mirrors/gpt4free
1
Fork 0
mirror of https://github.com/xtekky/gpt4free.git synced 2025-12-06 02:30:41 -08:00
Code Issues Projects Releases Packages Wiki Activity

Updated almost all documentation and added new documentation for the local interface

Browse source
This commit is contained in:
kqlio67 2024-10-19 20:21:14 +03:00
parent 3b4934f18c
commit d0b80e2bab
10 changed files with 887 additions and 331 deletions
Download patch file Download diff file Expand all files Collapse all files

48
README.md
View file

@ -1,3 +1,5 @@
![248433934-7886223b-c1d1-4260-82aa-da5741f303bb](https://github.com/xtekky/gpt4free/assets/98614666/ea012c87-76e0-496a-8ac4-e2de090cc6c9)
<a href="https://trendshift.io/repositories/1692" target="_blank"><img src="https://trendshift.io/api/badge/repositories/1692" alt="xtekky%2Fgpt4free | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
@ -27,23 +29,19 @@ docker pull hlohaus789/g4f
```
## 🆕 What's New
- Added `gpt-4o`, simply use `gpt-4o` in `chat.completion.create`.
- Installation Guide for Windows (.exe): 💻 [#installation-guide-for-windows](#installation-guide-for-windows-exe)
- Join our Telegram Channel: 📨 [telegram.me/g4f_channel](https://telegram.me/g4f_channel)
- Join our Discord Group: 💬 [discord.gg/XfybzPXPH5](https://discord.gg/XfybzPXPH5)
- `g4f` now supports 100% local inference: 🧠 [local-docs](https://g4f.mintlify.app/docs/core/usage/local)
- **For comprehensive details on new features and updates, please refer to our [Releases](https://github.com/xtekky/gpt4free/releases) page**
- **Installation Guide for Windows (.exe):** 💻 [#installation-guide-for-windows](#installation-guide-for-windows-exe)
- **Join our Telegram Channel:** 📨 [telegram.me/g4f_channel](https://telegram.me/g4f_channel)
- **Join our Discord Group:** 💬 [discord.gg/XfybzPXPH5](https://discord.gg/XfybzPXPH5)
## 🔻 Site Takedown
Is your site on this repository and you want to take it down? Send an email to takedown@g4f.ai with proof it is yours and it will be removed as fast as possible. To prevent reproduction please secure your API. 😉
## 🚀 Feedback and Todo
**You can always leave some feedback here:** https://forms.gle/FeWV9RLEedfdkmFN6
You can always leave some feedback here: https://forms.gle/FeWV9RLEedfdkmFN6
As per the survey, here is a list of improvements to come
**As per the survey, here is a list of improvements to come**
- [x] Update the repository to include the new openai library syntax (ex: `Openai()` class) | completed, use `g4f.client.Client`
- [ ] Golang implementation
- [ ] 🚧 Improve Documentation (in /docs & Guides, Howtos, & Do video tutorials)
@ -70,7 +68,8 @@ As per the survey, here is a list of improvements to come
- [Text Generation](#text-generation)
- [Image Generation](#image-generation)
- [Web UI](#web-ui)
- [Interference API](#interference-api)
- [Interference API](docs/interference.md)
- [Local inference](docs/local.md)
- [Configuration](#configuration)
- [🚀 Providers and Models](docs/providers-and-models.md)
- [🔗 Powered by gpt4free](#-powered-by-gpt4free)
@ -156,7 +155,6 @@ How do I load the project using git and installing the project requirements?
Read this tutorial and follow it step by step: [/docs/git](docs/git.md)
##### Install using Docker:
How do I build and run composer image from source?
Use docker-compose: [/docs/docker](docs/docker.md)
@ -181,7 +179,6 @@ Hello! How can I assist you today?
```
#### Image Generation
```python
from g4f.client import Client
@ -199,33 +196,26 @@ print(f"Generated image URL: {image_url}")
[![Image with cat](/docs/cat.jpeg)](docs/client.md)
**Full Documentation for Python API**
- AsyncClient API from G4F: [/docs/async_client](docs/async_client.md)
- Client API like the OpenAI Python library: [/docs/client](docs/client.md)
- Legacy API with python modules: [/docs/legacy](docs/legacy.md)
- **Async Client API from G4F:** [/docs/async_client](docs/async_client.md)
- **Client API like the OpenAI Python library:** [/docs/client](docs/client.md)
- **Legacy API with python modules:** [/docs/legacy](docs/legacy.md)
#### Web UI
To start the web interface, type the following codes in python:
**To start the web interface, type the following codes in python:**
```python
from g4f.gui import run_gui
run_gui()
```
or execute the following command:
```bash
python -m g4f.cli gui -port 8080 -debug
```
#### Interference API
You can use the Interference API to serve other OpenAI integrations with G4F.
See docs: [/docs/interference](docs/interference.md)
Access with: http://localhost:1337/v1
**See docs:** [/docs/interference](docs/interference-api.md)
**Access with:** http://localhost:1337/v1
### Configuration
@ -778,19 +768,15 @@ set G4F_PROXY=http://host:port
</table>
## 🤝 Contribute
We welcome contributions from the community. Whether you're adding new providers or features, or simply fixing typos and making small improvements, your input is valued. Creating a pull request is all it takes – our co-pilot will handle the code review process. Once all changes have been addressed, we'll merge the pull request into the main branch and release the updates at a later time.
