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import streamlit as st
import json
from typing import Iterable
from moa.agent import MOAgent
from moa.agent.moa import ResponseChunk
from streamlit_ace import st_ace
import copy
# Default configuration
default_config = {
"main_model": "llama-3.3-70b-versatile",
"cycles": 3,
"layer_agent_config": {}
}
layer_agent_config_def = {
"layer_agent_1": {
"system_prompt": "Think through your response step by step. {helper_response}",
"model_name": "llama3-8b-8192"
},
"layer_agent_2": {
"system_prompt": "Respond with a thought and then your response to the question. {helper_response}",
"model_name": "llama-3.2-1b-preview",
"temperature": 0.7
},
"layer_agent_3": {
"system_prompt": "You are an expert at logic and reasoning. Always take a logical approach to the answer. {helper_response}",
"model_name": "llama-3.2-3b-preview"
},
}
# Recommended Configuration
rec_config = {
"main_model": "llama-3.3-70b-versatile",
"cycles": 2,
"layer_agent_config": {}
}
layer_agent_config_rec = {
"layer_agent_1": {
"system_prompt": "Think through your response step by step. {helper_response}",
"model_name": "llama3-8b-8192",
"temperature": 0.1
},
"layer_agent_2": {
"system_prompt": "Respond with a thought and then your response to the question. {helper_response}",
"model_name": "llama3-8b-8192",
"temperature": 0.2
},
"layer_agent_3": {
"system_prompt": "You are an expert at logic and reasoning. Always take a logical approach to the answer. {helper_response}",
"model_name": "llama3-8b-8192",
"temperature": 0.4
},
"layer_agent_4": {
"system_prompt": "You are an expert planner agent. Create a plan for how to answer the human's query. {helper_response}",
"model_name": "mixtral-8x7b-32768",
"temperature": 0.5
},
}
def stream_response(messages: Iterable[ResponseChunk]):
layer_outputs = {}
for message in messages:
if message['response_type'] == 'intermediate':
layer = message['metadata']['layer']
if layer not in layer_outputs:
layer_outputs[layer] = []
layer_outputs[layer].append(message['delta'])
else:
# Display accumulated layer outputs
for layer, outputs in layer_outputs.items():
st.write(f"Layer {layer}")
cols = st.columns(len(outputs))
for i, output in enumerate(outputs):
with cols[i]:
st.expander(label=f"Agent {i+1}", expanded=False).write(output)
# Clear layer outputs for the next iteration
layer_outputs = {}
# Yield the main agent's output
yield message['delta']
def set_moa_agent(
main_model: str = default_config['main_model'],
cycles: int = default_config['cycles'],
layer_agent_config: dict[dict[str, any]] = copy.deepcopy(layer_agent_config_def),
main_model_temperature: float = 0.1,
override: bool = False
):
if override or ("main_model" not in st.session_state):
st.session_state.main_model = main_model
else:
if "main_model" not in st.session_state: st.session_state.main_model = main_model
if override or ("cycles" not in st.session_state):
st.session_state.cycles = cycles
else:
if "cycles" not in st.session_state: st.session_state.cycles = cycles
if override or ("layer_agent_config" not in st.session_state):
st.session_state.layer_agent_config = layer_agent_config
else:
if "layer_agent_config" not in st.session_state: st.session_state.layer_agent_config = layer_agent_config
if override or ("main_temp" not in st.session_state):
st.session_state.main_temp = main_model_temperature
else:
if "main_temp" not in st.session_state: st.session_state.main_temp = main_model_temperature
cls_ly_conf = copy.deepcopy(st.session_state.layer_agent_config)
if override or ("moa_agent" not in st.session_state):
st.session_state.moa_agent = MOAgent.from_config(
main_model=st.session_state.main_model,
cycles=st.session_state.cycles,
layer_agent_config=cls_ly_conf,
temperature=st.session_state.main_temp
)
del cls_ly_conf
del layer_agent_config
st.set_page_config(
page_title="Mixture-Of-Agents (MoA) Powered by Groq",
page_icon='/app/static/favicon.ico',
menu_items={
'About': "## Groq Mixture-Of-Agents (MoA) \n Powered by [Groq](https://groq.com)"
},
layout="wide"
)
valid_model_names = [
'llama3-70b-8192',
'llama3-8b-8192',
'llama-3.3-70b-versatile',
'llama-3.1-8b-instant',
'llama-3.2-3b-preview',
'llama-3.2-1b-preview',
'gemma2-9b-it',
'mixtral-8x7b-32768'
]
# st.image("./static/banner.png", width=500)
# st.write("---")
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
set_moa_agent()
# Sidebar for configuration
with st.sidebar:
# config_form = st.form("Agent Configuration", border=False)
st.title("MOA Configuration")
with st.form("Agent Configuration", border=False):
if st.form_submit_button("Use Recommended Config"):
try:
set_moa_agent(
main_model=rec_config['main_model'],
cycles=rec_config['cycles'],
layer_agent_config=layer_agent_config_rec,
override=True
)
st.session_state.messages = []
st.success("Configuration updated successfully!")
