gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

调整对应的调用

Browse Source
This commit is contained in:
UnCLAS-Prommer
2025-07-30 17:07:55 +08:00
parent 3c40ceda4c
commit 6c0edd0ad7
40 changed files with 580 additions and 1236 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
src/chat/utils/utils.py
View File

@@ -11,7 +11,7 @@ from typing import Optional, Tuple, Dict, List, Any
from src.common.logger import get_logger
from src.common.message_repository import find_messages, count_messages
from src.config.config import global_config
from src.config.config import global_config, model_config
from src.chat.message_receive.message import MessageRecv
from src.chat.message_receive.chat_stream import get_chat_manager
from src.llm_models.utils_model import LLMRequest
@@ -109,13 +109,11 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
return is_mentioned, reply_probability
async def get_embedding(text, request_type="embedding"):
async def get_embedding(text, request_type="embedding") -> Optional[List[float]]:
"""获取文本的embedding向量"""
# TODO: API-Adapter修改标记
llm = LLMRequest(model=global_config.model.embedding, request_type=request_type)
# return llm.get_embedding_sync(text)
llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type=request_type)
try:
embedding = await llm.get_embedding(text)
embedding, _ = await llm.get_embedding(text)
except Exception as e:
logger.error(f"获取embedding失败: {str(e)}")
embedding = None