###### Guide: How do i create a new Provider?
- Read: [/docs/guides/create_provider](docs/guides/create_provider.md)
###### Guide: How can AI help me with writing code?
- Read: [/docs/guides/help_me](docs/guides/help_me.md)
## 🙌 Contributors
A list of all contributors is available [here](https://github.com/xtekky/gpt4free/graphs/contributors)
<a href="https://github.com/xtekky" target="_blank"><img src="https://avatars.githubusercontent.com/u/98614666?v=4&s=45" width="45" title="xtekky"></a>

307
docs/async_client.md
View file

@ -1,183 +1,206 @@
# G4F - Async client API Guide
The G4F async client API is a powerful asynchronous interface for interacting with various AI models. This guide provides comprehensive information on how to use the API effectively, including setup, usage examples, best practices, and important considerations for optimal performance.
# How to Use the G4F AsyncClient API
The AsyncClient API is the asynchronous counterpart to the standard G4F Client API. It offers the same functionality as the synchronous API, but with the added benefit of improved performance due to its asynchronous nature.
## Compatibility Note
The G4F async client API is designed to be compatible with the OpenAI API, making it easy for developers familiar with OpenAI's interface to transition to G4F.
## Table of Contents
- [Introduction](#introduction)
- [Key Features](#key-features)
- [Getting Started](#getting-started)
- [Initializing the Client](#initializing-the-client)
- [Configuration](#configuration)
- [Usage Examples](#usage-examples)
- [Text Completions](#text-completions)
- [Streaming Completions](#streaming-completions)
- [Using a Vision Model](#using-a-vision-model)
- [Image Generation](#image-generation)
- [Concurrent Tasks](#concurrent-tasks-with-asynciogather)
- [Available Models and Providers](#available-models-and-providers)
- [Error Handling and Best Practices](#error-handling-and-best-practices)
- [Rate Limiting and API Usage](#rate-limiting-and-api-usage)
- [Conclusion](#conclusion)
## Introduction
The G4F async client API is an asynchronous version of the standard G4F Client API. It offers the same functionality as the synchronous API but with improved performance due to its asynchronous nature. This guide will walk you through the key features and usage of the G4F async client API.
Designed to maintain compatibility with the existing OpenAI API, the G4F AsyncClient API ensures a seamless transition for users already familiar with the OpenAI client.
## Key Features
- **Custom Providers**: Use custom providers for enhanced flexibility.
- **ChatCompletion Interface**: Interact with chat models through the ChatCompletion class.
- **Streaming Responses**: Get responses iteratively as they are received.
- **Non-Streaming Responses**: Generate complete responses in a single call.
- **Image Generation and Vision Models**: Support for image-related tasks.
The G4F AsyncClient API offers several key features:
- **Custom Providers:** The G4F Client API allows you to use custom providers. This feature enhances the flexibility of the API, enabling it to cater to a wide range of use cases.
- **ChatCompletion Interface:** The G4F package provides an interface for interacting with chat models through the ChatCompletion class. This class provides methods for creating both streaming and non-streaming responses.
- **Streaming Responses:** The ChatCompletion.create method can return a response iteratively as and when they are received if the stream parameter is set to True.
- **Non-Streaming Responses:** The ChatCompletion.create method can also generate non-streaming responses.
- **Image Generation and Vision Models:** The G4F Client API also supports image generation and vision models, expanding its utility beyond text-based interactions.
## Initializing the Client
To utilize the G4F `AsyncClient`, you need to create a new instance. Below is an example showcasing how to initialize the client with custom providers:
## Getting Started
### Initializing the Client
**To use the G4F `Client`, create a new instance:**
```python
from g4f.client import AsyncClient
from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
from g4f.client import Client
from g4f.Provider import OpenaiChat, Gemini
client = AsyncClient(
client = Client(
provider=OpenaiChat,
image_provider=Gemini,
# Add any other necessary parameters
# Add other parameters as needed
)
```
In this example:
- `provider` specifies the primary provider for generating text completions.
- `image_provider` specifies the provider for image-related functionalities.
## Configuration
You can configure the `AsyncClient` with additional settings, such as an API key for your provider and a proxy for all outgoing requests:
### Configuration
**Configure the `Client` with additional settings:**
```python
from g4f.client import AsyncClient
client = AsyncClient(
client = Client(
api_key="your_api_key_here",
proxies="http://user:pass@host",
# Add any other necessary parameters
# Add other parameters as needed
)
```
- `api_key`: Your API key for the provider.
- `proxies`: The proxy configuration for routing requests.
