mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-12-06 02:30:41 -08:00
* Update model configurations, provider implementations, and documentation - Updated model names and aliases for Qwen QVQ 72B and Qwen 2 72B (@TheFirstNoob) - Revised HuggingSpace class configuration, added default_image_model - Added llama-3.2-70b alias for Llama 3.2 70B model in AutonomousAI - Removed BlackboxCreateAgent class - Added gpt-4o alias for Copilot model - Moved api_key to Mhystical class attribute - Added models property with default_model value for Free2GPT - Simplified Jmuz class implementation - Improved image generation and model handling in DeepInfra - Standardized default models and removed aliases in Gemini - Replaced model aliases with direct model list in GlhfChat (@TheFirstNoob) - Removed trailing slash from image generation URL in PollinationsAI (https://github.com/xtekky/gpt4free/issues/2571) - Updated llama and qwen model configurations - Enhanced provider documentation and model details * Removed from (g4f/models.py) 'Yqcloud' provider from Default due to error 'ResponseStatusError: Response 429: 文字过长,请删减后重试。' * Update docs/providers-and-models.md * refactor(g4f/Provider/DDG.py): Add error handling and rate limiting to DDG provider - Add custom exception classes for rate limits, timeouts, and conversation limits - Implement rate limiting with sleep between requests (0.75s minimum delay) - Add model validation method to check supported models - Add proper error handling for API responses with custom exceptions - Improve session cookie handling for conversation persistence - Clean up User-Agent string and remove redundant code - Add proper error propagation through async generator Breaking changes: - New custom exceptions may require updates to error handling code - Rate limiting affects request timing and throughput - Model validation is now stricter Related: - Adds error handling similar to standard API clients - Improves reliability and robustness of chat interactions * Update g4f/models.py g4f/Provider/PollinationsAI.py * Update g4f/models.py * Restored provider which was not working and was disabled (g4f/Provider/DeepInfraChat.py) * Fixing a bug with Streaming Completions * Update g4f/Provider/PollinationsAI.py * Update g4f/Provider/Blackbox.py g4f/Provider/DDG.py * Added another model for generating images 'ImageGeneration2' to the 'Blackbox' provider * Update docs/providers-and-models.md * Update g4f/models.py g4f/Provider/Blackbox.py * Added a new OIVSCode provider from the Text Models and Vision (Image Upload) model * Update docs/providers-and-models.md * docs: add Conversation Memory class with context handling requested by @TheFirstNoob * Simplified README.md documentation added new docs/configuration.md documentation * Update add README.md docs/configuration.md * Update README.md * Update docs/providers-and-models.md g4f/models.py g4f/Provider/PollinationsAI.py * Added new model deepseek-r1 to Blackbox provider. @TheFirstNoob * Fixed bugs and updated docs/providers-and-models.md etc/unittest/client.py g4f/models.py g4f/Provider/. --------- Co-authored-by: kqlio67 <> Co-authored-by: H Lohaus <hlohaus@users.noreply.github.com>
602 lines
22 KiB
Python
602 lines
22 KiB
Python
from __future__ import annotations
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import os
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import time
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import random
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import string
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import asyncio
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import aiohttp
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import base64
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from typing import Union, AsyncIterator, Iterator, Awaitable, Optional
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from ..image import ImageResponse, copy_images
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from ..typing import Messages, ImageType
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from ..providers.types import ProviderType, BaseRetryProvider
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from ..providers.response import ResponseType, FinishReason, BaseConversation, SynthesizeData, ToolCalls, Usage
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from ..errors import NoImageResponseError
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from ..providers.retry_provider import IterListProvider
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from ..providers.asyncio import to_sync_generator
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from ..Provider.needs_auth import BingCreateImages, OpenaiAccount
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from ..tools.run_tools import async_iter_run_tools, iter_run_tools
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from .stubs import ChatCompletion, ChatCompletionChunk, Image, ImagesResponse
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from .image_models import ImageModels
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from .types import IterResponse, ImageProvider, Client as BaseClient
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from .service import get_model_and_provider, convert_to_provider
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from .helper import find_stop, filter_json, filter_none, safe_aclose
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from .. import debug
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ChatCompletionResponseType = Iterator[Union[ChatCompletion, ChatCompletionChunk, BaseConversation]]
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AsyncChatCompletionResponseType = AsyncIterator[Union[ChatCompletion, ChatCompletionChunk, BaseConversation]]
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try:
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anext # Python 3.8+
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except NameError:
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async def anext(aiter):
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try:
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return await aiter.__anext__()
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except StopAsyncIteration:
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raise StopIteration
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# Synchronous iter_response function
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def iter_response(
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response: Union[Iterator[Union[str, ResponseType]]],
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stream: bool,
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response_format: Optional[dict] = None,
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max_tokens: Optional[int] = None,
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stop: Optional[list[str]] = None
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) -> ChatCompletionResponseType:
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content = ""
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finish_reason = None
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tool_calls = None
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usage = None
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completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
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idx = 0
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if hasattr(response, '__aiter__'):
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response = to_sync_generator(response)
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for chunk in response:
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if isinstance(chunk, FinishReason):
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finish_reason = chunk.reason
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break
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elif isinstance(chunk, ToolCalls):
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tool_calls = chunk.get_list()
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continue
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elif isinstance(chunk, Usage):
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usage = chunk
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continue
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elif isinstance(chunk, BaseConversation):
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yield chunk
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continue
