Advanced Python Multi-Agent AI Application

This project demonstrates the development of an advanced multi-agent AI application using LangFlow, Streamlit, and retrieval-augmented generation (RAG). It integrates modern MLOps techniques, efficient use of multiple language models (LLMs), and an intuitive front-end interface.

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Features

• Multi-Agent AI:
  • Combines multiple LLMs for complex task routing.
  • Includes context-specific tools for mathematical and logical reasoning.
• Modular Design:
  • Uses LangFlow's low-code visual editor for modular AI flow construction.
  • Implements RAG to retrieve relevant notes or context dynamically.
• Interactive UI:
  • Built with Streamlit for a seamless and intuitive user experience.
• Scalable Backend:
  • AstraDB vector database integration for efficient vector operations.

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Tech Stack

• Programming Language: Python
• Backend: LangFlow, AstraDB
• Frontend: Streamlit
• AI Models: OpenAI GPT and custom tool-integrated models
• Database: AstraDB with vectorization capabilities

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Project Structure

├── ai.py
├── db.py
├── form_submit.py
├── profiles.py
├── main.py
├── requirements.txt
├── workflow.json
└── README.md

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Installation

Prerequisites

1. Python 3.8+
2. Environment Setup:
   • Install Python packages using pip install -r requirements.txt.
   • Create a .env file and configure the following:

   • LANGFLOW_TOKEN=<your_langflow_api_token>
     ASTRA_DB_APPLICATION_TOKEN=<your_astra_db_token>
     ASTRA_ENDPOINT=<your_astra_endpoint_url>
     OPENAI_API_KEY=<your_openai_api_key>

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/advanced-ai-multi-agent.git
   cd advanced-ai-multi-agent

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the LangFlow environment:

   • Export the LangFlow workflow JSON (workflow.json) via LangFlow UI.

4. Run the application:

   streamlit run main.py

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How It Works

Pipeline

1. User Inputs:

   • Collect user profiles, goals, and activity levels through Streamlit forms.
   • Retrieve or dynamically store contextual notes using AstraDB.

2. AI Routing:

   • Dynamically routes questions to specialized agents using LangFlow.
   • Incorporates retrieval-augmented generation for contextual responses.

3. Mathematical Operations:

   • Integrated calculator tool for advanced calculations.
   • Logical separation ensures error-free computations.

4. UI/UX:

   • Interactive inputs and results visualization through Streamlit.

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Usage

1. Start the LangFlow backend:

   • Host the API locally or remotely.

2. Run the main Streamlit app:

   streamlit run main.py

3. Interact:

   • Provide your personal information.
   • Retrieve or save notes.
   • Ask complex questions and observe dynamic AI task routing.

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Future Enhancements

• Add multi-language support for global use.
• Enhance the UI with modern frameworks.
• Introduce additional tools for broader AI capabilities.

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Contributing

We welcome contributions to enhance this project! Feel free to fork the repository, add features, and submit a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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