Creating Your Own LLM Agent Application from Scratch

Creating your own LLM (Large Language Model) agent application from scratch might seem daunting, but it's a rewarding and achievable project. Understanding the core components and essential tools required not only allows you to build an agent beyond simple text generation but also presents an opportunity for growth and learning. 

This guide will walk you through the process step-by-step, ensuring you have all the knowledge and resources needed to succeed and grow in your understanding of AI.

Introduction to LLM Agent Applications

LLM agent applications represent a significant leap forward in artificial intelligence. By leveraging large language models, these agents can perform various tasks across different scenarios with remarkable efficiency and accuracy.

This section explores the fundamentals of LLM agents, their evolution from basic chatbots, and their advanced capabilities.

Also Read: Introducing RAGA AI DNA

LLM Agents: Explanation and Significance

LLM agents are advanced programs that utilize large language models as their core computational engine. Unlike traditional chatbots that rely on pre-defined scripts, LLM agents can dynamically interpret and respond to user inputs.

This flexibility makes them invaluable for complex and varied tasks, providing businesses with tools to adapt to changing needs and environments. Some of the significance of the LLM agents are provided below:

• Dynamic Interaction: LLM agents can engage in complex and varied conversations, understanding context and nuances. This makes interactions more fluid and less robotic.

• Adaptive Learning: These agents continuously learn and improve from interactions, becoming more efficient. As they interact more with users, they refine their responses and actions, leading to better performance.

• Versatility: LLM agents can be applied across various domains and industries, from customer service to technical support. Businesses can deploy LLM agents in multiple areas, improving efficiency and effectiveness.

• Contextual Awareness: LLM agents can understand the context of the conversation, allowing them to provide more personalized, relevant, and accurate responses. This reduces the need for repetitive clarifications and enhances user satisfaction.

• Scalability: Businesses can scale their use of LLM agents quickly to handle increasing volumes of interactions without compromising quality. This is particularly beneficial during peak times or for growing companies.

LLM Agents vs Basic Chatbots

Early chatbots were limited to responding to specific commands and struggled with understanding context. On the other hand, LLM-powered agents represent a new era of AI-driven interactions. These agents can use large language models to comprehend and generate human-like responses, manage intricate dialogues, and make decisions based on contextual understanding.

Understanding this progression is crucial to know how to build LLM agents. Let's understand some of this progression:

• Early Limitations: Basic chatbots had restricted functionality, limited to specific commands and scripted responses. They often fail to understand the context or adapt to unexpected queries, leading to frustrating user experiences. These chatbots could not handle variations in user input or engage in multi-turn conversations effectively.

• Contextual Understanding: LLM agents can grasp the context of conversations, allowing for more natural and meaningful interactions. This means they can remember previous parts of the conversation, understand nuances, and provide relevant responses to the ongoing discussion. This contextual awareness leads to more coherent and engaging user interactions.

• Enhanced Decision-Making: These agents can make informed decisions based on the information provided, leading to more accurate and relevant responses. By understanding the intent behind user queries, LLM agents can choose appropriate actions and responses, whether retrieving information, performing calculations, or providing detailed explanations.

• Natural Language Processing (NLP) Capabilities: LLM-powered agents utilize advanced NLP techniques to understand and generate human language. This allows them to interpret various inputs, from simple questions to complex instructions, and respond appropriately. Their ability to process natural language enables more intuitive and user-friendly interactions.

• Multi-Tasking Abilities: Unlike basic chatbots, often designed for single-purpose tasks, LLM agents can handle multiple tasks simultaneously. They can seamlessly switch between different contexts and activities, making them more versatile and efficient. This multi-tasking capability is crucial for applications that require handling diverse user needs.

LLM Agents: Capability Overview

LLM agents go far beyond simple text generation, offering various capabilities that make them indispensable tools in various fields.

Understanding why you should build the project is essential to understanding how to build LLM agent. Here's a closer look at the capabilities of LLM agents in tasks beyond essential text generation.

Data Retrieval: LLM agents can access and retrieve information from various sources, providing accurate and timely responses. By integrating with databases, search engines, and other data repositories, these agents can pull in relevant information, summarize documents, and present findings coherently. This makes them invaluable for research, customer support, and information management.

Example: An LLM agent can search through a company's internal knowledge base to provide answers to employee queries, saving time and improving productivity.

Benefit: Enhances the efficiency of information retrieval, reducing the time spent on manual searches.

Mathematical Calculations: These agents can handle complex mathematical calculations, making them useful for technical and analytical tasks. They can evaluate mathematical expressions, solve equations, and perform statistical analyses, providing precise and reliable results.

Example: An LLM agent can be used in financial services to calculate investment returns, analyze risk, and generate financial forecasts.

Benefit: Reduces the likelihood of calculation errors and speeds up the data analysis process.

