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Diffstat (limited to 'docs/async_client.md')
| -rw-r--r-- | docs/async_client.md | 150 |
1 files changed, 85 insertions, 65 deletions
diff --git a/docs/async_client.md b/docs/async_client.md index 94e8cb5c..34ee8018 100644 --- a/docs/async_client.md +++ b/docs/async_client.md @@ -26,7 +26,7 @@ from g4f.Provider import BingCreateImages, OpenaiChat, Gemini client = AsyncClient( provider=OpenaiChat, image_provider=Gemini, - ... + # Add any other necessary parameters ) ``` @@ -44,7 +44,7 @@ from g4f.client import AsyncClient client = AsyncClient( api_key="your_api_key_here", proxies="http://user:pass@host", - ... + # Add any other necessary parameters ) ``` @@ -59,18 +59,20 @@ You can use the `ChatCompletions` endpoint to generate text completions. Here’ ```python import asyncio -from g4f.client import AsyncClient + +from g4f.client import Client async def main(): - client = AsyncClient() - response = await client.chat.completions.create( - [{"role": "user", "content": "say this is a test"}], - model="gpt-3.5-turbo" + client = Client() + response = await client.chat.completions.async_create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "say this is a test"}], + # Add any other necessary parameters ) - print(response.choices[0].message.content) asyncio.run(main()) + ``` ### Streaming Completions @@ -79,19 +81,23 @@ The `AsyncClient` also supports streaming completions. This allows you to proces ```python import asyncio -from g4f.client import AsyncClient + +from g4f.client import Client async def main(): - client = AsyncClient() - async for chunk in await client.chat.completions.create( - [{"role": "user", "content": "say this is a test"}], + client = Client() + stream = await client.chat.completions.async_create( model="gpt-4", + messages=[{"role": "user", "content": "say this is a test"}], stream=True, - ): - print(chunk.choices[0].delta.content or "", end="") - print() + # Add any other necessary parameters + ) + async for chunk in stream: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content or "", end="") asyncio.run(main()) + ``` In this example: @@ -102,23 +108,29 @@ In this example: 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 g4f import requests +import asyncio + from g4f.client import Client -from g4f.Provider import Bing -client = AsyncClient( - provider=Bing -) +image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/cat.jpeg", stream=True).raw +# Or: image = open("docs/cat.jpeg", "rb") -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) +async def main(): + client = Client() + response = await client.chat.completions.async_create( + model=g4f.models.default, + provider=g4f.Provider.Bing, + messages=[{"role": "user", "content": "What are on this image?"}], + image=image + # Add any other necessary parameters + ) + print(response.choices[0].message.content) + +asyncio.run(main()) + ``` ### Image Generation: @@ -127,32 +139,40 @@ You can generate images using a specified prompt: ```python import asyncio -from g4f.client import AsyncClient +from g4f.client import Client async def main(): - client = AsyncClient(image_provider='') - response = await client.images.generate( - prompt="a white siamese cat" - model="flux", - #n=1, - #size="1024x1024" - # ... + client = Client() + response = await client.images.async_generate( + prompt="a white siamese cat", + model="dall-e-3", + # Add any other necessary parameters ) image_url = response.data[0].url - print(image_url) + print(f"Generated image URL: {image_url}") asyncio.run(main()) + ``` #### Base64 as the response format ```python -response = await client.images.generate( - prompt="a cool cat", - response_format="b64_json" -) +import asyncio +from g4f.client import Client -base64_text = response.data[0].b64_json +async def main(): + client = Client() + response = await client.images.async_generate( + prompt="a white siamese cat", + model="dall-e-3", + response_format="b64_json" + # Add any other necessary parameters + ) + base64_text = response.data[0].b64_json + print(base64_text) + +asyncio.run(main()) ``` ### Example usage with asyncio.gather @@ -161,34 +181,34 @@ Start two tasks at the same time: ```python import asyncio -import g4f -from g4f.client import AsyncClient + +from g4f.client import Client async def main(): - client = AsyncClient( - provider=OpenaiChat, - image_provider=BingCreateImages, + client = Client() + + task1 = client.chat.completions.async_create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Say this is a test"}], + ) + task2 = client.images.generate( + model="dall-e-3", + prompt="a white siamese cat", ) - # Task for text completion - async def text_task(): - response = await client.chat.completions.create( - [{"role": "user", "content": "Say this is a test"}], - model="gpt-3.5-turbo", - ) - print(response.choices[0].message.content) - print() - - # Task for image generation - async def image_task(): - response = await client.images.generate( - "a white siamese cat", - model="flux", - ) - print(f"Image generated: {response.data[0].url}") - - # Execute both tasks asynchronously - await asyncio.gather(text_task(), image_task()) + responses = await asyncio.gather(task1, task2) + + chat_response, image_response = responses + + print("Chat Response:") + print(chat_response.choices[0].message.content) + + print("\nImage Response:") + image_url = image_response.data[0].url + print(image_url) asyncio.run(main()) + ``` + +[Return to Home](/) |