The G4F AsyncClient API is a powerful asynchronous interface for interacting with various AI models. This guide provides comprehensive information on how to use the API effectively, including setup, usage examples, best practices, and important considerations for optimal performance.
The G4F AsyncClient API is designed to be compatible with the OpenAI API, making it easy for developers familiar with OpenAI's interface to transition to G4F.
- Introduction
- Key Features
- Getting Started
- Initializing the Client
- Creating Chat Completions
- Configuration
- Explanation of Parameters
- Usage Examples
- Text Completions
- Streaming Completions
- Using a Vision Model
- Image Generation
- Concurrent Tasks
- Available Models and Providers
- Error Handling and Best Practices
- Rate Limiting and API Usage
- Conclusion
The G4F AsyncClient API is an asynchronous version of the standard G4F Client API. It offers the same functionality as the synchronous API but with improved performance due to its asynchronous nature. This guide will walk you through the key features and usage of the G4F AsyncClient API.
- Custom Providers: Use custom providers for enhanced flexibility.
- ChatCompletion Interface: Interact with chat models through the ChatCompletion class.
- Streaming Responses: Get responses iteratively as they are received.
- Non-Streaming Responses: Generate complete responses in a single call.
- Image Generation and Vision Models: Support for image-related tasks.
To use the G4F AsyncClient, create a new instance:
from g4f.client import AsyncClient
from g4f.Provider import OpenaiChat, Gemini
client = AsyncClient(
provider=OpenaiChat,
image_provider=Gemini,
# Add other parameters as needed
)Here’s an improved example of creating chat completions:
response = await client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": "Say this is a test"
}
],
web_search = False
# Add other parameters as needed
)This example:
- Asks a specific question
Say this is a test - Configures various parameters like temperature and max_tokens for more control over the output
- Disables streaming for a complete response
You can adjust these parameters based on your specific needs.
Configure the AsyncClient with additional settings:
client = AsyncClient(
api_key="your_api_key_here",
proxies="http://user:pass@host",
# Add other parameters as needed
)When using the G4F to create chat completions or perform related tasks, you can configure the following parameters:
-
model:
Specifies the AI model to be used for the task. Examples include"gpt-4o"for GPT-4 Optimized or"gpt-4o-mini"for a lightweight version. The choice of model determines the quality and speed of the response. Always ensure the selected model is supported by the provider. -
messages:
A list of dictionaries representing the conversation context. Each dictionary contains two keys: -role: Defines the role of the message sender, such as"user"(input from the user) or"system"(instructions to the AI).
-content: The actual text of the message.
Example:[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What day is it today?"} ] -
web_search:
(Optional) A Boolean flag indicating whether to enable internet-based search capabilities for the task. If True, the system performs a web search using the DuckDuckGo search engine to retrieve up-to-date information. This is particularly useful for obtaining real-time or specific details not contained within the model's training. -
provider:
Specifies the backend provider for the API. Examples includeg4f.Provider.Blackboxorg4f.Provider.OpenaiChat. Each provider may support a different subset of models and features, so select one that matches your requirements.
Generate text completions using the ChatCompletions endpoint:
import asyncio
from g4f.client import AsyncClient
async def main():
client = AsyncClient()
response = await client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": "Say this is a test"
}
]
)
print(response.choices[0].message.content)
asyncio.run(main())Process responses incrementally as they are generated:
import asyncio
from g4f.client import AsyncClient
async def main():
client = AsyncClient()
stream = client.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "Say this is a test"
}
],
stream=True,
)
async for chunk in stream:
if chunk.choices and chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
asyncio.run(main())Analyze an image and generate a description:
import g4f
import requests
import asyncio
from g4f.client import AsyncClient
async def main():
client = AsyncClient(
provider=g4f.Provider.CopilotAccount
)
image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/images/cat.jpeg", stream=True).raw
response = await client.chat.completions.create(
model=g4f.models.default,
messages=[
{
"role": "user",
"content": "What's in this image?"
