import asyncio
import json
import re
from datetime import datetime
from typing import Tuple, Union, Dict, Any
import aiohttp
from aiohttp.client import ClientResponse
from src.common.logger import get_module_logger
import base64
from PIL import Image
import io
import os
from src.common.database.database import db # 确保 db 被导入用于 create_tables
from src.common.database.database_model import LLMUsage # 导入 LLMUsage 模型
from src.config.config import global_config
from rich.traceback import install
install(extra_lines=3)
logger = get_module_logger("model_utils")
class PayLoadTooLargeError(Exception):
"""自定义异常类，用于处理请求体过大错误"""
def __init__(self, message: str):
super().__init__(message)
self.message = message
def __str__(self):
return "请求体过大，请尝试压缩图片或减少输入内容。"
class RequestAbortException(Exception):
"""自定义异常类，用于处理请求中断异常"""
def __init__(self, message: str, response: ClientResponse):
super().__init__(message)
self.message = message
self.response = response
def __str__(self):
return self.message
class PermissionDeniedException(Exception):
"""自定义异常类，用于处理访问拒绝的异常"""
def __init__(self, message: str):
super().__init__(message)
self.message = message
def __str__(self):
return self.message
# 常见Error Code Mapping
error_code_mapping = {
400: "参数不正确",
401: "API key 错误，认证失败，请检查/config/bot_config.toml和.env中的配置是否正确哦~",
402: "账号余额不足",
403: "需要实名,或余额不足",
404: "Not Found",
429: "请求过于频繁，请稍后再试",
500: "服务器内部故障",
503: "服务器负载过高",
}
async def _safely_record(request_content: Dict[str, Any], payload: Dict[str, Any]):
image_base64: str = request_content.get("image_base64")
image_format: str = request_content.get("image_format")
if (
image_base64
and payload
and isinstance(payload, dict)
and "messages" in payload
and len(payload["messages"]) > 0
):
if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
content = payload["messages"][0]["content"]
if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
payload["messages"][0]["content"][1]["image_url"]["url"] = (
f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,"
f"{image_base64[:10]}...{image_base64[-10:]}"
)
return payload
class LLMRequest:
# 定义需要转换的模型列表，作为类变量避免重复
MODELS_NEEDING_TRANSFORMATION = [
"o1",
"o1-2024-12-17",
"o1-mini",
"o1-mini-2024-09-12",
"o1-preview",
"o1-preview-2024-09-12",
"o1-pro",
"o1-pro-2025-03-19",
"o3",
"o3-2025-04-16",
"o3-mini",
"o3-mini-2025-01-31o4-mini",
"o4-mini-2025-04-16",
]
def __init__(self, model: dict, **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 = os.environ[f"{model['provider']}_KEY"]
self.base_url = os.environ[f"{model['provider']}_BASE_URL"]
except AttributeError as e:
logger.error(f"原始 model dict 信息：{model}")
logger.error(f"配置错误：找不到对应的配置项 - {str(e)}")
raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e
self.model_name: str = model["name"]
self.params = kwargs
self.enable_thinking = model.get("enable_thinking", False)
self.temp = model.get("temp", 0.7)
self.thinking_budget = model.get("thinking_budget", 4096)
self.stream = model.get("stream", False)
self.pri_in = model.get("pri_in", 0)
self.pri_out = model.get("pri_out", 0)
# 获取数据库实例
self._init_database()
# 从 kwargs 中提取 request_type，如果没有提供则默认为 "default"
self.request_type = kwargs.pop("request_type", "default")
@staticmethod
def _init_database():
"""初始化数据库集合"""
try:
# 使用 Peewee 创建表，safe=True 表示如果表已存在则不会抛出错误
db.create_tables([LLMUsage], safe=True)
logger.debug("LLMUsage 表已初始化/确保存在。")
except Exception as e:
logger.error(f"创建 LLMUsage 表失败: {str(e)}")
def _record_usage(
self,
prompt_tokens: int,
completion_tokens: int,
total_tokens: int,
