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Refactor Image Processing and Error Handling in g4f Client Module
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92 changes: 48 additions & 44 deletions docs/async_client.md
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# 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.

# G4F - AsyncClient API Guide
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.


## 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.
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.

## Table of Contents
- [Introduction](#introduction)
Expand All @@ -26,7 +27,7 @@ The G4F async client API is designed to be compatible with the OpenAI API, makin


## 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.
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.


## Key Features
Expand All @@ -39,13 +40,13 @@ The G4F async client API is an asynchronous version of the standard G4F Client A


## Getting Started
### Initializing the Client
**To use the G4F `Client`, create a new instance:**
### Initializing the AsyncClient
**To use the G4F `AsyncClient`, create a new instance:**
```python
from g4f.client import Client
from g4f.client import AsyncClient
from g4f.Provider import OpenaiChat, Gemini

client = Client(
client = AsyncClient(
provider=OpenaiChat,
image_provider=Gemini,
# Add other parameters as needed
Expand All @@ -56,7 +57,7 @@ client = Client(
## Creating Chat Completions
**Here’s an improved example of creating chat completions:**
```python
response = await async_client.chat.completions.create(
response = await client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
Expand All @@ -77,9 +78,9 @@ You can adjust these parameters based on your specific needs.


### Configuration
**Configure the `Client` with additional settings:**
**Configure the `AsyncClient` with additional settings:**
```python
client = Client(
client = AsyncClient(
api_key="your_api_key_here",
proxies="http://user:pass@host",
# Add other parameters as needed
Expand All @@ -93,12 +94,12 @@ client = Client(
**Generate text completions using the ChatCompletions endpoint:**
```python
import asyncio
from g4f.client import Client
from g4f.client import AsyncClient

async def main():
client = Client()
client = AsyncClient()

response = await client.chat.completions.async_create(
response = await client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
Expand All @@ -119,12 +120,12 @@ asyncio.run(main())
**Process responses incrementally as they are generated:**
```python
import asyncio
from g4f.client import Client
from g4f.client import AsyncClient

async def main():
client = Client()
stream = await client.chat.completions.async_create(
client = AsyncClient()

stream = client.chat.completions.create(
model="gpt-4",
messages=[
{
Expand All @@ -136,7 +137,7 @@ async def main():
)

async for chunk in stream:
if chunk.choices[0].delta.content:
if chunk.choices and chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")

asyncio.run(main())
Expand All @@ -150,14 +151,14 @@ asyncio.run(main())
import g4f
import requests
import asyncio
from g4f.client import Client
from g4f.client import AsyncClient

async def main():
client = Client()
client = AsyncClient()

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(
response = await client.chat.completions.create(
model=g4f.models.default,
provider=g4f.Provider.Bing,
messages=[
Expand All @@ -180,12 +181,12 @@ asyncio.run(main())
**Generate images using a specified prompt:**
```python
import asyncio
from g4f.client import Client
from g4f.client import AsyncClient

async def main():
client = Client()
client = AsyncClient()

response = await client.images.async_generate(
response = await client.images.generate(
prompt="a white siamese cat",
model="flux"
)
Expand All @@ -201,12 +202,12 @@ asyncio.run(main())
#### Base64 Response Format
```python
import asyncio
from g4f.client import Client
from g4f.client import AsyncClient

async def main():
client = Client()
client = AsyncClient()

response = await client.images.async_generate(
response = await client.images.generate(
prompt="a white siamese cat",
model="flux",
response_format="b64_json"
Expand All @@ -224,13 +225,13 @@ asyncio.run(main())
**Execute multiple tasks concurrently:**
```python
import asyncio
from g4f.client import Client
from g4f.client import AsyncClient

async def main():
client = Client()
client = AsyncClient()

task1 = client.chat.completions.async_create(
model="gpt-4o-mini",
task1 = client.chat.completions.create(
model=None,
messages=[
{
"role": "user",
Expand All @@ -239,18 +240,21 @@ async def main():
]
)

task2 = client.images.async_generate(
task2 = client.images.generate(
model="flux",
prompt="a white siamese cat"
)

chat_response, image_response = await asyncio.gather(task1, task2)

print("Chat Response:")
print(chat_response.choices[0].message.content)

print("Image Response:")
print(image_response.data[0].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())
```
Expand Down Expand Up @@ -286,7 +290,7 @@ client = AsyncClient(provider=g4f.Provider.OpenaiChat)

# or

response = await client.chat.completions.async_create(
response = await client.chat.completions.create(
model="gpt-4",
provider=g4f.Provider.Bing,
messages=[
Expand All @@ -306,7 +310,7 @@ Implementing proper error handling and following best practices is crucial when
1. **Use try-except blocks to catch and handle exceptions:**
```python
try:
response = await client.chat.completions.async_create(
response = await client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
Expand Down Expand Up @@ -368,7 +372,7 @@ logger = logging.getLogger(__name__)

async def make_api_call():
try:
response = await client.chat.completions.async_create(...)
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}")
Expand All @@ -387,7 +391,7 @@ def get_cached_response(query):
```

## 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.
The 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.

Expand Down
0 docs/legacy/legacy.md → docs/legacy.md
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