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添加中转渠道支持功能 #2208

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52 changes: 50 additions & 2 deletions config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,9 @@
"""

# [step 1-1]>> ( 接入OpenAI模型家族 ) API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
from pickle import TRUE


API_KEY = "在此处填写APIKEY" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"

# [step 1-2]>> ( 强烈推荐！接入通义家族 & 大模型服务平台百炼 ) 接入通义千问在线大模型，api-key获取地址 https://dashscope.console.aliyun.com/
Expand All @@ -16,6 +19,36 @@
# [step 1-3]>> ( 接入 deepseek-reasoner, 即 deepseek-r1 ) 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
DEEPSEEK_API_KEY = ""

# [step 1-4]>> ( 接入中转渠道 ) 中转渠道配置，支持采用OpenAI接口模式的中转渠道商
ZHONGZHUAN_ENABLE = TRUE # 是否启用中转渠道，默认关闭
ZHONGZHUAN_ENDPOINT = "https://test.com/v1/chat/completions" # 中转渠道的完整API端点
ZHONGZHUAN_API_KEY = "sk-xxxxxxxxxxxxxxx" # 中转渠道的API KEY，如果为空则使用API_KEY
ZHONGZHUAN_MODELS = [
# 中转渠道支持的模型列表，使用原始模型名称
"o3-mini-all",
"gpt-4.1",
"gpt-4o",
"gpt-4o-mini",
"claude-sonnet-4-20250514-thinking",
"claude-sonnet-4-20250514",
"gemini-2.5-pro-preview-03-25",
# 可以添加更多模型...
]
# 配置说明：
# 1. 将ZHONGZHUAN_ENABLE设置为True启用中转渠道
# 2. 将ZHONGZHUAN_ENDPOINT设置为你的中转渠道商提供的完整API端点（包含/chat/completions）
# 3. 将ZHONGZHUAN_API_KEY设置为你的中转渠道商提供的API KEY（可选）
# 4. 在ZHONGZHUAN_MODELS中配置你想要使用的模型，使用原始模型名称
# 5. 系统将自动把ZHONGZHUAN_MODELS中的模型添加到AVAIL_LLM_MODELS中，无需重复配置
# 6. 对于同时在两个列表中的模型，将自动使用中转渠道访问
#
# 示例配置：
# ZHONGZHUAN_ENABLE = True
# ZHONGZHUAN_ENDPOINT = "https://api.your-provider.com/v1/chat/completions"
# ZHONGZHUAN_API_KEY = "your-api-key-here"
# ZHONGZHUAN_MODELS = ["o3-mini-all", "gpt-4.1", "claude-sonnet-4-20250514"]
# 然后可以直接设置LLM_MODEL = "o3-mini-all"（将通过中转渠道访问）

# [step 2]>> 改为True应用代理。如果使用本地或无地域限制的大模型时，此处不修改；如果直接在海外服务器部署，此处不修改
USE_PROXY = False
if USE_PROXY:
Expand Down Expand Up @@ -49,6 +82,14 @@
"dashscope-qwen3-14b", "dashscope-qwen3-235b-a22b", "dashscope-qwen3-32b",
]

# 自动将中转渠道模型添加到可用模型列表中，避免用户重复配置
# 对于同时在AVAIL_LLM_MODELS和ZHONGZHUAN_MODELS中的模型，将自动使用中转渠道
if ZHONGZHUAN_ENABLE and ZHONGZHUAN_MODELS:
for model in ZHONGZHUAN_MODELS:
if model not in AVAIL_LLM_MODELS:
AVAIL_LLM_MODELS.append(model)
print(f"[中转渠道] 已启用，共{len(ZHONGZHUAN_MODELS)}个模型将通过中转渠道访问: {', '.join(ZHONGZHUAN_MODELS)}")

EMBEDDING_MODEL = "text-embedding-3-small"

# --- --- --- ---
Expand Down Expand Up @@ -158,7 +199,7 @@
DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']


# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
# 定义界面上"询问多个GPT模型"插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"


