from __future__ import annotations
import re
from aiohttp import ClientSession
import json
from typing import List
from ...typing import AsyncResult, Messages
from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..helper import format_prompt
def clean_response(text: str) -> str:
"""Clean response from unwanted patterns."""
patterns = [
r"One message exceeds the \d+chars per message limit\..+https:\/\/discord\.com\/invite\/\S+",
r"Rate limit \(\d+\/minute\) exceeded\. Join our discord for more: .+https:\/\/discord\.com\/invite\/\S+",
r"Rate limit \(\d+\/hour\) exceeded\. Join our discord for more: https:\/\/discord\.com\/invite\/\S+",
r"</s>", # zephyr-7b-beta
]
for pattern in patterns:
text = re.sub(pattern, '', text)
return text.strip()
def split_message(message: dict, chunk_size: int = 995) -> List[dict]:
"""Split a message into chunks of specified size."""
content = message.get('content', '')
if len(content) <= chunk_size:
return [message]
chunks = []
while content:
chunk = content[:chunk_size]
content = content[chunk_size:]
chunks.append({
'role': message['role'],
'content': chunk
})
return chunks
def split_messages(messages: Messages, chunk_size: int = 995) -> Messages:
"""Split all messages that exceed chunk_size into smaller messages."""
result = []
for message in messages:
result.extend(split_message(message, chunk_size))
return result
class AirforceChat(AsyncGeneratorProvider, ProviderModelMixin):
label = "AirForce Chat"
api_endpoint_completions = "https://api.airforce/chat/completions" # Замініть на реальний ендпоінт
supports_stream = True
supports_system_message = True
supports_message_history = True
default_model = 'llama-3-70b-chat'
text_models = [
# anthropic
'claude-3-haiku-20240307',
'claude-3-sonnet-20240229',
'claude-3-5-sonnet-20240620',
'claude-3-5-sonnet-20241022',
'claude-3-opus-20240229',
# openai
'chatgpt-4o-latest',
'gpt-4',
'gpt-4-turbo',
'gpt-4o-2024-05-13',
'gpt-4o-mini-2024-07-18',
'gpt-4o-mini',
'gpt-4o-2024-08-06',
'gpt-3.5-turbo',
'gpt-3.5-turbo-0125',
'gpt-3.5-turbo-1106',
'gpt-4o',
'gpt-4-turbo-2024-04-09',
'gpt-4-0125-preview',
'gpt-4-1106-preview',
# meta-llama
default_model,
'llama-3-70b-chat-turbo',
'llama-3-8b-chat',
'llama-3-8b-chat-turbo',
'llama-3-70b-chat-lite',
'llama-3-8b-chat-lite',
'llama-2-13b-chat',
'llama-3.1-405b-turbo',
'llama-3.1-70b-turbo',
'llama-3.1-8b-turbo',
'LlamaGuard-2-8b',
'llamaguard-7b',
'Llama-Vision-Free',
'Llama-Guard-7b',
'Llama-3.2-90B-Vision-Instruct-Turbo',
'Meta-Llama-Guard-3-8B',
'Llama-3.2-11B-Vision-Instruct-Turbo',
'Llama-Guard-3-11B-Vision-Turbo',
'Llama-3.2-3B-Instruct-Turbo',
'Llama-3.2-1B-Instruct-Turbo',
'llama-2-7b-chat-int8',
'llama-2-7b-chat-fp16',
'Llama 3.1 405B Instruct',
'Llama 3.1 70B Instruct',
'Llama 3.1 8B Instruct',
# mistral-ai
'Mixtral-8x7B-Instruct-v0.1',
'Mixtral-8x22B-Instruct-v0.1',
'Mistral-7B-Instruct-v0.1',
'Mistral-7B-Instruct-v0.2',
'Mistral-7B-Instruct-v0.3',
# Gryphe
'MythoMax-L2-13b-Lite',
