gpt4free/g4f/client/__init__.py
kqlio67 9def1aa71f
Update model configurations, provider implementations, and documentation (#2577)
* 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>
2025-01-24 03:47:57 +01:00

602 lines
22 KiB
Python

from __future__ import annotations
import os
import time
import random
import string
import asyncio
import aiohttp
import base64
from typing import Union, AsyncIterator, Iterator, Awaitable, Optional
from ..image import ImageResponse, copy_images
from ..typing import Messages, ImageType
from ..providers.types import ProviderType, BaseRetryProvider
from ..providers.response import ResponseType, FinishReason, BaseConversation, SynthesizeData, ToolCalls, Usage
from ..errors import NoImageResponseError
from ..providers.retry_provider import IterListProvider
from ..providers.asyncio import to_sync_generator
from ..Provider.needs_auth import BingCreateImages, OpenaiAccount
from ..tools.run_tools import async_iter_run_tools, iter_run_tools
from .stubs import ChatCompletion, ChatCompletionChunk, Image, ImagesResponse
from .image_models import ImageModels
from .types import IterResponse, ImageProvider, Client as BaseClient
from .service import get_model_and_provider, convert_to_provider
from .helper import find_stop, filter_json, filter_none, safe_aclose
from .. import debug
ChatCompletionResponseType = Iterator[Union[ChatCompletion, ChatCompletionChunk, BaseConversation]]
AsyncChatCompletionResponseType = AsyncIterator[Union[ChatCompletion, ChatCompletionChunk, BaseConversation]]
try:
anext # Python 3.8+
except NameError:
async def anext(aiter):
try:
return await aiter.__anext__()
except StopAsyncIteration:
raise StopIteration
# Synchronous iter_response function
def iter_response(
response: Union[Iterator[Union[str, ResponseType]]],
stream: bool,
response_format: Optional[dict] = None,
max_tokens: Optional[int] = None,
stop: Optional[list[str]] = None
) -> ChatCompletionResponseType:
content = ""
finish_reason = None
tool_calls = None
usage = None
completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
idx = 0
if hasattr(response, '__aiter__'):
response = to_sync_generator(response)
for chunk in response:
if isinstance(chunk, FinishReason):
finish_reason = chunk.reason
break
elif isinstance(chunk, ToolCalls):
tool_calls = chunk.get_list()
continue
elif isinstance(chunk, Usage):
usage = chunk
continue
elif isinstance(chunk, BaseConversation):
yield chunk
continue
elif isinstance(chunk, SynthesizeData) or not chunk:
continue
chunk = str(chunk)
content += chunk
if max_tokens is not None and idx + 1 >= max_tokens:
finish_reason = "length"
first, content, chunk = find_stop(stop, content, chunk if stream else None)
if first != -1:
finish_reason = "stop"
if stream:
yield ChatCompletionChunk.model_construct(chunk, None, completion_id, int(time.time()))
if finish_reason is not None:
break
idx += 1
if usage is None:
usage = Usage(prompt_tokens=0, completion_tokens=idx, total_tokens=idx)
finish_reason = "stop" if finish_reason is None else finish_reason
if stream:
yield ChatCompletionChunk.model_construct(
None, finish_reason, completion_id, int(time.time()),
usage=usage.get_dict()
)
else:
if response_format is not None and "type" in response_format:
if response_format["type"] == "json_object":
content = filter_json(content)
yield ChatCompletion.model_construct(
content, finish_reason, completion_id, int(time.time()),
usage=usage.get_dict(), **filter_none(tool_calls=tool_calls)
)
# Synchronous iter_append_model_and_provider function
def iter_append_model_and_provider(response: ChatCompletionResponseType, last_model: str, last_provider: ProviderType) -> ChatCompletionResponseType:
if isinstance(last_provider, BaseRetryProvider):
last_provider = last_provider.last_provider
for chunk in response:
if isinstance(chunk, (ChatCompletion, ChatCompletionChunk)):
if last_provider is not None:
chunk.model = getattr(last_provider, "last_model", last_model)
chunk.provider = last_provider.__name__
yield chunk
async def async_iter_response(
