Building an AI Agent with Tools

Introduction

• Overview of building an AI agent that utilizes various tools.
• Tools will be selected automatically by the AI based on the input.
• Accessible for beginner to intermediate programmers.

Demo of Agent Functionality

• Agent can answer questions using Retrieval Augmented Generation (RAG).
• RAG allows the AI to reason using additional data sources rather than relying solely on pre-trained data.

Data Sources Used

1. Population CSV file
   • Contains structured data regarding population density, changes, etc.
   • Easy for the AI model to ingest.
2. PDF about Canada
   • Included as a single example for demo purposes.
   • The model can switch between data sources depending on the question.

Notetaking Feature

• Agent can take notes and save them in a notes.txt file.
• Demonstrates the ability to interact with external systems and tools.

Step-by-Step Tutorial

• Introduction to the technology stack and tools used for building the agent.

Setting Up the Environment

1. Create a virtual environment.
   • Command: python3 -m venv AI
2. Activate the environment depending on OS.
3. Install necessary packages:
   • llama-index for data ingestion.
   • PyPDF2 for reading PDFs.
   • pandas for handling CSV files.
   • python-dotenv for loading environment variables.

Preparing Data Sources

• Download required datasets:
  1. World population by country (CSV) from Kaggle.
  2. PDF version of Canada's Wikipedia page.
• Organize files into a Data directory.

Ingesting and Querying Data

• Use pandas to read and query the population CSV file.
• Create a query engine to handle queries about the data.
• Implement a basic mechanism to test query results.

Integrating Note-Taking Capability

• Create a function to save notes to a text file.
• Use the Llama Index to wrap the function as a tool for the agent.

Setting Up the Agent

• Define the tools available to the agent (e.g., query engine for population data, note-saver).
• Use OpenAI's API for LLM and initialize the agent with all tools available.

Working with Unstructured Data

• Implement a PDF reader for handling unstructured data using Llama Index.
• Create a vector store index for the loaded PDF, enabling efficient queries over unstructured data.

Final Implementation

• Combine all components into the final agent.
• Test the agent with various queries, ensuring it correctly utilizes the tools and provides accurate responses.

Conclusion

• Summary of the capabilities of agent-based AI.
• Encouragement to explore further applications and extensions of this technology.