## Using AsyncClient
## Usage Examples
### Text Completions
You can use the `ChatCompletions` endpoint to generate text completions. Here’s how you can do it:
**Generate text completions using the ChatCompletions endpoint:**
```python
import asyncio
from g4f.client import Client
async def main():
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
messages=[
{
"role": "user",
"content": "Say this is a test"
}
]
)
print(response.choices[0].message.content)
asyncio.run(main())
```
### Streaming Completions
The `AsyncClient` also supports streaming completions. This allows you to process the response incrementally as it is generated:
**Process responses incrementally as they are generated:**
```python
import asyncio
from g4f.client import Client
async def main():
client = Client()
stream = await client.chat.completions.async_create(
model="gpt-4",
messages=[{"role": "user", "content": "say this is a test"}],
messages=[
{
"role": "user",
"content": "Say this is a test"
}
],
stream=True,
# Add any other necessary parameters
)
async for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content or "", end="")
print(chunk.choices[0].delta.content, end="")
asyncio.run(main())
```
In this example:
- `stream=True` enables streaming of the response.
### Example: Using a Vision Model
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.
### Using a Vision Model
**Analyze an image and generate a description:**
```python
import g4f
import requests
import asyncio
from g4f.client import Client
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")
async def main():
client = Client()
image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/heads/main/docs/cat.jpeg", stream=True).raw
response = await client.chat.completions.async_create(
model=g4f.models.default,
provider=g4f.Provider.Bing,
messages=[{"role": "user", "content": "What are on this image?"}],
messages=[
{
"role": "user",
"content": "What's in this image?"
}
],
image=image
# Add any other necessary parameters
)
print(response.choices[0].message.content)
asyncio.run(main())
```
### Image Generation:
You can generate images using a specified prompt:
### Image Generation
**Generate images using a specified prompt:**
```python
import asyncio
from g4f.client import Client
async def main():
client = Client()
response = await client.images.async_generate(
prompt="a white siamese cat",
model="dall-e-3",
# Add any other necessary parameters
model="dall-e-3"
)
image_url = response.data[0].url
print(f"Generated image URL: {image_url}")
asyncio.run(main())
```
#### Base64 as the response format
#### Base64 Response Format
```python
import asyncio
from g4f.client import Client
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
Start two tasks at the same time:
### Concurrent Tasks with asyncio.gather
**Execute multiple tasks concurrently:**
```python
import asyncio
from g4f.client import Client
async def main():
@ -185,25 +208,165 @@ async def main():
task1 = client.chat.completions.async_create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Say this is a test"}],
messages=[
{
"role": "user",
"content": "Say this is a test"
}
]
)
task2 = client.images.async_generate(
model="dall-e-3",
prompt="a white siamese cat",
prompt="a white siamese cat"
)
responses = await asyncio.gather(task1, task2)
chat_response, image_response = responses
chat_response, image_response = await asyncio.gather(task1, task2)
print("Chat Response:")
print(chat_response.choices[0].message.content)
print("\nImage Response:")
image_url = image_response.data[0].url
print(image_url)
print("Image Response:")
print(image_response.data[0].url)
asyncio.run(main())
```
## Available Models and Providers
The G4F AsyncClient supports a wide range of AI models and providers, allowing you to choose the best option for your specific use case. **Here's a brief overview of the available models and providers:**
### Models
- GPT-3.5-Turbo
- GPT-4
- DALL-E 3
- Gemini
- Claude (Anthropic)
- And more...
### Providers
- OpenAI
- Google (for Gemini)
- Anthropic
- Bing
- Custom providers
**To use a specific model or provider, specify it when creating the client or in the API call:**
```python
client = AsyncClient(provider=g4f.Provider.OpenaiChat)
# or
response = await client.chat.completions.async_create(
model="gpt-4",
provider=g4f.Provider.Bing,
messages=[
{
"role": "user",
"content": "Hello, world!"
}
]
)
```
## Error Handling and Best Practices
Implementing proper error handling and following best practices is crucial when working with the G4F AsyncClient API. This ensures your application remains robust and can gracefully handle various scenarios. **Here are some key practices to follow:**
1. **Use try-except blocks to catch and handle exceptions:**
```python
try:
response = await client.chat.completions.async_create(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "Hello, world!"
}
]
)
except Exception as e:
print(f"An error occurred: {e}")
```
2. **Check the response status and handle different scenarios:**
```python
if response.choices:
print(response.choices[0].message.content)
else:
print("No response generated")
```
3. **Implement retries for transient errors:**
```python
import asyncio
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
async def make_api_call():
# Your API call here
pass
```
## Rate Limiting and API Usage
When working with the G4F AsyncClient API, it's important to implement rate limiting and monitor your API usage. This helps ensure fair usage, prevents overloading the service, and optimizes your application's performance. Here are some key strategies to consider:
1. **Implement rate limiting in your application:**
```python
import asyncio
from aiolimiter import AsyncLimiter
rate_limit = AsyncLimiter(max_rate=10, time_period=1) # 10 requests per second
async def make_api_call():
async with rate_limit:
# Your API call here
pass
```
2. **Monitor your API usage and implement logging:**
```python
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
async def make_api_call():
try:
response = await client.chat.completions.async_create(...)
logger.info(f"API call successful. Tokens used: {response.usage.total_tokens}")
except Exception as e:
logger.error(f"API call failed: {e}")
```
3. **Use caching to reduce API calls for repeated queries:**
```python
from functools import lru_cache
@lru_cache(maxsize=100)
def get_cached_response(query):
# Your API call here
pass
```
## Conclusion
The G4F async client API provides a powerful and flexible way to interact with various AI models asynchronously. By leveraging its features and following best practices, you can build efficient and responsive applications that harness the power of AI for text generation, image analysis, and image creation.