Automated Workflows: LLM agents can streamline processes by automating repetitive tasks, improving efficiency and productivity. They can manage workflows, schedule tasks, send reminders, and handle routine administrative duties, freeing human resources for more strategic activities.

Example: An LLM agent can automate scheduling meetings, sending follow-up emails, and updating project management tools.

Benefit: Increases operational efficiency and allows employees to focus on higher-value tasks.

Natural Language Understanding (NLU): LLM agents can interpret and respond to complex queries, understanding the intent behind user inputs. This allows them to provide detailed and accurate responses, engage in meaningful dialogues, and offer solutions tailored to user needs.

Example: An LLM agent in a customer service role can understand and resolve complex customer issues by interpreting the underlying intent and context of the queries.

Benefit: Enhances the quality of customer interactions and improves user satisfaction.

Decision Support: These agents can analyze data, identify patterns, and provide accurate recommendations based on their findings. This makes them valuable for decision-making processes in various domains, including business strategy, healthcare, and logistics.

Example: An LLM agent can assist doctors by analyzing patient data and suggesting possible diagnoses or treatment plans.

Benefit: Supports informed decision-making and helps professionals make data-driven choices.

Content Summarization: LLM agents can summarize long documents, articles, or reports, extracting key information and presenting it concisely. This is useful for professionals who need to stay informed and updated but have limited time to read extensive materials.

Example: An LLM agent can provide executives with daily industry news summaries, highlighting the most important developments.

Benefit: Saves time and ensures users receive the most relevant information quickly.

Language Translation: These agents can translate text from one language to another, facilitating communication across different linguistic groups. They can handle complex translations and ensure that the meaning and context of the original text are accurately conveyed.

Example: An LLM agent can translate technical manuals, legal documents, or customer inquiries, enabling businesses to operate in multiple languages.

Benefit: Enhances global communication and expands market reach.

Sentiment Analysis: LLM agents can analyze text to determine the sentiment expressed, whether positive, negative, or neutral. This capability is valuable for understanding customer feedback, monitoring brand reputation, and gauging public opinion.

Example: An LLM agent can analyze social media posts to identify trends in customer sentiment regarding a new product launch.

Benefit: Provides insights into customer perceptions and helps businesses respond proactively to public sentiment.

Complex Query Handling: These agents can handle multi-faceted queries that require breaking down into simpler sub-questions and providing comprehensive answers. This is particularly useful in research and academic environments where detailed and multi-layered questions are common.

Example: An LLM agent can answer complex research questions by breaking them down into manageable parts and providing detailed responses.

Benefit: Enhances the depth and accuracy of responses to complex queries.

Read Also: A Guide to Evaluating LLM Applications and enabling Guardrails using Raga-LLM-Hub

LLM Agents: Core Components

Building an LLM agent involves several essential components that work together to create a functional and efficient application. 

Grasping these core elements is crucial to understanding how to build LLM agents that can handle various tasks effectively.

Let's understand the components that are necessary to build LLM agents:

LLM Agent: Base Requirements

The foundational element of any LLM agent is the large language model itself. This model must be trained on diverse text data to ensure it can understand and generate human-like text.

Choosing the right LLM, such as GPT-4, Llama-2, or another advanced model, is critical as it forms the backbone of the agent's capabilities. The selected LLM should be capable of handling your application's specific requirements, such as context understanding, language translation, or data retrieval.

LLM Agents: Introduction to Tools

Integrating various interaction tools is essential to extending the functionality of LLM agents beyond simple text generation. These tools enable the agent to perform various tasks, from searching the web for information to executing mathematical calculations. 

Below, we'll explore these interaction tools in detail.

Search Engines 

LLM agents can utilize search engines to retrieve information from the web. This involves sending queries to search engines like Google and processing the returned results to provide relevant answers. Integrating a custom search engine API allows the agent to access up-to-date information and deliver precise responses.

Example of integrating a Google Custom Search Engine in Python:

from googleapiclient.discovery import build
import os

def search(query):
    service = build("customsearch", "v1",
                    developerKey=os.environ.get("GOOGLE_API_KEY"))
    result = service.cse().list(q=query, cx=os.environ.get("GOOGLE_CSE_ID"), num=10).execute()
    return [item['snippet'] for item in result.get('items', [])]

query = "latest AI trends"
search_results = search(query)
print(search_results)

Calculator

For mathematical tasks, LLM agents can integrate calculator tools to evaluate expressions and perform calculations. Using libraries such as ‘py_expression_eval’, agents can process complex mathematical queries and return accurate results.

Example of implementing a simple calculator using ‘py_expression_eval’ in Python:

from py_expression_eval import Parser

parser = Parser()

def calculate(expression):
    return parser.parse(expression).evaluate({})

expression = "3 * (2 + 7)"
result = calculate(expression)
print(f"The result of the expression '{expression}' is: {result}")

Other Tools

Beyond search and calculation, LLM agents can integrate a variety of other tools, such as recommendation engines, classification algorithms, and summarization tools. These additional capabilities enable the agent to handle more specialized tasks and provide richer functionality.

