Mastering Large Language Models (LLM) with AI Agents

This repository includes a set of assets that are used in the above course, which dives deeper into the various problems and solutions when building an AI Agents system in enterprise environments.

The course is available on edX.

Simple AI Agent

Jupyter Notebooks

1. Simple AI Agent: This notebook introduces the fundamental concepts and implementation of AI Agents.
2. Embedding Model: This notebook examines the role of embedding models in RAG systems, including their applications and limitations.
3. Semantic Chunking: This notebook investigates the process of semantic chunking in RAG systems, focusing on its significance in information retrieval.
4. Contextual Retrieval: This notebook delves into the strategies for contextual retrieval in RAG systems, emphasizing the effective handling of numerical data and tables.
5. Reverse Hyde: This notebook explores the Reverse Hyde technique in RAG systems, highlighting its importance in addressing contextual relevance and query ambiguity.
6. Hybrid Search: This notebook discusses the integration of hybrid search capabilities in RAG systems, encompassing both temporal relevance and multi-lingual support.
7. Reranking: This notebook discusses the importance of reranking in RAG systems, focusing on techniques to refine the initial retrieval results for more accurate and relevant outputs.
8. Multi Modal Retrieval: This notebook explore the possibilities of retrieval from images and not limited to text.

Common Problems in AI Agents Systems and Their Solutions

The problematic part of a RAG application is usually the retrieval part. The system might retrieve the wrong documents, or only part of them and lead to wrong replies. This is the classical problem in machine learning of the precision-recall-tradeoff.

• Simple Agent
  • Prompt engineering basics
  • Generating code (SQL, GraphQL, Python, Rust) with schema and runtime.
  • AI Agent Tools
  • Scaling simple AI agents to production (Bedrock, for example)
• Complex Agent
  • Agent types: Planner, Orchestrator, Generator, Specific Task Executor, Evaluator
  • Solving puzzle such as Sudoku, from brute force to optimized and adjustable solution
  • Scaling complex AI agents to production (Step Functions, for example)
• Meta AI Agents
  • Domain specific meta AI agents (vision, Analytics, Time-series)
  • Meta AI concepts: Tools, Planner, Orchestrator, Generator..
  • Scaling meta AI agents to production

Set Up Local Jupyter

To set up the project, you have two options: using uv or traditional pip.

Using uv

pip install uv  # Install `uv`
uv venv --python cpython-3.12.3-macos-aarch64-none   # Create a virtual environment for MacOS with Apple Silicon
source .venv/bin/activate   # Activate the virtual environment.
uv pip compile requirements.in --output-file requirements.txt # Compile the project dependencies
uv pip install requirements.txt  # Sync the dependencies

If the virtual environment doesn't find pip or other modules:

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py  # get the pip package
python3 get-pip.py # Install the pip package
pip install ipykernel # Install ipykernel to add the support for Jupyter notebooks to VSC

To execute the notebooks in a Jupyter lab environment, you need to add the following commands

python3 -m pip install jupyterlab # Install Jupyter Lab and ipykernel to manager kernels for Jupyter
python3 -m ipykernel install --user --name=.venv --display-name="Python (.venv)" # Create kernel from the virtual environment
jupyter lab

And select the Python (.venv) from the kernel list.

Using traditional pip

1. Create a virtual environment using your preferred method (e.g., python -m venv myenv).
2. Activate the virtual environment.
3. Install the project dependencies using pip install -r requirements.txt.

Note: The uv method is recommended for its speed, ease of use and management of project dependencies. However, the traditional pip method is also supported for those familiar with it.

Setup in Google Colab

Follow the link to the first hands-on lab:

Before the first cells add the following commands

!git clone https://github.com/guyernest/advanced-rag.git
%cd advanced-rag
!pip install -q -r requirements.txt

You might need to restart the Colab runtime at the end of the dependencies installation. Remember to change back to the course folder:

%cd advanced-rag

Setup in SageMaker Studio Lab

Follow the link to the first hands-on lab:

Clone the GitHub repository when prompted by the service.

Before the first cells add the following commands:

%cd ai-agents
!pip install -q -r requirements.txt