except json.JSONDecodeError:
st.error("Invalid JSON in Layer Agent Configuration. Please check your input.")
except Exception as e:
st.error(f"Error updating configuration: {str(e)}")
# Main model selection
new_main_model = st.selectbox(
"Select Main Model",
options=valid_model_names,
index=valid_model_names.index(st.session_state.main_model)
)
# Cycles input
new_cycles = st.number_input(
"Number of Layers",
min_value=1,
max_value=10,
value=st.session_state.cycles
)
# Main Model Temperature
main_temperature = st.number_input(
label="Main Model Temperature",
value=0.1,
min_value=0.0,
max_value=1.0,
step=0.1
)
# Layer agent configuration
tooltip = "Agents in the layer agent configuration run in parallel _per cycle_. Each layer agent supports all initialization parameters of [Langchain's ChatGroq](https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html) class as valid dictionary fields."
st.markdown("Layer Agent Config", help=tooltip)
new_layer_agent_config = st_ace(
value=json.dumps(st.session_state.layer_agent_config, indent=2),
language='json',
placeholder="Layer Agent Configuration (JSON)",
show_gutter=False,
wrap=True,
auto_update=True
)
if st.form_submit_button("Update Configuration"):
try:
new_layer_config = json.loads(new_layer_agent_config)
set_moa_agent(
main_model=new_main_model,
cycles=new_cycles,
layer_agent_config=new_layer_config,
main_model_temperature=main_temperature,
override=True
)
st.session_state.messages = []
st.success("Configuration updated successfully!")
except json.JSONDecodeError:
st.error("Invalid JSON in Layer Agent Configuration. Please check your input.")
except Exception as e:
st.error(f"Error updating configuration: {str(e)}")
st.markdown("---")
st.markdown("""
### Credits
- MoA info: [Together AI](https://www.together.ai/blog/together-moa)
- Groq Models: [Groq](https://groq.com/)
- Paper: [arXiv:2406.04692](https://arxiv.org/abs/2406.04692)
- GitHub repo: [skapadia3214/groq-moa](https://github.com/skapadia3214/groq-moa)
- Webpage: [diegoromero.es](https://diegoromero.es)
""")
# Main app layout
st.header("Mixture of Agents (MoA)", anchor=False)
st.write("A demo of the Mixture of Agents architecture proposed by [Together AI](https://www.together.ai/blog/together-moa), Powered by [Groq](https://groq.com/) LLMs.")
st.image("/app/static/moa_groq.svg", caption="Mixture of Agents Workflow", width=1000)
# Display current configuration
with st.expander("Current MOA Configuration", expanded=False):
st.markdown(f"**Main Model**: ``{st.session_state.main_model}``")
st.markdown(f"**Main Model Temperature**: ``{st.session_state.main_temp:.1f}``")
st.markdown(f"**Layers**: ``{st.session_state.cycles}``")
st.markdown(f"**Layer Agents Config**:")
new_layer_agent_config = st_ace(
value=json.dumps(st.session_state.layer_agent_config, indent=2),
language='json',
placeholder="Layer Agent Configuration (JSON)",
show_gutter=False,
wrap=True,
readonly=True,
auto_update=True
)
# Chat interface
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if query := st.chat_input("Ask a question"):
st.session_state.messages.append({"role": "user", "content": query})
with st.chat_message("user"):
st.write(query)
moa_agent: MOAgent = st.session_state.moa_agent
with st.chat_message("assistant"):
message_placeholder = st.empty()
ast_mess = stream_response(moa_agent.chat(query, output_format='json'))
response = st.write_stream(ast_mess)
st.session_state.messages.append({"role": "assistant", "content": response}) |