'MythoMax-L2-13b',
# openchat
'openchat-3.5-0106',
# qwen
#'Qwen1.5-72B-Chat', Пуста відповідь
#'Qwen1.5-110B-Chat', Пуста відповідь
'Qwen2-72B-Instruct',
'Qwen2.5-7B-Instruct-Turbo',
'Qwen2.5-72B-Instruct-Turbo',
# google
'gemma-2b-it',
'gemma-2-9b-it',
'gemma-2-27b-it',
# gemini
'gemini-1.5-flash',
'gemini-1.5-pro',
# databricks
'dbrx-instruct',
# deepseek-ai
'deepseek-coder-6.7b-base',
'deepseek-coder-6.7b-instruct',
'deepseek-math-7b-instruct',
# NousResearch
'deepseek-math-7b-instruct',
'Nous-Hermes-2-Mixtral-8x7B-DPO',
'hermes-2-pro-mistral-7b',
# teknium
'openhermes-2.5-mistral-7b',
# microsoft
'WizardLM-2-8x22B',
'phi-2',
# upstage
'SOLAR-10.7B-Instruct-v1.0',
# pawan
'cosmosrp',
# liquid
'lfm-40b-moe',
# DiscoResearch
'discolm-german-7b-v1',
# tiiuae
'falcon-7b-instruct',
# defog
'sqlcoder-7b-2',
# tinyllama
'tinyllama-1.1b-chat',
# HuggingFaceH4
'zephyr-7b-beta',
]
models = [*text_models]
model_aliases = {
# anthropic
"claude-3-haiku": "claude-3-haiku-20240307",
"claude-3-sonnet": "claude-3-sonnet-20240229",
"claude-3.5-sonnet": "claude-3-5-sonnet-20240620",
"claude-3.5-sonnet": "claude-3-5-sonnet-20241022",
"claude-3-opus": "claude-3-opus-20240229",
# openai
"gpt-4o": "chatgpt-4o-latest",
#"gpt-4": "gpt-4",
#"gpt-4-turbo": "gpt-4-turbo",
"gpt-4o": "gpt-4o-2024-05-13",
"gpt-4o-mini": "gpt-4o-mini-2024-07-18",
#"gpt-4o-mini": "gpt-4o-mini",
"gpt-4o": "gpt-4o-2024-08-06",
"gpt-3.5-turbo": "gpt-3.5-turbo",
"gpt-3.5-turbo": "gpt-3.5-turbo-0125",
"gpt-3.5-turbo": "gpt-3.5-turbo-1106",
#"gpt-4o": "gpt-4o",
"gpt-4-turbo": "gpt-4-turbo-2024-04-09",
"gpt-4": "gpt-4-0125-preview",
"gpt-4": "gpt-4-1106-preview",
# meta-llama
"llama-3-70b": "llama-3-70b-chat",
"llama-3-8b": "llama-3-8b-chat",
"llama-3-8b": "llama-3-8b-chat-turbo",
"llama-3-70b": "llama-3-70b-chat-lite",
"llama-3-8b": "llama-3-8b-chat-lite",
"llama-2-13b": "llama-2-13b-chat",
"llama-3.1-405b": "llama-3.1-405b-turbo",
"llama-3.1-70b": "llama-3.1-70b-turbo",
"llama-3.1-8b": "llama-3.1-8b-turbo",
"llamaguard-2-8b": "LlamaGuard-2-8b",
"llamaguard-7b": "llamaguard-7b",
#"llama_vision_free": "Llama-Vision-Free", # Unknown
"llamaguard-7b": "Llama-Guard-7b",
"llama-3.2-90b": "Llama-3.2-90B-Vision-Instruct-Turbo",
"llamaguard-3-8b": "Meta-Llama-Guard-3-8B",
"llama-3.2-11b": "Llama-3.2-11B-Vision-Instruct-Turbo",
"llamaguard-3-11b": "Llama-Guard-3-11B-Vision-Turbo",
"llama-3.2-3b": "Llama-3.2-3B-Instruct-Turbo",
"llama-3.2-1b": "Llama-3.2-1B-Instruct-Turbo",
"llama-2-7b": "llama-2-7b-chat-int8",
"llama-2-7b": "llama-2-7b-chat-fp16",
"llama-3.1-405b": "Llama 3.1 405B Instruct",
"llama-3.1-70b": "Llama 3.1 70B Instruct",
"llama-3.1-8b": "Llama 3.1 8B Instruct",
# mistral-ai
"mixtral-8x7b": "Mixtral-8x7B-Instruct-v0.1",
"mixtral-8x22b": "Mixtral-8x22B-Instruct-v0.1",
"mixtral-8x7b": "Mistral-7B-Instruct-v0.1",
"mixtral-8x7b": "Mistral-7B-Instruct-v0.2",
"mixtral-8x7b": "Mistral-7B-Instruct-v0.3",
# Gryphe
"mythomax-13b": "MythoMax-L2-13b-Lite",
"mythomax-13b": "MythoMax-L2-13b",
