ADVANCED Python AI Multi-Agent Tutorial (RAG, Streamlit, Langflow & More!)

Education

Introduction

In this tutorial, you'll learn how to build an advanced multi-agent AI application using Python. We will employ multiple Large Language Models (LLMs) that can handle different tasks, including an LLM dedicated to routing tasks to other LLMs. Additionally, we will develop a full front end using Streamlit, allowing users to interact with the application effectively.

This project is one of the most advanced AI applications built on my channel, and despite the detailed nature of the explanation, I assure you that you'll learn numerous techniques and concepts that will empower you to create cool applications after following this tutorial.

Overview of Technologies Used

  • Python: The primary programming language for all coding.
  • Streamlit: A Python library for building interactive front-end applications.
  • Langflow: A low-code visual editor that helps build advanced AI flows.
  • Astrab: A vector database for implementing retrieval-augmented generation (RAG) features.

Don’t worry if some of these technologies are new to you; you can run this application locally on your computer. Wanglow and Astrab substantially streamline the development process, allowing you to customize your application efficiently.

Demo Walkthrough

To kick things off, let’s take a look at the demo of the final application. The user starts by filling out some personal data through a simple form created with Streamlit. This form includes fields for the user's name, age, weight, height, gender, activity level, and fitness goals.

Additionally, the application has an "Ask AI" feature where users can inquire about personalized workout routines. For instance, if you ask, "Can you suggest a leg day workout routine for me?" the AI will consider the user's note about their knee injury and lack of access to certain equipment when generating responses.

The AI utilizes a method called retrieval-augmented generation. When a user poses a question, it retrieves relevant notes and the user's profile information to create a highly tailored response.

Setting Up Langflow

To build the AI component of our application, we utilize Langflow, which is open-source and available for use without payment. You can quickly sign up for a free account and start creating projects. After logging in, you’ll arrive at the Langflow editor where you can create a new flow.

From this editor, you can establish prompts that will interact with the LLM. A typical flow will consist of input fields, prompt templates, and outputs that connect to different models. By outlining a simple prompt, such as "Calculate the recommended daily intake of protein, calories, fat, and carbohydrates," you can tailor the output according to the user’s input.

Connecting to Astrab

Next, we're going to utilize the Astrab database to store and retrieve notes. To set up an Astrab database, navigate to the Data Stacks website and create a free account. After setting it up, obtain the application token and insert it into your Langflow project so that the AI can retrieve relevant context for user queries.

This integration allows for intelligent searches based on the notes stored, providing users with contextually relevant workout suggestions and information.

Building with Streamlit

After setting up the AI components, we turned to Streamlit to create the user interface. We began by writing a form to collect personal data and fitness goals, and added functionality to save this data in Astrab. Essential functions import the user's current information and update the database accordingly.

We also incorporated a macro section where users could generate personalized nutrition information based on their goals. The AI picks up on this goal to calculate nutritional needs effectively.

Lastly, we designed a notes section where users can log notes related to their workouts. These notes are persisted in the Astrab database, and users can add or delete them seamlessly.

Asking the AI

The final aspect of our application was to allow users to interact with the AI freely. Users can submit questions, and the AI responds accordingly by accessing their notes and profile to deliver well-informed answers sans error.

Final Thoughts

This tutorial emphasized creating an advanced multi-agent AI application by leveraging Langflow for designing AI workflows, Streamlit for building user interfaces, and Astrab for storage and retrieval tasks. I hope you’re excited to harness the power of AI in your future projects!

Keywords

  • Python
  • AI Application
  • Multi-Agent
  • Streamlit
  • Langflow
  • Astrab
  • Retrieval-Augmented Generation (RAG)
  • LLMs

FAQ

What is Langflow?

Langflow is a low-code editor that allows developers to build complex AI flows visualizing how different components interact, making the development of AI applications simpler and faster.

How does retrieval-augmented generation work?

Retrieval-augmented generation combines the generation capabilities of LLMs with data retrieval from external sources (like databases or stored notes) to produce contextually relevant responses.

Can I run this application locally?

Yes, both Langflow and Astrab have features that enable you to run the entire application locally on your computer.

What is Astrab?

Astrab is a vector database provided by Data Stacks that supports quick similarity searches through vector representation of data. It is ideal for storing AI-generated data that requires retrieval based on relevance.

How can the AI provide personalized responses?

The AI examines user data, including their responses stored in the database and the attributes from their profile, to generate tailored suggestions and answers as per user queries.