Remember to handle errors gracefully, implement rate limiting, and monitor your API usage to ensure optimal performance and reliability in your applications.
---
[Return to Home](/)

166
docs/client.md
View file

@ -1,32 +1,51 @@
### G4F - Client API
# G4F Client API Guide
#### Introduction
## Table of Contents
- [Introduction](#introduction)
- [Getting Started](#getting-started)
- [Switching to G4F Client](#switching-to-g4f-client)
- [Initializing the Client](#initializing-the-client)
- [Configuration](#configuration)
- [Usage Examples](#usage-examples)
- [Text Completions](#text-completions)
- [Streaming Completions](#streaming-completions)
- [Image Generation](#image-generation)
- [Creating Image Variations](#creating-image-variations)
- [Advanced Usage](#advanced-usage)
- [Using a List of Providers with RetryProvider](#using-a-list-of-providers-with-retryprovider)
- [Using GeminiProVision](#using-geminiprovision)
- [Using a Vision Model](#using-a-vision-model)
- [Command-line Chat Program](#command-line-chat-program)
## 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
## Getting Started
### Switching to G4F Client
**To begin using the G4F Client, simply update your import statement in your Python code:**
**Switching to G4F Client:**
To begin using the G4F Client, simply update your import statement in your Python code:
Old Import:
**Old Import:**
```python
from openai import OpenAI
```
New Import:
**New Import:**
```python
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:
## Initializing the Client
To utilize the G4F Client, create a new instance. **Below is an example showcasing custom providers:**
```python
from g4f.client import Client
from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
@ -38,48 +57,60 @@ client = Client(
)
```
## 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:
**You can set an `api_key` for your provider in the client and define a proxy for all outgoing requests:**
```python
from g4f.client import Client
client = Client(
api_key="...",
api_key="your_api_key_here",
proxies="http://user:pass@host",
# Add any other necessary parameters
)
```
#### Usage Examples
**Text Completions:**
You can use the `ChatCompletions` endpoint to generate text completions as follows:
## Usage Examples
### Text Completions
**Generate text completions using the `ChatCompletions` endpoint:**
```python
from g4f.client import Client
client = Client()
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Say this is a test"}],
messages=[
{
"role": "user",
"content": "Say this is a test"
}
]
# Add any other necessary parameters
)
print(response.choices[0].message.content)
```
Also streaming are supported:
### Streaming Completions
**Process responses incrementally as they are generated:**
```python
from g4f.client import Client
client = Client()
stream = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Say this is a test"}],
messages=[
{
"role": "user",
"content": "Say this is a test"
}
],
stream=True,
)
@ -88,101 +119,104 @@ for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
```
**Image Generation:**
Generate images using a specified prompt:
### Image Generation
**Generate images using a specified prompt:**
```python
from g4f.client import Client
client = Client()
response = client.images.generate(
model="dall-e-3",
prompt="a white siamese cat",
prompt="a white siamese cat"
# Add any other necessary parameters
)
image_url = response.data[0].url
print(f"Generated image URL: {image_url}")
```
**Creating Image Variations:**
Create variations of an existing image:
### Creating Image Variations
**Create variations of an existing image:**
```python
from g4f.client import Client
client = Client()
response = client.images.create_variation(
image=open("cat.jpg", "rb"),
model="bing",
model="bing"
# Add any other necessary parameters
)
image_url = response.data[0].url
print(f"Generated image URL: {image_url}")
```
Original / Variant:
[![Original Image](/docs/cat.jpeg)](/docs/client.md) [![Variant Image](/docs/cat.webp)](/docs/client.md)
#### Use a list of providers with RetryProvider
## Advanced Usage
### Using a List of Providers with RetryProvider
```python
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"}],
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
### Using GeminiProVision
```python
from g4f.client import Client
from g4f.Provider.GeminiPro import GeminiPro
client = Client(
api_key="...",
api_key="your_api_key_here",
provider=GeminiPro
)
response = client.chat.completions.create(
model="gemini-pro-vision",
messages=[{"role": "user", "content": "What are on this image?"}],
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?
```
![Waterfall](/docs/waterfall.jpeg)
```
Bot: There is a waterfall in the middle of a jungle. There is a rainbow over...
```
### Example: Using a Vision Model
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.
### Using a Vision Model
**Analyze an image and generate a description:**
```python
import g4f
import requests
@ -192,17 +226,26 @@ image = requests.get("https://raw.githubusercontent.com/xtekky/gpt4free/refs/hea
# Or: image = open("docs/cat.jpeg", "rb")
client = Client()
response = client.chat.completions.create(
model=g4f.models.default,
messages=[{"role": "user", "content": "What are on this image?"}],
messages=[
{
"role": "user",
"content": "What are on this image?"
}
],
provider=g4f.Provider.Bing,
image=image,
image=image
# Add any other necessary parameters
)
print(response.choices[0].message.content)
```
#### Advanced example: A command-line program
## Command-line Chat Program
**Here's an example of a simple command-line chat program using the G4F Client:**
```python
import g4f
from g4f.client import Client
@ -238,8 +281,13 @@ while True:
# 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.
---
[Return to Home](/)

111
docs/docker.md
View file

@ -1,45 +1,114 @@
### G4F - Docker Setup
Easily set up and run the G4F project using Docker without the hassle of manual dependency installation.