# openchat
"openchat-3.5": "openchat-3.5-0106",
# qwen
#"qwen-1.5-72b": "Qwen1.5-72B-Chat", # Empty answer
#"qwen-1.5-110b": "Qwen1.5-110B-Chat", # Empty answer
"qwen-2-72b": "Qwen2-72B-Instruct",
"qwen-2-5-7b": "Qwen2.5-7B-Instruct-Turbo",
"qwen-2-5-72b": "Qwen2.5-72B-Instruct-Turbo",
# google
"gemma-2b": "gemma-2b-it",
"gemma-2-9b": "gemma-2-9b-it",
"gemma-2b-27b": "gemma-2-27b-it",
# gemini
"gemini-flash": "gemini-1.5-flash",
"gemini-pro": "gemini-1.5-pro",
# databricks
"dbrx-instruct": "dbrx-instruct",
# deepseek-ai
#"deepseek-coder": "deepseek-coder-6.7b-base",
"deepseek-coder": "deepseek-coder-6.7b-instruct",
#"deepseek-math": "deepseek-math-7b-instruct",
# NousResearch
#"deepseek-math": "deepseek-math-7b-instruct",
"hermes-2-dpo": "Nous-Hermes-2-Mixtral-8x7B-DPO",
"hermes-2": "hermes-2-pro-mistral-7b",
# teknium
"openhermes-2.5": "openhermes-2.5-mistral-7b",
# microsoft
"wizardlm-2-8x22b": "WizardLM-2-8x22B",
#"phi-2": "phi-2",
# upstage
"solar-10-7b": "SOLAR-10.7B-Instruct-v1.0",
# pawan
#"cosmosrp": "cosmosrp",
# liquid
"lfm-40b": "lfm-40b-moe",
# DiscoResearch
"german-7b": "discolm-german-7b-v1",
# tiiuae
#"falcon-7b": "falcon-7b-instruct",
# defog
#"sqlcoder-7b": "sqlcoder-7b-2",
# tinyllama
#"tinyllama-1b": "tinyllama-1.1b-chat",
# HuggingFaceH4
"zephyr-7b": "zephyr-7b-beta",
}
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
stream: bool = False,
proxy: str = None,
max_tokens: str = 4096,
temperature: str = 1,
top_p: str = 1,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
chunked_messages = split_messages(messages)
headers = {
'accept': '*/*',
'accept-language': 'en-US,en;q=0.9',
'authorization': 'Bearer missing api key',
'cache-control': 'no-cache',
'content-type': 'application/json',
'origin': 'https://llmplayground.net',
'pragma': 'no-cache',
'priority': 'u=1, i',
'referer': 'https://llmplayground.net/',
'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Linux"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'cross-site',
'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36'
}
data = {
"messages": chunked_messages,
"model": model,
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"stream": stream
}
async with ClientSession(headers=headers) as session:
async with session.post(cls.api_endpoint_completions, json=data, proxy=proxy) as response:
response.raise_for_status()
text = ""
if stream:
async for line in response.content:
line = line.decode('utf-8')
if line.startswith('data: '):
json_str = line[6:]
try:
chunk = json.loads(json_str)
if 'choices' in chunk and chunk['choices']:
content = chunk['choices'][0].get('delta', {}).get('content', '')
text += content # Збираємо дельти
except json.JSONDecodeError as e:
print(f"Error decoding JSON: {json_str}, Error: {e}")
elif line.strip() == "[DONE]":
break
yield clean_response(text)
else:
response_json = await response.json()
text = response_json["choices"][0]["message"]["content"]
yield clean_response(text)
