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gpt4free/g4f/Provider/Vercel.py

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from __future__ import annotations
import base64, json, uuid, quickjs, random
from curl_cffi.requests import AsyncSession
from ..typing import Any, TypedDict
from .base_provider import AsyncProvider
class Vercel(AsyncProvider):
url = "https://sdk.vercel.ai"
working = True
supports_gpt_35_turbo = True
model = "replicate:replicate/llama-2-70b-chat"
@classmethod
async def create_async(
cls,
model: str,
messages: list[dict[str, str]],
proxy: str = None,
**kwargs
) -> str:
if model in ["gpt-3.5-turbo", "gpt-4"]:
model = "openai:" + model
model = model if model else cls.model
proxies = None
if proxy:
if "://" not in proxy:
proxy = "http://" + proxy
proxies = {"http": proxy, "https": proxy}
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.{rand1}.{rand2} Safari/537.36".format(
rand1=random.randint(0,9999),
rand2=random.randint(0,9999)
),
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
"Accept-Encoding": "gzip, deflate, br",
"Accept-Language": "en-US,en;q=0.5",
"TE": "trailers",
}
async with AsyncSession(headers=headers, proxies=proxies, impersonate="chrome107") as session:
response = await session.get(cls.url + "/openai.jpeg")
response.raise_for_status()
custom_encoding = _get_custom_encoding(response.text)
headers = {
"Content-Type": "application/json",
"Custom-Encoding": custom_encoding,
}
data = _create_payload(model, messages)
response = await session.post(cls.url + "/api/generate", json=data, headers=headers)
response.raise_for_status()
return response.text
def _create_payload(model: str, messages: list[dict[str, str]]) -> dict[str, Any]:
if model not in model_info:
raise RuntimeError(f'Model "{model}" are not supported')
default_params = model_info[model]["default_params"]
return {
"messages": messages,
"playgroundId": str(uuid.uuid4()),
"chatIndex": 0,
"model": model
} | default_params
# based on https://github.com/ading2210/vercel-llm-api
def _get_custom_encoding(text: str) -> str:
data = json.loads(base64.b64decode(text, validate=True))
script = """
String.prototype.fontcolor = function() {{
return `<font>${{this}}</font>`
}}
var globalThis = {{marker: "mark"}};
({script})({key})
""".format(
script=data["c"], key=data["a"]
)
context = quickjs.Context() # type: ignore
token_data = json.loads(context.eval(script).json()) # type: ignore
token_data[2] = "mark"
token = {"r": token_data, "t": data["t"]}
token_str = json.dumps(token, separators=(",", ":")).encode("utf-16le")
return base64.b64encode(token_str).decode()
class ModelInfo(TypedDict):
id: str
default_params: dict[str, Any]
model_info: dict[str, ModelInfo] = {
"anthropic:claude-instant-v1": {
"id": "anthropic:claude-instant-v1",
"default_params": {
"temperature": 1,
"maxTokens": 200,
"topP": 1,
"topK": 1,
"presencePenalty": 1,
"frequencyPenalty": 1,
"stopSequences": ["\n\nHuman:"],
},
},
"anthropic:claude-v1": {
"id": "anthropic:claude-v1",
"default_params": {
"temperature": 1,
"maxTokens": 200,
"topP": 1,
"topK": 1,
"presencePenalty": 1,
"frequencyPenalty": 1,
"stopSequences": ["\n\nHuman:"],
},
},
"anthropic:claude-v2": {
"id": "anthropic:claude-v2",
"default_params": {
"temperature": 1,
"maxTokens": 200,
"topP": 1,
"topK": 1,
"presencePenalty": 1,
"frequencyPenalty": 1,
"stopSequences": ["\n\nHuman:"],
},
},
"replicate:a16z-infra/llama7b-v2-chat": {
"id": "replicate:a16z-infra/llama7b-v2-chat",
"default_params": {
"temperature": 0.75,
"maxTokens": 500,
"topP": 1,
"repetitionPenalty": 1,
},
},
"replicate:a16z-infra/llama13b-v2-chat": {
