mirror of
https://github.com/Balshgit/gpt_chat_bot.git
synced 2025-09-11 22:30:41 +03:00
516 lines
20 KiB
Python
516 lines
20 KiB
Python
import base64
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import json
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import os
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import queue
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import threading
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import execjs
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from curl_cffi import requests
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from ...typing import get_type_hints
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url = "https://play.vercel.ai"
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supports_stream = True
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needs_auth = False
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models = {
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"claude-instant-v1": "anthropic:claude-instant-v1",
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"claude-v1": "anthropic:claude-v1",
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"alpaca-7b": "replicate:replicate/alpaca-7b",
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"stablelm-tuned-alpha-7b": "replicate:stability-ai/stablelm-tuned-alpha-7b",
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"bloom": "huggingface:bigscience/bloom",
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"bloomz": "huggingface:bigscience/bloomz",
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"flan-t5-xxl": "huggingface:google/flan-t5-xxl",
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"flan-ul2": "huggingface:google/flan-ul2",
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"gpt-neox-20b": "huggingface:EleutherAI/gpt-neox-20b",
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"oasst-sft-4-pythia-12b-epoch-3.5": "huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"santacoder": "huggingface:bigcode/santacoder",
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"command-medium-nightly": "cohere:command-medium-nightly",
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"command-xlarge-nightly": "cohere:command-xlarge-nightly",
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"code-cushman-001": "openai:code-cushman-001",
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"code-davinci-002": "openai:code-davinci-002",
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"gpt-3.5-turbo": "openai:gpt-3.5-turbo",
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"text-ada-001": "openai:text-ada-001",
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"text-babbage-001": "openai:text-babbage-001",
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"text-curie-001": "openai:text-curie-001",
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"text-davinci-002": "openai:text-davinci-002",
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"text-davinci-003": "openai:text-davinci-003",
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}
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model = models.keys()
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vercel_models = {
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"anthropic:claude-instant-v1": {
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"id": "anthropic:claude-instant-v1",
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"provider": "anthropic",
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"providerHumanName": "Anthropic",
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"makerHumanName": "Anthropic",
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"minBillingTier": "hobby",
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"parameters": {
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"temperature": {"value": 1, "range": [0, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"topK": {"value": 1, "range": [1, 500]},
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"presencePenalty": {"value": 1, "range": [0, 1]},
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"frequencyPenalty": {"value": 1, "range": [0, 1]},
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"stopSequences": {"value": ["\n\nHuman:"], "range": []},
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},
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"name": "claude-instant-v1",
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},
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"anthropic:claude-v1": {
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"id": "anthropic:claude-v1",
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"provider": "anthropic",
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"providerHumanName": "Anthropic",
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"makerHumanName": "Anthropic",
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"minBillingTier": "hobby",
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"parameters": {
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"temperature": {"value": 1, "range": [0, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"topK": {"value": 1, "range": [1, 500]},
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"presencePenalty": {"value": 1, "range": [0, 1]},
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"frequencyPenalty": {"value": 1, "range": [0, 1]},
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"stopSequences": {"value": ["\n\nHuman:"], "range": []},
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},
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"name": "claude-v1",
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},
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"replicate:replicate/alpaca-7b": {
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"id": "replicate:replicate/alpaca-7b",
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"provider": "replicate",
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"providerHumanName": "Replicate",
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"makerHumanName": "Stanford",
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"parameters": {
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"temperature": {"value": 0.75, "range": [0.01, 5]},
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"maximumLength": {"value": 200, "range": [50, 512]},
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"topP": {"value": 0.95, "range": [0.01, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"repetitionPenalty": {"value": 1.1765, "range": [0.01, 5]},
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"stopSequences": {"value": [], "range": []},
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},
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"version": "2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21",
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"name": "alpaca-7b",
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},
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"replicate:stability-ai/stablelm-tuned-alpha-7b": {
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"id": "replicate:stability-ai/stablelm-tuned-alpha-7b",
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"provider": "replicate",
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"makerHumanName": "StabilityAI",
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"providerHumanName": "Replicate",
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"parameters": {
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"temperature": {"value": 0.75, "range": [0.01, 5]},
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"maximumLength": {"value": 200, "range": [50, 512]},
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"topP": {"value": 0.95, "range": [0.01, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"repetitionPenalty": {"value": 1.1765, "range": [0.01, 5]},
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"stopSequences": {"value": [], "range": []},
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},
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"version": "4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b",
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"name": "stablelm-tuned-alpha-7b",
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},
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"huggingface:bigscience/bloom": {
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"id": "huggingface:bigscience/bloom",
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"provider": "huggingface",
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"providerHumanName": "HuggingFace",
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"makerHumanName": "BigScience",
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"instructions": "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 0.95, "range": [0.01, 0.99]},
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"topK": {"value": 4, "range": [1, 500]},
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"repetitionPenalty": {"value": 1.03, "range": [0.1, 2]},
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},
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"name": "bloom",
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},
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"huggingface:bigscience/bloomz": {
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"id": "huggingface:bigscience/bloomz",
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"provider": "huggingface",
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"providerHumanName": "HuggingFace",
