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Merge branch 'debug' of https://github.com/SengokuCola/MaiMBot into debug

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SengokuCola
2025-03-08 01:02:26 +08:00
parent f65099e8ee d9a5852653
commit e2d8f68066
5 changed files with 70 additions and 13 deletions
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6
bot.py
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@@ -20,8 +20,12 @@ print(rainbow_text)
if not os.path.exists("config/bot_config.toml"):
logger.warning("检测到bot_config.toml不存在，正在从模板复制")
import shutil
# 检查config目录是否存在
if not os.path.exists("config"):
os.makedirs("config")
logger.info("创建config目录")
shutil.copy("templete/bot_config_template.toml", "config/bot_config.toml")
shutil.copy("template/bot_config_template.toml", "config/bot_config.toml")
logger.info("复制完成，请修改config/bot_config.toml和.env.prod中的配置后重新启动")
# 初始化.env 默认ENVIRONMENT=prod

4
src/plugins/chat/llm_generator.py
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@@ -21,7 +21,7 @@ config = driver.config
class ResponseGenerator:
def __init__(self):
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000)
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000,stream=True)
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000)
self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000)
self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7,max_tokens=1000)
@@ -194,6 +194,6 @@ class InitiativeMessageGenerate:
prompt = prompt_builder._build_initiative_prompt(
select_dot, prompt_template, memory
)
content, reasoning = self.model_r1.generate_response(prompt)
content, reasoning = self.model_r1.generate_response_async(prompt)
print(f"[DEBUG] {content} {reasoning}")
return content

73
src/plugins/models/utils_model.py
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@@ -1,5 +1,6 @@
import aiohttp
import asyncio
import json
import requests
import time
import re
@@ -138,7 +139,12 @@ class LLM_request:
}
api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
logger.info(f"发送请求到URL: {api_url}")
#判断是否为流式
stream_mode = self.params.get("stream", False)
if self.params.get("stream", False) is True:
logger.info(f"进入流式输出模式，发送请求到URL: {api_url}")
else:
logger.info(f"发送请求到URL: {api_url}")
logger.info(f"使用模型: {self.model_name}")
# 构建请求体
@@ -151,6 +157,9 @@ class LLM_request:
try:
# 使用上下文管理器处理会话
headers = await self._build_headers()
#似乎是openai流式必须要的东西,不过阿里云的qwq-plus加了这个没有影响
if stream_mode:
headers["Accept"] = "text/event-stream"
async with aiohttp.ClientSession() as session:
async with session.post(api_url, headers=headers, json=payload) as response:
@@ -173,12 +182,41 @@ class LLM_request:
elif response.status in policy["abort_codes"]:
logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
response.raise_for_status()
result = await response.json()
# 使用自定义处理器或默认处理
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
if stream_mode:
accumulated_content = ""
async for line_bytes in response.content:
line = line_bytes.decode("utf-8").strip()
if not line:
continue
if line.startswith("data:"):
data_str = line[5:].strip()
if data_str == "[DONE]":
break
try:
chunk = json.loads(data_str)
delta = chunk["choices"][0]["delta"]
delta_content = delta.get("content")
if delta_content is None:
delta_content = ""
accumulated_content += delta_content
except Exception as e:
logger.error(f"解析流式输出错误: {e}")
content = accumulated_content
reasoning_content = ""
think_match = re.search(r'<think>(.*?)</think>', content, re.DOTALL)
if think_match:
reasoning_content = think_match.group(1).strip()
content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
# 构造一个伪result以便调用自定义响应处理器或默认处理器
result = {"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]}
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
else:
result = await response.json()
# 使用自定义处理器或默认处理
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
except Exception as e:
if retry < policy["max_retries"] - 1:
@@ -195,8 +233,18 @@ class LLM_request:
async def _build_payload(self, prompt: str, image_base64: str = None) -> dict:
"""构建请求体"""
# 复制一份参数，避免直接修改 self.params
params_copy = dict(self.params)
if self.model_name.lower() == "o3-mini" or "o1-mini" or "o1" or "o1-2024-12-17" or "o1-preview-2024-09-12" or "o3-mini-2025-01-31" or "o1-mini-2024-09-12":
# 删除可能存在的 'temprature' 参数
params_copy.pop("temprature", None)
# 如果存在 'max_tokens' 参数，则将其替换为 'max_completion_tokens'
if "max_tokens" in params_copy:
params_copy["max_completion_tokens"] = params_copy.pop("max_tokens")
# 构造基础请求体，注意这里依然使用 global_config.max_response_length 填充 'max_tokens'
# 如果需要统一改为 max_completion_tokens，也可以在下面做相同的调整
if image_base64:
return {
payload = {
"model": self.model_name,
"messages": [
{
@@ -208,15 +256,20 @@ class LLM_request:
}
],
"max_tokens": global_config.max_response_length,
**self.params
**params_copy
}
else:
return {
payload = {
"model": self.model_name,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": global_config.max_response_length,
**self.params
**params_copy
}
# 如果是 o3-mini 模型，也将基础请求体中的 max_tokens 改为 max_completion_tokens
if self.model_name.lower() == "o3-mini" and "max_tokens" in payload:
payload["max_completion_tokens"] = payload.pop("max_tokens")
return payload
def _default_response_handler(self, result: dict, user_id: str = "system",
request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:

0
templete/auto_format.py → template/auto_format.py
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0
templete/bot_config_template.toml → template/bot_config_template.toml
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