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246 lines
6.0 KiB
Markdown
246 lines
6.0 KiB
Markdown
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### G4F - Client API
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#### Introduction
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Welcome to the G4F Client API, a cutting-edge tool for seamlessly integrating advanced AI capabilities into your Python applications. This guide is designed to facilitate your transition from using the OpenAI client to the G4F Client, offering enhanced features while maintaining compatibility with the existing OpenAI API.
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#### Getting Started
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**Switching to G4F Client:**
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To begin using the G4F Client, simply update your import statement in your Python code:
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Old Import:
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```python
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from openai import OpenAI
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```
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New Import:
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```python
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from g4f.client import Client as OpenAI
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```
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The G4F Client preserves the same familiar API interface as OpenAI, ensuring a smooth transition process.
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### Initializing the Client
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To utilize the G4F Client, create an new instance. Below is an example showcasing custom providers:
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```python
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from g4f.client import Client
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from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
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client = Client(
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provider=OpenaiChat,
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image_provider=Gemini,
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# Add any other necessary parameters
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)
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```
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## Configuration
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You can set an "api_key" for your provider in the client.
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And you also have the option to define a proxy for all outgoing requests:
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```python
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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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# Add any other necessary parameters
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)
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```
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#### Usage Examples
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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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```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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# 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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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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```
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**Image Generation:**
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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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# 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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# 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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[![Original Image](/docs/cat.jpeg)](/docs/client.md) [![Variant Image](/docs/cat.webp)](/docs/client.md)
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#### Use a list of providers with RetryProvider
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```python
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from g4f.client import Client
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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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)
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response = client.chat.completions.create(
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model="",
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messages=[{"role": "user", "content": "Hello"}],
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)
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print(response.choices[0].message.content)
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```
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```
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Using RetryProvider provider
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Using Phind provider
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How can I assist you today?
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```
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#### Advanced example using GeminiProVision
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```python
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from g4f.client import Client
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from g4f.Provider.GeminiPro import GeminiPro
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client = Client(
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api_key="...",
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provider=GeminiPro
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)
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response = client.chat.completions.create(
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model="gemini-pro-vision",
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messages=[{"role": "user", "content": "What are on this image?"}],
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image=open("docs/waterfall.jpeg", "rb")
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)
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print(response.choices[0].message.content)
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```
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```
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User: What are on this image?
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```
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![Waterfall](/docs/waterfall.jpeg)
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```
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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()
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# Initialize an empty conversation history
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messages = []
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while True:
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# Get user input
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user_input = input("You: ")
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# Check if the user wants to exit the chat
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if user_input.lower() == "exit":
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print("Exiting chat...")
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break # Exit the loop to end the conversation
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# Update the conversation history with the user's message
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messages.append({"role": "user", "content": user_input})
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try:
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# Get GPT's response
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response = client.chat.completions.create(
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messages=messages,
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model=g4f.models.default,
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)
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# Extract the GPT response and print it
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gpt_response = response.choices[0].message.content
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print(f"Bot: {gpt_response}")
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# Update the conversation history with GPT's response
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messages.append({"role": "assistant", "content": gpt_response})
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except Exception as e:
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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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