONFLOW_MODEL_R1, {"temperature": 0.7, "max_tokens": 1024} ) @@ -221,13 +221,13 @@ class LLMResponseGenerator: if global_config.API_USING == "deepseek": return await self._generate_base_response( message, - "deepseek-chat", + global_config.DEEPSEEK_MODEL_V3, {"temperature": 0.8, "max_tokens": 1024} ) else: return await self._generate_base_response( message, - "Pro/deepseek-ai/DeepSeek-V3", + global_config.SILICONFLOW_MODEL_V3, {"temperature": 0.8, "max_tokens": 1024} ) @@ -235,7 +235,7 @@ class LLMResponseGenerator: """使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复""" return await self._generate_base_response( message, - "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", + global_config.SILICONFLOW_MODEL_R1_DISTILL, {"temperature": 0.7, "max_tokens": 1024} ) @@ -270,9 +270,9 @@ class LLMResponseGenerator: loop = asyncio.get_event_loop() if global_config.API_USING == "deepseek": - model = "deepseek-chat" + model = global_config.DEEPSEEK_MODEL_V3 else: - model = "Pro/deepseek-ai/DeepSeek-V3" + model = global_config.SILICONFLOW_MODEL_V3 create_completion = partial( self.client.chat.completions.create, model=model, diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py index 81956ddc1..34ac4e714 100644 --- a/src/plugins/chat/topic_identifier.py +++ b/src/plugins/chat/topic_identifier.py @@ -3,6 +3,7 @@ from openai import OpenAI from .message import Message import jieba from nonebot import get_driver +from .config import global_config driver = get_driver() config = driver.config @@ -24,7 +25,7 @@ class TopicIdentifier: 消息内容：{text}""" response = self.client.chat.completions.create( - model="Pro/deepseek-ai/DeepSeek-V3", + model=global_config.SILICONFLOW_MODEL_V3, messages=[{"role": "user", "content": prompt}], temperature=0.8, max_tokens=10 diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py index 34e4e4af1..c2b6cf042 100644 --- a/src/plugins/memory_system/llm_module.py +++ b/src/plugins/memory_system/llm_module.py @@ -5,13 +5,13 @@ import time from nonebot import get_driver import aiohttp import asyncio - +from src.plugins.chat.config import global_config driver = get_driver() config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): + def __init__(self, model_name=global_config.SILICONFLOW_MODEL_V3, **kwargs): self.model_name = model_name self.params = kwargs self.api_key = config.siliconflow_key diff --git a/src/plugins/memory_system/llm_module_memory_make.py b/src/plugins/memory_system/llm_module_memory_make.py index 5b01a2917..f59354570 100644 --- a/src/plugins/memory_system/llm_module_memory_make.py +++ b/src/plugins/memory_system/llm_module_memory_make.py @@ -2,17 +2,17 @@ import os import requests from typing import Tuple, Union import time -from ..chat.config import BotConfig from nonebot import get_driver import aiohttp import asyncio +from src.plugins.chat.config import BotConfig, global_config driver = get_driver() config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): + def __init__(self, model_name=global_config.SILICONFLOW_MODEL_V3, **kwargs): self.model_name = model_name self.params = kwargs self.api_key = config.siliconflow_key diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index a33c4b279..63e3235a3 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -3,7 +3,7 @@ import os from typing import List, Dict from .schedule_llm_module import LLMModel from ...common.database import Database # 使用正确的导入语法 -from ..chat.config import global_config +from src.plugins.chat.config import global_config from nonebot import get_driver driver = get_driver() @@ -22,9 +22,9 @@ Database.initialize( class ScheduleGenerator: def __init__(self): if global_config.API_USING == "siliconflow": - self.llm_scheduler = LLMModel(model_name="Pro/deepseek-ai/DeepSeek-V3") + self.llm_scheduler = LLMModel(model_name=global_config.SILICONFLOW_MODEL_V3) elif global_config.API_USING == "deepseek": - self.llm_scheduler = LLMModel(model_name="deepseek-chat",api_using="deepseek") + self.llm_scheduler = LLMModel(model_name=global_config.DEEPSEEK_MODEL_V3) self.db = Database.get_instance() today = datetime.datetime.now() diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index cf88a8653..34dad6fec 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -3,20 +3,17 @@ import requests import aiohttp from typing import Tuple, Union from nonebot import get_driver - +from src.plugins.chat.config import global_config driver = get_driver() config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-R1",api_using=None, **kwargs): + def __init__(self, model_name=global_config.SILICONFLOW_MODEL_R1,api_using=None, **kwargs): if api_using == "deepseek": self.api_key = config.deep_seek_key self.base_url = config.deep_seek_base_url - if model_name != "Pro/deepseek-ai/DeepSeek-R1": - self.model_name = model_name - else: - self.model_name = "deepseek-reasoner" + self.model_name = global_config.DEEPSEEK_MODEL_R1 else: self.api_key = config.siliconflow_key self.base_url = config.siliconflow_base_url From 0bcd18c821e5fc351d73b39c17fbab92d2123774 Mon Sep 17 00:00:00 2001 From: HYY1116 Date: Sun, 2 Mar 2025 19:51:14 +0800 Subject: [PATCH 09/18] =?UTF-8?q?=E6=BC=8F=E6=94=B9=E4=B8=80=E5=B0=8F?= =?UTF-8?q?=E5=A4=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/chat/config.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 68ec6784b..54d125408 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -33,6 +33,7 @@ class BotConfig: EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） API_USING: str = "siliconflow" # 使用的API + API_PAID: bool = False # 是否使用付费API DEEPSEEK_MODEL_R1: str = "deepseek-reasoner" # deepseek-R1模型 DEEPSEEK_MODEL_V3: str = "deepseek-chat" # deepseek-V3模型 SILICONFLOW_MODEL_R1: str = "deepseek-ai/DeepSeek-R1" # siliconflow-R1模型 From 6e2d229cb74d98f21d3f3a91356173776c2353e8 Mon Sep 17 00:00:00 2001 From: HYY1116 Date: Sun, 2 Mar 2025 19:59:00 +0800 Subject: [PATCH 10/18] =?UTF-8?q?=E5=9B=9E=E6=9D=A5=E5=90=A7=EF=BC=8C?= =?UTF-8?q?=E6=88=91=E7=9A=84config?