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@@ -42,4 +42,48 @@ This project implements the Mixture-of-Agents architecture proposed in the follo
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- For more information about the Mixture-of-Agents concept, please refer to the [original research paper](https://arxiv.org/abs/2406.04692) and the [Together AI blog post](https://www.together.ai/blog/together-moa).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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+ For more information about the Mixture-of-Agents concept, please refer to the [original research paper](https://arxiv.org/abs/2406.04692) and the [Together AI blog post](https://www.together.ai/blog/together-moa).
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+ ----
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+ ## App Structure
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+ The app is structured into several main sections:
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+ 1. **Configuration Sidebar**:
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+ - Allows users to select the main model, number of cycles, and main model temperature.
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+ - Provides a JSON editor for configuring layer agents.
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+ - Includes a button to apply recommended configurations.
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+ 2. **Main Chat Interface**:
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+ - Displays the chat history and allows users to input questions.
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+ - Shows streamed responses from the MOA system, including intermediate outputs from layer agents.
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+ 3. **Current Configuration Display**:
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+ - Shows the current MOA configuration in an expandable section.
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+ 4. **Information Sections**:
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+ - Displays credits and related information.
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+ - Shows a visualization of the MOA workflow.
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+ ## Configuration Options
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+ - **Main Model**: Select from a list of available Groq LLMs.
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+ - **Number of Layers**: Choose the number of intermediate processing layers (1-10).
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+ - **Main Model Temperature**: Set the temperature for the main model's output (0.0-1.0).
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+ - **Layer Agent Configuration**: Customize the behavior of each layer agent using a JSON editor.
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+ ## How It Works
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+ 1. The app initializes with default or previously set configurations.
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+ 2. Users can update the configuration using the sidebar.
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+ 3. Questions are input through the chat interface.
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+ 4. The MOA system processes the input:
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+ - Layer agents process the input in parallel for each cycle.
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+ - The main model generates the final response.
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+ 5. Responses are streamed back to the user, showing both intermediate and final outputs.
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+ ## Notes
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+ - The app uses session state to maintain conversation history and configuration across interactions.
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+ - The MOA system is reset when the configuration is updated.