mirror of https://github.com/Mai-with-u/MaiBot.git
尝试减少不同群内的同义词混淆 以及允许用户设置指定群组记忆私有
用户设置指定群组记忆私有会导致记忆的语料严重减少 未设置群组记忆私有的记忆会开放对于所有群聊 因此要不就修改其他记忆配置 增强学习/记忆能力 或者增加单一群组的信息流数量 或者增加公开记忆的信息流数量 记忆检索时会按以下优先级返回记忆： 当前群组的记忆（如果群聊属于某个群组） 当前群聊的记忆（如果不属于任何群组） 公共记忆（无群组/群聊标识的记忆） 其他非私有群组的记忆 5. 跨群记忆连接 同一群组内的主题使用较强连接（相似度×10） 跨群组的相似主题使用较弱连接（相似度×5） 相同群组/群聊的相似主题会获得20%的相似度加成pull/273/head
Cindy-Master
parent
6ff1601722
commit
1e3ebdcdf0
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@ -135,7 +135,7 @@ class ChatBot:
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for pattern in global_config.ban_msgs_regex:
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if re.search(pattern, message.raw_message):
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info.group_id else '私聊'}]{userinfo.user_nickname}:{message.raw_message}"
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f"[{chat.group_info.group_name if chat.group_info.group_id else '私聊'}]{message.user_nickname}:{message.raw_message}"
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)
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logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
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return
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@ -6,8 +6,6 @@ import random
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import time
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import traceback
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from typing import Optional, Tuple
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from PIL import Image
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import io
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from loguru import logger
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from nonebot import get_driver
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@ -194,11 +192,11 @@ class EmojiManager:
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logger.error(f"获取标签失败: {str(e)}")
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return None
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async def _check_emoji(self, image_base64: str, image_format: str) -> str:
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async def _check_emoji(self, image_base64: str) -> str:
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try:
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prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容'
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content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
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content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
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logger.debug(f"输出描述: {content}")
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return content
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@ -239,7 +237,7 @@ class EmojiManager:
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image_bytes = base64.b64decode(image_base64)
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image_hash = hashlib.md5(image_bytes).hexdigest()
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image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
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# 检查是否已经注册过
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existing_emoji = self.db['emoji'].find_one({'filename': filename})
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description = None
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@ -280,7 +278,7 @@ class EmojiManager:
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if global_config.EMOJI_CHECK:
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check = await self._check_emoji(image_base64, image_format)
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check = await self._check_emoji(image_base64)
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if '是' not in check:
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os.remove(image_path)
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logger.info(f"描述: {description}")
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@ -4,8 +4,6 @@ import time
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import aiohttp
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import hashlib
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from typing import Optional, Union
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from PIL import Image
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import io
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from loguru import logger
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from nonebot import get_driver
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@ -121,7 +119,6 @@ class ImageManager:
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# 计算哈希值
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image_hash = hashlib.md5(image_bytes).hexdigest()
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image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
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# 查重
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existing = self.db.images.find_one({'hash': image_hash})
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@ -130,7 +127,7 @@ class ImageManager:
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# 生成文件名和路径
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timestamp = int(time.time())
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filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
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filename = f"{timestamp}_{image_hash[:8]}.jpg"
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file_path = os.path.join(self.IMAGE_DIR, filename)
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# 保存文件
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@ -241,7 +238,6 @@ class ImageManager:
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# 计算图片哈希
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image_bytes = base64.b64decode(image_base64)
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image_hash = hashlib.md5(image_bytes).hexdigest()
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image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
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# 查询缓存的描述
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cached_description = self._get_description_from_db(image_hash, 'emoji')
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@ -251,13 +247,13 @@ class ImageManager:
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# 调用AI获取描述
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prompt = "这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感"
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
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# 根据配置决定是否保存图片
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if global_config.EMOJI_SAVE:
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# 生成文件名和路径
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timestamp = int(time.time())
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filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
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filename = f"{timestamp}_{image_hash[:8]}.jpg"
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file_path = os.path.join(self.IMAGE_DIR, 'emoji',filename)
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try:
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@ -297,7 +293,6 @@ class ImageManager:
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# 计算图片哈希
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image_bytes = base64.b64decode(image_base64)
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image_hash = hashlib.md5(image_bytes).hexdigest()
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image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
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# 查询缓存的描述
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cached_description = self._get_description_from_db(image_hash, 'image')
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@ -307,7 +302,7 @@ class ImageManager:
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# 调用AI获取描述
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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。"
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
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print(f"描述是{description}")
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@ -319,7 +314,7 @@ class ImageManager:
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if global_config.EMOJI_SAVE:
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# 生成文件名和路径
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timestamp = int(time.time())
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filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
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filename = f"{timestamp}_{image_hash[:8]}.jpg"
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file_path = os.path.join(self.IMAGE_DIR,'image', filename)
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try:
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@ -104,7 +104,6 @@ class LLM_request:
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endpoint: str,
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prompt: str = None,
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image_base64: str = None,
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image_format: str = None,
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payload: dict = None,
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retry_policy: dict = None,
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response_handler: callable = None,
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@ -116,7 +115,6 @@ class LLM_request:
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endpoint: API端点路径 (如 "chat/completions")
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prompt: prompt文本
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image_base64: 图片的base64编码
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image_format: 图片格式
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payload: 请求体数据
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retry_policy: 自定义重试策略
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response_handler: 自定义响应处理器
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@ -153,7 +151,7 @@ class LLM_request:
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# 构建请求体
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if image_base64:
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payload = await self._build_payload(prompt, image_base64, image_format)
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payload = await self._build_payload(prompt, image_base64)
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elif payload is None:
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payload = await self._build_payload(prompt)
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@ -174,7 +172,7 @@ class LLM_request:
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if response.status == 413:
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logger.warning("请求体过大，尝试压缩...")
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image_base64 = compress_base64_image_by_scale(image_base64)
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payload = await self._build_payload(prompt, image_base64, image_format)
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payload = await self._build_payload(prompt, image_base64)
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elif response.status in [500, 503]:
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logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
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raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
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@ -296,7 +294,7 @@ class LLM_request:
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new_params["max_completion_tokens"] = new_params.pop("max_tokens")
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return new_params
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async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
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async def _build_payload(self, prompt: str, image_base64: str = None) -> dict:
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"""构建请求体"""
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# 复制一份参数，避免直接修改 self.params
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params_copy = await self._transform_parameters(self.params)
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@ -308,7 +306,7 @@ class LLM_request:
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}}
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
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]
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}
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],
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@ -393,14 +391,13 @@ class LLM_request:
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)
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return content, reasoning_content
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async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple[str, str]:
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async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]:
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"""根据输入的提示和图片生成模型的异步响应"""
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content, reasoning_content = await self._execute_request(
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endpoint="/chat/completions",
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prompt=prompt,
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image_base64=image_base64,
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image_format=image_format
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image_base64=image_base64
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)
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return content, reasoning_content
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Loading…
Reference in New Issue