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 - config/bot_config.toml | 46 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+), 1 deletion(-) create mode 100644 config/bot_config.toml diff --git a/.gitignore b/.gitignore index 097126da9..94b4bb855 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,6 @@ mongodb/ NapCat.Framework.Windows.Once/ log/ src/plugins/memory -config/bot_config.toml /test message_queue_content.txt message_queue_content.bat diff --git a/config/bot_config.toml b/config/bot_config.toml new file mode 100644 index 000000000..d668fcf59 --- /dev/null +++ b/config/bot_config.toml @@ -0,0 +1,46 @@ +[bot] +qq = 123456 #填入你的机器人QQ +nickname = "麦麦" #你希望bot被称呼的名字 + +[message] +min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 +max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 +emoji_chance = 0.2 # 麦麦使用表情包的概率 + +[emoji] +check_interval = 120 +register_interval = 10 + +[cq_code] +enable_pic_translate = false + + +[response] +api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 +api_paid = false #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 +model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 +model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 +model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 + +[memory] +build_memory_interval = 300 # 记忆构建间隔 + + + +[others] +enable_advance_output = true # 开启后输出更多日志,false关闭true开启 + + +[groups] + +talk_allowed = [ + 123456,12345678 +] #可以回复消息的群 + +talk_frequency_down = [ + 123456,12345678 +] #降低回复频率的群 + +ban_user_id = [ + 123456,12345678 +] #禁止回复消息的QQ号 From 5e9401c27aa1a026fbe5041c1bb7d74d4d88e0b3 Mon Sep 17 00:00:00 2001 From: HYY <95246895+HYY1116@users.noreply.github.com> Date: Sun, 2 Mar 2025 20:00:21 +0800 Subject: [PATCH 11/18] Update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 94b4bb855..8df5d72a8 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ NapCat.Framework.Windows.Once/ log/ src/plugins/memory /test +config/bot_config.toml message_queue_content.txt message_queue_content.bat message_queue_window.bat From f04ae091e285d28e23e926fb5aaffecd79835e84 Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 20:34:40 +0800 Subject: [PATCH 12/18] =?UTF-8?q?=E4=B8=8D=E5=86=8D=E9=9C=80=E8=A6=81?= =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8Dconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index 6a7aa09c0..225a90a1b 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,8 +2,7 @@ FROM nonebot/nb-cli:latest WORKDIR / COPY . /MaiMBot/ WORKDIR /MaiMBot -RUN mv config/bot_config_toml config/bot_config.toml RUN pip install --upgrade -r requirements.txt VOLUME [ "/MaiMBot/config" ] EXPOSE 8080 -ENTRYPOINT [ "nb","run" ] \ No newline at end of file +ENTRYPOINT [ "nb","run" ] From 12e759f3ad5a3c4d9b782aa01b55ec73aa88ad80 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 20:56:38 +0800 Subject: [PATCH 13/18] =?UTF-8?q?=E5=B1=8E=E5=B1=B1=E4=BF=AE=E8=A1=A5?= =?UTF-8?q?=E6=9C=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 修改了bot_toml加载逻辑 --- .gitignore | 2 +- config/bot_config.toml | 11 ++--- src/plugins/chat/config.py | 72 +++++++++++++++++++++++++----- src/plugins/chat/cq_code.py | 2 +- src/plugins/chat/prompt_builder.py | 4 +- 5 files changed, 72 insertions(+), 19 deletions(-) diff --git a/.gitignore b/.gitignore index 8df5d72a8..a9cdc192f 100644 --- a/.gitignore +++ b/.gitignore @@ -15,7 +15,7 @@ reasoning_window.bat queue_update.txt memory_graph.gml .env.* - +config/bot_config_dev.toml # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/config/bot_config.toml b/config/bot_config.toml index d668fcf59..4863da245 100644 --- a/config/bot_config.toml +++ b/config/bot_config.toml @@ -1,5 +1,5 @@ [bot] -qq = 123456 #填入你的机器人QQ +qq = 123 #填入你的机器人QQ nickname = "麦麦" #你希望bot被称呼的名字 [message] @@ -17,7 +17,7 @@ enable_pic_translate = false [response] api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 -api_paid = false #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 +api_paid = true #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 @@ -34,13 +34,14 @@ enable_advance_output = true # 开启后输出更多日志,false关闭true开启 [groups] talk_allowed = [ - 123456,12345678 + 123, + 123, ] #可以回复消息的群 talk_frequency_down = [ - 123456,12345678 + ] #降低回复频率的群 ban_user_id = [ - 123456,12345678 + ] #禁止回复消息的QQ号 diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 54d125408..b85cbc2e6 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -47,20 +47,59 @@ class BotConfig: enable_kuuki_read: bool = True # 是否启用读空气功能 @staticmethod - def get_default_config_path() -> str: - """获取默认配置文件路径""" + def get_config_dir() -> str: + """获取配置文件目录""" current_dir = os.path.dirname(os.path.abspath(__file__)) root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..')) config_dir = os.path.join(root_dir, 'config') - return os.path.join(config_dir, 'bot_config.toml') + if not os.path.exists(config_dir): + os.makedirs(config_dir) + return config_dir + + @staticmethod + def create_default_config(config_path: str) -> None: + """创建默认配置文件""" + default_config = """[bot] +qq = 1 # 填入你的机器人QQ +nickname = "麦麦" # 你希望bot被称呼的名字 + +[message] +min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 +max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 +emoji_chance = 0.2 # 麦麦使用表情包的概率 + +[emoji] +check_interval = 120 +register_interval = 10 + +[cq_code] +enable_pic_translate = false + +[response] +api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek +api_paid = false # 是否使用付费api +model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 +model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 +model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 + +[memory] +build_memory_interval = 300 # 记忆构建间隔 + +[others] +enable_advance_output = false # 开启后输出更多日志 + +[groups] +talk_allowed = [] # 可以回复消息的群 +talk_frequency_down = [] # 降低回复频率的群 +ban_user_id = [] # 禁止回复消息的QQ号 +""" + with open(config_path, "w", encoding="utf-8") as f: + f.write(default_config) + logger.success(f"已创建默认配置文件: {config_path}") @classmethod def load_config(cls, config_path: str = None) -> "BotConfig": """从TOML配置文件加载配置""" - if config_path is None: - config_path = cls.get_default_config_path() - logger.info(f"使用默认配置文件路径: {config_path}") - config = cls() if os.path.exists(config_path): with open(config_path, "rb") as f: @@ -119,12 +158,25 @@ class BotConfig: return config # 获取配置文件路径 -bot_config_path = BotConfig.get_default_config_path() -config_dir = os.path.dirname(bot_config_path) -logger.info(f"尝试从 {bot_config_path} 加载机器人配置") +bot_config_floder_path = BotConfig.get_config_dir() +print(f"正在品鉴配置文件目录: {bot_config_floder_path}") +bot_config_path = os.path.join(bot_config_floder_path, "bot_config_dev.toml") +if not os.path.exists(bot_config_path): + # 如果开发环境配置文件不存在，则使用默认配置文件 + bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml") + logger.info("使用默认配置文件") +else: + logger.info("已找到开发环境配置文件") + global_config = BotConfig.load_config(config_path=bot_config_path) + +# config_dir = os.path.dirname(bot_config_path) + +# logger.info(f"尝试从 {bot_config_path} 加载机器人配置") +# global_config = BotConfig.load_config(config_path=bot_config_path) + @dataclass class LLMConfig: """机器人配置类""" diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index ae5d8a257..386124c92 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -349,7 +349,7 @@ class CQCode: # 创建Message对象 from .message import Message if self.reply_message == None: - print(f"\033[1;31m[错误]\033[0m 回复消息为空") + # print(f"\033[1;31m[错误]\033[0m 回复消息为空") return '[回复某人消息]' if self.reply_message.sender.user_id: diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 01fe3e82d..272229416 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -150,9 +150,9 @@ class PromptBuilder: prompt_personality = '' personality_choice = random.random() if personality_choice < 4/6: # 