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author | Heiner Lohaus <hlohaus@users.noreply.github.com> | 2024-05-20 15:59:06 +0200 |
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committer | Heiner Lohaus <hlohaus@users.noreply.github.com> | 2024-05-20 15:59:06 +0200 |
commit | 77a1f500a88a040aca5935510cce5e2356f652d2 (patch) | |
tree | 936bc18ad7bc053cedba9897d6032c032e5bb19f | |
parent | Merge pull request #1984 from hlohaus/leech (diff) | |
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-rw-r--r-- | docs/async_client.md | 43 | ||||
-rw-r--r-- | etc/examples/image_api.py | 9 | ||||
-rw-r--r-- | g4f/gui/client/static/css/style.css | 3 |
3 files changed, 51 insertions, 4 deletions
diff --git a/docs/async_client.md b/docs/async_client.md index 4827d11b..ad08302c 100644 --- a/docs/async_client.md +++ b/docs/async_client.md @@ -9,15 +9,39 @@ Designed to maintain compatibility with the existing OpenAI API, the G4F AsyncCl 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, create a new instance. Below is an example showcasing custom providers: + +```python +from g4f.client import AsyncClient +from g4f.Provider import BingCreateImages, OpenaiChat, Gemini -- **Image Generation and Vision Models:** The G4F Client API also supports image generation and vision models, expanding its utility beyond text-based interactions. +client = AsyncClient( + provider=OpenaiChat, + image_provider=Gemini, + ... +) +``` + +## 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: + +```python +from g4f.client import AsyncClient + +client = AsyncClient( + api_key="...", + proxies="http://user:pass@host", + ... +) +``` ## Using AsyncClient @@ -62,6 +86,17 @@ response = await client.images.generate( image_url = response.data[0].url ``` +#### Base64 as the response format + +```python +response = await client.images.generate( + prompt="a cool cat", + response_format="b64_json" +) + +base64_text = response.data[0].b64_json +``` + ### Example usage with asyncio.gather Start two tasks at the same time: diff --git a/etc/examples/image_api.py b/etc/examples/image_api.py new file mode 100644 index 00000000..dbae22ed --- /dev/null +++ b/etc/examples/image_api.py @@ -0,0 +1,9 @@ +import requests +url = "http://localhost:1337/v1/images/generations" +body = { + "prompt": "heaven for dogs", + "provider": "OpenaiAccount", + "response_format": "b64_json", +} +data = requests.post(url, json=body, stream=True).json() +print(data)
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