How To Develop An AI Agent

 

                                                                            image credit: wikipedia

What is an LLM Agent?

An LLM agent is an autonomous entity that leverages a Large Language Model (LLM) to perceive its environment, make decisions, and take actions to achieve specific goals. Unlike a simple LLM application that passively responds to prompts, an agent actively interacts with its environment.

Key Characteristics of LLM Agents:

• Autonomy: Agents can operate independently, without constant human intervention.
• Goal-Oriented: They are designed to achieve specific objectives.
• Perception: Agents can sense their environment through input from various sources (text, APIs, tools).
• Decision-Making: They use the LLM to process information and determine the best course of action.
• Action Execution: Agents can execute actions by interacting with external systems (APIs, tools, databases).
• Memory: They can retain and utilize past interactions and information.
• Planning: Advanced agents can plan complex sequences of actions.

How LLM Agents Work:

The core workflow of an LLM agent typically involves these steps:

1. Perception: The agent receives input from its environment (e.g., user query, API response).
2. Planning: Using the LLM, the agent analyzes the input, breaks down the goal into sub-tasks, and formulates a plan.
3. Action Selection: Based on the plan and available tools, the agent selects the next action to execute.
4. Action Execution: The agent executes the selected action (e.g., makes an API call, searches the web).
5. Observation: The agent observes the outcome of the action (e.g., API response, search results).
6. Memory Update: The agent stores the observation and updates its internal memory.
7. Iteration: The agent repeats steps 2-6 until the goal is achieved.

Architecture of LLM Agents:

A typical LLM agent architecture comprises the following components:

• LLM (Large Language Model): The brain of the agent, responsible for reasoning, planning, and decision-making.
• Prompt Manager: This component is responsible for creating and formatting effective prompts for the LLM. It manages context, memory, and instructions.
• Memory Module: Stores past interactions, knowledge, and observations. This enables the agent to maintain context and learn from its experiences.
• Tools/Plugins: These are external resources that the agent can use to interact with the environment (e.g., search engines, APIs, databases, code interpreters).
• Action Executor: This component executes the actions selected by the agent.
• Planning Module: This component generates plans and breaks down goals into sub-tasks.
• Observation Processor: Handles and formats the responses from the tools.

Key Components in Detail:

• LLM:
  • The LLM is the core of the agent, providing the reasoning and language capabilities.
  • Choosing the right LLM (e.g., GPT-3.5, GPT-4, PaLM 2) depends on the complexity of the task.
• Prompt Engineering:
  • Effective prompt engineering is crucial for guiding the LLM's behavior.
  • Techniques like few-shot learning, chain-of-thought prompting, and role-playing are used to improve performance.
• Memory:
  • Memory can be short-term (e.g., conversation history) or long-term (e.g., knowledge base).
  • Techniques like vector databases and embeddings are used to store and retrieve information efficiently.
• Tools:
  • Tools extend the agent's capabilities beyond the LLM's inherent knowledge.
  • Examples include:
    • Search engines (e.g., Google Search API)
    • APIs (e.g., weather APIs, stock market APIs)
    • Databases
    • Code execution environments.
• Planning:
  • Planning is essential for complex tasks that require multiple steps.
  • Agents can use techniques like hierarchical planning and task decomposition.
• Function Calling:
  • This is a very important part of modern agents. It allows the LLM to call external functions.
  • This allows the LLM to interact with the real world.

Agent Frameworks:

Several frameworks simplify the development of LLM agents:

• LangChain: A popular framework for building LLM applications, including agents. It provides tools for prompt management, memory, and tool integration.
• AutoGPT: An experimental open-source application that demonstrates autonomous LLM agents.
• BabyAGI: another open source project focused on task driven agents.
• Microsoft Semantic Kernel: Microsofts SDK for creating agents.

Challenges and Considerations:

• Hallucinations: LLMs can generate inaccurate or fabricated information.
• Bias: LLMs can inherit biases from their training data.
• Safety: Agents can potentially perform unintended or harmful actions.
• Cost: LLM API usage can be expensive.
• Complexity: Building robust and reliable agents can be challenging.

In summary:

LLM agents represent a significant advancement in AI, enabling autonomous and intelligent systems. By combining the power of LLMs with tools, memory, and planning capabilities, agents can perform complex tasks and interact with the world in a more sophisticated way.

Simple Tutorial: Building an LLM Agent-Based Application

This tutorial will guide you through the basic steps of creating a simple LLM agent-based application. We'll use Python and a popular LLM API (like OpenAI's) to build an agent that can perform a specific task.

