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-
-# How to Use the G4F AsyncClient API
-
-The AsyncClient API is the asynchronous counterpart to the standard G4F Client API. It offers the same functionality as the synchronous API, but with the added benefit of improved performance due to its asynchronous nature.
-
-Designed to maintain compatibility with the existing OpenAI API, the G4F AsyncClient API ensures a seamless transition for users already familiar with the OpenAI client.
+# G4F - Async client API Guide
+The G4F async client 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.
+
+
+## Compatibility Note
+The G4F async client API is designed to be compatible with the OpenAI API, making it easy for developers familiar with OpenAI's interface to transition to G4F.
+
+## Table of Contents
+   - [Introduction](#introduction)
+   - [Key Features](#key-features)
+   - [Getting Started](#getting-started)
+   - [Initializing the Client](#initializing-the-client)
+   - [Configuration](#configuration)
+   - [Usage Examples](#usage-examples)
+   - [Text Completions](#text-completions)
+   - [Streaming Completions](#streaming-completions)
+   - [Using a Vision Model](#using-a-vision-model)
+   - [Image Generation](#image-generation)
+   - [Concurrent Tasks](#concurrent-tasks-with-asynciogather)
+   - [Available Models and Providers](#available-models-and-providers)
+   - [Error Handling and Best Practices](#error-handling-and-best-practices)
+   - [Rate Limiting and API Usage](#rate-limiting-and-api-usage)
+   - [Conclusion](#conclusion)
+
+  
+
+## Introduction
+The G4F async client 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 async client API.
+  
 
 ## Key Features
+   - **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.
 
-The G4F AsyncClient API offers several key features:
-
-- **Custom Providers:** The G4F Client API allows you to use custom providers. This feature enhances the flexibility of the API, enabling it to cater to a wide range of use cases.
-- **ChatCompletion Interface:** The G4F package provides an interface for interacting with chat models through the ChatCompletion class. This class provides methods for creating both streaming and non-streaming responses.
-- **Streaming Responses:** The ChatCompletion.create method can return a response iteratively as and when they are received if the stream parameter is set to True.
-- **Non-Streaming Responses:** The ChatCompletion.create method can also generate non-streaming responses.
-- **Image Generation and Vision Models:** The G4F Client API also supports image generation and vision models, expanding its utility beyond text-based interactions.
-
-## Initializing the Client
-
-To utilize the G4F `AsyncClient`, you need to create a new instance. Below is an example showcasing how to initialize the client with custom providers:
+  
 
+## Getting Started
+### Initializing the Client
+**To use the G4F `Client`, create a new instance:**
 ```python
-from g4f.client import AsyncClient
-from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
+from g4f.client import Client
+from g4f.Provider import OpenaiChat, Gemini
 
-client = AsyncClient(
+client = Client(
     provider=OpenaiChat,
     image_provider=Gemini,
-    # Add any other necessary parameters
+    # Add other parameters as needed
 )
 ```
 
-In this example:
-- `provider` specifies the primary provider for generating text completions.
-- `image_provider` specifies the provider for image-related functionalities.
-
-## Configuration
-
-You can configure the `AsyncClient` with additional settings, such as an API key for your provider and a proxy for all outgoing requests:
+  
 
+### Configuration
+**Configure the `Client` with additional settings:**
 ```python
-from g4f.client import AsyncClient
-
-client = AsyncClient(
+client = Client(
     api_key="your_api_key_here",
     proxies="http://user:pass@host",
-    # Add any other necessary parameters
+    # Add other parameters as needed
 )
 ```
 
-- `api_key`: Your API key for the provider.
-- `proxies`: The proxy configuration for routing requests.
-
-## Using AsyncClient
+  
 
+## Usage Examples
 ### Text Completions
-
-You can use the `ChatCompletions` endpoint to generate text completions. Here’s how you can do it:
-
+**Generate text completions using the ChatCompletions endpoint:**
 ```python
 import asyncio
-
 from g4f.client import Client
 
 async def main():
     client = Client()
+    
     response = await client.chat.completions.async_create(
         model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": "say this is a test"}],
-        # Add any other necessary parameters
+        messages=[
+            {
+                "role": "user",
+                "content": "Say this is a test"
+            }
+        ]
     )
+    
     print(response.choices[0].message.content)
 
 asyncio.run(main())
 ```
 
-### Streaming Completions
-
-The `AsyncClient` also supports streaming completions. This allows you to process the response incrementally as it is generated:
+  
 
+### Streaming Completions
+**Process responses incrementally as they are generated:**
 ```python
 import asyncio
-
 from g4f.client import Client
 
 async def main():
     client = Client()
+    
     stream = await client.chat.completions.async_create(
         model="gpt-4",
-        messages=[{"role": "user", "content": "say this is a test"}],
+        messages=[
+            {
+                "role": "user",
+                "content": "Say this is a test"
+            }
+        ],
         stream=True,
-        # Add any other necessary parameters
     )
+    
     async for chunk in stream:
         if chunk.choices[0].delta.content:
-            print(chunk.choices[0].delta.content or "", end="")
+            print(chunk.choices[0].delta.content, end="")
 
 asyncio.run(main())
 ```
 