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"makerHumanName": "BigScience",
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"instructions": 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".',
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 0.95, "range": [0.01, 0.99]},
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"topK": {"value": 4, "range": [1, 500]},
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"repetitionPenalty": {"value": 1.03, "range": [0.1, 2]},
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},
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"name": "bloomz",
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},
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"huggingface:google/flan-t5-xxl": {
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"id": "huggingface:google/flan-t5-xxl",
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"provider": "huggingface",
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"makerHumanName": "Google",
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"providerHumanName": "HuggingFace",
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"name": "flan-t5-xxl",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 0.95, "range": [0.01, 0.99]},
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"topK": {"value": 4, "range": [1, 500]},
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"repetitionPenalty": {"value": 1.03, "range": [0.1, 2]},
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},
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},
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"huggingface:google/flan-ul2": {
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"id": "huggingface:google/flan-ul2",
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"provider": "huggingface",
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"providerHumanName": "HuggingFace",
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"makerHumanName": "Google",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 0.95, "range": [0.01, 0.99]},
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"topK": {"value": 4, "range": [1, 500]},
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"repetitionPenalty": {"value": 1.03, "range": [0.1, 2]},
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},
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"name": "flan-ul2",
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},
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"huggingface:EleutherAI/gpt-neox-20b": {
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"id": "huggingface:EleutherAI/gpt-neox-20b",
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"provider": "huggingface",
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"providerHumanName": "HuggingFace",
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"makerHumanName": "EleutherAI",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 0.95, "range": [0.01, 0.99]},
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"topK": {"value": 4, "range": [1, 500]},
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"repetitionPenalty": {"value": 1.03, "range": [0.1, 2]},
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"stopSequences": {"value": [], "range": []},
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},
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"name": "gpt-neox-20b",
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},
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"huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5": {
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"id": "huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"provider": "huggingface",
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"providerHumanName": "HuggingFace",
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"makerHumanName": "OpenAssistant",
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"parameters": {
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"typicalP": {"value": 0.2, "range": [0.1, 0.99]},
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"repetitionPenalty": {"value": 1, "range": [0.1, 2]},
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},
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"name": "oasst-sft-4-pythia-12b-epoch-3.5",
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},
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"huggingface:bigcode/santacoder": {
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"id": "huggingface:bigcode/santacoder",
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"provider": "huggingface",
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"providerHumanName": "HuggingFace",
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"makerHumanName": "BigCode",
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"instructions": 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.',
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 0.95, "range": [0.01, 0.99]},
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"topK": {"value": 4, "range": [1, 500]},
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"repetitionPenalty": {"value": 1.03, "range": [0.1, 2]},
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},
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"name": "santacoder",
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},
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"cohere:command-medium-nightly": {
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"id": "cohere:command-medium-nightly",
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"provider": "cohere",
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"providerHumanName": "Cohere",
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"makerHumanName": "Cohere",
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"name": "command-medium-nightly",
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"parameters": {
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"temperature": {"value": 0.9, "range": [0, 2]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0, 1]},
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"topK": {"value": 0, "range": [0, 500]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"cohere:command-xlarge-nightly": {
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"id": "cohere:command-xlarge-nightly",
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"provider": "cohere",
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"providerHumanName": "Cohere",
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"makerHumanName": "Cohere",
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"name": "command-xlarge-nightly",
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"parameters": {
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"temperature": {"value": 0.9, "range": [0, 2]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0, 1]},
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"topK": {"value": 0, "range": [0, 500]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"openai:gpt-4": {
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"id": "openai:gpt-4",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"name": "gpt-4",
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"minBillingTier": "pro",
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"parameters": {
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"temperature": {"value": 0.7, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"openai:code-cushman-001": {
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"id": "openai:code-cushman-001",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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"name": "code-cushman-001",
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},
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"openai:code-davinci-002": {
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"id": "openai:code-davinci-002",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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"name": "code-davinci-002",
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},
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"openai:gpt-3.5-turbo": {
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"id": "openai:gpt-3.5-turbo",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"parameters": {
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"temperature": {"value": 0.7, "range": [0, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"topK": {"value": 1, "range": [1, 500]},
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"presencePenalty": {"value": 1, "range": [0, 1]},
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"frequencyPenalty": {"value": 1, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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"name": "gpt-3.5-turbo",
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},
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"openai:text-ada-001": {
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"id": "openai:text-ada-001",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"name": "text-ada-001",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"openai:text-babbage-001": {
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"id": "openai:text-babbage-001",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"name": "text-babbage-001",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"openai:text-curie-001": {
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"id": "openai:text-curie-001",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"name": "text-curie-001",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"openai:text-davinci-002": {
