playground + flow docs fix

docs/swarms/structs/flow.md

@@ -108,8 +108,13 @@ Here are three usage examples:
 
 ```python
 from swarms.structs import Flow
+# Select any Language model from the models folder
+from swarms.models import Mistral, OpenAIChat
 
-flow = Flow(llm=my_language_model, max_loops=5)
+llm = Mistral()
+# llm = OpenAIChat()
+
+flow = Flow(llm=llm, max_loops=5)
 
 # Define a starting task or message
 initial_task = "Generate an long form analysis on the transformer model architecture."
@@ -126,7 +131,7 @@ from swarms.structs import Flow
 def stop_when_repeats(response: str) -> bool:
     return "Stop" in response.lower()
 
-flow = Flow(llm=my_language_model, max_loops=5, stopping_condition=stop_when_repeats)
+flow = Flow(llm=llm, max_loops=5, stopping_condition=stop_when_repeats)
 ```
 
 ### Example 3: Interactive Conversation
@@ -134,7 +139,7 @@ flow = Flow(llm=my_language_model, max_loops=5, stopping_condition=stop_when_rep
 ```python
 from swarms.structs import Flow
 
-flow = Flow(llm=my_language_model, max_loops=5, interactive=True)
+flow = Flow(llm=llm, max_loops=5, interactive=True)
 
 # Provide initial task
 initial_task = "Rank and prioritize the following financial documents and cut out 30% of our expenses"

playground/models/multitemp.py

@@ -1,56 +0,0 @@
-from swarms.models import OpenAIChat  # Replace with your actual OpenAIChat import
-
-if __name__ == "__main__":
-    api_key = ""  # Your OpenAI API key here
-    agent = MultiTempAgent(api_key)
-
-    prompt = "Write a blog post about health and wellness"
-    final_output = agent.run(prompt)
-
-    print("Final chosen output:")
-    print(final_output)
-
-
-class MultiTempAgent:
-    def __init__(self, api_key, default_temp=0.5, alt_temps=[0.2, 0.7, 0.9]):
-        self.api_key = api_key
-        self.default_temp = default_temp
-        self.alt_temps = alt_temps
-
-    def ask_user_feedback(self, text):
-        print(f"Generated text: {text}")
-        feedback = input("Are you satisfied with this output? (yes/no): ")
-        return feedback.lower() == "yes"
-
-    def present_options_to_user(self, outputs):
-        print("Alternative outputs:")
-        for temp, output in outputs.items():
-            print(f"Temperature {temp}: {output}")
-        chosen_temp = float(input("Choose the temperature of the output you like: "))
-        return outputs.get(chosen_temp, "Invalid temperature chosen.")
-
-    def run(self, prompt):
-        try:
-            llm = OpenAIChat(openai_api_key=self.api_key, temperature=self.default_temp)
-            initial_output = llm(prompt)  # Using llm as a callable
-        except Exception as e:
-            print(f"Error generating initial output: {e}")
-            initial_output = None
-
-        user_satisfied = self.ask_user_feedback(initial_output)
-
-        if user_satisfied:
-            return initial_output
-        else:
-            outputs = {}
-            for temp in self.alt_temps:
-                try:
-                    llm = OpenAIChat(
-                        openai_api_key=self.api_key, temperature=temp
-                    )  # Re-initializing
-                    outputs[temp] = llm(prompt)  # Using llm as a callable
-                except Exception as e:
-                    print(f"Error generating text at temperature {temp}: {e}")
-                    outputs[temp] = None
-            chosen_output = self.present_options_to_user(outputs)
-            return chosen_output

playground/models/openai_model.py

@@ -1,6 +1,6 @@
 from swarms.models.openai_models import OpenAIChat
 
-openai = OpenAIChat(openai_api_key="", verbose=False)
+openai = OpenAIChat(openai_api_key="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", verbose=False)
 
-chat = openai("Are quantum fields everywhere?")
+chat = openai("What are quantum fields?")
 print(chat)

playground/structs/flow.py

@@ -0,0 +1,35 @@
+from swarms.models import OpenAIChat
+from swarms.structs import Flow
+
+api_key = ""
+
+# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
+llm = OpenAIChat(
+    # model_name="gpt-4"
+    openai_api_key=api_key,
+    temperature=0.5,
+    # max_tokens=100,
+)
+
+## Initialize the workflow
+flow = Flow(
+    llm=llm,
+    max_loops=2,
+    dashboard=True,
+    # stopping_condition=None,  # You can define a stopping condition as needed.
+    # loop_interval=1,
+    # retry_attempts=3,
+    # retry_interval=1,
+    # interactive=False,  # Set to 'True' for interactive mode.
