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-rw-r--r--g4f/Provider/Vercel.py337
1 files changed, 337 insertions, 0 deletions
diff --git a/g4f/Provider/Vercel.py b/g4f/Provider/Vercel.py
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+++ b/g4f/Provider/Vercel.py
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+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": [],
+ },
+ },
+}
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