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import uuid
import json
import time
import os
import re
import random
import requests
from flask import Flask, request, Response, stream_with_context, jsonify
from flask_cors import CORS
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor
app = Flask(__name__)
CORS(app, resources={r"/*": {"origins": "*"}})
executor = ThreadPoolExecutor(max_workers=10)
# 使用LRU缓存,最大容量为10,用于缓存文件内容
@lru_cache(maxsize=10)
def read_file(filename):
try:
with open(filename, 'r') as f:
return f.read().strip()
except FileNotFoundError:
print(f"文件 {filename} 未找到")
return ""
except Exception as e:
print(f"读取文件 {filename} 时发生错误: {e}")
return ""
# 从环境变量或文件中获取信息
def get_env_or_file(env_var, filename):
return os.environ.get(env_var) or read_file(filename)
NOTDIAMOND_URLS = [
'https://chat.notdiamond.ai/mini-chat'
]
# 从预设的URL列表中随机选择一个
def get_notdiamond_url():
return random.choice(NOTDIAMOND_URLS)
# 使用LRU缓存,缓存头信息,最大容量为1
@lru_cache(maxsize=1)
def get_notdiamond_headers():
return {
'accept': 'text/event-stream',
'accept-language': 'zh-CN,zh;q=0.9',
'content-type': 'application/json',
'next-action': get_env_or_file('NEXT_ACTION', 'next_action.txt'),
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36',
'cookie': get_env_or_file('COOKIES', 'cookies.txt')
}
# 模型信息字典,包含各模型的提供商和映射
MODEL_INFO = {
"gpt-4-turbo-2024-04-09": {
"provider": "openai",
"mapping": "gpt-4-turbo-2024-04-09"
},
"gemini-1.5-pro-exp-0801": {
"provider": "google",
"mapping": "models/gemini-1.5-pro-exp-0801"
},
"Meta-Llama-3.1-70B-Instruct-Turbo": {
"provider": "togetherai",
"mapping": "meta.llama3-1-70b-instruct-v1:0"
},
"Meta-Llama-3.1-405B-Instruct-Turbo": {
"provider": "togetherai",
"mapping": "meta.llama3-1-405b-instruct-v1:0"
},
"llama-3.1-sonar-large-128k-online": {
"provider": "perplexity",
"mapping": "llama-3.1-sonar-large-128k-online"
},
"gemini-1.5-pro-latest": {
"provider": "google",
"mapping": "models/gemini-1.5-pro-latest"
},
"claude-3-5-sonnet-20240620": {
"provider": "anthropic",
"mapping": "anthropic.claude-3-5-sonnet-20240620-v1:0"
},
"claude-3-haiku-20240307": {
"provider": "anthropic",
"mapping": "anthropic.claude-3-haiku-20240307-v1:0"
},
"gpt-4o-mini": {
"provider": "openai",
"mapping": "gpt-4o-mini"
},
"gpt-4o": {
"provider": "openai",
"mapping": "gpt-4o"
},
"mistral-large-2407": {
"provider": "mistral",
"mapping": "mistral.mistral-large-2407-v1:0"
}
}
# 生成系统指纹,用于追踪和安全目的
def generate_system_fingerprint():
return f"fp_{uuid.uuid4().hex[:10]}"
# 创建OpenAI格式的分块内容
def create_openai_chunk(content, model, finish_reason=None, usage=None):
system_fingerprint = generate_system_fingerprint()
chunk = {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"system_fingerprint": system_fingerprint,
"choices": [
{
"index": 0,
"delta": {"content": content} if content else {},
"logprobs": None,
"finish_reason": finish_reason
}
]
}
if usage is not None:
chunk["usage"] = usage
return chunk
# 处理带美元符号的字符串
def process_dollars(s):
return s.replace('$$', '$')
def stream_notdiamond_response(response, model):
buffer = ""
last_content = ""
total_tokens = 0
for chunk in response.iter_content(chunk_size=1024):
if chunk:
buffer += chunk.decode('utf-8')
lines = buffer.split('\n')
buffer = lines.pop()
for line in lines:
if line.strip():
match = re.match(r'^([0-9a-zA-Z]+):(.*)$', line)
if match:
try:
_, content = match.groups()
data = json.loads(content)
content = ''
if 'output' in data and 'curr' in data['output']:
content = process_dollars(data['output']['curr'])
elif 'curr' in data:
content = process_dollars(data['curr'])
elif 'diff' in data and isinstance(data['diff'], list):
if len(data['diff']) > 1:
new_content = process_dollars(data['diff'][1])
content = last_content + new_content
elif len(data['diff']) == 1:
content = last_content
if content:
total_tokens += len(content.split())
last_content = content
yield create_openai_chunk(content, model)
except json.JSONDecodeError:
print(f"Error processing line: {line}") # 可考虑替换为日志记录
usage = {
"prompt_tokens": 0,
"completion_tokens": total_tokens,
"total_tokens": total_tokens
}
yield create_openai_chunk('', model, 'stop', usage=usage)
# 处理非流式响应
def handle_non_stream_response(response, model):
full_content = ""
for chunk in stream_notdiamond_response(response, model):
if chunk['choices'][0]['delta'].get('content'):
full_content += chunk['choices'][0]['delta']['content']
return jsonify({
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion",
"created": int(time.time()),
"model": model,
"system_fingerprint": generate_system_fingerprint(),
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": full_content
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": len(full_content) // 4,
"completion_tokens": len(full_content) // 4,
"total_tokens": len(full_content) // 2
}
})
# 生成流式响应
def generate_stream_response(response, model):
for chunk in stream_notdiamond_response(response, model):
yield f"data: {json.dumps(chunk)}\n\n"
yield "data: [DONE]\n\n"
# 获取模型列表的API
@app.route('/ai/v1/models', methods=['GET'])
def proxy_models():
models = [
{
"id": model_id,
"object": "model",
"provider": info["provider"]
} for model_id, info in MODEL_INFO.items()
]
return jsonify({
"object": "list",
"data": models
})
# 处理请求的API
@app.route('/ai/v1/chat/completions', methods=['POST'])
def handle_request():
try:
request_data = request.get_json()
messages = request_data.get('messages')
model_id = request_data.get('model', '')
model = MODEL_INFO.get(model_id, {}).get('mapping', model_id)
stream = request_data.get('stream', False)
payload = {
"messages": messages,
"model": model,
"stream": stream,
"frequency_penalty": request_data.get('frequency_penalty', 0),
"presence_penalty": request_data.get('presence_penalty', 0),
"temperature": request_data.get('temperature', 0.8),
"top_p": request_data.get('top_p', 1)
}
headers = get_notdiamond_headers()
url = get_notdiamond_url()
future = executor.submit(requests.post, url, headers=headers, json=[payload], stream=True)
response = future.result()
response.raise_for_status()
if stream:
return Response(stream_with_context(generate_stream_response(response, model)), content_type='text/event-stream')
else:
return handle_non_stream_response(response, model)
except json.JSONDecodeError as e:
return jsonify({
'error': 'Invalid JSON format',
'details': str(e)
}), 400 # 返回400错误,因为请求数据格式不正确
except requests.exceptions.RequestException as e:
return jsonify({
'error': 'Request failed',
'details': str(e)
}), 502 # 返回502错误,表示请求失败
except Exception as e:
return jsonify({
'error': 'Internal Server Error',
'details': str(e)
}), 500
if __name__ == '__main__':
port = int(os.environ.get("PORT", 8000))
app.run(debug=True, host='0.0.0.0', port=port, threaded=True) |