response: AsyncIterator[Union[str, ResponseType]],
stream: bool,
response_format: Optional[dict] = None,
max_tokens: Optional[int] = None,
stop: Optional[list[str]] = None
) -> AsyncChatCompletionResponseType:
content = ""
finish_reason = None
completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
idx = 0
tool_calls = None
usage = None
try:
async for chunk in response:
if isinstance(chunk, FinishReason):
finish_reason = chunk.reason
break
elif isinstance(chunk, BaseConversation):
yield chunk
continue
elif isinstance(chunk, ToolCalls):
tool_calls = chunk.get_list()
continue
elif isinstance(chunk, Usage):
usage = chunk
continue
elif isinstance(chunk, SynthesizeData) or not chunk:
continue
chunk = str(chunk)
content += chunk
idx += 1
if max_tokens is not None and idx >= max_tokens:
finish_reason = "length"
first, content, chunk = find_stop(stop, content, chunk if stream else None)
if first != -1:
finish_reason = "stop"
if stream:
yield ChatCompletionChunk.model_construct(chunk, None, completion_id, int(time.time()))
if finish_reason is not None:
break
finish_reason = "stop" if finish_reason is None else finish_reason
if usage is None:
usage = Usage(prompt_tokens=0, completion_tokens=idx, total_tokens=idx)
if stream:
yield ChatCompletionChunk.model_construct(
None, finish_reason, completion_id, int(time.time()),
usage=usage.get_dict()
)
else:
if response_format is not None and "type" in response_format:
if response_format["type"] == "json_object":
content = filter_json(content)
yield ChatCompletion.model_construct(
content, finish_reason, completion_id, int(time.time()),
usage=usage.get_dict(), **filter_none(tool_calls=tool_calls)
)
finally:
await safe_aclose(response)
async def async_iter_append_model_and_provider(
response: AsyncChatCompletionResponseType,
last_model: str,
last_provider: ProviderType
) -> AsyncChatCompletionResponseType:
last_provider = None
try:
if isinstance(last_provider, BaseRetryProvider):
if last_provider is not None:
last_provider = last_provider.last_provider
async for chunk in response:
if isinstance(chunk, (ChatCompletion, ChatCompletionChunk)):
if last_provider is not None:
chunk.model = getattr(last_provider, "last_model", last_model)
chunk.provider = last_provider.__name__
yield chunk
finally:
await safe_aclose(response)
class Client(BaseClient):
def __init__(
self,
provider: Optional[ProviderType] = None,
image_provider: Optional[ImageProvider] = None,
**kwargs
) -> None:
super().__init__(**kwargs)
self.chat: Chat = Chat(self, provider)
self.images: Images = Images(self, image_provider)
class Completions:
def __init__(self, client: Client, provider: Optional[ProviderType] = None):
self.client: Client = client
self.provider: ProviderType = provider
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
) -> ChatCompletion:
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 = iter_run_tools(
provider.get_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
)
if not hasattr(response, '__iter__'):
response = [response]
response = iter_response(response, stream, response_format, max_tokens, stop)
response = iter_append_model_and_provider(response, model, provider)
if stream:
return response
else:
return next(response)
def stream(
self,
messages: Messages,
model: str,
**kwargs
) -> IterResponse:
return self.create(messages, model, stream=True, **kwargs)
class Chat:
completions: Completions
def __init__(self, client: Client, provider: Optional[ProviderType] = None):
self.completions = Completions(client, provider)
class Images:
def __init__(self, client: Client, provider: Optional[ProviderType] = None):
self.client: Client = client
self.provider: Optional[ProviderType] = provider
self.models: ImageModels = ImageModels(client)
def generate(
self,
prompt: str,
model: str = None,
provider: Optional[ProviderType] = None,
response_format: Optional[str] = None,
proxy: Optional[str] = None,
**kwargs
) -> ImagesResponse:
"""
Synchronous generate method that runs the async_generate method in an event loop.