user_id: str = "system",
request_type: str = None,
endpoint: str = "/chat/completions",
):
"""记录模型使用情况到数据库
Args:
prompt_tokens: 输入token数
completion_tokens: 输出token数
total_tokens: 总token数
user_id: 用户ID，默认为system
request_type: 请求类型
endpoint: API端点
"""
# 如果 request_type 为 None，则使用实例变量中的值
if request_type is None:
request_type = self.request_type
try:
# 使用 Peewee 模型创建记录
LLMUsage.create(
model_name=self.model_name,
user_id=user_id,
request_type=request_type,
endpoint=endpoint,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
cost=self._calculate_cost(prompt_tokens, completion_tokens),
status="success",
timestamp=datetime.now(), # Peewee 会处理 DateTimeField
)
logger.trace(
f"Token使用情况 - 模型: {self.model_name}, "
f"用户: {user_id}, 类型: {request_type}, "
f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
f"总计: {total_tokens}"
)
except Exception as e:
logger.error(f"记录token使用情况失败: {str(e)}")
def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
"""计算API调用成本
使用模型的pri_in和pri_out价格计算输入和输出的成本
Args:
prompt_tokens: 输入token数量
completion_tokens: 输出token数量
Returns:
float: 总成本（元）
"""
# 使用模型的pri_in和pri_out计算成本
input_cost = (prompt_tokens / 1000000) * self.pri_in
output_cost = (completion_tokens / 1000000) * self.pri_out
return round(input_cost + output_cost, 6)
async def _prepare_request(
self,
endpoint: str,
prompt: str = None,
image_base64: str = None,
image_format: str = None,
payload: dict = None,
retry_policy: dict = None,
) -> Dict[str, Any]:
"""配置请求参数
Args:
endpoint: API端点路径 (如 "chat/completions")
prompt: prompt文本
image_base64: 图片的base64编码
image_format: 图片格式
payload: 请求体数据
retry_policy: 自定义重试策略
request_type: 请求类型
"""
# 合并重试策略
default_retry = {
"max_retries": 3,
"base_wait": 10,
"retry_codes": [429, 413, 500, 503],
"abort_codes": [400, 401, 402, 403],
}
policy = {**default_retry, **(retry_policy or {})}
api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
stream_mode = self.stream
# 构建请求体
if image_base64:
payload = await self._build_payload(prompt, image_base64, image_format)
elif payload is None:
payload = await self._build_payload(prompt)
if stream_mode:
payload["stream"] = stream_mode
return {
"policy": policy,
"payload": payload,
"api_url": api_url,
"stream_mode": stream_mode,
"image_base64": image_base64, # 保留必要的exception处理所需的原始数据
"image_format": image_format,
"prompt": prompt,
}
async def _execute_request(
self,
endpoint: str,
prompt: str = None,
image_base64: str = None,
image_format: str = None,
payload: dict = None,
retry_policy: dict = None,
response_handler: callable = None,
user_id: str = "system",
request_type: str = None,
):
"""统一请求执行入口
Args:
endpoint: API端点路径 (如 "chat/completions")
prompt: prompt文本
image_base64: 图片的base64编码
image_format: 图片格式
payload: 请求体数据
retry_policy: 自定义重试策略
response_handler: 自定义响应处理器
user_id: 用户ID
request_type: 请求类型
"""
# 获取请求配置
request_content = await self._prepare_request(
endpoint, prompt, image_base64, image_format, payload, retry_policy
)
if request_type is None:
request_type = self.request_type
for retry in range(request_content["policy"]["max_retries"]):
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 request_content["stream_mode"]:
headers["Accept"] = "text/event-stream"
async with aiohttp.ClientSession() as session:
async with session.post(
request_content["api_url"], headers=headers, json=request_content["payload"]
) as response:
handled_result = await self._handle_response(
response, request_content, retry, response_handler, user_id, request_type, endpoint
)
return handled_result
except Exception as e:
handled_payload, count_delta = await self._handle_exception(e, retry, request_content)
retry += count_delta # 降级不计入重试次数
if handled_payload:
# 如果降级成功，重新构建请求体
request_content["payload"] = handled_payload
continue
logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败")
raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败")
async def _handle_response(
self,
response: ClientResponse,
request_content: Dict[str, Any],
retry_count: int,
response_handler: callable,
user_id,
request_type,
endpoint,
) -> Union[Dict[str, Any], None]:
policy = request_content["policy"]
stream_mode = request_content["stream_mode"]
if response.status in policy["retry_codes"] or response.status in policy["abort_codes"]:
await self._handle_error_response(response, retry_count, policy)
return None
response.raise_for_status()
result = {}
if stream_mode:
# 将流式输出转化为非流式输出
result = await self._handle_stream_output(response)
else:
result = await response.json()
return (
response_handler(result)
if response_handler
else self._default_response_handler(result, user_id, request_type, endpoint)
)
async def _handle_stream_output(self, response: ClientResponse) -> Dict[str, Any]:
flag_delta_content_finished = False
accumulated_content = ""
usage = None # 初始化usage变量，避免未定义错误
reasoning_content = ""
content = ""
tool_calls = None # 初始化工具调用变量
async for line_bytes in response.content:
try:
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)
if flag_delta_content_finished:
chunk_usage = chunk.get("usage", None)
if chunk_usage:
usage = chunk_usage # 获取token用量
else:
delta = chunk["choices"][0]["delta"]
delta_content = delta.get("content")
if delta_content is None:
delta_content = ""
accumulated_content += delta_content
# 提取工具调用信息
if "tool_calls" in delta:
if tool_calls is None:
tool_calls = delta["tool_calls"]
else:
# 合并工具调用信息
tool_calls.extend(delta["tool_calls"])
# 检测流式输出文本是否结束
finish_reason = chunk["choices"][0].get("finish_reason")
if delta.get("reasoning_content", None):
reasoning_content += delta["reasoning_content"]
if finish_reason == "stop" or finish_reason == "tool_calls":
chunk_usage = chunk.get("usage", None)
if chunk_usage:
usage = chunk_usage
break
# 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
flag_delta_content_finished = True
except Exception as e:
logger.exception(f"模型 {self.model_name} 解析流式输出错误: {str(e)}")
except Exception as e:
if isinstance(e, GeneratorExit):
log_content = f"模型 {self.model_name} 流式输出被中断，正在清理资源..."
else:
log_content = f"模型 {self.model_name} 处理流式输出时发生错误: {str(e)}"
logger.warning(log_content)
# 确保资源被正确清理
try:
await response.release()
except Exception as cleanup_error:
logger.error(f"清理资源时发生错误: {cleanup_error}")
# 返回已经累积的内容
content = accumulated_content
if not content:
content = accumulated_content
think_match = re.search(r"(.*?)", content, re.DOTALL)
if think_match:
reasoning_content = think_match.group(1).strip()
content = re.sub(r".*?", "", content, flags=re.DOTALL).strip()
# 构建消息对象
message = {
"content": content,
"reasoning_content": reasoning_content,
}
# 如果有工具调用，添加到消息中
if tool_calls:
message["tool_calls"] = tool_calls
result = {
"choices": [{"message": message}],
"usage": usage,
}
return result
async def _handle_error_response(
self, response: ClientResponse, retry_count: int, policy: Dict[str, Any]
) -> Union[Dict[str, any]]:
if response.status in policy["retry_codes"]:
wait_time = policy["base_wait"] * (2**retry_count)
logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
if response.status == 413:
logger.warning("请求体过大，尝试压缩...")