Expand Down Expand Up @@ -361,7 +402,7 @@
AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN = 30*1000
# 无条件丢弃x以上的轮数
AUTO_CONTEXT_MAX_ROUND = 64
# 在裁剪上下文时，倒数第x次对话能“最多”保留的上下文token的比例占 AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN 的多少
# 在裁剪上下文时，倒数第x次对话能"最多"保留的上下文token的比例占 AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN 的多少
AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01]


Expand Down Expand Up @@ -469,4 +510,11 @@
└── MATHPIX_APPKEY


"zhongzhuan-..." 中转渠道模型配置
├── ZHONGZHUAN_ENABLE
├── ZHONGZHUAN_ENDPOINT
├── ZHONGZHUAN_API_KEY
└── ZHONGZHUAN_MODELS


"""
28 changes: 25 additions & 3 deletions request_llms/bridge_all.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@

"""
该文件中主要包含2个函数，是所有LLM的通用接口，它们会继续向下调用更底层的LLM模型，处理多模型并行等细节

Expand Down Expand Up @@ -115,6 +114,12 @@ def decode(self, *args, **kwargs):
# 开始初始化模型
AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]

# 获取中转渠道配置
ZHONGZHUAN_ENABLE, ZHONGZHUAN_ENDPOINT, ZHONGZHUAN_API_KEY, ZHONGZHUAN_MODELS = get_conf(
"ZHONGZHUAN_ENABLE", "ZHONGZHUAN_ENDPOINT", "ZHONGZHUAN_API_KEY", "ZHONGZHUAN_MODELS"
)

# -=-=-=-=-=-=- 以下这部分是最早加入的最稳定的模型 -=-=-=-=-=-=-
model_info = {
# openai
Expand Down Expand Up @@ -1415,6 +1420,23 @@ def decode(self, *args, **kwargs):
},
})

# -=-=-=-=-=-=- 中转渠道模型对齐支持 -=-=-=-=-=-=-
# 为中转渠道模型创建统一的model_info配置
if ZHONGZHUAN_ENABLE and ZHONGZHUAN_MODELS:
# 为每个中转渠道模型创建统一的model_info配置
# 注意：模型列表的合并已在config.py中处理
for model in ZHONGZHUAN_MODELS:
model_info.update({
model: {
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": ZHONGZHUAN_ENDPOINT,
"has_multimodal_capacity": True,
"max_token": 12800000,
"tokenizer": tokenizer_gpt4,
"token_cnt": get_token_num_gpt4,
}
})

# -=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=-=-=
# -=-=-=-=-=-=-=-=-=- ☝️ 以上是模型路由 -=-=-=-=-=-=-=-=-=
Expand Down Expand Up @@ -1459,11 +1481,11 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
model = llm_kwargs['llm_model']
n_model = 1
if '&' not in model:
# 如果只询问“一个”大语言模型（多数情况）：
# 如果只询问"一个"大语言模型（多数情况）：
method = model_info[model]["fn_without_ui"]
return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_silence)
else:
# 如果同时询问“多个”大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
# 如果同时询问"多个"大语言模型，这个稍微啰嗦一点，但思路相同，您不必读这个else分支
executor = ThreadPoolExecutor(max_workers=4)
models = model.split('&')
n_model = len(models)
Expand Down
30 changes: 25 additions & 5 deletions request_llms/bridge_chatgpt.py
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Expand Up @@ -241,9 +241,19 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
return
elif not is_any_api_key(chatbot._cookies['api_key']):
chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
return
# 对于中转渠道模型，额外检查中转渠道API key
is_zhongzhuan_valid = False
try:
ZHONGZHUAN_ENABLE, ZHONGZHUAN_MODELS, ZHONGZHUAN_API_KEY = get_conf("ZHONGZHUAN_ENABLE", "ZHONGZHUAN_MODELS", "ZHONGZHUAN_API_KEY")
if ZHONGZHUAN_ENABLE and llm_kwargs['llm_model'] in ZHONGZHUAN_MODELS and ZHONGZHUAN_API_KEY:
is_zhongzhuan_valid = is_any_api_key(ZHONGZHUAN_API_KEY)
except Exception:
pass

if not is_zhongzhuan_valid:
chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
return

user_input = inputs
if additional_fn is not None:
Expand All @@ -269,12 +279,22 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith

# check mis-behavior
if is_the_upload_folder(user_input):
chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击\"**函数插件区**\"按钮进行处理，请勿点击\"提交\"按钮或者\"基础功能区\"按钮。")
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
time.sleep(2)

try:
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity, stream)
# 对于中转渠道模型，需要确保使用正确的API key
llm_kwargs_modified = llm_kwargs.copy()
try:
ZHONGZHUAN_ENABLE, ZHONGZHUAN_MODELS, ZHONGZHUAN_API_KEY = get_conf("ZHONGZHUAN_ENABLE", "ZHONGZHUAN_MODELS", "ZHONGZHUAN_API_KEY")
if ZHONGZHUAN_ENABLE and llm_kwargs['llm_model'] in ZHONGZHUAN_MODELS and ZHONGZHUAN_API_KEY:
# 确保中转渠道模型使用正确的API key
llm_kwargs_modified['api_key'] = ZHONGZHUAN_API_KEY
except Exception:
pass

headers, payload = generate_payload(inputs, llm_kwargs_modified, history, system_prompt, image_base64_array, has_multimodal_capacity, stream)
except RuntimeError as e:
chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
Expand Down
29 changes: 27 additions & 2 deletions shared_utils/key_pattern_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,8 +45,17 @@ def is_cohere_api_key(key):


def is_any_api_key(key):
# key 一般只包含字母、数字、下划线、逗号、中划线
if not re.match(r"^[a-zA-Z0-9_\-,]+$", key):
# 首先检查是否为中转渠道API KEY
try:
ZHONGZHUAN_ENABLE, ZHONGZHUAN_API_KEY = get_conf("ZHONGZHUAN_ENABLE", "ZHONGZHUAN_API_KEY")
if ZHONGZHUAN_ENABLE and ZHONGZHUAN_API_KEY and key == ZHONGZHUAN_API_KEY:
return True
except Exception:
pass

# key 一般只包含字母、数字、下划线、逗号、中划线，但为了支持更多中转渠道，适当放宽限制
# 允许点号(.)，用于支持某些中转渠道的特殊格式
if not re.match(r"^[a-zA-Z0-9_\-,\.]+$", key):
# 如果配置了 CUSTOM_API_KEY_PATTERN，再检查以下以免误杀
if CUSTOM_API_KEY_PATTERN := get_conf('CUSTOM_API_KEY_PATTERN'):
return bool(re.match(CUSTOM_API_KEY_PATTERN, key))
Expand Down Expand Up @@ -93,6 +102,22 @@ def select_api_key(keys, llm_model):
avail_key_list = []
key_list = keys.split(',')

# 中转渠道API KEY处理
try:
ZHONGZHUAN_ENABLE, ZHONGZHUAN_MODELS, ZHONGZHUAN_API_KEY = get_conf("ZHONGZHUAN_ENABLE", "ZHONGZHUAN_MODELS", "ZHONGZHUAN_API_KEY")
if ZHONGZHUAN_ENABLE and llm_model in ZHONGZHUAN_MODELS:
# 如果模型在中转渠道列表中，优先使用中转渠道的API KEY
if ZHONGZHUAN_API_KEY:
return ZHONGZHUAN_API_KEY
# 如果没有设置专门的中转渠道API KEY，则使用OpenAI格式的key（中转渠道一般采用OpenAI接口格式）
for k in key_list:
if is_openai_api_key(k): avail_key_list.append(k)
if len(avail_key_list) > 0:
return random.choice(avail_key_list)
except Exception:
# 如果获取中转渠道配置失败，继续使用原有逻辑
pass

if llm_model.startswith('gpt-') or llm_model.startswith('chatgpt-') or \
llm_model.startswith('one-api-') or is_o_family_for_openai(llm_model):
for k in key_list:
Expand Down