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.
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.
To utilize the G4F Client, create a 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,
# Add any other necessary parameters
)
Here’s an improved example of creating chat completions:
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": "Say this is a test"
}
]
# Add any other necessary parameters
)
This example:
Say this is a testYou can adjust these parameters based on your specific needs.
You can set an api_key for your provider in the client and define a proxy for all outgoing requests:
from g4f.client import Client
client = Client(
api_key="your_api_key_here",
proxies="http://user:pass@host",
# Add any other necessary parameters
)
When using the G4F to create chat completions or perform related tasks, you can configure the following parameters:
model:
Specifies the AI model to be used for the task. Examples include "gpt-4o" for GPT-4 Optimized or "gpt-4o-mini" for a lightweight version. The choice of model determines the quality and speed of the response. Always ensure the selected model is supported by the provider.
messages:role: Defines the role of the message sender, such as "user" (input from the user) or "system" (instructions to the AI).content: The actual text of the message.[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What day is it today?"}
]
provider:
(Optional) Specifies the backend provider for the API. Examples include g4f.Provider.Blackbox or g4f.Provider.OpenaiChat. Each provider may support a different subset of models and features, so select one that matches your requirements.
web_search (Optional):The web_search argument is limited to specific providers, including:
If your chosen provider does not support web_search, it will not function as expected.
Alternative Solution:
Instead of relying on the web_search argument, you can use the more versatile Search Tool Support, which allows for highly customizable web search operations. The search tool enables you to define parameters such as query, number of results, word limit, and timeout, offering greater control over search capabilities.
Generate text completions using the ChatCompletions endpoint:
from g4f.client import Client
client = Client()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": "Say this is a test"
}
],
web_search = False
)
print(response.choices[0].message.content)
Process responses incrementally as they are generated:
from g4f.client import Client
client = Client()
stream = client.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "Say this is a test"
}
],
stream=True,
web_search = False
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content or "", end="")
Analyze an image and generate a description:
import g4f
import requests
from g4f.client import Client
from g4f.Provider.GeminiPro import GeminiPro
# Initialize the GPT client with the desired provider and api key
client = Client(
api_key="your_api_key_here",
provider=GeminiPro
)
image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/images/cat.jpeg", stream=True).raw
# Or: image = open("docs/images/cat.jpeg", "rb")
response = client.chat.completions.create(
model=g4f.models.default,
messages=[
{
"role": "user",
"content": "What's in this image?"
}
],
image=image
# Add any other necessary parameters
)
print(response.choices[0].message.content)
The response_format parameter is optional and can have the following values:
Generate images using a specified prompt:
from g4f.client import Client
client = Client()
response = client.images.generate(
model="flux",
prompt="a white siamese cat",
response_format="url"
# Add any other necessary parameters
)
image_url = response.data[0].url
print(f"Generated image URL: {image_url}")
from g4f.client import Client
client = Client()
response = client.images.generate(
model="flux",
prompt="a white siamese cat",
response_format="b64_json"
# Add any other necessary parameters
)
base64_text = response.data[0].b64_json
print(base64_text)
Create variations of an existing image:
from g4f.client import Client
from g4f.Provider import OpenaiChat
client = Client(
image_provider=OpenaiChat
)
response = client.images.create_variation(
image=open("docs/images/cat.jpg", "rb"),
model="dall-e-3",
# Add any other necessary parameters
)
image_url = response.data[0].url
print(f"Generated image URL: {image_url}")
To maintain a coherent conversation, it’s important to store the context or history of the dialogue. This can be achieved by appending both the user’s inputs and the bot’s responses to a messages list. This allows the model to reference past exchanges when generating responses.
The conversation history consists of messages with different roles:
system: Initial instructions that define the AI’s behavioruser: Messages from the userassistant: Responses from the AIThe following example demonstrates how to implement conversation memory with the G4F:
from g4f.client import Client
class Conversation:
def __init__(self):
self.client = Client()
self.history = [
{
"role": "system",
"content": "You are a helpful assistant."
}
]
def add_message(self, role, content):
self.history.append({
"role": role,
"content": content
})
def get_response(self, user_message):
# Add user message to history
self.add_message("user", user_message)
# Get response from AI
response = self.client.chat.completions.create(
model="gpt-4o-mini",
messages=self.history,
web_search=False
)
# Add AI response to history
assistant_response = response.choices[0].message.content
self.add_message("assistant", assistant_response)
return assistant_response
def main():
conversation = Conversation()
print("=" * 50)
print("G4F Chat started (type 'exit' to end)".center(50))
print("=" * 50)
print("\nAI: Hello! How can I assist you today?")
while True:
user_input = input("\nYou: ")
if user_input.lower() == 'exit':
print("\nGoodbye!")
break
response = conversation.get_response(user_input)
print("\nAI:", response)
if __name__ == "__main__":
main()
Key Features:
Usage Example:
conversation = Conversation()
response = conversation.get_response("Hello, how are you?")
print(response)
Note: The conversation history grows with each interaction. For long conversations, you might want to implement a method to limit the history size or clear old messages to manage token usage.
The Search Tool Support feature enables triggering a web search during chat completions. This is useful for retrieving real-time or specific data, offering a more flexible solution than web_search.
Example Usage:
from g4f.client import Client
client = Client()
tool_calls = [
{
"function": {
"arguments": {
"query": "Latest advancements in AI",
"max_results": 5,
"max_words": 2500,
"backend": "auto",
"add_text": True,
"timeout": 5
},
"name": "search_tool"
},
"type": "function"
}
]
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "user", "content": "Tell me about recent advancements in AI."}
],
tool_calls=tool_calls
)
print(response.choices[0].message.content)
Parameters for search_tool:
query: The search query string.max_results: Number of search results to retrieve.max_words: Maximum number of words in the response.backend: The backend used for search (e.g., "api").add_text: Whether to include text snippets in the response.timeout: Maximum time (in seconds) for the search operation.Advantages of Search Tool Support:
web_search support.from g4f.client import Client
from g4f.Provider import RetryProvider, Phind, FreeChatgpt, Liaobots
import g4f.debug
g4f.debug.logging = True
g4f.debug.version_check = False
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)
Here’s an example of a simple command-line chat program using the G4F Client:
import g4f
from g4f.client import Client
# Initialize the GPT client with the desired provider
client = Client()
# 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}")
This guide provides a comprehensive overview of the G4F Client API, demonstrating its versatility in handling various AI tasks, from text generation to image analysis and creation. By leveraging these features, you can build powerful and responsive applications that harness the capabilities of advanced AI models.