第一种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧，你正在浏览qq群,{promt_info_prompt}, + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，现在学习心理学和脑科学，你会刷贴吧，你正在浏览qq群,{promt_info_prompt}, 现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{is_bot_prompt} - 请注意把握群里的聊天内容，不要回复的太有条理，可以有个性。''' + 请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。''' elif personality_choice < 1: # 第二种人格 prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，是一个女大学生，你有黑色头发，你会刷小红书, 你正在浏览qq群,{promt_info_prompt}, From 927ae4484f86cdda55f23e2ef08c94df03cab463 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 20:59:50 +0800 Subject: [PATCH 14/18] Update .gitignore --- .gitignore | 5 ----- 1 file changed, 5 deletions(-) diff --git a/.gitignore b/.gitignore index a9cdc192f..48c0c13c3 100644 --- a/.gitignore +++ b/.gitignore @@ -2,16 +2,11 @@ data/ mongodb/ NapCat.Framework.Windows.Once/ log/ -src/plugins/memory /test -config/bot_config.toml message_queue_content.txt message_queue_content.bat message_queue_window.bat message_queue_window.txt -reasoning_content.txt -reasoning_content.bat -reasoning_window.bat queue_update.txt memory_graph.gml .env.* From 65d983f37f9c1f6eec120ee7b1ca93edb2330b24 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 21:03:56 +0800 Subject: [PATCH 15/18] =?UTF-8?q?=E8=B0=A2=E8=B0=A2=E5=A4=A7=E5=AE=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1 --- .env | 2 +- bot.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.env b/.env index 6e242e1f5..709d22d34 100644 --- a/.env +++ b/.env @@ -1,5 +1,5 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod -ENVIRONMENT=prod +ENVIRONMENT=dev HOST=127.0.0.1 PORT=8080 diff --git a/bot.py b/bot.py index 8741eca7f..489ab1c36 100644 --- a/bot.py +++ b/bot.py @@ -29,7 +29,7 @@ env_file = f".env.{env}" if env_file == ".env.dev" and os.path.exists(env_file): logger.success("加载开发环境变量配置") load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 -elif env_file == ".env.prod" and os.path.exists(env_file): +elif os.path.exists(".env.prod"): logger.success("加载环境变量配置") load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 else: From 6462c3b3606d1bedc62fc7818a7d32c3d1e182a6 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 23:51:47 +0800 Subject: [PATCH 16/18] =?UTF-8?q?v0.4.0=20=E6=94=AF=E6=8C=81=E4=BB=BB?= =?UTF-8?q?=E6=84=8F=E6=9B=BF=E6=8D=A2=E7=9A=84=E6=A8=A1=E5=9E=8B=EF=BC=8C?= =?UTF-8?q?=E6=94=B9=E8=BF=9B=E9=85=8D=E7=BD=AE=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 好好好好好好好好好 --- .env.prod | 8 +- bot.py | 35 +-- config/auto_format.py | 46 ++++ config/bot_config.toml | 62 +++-- docs/qq.png | Bin 195176 -> 0 bytes src/plugins/chat/__init__.py | 1 + src/plugins/chat/bot.py | 4 +- src/plugins/chat/config.py | 81 ++---- src/plugins/chat/cq_code.py | 106 ++------ src/plugins/chat/emoji_manager.py | 107 ++------ src/plugins/chat/llm_generator.py | 287 +++++--------------- src/plugins/memory_system/llm_module.py | 68 ----- src/plugins/memory_system/memory.py | 9 +- src/plugins/models/utils_model.py | 199 ++++++++++++++ src/plugins/schedule/schedule_generator.py | 29 +- src/plugins/schedule/schedule_llm_module.py | 61 ----- 16 files changed, 459 insertions(+), 644 deletions(-) create mode 100644 config/auto_format.py delete mode 100644 docs/qq.png delete mode 100644 src/plugins/memory_system/llm_module.py create mode 100644 src/plugins/models/utils_model.py delete mode 100644 src/plugins/schedule/schedule_llm_module.py diff --git a/.env.prod b/.env.prod index 3d795978c..f00cd5169 100644 --- a/.env.prod +++ b/.env.prod @@ -16,9 +16,11 @@ MONGODB_PASSWORD = "" # 默认空值 MONGODB_AUTH_SOURCE = "" # 默认空值 #key and url -CHAT_ANY_WHERE_KEY= -SILICONFLOW_KEY= + CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 + DEEP_SEEK_KEY= -DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file +CHAT_ANY_WHERE_KEY= +SILICONFLOW_KEY= \ No newline at end of file diff --git a/bot.py b/bot.py index 489ab1c36..cf1b82ff6 100644 --- a/bot.py +++ b/bot.py @@ -36,28 +36,18 @@ else: logger.error(f"{env}对应的环境配置文件{env_file}不存在,请修改.env文件中的ENVIRONMENT变量为 prod.") exit(1) -nonebot.init( - # 从环境变量中读取配置 - websocket_port=os.getenv("PORT", 8080), - host=os.getenv("HOST", "127.0.0.1"), - log_level="INFO", - # 添加自定义配置 - mongodb_host=os.getenv("MONGODB_HOST", "127.0.0.1"), - mongodb_port=os.getenv("MONGODB_PORT", 27017), - database_name=os.getenv("DATABASE_NAME", "MegBot"), - mongodb_username=os.getenv("MONGODB_USERNAME", ""), - mongodb_password=os.getenv("MONGODB_PASSWORD", ""), - mongodb_auth_source=os.getenv("MONGODB_AUTH_SOURCE", ""), - # API相关配置 - chat_any_where_key=os.getenv("CHAT_ANY_WHERE_KEY", ""), - siliconflow_key=os.getenv("SILICONFLOW_KEY", ""), - chat_any_where_base_url=os.getenv("CHAT_ANY_WHERE_BASE_URL", "https://api.chatanywhere.tech/v1"), - siliconflow_base_url=os.getenv("SILICONFLOW_BASE_URL", "https://api.siliconflow.cn/v1/"), - deep_seek_key=os.getenv("DEEP_SEEK_KEY", ""), - deep_seek_base_url=os.getenv("DEEP_SEEK_BASE_URL", "https://api.deepseek.com/v1"), - # 插件配置 - plugins=os.getenv("PLUGINS", ["src2.plugins.chat"]) -) +# 获取所有环境变量 +env_config = {key: os.getenv(key) for key in os.environ} + +# 设置基础配置 +base_config = { + "websocket_port": int(env_config.get("PORT", 8080)), + "host": env_config.get("HOST", "127.0.0.1"), + "log_level": "INFO", +} + +# 合并配置 +nonebot.init(**base_config, **env_config) # 注册适配器 driver = nonebot.get_driver() @@ -67,4 +57,5 @@ driver.register_adapter(Adapter) nonebot.load_plugins("src/plugins") if __name__ == "__main__": + nonebot.run() \ No newline at end of file diff --git a/config/auto_format.py b/config/auto_format.py new file mode 100644 index 000000000..9bc27da29 --- /dev/null +++ b/config/auto_format.py @@ -0,0 +1,46 @@ +import tomli +import tomli_w +import sys +from pathlib import Path +import os + +def sync_configs(): + # 读取两个配置文件 + try: + with open('bot_config_dev.toml', 'rb') as f: # tomli需要使用二进制模式读取 + dev_config = tomli.load(f) + + with open('bot_config.toml', 'rb') as f: + prod_config = tomli.load(f) + except FileNotFoundError as e: + print(f"错误：找不到配置文件 - {e}") + sys.exit(1) + except tomli.TOMLDecodeError as e: + print(f"错误：TOML格式解析失败 - {e}") + sys.exit(1) + + # 递归合并配置 + def merge_configs(source, target): + for key, value in source.items(): + if key not in target: + target[key] = value + elif isinstance(value, dict) and isinstance(target[key], dict): + merge_configs(value, target[key]) + + # 将dev配置的新属性合并到prod配置中 + merge_configs(dev_config, prod_config) + + # 保存更新后的配置 + try: + with open('bot_config.toml', 'wb') as f: # tomli_w需要使用二进制模式写入 + tomli_w.dump(prod_config, f) + print("配置文件同步完成！") + except Exception as e: + print(f"错误：保存配置文件失败 - {e}") + sys.exit(1) + +if __name__ == '__main__': + # 确保在正确的目录下运行 + script_dir = Path(__file__).parent + os.chdir(script_dir) + sync_configs() diff --git a/config/bot_config.toml b/config/bot_config.toml index 4863da245..83526945c 100644 --- a/config/bot_config.toml +++ b/config/bot_config.toml @@ -1,11 +1,11 @@ [bot] -qq = 123 #填入你的机器人QQ -nickname = "麦麦" #你希望bot被称呼的名字 +qq = 123 +nickname = "麦麦" [message] -min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 -max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 -emoji_chance = 0.2 # 麦麦使用表情包的概率 +min_text_length = 2 +max_context_size = 15 +emoji_chance = 0.2 [emoji] check_interval = 120 @@ -14,34 +14,48 @@ register_interval = 10 [cq_code] enable_pic_translate = false - [response] -api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 -api_paid = true #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 -model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 -model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 -model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 +api_using = "siliconflow" +api_paid = true +model_r1_probability = 0.8 +model_v3_probability = 0.1 +model_r1_distill_probability = 0.1 [memory] -build_memory_interval = 300 # 记忆构建间隔 - - +build_memory_interval = 300 [others] -enable_advance_output = true # 开启后输出更多日志,false关闭true开启 - +enable_advance_output = true [groups] - talk_allowed = [ - 123, - 123, -] #可以回复消息的群 + 123, + 123, +] +talk_frequency_down = [] +ban_user_id = [] -talk_frequency_down = [ +[model.llm_reasoning] +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" -] #降低回复频率的群 +[model.llm_reasoning_minor] +name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" -ban_user_id = [ +[model.llm_normal] +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" -] #禁止回复消息的QQ号 +[model.llm_normal_minor] +name = "deepseek-ai/DeepSeek-V2.5" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.vlm] +name = "deepseek-ai/deepseek-vl2" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" diff --git a/docs/qq.png b/docs/qq.png deleted file mode 100644 index 3c31347b658e6977f3cda32125b1b4d55f907c9f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 195176 zcmd3NV|!*z({=2L?M!T(v2EM7t%*6YZQHi(iEZ0X-dy)_JpbYS+WW)quCD4@wN_P_ zysX$S7;G3IAfR6o;=+nRK)_BwKp=!r5I-w!;$f;lK!iXN!UD?fnHODVwYbYJbZ;)t z2OO`sxCfai5{wDR5@Ctr)KaRoBonuqGt1p7K8N4cj!D<=_g4tpYx9hFV&nml`#w^b+ ze8OaP!!Xihx2D$RcF?*(WGR}uPM5Gg?*En#Ib$&=jefqWSA+h4vXr^k*fsLMO-_(F zRB_p}4C+68RP4XnzD#iYUAw9`)2MYohIbt+F%dtO8B>bVg{mpirVimV=9YosiFg}RkGc)r~lpAHJArR zfCpka`)F*|Ec#U`Rv{UXZgrwUnKE{+3{VY9C1^?p4zYxOAiv%-4R?{~S)e+OPAycLREXM9{i=5o|ER1ocn_ouOu_!Zd@BJ=B7$@3nV> 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zunBy`R2!m57&6crivT1;sYE!jUp=>p!wash)Ah8Nv7GZs_Z)D}za%ZQFXCt@1e^jS zlc3y2Z#Zrv-l0wRB@gn1N-}-coQVhhaX^pv4oe81-H8!#Mk?~VjNF8CuR z9-Qb0*yEf3F&y;Egt!e?=z+YS0;0ZNP- z`kpR(IVEkun610@`=#lv2_KlTnsbU*+5)Z z6UlCeXos@?n&Kqm^gUYq?+s$ZxcJ_KC|KBT*xhzm+ z#^3Mf39c#ozy1Oa9RedzVnI|C=VR+Q=62UuoN6EzYL@E6YNpYuxp}SN^@kkE!I``0t(` tP=-L?9sc*R+ None: - """创建默认配置文件""" - default_config = """[bot] -qq = 1 # 填入你的机器人QQ -nickname = "麦麦" # 你希望bot被称呼的名字 - -[message] -min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 -max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 -emoji_chance = 0.2 # 麦麦使用表情包的概率 - -[emoji] -check_interval = 120 -register_interval = 10 - -[cq_code] -enable_pic_translate = false - -[response] -api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek -api_paid = false # 是否使用付费api -model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 -model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 -model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 - -[memory] -build_memory_interval = 300 # 记忆构建间隔 - -[others] -enable_advance_output = false # 开启后输出更多日志 - -[groups] -talk_allowed = [] # 可以回复消息的群 -talk_frequency_down = [] # 降低回复频率的群 -ban_user_id = [] # 禁止回复消息的QQ号 -""" - with open(config_path, "w", encoding="utf-8") as f: - f.write(default_config) - logger.success(f"已创建默认配置文件: {config_path}") @classmethod def load_config(cls, config_path: str = None) -> "BotConfig": @@ -127,9 +89,26 @@ ban_user_id = [] # 禁止回复消息的QQ号 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) - if response_config.get("api_using", config.API_PAID): - config.SILICONFLOW_MODEL_R1 = "Pro/deepseek-ai/DeepSeek-R1" - config.SILICONFLOW_MODEL_V3 = "Pro/deepseek-ai/DeepSeek-V3" + config.API_PAID = response_config.get("api_paid", config.API_PAID) + + # 加载模型配置 + if "model" in toml_dict: + model_config = toml_dict["model"] + + if "llm_reasoning" in model_config: + config.llm_reasoning = model_config["llm_reasoning"] + + if "llm_reasoning_minor" in model_config: + config.llm_reasoning_minor = model_config["llm_reasoning_minor"] + + if "llm_normal" in model_config: + config.llm_normal = model_config["llm_normal"] + + if "llm_normal_minor" in model_config: + config.llm_normal_minor = model_config["llm_normal_minor"] + + if "vlm" in model_config: + config.vlm = model_config["vlm"] # 消息配置 if "message" in toml_dict: @@ -172,10 +151,6 @@ else: global_config = BotConfig.load_config(config_path=bot_config_path) -# config_dir = os.path.dirname(bot_config_path) - -# logger.info(f"尝试从 {bot_config_path} 加载机器人配置") -# global_config = BotConfig.load_config(config_path=bot_config_path) @dataclass class LLMConfig: diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index 386124c92..4d70736cd 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -12,6 +12,7 @@ import time import asyncio from .utils_image import storage_image,storage_emoji from .utils_user import get_user_nickname +from ..models.utils_model import LLM_request #解析各种CQ码 #包含CQ码类 import urllib3 @@ -57,6 +58,11 @@ class CQCode: translated_plain_text: Optional[str] = None reply_message: Dict = None # 存储回复消息 image_base64: Optional[str] = None + _llm: Optional[LLM_request] = None + + def __post_init__(self): + """初始化LLM实例""" + self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300) def translate(self): """根据CQ码类型进行相应的翻译处理""" @@ -161,7 +167,7 @@ class CQCode: # 将 base64 字符串转换为字节类型 image_bytes = base64.b64decode(base64_str) storage_emoji(image_bytes) - return self.get_image_description(base64_str) + return self.get_emoji_description(base64_str) else: return '[表情包]' @@ -181,93 +187,23 @@ class CQCode: def get_emoji_description(self, image_base64: str) -> str: """调用AI接口获取表情包描述""" - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } - - payload = { - "model": "deepseek-ai/deepseek-vl2", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。" - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - "max_tokens": 50, - "temperature": 0.4 - } - - response = requests.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload, - timeout=30 - ) - - if response.status_code == 200: - result_json = response.json() - if "choices" in result_json and len(result_json["choices"]) > 0: - description = result_json["choices"][0]["message"]["content"] - return f"[表情包：{description}]" - - raise ValueError(f"AI接口调用失败: {response.text}") + try: + prompt = "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。" + description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + return f"[表情包：{description}]" + except Exception as e: + print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}") + return "[表情包]" def get_image_description(self, image_base64: str) -> str: """调用AI接口获取普通图片描述""" - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } - - payload = { - "model": "deepseek-ai/deepseek-vl2", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。" - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - "max_tokens": 300, - "temperature": 0.6 - } - - response = requests.