Piyush Garg - Building AI Agent from Scratch with OpenAI and Postgres
The video builds on a previous tutorial by creating a real-world AI agent using a to-do application. The focus is on integrating AI with a real database and ORM (Object-Relational Mapping) to perform CRUD operations. The tutorial uses PostgreSQL for the database and Drizzle ORM for managing database interactions. The AI agent is designed to understand natural language commands to manage to-do tasks, demonstrating how AI can interact with databases securely. The video also covers setting up a Docker environment for PostgreSQL, creating a schema for to-do tasks, and using OpenAI's API for natural language processing. The tutorial provides a step-by-step guide to setting up the environment, writing the necessary code, and testing the AI agent's functionality. The practical application shows how AI can be used to automate task management, providing a foundation for more complex AI-driven applications.
Key Points:
- Use PostgreSQL and Drizzle ORM to manage database operations for AI agents.
- Integrate OpenAI's API to process natural language commands for task management.
- Set up a Docker environment to run PostgreSQL securely and efficiently.
- Create a schema for to-do tasks to perform CRUD operations using AI.
- Demonstrate AI's capability to automate and manage tasks through natural language processing.
Details:
1. Introduction to AI Agents Part 2 ЁЯдЦ
1.1. Recap of Part 1
1.2. Exploring Complex AI Agents
2. Recap and Audience Feedback on Part 1 ЁЯУЛ
2.1. Recap of Part 1
2.2. Audience Feedback on Part 1
3. Conceptualizing a Real Todo App with AI ЁЯУ▒
- The project aims to develop a fully functional Todo app that goes beyond a basic proof of concept, focusing on real-world application and practical utility.
- Key components include the integration of a real database and an Object-Relational Mapping (ORM) system, such as PostgreSQL and Sequelize, to manage data efficiently.
- AI components are integrated to enhance the app's functionality, providing practical features that can be utilized in real-world scenarios.
- The project emphasizes simplicity and effectiveness, ensuring that the app is user-friendly while leveraging AI to improve usability and productivity.
- Specific AI features include task prioritization and personalized reminders, which are designed to increase user engagement and retention.
4. Understanding Todo App Basics and AI Integration ЁЯза
- The project focuses on developing a Todo application with AI integration, serving as a foundational learning tool for AI functionalities.
- Todo apps are pivotal as they demonstrate CRUD operations in a database, which are critical for many real-world applications.
- The simplicity of a Todo app makes it ideal for illustrating AI integration into software development processes.
- Integrating AI into a Todo app can enhance functionality beyond basic operations, such as using AI for task prioritization or predictive analytics, offering practical insights into AI's role in software.
- This project not only teaches basic programming skills but also provides hands-on experience in leveraging AI to solve common software challenges.
5. Setting Up PostgreSQL Database with Docker ЁЯР│
- Integrate AI agents with databases to enable natural language task management, enhancing user experience through intelligent interaction.
- Ensure AI models, like those from OpenAI, have secure access to data sources such as PostgreSQL, safeguarding sensitive information while allowing effective data utilization.
- Utilize Docker to streamline the setup of PostgreSQL databases, ensuring easy deployment and management, which supports the functionalities required by AI agents.
- Docker provides a portable and consistent environment, making it ideal for managing database configurations and dependencies, thus reducing setup time and errors.
- Implement detailed steps for setting up PostgreSQL with Docker, including pulling the official PostgreSQL image, configuring environment variables, and setting up persistent data storage.
- Example: The setup process can be completed in minutes using Docker commands like 'docker pull postgres' and 'docker run', which simplify deployment and ensure consistency across different environments.
6. Project Environment and Drizzle ORM Setup тЪЩя╕П
6.1. Introduction and Preparation
6.2. Terminal Setup and VS Code Initialization
6.3. Using PNPM for Package Management
6.4. Docker Setup for PostgreSQL
6.5. Environment Variables Configuration
7. Configuring Docker Compose for PostgreSQL ЁЯУж
7.1. Environment Variable Configuration
7.2. Volume Mounting for Data Persistence
8. Database Schema and Drizzle ORM Configuration ЁЯУК
8.1. Database Schema Installation and Configuration
8.2. Drizzle ORM Configuration
8.3. Troubleshooting Tips
9. Running Migrations and Using TablePlus ЁЯЫая╕П
9.1. Setup and Configuration for Migrations
9.2. Executing Migrations and Verification
10. Implementing CRUD Operations with Drizzle ORM ЁЯЦ▒я╕П
- Drizzle ORM requires the installation of Drizzle Kit and Drizzle Studio to facilitate development.
- Add a command in your package.json to open Drizzle Studio with 'npm run studio' for streamlined access.
- Key CRUD operationsтАФcreate, read, update, and deleteтАФare implemented using Drizzle ORM, focusing on a 'todo' table.
- Functions such as 'getAllTodos', 'createTodo', and 'searchTodo' are used to interact with the database.
- 'getAllTodos' retrieves all entries from the 'todo' table efficiently using asynchronous database calls.
- 'createTodo' adds a new record into the 'todo' table, simplifying the process to just needing a string input for the 'todo'.
- The flexibility of Drizzle ORM allows for file organization in either a single consolidated file or across multiple files for better management.
- For example, using 'getAllTodos', developers can quickly fetch all todos, enhancing data retrieval processes.
11. Building an AI Assistant with OpenAI ЁЯМР
11.1. Search and CRUD Operations
11.2. OpenAI SDK Integration
11.3. AI Assistant Setup and Tools
12. Prompt Engineering and Tool Integration ЁЯЪА
12.1. Query and Schema Integration
12.2. AI Assistant Planning and Tool Utilization
12.3. Execution of AI Actions
12.4. Observations and Outputs
13. Looping and Interacting with OpenAI API ЁЯФД
13.1. Observation and Output
13.2. System Prompt and Tools Setup
13.3. Running a Loop for Continuous Interaction
13.4. Message Handling and User Queries
13.5. Autoprompting and Result Handling
13.6. Tool Call and Function Extraction
13.7. Function Execution and Observations
14. Testing and Debugging the AI Agent ЁЯРЮ
14.1. Configuration and Initialization
14.2. User Interaction
14.3. Task Management
14.4. Debugging and Corrections
15. Refining the AI Agent and Search Functionality ЁЯФН
- Initially, the search functionality failed to locate the 'to-do' related to video recordings due to a bug in the code.
- Debugging involved adding console log statements to provide better context and trace the issue during the search process.
- The investigation revealed that the search code was not implemented correctly, resulting in empty search results.
- The query was corrected, leading to the successful identification of the video-related 'to-do', demonstrating the importance of precise search algorithms.
- The process underscores the need for robust debugging tools and methodologies in AI agent development to ensure functionality.