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elif isinstance(chunk, SynthesizeData) or not chunk:
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continue
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chunk = str(chunk)
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content += chunk
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if max_tokens is not None and idx + 1 >= max_tokens:
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finish_reason = "length"
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first, content, chunk = find_stop(stop, content, chunk if stream else None)
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if first != -1:
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finish_reason = "stop"
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if stream:
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yield ChatCompletionChunk.model_construct(chunk, None, completion_id, int(time.time()))
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if finish_reason is not None:
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break
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idx += 1
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if usage is None:
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usage = Usage(prompt_tokens=0, completion_tokens=idx, total_tokens=idx)
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finish_reason = "stop" if finish_reason is None else finish_reason
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if stream:
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yield ChatCompletionChunk.model_construct(
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None, finish_reason, completion_id, int(time.time()),
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usage=usage.get_dict()
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)
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else:
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if response_format is not None and "type" in response_format:
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if response_format["type"] == "json_object":
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content = filter_json(content)
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yield ChatCompletion.model_construct(
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content, finish_reason, completion_id, int(time.time()),
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usage=usage.get_dict(), **filter_none(tool_calls=tool_calls)
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)
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# Synchronous iter_append_model_and_provider function
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def iter_append_model_and_provider(response: ChatCompletionResponseType, last_model: str, last_provider: ProviderType) -> ChatCompletionResponseType:
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if isinstance(last_provider, BaseRetryProvider):
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last_provider = last_provider.last_provider
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for chunk in response:
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if isinstance(chunk, (ChatCompletion, ChatCompletionChunk)):
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if last_provider is not None:
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chunk.model = getattr(last_provider, "last_model", last_model)
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chunk.provider = last_provider.__name__
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yield chunk
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async def async_iter_response(
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response: AsyncIterator[Union[str, ResponseType]],
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stream: bool,
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response_format: Optional[dict] = None,
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max_tokens: Optional[int] = None,
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stop: Optional[list[str]] = None
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) -> AsyncChatCompletionResponseType:
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content = ""
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finish_reason = None
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completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
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idx = 0
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tool_calls = None
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usage = None
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try:
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async for chunk in response:
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if isinstance(chunk, FinishReason):
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finish_reason = chunk.reason
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break
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elif isinstance(chunk, BaseConversation):
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yield chunk
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continue
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elif isinstance(chunk, ToolCalls):
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tool_calls = chunk.get_list()
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continue
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elif isinstance(chunk, Usage):
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usage = chunk
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continue
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elif isinstance(chunk, SynthesizeData) or not chunk:
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continue
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chunk = str(chunk)
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content += chunk
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idx += 1
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if max_tokens is not None and idx >= max_tokens:
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finish_reason = "length"
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first, content, chunk = find_stop(stop, content, chunk if stream else None)
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if first != -1:
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finish_reason = "stop"
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if stream:
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yield ChatCompletionChunk.model_construct(chunk, None, completion_id, int(time.time()))
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if finish_reason is not None:
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break
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finish_reason = "stop" if finish_reason is None else finish_reason
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if usage is None:
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usage = Usage(prompt_tokens=0, completion_tokens=idx, total_tokens=idx)
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if stream:
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yield ChatCompletionChunk.model_construct(
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None, finish_reason, completion_id, int(time.time()),
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usage=usage.get_dict()
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)
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else:
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if response_format is not None and "type" in response_format:
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if response_format["type"] == "json_object":
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content = filter_json(content)
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yield ChatCompletion.model_construct(
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content, finish_reason, completion_id, int(time.time()),
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usage=usage.get_dict(), **filter_none(tool_calls=tool_calls)
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)
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finally:
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await safe_aclose(response)
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async def async_iter_append_model_and_provider(
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response: AsyncChatCompletionResponseType,
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last_model: str,
