diff --git a/py/ai.py b/py/ai.py
index 0207180..d4cd09d 100644
--- a/py/ai.py
+++ b/py/ai.py
@@ -4,10 +4,11 @@ import google.generativeai as genai
 import anthropic
 import ollama
 
-
 class AI:
     @staticmethod
     def process_local(model, messages, return_class, access_token):
+        """Process chat messages using the Ollama model locally."""
+        # Stream the chat response from the Ollama model
         stream = ollama.chat(
             model=model,
             messages=messages,
@@ -15,50 +16,61 @@ class AI:
             options={"temperature": 0.5},
         )
 
+        # Initialize the AI response for the given access token
         with return_class.ai_response_lock:
             return_class.ai_response[access_token] = ""
 
+        # Collect the response chunks and append to the response for the given access token
         for chunk in stream:
             with return_class.ai_response_lock:
                 return_class.ai_response[access_token] += chunk["message"]["content"]
 
     @staticmethod
     def process_mistralai(model, messages, return_class, access_token, api_key):
+        """Process chat messages using the Mistral AI model."""
         client = Mistral(api_key=api_key)
 
+        # Stream the chat response from the Mistral model
         stream_response = client.chat.stream(model=model, messages=messages)
 
+        # Initialize the AI response for the given access token
         with return_class.ai_response_lock:
             return_class.ai_response[access_token] = ""
 
+        # Collect the response chunks and append to the response for the given access token
         for chunk in stream_response:
             with return_class.ai_response_lock:
-                return_class.ai_response[access_token] += chunk.data.choices[
-                    0
-                ].delta.content
+                return_class.ai_response[access_token] += chunk.data.choices[0].delta.content
 
     @staticmethod
     def process_openai(model, messages, return_class, access_token, api_key):
+        """Process chat messages using the OpenAI model."""
         client = OpenAI(api_key=api_key)
 
+        # Stream the chat response from the OpenAI model
         stream_response = client.chat.completions.create(
             model=model, messages=messages, stream=True
         )
 
+        # Initialize the AI response for the given access token
         with return_class.ai_response_lock:
             return_class.ai_response[access_token] = ""
 
+        # Collect the response chunks and append to the response for the given access token
         for chunk in stream_response:
             with return_class.ai_response_lock:
                 return_class.ai_response[access_token] += chunk.choices[0].delta.content
 
     @staticmethod
     def process_anthropic(model, messages, return_class, access_token, api_key):
+        """Process chat messages using the Anthropic model."""
         client = anthropic.Anthropic(api_key=api_key)
 
+        # Initialize the AI response for the given access token
         with return_class.ai_response_lock:
             return_class.ai_response[access_token] = ""
 
+        # Stream the chat response from the Anthropic model
         with client.messages.stream(
             max_tokens=1024,
             model=model,
@@ -70,24 +82,27 @@ class AI:
 
     @staticmethod
     def process_google(model, messages, return_class, access_token, api_key):
-        message = messages[-1]["content"]
-        messages.pop()
+        """Process chat messages using the Google Generative AI model."""
+        message = messages[-1]["content"]  # Get the latest message content
+        messages.pop()  # Remove the latest message from the list
 
+        # Prepare messages for the Google Generative AI format
         for msg in messages:
             msg["parts"] = msg.pop()["content"]
 
+        # Change 'assistant' role to 'model' for compatibility
         for msg in messages:
             if msg["role"] == "assistant":
                 msg["role"] = "model"
 
+        # Configure the Google Generative AI client
         genai.configure(api_key=api_key)
 
+        # Start a chat session with the specified model and message history
         model = genai.GenerativeModel(model)
+        chat = model.start_chat(history=messages)
 
-        chat = model.start_chat(
-            history=messages,
-        )
-
+        # Send the message and stream the response
         response = chat.send_message(message, stream=True)
         for chunk in response:
             return_class.ai_response[access_token] += chunk.text