From 5584624de571a37db037d953b56a998dd33610fd Mon Sep 17 00:00:00 2001
From: ShubhamSaboo <shubhamsaboo111@gmail.com>
Date: Thu, 15 Aug 2024 20:35:47 -0700
Subject: [PATCH] Added new demo

---
 mixture_of_agents/mixture-of-agents.py | 103 +++++++++++++++++++++++++
 mixture_of_agents/requirements.txt     |   3 +
 2 files changed, 106 insertions(+)
 create mode 100644 mixture_of_agents/mixture-of-agents.py
 create mode 100644 mixture_of_agents/requirements.txt

diff --git a/mixture_of_agents/mixture-of-agents.py b/mixture_of_agents/mixture-of-agents.py
new file mode 100644
index 0000000..9eac080
--- /dev/null
+++ b/mixture_of_agents/mixture-of-agents.py
@@ -0,0 +1,103 @@
+import streamlit as st
+import asyncio
+import os
+from together import AsyncTogether, Together
+
+# Set up the Streamlit app
+st.title("Mixture-of-Agents LLM App")
+
+# Get API key from the user
+together_api_key = st.text_input("Enter your Together API Key:", type="password")
+
+if together_api_key:
+    os.environ["TOGETHER_API_KEY"] = together_api_key
+    client = Together(api_key=together_api_key)
+    async_client = AsyncTogether(api_key=together_api_key)
+
+    # Define the models
+    reference_models = [
+        "Qwen/Qwen2-72B-Instruct",
+        "Qwen/Qwen1.5-72B-Chat",
+        "mistralai/Mixtral-8x22B-Instruct-v0.1",
+        "databricks/dbrx-instruct",
+    ]
+    aggregator_model = "mistralai/Mixtral-8x22B-Instruct-v0.1"
+
+    # Define the aggregator system prompt
+    aggregator_system_prompt = """You have been provided with a set of responses from various open-source models to the latest user query. Your task is to synthesize these responses into a single, high-quality response. It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability. Responses from models:"""
+
+    # Get user input
+    user_prompt = st.text_input("Enter your question:")
+
+    async def run_llm(model):
+        """Run a single LLM call with a reference model."""
+        response = await async_client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": user_prompt}],
+            temperature=0.7,
+            max_tokens=512,
+        )
+        return model, response.choices[0].message.content
+
+    async def main():
+        results = await asyncio.gather(*[run_llm(model) for model in reference_models])
+        
+        # Display individual model responses
+        st.subheader("Individual Model Responses:")
+        for model, response in results:
+            with st.expander(f"Response from {model}"):
+                st.write(response)
+        
+        # Aggregate responses
+        st.subheader("Aggregated Response:")
+        finalStream = client.chat.completions.create(
+            model=aggregator_model,
+            messages=[
+                {"role": "system", "content": aggregator_system_prompt},
+                {"role": "user", "content": ",".join(response for _, response in results)},
+            ],
+            stream=True,
+        )
+        
+        # Display aggregated response
+        response_container = st.empty()
+        full_response = ""
+        for chunk in finalStream:
+            content = chunk.choices[0].delta.content or ""
+            full_response += content
+            response_container.markdown(full_response + "▌")
+        response_container.markdown(full_response)
+
+    if st.button("Get Answer"):
+        if user_prompt:
+            asyncio.run(main())
+        else:
+            st.warning("Please enter a question.")
+
+else:
+    st.warning("Please enter your Together API key to use the app.")
+
+# Add some information about the app
+st.sidebar.title("About this app")
+st.sidebar.write(
+    "This app demonstrates a Mixture-of-Agents approach using multiple Language Models (LLMs) "
+    "to answer a single question."
+)
+
+st.sidebar.subheader("How it works:")
+st.sidebar.markdown(
+    """
+    1. The app sends your question to multiple LLMs:
+        - Qwen/Qwen2-72B-Instruct
+        - Qwen/Qwen1.5-72B-Chat
+        - mistralai/Mixtral-8x22B-Instruct-v0.1
+        - databricks/dbrx-instruct
+    2. Each model provides its own response
+    3. All responses are then aggregated using Mixtral-8x22B-Instruct-v0.1
+    4. The final aggregated response is displayed
+    """
+)
+
+st.sidebar.write(
+    "This approach allows for a more comprehensive and balanced answer by leveraging multiple AI models."
+)
\ No newline at end of file
diff --git a/mixture_of_agents/requirements.txt b/mixture_of_agents/requirements.txt
new file mode 100644
index 0000000..42fd57b
--- /dev/null
+++ b/mixture_of_agents/requirements.txt
@@ -0,0 +1,3 @@
+streamlit
+asyncio
+together
\ No newline at end of file