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last_provider: ProviderType
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) -> AsyncChatCompletionResponseType:
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last_provider = None
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try:
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if isinstance(last_provider, BaseRetryProvider):
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if last_provider is not None:
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last_provider = last_provider.last_provider
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async for chunk in response:
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if isinstance(chunk, (ChatCompletion, ChatCompletionChunk)):
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if last_provider is not None:
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chunk.model = getattr(last_provider, "last_model", last_model)
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chunk.provider = last_provider.__name__
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yield chunk
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finally:
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await safe_aclose(response)
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class Client(BaseClient):
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def __init__(
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self,
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provider: Optional[ProviderType] = None,
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image_provider: Optional[ImageProvider] = None,
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**kwargs
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) -> None:
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super().__init__(**kwargs)
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self.chat: Chat = Chat(self, provider)
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self.images: Images = Images(self, image_provider)
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class Completions:
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def __init__(self, client: Client, provider: Optional[ProviderType] = None):
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self.client: Client = client
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self.provider: ProviderType = provider
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def create(
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self,
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messages: Messages,
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model: str,
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provider: Optional[ProviderType] = None,
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stream: Optional[bool] = False,
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proxy: Optional[str] = None,
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image: Optional[ImageType] = None,
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image_name: Optional[str] = None,
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response_format: Optional[dict] = None,
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max_tokens: Optional[int] = None,
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stop: Optional[Union[list[str], str]] = None,
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api_key: Optional[str] = None,
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ignored: Optional[list[str]] = None,
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ignore_working: Optional[bool] = False,
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ignore_stream: Optional[bool] = False,
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**kwargs
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) -> ChatCompletion:
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model, provider = get_model_and_provider(
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model,
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self.provider if provider is None else provider,
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stream,
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ignore_working,
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ignore_stream,
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)
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stop = [stop] if isinstance(stop, str) else stop
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if image is not None:
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kwargs["images"] = [(image, image_name)]
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if ignore_stream:
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kwargs["ignore_stream"] = True
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response = iter_run_tools(
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provider.get_create_function(),
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model,
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messages,
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stream=stream,
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**filter_none(
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proxy=self.client.proxy if proxy is None else proxy,
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max_tokens=max_tokens,
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stop=stop,
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api_key=self.client.api_key if api_key is None else api_key
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),
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**kwargs
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)
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if not hasattr(response, '__iter__'):
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response = [response]
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response = iter_response(response, stream, response_format, max_tokens, stop)
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response = iter_append_model_and_provider(response, model, provider)
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if stream:
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return response
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else:
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return next(response)
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def stream(
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self,
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messages: Messages,
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model: str,
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**kwargs
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) -> IterResponse:
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return self.create(messages, model, stream=True, **kwargs)
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class Chat:
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completions: Completions
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def __init__(self, client: Client, provider: Optional[ProviderType] = None):
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self.completions = Completions(client, provider)
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class Images:
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def __init__(self, client: Client, provider: Optional[ProviderType] = None):
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self.client: Client = client
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self.provider: Optional[ProviderType] = provider
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self.models: ImageModels = ImageModels(client)
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def generate(
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self,
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prompt: str,
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model: str = None,
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provider: Optional[ProviderType] = None,
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response_format: Optional[str] = None,
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proxy: Optional[str] = None,
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**kwargs
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) -> ImagesResponse:
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"""
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Synchronous generate method that runs the async_generate method in an event loop.
