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Improve readme
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README.md
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README.md
@ -441,19 +441,6 @@ While we wait for gpt-5, here is a list of new models that are at least better t
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| Replicate | `g4f.Provider.Replicate` | stability-ai/sdxl| llava-v1.6-34b | [replicate.com](https://replicate.com) |
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| You.com | `g4f.Provider.You` | dall-e-3| ✔️ | [you.com](https://you.com) |
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```python
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import requests
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from g4f.client import Client
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client = Client()
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image = requests.get("https://change_me.jpg", stream=True).raw
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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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image=image
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)
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print(response.choices[0].message.content)
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```
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## 🔗 Powered by gpt4free
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@ -16,7 +16,7 @@ The G4F AsyncClient API offers several key features:
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## Initializing the Client
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To utilize the G4F AsyncClient, create a new instance. Below is an example showcasing custom providers:
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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:
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```python
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from g4f.client import AsyncClient
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@ -29,25 +29,32 @@ client = AsyncClient(
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)
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```
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In this example:
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- `provider` specifies the primary provider for generating text completions.
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- `image_provider` specifies the provider for image-related functionalities.
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## Configuration
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You can set an "api_key" for your provider in the client. You also have the option to define a proxy for all outgoing requests:
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You can configure the `AsyncClient` with additional settings, such as an API key for your provider and a proxy for all outgoing requests:
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```python
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from g4f.client import AsyncClient
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client = AsyncClient(
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api_key="...",
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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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)
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```
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- `api_key`: Your API key for the provider.
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- `proxies`: The proxy configuration for routing requests.
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## Using AsyncClient
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### Text Completions:
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### Text Completions
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You can use the ChatCompletions endpoint to generate text completions as follows:
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You can use the `ChatCompletions` endpoint to generate text completions. Here’s how you can do it:
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```python
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response = await client.chat.completions.create(
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@ -58,7 +65,9 @@ response = await client.chat.completions.create(
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print(response.choices[0].message.content)
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```
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Streaming completions are also supported:
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### Streaming Completions
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The `AsyncClient` also supports streaming completions. This allows you to process the response incrementally as it is generated:
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```python
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stream = client.chat.completions.create(
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@ -72,6 +81,33 @@ async for chunk in stream:
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print(chunk.choices[0].delta.content or "", end="")
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```
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In this example:
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- `stream=True` enables streaming of the response.
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### Example: Using a Vision Model
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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 requests
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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://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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image=image
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)
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print(response.choices[0].message.content)
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```
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### Image Generation:
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You can generate images using a specified prompt:
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