[DOCS UPDATE]

docs/swarms/structs/group_chat.md

@@ -62,25 +62,29 @@ The GroupChat system consists of several key components:
 ## Basic Usage
 
 ```python
+
 import os
 from dotenv import load_dotenv
 from swarm_models import OpenAIChat
-from swarms import Agent, GroupChat
-from loguru import logger
+from swarms import Agent, GroupChat, expertise_based
+
+
+if __name__ == "__main__":
 
-# Load environment variables
     load_dotenv()
+
+    # Get the OpenAI API key from the environment variable
     api_key = os.getenv("OPENAI_API_KEY")
 
-# Initialize LLM
+    # Create an instance of the OpenAIChat class
     model = OpenAIChat(
         openai_api_key=api_key,
         model_name="gpt-4o-mini",
-    temperature=0.1
+        temperature=0.1,
     )
 
-# Create financial analyst agent
-financial_analyst = Agent(
+    # Example agents
+    agent1 = Agent(
         agent_name="Financial-Analysis-Agent",
         system_prompt="You are a financial analyst specializing in investment strategies.",
         llm=model,
@@ -89,36 +93,43 @@ financial_analyst = Agent(
         dashboard=False,
         verbose=True,
         dynamic_temperature_enabled=True,
+        user_name="swarms_corp",
         retry_attempts=1,
         context_length=200000,
-    output_type="string"
+        output_type="string",
+        streaming_on=False,
     )
 
-# Create tax advisor agent
-tax_advisor = Agent(
+    agent2 = Agent(
         agent_name="Tax-Adviser-Agent",
-    system_prompt="You are a tax adviser providing clear tax guidance.",
+        system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
         llm=model,
         max_loops=1,
         autosave=False,
         dashboard=False,
         verbose=True,
         dynamic_temperature_enabled=True,
+        user_name="swarms_corp",
         retry_attempts=1,
         context_length=200000,
-    output_type="string"
+        output_type="string",
+        streaming_on=False,
     )
 
-# Initialize group chat
+    agents = [agent1, agent2]
+
     chat = GroupChat(
         name="Investment Advisory",
         description="Financial and tax analysis group",
-    agents=[financial_analyst, tax_advisor],
-    speaker_fn=expertise_based
+        agents=agents,
+        speaker_fn=expertise_based,
+    )
+
+    history = chat.run(
+        "How to optimize tax strategy for investments?"
     )
+    print(history.model_dump_json(indent=2))
 
-# Run conversation
-history = chat.run("How to optimize tax strategy for investments?")
 ```
 
 ## Speaker Functions

swarm_arange_demo.py

@@ -1,8 +1,10 @@
-from swarms.structs.swarm_arange import SwarmRearrange
 import os
-from swarms import Agent, AgentRearrange
+
 from swarm_models import OpenAIChat
 
+from swarms import Agent, AgentRearrange
+from swarms.structs.swarm_arange import SwarmRearrange
+
 # model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
 company = "TGSC"