mirror of
https://github.com/xtekky/gpt4free.git
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134 lines
4.8 KiB
Python
134 lines
4.8 KiB
Python
from __future__ import annotations
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import json
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from typing import AsyncGenerator
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from aiohttp import ClientSession
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from ..typing import AsyncResult, Messages
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from .helper import format_prompt
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class Snova(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://fast.snova.ai"
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api_endpoint = "https://fast.snova.ai/api/completion"
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working = True
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supports_stream = True
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supports_system_message = True
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supports_message_history = True
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default_model = 'Meta-Llama-3.1-8B-Instruct'
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models = [
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'Meta-Llama-3.1-8B-Instruct',
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'Meta-Llama-3.1-70B-Instruct',
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'Meta-Llama-3.1-405B-Instruct',
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'Samba-CoE',
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'ignos/Mistral-T5-7B-v1',
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'v1olet/v1olet_merged_dpo_7B',
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'macadeliccc/WestLake-7B-v2-laser-truthy-dpo',
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'cookinai/DonutLM-v1',
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]
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model_aliases = {
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"llama-3.1-8b": "Meta-Llama-3.1-8B-Instruct",
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"llama-3.1-70b": "Meta-Llama-3.1-70B-Instruct",
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"llama-3.1-405b": "Meta-Llama-3.1-405B-Instruct",
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"mistral-7b": "ignos/Mistral-T5-7B-v1",
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"samba-coe-v0.1": "Samba-CoE",
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"v1olet-merged-7b": "v1olet/v1olet_merged_dpo_7B",
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"westlake-7b-v2": "macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
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"donutlm-v1": "cookinai/DonutLM-v1",
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}
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@classmethod
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def get_model(cls, model: str) -> str:
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if model in cls.models:
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return model
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elif model in cls.model_aliases:
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return cls.model_aliases[model]
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else:
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return cls.default_model
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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proxy: str = None,
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**kwargs
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) -> AsyncGenerator[str, None]:
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model = cls.get_model(model)
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headers = {
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"accept": "text/event-stream",
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"accept-language": "en-US,en;q=0.9",
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"cache-control": "no-cache",
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"content-type": "application/json",
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"origin": cls.url,
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"pragma": "no-cache",
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"priority": "u=1, i",
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"referer": f"{cls.url}/",
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"sec-ch-ua": '"Chromium";v="127", "Not)A;Brand";v="99"',
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": '"Linux"',
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"sec-fetch-dest": "empty",
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"sec-fetch-mode": "cors",
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"sec-fetch-site": "same-origin",
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"user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36"
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}
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async with ClientSession(headers=headers) as session:
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data = {
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"body": {
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant."
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},
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{
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"role": "user",
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"content": format_prompt(messages),
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"id": "1-id",
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"ref": "1-ref",
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"revision": 1,
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"draft": False,
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"status": "done",
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"enableRealTimeChat": False,
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"meta": None
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}
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],
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"max_tokens": 1000,
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"stop": ["<|eot_id|>"],
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"stream": True,
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"stream_options": {"include_usage": True},
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"model": model
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},
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"env_type": "tp16"
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}
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async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
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response.raise_for_status()
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full_response = ""
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async for line in response.content:
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line = line.decode().strip()
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if line.startswith("data: "):
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data = line[6:]
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if data == "[DONE]":
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break
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try:
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json_data = json.loads(data)
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choices = json_data.get("choices", [])
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if choices:
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delta = choices[0].get("delta", {})
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content = delta.get("content", "")
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full_response += content
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except json.JSONDecodeError:
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continue
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except Exception as e:
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print(f"Error processing chunk: {e}")
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print(f"Problematic data: {data}")
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continue
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yield full_response.strip()
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