# G4F Docker Setup
1. **Prerequisites:**
- [Install Docker](https://docs.docker.com/get-docker/)
- [Install Docker Compose](https://docs.docker.com/compose/install/)
## Table of Contents
- [Prerequisites](#prerequisites)
- [Installation and Setup](#installation-and-setup)
- [Testing the API](#testing-the-api)
- [Troubleshooting](#troubleshooting)
- [Stopping the Service](#stopping-the-service)
2. **Clone the Repository:**
## Prerequisites
**Before you begin, ensure you have the following installed on your system:**
- [Docker](https://docs.docker.com/get-docker/)
- [Docker Compose](https://docs.docker.com/compose/install/)
- Python 3.7 or higher
- pip (Python package manager)
**Note:** If you encounter issues with Docker, you can run the project directly using Python.
## Installation and Setup
### Docker Method (Recommended)
1. **Clone the Repository**
```bash
git clone https://github.com/xtekky/gpt4free.git
```
3. **Navigate to the Project Directory:**
```bash
cd gpt4free
```
4. **Build the Docker Image:**
2. **Build and Run with Docker Compose**
```bash
docker pull selenium/node-chrome
docker-compose build
docker-compose up --build
```
5. **Start the Service:**
3. **Access the API**
The server will be accessible at `http://localhost:1337`
### Non-Docker Method
If you encounter issues with Docker, you can run the project directly using Python:
1. **Clone the Repository**
```bash
docker-compose up
git clone https://github.com/xtekky/gpt4free.git
cd gpt4free
```
Your server will now be accessible at `http://localhost:1337`. Interact with the API or run tests as usual.
2. **Install Dependencies**
```bash
pip install -r requirements.txt
```
To stop the Docker containers, simply run:
3. **Run the Server**
```bash
python -m g4f.api.run
```
4. **Access the API**
The server will be accessible at `http://localhost:1337`
## Testing the API
**You can test the API using curl or by creating a simple Python script:**
### Using curl
```bash
curl -X POST -H "Content-Type: application/json" -d '{"prompt": "What is the capital of France?"}' http://localhost:1337/chat/completions
```
### Using Python
**Create a file named `test_g4f.py` with the following content:**
```python
import requests
url = "http://localhost:1337/v1/chat/completions"
body = {
"model": "gpt-3.5-turbo",
"stream": False,
"messages": [
{"role": "assistant", "content": "What can you do?"}
]
}
json_response = requests.post(url, json=body).json().get('choices', [])
for choice in json_response:
print(choice.get('message', {}).get('content', ''))
```
**Run the script:**
```bash
python test_g4f.py
```
## Troubleshooting
- If you encounter issues with Docker, try running the project directly using Python as described in the Non-Docker Method.
- Ensure that you have the necessary permissions to run Docker commands. You might need to use `sudo` or add your user to the `docker` group.
- If the server doesn't start, check the logs for any error messages and ensure all dependencies are correctly installed.
**_For more detailed information on API endpoints and usage, refer to the [G4F API documentation](docs/interference-api.md)._**
## Stopping the Service
### Docker Method
**To stop the Docker containers, use the following command:**
```bash
docker-compose down
```
> [!Note]
> Changes made to local files reflect in the Docker container due to volume mapping in `docker-compose.yml`. However, if you add or remove dependencies, rebuild the Docker image using `docker-compose build`.
### Non-Docker Method
If you're running the server directly with Python, you can stop it by pressing Ctrl+C in the terminal where it's running.
---
[Return to Home](/)

115
docs/git.md
View file

@ -1,66 +1,129 @@
### G4F - Installation Guide
Follow these steps to install G4F from the source code:
# G4F - Git Installation Guide
1. **Clone the Repository:**
This guide provides step-by-step instructions for installing G4F from the source code using Git.
## Table of Contents
1. [Prerequisites](#prerequisites)
2. [Installation Steps](#installation-steps)
1. [Clone the Repository](#1-clone-the-repository)
2. [Navigate to the Project Directory](#2-navigate-to-the-project-directory)
3. [Set Up a Python Virtual Environment](#3-set-up-a-python-virtual-environment-recommended)
4. [Activate the Virtual Environment](#4-activate-the-virtual-environment)
5. [Install Dependencies](#5-install-dependencies)
6. [Verify Installation](#6-verify-installation)
3. [Usage](#usage)
4. [Troubleshooting](#troubleshooting)
5. [Additional Resources](#additional-resources)
---
## Prerequisites
Before you begin, ensure you have the following installed on your system:
- Git
- Python 3.7 or higher
- pip (Python package installer)
## Installation Steps
### 1. Clone the Repository
**Open your terminal and run the following command to clone the G4F repository:**
```bash
git clone https://github.com/xtekky/gpt4free.git
```
2. **Navigate to the Project Directory:**
### 2. Navigate to the Project Directory
**Change to the project directory:**
```bash
cd gpt4free
```
3. **(Optional) Create a Python Virtual Environment:**
It's recommended to isolate your project dependencies. You can follow the [Python official documentation](https://docs.python.org/3/tutorial/venv.html) for virtual environments.
