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docs/client.md
+48
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XFEstudio/gpt4free
refactor(docs/client.): Update G4F Client API documentation to reflect changes in API usage and add examples
dc9a6a9d
代码差异
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### G4F - Client API
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#### Introduction
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client = Client(
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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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@@ -48,7 +49,7 @@ from g4f.client import Client
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client = Client(
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api_key="...",
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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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@@ -59,10 +60,13 @@ client = Client(
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You can use the `ChatCompletions` endpoint to generate text completions as follows:
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```python
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from g4f.client import Client
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client = Client()
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response = client.chat.completions.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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# Add any other necessary parameters
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)
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print(response.choices[0].message.content)
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```
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Also streaming are supported:
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```python
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from g4f.client import Client
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client = Client()
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stream = client.chat.completions.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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)
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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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Generate images using a specified prompt:
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```python
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from g4f.client import Client
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client = Client()
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response = 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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# Add any other necessary parameters
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)
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image_url = response.data[0].url
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print(f"Generated image URL: {image_url}")
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```
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**Creating Image Variations:**
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Create variations of an existing image:
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```python
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from g4f.client import Client
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client = Client()
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response = client.images.create_variation(
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image=open("cat.jpg", "rb"),
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model="bing",
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...
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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(f"Generated image URL: {image_url}")
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```
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Original / Variant:
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@@ -120,6 +136,7 @@ from g4f.Provider import RetryProvider, Phind, FreeChatgpt, Liaobots
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import g4f.debug
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g4f.debug.logging = True
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g4f.debug.version_check = False
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client = Client(
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provider=RetryProvider([Phind, FreeChatgpt, Liaobots], shuffle=False)
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Bot: There is a waterfall in the middle of a jungle. There is a rainbow over...
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```
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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 g4f
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import requests
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from g4f.client import Client
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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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client = Client()
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response = client.chat.completions.create(
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model=g4f.models.default,
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messages=[{"role": "user", "content": "What are on this image?"}],
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provider=g4f.Provider.Bing,
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image=image,
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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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```
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#### Advanced example: A command-line program
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```python
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import g4f
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from g4f.client import Client
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# Initialize the GPT client with the desired provider
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client = Client(provider=g4f.Provider.Bing)
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client = Client()
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# Initialize an empty conversation history
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messages = []
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print(f"An error occurred: {e}")
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```
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[Return to Home](/)
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[Return to Home](/)