"id": "replicate:a16z-infra/llama13b-v2-chat",
"default_params": {
"temperature": 0.75,
"maxTokens": 500,
"topP": 1,
"repetitionPenalty": 1,
},
},
"replicate:replicate/llama-2-70b-chat": {
"id": "replicate:replicate/llama-2-70b-chat",
"default_params": {
"temperature": 0.75,
"maxTokens": 1000,
"topP": 1,
"repetitionPenalty": 1,
},
},
"huggingface:bigscience/bloom": {
"id": "huggingface:bigscience/bloom",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 0.95,
"topK": 4,
"repetitionPenalty": 1.03,
},
},
"huggingface:google/flan-t5-xxl": {
"id": "huggingface:google/flan-t5-xxl",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 0.95,
"topK": 4,
"repetitionPenalty": 1.03,
},
},
"huggingface:EleutherAI/gpt-neox-20b": {
"id": "huggingface:EleutherAI/gpt-neox-20b",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 0.95,
"topK": 4,
"repetitionPenalty": 1.03,
"stopSequences": [],
},
},
"huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5": {
"id": "huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"default_params": {"maxTokens": 200, "typicalP": 0.2, "repetitionPenalty": 1},
},
"huggingface:OpenAssistant/oasst-sft-1-pythia-12b": {
"id": "huggingface:OpenAssistant/oasst-sft-1-pythia-12b",
"default_params": {"maxTokens": 200, "typicalP": 0.2, "repetitionPenalty": 1},
},
"huggingface:bigcode/santacoder": {
"id": "huggingface:bigcode/santacoder",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 0.95,
"topK": 4,
"repetitionPenalty": 1.03,
},
},
"cohere:command-light-nightly": {
"id": "cohere:command-light-nightly",
"default_params": {
"temperature": 0.9,
"maxTokens": 200,
"topP": 1,
"topK": 0,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"cohere:command-nightly": {
"id": "cohere:command-nightly",
"default_params": {
"temperature": 0.9,
"maxTokens": 200,
"topP": 1,
"topK": 0,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:gpt-4": {
"id": "openai:gpt-4",
"default_params": {
"temperature": 0.7,
"maxTokens": 500,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:gpt-4-0613": {
"id": "openai:gpt-4-0613",
"default_params": {
"temperature": 0.7,
"maxTokens": 500,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:code-davinci-002": {
"id": "openai:code-davinci-002",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:gpt-3.5-turbo": {
"id": "openai:gpt-3.5-turbo",
"default_params": {
"temperature": 0.7,
"maxTokens": 500,
"topP": 1,
"topK": 1,
"presencePenalty": 1,
"frequencyPenalty": 1,
"stopSequences": [],
},
},
"openai:gpt-3.5-turbo-16k": {
"id": "openai:gpt-3.5-turbo-16k",
"default_params": {
"temperature": 0.7,
"maxTokens": 500,
"topP": 1,
"topK": 1,
"presencePenalty": 1,
"frequencyPenalty": 1,
"stopSequences": [],
},
},
"openai:gpt-3.5-turbo-16k-0613": {
"id": "openai:gpt-3.5-turbo-16k-0613",
"default_params": {
"temperature": 0.7,
"maxTokens": 500,
"topP": 1,
"topK": 1,
"presencePenalty": 1,
"frequencyPenalty": 1,
"stopSequences": [],
},
},
"openai:text-ada-001": {
"id": "openai:text-ada-001",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:text-babbage-001": {
"id": "openai:text-babbage-001",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:text-curie-001": {
"id": "openai:text-curie-001",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:text-davinci-002": {
"id": "openai:text-davinci-002",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
"openai:text-davinci-003": {
"id": "openai:text-davinci-003",
"default_params": {
"temperature": 0.5,
"maxTokens": 200,
"topP": 1,
"presencePenalty": 0,
"frequencyPenalty": 0,
"stopSequences": [],
},
},
}
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