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| -rw-r--r-- | README.md | 13 | ||||
| -rw-r--r-- | docs/async_client.md | 48 |
2 files changed, 42 insertions, 19 deletions
@@ -441,19 +441,6 @@ While we wait for gpt-5, here is a list of new models that are at least better t | Replicate | `g4f.Provider.Replicate` | stability-ai/sdxl| llava-v1.6-34b | [replicate.com](https://replicate.com) | | You.com | `g4f.Provider.You` | dall-e-3| ✔️ | [you.com](https://you.com) | -```python -import requests -from g4f.client import Client - -client = Client() -image = requests.get("https://change_me.jpg", stream=True).raw -response = client.chat.completions.create( - "", - messages=[{"role": "user", "content": "what is in this picture?"}], - image=image -) -print(response.choices[0].message.content) -``` ## 🔗 Powered by gpt4free diff --git a/docs/async_client.md b/docs/async_client.md index ad08302c..003cfb20 100644 --- a/docs/async_client.md +++ b/docs/async_client.md @@ -16,7 +16,7 @@ The G4F AsyncClient API offers several key features: ## Initializing the Client -To utilize the G4F AsyncClient, create a new instance. Below is an example showcasing custom providers: +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: ```python from g4f.client import AsyncClient @@ -29,25 +29,32 @@ client = AsyncClient( ) ``` +In this example: +- `provider` specifies the primary provider for generating text completions. +- `image_provider` specifies the provider for image-related functionalities. + ## Configuration -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: +You can configure the `AsyncClient` with additional settings, such as an API key for your provider and a proxy for all outgoing requests: ```python from g4f.client import AsyncClient client = AsyncClient( - api_key="...", + api_key="your_api_key_here", proxies="http://user:pass@host", ... ) ``` +- `api_key`: Your API key for the provider. +- `proxies`: The proxy configuration for routing requests. + ## Using AsyncClient -### Text Completions: +### Text Completions -You can use the ChatCompletions endpoint to generate text completions as follows: +You can use the `ChatCompletions` endpoint to generate text completions. Here’s how you can do it: ```python response = await client.chat.completions.create( @@ -58,7 +65,9 @@ response = await client.chat.completions.create( print(response.choices[0].message.content) ``` -Streaming completions are also supported: +### Streaming Completions + +The `AsyncClient` also supports streaming completions. This allows you to process the response incrementally as it is generated: ```python stream = client.chat.completions.create( @@ -72,6 +81,33 @@ async for chunk in stream: print(chunk.choices[0].delta.content or "", end="") ``` +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. + +```python +import requests +from g4f.client import Client +from g4f.Provider import Bing + +client = AsyncClient( + provider=Bing +) + +image = requests.get("https://my_website/image.jpg", stream=True).raw +# Or: image = open("local_path/image.jpg", "rb") + +response = client.chat.completions.create( + "", + messages=[{"role": "user", "content": "what is in this picture?"}], + image=image +) +print(response.choices[0].message.content) +``` + ### Image Generation: You can generate images using a specified prompt: |