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swarms
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playground + flow docs fix

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Former-commit-id: 336bffea19
jojo-group-chat
Kye 2 years ago
parent b3a39e578d
commit 690433d39f
8 changed files with 120 additions and 140 deletions
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11
docs/swarms/structs/flow.md
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@ -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"

0
simple_agent.py → playground/agents/simple_agent.py
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56
playground/models/multitemp.py
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@ -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

4
playground/models/openai_model.py
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@ -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)

35
playground/structs/flow.py
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@ -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)

31
playground/structs/sequential_workflow.py
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@ -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}")

39
playground/swarms/godmode.py
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@ -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)

84
playground/swarms/groupchat.py
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@ -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")

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