"""
return asyncio.run(self.async_generate(prompt, model, provider, response_format, proxy, **kwargs))
async def get_provider_handler(self, model: Optional[str], provider: Optional[ImageProvider], default: ImageProvider) -> ImageProvider:
if provider is None:
provider_handler = self.provider
if provider_handler is None:
provider_handler = self.models.get(model, default)
elif isinstance(provider, str):
provider_handler = convert_to_provider(provider)
else:
provider_handler = provider
if provider_handler is None:
return default
return provider_handler
async def async_generate(
self,
prompt: str,
model: Optional[str] = None,
provider: Optional[ProviderType] = None,
response_format: Optional[str] = None,
proxy: Optional[str] = None,
**kwargs
) -> ImagesResponse:
provider_handler = await self.get_provider_handler(model, provider, BingCreateImages)
provider_name = provider_handler.__name__ if hasattr(provider_handler, "__name__") else type(provider_handler).__name__
if proxy is None:
proxy = self.client.proxy
error = None
response = None
if isinstance(provider_handler, IterListProvider):
for provider in provider_handler.providers:
try:
response = await self._generate_image_response(provider, provider.__name__, model, prompt, **kwargs)
if response is not None:
provider_name = provider.__name__
break
except Exception as e:
error = e
debug.log(f"Image provider {provider.__name__}: {e}")
else:
response = await self._generate_image_response(provider_handler, provider_name, model, prompt, **kwargs)
if isinstance(response, ImageResponse):
return await self._process_image_response(
response,
model,
provider_name,
response_format,
proxy
)
if response is None:
if error is not None:
raise error
raise NoImageResponseError(f"No image response from {provider_name}")
raise NoImageResponseError(f"Unexpected response type: {type(response)}")
async def _generate_image_response(
self,
provider_handler,
provider_name,
model: str,
prompt: str,
prompt_prefix: str = "Generate a image: ",
**kwargs
) -> ImageResponse:
messages = [{"role": "user", "content": f"{prompt_prefix}{prompt}"}]
response = None
if hasattr(provider_handler, "create_async_generator"):
async for item in provider_handler.create_async_generator(
model,
messages,
stream=True,
prompt=prompt,
**kwargs
):
if isinstance(item, ImageResponse):
response = item
break
elif hasattr(provider_handler, "create_completion"):
for item in provider_handler.create_completion(
model,
messages,
True,
prompt=prompt,
**kwargs
):
if isinstance(item, ImageResponse):
response = item
break
else:
raise ValueError(f"Provider {provider_name} does not support image generation")
return response
def create_variation(
self,
image: ImageType,
model: str = None,
provider: Optional[ProviderType] = None,
response_format: Optional[str] = None,
**kwargs
) -> ImagesResponse:
return asyncio.run(self.async_create_variation(
image, model, provider, response_format, **kwargs
))
async def async_create_variation(
self,
image: ImageType,
model: Optional[str] = None,
provider: Optional[ProviderType] = None,
response_format: Optional[str] = None,
proxy: Optional[str] = None,
**kwargs
) -> ImagesResponse:
provider_handler = await self.get_provider_handler(model, provider, OpenaiAccount)
provider_name = provider_handler.__name__ if hasattr(provider_handler, "__name__") else type(provider_handler).__name__
if proxy is None:
proxy = self.client.proxy
prompt = "create a variation of this image"
if image is not None:
kwargs["images"] = [(image, None)]
error = None
response = None
if isinstance(provider_handler, IterListProvider):
for provider in provider_handler.providers:
try:
response = await self._generate_image_response(provider, provider.__name__, model, prompt, **kwargs)
if response is not None:
provider_name = provider.__name__
break
except Exception as e:
error = e
debug.log(f"Image provider {provider.__name__}: {e}")
else:
response = await self._generate_image_response(provider_handler, provider_name, model, prompt, **kwargs)
if isinstance(response, ImageResponse):
return await self._process_image_response(response, model, provider_name, response_format, proxy)
if response is None:
if error is not None:
raise error
raise NoImageResponseError(f"No image response from {provider_name}")
raise NoImageResponseError(f"Unexpected response type: {type(response)}")
async def _process_image_response(
self,
response: ImageResponse,
model: str,
provider: str,
response_format: Optional[str] = None,
proxy: str = None
) -> ImagesResponse:
if response_format == "url":
# Return original URLs without saving locally
images = [Image.model_construct(url=image, revised_prompt=response.alt) for image in response.get_list()]
elif response_format == "b64_json":
# Convert URLs directly to base64 without saving
async def get_b64_from_url(url: str) -> Image:
async with aiohttp.ClientSession() as session:
async with session.get(url, proxy=proxy) as resp:
if resp.status == 200:
image_data = await resp.read()
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
)