Harness the Power of AI Agents with LlamaIndex: A Step-by-Step Guide

Imagine a personal assistant that understands your requests and executes them flawlessly, whether it's a quick calculation or retrieving the latest market news. This article explores building such AI agents using the LlamaIndex framework. We'll guide you through creating these intelligent agents, leveraging LLMs' function-calling capabilities for efficient task completion. This guide is suitable for both AI newcomers and experienced developers.

Key Learning Objectives:

• Grasp the fundamentals of AI agents and their problem-solving capabilities.
• Implement AI agents using the LlamaIndex framework.
• Utilize LLM function-calling for efficient task execution.
• Integrate web search tools into your AI agents.
• Gain practical experience building and customizing AI agents in Python.

This article is part of the Data Science Blogathon.

Table of Contents:

• Understanding AI Agents
• Introducing LlamaIndex
• Implementing AI Agents with LlamaIndex
  • Step 1: Obtaining the API Key
  • Step 2: Installing Necessary Libraries
  • Step 3: Setting Environment Variables
  • Step 4: Initializing the LLM
  • Step 5: Defining Custom Functions
  • Step 6: Creating Function Tool Objects
  • Step 7: Using predict_and_call with User Queries
  • Step 8: Putting it All Together
• Advanced Customization Options
• Frequently Asked Questions

Understanding AI Agents:

AI agents are sophisticated digital assistants. They don't just respond; they analyze, understand, and decide how best to execute commands. This includes answering questions, performing calculations, or retrieving information—all with minimal human intervention. They process natural language, identify key details, and utilize their capabilities for optimal responses.

Why Use AI Agents?

AI agents are revolutionizing technology interaction. They automate repetitive tasks, improve decision-making, and personalize experiences, making them valuable across various industries. From finance to healthcare, they streamline operations, enhance customer service, and provide valuable insights.

Introducing LlamaIndex:

LlamaIndex is a powerful framework for simplifying AI agent creation using Large Language Models (LLMs). It leverages LLMs like OpenAI's models, enabling developers to build intelligent agents with minimal code. LlamaIndex allows integration of custom Python functions, seamlessly combining them with the LLM for diverse task handling.

LlamaIndex Key Features:

• Function Calling: Enables AI agents to call specific functions based on user queries.
• Tool Integration: Supports integration of various tools, including web search and data analysis.
• User-Friendliness: Designed for ease of use for both beginners and experienced developers.
• Customization: Supports custom functions and advanced features like pydantic models.

Implementing AI Agents with LlamaIndex:

We'll use OpenAI's GPT-4o and Bing search for web queries (LlamaIndex integrates with Bing).

Step 1: Obtaining the API Key

Obtain a Bing Search API key by creating a Bing resource (a free tier is available).

Step 2: Installing Necessary Libraries

Install required libraries:

!pip install llama_index llama-index-core llama-index-llms-openai llama-index-tools-bing-search

Step 3: Setting Environment Variables

Set your API keys as environment variables:

import os

os.environ["OPENAI_API_KEY"] = "sk-proj-<openai_api_key>"
os.environ['BING_API_KEY'] = "<bing_api_key>"</bing_api_key></openai_api_key>

Step 4: Initializing the LLM

Initialize the GPT-4o LLM:

from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4o")
llm.complete("1 1=")

Step 5: Defining Custom Functions

Create functions for your agent:

from llama_index.tools.bing_search import BingSearchToolSpec

def addition_tool(a:int, b:int) -> int:
    """Returns sum of inputs"""
    return a   b

def web_search_tool(query:str) -> str:
  """Retrieves latest stock news using Bing Search"""
  bing_tool = BingSearchToolSpec(api_key=os.getenv('BING_API_KEY'))
  return bing_tool.bing_news_search(query=query)

Step 6: Creating Function Tool Objects

Create function tool objects:

from llama_index.core.tools import FunctionTool

add_tool = FunctionTool.from_defaults(fn=addition_tool)
search_tool = FunctionTool.from_defaults(fn=web_search_tool)

Step 7: Using predict_and_call with User Queries

query = "what is the current market price of apple"

response = llm.predict_and_call(
    tools=[add_tool, search_tool],
    user_msg=query, verbose = True
)

Step 8: Putting it All Together

Combine all steps into a single code block (refer to the original article for the complete code).

Advanced Customization Options:

Enhance function definitions using pydantic models for improved type checking and validation. Handle complex queries by creating additional tools or refining existing ones, adding error handling and custom logic.

Conclusion:

AI agents, empowered by frameworks like LlamaIndex, offer a powerful way to interact with technology. They can process inputs, reason, access information, and execute actions efficiently. This guide provides a foundational understanding of building and customizing these agents.

Key Takeaways:

• Agents can select functions based on user queries.
• Function calling relies on LLM's ability to interpret function names and descriptions.
• LlamaIndex simplifies AI agent implementation.
• Function-calling requires LLMs with function-calling support.

Frequently Asked Questions: (Refer to the original article for the FAQs)

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