+    # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
+)
+
+# out = flow.load_state("flow_state.json")
+# temp = flow.dynamic_temperature()
+# filter = flow.add_response_filter("Trump")
+out = flow.run("Generate a 10,000 word blog on health and wellness.")
+# out = flow.validate_response(out)
+# out = flow.analyze_feedback(out)
+# out = flow.print_history_and_memory()
+# # out = flow.save_state("flow_state.json")
+# print(out)

playground/structs/sequential_workflow.py

@@ -0,0 +1,31 @@
+from swarms.models import OpenAIChat
+from swarms.structs import Flow
+from swarms.structs.sequential_workflow import SequentialWorkflow
+
+# Example usage
+llm = OpenAIChat(
+    temperature=0.5,
+    max_tokens=3000,
+)
+
+# Initialize the Flow with the language flow
+flow1 = Flow(llm=llm, max_loops=1, dashboard=False)
+
+# Create another Flow for a different task
+flow2 = Flow(llm=llm, max_loops=1, dashboard=False)
+
+# Create the workflow
+workflow = SequentialWorkflow(max_loops=1)
+
+# Add tasks to the workflow
+workflow.add("Generate a 10,000 word blog on health and wellness.", flow1)
+
+# Suppose the next task takes the output of the first task as input
+workflow.add("Summarize the generated blog", flow2)
+
+# Run the workflow
+workflow.run()
+
+# Output the results
+for task in workflow.tasks:
+    print(f"Task: {task.description}, Result: {task.result}")

playground/swarms/godmode.py

@@ -1,39 +1,16 @@
+from swarms.swarms import GodMode
 from swarms.models import OpenAIChat
 
-from swarms.swarms import GodMode
-from swarms.workers.worker import Worker
+api_key = ""
+
+llm = OpenAIChat(openai_api_key=api_key)
 
-llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
 
-worker1 = Worker(
-    llm=llm,
-    ai_name="Bumble Bee",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-worker2 = Worker(
-    llm=llm,
-    ai_name="Optimus Prime",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-worker3 = Worker(
-    llm=llm,
-    ai_name="Megatron",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
-    temperature=0.5,
-)
-# Usage
-agents = [worker1, worker2, worker3]
+llms = [llm, llm, llm]
 
-god_mode = GodMode(agents)
+god_mode = GodMode(llms)
 
-task = "What are the biggest risks facing humanity?"
+task = "Generate a 10,000 word blog on health and wellness."
 
+out = god_mode.run(task)
 god_mode.print_responses(task)

playground/swarms/groupchat.py

@@ -1,61 +1,49 @@
-from swarms.models import OpenAIChat
-from swarms.swarms import GroupChat, GroupChatManager
-from swarms.workers import Worker
+from swarms import OpenAI, Flow
+from swarms.swarms.groupchat import GroupChatManager, GroupChat
 
-llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
 
-node = Worker(
-    llm=llm,
-    ai_name="Optimus Prime",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
+api_key = ""
+
+llm = OpenAI(
+    openai_api_key=api_key,
     temperature=0.5,
+    max_tokens=3000,
 )
 
-node2 = Worker(
+# Initialize the flow
+flow1 = Flow(
     llm=llm,
-    ai_name="Optimus Prime",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
-    temperature=0.5,
+    max_loops=1,
+    system_message="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE",
+    name="silly",
+    dashboard=True,
 )
-
-node3 = Worker(
+flow2 = Flow(
     llm=llm,
-    ai_name="Optimus Prime",
-    ai_role="Worker in a swarm",
-    external_tools=None,
-    human_in_the_loop=False,
-    temperature=0.5,
+    max_loops=1,
+    system_message="YOU ARE VERY SMART AND ANSWER RIDDLES",
+    name="detective",
+    dashboard=True,
 )
-
-nodes = [node, node2, node3]
-
-messages = [
-    {
-        "role": "system",
-        "context": "Create an a small feedforward in pytorch",
-    }
-]
-
-group = GroupChat(
-    workers=nodes,
-    messages=messages,
-    max_rounds=3,
+flow3 = Flow(
+    llm=llm,
+    max_loops=1,
+    system_message="YOU MAKE RIDDLES",
+    name="riddler",
+    dashboard=True,
 )
-
-
-manager = GroupChatManager(
-    groupchat=group,
-    max_consecutive_auto_reply=3,
+manager = Flow(
+    llm=llm,
+    max_loops=1,
+    system_message="YOU ARE A GROUP CHAT MANAGER",
+    name="manager",
+    dashboard=True,
 )
 
-output = group.run(
-    messages,
-    sender=node,
-    config=group,
-)
 
-print(output)
+# Example usage:
+agents = [flow1, flow2, flow3]
+
+group_chat = GroupChat(agents=agents, messages=[], max_round=10)
+chat_manager = GroupChatManager(groupchat=group_chat, selector=manager)
+chat_history = chat_manager("Write me a riddle")