raise PayLoadTooLargeError("请求体过大")
elif response.status in [500, 503]:
logger.error(
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
)
raise RuntimeError("服务器负载过高，模型回复失败QAQ")
else:
logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
raise RuntimeError("请求限制(429)")
elif response.status in policy["abort_codes"]:
if response.status != 403:
raise RequestAbortException("请求出现错误，中断处理", response)
else:
raise PermissionDeniedException("模型禁止访问")
async def _handle_exception(
self, exception, retry_count: int, request_content: Dict[str, Any]
) -> Union[Tuple[Dict[str, Any], int], Tuple[None, int]]:
policy = request_content["policy"]
payload = request_content["payload"]
wait_time = policy["base_wait"] * (2**retry_count)
keep_request = False
if retry_count < policy["max_retries"] - 1:
keep_request = True
if isinstance(exception, RequestAbortException):
response = exception.response
logger.error(
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
)
print(request_content)
print(response)
# 尝试获取并记录服务器返回的详细错误信息
try:
error_json = await response.json()
if error_json and isinstance(error_json, list) and len(error_json) > 0:
# 处理多个错误的情况
for error_item in error_json:
if "error" in error_item and isinstance(error_item["error"], dict):
error_obj: dict = error_item["error"]
error_code = error_obj.get("code")
error_message = error_obj.get("message")
error_status = error_obj.get("status")
logger.error(
f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}"
)
elif isinstance(error_json, dict) and "error" in error_json:
# 处理单个错误对象的情况
error_obj = error_json.get("error", {})
error_code = error_obj.get("code")
error_message = error_obj.get("message")
error_status = error_obj.get("status")
logger.error(f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}")
else:
# 记录原始错误响应内容
logger.error(f"服务器错误响应: {error_json}")
except Exception as e:
logger.warning(f"无法解析服务器错误响应: {str(e)}")
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
elif isinstance(exception, PermissionDeniedException):
# 只针对硅基流动的V3和R1进行降级处理
if self.model_name.startswith("Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/":
old_model_name = self.model_name
self.model_name = self.model_name[4:] # 移除"Pro/"前缀
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
# 对全局配置进行更新
if global_config.model.normal_chat_2.get("name") == old_model_name:
global_config.model.normal_chat_2["name"] = self.model_name
logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
if global_config.model.normal_chat_1.get("name") == old_model_name:
global_config.model.normal_chat_1["name"] = self.model_name
logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
if payload and "model" in payload:
payload["model"] = self.model_name
await asyncio.sleep(wait_time)
return payload, -1
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(403)}")
elif isinstance(exception, PayLoadTooLargeError):
if keep_request:
image_base64 = request_content["image_base64"]
compressed_image_base64 = compress_base64_image_by_scale(image_base64)
new_payload = await self._build_payload(
request_content["prompt"], compressed_image_base64, request_content["image_format"]
)
return new_payload, 0
else:
return None, 0
elif isinstance(exception, aiohttp.ClientError) or isinstance(exception, asyncio.TimeoutError):
if keep_request:
logger.error(f"模型 {self.model_name} 网络错误，等待{wait_time}秒后重试... 错误: {str(exception)}")
await asyncio.sleep(wait_time)
return None, 0
else:
logger.critical(f"模型 {self.model_name} 网络错误达到最大重试次数: {str(exception)}")
raise RuntimeError(f"网络请求失败: {str(exception)}")
elif isinstance(exception, aiohttp.ClientResponseError):
# 处理aiohttp抛出的，除了policy中的status的响应错误
if keep_request:
logger.error(
f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {exception.status}, 错误: {exception.message}"
)
try:
error_text = await exception.response.text()
error_json = json.loads(error_text)
if isinstance(error_json, list) and len(error_json) > 0:
# 处理多个错误的情况
for error_item in error_json:
if "error" in error_item and isinstance(error_item["error"], dict):
error_obj = error_item["error"]
logger.error(
f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
f"状态={error_obj.get('status')}, "
f"消息={error_obj.get('message')}"
)
elif isinstance(error_json, dict) and "error" in error_json:
error_obj = error_json.get("error", {})
logger.error(
f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
f"状态={error_obj.get('status')}, "
f"消息={error_obj.get('message')}"
)
else:
logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
except (json.JSONDecodeError, TypeError) as json_err:
logger.warning(
f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}"
)
except Exception as parse_err:
logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
await asyncio.sleep(wait_time)
return None, 0
else:
logger.critical(
f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {exception.status}, 错误: {exception.message}"
)
# 安全地检查和记录请求详情
handled_payload = await _safely_record(request_content, payload)
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {handled_payload[:100]}")
raise RuntimeError(
f"模型 {self.model_name} API请求失败: 状态码 {exception.status}, {exception.message}"
)
else:
if keep_request:
logger.error(f"模型 {self.model_name} 请求失败，等待{wait_time}秒后重试... 错误: {str(exception)}")
await asyncio.sleep(wait_time)
return None, 0
else:
logger.critical(f"模型 {self.model_name} 请求失败: {str(exception)}")
# 安全地检查和记录请求详情
handled_payload = await _safely_record(request_content, payload)
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {handled_payload[:100]}")
raise RuntimeError(f"模型 {self.model_name} API请求失败: {str(exception)}")
async def _transform_parameters(self, params: dict) -> dict:
"""
根据模型名称转换参数：
- 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temperature' 参数，
并将 'max_tokens' 重命名为 'max_completion_tokens'
"""
# 复制一份参数，避免直接修改原始数据
new_params = dict(params)
if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION:
# 删除 'temperature' 参数（如果存在），但避免删除我们在_build_payload中添加的自定义温度
if "temperature" in new_params and new_params["temperature"] == 0.7:
new_params.pop("temperature")
# 如果存在 'max_tokens'，则重命名为 'max_completion_tokens'
if "max_tokens" in new_params:
new_params["max_completion_tokens"] = new_params.pop("max_tokens")
return new_params
async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
"""构建请求体"""
# 复制一份参数，避免直接修改 self.params
params_copy = await self._transform_parameters(self.params)
if image_base64:
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"},
},
],
}
]
else:
messages = [{"role": "user", "content": prompt}]
payload = {
"model": self.model_name,
"messages": messages,
**params_copy,
}
# 添加temp参数（如果不是默认值0.7）
if self.temp != 0.7:
payload["temperature"] = self.temp
# 添加enable_thinking参数（如果不是默认值False）
if not self.enable_thinking:
payload["enable_thinking"] = False
if self.thinking_budget != 4096:
payload["thinking_budget"] = self.thinking_budget
if "max_tokens" not in payload and "max_completion_tokens" not in payload:
payload["max_tokens"] = global_config.model.model_max_output_length
# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION 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 = None, endpoint: str = "/chat/completions"
) -> Tuple:
"""默认响应解析"""
if "choices" in result and result["choices"]:
message = result["choices"][0]["message"]
content = message.get("content", "")
content, reasoning = self._extract_reasoning(content)
reasoning_content = message.get("model_extra", {}).get("reasoning_content", "")
if not reasoning_content:
reasoning_content = message.get("reasoning_content", "")
if not reasoning_content:
reasoning_content = reasoning
# 提取工具调用信息
tool_calls = message.get("tool_calls", None)
# 记录token使用情况
usage = result.get("usage", {})
if usage:
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
total_tokens = usage.get("total_tokens", 0)
self._record_usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
user_id=user_id,
request_type=request_type if request_type is not None else self.request_type,
endpoint=endpoint,
)
# 只有当tool_calls存在且不为空时才返回
if tool_calls:
logger.debug(f"检测到工具调用: {tool_calls}")
return content, reasoning_content, tool_calls
else:
return content, reasoning_content
return "没有返回结果", ""
@staticmethod
def _extract_reasoning(content: str) -> Tuple[str, str]:
"""CoT思维链提取"""
match = re.search(r"(?:)?(.*?)", content, re.DOTALL)
content = re.sub(r"(?:)?.*?", "", content, flags=re.DOTALL, count=1).strip()
if match:
reasoning = match.group(1).strip()
else:
reasoning = ""
return content, reasoning
async def _build_headers(self, no_key: bool = False) -> dict:
"""构建请求头"""
if no_key:
return {"Authorization": "Bearer **********", "Content-Type": "application/json"}
else:
return {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
# 防止小朋友们截图自己的key
async def generate_response(self, prompt: str) -> Tuple:
"""根据输入的提示生成模型的异步响应"""
response = await self._execute_request(endpoint="/chat/completions", prompt=prompt)
# 根据返回值的长度决定怎么处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, reasoning_content, self.model_name, tool_calls
else:
content, reasoning_content = response
