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from __future__ import annotations
import base64
import json
import uuid
import quickjs
from curl_cffi import requests
from ..typing import Any, CreateResult, TypedDict
from .base_provider import BaseProvider
class Vercel(BaseProvider):
url = "https://play.vercel.ai"
working = True
supports_gpt_35_turbo = True
@staticmethod
def create_completion(
model: str,
messages: list[dict[str, str]],
stream: bool, **kwargs: Any) -> CreateResult:
if model in ["gpt-3.5-turbo", "gpt-4"]:
model = "openai:" + model
yield _chat(model_id=model, messages=messages)
def _chat(model_id: str, messages: list[dict[str, str]]) -> str:
session = requests.Session(impersonate="chrome107")
url = "https://sdk.vercel.ai/api/generate"
header = _create_header(session)
payload = _create_payload(model_id, messages)
response = session.post(url=url, headers=header, json=payload)
response.raise_for_status()
return response.text
def _create_payload(model_id: str, messages: list[dict[str, str]]) -> dict[str, Any]:
default_params = model_info[model_id]["default_params"]
return {
"messages": messages,
"playgroundId": str(uuid.uuid4()),
"chatIndex": 0,
"model": model_id} | default_params
def _create_header(session: requests.Session):
custom_encoding = _get_custom_encoding(session)
return {"custom-encoding": custom_encoding}
# based on https://github.com/ading2210/vercel-llm-api
def _get_custom_encoding(session: requests.Session):
url = "https://sdk.vercel.ai/openai.jpeg"
response = session.get(url=url)
data = json.loads(base64.b64decode(response.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,
},
},
"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": [],
},
},
}
|
