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修复gemini接口，添加豆包接口

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skestar
2025-08-02 22:46:57 +08:00
parent 160d716fbe
commit aef6e2abef
12 changed files with 420 additions and 11 deletions
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2
models/__init__.py
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@@ -4,6 +4,7 @@ from .openai import OpenAIModel
from .deepseek import DeepSeekModel
from .alibaba import AlibabaModel
from .google import GoogleModel
from .doubao import DoubaoModel
from .factory import ModelFactory
__all__ = [
@@ -13,5 +14,6 @@ __all__ = [
'DeepSeekModel',
'AlibabaModel',
'GoogleModel',
'DoubaoModel',
'ModelFactory'
]

3
models/alibaba.py
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@@ -4,12 +4,13 @@ from openai import OpenAI
from .base import BaseModel
class AlibabaModel(BaseModel):
def __init__(self, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None, model_name: str = None):
def __init__(self, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None, model_name: str = None, api_base_url: str = None):
# 如果没有提供模型名称，才使用默认值
self.model_name = model_name if model_name else "QVQ-Max-2025-03-25"
print(f"初始化阿里巴巴模型: {self.model_name}")
# 在super().__init__之前设置model_name，这样get_default_system_prompt能使用它
super().__init__(api_key, temperature, system_prompt, language)
self.api_base_url = api_base_url # 存储API基础URL
def get_default_system_prompt(self) -> str:
"""根据模型名称返回不同的默认系统提示词"""

3
models/deepseek.py
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@@ -6,9 +6,10 @@ from openai import OpenAI
from .base import BaseModel
class DeepSeekModel(BaseModel):
def __init__(self, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None, model_name: str = "deepseek-reasoner"):
def __init__(self, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None, model_name: str = "deepseek-reasoner", api_base_url: str = None):
super().__init__(api_key, temperature, system_prompt, language)
self.model_name = model_name
self.api_base_url = api_base_url # 存储API基础URL
def get_default_system_prompt(self) -> str:
return """You are an expert at analyzing questions and providing detailed solutions. When presented with an image of a question:

312
models/doubao.py Normal file
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@@ -0,0 +1,312 @@
import json
import os
import base64
from typing import Generator, Dict, Any, Optional
import requests
from .base import BaseModel
class DoubaoModel(BaseModel):
"""
豆包API模型实现类
支持字节跳动的豆包AI模型，可处理文本和图像输入
"""
def __init__(self, api_key: str, temperature: float = 0.7, system_prompt: str = None, language: str = None, model_name: str = None, api_base_url: str = None):
"""
初始化豆包模型
Args:
api_key: 豆包API密钥
temperature: 生成温度
system_prompt: 系统提示词
language: 首选语言
model_name: 指定具体模型名称，如不指定则使用默认值
api_base_url: API基础URL，用于设置自定义API端点
"""
super().__init__(api_key, temperature, system_prompt, language)
self.model_name = model_name or self.get_model_identifier()
self.base_url = api_base_url or "https://ark.cn-beijing.volces.com/api/v3"
self.max_tokens = 4096 # 默认最大输出token数
def get_default_system_prompt(self) -> str:
return """你是一个专业的问题分析专家。当看到问题图片时：
1. 仔细阅读并理解问题
2. 分解问题的关键组成部分
3. 提供清晰的分步解决方案
4. 如果相关，解释涉及的概念或理论
5. 如果有多种方法，优先解释最有效的方法"""
def get_model_identifier(self) -> str:
"""返回默认的模型标识符"""
return "doubao-seed-1-6-250615" # Doubao-Seed-1.6
def get_actual_model_name(self) -> str:
"""根据配置的模型名称返回实际的API调用标识符"""
# 豆包API的实际模型名称映射
model_mapping = {
"doubao-seed-1-6-250615": "doubao-seed-1-6-250615"
}
return model_mapping.get(self.model_name, "doubao-seed-1-6-250615")
def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
"""流式生成文本响应"""
try:
yield {"status": "started"}
# 设置环境变量代理（如果提供）
original_proxies = None
if proxies:
original_proxies = {
'http_proxy': os.environ.get('http_proxy'),
'https_proxy': os.environ.get('https_proxy')
}
if 'http' in proxies:
os.environ['http_proxy'] = proxies['http']
if 'https' in proxies:
os.environ['https_proxy'] = proxies['https']
try:
# 构建请求头
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# 构建消息 - 根据官方API文档，暂时不使用系统提示词
messages = []
# 添加用户查询
user_content = text
if self.language and self.language != 'auto':
user_content = f"请使用{self.language}回答以下问题: {text}"
messages.append({
"role": "user",
"content": user_content
})
# 构建请求数据
data = {
"model": self.get_actual_model_name(),
"messages": messages,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"stream": True
}
# 发送流式请求
response = requests.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=data,
stream=True,
proxies=proxies if proxies else None,
timeout=60
)
if response.status_code != 200:
error_text = response.text
raise Exception(f"HTTP {response.status_code}: {error_text}")
response.raise_for_status()
# 初始化响应缓冲区
response_buffer = ""
# 处理流式响应
for line in response.iter_lines():
if not line:
continue
line = line.decode('utf-8')
if not line.startswith('data: '):
continue
line = line[6:] # 移除 'data: ' 前缀
if line == '[DONE]':
break
try:
chunk_data = json.loads(line)
choices = chunk_data.get('choices', [])
if choices and len(choices) > 0:
delta = choices[0].get('delta', {})
content = delta.get('content', '')
if content:
response_buffer += content
# 发送响应进度
yield {
"status": "streaming",
"content": response_buffer
}
except json.JSONDecodeError:
continue
# 确保发送完整的最终内容
yield {
"status": "completed",
"content": response_buffer
}
finally:
# 恢复原始代理设置
if original_proxies:
for key, value in original_proxies.items():
if value is None:
if key in os.environ:
del os.environ[key]
else:
os.environ[key] = value
except Exception as e:
yield {
"status": "error",
"error": f"豆包API错误: {str(e)}"
}
def analyze_image(self, image_data: str, proxies: dict = None) -> Generator[dict, None, None]:
"""分析图像并流式生成响应"""
try:
yield {"status": "started"}
# 设置环境变量代理（如果提供）
original_proxies = None
if proxies:
original_proxies = {
'http_proxy': os.environ.get('http_proxy'),
'https_proxy': os.environ.get('https_proxy')
}
if 'http' in proxies:
os.environ['http_proxy'] = proxies['http']
if 'https' in proxies:
os.environ['https_proxy'] = proxies['https']
try:
# 构建请求头
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# 处理图像数据
if image_data.startswith('data:image'):
# 如果是data URI，提取base64部分
image_data = image_data.split(',', 1)[1]
# 构建用户消息 - 使用豆包API官方示例格式
# 首先检查图像数据的格式，确保是有效的图像
image_format = "jpeg" # 默认使用jpeg
if image_data.startswith('/9j/'): # JPEG magic number in base64
image_format = "jpeg"
elif image_data.startswith('iVBORw0KGgo'): # PNG magic number in base64
image_format = "png"
user_content = [
{
"type": "text",
"text": f"请使用{self.language}分析这张图片并提供详细解答。" if self.language and self.language != 'auto' else "请分析这张图片并提供详细解答?"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/{image_format};base64,{image_data}"
}
}
]
messages = [
{
"role": "user",
"content": user_content
}
]
# 构建请求数据
data = {
"model": self.get_actual_model_name(),
"messages": messages,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"stream": True
}
# 发送流式请求
response = requests.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=data,
stream=True,
proxies=proxies if proxies else None,
timeout=60
)
if response.status_code != 200:
error_text = response.text
raise Exception(f"HTTP {response.status_code}: {error_text}")
response.raise_for_status()
# 初始化响应缓冲区
response_buffer = ""
# 处理流式响应
for line in response.iter_lines():
if not line:
continue
line = line.decode('utf-8')
if not line.startswith('data: '):
continue
line = line[6:] # 移除 'data: ' 前缀
if line == '[DONE]':
break
try:
chunk_data = json.loads(line)
choices = chunk_data.get('choices', [])
if choices and len(choices) > 0:
delta = choices[0].get('delta', {})
content = delta.get('content', '')
if content:
response_buffer += content
# 发送响应进度
yield {
"status": "streaming",
"content": response_buffer
}
except json.JSONDecodeError:
continue
# 确保发送完整的最终内容
yield {
"status": "completed",
"content": response_buffer
}
finally:
# 恢复原始代理设置
if original_proxies:
for key, value in original_proxies.items():
if value is None:
if key in os.environ:
del os.environ[key]
else:
os.environ[key] = value
except Exception as e:
yield {
"status": "error",
"error": f"豆包图像分析错误: {str(e)}"
}

19
models/factory.py
View File

@@ -114,6 +114,25 @@ class ModelFactory:
)
# 对于阿里巴巴模型，也需要传递正确的模型名称
elif 'qwen' in model_name.lower() or 'qvq' in model_name.lower() or 'alibaba' in model_name.lower():
return model_class(
api_key=api_key,
temperature=temperature,
system_prompt=system_prompt,
language=language,
model_name=model_name
)
# 对于Google模型，也需要传递正确的模型名称
elif 'gemini' in model_name.lower() or 'google' in model_name.lower():
return model_class(
api_key=api_key,
temperature=temperature,
system_prompt=system_prompt,
language=language,
model_name=model_name,
api_base_url=api_base_url
)
# 对于豆包模型，也需要传递正确的模型名称
elif 'doubao' in model_name.lower():
return model_class(
api_key=api_key,
temperature=temperature,

2
models/google.py
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@@ -46,7 +46,7 @@ class GoogleModel(BaseModel):
def get_model_identifier(self) -> str:
"""返回默认的模型标识符"""
return "gemini-2.5-pro-preview-03-25"
return "gemini-2.0-flash" # 使用有免费配额的模型作为默认值
def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
"""流式生成文本响应"""