### 3. Set Up a Python Virtual Environment (Recommended)
**It's best practice to use a virtual environment to manage project dependencies:**
```bash
python3 -m venv venv
```
4. **Activate the Virtual Environment:**
- On Windows:
### 4. Activate the Virtual Environment
**Activate the virtual environment based on your operating system:**
- **Windows:**
```bash
.\venv\Scripts\activate
```
- On macOS and Linux:
- **macOS and Linux:**
```bash
source venv/bin/activate
```
5. **Install Minimum Requirements:**
Install the minimum required packages:
### 5. Install Dependencies
**You have two options for installing dependencies:**
#### Option A: Install Minimum Requirements
**For a lightweight installation, use:**
```bash
pip install -r requirements-min.txt
```
6. **Or Install All Packages from `requirements.txt`:**
If you prefer, you can install all packages listed in `requirements.txt`:
#### Option B: Install All Packages
**For a full installation with all features, use:**
```bash
pip install -r requirements.txt
```
7. **Start Using the Repository:**
### 6. Verify Installation
You can now create Python scripts and utilize the G4F functionalities. Here's a basic example:
Create a `test.py` file in the root folder and start using the repository:
**Create a `g4f-test.py` file in the root folder and start using the repository:**
```python
import g4f
# Your code here
```
## Usage
**After installation, you can start using G4F in your Python scripts. Here's a basic example:**
```python
import g4f
# Your G4F code here
# For example:
from g4f.client import Client
client = Client()
response = client.chat.completions.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)
```
## Troubleshooting
**If you encounter any issues during installation or usage:**
1. Ensure all prerequisites are correctly installed.
2. Check that you're in the correct directory and the virtual environment is activated.
3. Try reinstalling the dependencies.
4. Consult the [G4F documentation](https://github.com/xtekky/gpt4free) for more detailed information.
## Additional Resources
- [G4F GitHub Repository](https://github.com/xtekky/gpt4free)
- [Python Virtual Environments Guide](https://docs.python.org/3/tutorial/venv.html)
- [pip Documentation](https://pip.pypa.io/en/stable/)
---
**_For more information or support, please visit the [G4F GitHub Issues page](https://github.com/xtekky/gpt4free/issues)._**
---
[Return to Home](/)

110
docs/interference-api.md Normal file
View file

@ -0,0 +1,110 @@
# G4F - Interference API Usage Guide
## Table of Contents
- [Introduction](#introduction)
- [Running the Interference API](#running-the-interference-api)
- [From PyPI Package](#from-pypi-package)
- [From Repository](#from-repository)
- [Usage with OpenAI Library](#usage-with-openai-library)
- [Usage with Requests Library](#usage-with-requests-library)
- [Key Points](#key-points)
## Introduction
The Interference API allows you to serve other OpenAI integrations with G4F. It acts as a proxy, translating requests to the OpenAI API into requests to the G4F providers.
## Running the Interference API
### From PyPI Package
**You can run the Interference API directly from the G4F PyPI package:**
```python
from g4f.api import run_api
run_api()
```
### From Repository
Alternatively, you can run the Interference API from the cloned repository.
**Run the server with:**
```bash
g4f api
```
or
```bash
python -m g4f.api.run
```
## Usage with OpenAI Library
```python
from openai import OpenAI
client = OpenAI(
api_key="",
# Change the API base URL to the local interference API
base_url="http://localhost:1337/v1"
)
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "write a poem about a tree"}],
stream=True,
)
if isinstance(response, dict):
# Not streaming
print(response.choices[0].message.content)
else:
# Streaming
for token in response:
content = token.choices[0].delta.content
if content is not None:
print(content, end="", flush=True)
```
## Usage with Requests Library
You can also send requests directly to the Interference API using the requests library.
**Send a POST request to `/v1/chat/completions` with the request body containing the model and other parameters:**
```python
import requests
url = "http://localhost:1337/v1/chat/completions"
body = {
"model": "gpt-3.5-turbo",
"stream": False,
"messages": [
{"role": "assistant", "content": "What can you do?"}
]
}
json_response = requests.post(url, json=body).json().get('choices', [])
for choice in json_response:
print(choice.get('message', {}).get('content', ''))
```
## Key Points
- The Interference API translates OpenAI API requests into G4F provider requests
- You can run it from the PyPI package or the cloned repository
- It supports usage with the OpenAI Python library by changing the `base_url`
- Direct requests can be sent to the API endpoints using libraries like `requests`
**_The Interference API allows easy integration of G4F with existing OpenAI-based applications and tools._**
---
[Return to Home](/)

69
docs/interference.md
View file

@ -1,69 +0,0 @@
### Interference openai-proxy API
#### Run interference API from PyPi package
```python
from g4f.api import run_api
run_api()
```
#### Run interference API from repo
Run server:
```sh
g4f api
```
or
```sh
python -m g4f.api.run
```
```python
from openai import OpenAI
client = OpenAI(
api_key="",
# Change the API base URL to the local interference API
base_url="http://localhost:1337/v1"
)
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "write a poem about a tree"}],
stream=True,
)
if isinstance(response, dict):
# Not streaming
print(response.choices[0].message.content)
else:
# Streaming
for token in response:
content = token.choices[0].delta.content
if content is not None:
print(content, end="", flush=True)
```
#### API usage (POST)
Send the POST request to /v1/chat/completions with body containing the `model` method. This example uses python with requests library:
```python
import requests
url = "http://localhost:1337/v1/chat/completions"
body = {
"model": "gpt-3.5-turbo",
"stream": False,
"messages": [
{"role": "assistant", "content": "What can you do?"}
]
}
json_response = requests.post(url, json=body).json().get('choices', [])
for choice in json_response:
print(choice.get('message', {}).get('content', ''))
```
[Return to Home](/)

164
docs/local.md Normal file
View file

@ -0,0 +1,164 @@
### G4F - Local Usage Guide
### Table of Contents
1. [Introduction](#introduction)
2. [Required Dependencies](#required-dependencies)
3. [Basic Usage Example](#basic-usage-example)
4. [Supported Models](#supported-models)
5. [Performance Considerations](#performance-considerations)
6. [Troubleshooting](#troubleshooting)
#### Introduction
This guide explains how to use g4f to run language models locally. G4F (GPT4Free) allows you to interact with various language models on your local machine, providing a flexible and private solution for natural language processing tasks.