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload, - timeout=30 - ) - - if response.status_code == 200: - result_json = response.json() - if "choices" in result_json and len(result_json["choices"]) > 0: - description = result_json["choices"][0]["message"]["content"] - return f"[图片：{description}]" - - raise ValueError(f"AI接口调用失败: {response.text}") + try: + prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。" + description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + return f"[图片：{description}]" + except Exception as e: + print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}") + return "[图片]" def translate_forward(self) -> str: """处理转发消息""" diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index c8c9dc814..58713e296 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -14,6 +14,8 @@ import asyncio import time from nonebot import get_driver +from ..chat.config import global_config +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config @@ -43,6 +45,7 @@ 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=50) def _ensure_emoji_dir(self): """确保表情存储目录存在""" @@ -87,55 +90,23 @@ class EmojiManager: print(f"\033[1;31m[错误]\033[0m 记录表情使用失败: {str(e)}") async def _get_emotion_from_text(self, text: str) -> List[str]: - """从文本中识别情感关键词，使用DeepSeek API进行分析 + """从文本中识别情感关键词 Args: text: 输入文本 Returns: List[str]: 匹配到的情感标签列表 """ try: - # 准备请求数据 - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } + prompt = f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。' - payload = { - "model": "deepseek-ai/DeepSeek-V3", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。' - } - ] - } - ], - "max_tokens": 50, - "temperature": 0.3 - } + content, _ = await self.llm.generate_response(prompt) + emotion = content.strip().lower() - async with aiohttp.ClientSession() as session: - async with session.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload - ) as response: - if response.status != 200: - print(f"\033[1;31m[错误]\033[0m API请求失败: {await response.text()}") - return ['neutral'] - - result = json.loads(await response.text()) - if "choices" in result and len(result["choices"]) > 0: - emotion = result["choices"][0]["message"]["content"].strip().lower() - # 确保返回的标签是有效的 - if emotion in self.EMOTION_KEYWORDS: - print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}") - return [emotion] # 返回单个情感标签的列表 + if emotion in self.EMOTION_KEYWORDS: + print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}") + return [emotion] - return ['neutral'] # 如果无法识别情感，返回neutral + return ['neutral'] except Exception as e: print(f"\033[1;31m[错误]\033[0m 情感分析失败: {str(e)}") @@ -250,52 +221,20 @@ class EmojiManager: async def _get_emoji_tag(self, image_base64: str) -> str: """获取表情包的标签""" - async with aiohttp.ClientSession() as session: - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } + try: + prompt = '这是一个表情包，请从"happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"中选出1个情感标签。只输出标签，不要输出其他任何内容，只输出情感标签就好' - payload = { - "model": "deepseek-ai/deepseek-vl2", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": '这是一个表情包，请从"happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"中选出1个情感标签。只输出标签，不要输出其他任何内容，只输出情感标签就好' - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - "max_tokens": 60, - "temperature": 0.3 - } + content, _ = await self.llm.generate_response_for_image(prompt, image_base64) + tag_result = content.strip().lower() - async with session.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload - ) as response: - if response.status == 200: - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - tag_result = result["choices"][0]["message"]["content"].strip().lower() - - valid_tags = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"] - for tag_match in valid_tags: - if tag_match in tag_result or tag_match == tag_result: - return tag_match - print(f"\033[1;33m[警告]\033[0m 无效的标签: {tag_match}, 跳过") - else: - print(f"\033[1;31m[错误]\033[0m 获取标签失败, 状态码: {response.status}") + valid_tags = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"] + for tag_match in valid_tags: + if tag_match in tag_result or tag_match == tag_result: + return tag_match + print(f"\033[1;33m[警告]\033[0m 无效的标签: {tag_result}, 跳过") + + except Exception as e: + print(f"\033[1;31m[错误]\033[0m 获取标签失败: {str(e)}") print(f"\033[1;32m[调试信息]\033[0m 使用默认标签: neutral") return "skip" # 默认标签 diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index 682be8a87..a9c028827 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -13,249 +13,113 @@ from .prompt_builder import prompt_builder from .config import global_config from .utils import process_llm_response from nonebot import get_driver +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config -class LLMResponseGenerator: +class ResponseGenerator: def __init__(self): - if global_config.API_USING == "siliconflow": - self.client = OpenAI( - api_key=config.siliconflow_key, - base_url=config.siliconflow_base_url - ) - elif global_config.API_USING == "deepseek": - self.client = OpenAI( - api_key=config.deep_seek_key, - base_url=config.deep_seek_base_url - ) - + self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7) + self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7) + self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7) self.db = Database.get_instance() - - # 当前使用的模型类型 self.current_model_type = 'r1' # 默认使用 R1 async def generate_response(self, message: Message) -> Optional[Union[str, List[str]]]: """根据当前模型类型选择对应的生成函数""" - # 从global_config中获取模型概率值 - model_r1_probability = global_config.MODEL_R1_PROBABILITY - model_v3_probability = global_config.MODEL_V3_PROBABILITY - model_r1_distill_probability = global_config.MODEL_R1_DISTILL_PROBABILITY - - # 生成随机数并根据概率选择模型 + # 从global_config中获取模型概率值并选择模型 rand = random.random() - if rand < model_r1_probability: + if rand < global_config.MODEL_R1_PROBABILITY: self.current_model_type = 'r1' - elif rand < model_r1_probability + model_v3_probability: + current_model = self.model_r1 + elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY: self.current_model_type = 'v3' + current_model = self.model_v3 else: - self.current_model_type = 'r1_distill' # 默认使用 R1-Distill - + self.current_model_type = 'r1_distill' + current_model = self.model_r1_distill print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++") - if self.current_model_type == 'r1': - model_response = await self._generate_r1_response(message) - elif self.current_model_type == 'v3': - model_response = await self._generate_v3_response(message) - else: - model_response = await