}
],
image=image
)
print(response.choices[0].message.content)
asyncio.run(main())The response_format parameter is optional and can have the following values:
- If not specified (default): The image will be saved locally, and a local path will be returned (e.g., "/images/1733331238_cf9d6aa9-f606-4fea-ba4b-f06576cba309.jpg").
- "url": Returns a URL to the generated image.
- "b64_json": Returns the image as a base64-encoded JSON string.
Generate images using a specified prompt:
import asyncio
from g4f.client import AsyncClient
async def main():
client = AsyncClient()
response = await client.images.generate(
prompt="a white siamese cat",
model="flux",
response_format="url"
# Add any other necessary parameters
)
image_url = response.data[0].url
print(f"Generated image URL: {image_url}")
asyncio.run(main())import asyncio
from g4f.client import AsyncClient
async def main():
client = AsyncClient()
response = await client.images.generate(
prompt="a white siamese cat",
model="flux",
response_format="b64_json"
# Add any other necessary parameters
)
base64_text = response.data[0].b64_json
print(base64_text)
asyncio.run(main())Execute multiple tasks concurrently:
import asyncio
from g4f.client import AsyncClient
async def main():
client = AsyncClient()
task1 = client.chat.completions.create(
model=None,
messages=[
{
"role": "user",
"content": "Say this is a test"
}
]
)
task2 = client.images.generate(
model="flux",
prompt="a white siamese cat",
response_format="url"
)
try:
chat_response, image_response = await asyncio.gather(task1, task2)
print("Chat Response:")
print(chat_response.choices[0].message.content)
print("\nImage Response:")
print(image_response.data[0].url)
except Exception as e:
print(f"An error occurred: {e}")
asyncio.run(main())The G4F AsyncClient supports a wide range of AI models and providers, allowing you to choose the best option for your specific use case. Here's a brief overview of the available models and providers:
- GPT-3.5-Turbo
- GPT-4o-Mini
- GPT-4
- DALL-E 3
- Gemini
- Claude (Anthropic)
- And more...
- OpenAI
- Google (for Gemini)
- Anthropic
- Microsoft Copilot
- Custom providers
To use a specific model or provider, specify it when creating the client or in the API call:
client = AsyncClient(provider=g4f.Provider.OpenaiChat)
# or
response = await client.chat.completions.create(
model="gpt-4",
provider=g4f.Provider.CopilotAccount,
messages=[
{
"role": "user",
"content": "Hello, world!"
}
]
)Implementing proper error handling and following best practices is crucial when working with the G4F AsyncClient API. This ensures your application remains robust and can gracefully handle various scenarios. Here are some key practices to follow:
- Use try-except blocks to catch and handle exceptions:
try:
response = await client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": "Hello, world!"
}
]
)
except Exception as e:
print(f"An error occurred: {e}")- Check the response status and handle different scenarios:
if response.choices:
print(response.choices[0].message.content)
else:
print("No response generated")- Implement retries for transient errors:
import asyncio
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
async def make_api_call():
# Your API call here
passWhen working with the G4F AsyncClient API, it's important to implement rate limiting and monitor your API usage. This helps ensure fair usage, prevents overloading the service, and optimizes your application's performance. Here are some key strategies to consider:
- Implement rate limiting in your application:
import asyncio
from aiolimiter import AsyncLimiter
rate_limit = AsyncLimiter(max_rate=10, time_period=1) # 10 requests per second
async def make_api_call():
async with rate_limit:
# Your API call here
pass- Monitor your API usage and implement logging:
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
async def make_api_call():
try:
response = await client.chat.completions.create(...)
logger.info(f"API call successful. Tokens used: {response.usage.total_tokens}")
except Exception as e:
logger.error(f"API call failed: {e}")- Use caching to reduce API calls for repeated queries:
from functools import lru_cache
@lru_cache(maxsize=100)
def get_cached_response(query):
# Your API call here
passThe G4F AsyncClient API provides a powerful and flexible way to interact with various AI models asynchronously. By leveraging its features and following best practices, you can build efficient and responsive applications that harness the power of AI for text generation, image analysis, and image creation.
Remember to handle errors gracefully, implement rate limiting, and monitor your API usage to ensure optimal performance and reliability in your applications.