71
src/chat/utils/utils_image.py
View File

@@ -14,7 +14,7 @@ from rich.traceback import install
from src.common.logger import get_logger
from src.common.database.database import db
from src.common.database.database_model import Images, ImageDescriptions
from src.config.config import global_config
from src.config.config import global_config, model_config
from src.llm_models.utils_model import LLMRequest
install(extra_lines=3)
@@ -37,7 +37,7 @@ class ImageManager:
self._ensure_image_dir()
self._initialized = True
self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image")
self.vlm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="image")
try:
db.connect(reuse_if_open=True)
@@ -107,6 +107,7 @@ class ImageManager:
# 优先使用EmojiManager查询已注册表情包的描述
try:
from src.chat.emoji_system.emoji_manager import get_emoji_manager
emoji_manager = get_emoji_manager()
cached_emoji_description = await emoji_manager.get_emoji_description_by_hash(image_hash)
if cached_emoji_description:
@@ -116,13 +117,12 @@ class ImageManager:
logger.debug(f"查询EmojiManager时出错: {e}")
# 查询ImageDescriptions表的缓存描述
cached_description = self._get_description_from_db(image_hash, "emoji")
if cached_description:
if cached_description := self._get_description_from_db(image_hash, "emoji"):
logger.info(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
return f"[表情包：{cached_description}]"
# === 二步走识别流程 ===
# 第一步：VLM视觉分析 - 生成详细描述
if image_format in ["gif", "GIF"]:
image_base64_processed = self.transform_gif(image_base64)
@@ -130,10 +130,16 @@ class ImageManager:
logger.warning("GIF转换失败，无法获取描述")
return "[表情包(GIF处理失败)]"
vlm_prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64_processed, "jpg")
detailed_description, _ = await self.vlm.generate_response_for_image(
vlm_prompt, image_base64_processed, "jpg", temperature=0.4, max_tokens=300
)
else:
vlm_prompt = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64, image_format)
vlm_prompt = (
"这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
)
detailed_description, _ = await self.vlm.generate_response_for_image(
vlm_prompt, image_base64, image_format, temperature=0.4, max_tokens=300
)
if detailed_description is None:
logger.warning("VLM未能生成表情包详细描述")
@@ -150,31 +156,32 @@ class ImageManager:
3. 输出简短精准，不要解释
4. 如果有多个词用逗号分隔
"""
# 使用较低温度确保输出稳定
emotion_llm = LLMRequest(model=global_config.model.utils, temperature=0.3, max_tokens=50, request_type="emoji")
emotion_result, _ = await emotion_llm.generate_response_async(emotion_prompt)
emotion_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="emoji")
emotion_result, _ = await emotion_llm.generate_response_async(
emotion_prompt, temperature=0.3, max_tokens=50
)
if emotion_result is None:
logger.warning("LLM未能生成情感标签，使用详细描述的前几个词")
# 降级处理：从详细描述中提取关键词
import jieba
words = list(jieba.cut(detailed_description))
emotion_result = "，".join(words[:2]) if len(words) >= 2 else (words[0] if words else "表情")
# 处理情感结果，取前1-2个最重要的标签
emotions = [e.strip() for e in emotion_result.replace("，", ",").split(",") if e.strip()]
final_emotion = emotions[0] if emotions else "表情"
# 如果有第二个情感且不重复，也包含进来
if len(emotions) > 1 and emotions[1] != emotions[0]:
final_emotion = f"{emotions[0]}，{emotions[1]}"
logger.info(f"[emoji识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}")
# 再次检查缓存，防止并发写入时重复生成
cached_description = self._get_description_from_db(image_hash, "emoji")
if cached_description:
if cached_description := self._get_description_from_db(image_hash, "emoji"):
logger.warning(f"虽然生成了描述，但是找到缓存表情包描述: {cached_description}")
return f"[表情包：{cached_description}]"
@@ -242,9 +249,7 @@ class ImageManager:
logger.debug(f"[缓存命中] 使用Images表中的图片描述: {existing_image.description[:50]}...")
return f"[图片：{existing_image.description}]"
# 查询ImageDescriptions表的缓存描述
cached_description = self._get_description_from_db(image_hash, "image")
if cached_description:
if cached_description := self._get_description_from_db(image_hash, "image"):
logger.debug(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
return f"[图片：{cached_description}]"
@@ -252,7 +257,9 @@ class ImageManager:
image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore
prompt = global_config.custom_prompt.image_prompt
logger.info(f"[VLM调用] 为图片生成新描述 (Hash: {image_hash[:8]}...)")
description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
description, _ = await self.vlm.generate_response_for_image(
prompt, image_base64, image_format, temperature=0.4, max_tokens=300
)
if description is None:
logger.warning("AI未能生成图片描述")
@@ -445,10 +452,7 @@ class ImageManager:
image_bytes = base64.b64decode(image_base64)
image_hash = hashlib.md5(image_bytes).hexdigest()
# 检查图片是否已存在
existing_image = Images.get_or_none(Images.emoji_hash == image_hash)
if existing_image:
if existing_image := Images.get_or_none(Images.emoji_hash == image_hash):
# 检查是否缺少必要字段，如果缺少则创建新记录
if (
not hasattr(existing_image, "image_id")
@@ -524,9 +528,7 @@ class ImageManager:
# 优先检查是否已有其他相同哈希的图片记录包含描述
existing_with_description = Images.get_or_none(
(Images.emoji_hash == image_hash) &
(Images.description.is_null(False)) &
(Images.description != "")
(Images.emoji_hash == image_hash) & (Images.description.is_null(False)) & (Images.description != "")
)
if existing_with_description and existing_with_description.id != image.id:
logger.debug(f"[缓存复用] 从其他相同图片记录复用描述: {existing_with_description.description[:50]}...")
@@ -538,8 +540,7 @@ class ImageManager:
return
# 检查ImageDescriptions表的缓存描述
cached_description = self._get_description_from_db(image_hash, "image")
if cached_description:
if cached_description := self._get_description_from_db(image_hash, "image"):
logger.debug(f"[缓存复用] 从ImageDescriptions表复用描述: {cached_description[:50]}...")
image.description = cached_description
image.vlm_processed = True
@@ -554,15 +555,15 @@ class ImageManager:
# 获取VLM描述
logger.info(f"[VLM异步调用] 为图片生成描述 (ID: {image_id}, Hash: {image_hash[:8]}...)")
description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
description, _ = await self.vlm.generate_response_for_image(
prompt, image_base64, image_format, temperature=0.4, max_tokens=300
)
if description is None:
logger.warning("VLM未能生成图片描述")
description = "无法生成描述"
# 再次检查缓存，防止并发写入时重复生成
cached_description = self._get_description_from_db(image_hash, "image")
if cached_description:
if cached_description := self._get_description_from_db(image_hash, "image"):
logger.warning(f"虽然生成了描述，但是找到缓存图片描述: {cached_description}")
description = cached_description
@@ -606,7 +607,7 @@ def image_path_to_base64(image_path: str) -> str:
raise FileNotFoundError(f"图片文件不存在: {image_path}")
with open(image_path, "rb") as f:
image_data = f.read()
if not image_data:
if image_data := f.read():
return base64.b64encode(image_data).decode("utf-8")
else:
raise IOError(f"读取图片文件失败: {image_path}")
return base64.b64encode(image_data).decode("utf-8")

4
src/chat/utils/utils_voice.py
View File

@@ -1,6 +1,6 @@
import base64
from src.config.config import global_config
from src.config.config import global_config, model_config
from src.llm_models.utils_model import LLMRequest
from src.common.logger import get_logger
@@ -20,7 +20,7 @@ async def get_voice_text(voice_base64: str) -> str:
if isinstance(voice_base64, str):
voice_base64 = voice_base64.encode("ascii", errors="ignore").decode("ascii")
voice_bytes = base64.b64decode(voice_base64)
_llm = LLMRequest(model=global_config.model.voice, request_type="voice")
_llm = LLMRequest(model_set=model_config.model_task_config.voice, request_type="voice")
text = await _llm.generate_response_for_voice(voice_bytes)
if text is None:
logger.warning("未能生成语音文本")