Agentic AI Workshop 1: Building AI Apps with RAG and LLM Workflows

This hands-on 1-hour workshop introduces participants to building intelligent AI applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Attendees will progress from basic LLM API calls to creating a fully functional RAG-based chatbot that can answer questions using custom documents. The workshop emphasizes practical coding with immediate results, using popular tools like OpenAI's API, ChromaDB, and Gradio for interactive interfaces.

Target Audience: Developers and technical professionals with basic Python knowledge who want to understand and build agentic AI applications.

Key Takeaways:

• Understand core concepts of agentic AI and prompt engineering
• Make API calls to LLMs for various tasks (text, image, audio generation)
• Learn how embeddings and vector databases enable semantic search
• Build a complete RAG-based chatbot from scratch

---

Workshop Outline

Part 1: Introduction (10-12 minutes)

Slides covering:

• What is Agentic AI?
• Basics of Prompt Engineering
• LLM Workflows vs Multi-Agent Systems
• Understanding Embeddings and Vector Databases
• Introduction to Retrieval-Augmented Generation (RAG)

---

Part 2: Coding Session - Building Blocks (40-45 minutes)

Notebook 1: Basic LLM API Interactions (15-18 minutes)

1. Basic API call to an LLM - text in, text out
2. Image generation example - text to image
3. Sound generation example - text to audio
4. Create a Gradio UI for interactive testing
5. Implement streaming output for real-time responses
6. Build a multi-turn chat conversation

Technologies: OpenAI Python SDK, Gradio

Notebook 2: Embeddings and Similarity Search (8-10 minutes)

1. Generate embeddings for different documents
2. Calculate cosine similarity between documents
3. Find the most similar documents to a query (without vector DB yet)

Technologies: OpenAI Embeddings API

Notebook 3: RAG-Based Chatbot with Vector DB (15-18 minutes)

1. Ingest documents into ChromaDB (using 5 Ready Tensor publications)
2. Implement semantic document retrieval
3. Build a complete RAG pipeline
4. Implement in a Gradio Chat UI with RAG

Technologies: ChromaDB, OpenAI API, Gradio

---

Part 3: Wrap-Up (5 minutes)

Slides covering:

• Summary of what we covered
• Limitations and cautions of current approaches
• Next steps and advanced topics to explore
• Resources for continued learning

---

Technical Stack

• LLM Provider: OpenAI (GPT-4/GPT-3.5)
• Embeddings: OpenAI text-embedding-3-small
• Vector Database: ChromaDB
• UI Framework: Gradio
• Development Environment: Jupyter Notebooks
• Language: Python 3.11+

---

Pre-Workshop Requirements

Attendees should:

• Have Python 3.11 or higher installed
• Obtain an OpenAI API key
• Install required packages: openai, chromadb, gradio, numpy
• Have basic familiarity with Python and Jupyter notebooks

---

Setup Instructions

Follow these steps to get your environment ready for the workshop.

1. Clone and enter the repository

git clone <your-repo-url>
cd <your-repo-folder>

2. Create and activate a virtual environment

It’s best to isolate workshop dependencies. You can use either venv or conda.

Using venv (Python 3.9+ recommended):

python3 -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

Using conda:

conda create -n agentic_ai python=3.11 -y
conda activate agentic_ai

3. Install dependencies

pip install -r requirements.txt

4. Set your OpenAI API key

You need an API key with access to GPT-4o models. Create a .env file in the project root:

Create .env file:

# Create .env file in the project root
touch .env

Add your API key to the .env file:

OPENAI_API_KEY=your_api_key_here

Important: Never commit the .env file to version control. It's already included in .gitignore.

5. Launch Jupyter Notebook

jupyter notebook

Open one of the notebooks (part1_basic_llm.ipynb, part2_embeddings_cosine.ipynb, or part3_rag_chromadb.ipynb) to get started.

---

⚡ Tip: If you run into dependency conflicts, you can upgrade packages individually, e.g.:

pip install --upgrade openai gradio chromadb

---