## Usage
#### Local inference
How to use g4f to run language models locally
#### Required dependencies
**Make sure to install the required dependencies by running:**
```bash
pip install g4f[local]
```
or
```bash
pip install -U gpt4all
```
#### Basic usage example
```python
from g4f.local import LocalClient
client = LocalClient()
response = client.chat.completions.create(
model = 'orca-mini-3b',
messages = [{"role": "user", "content": "hi"}],
stream = True
)
for token in response:
print(token.choices[0].delta.content or "")
```
Upon first use, there will be a prompt asking you if you wish to download the model. If you respond with `y`, g4f will go ahead and download the model for you.
You can also manually place supported models into `./g4f/local/models/`
**You can get a list of the current supported models by running:**
```python
from g4f.local import LocalClient
client = LocalClient()
client.list_models()
```
```json
{
"mistral-7b": {
"path": "mistral-7b-openorca.gguf2.Q4_0.gguf",
"ram": "8",
"prompt": "<|im_start|>user\n%1<|im_end|>\n<|im_start|>assistant\n",
"system": "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. For multi-step problems, write out your reasoning for each step.\n<|im_end|>"
},
"mistral-7b-instruct": {
"path": "mistral-7b-instruct-v0.1.Q4_0.gguf",
"ram": "8",
"prompt": "[INST] %1 [/INST]",
"system": None
},
"gpt4all-falcon": {
"path": "gpt4all-falcon-newbpe-q4_0.gguf",
"ram": "8",
"prompt": "### Instruction:\n%1\n### Response:\n",
"system": None
},
"orca-2": {
"path": "orca-2-13b.Q4_0.gguf",
"ram": "16",
"prompt": None,
"system": None
},
"wizardlm-13b": {
"path": "wizardlm-13b-v1.2.Q4_0.gguf",
"ram": "16",
"prompt": None,
"system": None
},
"nous-hermes-llama2": {
"path": "nous-hermes-llama2-13b.Q4_0.gguf",
"ram": "16",
"prompt": "### Instruction:\n%1\n### Response:\n",
"system": None
},
"gpt4all-13b-snoozy": {
"path": "gpt4all-13b-snoozy-q4_0.gguf",
"ram": "16",
"prompt": None,
"system": None
},
"mpt-7b-chat": {
"path": "mpt-7b-chat-newbpe-q4_0.gguf",
"ram": "8",
"prompt": "<|im_start|>user\n%1<|im_end|>\n<|im_start|>assistant\n",
"system": "<|im_start|>system\n- You are a helpful assistant chatbot trained by MosaicML.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>"
},
"orca-mini-3b": {
"path": "orca-mini-3b-gguf2-q4_0.gguf",
"ram": "4",
"prompt": "### User:\n%1\n### Response:\n",
"system": "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
},
"replit-code-3b": {
"path": "replit-code-v1_5-3b-newbpe-q4_0.gguf",
"ram": "4",
"prompt": "%1",
"system": None
},
"starcoder": {
"path": "starcoder-newbpe-q4_0.gguf",
"ram": "4",
"prompt": "%1",
"system": None
},
"rift-coder-7b": {
"path": "rift-coder-v0-7b-q4_0.gguf",
"ram": "8",
"prompt": "%1",
"system": None
},
"all-MiniLM-L6-v2": {
"path": "all-MiniLM-L6-v2-f16.gguf",
"ram": "1",
"prompt": None,
"system": None
},
"mistral-7b-german": {
"path": "em_german_mistral_v01.Q4_0.gguf",
"ram": "8",
"prompt": "USER: %1 ASSISTANT: ",
"system": "Du bist ein hilfreicher Assistent. "
}
}
```
#### Performance Considerations
**When running language models locally, consider the following:**
- RAM requirements vary by model size (see the 'ram' field in the model list).
- CPU/GPU capabilities affect inference speed.
- Disk space is needed to store the model files.
#### Troubleshooting
**Common issues and solutions:**
1. **Model download fails**: Check your internet connection and try again.
2. **Out of memory error**: Choose a smaller model or increase your system's RAM.