return content, reasoning_content, self.model_name
async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple:
"""根据输入的提示和图片生成模型的异步响应"""
response = await self._execute_request(
endpoint="/chat/completions", prompt=prompt, image_base64=image_base64, image_format=image_format
)
# 根据返回值的长度决定怎么处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, reasoning_content, tool_calls
else:
content, reasoning_content = response
return content, reasoning_content
async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
"""异步方式根据输入的提示生成模型的响应"""
# 构建请求体，不硬编码max_tokens
data = {
"model": self.model_name,
"messages": [{"role": "user", "content": prompt}],
**self.params,
**kwargs,
}
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
# 原样返回响应，不做处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, (reasoning_content, self.model_name, tool_calls)
else:
content, reasoning_content = response
return content, (reasoning_content, self.model_name)
async def generate_response_tool_async(self, prompt: str, tools: list, **kwargs) -> tuple[str, str, list]:
"""异步方式根据输入的提示生成模型的响应"""
# 构建请求体，不硬编码max_tokens
data = {
"model": self.model_name,
"messages": [{"role": "user", "content": prompt}],
**self.params,
**kwargs,
"tools": tools,
}
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
logger.debug(f"向模型 {self.model_name} 发送工具调用请求，包含 {len(tools)} 个工具，返回结果: {response}")
# 检查响应是否包含工具调用
if len(response) == 3:
content, reasoning_content, tool_calls = response
logger.debug(f"收到工具调用响应，包含 {len(tool_calls) if tool_calls else 0} 个工具调用")
return content, reasoning_content, tool_calls
else:
content, reasoning_content = response
logger.debug("收到普通响应，无工具调用")
return content, reasoning_content, None
async def get_embedding(self, text: str) -> Union[list, None]:
"""异步方法：获取文本的embedding向量
Args:
text: 需要获取embedding的文本
Returns:
list: embedding向量，如果失败则返回None
"""
if len(text) < 1:
logger.debug("该消息没有长度，不再发送获取embedding向量的请求")
return None
def embedding_handler(result):
"""处理响应"""
if "data" in result and len(result["data"]) > 0:
# 提取 token 使用信息
usage = result.get("usage", {})
if usage:
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
total_tokens = usage.get("total_tokens", 0)
# 记录 token 使用情况
self._record_usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
user_id="system", # 可以根据需要修改 user_id
# request_type="embedding", # 请求类型为 embedding
request_type=self.request_type, # 请求类型为 text
endpoint="/embeddings", # API 端点
)
return result["data"][0].get("embedding", None)
return result["data"][0].get("embedding", None)
return None
embedding = await self._execute_request(
endpoint="/embeddings",
prompt=text,
payload={"model": self.model_name, "input": text, "encoding_format": "float"},
retry_policy={"max_retries": 2, "base_wait": 6},
response_handler=embedding_handler,
)
return embedding
def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
"""压缩base64格式的图片到指定大小
Args:
base64_data: base64编码的图片数据
target_size: 目标文件大小（字节），默认0.8MB
Returns:
str: 压缩后的base64图片数据
"""
try:
# 将base64转换为字节数据
image_data = base64.b64decode(base64_data)
# 如果已经小于目标大小，直接返回原图
if len(image_data) <= 2 * 1024 * 1024:
return base64_data
# 将字节数据转换为图片对象
img = Image.open(io.BytesIO(image_data))
# 获取原始尺寸
original_width, original_height = img.size
# 计算缩放比例
scale = min(1.0, (target_size / len(image_data)) ** 0.5)
# 计算新的尺寸
new_width = int(original_width * scale)
new_height = int(original_height * scale)
# 创建内存缓冲区
output_buffer = io.BytesIO()
# 如果是GIF，处理所有帧
if getattr(img, "is_animated", False):
frames = []
for frame_idx in range(img.n_frames):
img.seek(frame_idx)
new_frame = img.copy()
new_frame = new_frame.resize((new_width // 2, new_height // 2), Image.Resampling.LANCZOS) # 动图折上折
frames.append(new_frame)
# 保存到缓冲区
frames[0].save(
output_buffer,
format="GIF",
save_all=True,
append_images=frames[1:],
optimize=True,
duration=img.info.get("duration", 100),
loop=img.info.get("loop", 0),
)
else:
# 处理静态图片
resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
# 保存到缓冲区，保持原始格式
if img.format == "PNG" and img.mode in ("RGBA", "LA"):
resized_img.save(output_buffer, format="PNG", optimize=True)
else:
resized_img.save(output_buffer, format="JPEG", quality=95, optimize=True)
# 获取压缩后的数据并转换为base64
compressed_data = output_buffer.getvalue()
logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
logger.info(f"压缩前大小: {len(image_data) / 1024:.1f}KB, 压缩后大小: {len(compressed_data) / 1024:.1f}KB")
return base64.b64encode(compressed_data).decode("utf-8")
except Exception as e:
logger.error(f"压缩图片失败: {str(e)}")
import traceback
logger.error(traceback.format_exc())
return base64_data