Prerequisites:

1. Python: Ensure you have Python 3.6+ installed.

2. LLM API Key: Obtain an API key from your chosen LLM provider (e.g., OpenAI, Google Cloud AI).

3. Libraries: Install necessary libraries using pip:

   Bash

   pip install openai requests
   

Step 1: Setting up the API Connection

First, we need to establish a connection to the LLM API. We'll use the openai library in this example.

Python

import openai
import os

# Replace with your actual API key
openai.api_key = os.getenv("OPENAI_API_KEY") #or "YOUR_API_KEY"

Note: It's best practice to store your API key in an environment variable (OPENAI_API_KEY) for security.

Step 2: Defining the Agent's Task

Let's create a simple agent that can summarize text. We'll define the task and provide instructions to the LLM.

Python

def summarize_text(text):
    """
    Summarizes the given text using the LLM.

    Args:
        text (str): The text to summarize.

    Returns:
        str: The summarized text.
    """

    prompt = f"""
    Summarize the following text:

    {text}

    Summary:
    """

    try:
        response = openai.Completion.create(
            engine="text-davinci-003",  # Or another suitable engine
            prompt=prompt,
            max_tokens=150,  # Adjust as needed
            n=1,
            stop=None,
            temperature=0.7, # Adjust to control randomness
        )

        summary = response.choices[0].text.strip()
        return summary

    except Exception as e:
        return f"Error: {e}"

Explanation:

• summarize_text(text): This function takes the text to be summarized as input.
• prompt: We construct a prompt that instructs the LLM to summarize the given text. The prompt is crucial for guiding the LLM's behavior.
• openai.Completion.create(...): This function sends the prompt to the LLM API and receives a response.
• engine: Specifies the LLM model to use.
• max_tokens: Limits the length of the generated summary.
• temperature: Controls the randomness of the LLM's output. Lower values result in more deterministic output.
• Error Handling: The try...except block handles potential errors during the API call.

Step 3: Creating a Simple User Interface (Optional)

For a basic interactive experience, we can create a simple command-line interface.

Python

def main():
    """
    Main function to interact with the summarization agent.
    """

    text = input("Enter the text to summarize: ")
    summary = summarize_text(text)
    print("\nSummary:")
    print(summary)

if __name__ == "__main__":
    main()

Step 4: Running the Application

Save the code as a Python file (e.g., summarizer.py) and run it from your terminal:

Bash

python summarizer.py

You'll be prompted to enter the text you want to summarize. The agent will then generate a summary and display it.

Expanding the Agent's Capabilities:

This is a very basic example. To create more complex agents, you can:

• Add Tools: Integrate external tools (e.g., search engines, APIs) to allow the agent to perform more complex tasks.
• Implement Memory: Store previous interactions and information to enable the agent to maintain context.
• Use Prompt Engineering: Refine the prompts to guide the LLM's behavior more precisely.
• Utilize Agent Frameworks: Explore frameworks like LangChain or AutoGPT to simplify agent development.
• Create Function Calling: use the newer openai function calling to create agents that can use tools.
• Utilize Chat Completions: For conversational agents, use the chat completions endpoints, which are designed for multi-turn conversations.

Example of function calling (Very basic):

Python

import openai
import json
import os

openai.api_key = os.getenv("OPENAI_API_KEY")

def get_current_weather(location, unit="fahrenheit"):
    """Gets the current weather for a location."""
    weather_info = {
        "location": location,
        "temperature": "72",
        "unit": unit,
        "forecast": ["sunny", "windy"],
    }
    return json.dumps(weather_info)

def run_conversation():
    messages = [{"role": "user", "content": "What's the weather in Boston?"}]
    functions = [
        {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        }
    ]

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-0613",
        messages=messages,
        functions=functions,
        function_call="auto",  # auto is default, but we'll be explicit
    )

    response_message = response["choices"][0]["message"]

    if response_message.get("function_call"):
        function_name = response_message["function_call"]["name"]
        function_args = json.loads(response_message["function_call"]["arguments"])
        function_response = get_current_weather(
            location=function_args.get("location"),
            unit=function_args.get("unit"),
        )

        messages.append(response_message)
        messages.append(
            {
                "role": "function",
                "name": function_name,
                "content": function_response,
            }
        )
        second_response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo-0613",
            messages=messages,
        )
        return second_response["choices"][0]["message"]["content"]
    else:
        return response_message["content"]

print(run_conversation())

This extended example shows how to use function calling. The LLM can now call an external function and use the result to create a response.

Remember to consult the documentation of your chosen LLM API for more advanced features and capabilities.

You can use already developed code as a template  https://github.com/dhirajpatra/ai_agent_ollama