self._generate_r1_distill_response(message) - # 打印情感标签 - print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') - model_response, emotion = await self._process_response(model_response) + model_response = await self._generate_response_with_model(message, current_model) if model_response: - print(f"为 '{model_response}' 获取到的情感标签为：{emotion}") - - return model_response, emotion + print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') + model_response, emotion = await self._process_response(model_response) + if model_response: + print(f"为 '{model_response}' 获取到的情感标签为：{emotion}") + return model_response, emotion + return None, [] - async def _generate_base_response( - self, - message: Message, - model_name: str, - model_params: Optional[Dict[str, Any]] = None - ) -> Optional[str]: + async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]: + """使用指定的模型生成回复""" sender_name = message.user_nickname or f"用户{message.user_id}" # 获取关系值 - if relationship_manager.get_relationship(message.user_id): - relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value + relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0 + if relationship_value != 0.0: print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}") - else: - relationship_value = 0.0 - - ''' 构建prompt ''' - prompt,prompt_check = prompt_builder._build_prompt( + # 构建prompt + prompt, prompt_check = prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, relationship_value=relationship_value, group_id=message.group_id ) - - - # 设置默认参数 - default_params = { - "model": model_name, - "messages": [{"role": "user", "content": prompt}], - "stream": False, - "max_tokens": 1024, - "temperature": 0.7 - } - - default_params_check = { - "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", - "messages": [{"role": "user", "content": prompt_check}], - "stream": False, - "max_tokens": 1024, - "temperature": 0.7 - } - - # 更新参数 - if model_params: - default_params.update(model_params) - - - def create_completion(): - return self.client.chat.completions.create(**default_params) - - def create_completion_check(): - return self.client.chat.completions.create(**default_params_check) - - loop = asyncio.get_event_loop() # 读空气模块 - air = 0 - reasoning_content_check='' - content_check='' if global_config.enable_kuuki_read: - response_check = await loop.run_in_executor(None, create_completion_check) - if response_check: - reasoning_content_check = "" - if hasattr(response_check.choices[0].message, "reasoning"): - reasoning_content_check = response_check.choices[0].message.reasoning or reasoning_content_check - elif hasattr(response_check.choices[0].message, "reasoning_content"): - reasoning_content_check = response_check.choices[0].message.reasoning_content or reasoning_content_check - content_check = response_check.choices[0].message.content - print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") - if 'yes' not in content_check.lower(): - air = 1 - #稀释读空气的判定 - if air == 1 and random.random() < 0.3: - self.db.db.reasoning_logs.insert_one({ - 'time': time.time(), - 'group_id': message.group_id, - 'user': sender_name, - 'message': message.processed_plain_text, - 'model': model_name, - 'reasoning_check': reasoning_content_check, - 'response_check': content_check, - 'reasoning': "", - 'response': "", - 'prompt': prompt, - 'prompt_check': prompt_check, - 'model_params': default_params - }) - return None - - - - + content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check) + print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") + if 'yes' not in content_check.lower() and random.random() < 0.3: + self._save_to_db( + message=message, + sender_name=sender_name, + prompt=prompt, + prompt_check=prompt_check, + content="", + content_check=content_check, + reasoning_content="", + reasoning_content_check=reasoning_content_check + ) + return None - response = await loop.run_in_executor(None, create_completion) + # 生成回复 + content, reasoning_content = await model.generate_response(prompt) - # 检查响应内容 - if not response: - print("请求未返回任何内容") - return None - - if not response.choices or not response.choices[0].message.content: - print("请求返回的内容无效:", response) - return None - - content = response.choices[0].message.content - - # 获取推理内容 - reasoning_content = "" - if hasattr(response.choices[0].message, "reasoning"): - reasoning_content = response.choices[0].message.reasoning or reasoning_content - elif hasattr(response.choices[0].message, "reasoning_content"): - reasoning_content = response.choices[0].message.reasoning_content or reasoning_content - # 保存到数据库 + self._save_to_db( + message=message, + sender_name=sender_name, + prompt=prompt, + prompt_check=prompt_check, + content=content, + content_check=content_check if global_config.enable_kuuki_read else "", + reasoning_content=reasoning_content, + reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else "" + ) + + return content + + def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str, + content: str, content_check: str, reasoning_content: str, reasoning_content_check: str): + """保存对话记录到数据库""" self.db.db.reasoning_logs.insert_one({ 'time': time.time(), 'group_id': message.group_id, 'user': sender_name, 'message': message.processed_plain_text, - 'model': model_name, + 'model': self.current_model_type, 'reasoning_check': reasoning_content_check, 'response_check': content_check, 'reasoning': reasoning_content, 'response': content, 'prompt': prompt, - 'prompt_check': prompt_check, - 'model_params': default_params + 'prompt_check': prompt_check }) - - return content - - async def _generate_r1_response(self, message: Message) -> Optional[str]: - """使用 DeepSeek-R1 模型生成回复""" - if global_config.API_USING == "deepseek": - return await self._generate_base_response( - message, - global_config.DEEPSEEK_MODEL_R1, - {"temperature": 0.7, "max_tokens": 1024} - ) - else: - return await self._generate_base_response( - message, - global_config.SILICONFLOW_MODEL_R1, - {"temperature": 0.7, "max_tokens": 1024} - ) - - async def _generate_v3_response(self, message: Message) -> Optional[str]: - """使用 DeepSeek-V3 模型生成回复""" - if global_config.API_USING == "deepseek": - return await self._generate_base_response( - message, - global_config.DEEPSEEK_MODEL_V3, - {"temperature": 0.8, "max_tokens": 1024} - ) - else: - return await self._generate_base_response( - message, - global_config.SILICONFLOW_MODEL_V3, - {"temperature": 0.8, "max_tokens": 1024} - ) - - async def _generate_r1_distill_response(self, message: Message) -> Optional[str]: - """使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复""" - return await self._generate_base_response( - message, - global_config.SILICONFLOW_MODEL_R1_DISTILL, - {"temperature": 0.7, "max_tokens": 1024} - ) - - async def _get_group_chat_context(self, message: Message) -> str: - """获取群聊上下文""" - recent_messages = self.db.db.messages.find( - {"group_id": message.group_id} - ).sort("time", -1).limit(15) - - messages_list = list(recent_messages)[::-1] - group_chat = "" - - for msg_dict in messages_list: - time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(msg_dict['time'])) - display_name = msg_dict.get('user_nickname', f"用户{msg_dict['user_id']}") - content = msg_dict.get('processed_plain_text', msg_dict['plain_text']) - - group_chat += f"[{time_str}] {display_name}: {content}\n" - - return group_chat async def _get_emotion_tags(self, content: str) -> List[str]: """提取情感标签""" @@ -266,33 +130,12 @@ class LLMResponseGenerator: 输出： ''' - messages = [{"role": "user", "content": prompt}] - - loop = asyncio.get_event_loop() - if global_config.API_USING == "deepseek": - model = global_config.DEEPSEEK_MODEL_V3 - else: - model = global_config.SILICONFLOW_MODEL_V3 - create_completion = partial( - self.client.chat.completions.create, - model=model, - messages=messages, - stream=False, - max_tokens=30, - temperature=0.6 - ) - response = await loop.run_in_executor(None, create_completion) - - if response.choices[0].message.content: - # 确保返回的是列表格式 - emotion_tag = response.choices[0].message.content.strip() - return [emotion_tag] # 将单个标签包装成列表返回 - - return ["neutral"] # 如果无法获取情感标签，返回默认值 + content, _ = await self.model_v3.generate_response(prompt) + return [content.strip()] if content else ["neutral"] except Exception as e: print(f"获取情感标签时出错: {e}") - return ["neutral"] # 发生错误时返回默认值 + return ["neutral"] async def _process_response(self, content: str) -> Tuple[List[str], List[str]]: """处理响应内容，返回处理后的内容和情感标签""" @@ -300,10 +143,6 @@ class LLMResponseGenerator: return None, [] emotion_tags = await self._get_emotion_tags(content) - processed_response = process_llm_response(content) - return processed_response, emotion_tags - -# 创建全局实例 -llm_response = LLMResponseGenerator() \ No newline at end of file + return processed_response, emotion_tags \ No newline at end of file diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py deleted file mode 100644 index c2b6cf042..000000000 --- a/src/plugins/memory_system/llm_module.py +++ /dev/null @@ -1,68 +0,0 @@ -import os -import requests -from typing import Tuple, Union -import time -from nonebot import get_driver -import aiohttp -import asyncio -from src.plugins.chat.config import global_config -driver = get_driver() -config = driver.config - -class LLMModel: - # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name=global_config.SILICONFLOW_MODEL_V3, **kwargs): - self.model_name = model_name - self.params = kwargs - self.api_key = config.siliconflow_key - self.base_url = config.siliconflow_base_url - - async def generate_response(self, prompt: str) -> Tuple[str, str]: - """根据输入的提示生成模型的响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - # 构建请求体 - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - "temperature": 0.5, - **self.params - } - - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - - max_retries = 3 - base_wait_time = 15 - - for retry in range(max_retries): - try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content - return "没有返回结果", "" - - except Exception as e: - if retry < max_retries - 1: # 如果还有重试机会 - wait_time = base_wait_time * (2 ** retry) - print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - await asyncio.sleep(wait_time) - else: - return f"请求失败: {str(e)}", "" - - return "达到最大重试次数，请求仍然失败", "" \ No newline at end of file diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index cab41f903..a051192a5 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -1,19 +1,16 @@ # -*- coding: utf-8 -*- import os import jieba -from .llm_module import LLMModel import networkx as nx import matplotlib.pyplot as plt -import math from collections import Counter import datetime import random import time from ..chat.config import global_config -import sys from ...common.database import Database # 使用正确的导入语法 from ..chat.utils import calculate_information_content, get_cloest_chat_from_db - +from ..models.utils_model import LLM_request class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 @@ -169,8 +166,8 @@ class Memory_graph: class Hippocampus: def __init__(self,memory_graph:Memory_graph): self.memory_graph = memory_graph - self.llm_model = LLMModel() - self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5") + self.llm_model = LLM_request(model = global_config.llm_normal,temperature=0.5) + self.llm_model_small = LLM_request(model = global_config.llm_normal_minor,temperature=0.5) def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}): current_timestamp = datetime.datetime.now().timestamp() diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py new file mode 100644 index 000000000..5f4d02df5 --- /dev/null +++ b/src/plugins/models/utils_model.py @@ -0,0 +1,199 @@ +import aiohttp +import asyncio +import requests +import time +from typing import Tuple, Union +from nonebot import get_driver +from ..chat.config import global_config +driver = get_driver() +config = driver.config + +class LLM_request: + def __init__(self, model = global_config.llm_normal,**kwargs): + # 将大写的配置键转换为小写并从config中获取实际值 + try: + self.api_key = getattr(config, model["key"]) + self.base_url = getattr(config, model["base_url"]) + except AttributeError as e: + raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") + self.model_name = model["name"] + self.params = kwargs + + async def generate_response(self, prompt: str) -> Tuple[str, str]: + """根据输入的提示生成模型的异步响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [{"role": "user", "content": prompt}], + **self.params + } + + # 发送请求到完整的chat/completions端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + + max_retries = 3 + base_wait_time = 15 + + for retry in range(max_retries): + try: + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + return f"请求失败: {str(e)}", "" + + return "达到最大重试次数，请求仍然失败", "" + + async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]: + """根据输入的提示和图片生成模型的异步响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt + }, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_base64}" + } + } + ] + } + ], + **self.params + } + + # 发送请求到完整的chat/completions端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + + max_retries = 3 + base_wait_time = 15 + + for retry in range(max_retries): + try: + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + return f"请求失败: {str(e)}", "" + + return "达到最大重试次数，请求仍然失败", "" + + def generate_response_for_image_sync(self, prompt: str, image_base64: str) -> Tuple[str, str]: + """同步方法：根据输入的提示和图片生成模型的响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt + }, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_base64}" + } + } + ] + } + ], + **self.params + } + + # 发送请求到完整的chat/completions端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + + max_retries = 3 + base_wait_time = 15 + + for retry in range(max_retries): + try: + response = requests.post(api_url, headers=headers, json=data, timeout=30) + + if response.status_code == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + time.