3. **Slow inference**: Consider using a GPU or a more powerful CPU.
[Return to Home](/)

57
docs/providers-and-models.md
View file

@ -1,16 +1,20 @@
# G4F - Providers and Models
This document provides an overview of various AI providers and models, including text generation, image generation, and vision capabilities. It aims to help users navigate the diverse landscape of AI services and choose the most suitable option for their needs.
## 🚀 Providers and Models
- [Providers](#Providers)
## Table of Contents
- [Providers](#providers)
- [Models](#models)
- [Text Model](#text-model)
- [Image Model](#image-model)
- [Text Models](#text-models)
- [Image Models](#image-models)
- [Vision Models](#vision-models)
- [Conclusion and Usage Tips](#conclusion-and-usage-tips)
---
#### Providers
|Website|Provider|Text Model|Image Model|Vision Model|Stream|Status|Auth|
|--|--|--|--|--|--|--|--|
## Providers
| Provider | Text Models | Image Models | Vision Models | Stream | Status | Auth |
|----------|-------------|--------------|---------------|--------|--------|------|
|[ai4chat.co](https://www.ai4chat.co)|`g4f.Provider.Ai4Chat`|`gpt-4`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[chat.ai365vip.com](https://chat.ai365vip.com)|`g4f.Provider.AI365VIP`|`gpt-3.5-turbo, gpt-4o`|❌|❌|?|![Cloudflare](https://img.shields.io/badge/Cloudflare-f48d37)|❌|
|[aichatfree.info](https://aichatfree.info)|`g4f.Provider.AIChatFree`|`gemini-pro`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
@ -101,14 +105,11 @@
|[whiterabbitneo.com](https://www.whiterabbitneo.com)|`g4f.Provider.WhiteRabbitNeo`|✔|❌|❌|?|![Unknown](https://img.shields.io/badge/Unknown-grey)|✔|
|[you.com](https://you.com)|`g4f.Provider.You`|✔|✔|✔|✔|![Unknown](https://img.shields.io/badge/Unknown-grey)|❌+✔|
## Models
---
### Models
#### Text Model
|Model|Base Provider|Provider|Website|
|--|--|--|-|
### Text Models
| Model | Base Provider | Providers | Website |
|-------|---------------|-----------|---------|
|gpt-3|OpenAI|1+ Providers|[platform.openai.com](https://platform.openai.com/docs/models/gpt-base)|
|gpt-3.5-turbo|OpenAI|5+ Providers|[platform.openai.com](https://platform.openai.com/docs/models/gpt-3-5-turbo)|
|gpt-4|OpenAI|9+ Providers|[platform.openai.com](https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4)|
@ -195,10 +196,10 @@
|german-7b|TheBloke|1+ Providers|[huggingface.co](https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-GGUF)|
|tinyllama-1.1b|TinyLlama|1+ Providers|[huggingface.co](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)|
|cybertron-7b|TheBloke|1+ Providers|[huggingface.co](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16)|
---
### Image Model
|Model|Base Provider|Provider|Website|
|--|--|--|-|
### Image Models
| Model | Base Provider | Providers | Website |
|-------|---------------|-----------|---------|
|sdxl|Stability AI|3+ Providers|[huggingface.co](https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl)|
|sd-3|Stability AI|1+ Providers|[huggingface.co](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3)|
|playground-v2.5|Playground AI|1+ Providers|[huggingface.co](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic)|
@ -218,6 +219,26 @@
|emi||1+ Providers|[]()|
|any-dark||1+ Providers|[]()|
### Vision Models
| Model | Base Provider | Providers | Website |
|-------|---------------|-----------|---------|
|gpt-4-vision|OpenAI|1+ Providers|[openai.com](https://openai.com/research/gpt-4v-system-card)|
|gemini-pro-vision|Google DeepMind|1+ Providers | [deepmind.google](https://deepmind.google/technologies/gemini/)|
|blackboxai|Blackbox AI|1+ Providers|[docs.blackbox.chat](https://docs.blackbox.chat/blackbox-ai-1)|
|minicpm-llama-3-v2.5|OpenBMB|1+ Providers | [huggingface.co](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5)|
## Conclusion and Usage Tips
This document provides a comprehensive overview of various AI providers and models available for text generation, image generation, and vision tasks. **When choosing a provider or model, consider the following factors:**
1. **Availability**: Check the status of the provider to ensure it's currently active and accessible.
2. **Model Capabilities**: Different models excel at different tasks. Choose a model that best fits your specific needs, whether it's text generation, image creation, or vision-related tasks.
3. **Authentication**: Some providers require authentication, while others don't. Consider this when selecting a provider for your project.
4. **Streaming Support**: If real-time responses are important for your application, prioritize providers that offer streaming capabilities.
5. **Vision Models**: For tasks requiring image understanding or multimodal interactions, look for providers offering vision models.
Remember to stay updated with the latest developments in the AI field, as new models and providers are constantly emerging and evolving.
---
Last Updated: 2024-10-19
[Return to Home](/)

1
docs/requirements.md
View file

@ -43,4 +43,5 @@ Install all packages and uninstall this package for disabling the webdriver:
pip uninstall undetected-chromedriver
```
---
[Return to Home](/)