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"""
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return asyncio.run(self.async_generate(prompt, model, provider, response_format, proxy, **kwargs))
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async def get_provider_handler(self, model: Optional[str], provider: Optional[ImageProvider], default: ImageProvider) -> ImageProvider:
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if provider is None:
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provider_handler = self.provider
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if provider_handler is None:
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provider_handler = self.models.get(model, default)
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elif isinstance(provider, str):
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provider_handler = convert_to_provider(provider)
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else:
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provider_handler = provider
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if provider_handler is None:
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return default
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return provider_handler
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async def async_generate(
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self,
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prompt: str,
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model: Optional[str] = None,
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provider: Optional[ProviderType] = None,
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response_format: Optional[str] = None,
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proxy: Optional[str] = None,
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**kwargs
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) -> ImagesResponse:
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provider_handler = await self.get_provider_handler(model, provider, BingCreateImages)
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provider_name = provider_handler.__name__ if hasattr(provider_handler, "__name__") else type(provider_handler).__name__
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if proxy is None:
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proxy = self.client.proxy
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error = None
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response = None
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if isinstance(provider_handler, IterListProvider):
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for provider in provider_handler.providers:
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try:
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response = await self._generate_image_response(provider, provider.__name__, model, prompt, **kwargs)
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if response is not None:
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provider_name = provider.__name__
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break
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except Exception as e:
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error = e
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debug.log(f"Image provider {provider.__name__}: {e}")
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else:
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response = await self._generate_image_response(provider_handler, provider_name, model, prompt, **kwargs)
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if isinstance(response, ImageResponse):
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return await self._process_image_response(
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response,
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model,
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provider_name,
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response_format,
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proxy
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)
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if response is None:
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if error is not None:
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raise error
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raise NoImageResponseError(f"No image response from {provider_name}")
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raise NoImageResponseError(f"Unexpected response type: {type(response)}")
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async def _generate_image_response(
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self,
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provider_handler,
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provider_name,
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model: str,
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prompt: str,
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prompt_prefix: str = "Generate a image: ",
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**kwargs
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) -> ImageResponse:
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messages = [{"role": "user", "content": f"{prompt_prefix}{prompt}"}]
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response = None
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if hasattr(provider_handler, "create_async_generator"):
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async for item in provider_handler.create_async_generator(
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model,
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messages,
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stream=True,
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prompt=prompt,
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**kwargs
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):
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if isinstance(item, ImageResponse):
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response = item
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break
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elif hasattr(provider_handler, "create_completion"):
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for item in provider_handler.create_completion(
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model,
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messages,
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True,
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prompt=prompt,
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**kwargs
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):
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if isinstance(item, ImageResponse):
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response = item
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break
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else:
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raise ValueError(f"Provider {provider_name} does not support image generation")
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return response
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def create_variation(
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self,
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image: ImageType,
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model: str = None,
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provider: Optional[ProviderType] = None,
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response_format: Optional[str] = None,
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**kwargs
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) -> ImagesResponse:
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return asyncio.run(self.async_create_variation(
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image, model, provider, response_format, **kwargs
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))
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async def async_create_variation(
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self,
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image: ImageType,
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model: Optional[str] = None,
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provider: Optional[ProviderType] = None,