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"id": "openai:text-davinci-002",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"name": "text-davinci-002",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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"openai:text-davinci-003": {
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"id": "openai:text-davinci-003",
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"provider": "openai",
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"providerHumanName": "OpenAI",
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"makerHumanName": "OpenAI",
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"name": "text-davinci-003",
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"parameters": {
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"temperature": {"value": 0.5, "range": [0.1, 1]},
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"maximumLength": {"value": 200, "range": [50, 1024]},
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"topP": {"value": 1, "range": [0.1, 1]},
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"presencePenalty": {"value": 0, "range": [0, 1]},
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"frequencyPenalty": {"value": 0, "range": [0, 1]},
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"stopSequences": {"value": [], "range": []},
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},
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},
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}
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# based on https://github.com/ading2210/vercel-llm-api // modified
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class Client:
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def __init__(self):
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self.session = requests.Session()
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self.headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36",
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"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "en-US,en;q=0.5",
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"Te": "trailers",
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"Upgrade-Insecure-Requests": "1",
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}
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self.session.headers.update(self.headers)
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def get_token(self):
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b64 = self.session.get("https://sdk.vercel.ai/openai.jpeg").text
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data = json.loads(base64.b64decode(b64))
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code = "const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}" % (data["c"], data["a"])
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token_string = json.dumps(
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separators=(",", ":"),
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obj={"r": execjs.compile(code).call("token"), "t": data["t"]},
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)
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return base64.b64encode(token_string.encode()).decode()
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def get_default_params(self, model_id):
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return {key: param["value"] for key, param in vercel_models[model_id]["parameters"].items()}
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def generate(self, model_id: str, prompt: str, params: dict = {}):
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if not ":" in model_id:
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model_id = models[model_id]
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defaults = self.get_default_params(model_id)
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payload = (
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defaults
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| params
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| {
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"prompt": prompt,
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"model": model_id,
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}
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)
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headers = self.headers | {
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"Accept-Encoding": "gzip, deflate, br",
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"Custom-Encoding": self.get_token(),
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"Host": "sdk.vercel.ai",
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"Origin": "https://sdk.vercel.ai",
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"Referrer": "https://sdk.vercel.ai",
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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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}
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chunks_queue = queue.Queue()
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error = None
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response = None
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def callback(data):
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chunks_queue.put(data.decode())
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def request_thread():
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nonlocal response, error
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for _ in range(3):
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try:
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response = self.session.post(
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"https://sdk.vercel.ai/api/generate",
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json=payload,
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headers=headers,
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content_callback=callback,
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)
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response.raise_for_status()
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except Exception as e:
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if _ == 2:
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error = e
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else:
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continue
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thread = threading.Thread(target=request_thread, daemon=True)
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thread.start()
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text = ""
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index = 0
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while True:
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try:
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chunk = chunks_queue.get(block=True, timeout=0.1)
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except queue.Empty:
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if error:
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raise error
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elif response:
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break
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else:
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continue
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text += chunk
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lines = text.split("\n")
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if len(lines) - 1 > index:
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new = lines[index:-1]
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for word in new:
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yield json.loads(word)
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index = len(lines) - 1
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def _create_completion(model: str, messages: list, stream: bool, **kwargs):
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yield "Vercel is currently not working."
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return
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conversation = "This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n"
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for message in messages:
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conversation += "%s: %s\n" % (message["role"], message["content"])
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conversation += "assistant: "
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completion = Client().generate(model, conversation)
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for token in completion:
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yield token
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params = f"g4f.Providers.{os.path.basename(__file__)[:-3]} supports: " + "(%s)" % ", ".join(
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[
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f"{name}: {get_type_hints(_create_completion)[name].__name__}"
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for name in _create_completion.__code__.co_varnames[: _create_completion.__code__.co_argcount]
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]
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)
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