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refactor(docs): Update AsyncClient API documentation to reflect changes in API usage and add asyncio examples
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kqlio67
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1 changed files with 83 additions and 63 deletions
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@ -26,7 +26,7 @@ from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
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client = AsyncClient(
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provider=OpenaiChat,
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image_provider=Gemini,
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...
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# Add any other necessary parameters
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)
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```
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@ -44,7 +44,7 @@ from g4f.client import AsyncClient
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client = AsyncClient(
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api_key="your_api_key_here",
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proxies="http://user:pass@host",
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...
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# Add any other necessary parameters
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)
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```
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@ -59,18 +59,20 @@ You can use the `ChatCompletions` endpoint to generate text completions. Here’
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```python
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import asyncio
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from g4f.client import AsyncClient
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from g4f.client import Client
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async def main():
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client = AsyncClient()
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response = await client.chat.completions.create(
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[{"role": "user", "content": "say this is a test"}],
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model="gpt-3.5-turbo"
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client = Client()
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response = await client.chat.completions.async_create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "say this is a test"}],
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# Add any other necessary parameters
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)
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print(response.choices[0].message.content)
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asyncio.run(main())
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```
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### Streaming Completions
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@ -79,19 +81,23 @@ The `AsyncClient` also supports streaming completions. This allows you to proces
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```python
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import asyncio
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from g4f.client import AsyncClient
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from g4f.client import Client
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async def main():
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client = AsyncClient()
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async for chunk in await client.chat.completions.create(
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[{"role": "user", "content": "say this is a test"}],
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client = Client()
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stream = await client.chat.completions.async_create(
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model="gpt-4",
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messages=[{"role": "user", "content": "say this is a test"}],
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stream=True,
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):
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# Add any other necessary parameters
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)
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async for chunk in stream:
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if chunk.choices[0].delta.content:
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print(chunk.choices[0].delta.content or "", end="")
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print()
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asyncio.run(main())
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```
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In this example:
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@ -102,23 +108,29 @@ In this example:
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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.
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```python
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import g4f
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import requests
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import asyncio
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from g4f.client import Client
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from g4f.Provider import Bing
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client = AsyncClient(
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provider=Bing
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)
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image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/cat.jpeg", stream=True).raw
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# Or: image = open("docs/cat.jpeg", "rb")
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image = requests.get("https://my_website/image.jpg", stream=True).raw
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# Or: image = open("local_path/image.jpg", "rb")
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response = client.chat.completions.create(
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"",
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messages=[{"role": "user", "content": "what is in this picture?"}],
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async def main():
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client = Client()
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response = await client.chat.completions.async_create(
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model=g4f.models.default,
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provider=g4f.Provider.Bing,
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messages=[{"role": "user", "content": "What are on this image?"}],
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image=image
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)
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print(response.choices[0].message.content)
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# Add any other necessary parameters
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)
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print(response.choices[0].message.content)
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asyncio.run(main())
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```
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### Image Generation:
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@ -127,32 +139,40 @@ You can generate images using a specified prompt:
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```python
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import asyncio
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from g4f.client import AsyncClient
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from g4f.client import Client
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async def main():
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client = AsyncClient(image_provider='')
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response = await client.images.generate(
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prompt="a white siamese cat"
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model="flux",
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#n=1,
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#size="1024x1024"
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# ...
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client = Client()
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response = await client.images.async_generate(
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prompt="a white siamese cat",
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model="dall-e-3",
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# Add any other necessary parameters
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)
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image_url = response.data[0].url
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print(image_url)
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print(f"Generated image URL: {image_url}")
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asyncio.run(main())
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```
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#### Base64 as the response format
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```python
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response = await client.images.generate(
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prompt="a cool cat",
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response_format="b64_json"
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)
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import asyncio
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from g4f.client import Client
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base64_text = response.data[0].b64_json
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async def main():
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client = Client()
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response = await client.images.async_generate(
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prompt="a white siamese cat",
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model="dall-e-3",
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response_format="b64_json"
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# Add any other necessary parameters
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)
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base64_text = response.data[0].b64_json
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print(base64_text)
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asyncio.run(main())
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```
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### Example usage with asyncio.gather
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@ -161,34 +181,34 @@ Start two tasks at the same time:
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```python
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import asyncio
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import g4f
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from g4f.client import AsyncClient
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from g4f.client import Client
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async def main():
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client = AsyncClient(
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provider=OpenaiChat,
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image_provider=BingCreateImages,
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)
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client = Client()
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# Task for text completion
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async def text_task():
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response = await client.chat.completions.create(
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[{"role": "user", "content": "Say this is a test"}],
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task1 = client.chat.completions.async_create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Say this is a test"}],
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)
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print(response.choices[0].message.content)
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print()
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# Task for image generation
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async def image_task():
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response = await client.images.generate(
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"a white siamese cat",
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model="flux",
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task2 = client.images.generate(
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model="dall-e-3",
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prompt="a white siamese cat",
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)
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print(f"Image generated: {response.data[0].url}")
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# Execute both tasks asynchronously
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await asyncio.gather(text_task(), image_task())
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responses = await asyncio.gather(task1, task2)
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chat_response, image_response = responses
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print("Chat Response:")
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print(chat_response.choices[0].message.content)
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print("\nImage Response:")
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image_url = image_response.data[0].url
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print(image_url)
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asyncio.run(main())
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```
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[Return to Home](/)
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