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response_format: Optional[str] = None,
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proxy: Optional[str] = None,
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**kwargs
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) -> ImagesResponse:
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provider_handler = await self.get_provider_handler(model, provider, OpenaiAccount)
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provider_name = provider_handler.__name__ if hasattr(provider_handler, "__name__") else type(provider_handler).__name__
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if proxy is None:
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proxy = self.client.proxy
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prompt = "create a variation of this image"
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if image is not None:
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kwargs["images"] = [(image, None)]
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error = None
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response = None
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if isinstance(provider_handler, IterListProvider):
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for provider in provider_handler.providers:
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try:
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response = await self._generate_image_response(provider, provider.__name__, model, prompt, **kwargs)
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if response is not None:
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provider_name = provider.__name__
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break
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except Exception as e:
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error = e
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debug.log(f"Image provider {provider.__name__}: {e}")
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else:
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response = await self._generate_image_response(provider_handler, provider_name, model, prompt, **kwargs)
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if isinstance(response, ImageResponse):
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return await self._process_image_response(response, model, provider_name, response_format, proxy)
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if response is None:
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if error is not None:
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raise error
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raise NoImageResponseError(f"No image response from {provider_name}")
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raise NoImageResponseError(f"Unexpected response type: {type(response)}")
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async def _process_image_response(
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self,
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response: ImageResponse,
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model: str,
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provider: str,
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response_format: Optional[str] = None,
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proxy: str = None
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) -> ImagesResponse:
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if response_format == "url":
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# Return original URLs without saving locally
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images = [Image.model_construct(url=image, revised_prompt=response.alt) for image in response.get_list()]
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elif response_format == "b64_json":
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# Convert URLs directly to base64 without saving
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async def get_b64_from_url(url: str) -> Image:
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async with aiohttp.ClientSession() as session:
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async with session.get(url, proxy=proxy) as resp:
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if resp.status == 200:
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image_data = await resp.read()
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b64_data = base64.b64encode(image_data).decode()
|
|
return Image.model_construct(b64_json=b64_data, revised_prompt=response.alt)
|
|
images = await asyncio.gather(*[get_b64_from_url(image) for image in response.get_list()])
|
|
else:
|
|
# Save locally for None (default) case
|
|
images = await copy_images(response.get_list(), response.get("cookies"), proxy)
|
|
images = [Image.model_construct(url=f"/images/{os.path.basename(image)}", revised_prompt=response.alt) for image in images]
|
|
|
|
return ImagesResponse.model_construct(
|
|
created=int(time.time()),
|
|
data=images,
|
|
model=model,
|
|
provider=provider
|
|
)
|
|
|
|
class AsyncClient(BaseClient):
|
|
def __init__(
|
|
self,
|
|
provider: Optional[ProviderType] = None,
|
|
image_provider: Optional[ImageProvider] = None,
|
|
**kwargs
|
|
) -> None:
|
|
super().__init__(**kwargs)
|
|
self.chat: AsyncChat = AsyncChat(self, provider)
|
|
self.images: AsyncImages = AsyncImages(self, image_provider)
|
|
|
|
class AsyncChat:
|
|
completions: AsyncCompletions
|
|
|
|
def __init__(self, client: AsyncClient, provider: Optional[ProviderType] = None):
|
|
self.completions = AsyncCompletions(client, provider)
|
|
|
|
class AsyncCompletions:
|
|
def __init__(self, client: AsyncClient, provider: Optional[ProviderType] = None):
|
|
self.client: AsyncClient = client
|
|
self.provider: ProviderType = provider
|
|
|
|
async def create(
|
|
self,
|
|
messages: Messages,
|
|
model: str,
|
|
provider: Optional[ProviderType] = None,
|
|
stream: Optional[bool] = False,
|
|
proxy: Optional[str] = None,
|
|
image: Optional[ImageType] = None,
|
|
image_name: Optional[str] = None,
|
|
response_format: Optional[dict] = None,
|
|
max_tokens: Optional[int] = None,
|
|
stop: Optional[Union[list[str], str]] = None,
|
|
api_key: Optional[str] = None,
|
|
ignored: Optional[list[str]] = None,
|
|
ignore_working: Optional[bool] = False,
|
|
ignore_stream: Optional[bool] = False,
|
|
**kwargs
|
|
) -> Awaitable[ChatCompletion, AsyncIterator[ChatCompletionChunk]]:
|
|
model, provider = get_model_and_provider(
|
|
model,
|
|
self.provider if provider is None else provider,
|
|
stream,
|
|
ignore_working,
|
|
ignore_stream,
|
|
)
|
|
stop = [stop] if isinstance(stop, str) else stop
|
|
if image is not None:
|
|
kwargs["images"] = [(image, image_name)]
|
|
if ignore_stream:
|
|
kwargs["ignore_stream"] = True
|
|
|
|
response = async_iter_run_tools(
|
|
provider.get_async_create_function(),
|
|
model,
|
|
messages,
|
|
stream=stream,
|
|
**filter_none(
|
|
proxy=self.client.proxy if proxy is None else proxy,
|
|
max_tokens=max_tokens,
|
|
stop=stop,
|
|
api_key=self.client.api_key if api_key is None else api_key
|
|
),
|
|
**kwargs
|
|
)
|
|
|
|
response = async_iter_response(response, stream, response_format, max_tokens, stop)
|
|
response = async_iter_append_model_and_provider(response, model, provider)
|
|
|
|
if stream:
|
|
return response
|
|
else:
|
|
return await anext(response)
|
|
|
|
def stream(
|
|
self,
|
|
messages: Messages,
|
|
model: str,
|
|
**kwargs
|
|
) -> AsyncIterator[ChatCompletionChunk, BaseConversation]:
|
|
return self.create(messages, model, stream=True, **kwargs)
|
|
|
|
class AsyncImages(Images):
|
|
def __init__(self, client: AsyncClient, provider: Optional[ProviderType] = None):
|
|
self.client: AsyncClient = client
|
|
self.provider: Optional[ProviderType] = provider
|
|
self.models: ImageModels = ImageModels(client)
|
|
|
|
async def generate(
|
|
self,
|
|
prompt: str,
|
|
model: Optional[str] = None,
|
|
provider: Optional[ProviderType] = None,
|
|
response_format: Optional[str] = None,
|
|
**kwargs
|
|
) -> ImagesResponse:
|
|
return await self.async_generate(prompt, model, provider, response_format, **kwargs)
|
|
|
|
async def create_variation(
|
|
self,
|
|
image: ImageType,
|
|
model: str = None,
|
|
provider: ProviderType = None,
|
|
response_format: Optional[str] = None,
|
|
**kwargs
|
|
) -> ImagesResponse:
|
|
return await self.async_create_variation(
|
|
image, model, provider, response_format, **kwargs
|
|
)
|