gpt4free/docs/client.md

G4F - Client API

Introduction

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.

Getting Started

Switching to G4F Client:

To begin using the G4F Client, simply update your import statement in your Python code:

Old Import:

from openai import OpenAI

New Import:

from g4f.client import Client as OpenAI

The G4F Client preserves the same familiar API interface as OpenAI, ensuring a smooth transition process.

Initializing the Client

To utilize the G4F Client, create an new instance. Below is an example showcasing custom providers:

from g4f.client import Client
from g4f.Provider import BingCreateImages, OpenaiChat, Gemini

client = Client(
    provider=OpenaiChat,
    image_provider=Gemini,
    ...
)

Configuration

You can set an "api_key" for your provider in the client. And you also have the option to define a proxy for all outgoing requests:

from g4f.client import Client

client = Client(
    api_key="...",
    proxies="http://user:pass@host",
    ...
)

Usage Examples

Text Completions:

You can use the ChatCompletions endpoint to generate text completions as follows:

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Say this is a test"}],
    ...
)
print(response.choices[0].message.content)

Also streaming are supported:

stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Say this is a test"}],
    stream=True,
    ...
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content or "", end="")

Image Generation:

Generate images using a specified prompt:

response = client.images.generate(
    model="dall-e-3",
    prompt="a white siamese cat",
    ...
)

image_url = response.data[0].url

Creating Image Variations:

Create variations of an existing image:

response = client.images.create_variation(
    image=open("cat.jpg", "rb"),
    model="bing",
    ...
)

image_url = response.data[0].url

Original / Variant:

Use a list of providers with RetryProvider

from g4f.client import Client
from g4f.Provider import RetryProvider, Phind, FreeChatgpt, Liaobots

import g4f.debug
g4f.debug.logging = True

client = Client(
    provider=RetryProvider([Phind, FreeChatgpt, Liaobots], shuffle=False)
)
response = client.chat.completions.create(
    model="",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Using RetryProvider provider
Using Phind provider
How can I assist you today?

Advanced example using GeminiProVision

from g4f.client import Client
from g4f.Provider.GeminiPro import GeminiPro

client = Client(
    api_key="...",
    provider=GeminiPro
)
response = client.chat.completions.create(
    model="gemini-pro-vision",
    messages=[{"role": "user", "content": "What are on this image?"}],
    image=open("docs/waterfall.jpeg", "rb")
)
print(response.choices[0].message.content)

User: What are on this image?

Bot: There is a waterfall in the middle of a jungle. There is a rainbow over...

Advanced example: A command-line program

import g4f
from g4f.client import Client

# Initialize the GPT client with the desired provider
client = Client(provider=g4f.Provider.Bing)

# Initialize an empty conversation history
messages = []

while True:
    # Get user input
    user_input = input("You: ")
    
    # Check if the user wants to exit the chat
    if user_input.lower() == "exit":
        print("Exiting chat...")
        break  # Exit the loop to end the conversation

    # Update the conversation history with the user's message
    messages.append({"role": "user", "content": user_input})

    try:
        # Get GPT's response
        response = client.chat.completions.create(
            messages=messages,
            model=g4f.models.default,
        )

        # Extract the GPT response and print it
        gpt_response = response.choices[0].message.content
        print(f"Bot: {gpt_response}")

        # Update the conversation history with GPT's response
        messages.append({"role": "assistant", "content": gpt_response})
    except Exception as e:
        print(f"An error occurred: {e}")

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