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + time.sleep(wait_time) + else: + return f"请求失败: {str(e)}", "" + + return "达到最大重试次数，请求仍然失败", "" diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index 63e3235a3..24b2a32dd 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -1,10 +1,10 @@ import datetime import os from typing import List, Dict -from .schedule_llm_module import LLMModel from ...common.database import Database # 使用正确的导入语法 from src.plugins.chat.config import global_config from nonebot import get_driver +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config @@ -21,22 +21,27 @@ Database.initialize( class ScheduleGenerator: def __init__(self): - if global_config.API_USING == "siliconflow": - self.llm_scheduler = LLMModel(model_name=global_config.SILICONFLOW_MODEL_V3) - elif global_config.API_USING == "deepseek": - self.llm_scheduler = LLMModel(model_name=global_config.DEEPSEEK_MODEL_V3) + #根据global_config.llm_normal这一字典配置指定模型 + # self.llm_scheduler = LLMModel(model = global_config.llm_normal,temperature=0.9) + self.llm_scheduler = LLM_request(model = global_config.llm_normal,temperature=0.9) self.db = Database.get_instance() - + self.today_schedule_text = "" + self.today_schedule = {} + self.tomorrow_schedule_text = "" + self.tomorrow_schedule = {} + self.yesterday_schedule_text = "" + self.yesterday_schedule = {} + + async def initialize(self): today = datetime.datetime.now() tomorrow = datetime.datetime.now() + datetime.timedelta(days=1) yesterday = datetime.datetime.now() - datetime.timedelta(days=1) - self.today_schedule_text, self.today_schedule = self.generate_daily_schedule(target_date=today) - - self.tomorrow_schedule_text, self.tomorrow_schedule = self.generate_daily_schedule(target_date=tomorrow,read_only=True) - self.yesterday_schedule_text, self.yesterday_schedule = self.generate_daily_schedule(target_date=yesterday,read_only=True) + self.today_schedule_text, self.today_schedule = await self.generate_daily_schedule(target_date=today) + self.tomorrow_schedule_text, self.tomorrow_schedule = await self.generate_daily_schedule(target_date=tomorrow,read_only=True) + self.yesterday_schedule_text, self.yesterday_schedule = await self.generate_daily_schedule(target_date=yesterday,read_only=True) - def generate_daily_schedule(self, target_date: datetime.datetime = None,read_only:bool = False) -> Dict[str, str]: + async def generate_daily_schedule(self, target_date: datetime.datetime = None,read_only:bool = False) -> Dict[str, str]: if target_date is None: target_date = datetime.datetime.now() @@ -60,7 +65,7 @@ class ScheduleGenerator: 3. 晚上的计划和休息时间 请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用逗号,隔开时间与活动，格式为"时间,活动"，例如"08:00,起床"。""" - schedule_text, _ = self.llm_scheduler.generate_response(prompt) + schedule_text, _ = await self.llm_scheduler.generate_response(prompt) # print(self.schedule_text) self.db.db.schedule.insert_one({"date": date_str, "schedule": schedule_text}) else: diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py deleted file mode 100644 index 21e3f7681..000000000 --- a/src/plugins/schedule/schedule_llm_module.py +++ /dev/null @@ -1,61 +0,0 @@ -import os -import requests -import aiohttp -from typing import Tuple, Union -from nonebot import get_driver -from src.plugins.chat.config import global_config -driver = get_driver() -config = driver.config - -class LLMModel: - # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name=global_config.SILICONFLOW_MODEL_R1,api_using=None, **kwargs): - if api_using == "deepseek": - self.api_key = config.deep_seek_key - self.base_url = config.deep_seek_base_url - self.model_name = global_config.DEEPSEEK_MODEL_R1 - else: - self.api_key = config.siliconflow_key - self.base_url = config.siliconflow_base_url - self.model_name = model_name - self.params = kwargs - - def generate_response(self, prompt: str) -> Tuple[str, str]: - """根据输入的提示生成模型的响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - # 构建请求体 - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - "temperature": 0.9, - **self.params - } - - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - - try: - response = requests.post(api_url, headers=headers, json=data) - response.raise_for_status() # 检查响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content # 返回内容和推理内容 - return "没有返回结果", "" # 返回两个值 - - except Exception as e: - return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串 - -# 示例用法 -if __name__ == "__main__": - model = LLMModel() # 默认使用 DeepSeek-V3 模型 - prompt = "你好，你喜欢我吗？" - result, reasoning = model.generate_response(prompt) - print("回复内容:", result) - print("推理内容:", reasoning) \ No newline at end of file From 7ccd27520298be5f0d9983c1b01d312037fa059f Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Mon, 3 Mar 2025 01:19:22 +0800 Subject: [PATCH 17/18] Update .env --- .env | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/.env b/.env index 709d22d34..06c26cdf3 100644 --- a/.env +++ b/.env @@ -1,26 +1,26 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod -ENVIRONMENT=dev -HOST=127.0.0.1 -PORT=8080 +ENVIRONMENT=prod +# HOST=127.0.0.1 +# PORT=8080 -COMMAND_START=["/"] +# COMMAND_START=["/"] -# 插件配置 -PLUGINS=["src2.plugins.chat"] +# # 插件配置 +# PLUGINS=["src2.plugins.chat"] -# 默认配置 -MONGODB_HOST=127.0.0.1 -MONGODB_PORT=27017 -DATABASE_NAME=MegBot +# # 默认配置 +# MONGODB_HOST=127.0.0.1 +# MONGODB_PORT=27017 +# DATABASE_NAME=MegBot -MONGODB_USERNAME = "" # 默认空值 -MONGODB_PASSWORD = "" # 默认空值 -MONGODB_AUTH_SOURCE = "" # 默认空值 +# MONGODB_USERNAME = "" # 默认空值 +# MONGODB_PASSWORD = "" # 默认空值 +# MONGODB_AUTH_SOURCE = "" # 默认空值 -#key and url -CHAT_ANY_WHERE_KEY= -SILICONFLOW_KEY= -CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 -SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ -DEEP_SEEK_KEY= -DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file +# #key and url +# CHAT_ANY_WHERE_KEY= +# SILICONFLOW_KEY= +# CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 +# SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +# DEEP_SEEK_KEY= +# DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file From d805e6ea2ccfdcc64f99b7e76a80597c9d45e47a Mon Sep 17 00:00:00 2001 From: KawaiiYusora Date: Mon, 3 Mar 2025 01:25:32 +0800 Subject: [PATCH 18/18] =?UTF-8?q?env.prod=E6=97=A0=E6=B3=95=E5=8A=A0?= =?UTF-8?q?=E8=BD=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- bot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bot.py b/bot.py index cf1b82ff6..78bf34b32 100644 --- a/bot.py +++ b/bot.py @@ -31,7 +31,7 @@ if env_file == ".env.dev" and os.path.exists(env_file): load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 elif os.path.exists(".env.prod"): logger.success("加载环境变量配置") - load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 + load_dotenv(".env.prod", override=True) # override=True 允许覆盖已存在的环境变量 else: logger.error(f"{env}对应的环境配置文件{env_file}不存在,请修改.env文件中的ENVIRONMENT变量为 prod.") exit(1)