Learn how to build text-to-query agents so you can have conversations with your data!

Text-to-query agents

Get set up for the hands-on exercises

The text-to-query workflow

Writing MongoDB queries

More complex aggregations

A text-to-query workflow in practice

Text-to-Query Agents: Applications and Architecture

Make your text-to-query workflow agentic! Building and orchestrating AI agents with LangGraph will allow you to create smart and dynamic text-to-query workflows.

The text-to-query agent architecture

Building the agent's toolbox

Creating the text-to-query prompt

Orchestrating the text-to-query agent

Automating Text-to-Query Workflows with Agents

Integrate a short-term memory into your text-to-query agent so it doesn't have to re-run queries to combine insights.

Memory and checkpointing in LangGraph

Adding memory to the agent

Congratulations!

Adding Memory to the Agent

Have you ever wished that your could chat with your database? This is now a possibility with LLMs and AI agents. In this course, you'll learn how to interact and chat with a MongoDB database instance, using LangGraph for agent orchestration. You'll learn about the challenges and value of text-to-query workflows, build your own to answer data-related questions about a movies dataset, and even implement memory checkpointing to pick up your conversations where they left off!

Introduction to Functions in Python

Discover how to talk to your data using text-to-query AI agents with MongoDB and LangGraph.

Building the agent's toolbox

Text-to-Query Agents with MongoDB and LangGraph

Exercise instructions

Hands-on interactive exercise