From 0846992ebff89a2f8e5cf96f1f1a588c240df271 Mon Sep 17 00:00:00 2001
From: Kye Gomez <kye@swarms.world>
Date: Mon, 23 Dec 2024 11:14:04 -0800
Subject: [PATCH] [DOCS UPDATE]
---
docs/swarms/structs/group_chat.md | 111 ++++++++++++++++--------------
swarm_arange_demo.py | 6 +-
2 files changed, 65 insertions(+), 52 deletions(-)
diff --git a/docs/swarms/structs/group_chat.md b/docs/swarms/structs/group_chat.md
index 84919e31..4bd1a04a 100644
--- a/docs/swarms/structs/group_chat.md
+++ b/docs/swarms/structs/group_chat.md
@@ -62,63 +62,74 @@ 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
-
-# Load environment variables
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-# Initialize LLM
-model = OpenAIChat(
- openai_api_key=api_key,
- model_name="gpt-4o-mini",
- temperature=0.1
-)
+from swarms import Agent, GroupChat, expertise_based
-# Create financial analyst agent
-financial_analyst = Agent(
- agent_name="Financial-Analysis-Agent",
- system_prompt="You are a financial analyst specializing in investment strategies.",
- llm=model,
- max_loops=1,
- autosave=False,
- dashboard=False,
- verbose=True,
- dynamic_temperature_enabled=True,
- retry_attempts=1,
- context_length=200000,
- output_type="string"
-)
-# Create tax advisor agent
-tax_advisor = Agent(
- agent_name="Tax-Adviser-Agent",
- system_prompt="You are a tax adviser providing clear tax guidance.",
- llm=model,
- max_loops=1,
- autosave=False,
- dashboard=False,
- verbose=True,
- dynamic_temperature_enabled=True,
- retry_attempts=1,
- context_length=200000,
- output_type="string"
-)
+if __name__ == "__main__":
-# Initialize group chat
-chat = GroupChat(
- name="Investment Advisory",
- description="Financial and tax analysis group",
- agents=[financial_analyst, tax_advisor],
- speaker_fn=expertise_based
-)
+ load_dotenv()
+
+ # Get the OpenAI API key from the environment variable
+ api_key = os.getenv("OPENAI_API_KEY")
+
+ # Create an instance of the OpenAIChat class
+ model = OpenAIChat(
+ openai_api_key=api_key,
+ model_name="gpt-4o-mini",
+ temperature=0.1,
+ )
+
+ # Example agents
+ agent1 = Agent(
+ agent_name="Financial-Analysis-Agent",
+ system_prompt="You are a financial analyst specializing in investment strategies.",
+ 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",
+ streaming_on=False,
+ )
+
+ agent2 = Agent(
+ agent_name="Tax-Adviser-Agent",
+ 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",
+ streaming_on=False,
+ )
+
+ agents = [agent1, agent2]
+
+ chat = GroupChat(
+ name="Investment Advisory",
+ description="Financial and tax analysis group",
+ 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
diff --git a/swarm_arange_demo.py b/swarm_arange_demo.py
index 713c2cfb..d9457ac5 100644
--- a/swarm_arange_demo.py
+++ b/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"