1 files changed, 191 insertions, 64 deletions

diff --git a/docs/client.md b/docs/client.md
index a889443c..388b2e4b 100644
--- a/docs/client.md
+++ b/docs/client.md
@@ -1,31 +1,52 @@
-### G4F - Client API
-
-#### Introduction
 
+# G4F Client API Guide
+ 
+
+## Table of Contents
+   - [Introduction](#introduction)
+   - [Getting Started](#getting-started)
+   - [Switching to G4F Client](#switching-to-g4f-client)
+   - [Initializing the Client](#initializing-the-client)
+   - [Creating Chat Completions](#creating-chat-completions)
+   - [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
-
-**Switching to G4F Client:**
-
-To begin using the G4F Client, simply update your import statement in your Python code:
+## Getting Started
+### 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:
+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 a new instance. **Below is an example showcasing custom providers:**
 ```python
 from g4f.client import Client
 from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
@@ -33,143 +54,244 @@ from g4f.Provider import BingCreateImages, OpenaiChat, Gemini
 client = Client(
     provider=OpenaiChat,
     image_provider=Gemini,
-    ...
+    # Add any other necessary parameters
 )
 ```
 
-## Configuration
+## Creating Chat Completions
+**Here’s an improved example of creating chat completions:**
+```python
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    messages=[
+        {
+            "role": "user",
+            "content": "Say this is a test"
+        }
+    ]
+    # Add any other necessary parameters
+)
+```
+
+**This example:**
+   - Asks a specific question `Say this is a test`
+   - Configures various parameters like temperature and max_tokens for more control over the output
+   - Disables streaming for a complete response
 
-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 adjust these parameters based on your specific needs.
+ 
 
+## Configuration
+**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:**
+## Usage Examples
+### Text Completions
+**Generate text completions using the `ChatCompletions` endpoint:** 
+```python
+from g4f.client import Client
 
-You can use the `ChatCompletions` endpoint to generate text completions as follows:
+client = Client()
 
-```python
 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,
-    ...
 )
+
 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:
+  
 
+### 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",
-    ...
+    model="flux",
+    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:
+#### Base64 Response Format
+```python
+from g4f.client import Client
+
+client = Client()
 
+response = client.images.generate(
+    model="flux",
+    prompt="a white siamese cat",
+    response_format="b64_json"
+)
+
+base64_text = response.data[0].b64_json
+print(base64_text)
+```
+
+  
+
+### 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?
+print(response.choices[0].message.content)
 ```
 
-#### 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...
+  
+### Using a Vision Model
+**Analyze an image and generate a description:**
+```python
+import g4f
+import requests
+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")
+
+client = Client()
+
+response = client.chat.completions.create(
+    model=g4f.models.default,
+    messages=[
+        {
+            "role": "user",
+            "content": "What are on this image?"
+        }
+    ],
+    provider=g4f.Provider.Bing,
+    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
 
 # Initialize the GPT client with the desired provider
-client = Client(provider=g4f.Provider.Bing)
+client = Client()
 
 # Initialize an empty conversation history
 messages = []
@@ -177,7 +299,7 @@ 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...")
@@ -199,8 +321,13 @@ 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](/)
\ No newline at end of file
+---  
+[Return to Home](/)