-In this example:
-- `stream=True` enables streaming of the response.
-
-### Example: Using a Vision Model
-
-The following code snippet demonstrates how to use a vision model to analyze an image and generate a description based on the content of the image. This example shows how to fetch an image, send it to the model, and then process the response.
+  
 
+### Using a Vision Model
+**Analyze an image and generate a description:**
 ```python
 import g4f
 import requests
 import asyncio
-
 from g4f.client import Client
 
-image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/cat.jpeg", stream=True).raw
-# Or: image = open("docs/cat.jpeg", "rb")
-
-
 async def main():
     client = Client()
+    
+    image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/cat.jpeg", stream=True).raw
+    
     response = await client.chat.completions.async_create(
         model=g4f.models.default,
         provider=g4f.Provider.Bing,
-        messages=[{"role": "user", "content": "What are on this image?"}],
+        messages=[
+            {
+                "role": "user",
+                "content": "What's in this image?"
+            }
+        ],
         image=image
-        # Add any other necessary parameters
     )
+    
     print(response.choices[0].message.content)
 
 asyncio.run(main())
 ```
 
-### Image Generation:
-
-You can generate images using a specified prompt:
+  
 
+### Image Generation
+**Generate images using a specified prompt:**
 ```python
 import asyncio
 from g4f.client import Client
 
 async def main():
     client = Client()
+    
     response = await client.images.async_generate(
         prompt="a white siamese cat",
-        model="dall-e-3",
-        # Add any other necessary parameters
+        model="dall-e-3"
     )
+    
     image_url = response.data[0].url
     print(f"Generated image URL: {image_url}")
 
 asyncio.run(main())
 ```
 
-#### Base64 as the response format
+  
 
+#### Base64 Response Format
 ```python
 import asyncio
 from g4f.client import Client
 
 async def main():
     client = Client()
+    
     response = await client.images.async_generate(
         prompt="a white siamese cat",
         model="dall-e-3",
         response_format="b64_json"
-        # Add any other necessary parameters
     )
+    
     base64_text = response.data[0].b64_json
     print(base64_text)
 
 asyncio.run(main())
 ```
 
-### Example usage with asyncio.gather
-
-Start two tasks at the same time:
+  
 
+### Concurrent Tasks with asyncio.gather
+**Execute multiple tasks concurrently:**
 ```python
 import asyncio
-
 from g4f.client import Client
 
 async def main():
     client = Client()
-
+    
     task1 = client.chat.completions.async_create(
         model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": "Say this is a test"}],
+        messages=[
+            {
+                "role": "user",
+                "content": "Say this is a test"
+            }
+        ]
     )
+    
     task2 = client.images.async_generate(
         model="dall-e-3",
-        prompt="a white siamese cat",
+        prompt="a white siamese cat"
     )
-
-    responses = await asyncio.gather(task1, task2)
     
-    chat_response, image_response = responses
-
+    chat_response, image_response = await asyncio.gather(task1, task2)
+    
     print("Chat Response:")
     print(chat_response.choices[0].message.content)
-
-    print("\nImage Response:")
-    image_url = image_response.data[0].url
-    print(image_url)
+    
+    print("Image Response:")
+    print(image_response.data[0].url)
 
 asyncio.run(main())
 ```
 
+  
+
+## Available Models and Providers
+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:**
+
+### Models
+   - GPT-3.5-Turbo
+   - GPT-4
+   - DALL-E 3
+   - Gemini
+   - Claude (Anthropic)
+   - And more...
+
+  
+
+### Providers
+   - OpenAI
+   - Google (for Gemini)
+   - Anthropic
+   - Bing
+   - Custom providers
+
+  
+
+**To use a specific model or provider, specify it when creating the client or in the API call:**
+```python
+client = AsyncClient(provider=g4f.Provider.OpenaiChat)
+
+# or
+
+response = await client.chat.completions.async_create(
+    model="gpt-4",
+    provider=g4f.Provider.Bing,
+    messages=[
+        {
+            "role": "user",
+            "content": "Hello, world!"
+        }
+    ]
+)
+```
+
+  
+
+## Error Handling and Best Practices
+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:**
+
+1. **Use try-except blocks to catch and handle exceptions:**
+```python
+try:
+    response = await client.chat.completions.async_create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "user",
+                "content": "Hello, world!"
+            }
+        ]
+    )
+except Exception as e:
+    print(f"An error occurred: {e}")
+```
+
+2. **Check the response status and handle different scenarios:**
+```python
+if response.choices:
+    print(response.choices[0].message.content)
+else:
+    print("No response generated")
+```
+
+3. **Implement retries for transient errors:**
+```python
+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
+    pass
+```
+
+  
+
+## Rate Limiting and API Usage
+When 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:
+  
+
+1. **Implement rate limiting in your application:**
+```python
+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
+```
+
+  
+
+2. **Monitor your API usage and implement logging:**
+```python
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+async def make_api_call():
+    try:
+        response = await client.chat.completions.async_create(...)
+        logger.info(f"API call successful. Tokens used: {response.usage.total_tokens}")
+    except Exception as e:
+        logger.error(f"API call failed: {e}")
+```
+
+  
+
+3. **Use caching to reduce API calls for repeated queries:**
+```python
+from functools import lru_cache
+
+@lru_cache(maxsize=100)
+def get_cached_response(query):
+    # Your API call here
+    pass
+```
+
+## Conclusion
+The G4F async client 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.
+
+---
+
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