Llama 3: Step by Step Guide to start building LLMs and Building Ai Agents.

Building AI agents using Llama 3’s function calling capabilities offers a powerful way to create intelligent applications that can understand and respond to user queries effectively. This guide will walk you through the essential steps to set up and deploy AI agents with Llama 3, including code snippets and key concepts.

Introduction to Llama 3

Llama 3, developed by Meta, is a state-of-the-art language model that supports advanced natural language processing tasks. The model is available in various sizes, including 8B and 70B parameters, enabling developers to choose the appropriate version based on their computational resources and application needs. Llama 3’s function calling capabilities allow it to interact with external tools, making it suitable for building complex AI agents that can perform specific tasks based on user input.

Setting Up Your Environment

To get started, ensure you have the necessary libraries installed. You can use the following command to install the required packages:

pip install llama-agents llama-index-agent-openai llama-index-embeddings-openai

Importing Libraries

Next, import the libraries needed for your AI agent:

from llama_agents import (
    AgentService,
    ControlPlaneServer,
    SimpleMessageQueue,
    AgentOrchestrator,
)
from llama_index.core.agent import FunctionCallingAgentWorker
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
import logging
import os
import nest_asyncio

nest_asyncio.apply()

Configuring Logging

Set the logging level to see system operations in the outpu

logging.getLogger("llama_agents").setLevel(logging.INFO)

Setting Up the API Key

If you’re using an API like OpenAI, set your API key in the environment:

os.environ['OPENAI_API_KEY'] = 'your_api_key_here'

Creating the AI Agent

Message Queue and Control Plane

Set up a message queue and control plane for managing communication between agents:

message_queue = SimpleMessageQueue()

control_plane = ControlPlaneServer(
    message_queue=message_queue,
    orchestrator=AgentOrchestrator(llm=OpenAI())
)

Defining a Tool

Create a user-defined tool that your agent can use. For example, a tool that returns synonyms:

def get_the_syno() -> str:
    """Returns the word synonym."""
    return "The synonym of the word Artificial Intelligence is: Expert Systems."

tool_1 = FunctionTool.from_defaults(fn=get_the_syno)

Creating the Agent Service

Define the agent and create the agent service:

worker1 = FunctionCallingAgentWorker.from_tools([tool_1], llm=OpenAI())
agent1 = worker1.as_agent()
agent_service_1 = AgentService(
    agent=agent1,
    message_queue=message_queue,
    description="Word Synonym Finder",
    service_name="synonym_finder",
    host="localhost",
    port=5000
)

Testing the Agent

To test your agent, you can simulate user messages and see how the agent responds:

messages = [
    ChatMessage.from_system("You are a helpful assistant."),
    ChatMessage.from_user("What is a synonym for Artificial Intelligence?")
]

response = chat_generator.run(messages=messages)
print(response)

The expected output will include the tool call that retrieves the synonym for “Artificial Intelligence.”

Conclusion

Building AI agents with Llama 3 involves setting up the environment, defining tools, and creating a service that can respond to user queries. As you develop your AI agents, consider factors such as task completion time and the efficiency of API calls. Reducing hallucinations and ensuring accurate responses are critical challenges in this field. By leveraging Llama 3’s capabilities, you can create sophisticated AI agents that enhance user experiences.

This guide should provide a solid foundation for anyone looking to build AI agents using Llama 3’s function calling capabilities. For further exploration, consider reviewing the extensive documentation provided by Meta and experimenting with different configurations and tools.