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] =?UTF-8?q?v0.3.3=20=E5=BC=82=E6=AD=A5=E5=A4=84=E7=90=86?= =?UTF-8?q?=E8=AE=B0=E5=BF=86=EF=BC=8C=E4=BF=AE=E5=A4=8D=E4=BA=86GUI?= 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