Recommendation Engines: Agents can suggest products or content based on user preferences and behavior.

Classification Algorithms: These can be used to categorize text or data into predefined groups.

Summarization Tools: Agents can condense lengthy documents or articles into concise summaries.

LLM Agent: Role in Managing Interactions and Decision Making

LLM agent is the orchestrator of interactions, managing how and when to use the integrated tools based on user inputs and the conversation context.

It must decide which tool to deploy, how to interpret the results, and how to return the information to the user. This involves:

Interpreting User Inputs: Understanding the intent behind user queries and determining the appropriate action.

Tool Selection: Choosing the right tool based on the query (e.g., using a search engine for information retrieval or a calculator for mathematical queries).

Executing Actions: Running the selected tool with the given inputs and handling the output.

Response Generation: Crafting a coherent and contextually relevant response to the user based on the tool's output and any additional context.

This comprehensive management ensures that the agent delivers accurate and relevant results, enhancing the user experience and making the agent a valuable asset for various applications.

With a clear understanding of the core components of LLM agents, we can now move on to selecting the right framework for your agent, exploring the available options, and determining the best fit for your specific needs.

Also Read: AI’s Missing Piece: Comprehensive AI Testing

Framework Selection to Build LLM Agent

Choosing the right framework is a critical step in knowing how to build LLM agents. The framework you select will influence the agent's capabilities, performance, and ease of development.

This section provides an overview of single-agent and multi-agent frameworks, guides you through selecting popular frameworks like LangChain, LlamaIndex, and Haystack, and discusses considerations for aligning your choice with project-specific needs and capabilities.

Single-Agent vs. Multi-Agent Frameworks

Single-agent frameworks are designed to support the development of individual agents that operate independently. These frameworks provide the tools and libraries to create an agent to handle tasks independently. They are simpler to implement and manage, making them ideal for straightforward applications where one agent is sufficient to meet the requirements.

• Single-Agent Frameworks: Focus on creating one robust agent.

• Advantages: Simplicity, ease of management, straightforward implementation.

• Disadvantages: Limited to the capabilities of a single agent, may not handle complex or varied tasks as efficiently.

On the other hand, multi-agent frameworks allow for the creation of multiple agents that can interact and collaborate to perform tasks. These frameworks are more complex but offer greater flexibility and scalability. They are suitable for applications requiring a team of agents to work together, such as handling various aspects of a large project or interacting with different systems.

• Multi-Agent Frameworks: Enable multiple agents to collaborate and share tasks.

• Advantages: Flexibility, scalability, ability to handle complex tasks by distributing workload.

• Disadvantages: Increased complexity, more challenging to implement and manage.

Choosing Right Framework: LangChain, LlamaIndex, Haystack

Selecting the appropriate framework depends on your specific project needs and your required capabilities. Here's a closer look at three popular frameworks: LangChain, LlamaIndex, and Haystack.

LangChain

This framework is designed to facilitate the creation of complex workflows involving language models. It is ideal for applications that require combining multiple tasks or integrating various tools and APIs.

Example of setting up a basic LangChain workflow in Python:

from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI

# Load the necessary tools
tools = load_tools(["serpapi", "llm-math"])

# Initialize the agent
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)

# Run the agent
result = agent.run("What is the sum of the latest stock prices?")
print(result)

LlamaIndex

LlamaIndex focuses on providing robust indexing and retrieval capabilities. It is particularly useful for applications that need to manage large datasets and perform efficient searches.

Example of creating an index and performing a search with LlamaIndex in Python:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

# Create the index
documents = SimpleDirectoryReader("./documents").load_data()
index = VectorStoreIndex.from_documents(documents)

# Perform a search
query = "first document"
response = index.query(query)
print(response)

Haystack

Haystack is an open-source framework designed for building search systems. Haystack offers a comprehensive suite of tools for integrating with various data sources, performing semantic searches, and providing accurate results.

Example of setting up a basic search pipeline with Haystack in Python:

from haystack.document_store.memory import InMemoryDocumentStore
from haystack.retriever.sparse import BM25Retriever
from haystack.pipeline import ExtractiveQAPipeline
from haystack.reader.farm import FARMReader

# Initialize components
document_store = InMemoryDocumentStore()
retriever = BM25Retriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")

# Create the pipeline
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)

# Add documents to the document store
documents = [{"content": "This is a test document."}]
document_store.write_documents(documents)

# Run the pipeline
result = pipeline.run(query="What is this?", top_k_retriever=10, top_k_reader=5)
print(result)

LLM Agent: Project-Specific Needs and Capabilities

When selecting a framework, it is essential to consider your project's specific needs and capabilities. Here are some key factors to keep in mind:

• Scalability: Consider whether your LLM agent application will need to scale up to handle a larger volume of interactions or more complex tasks. Multi-agent frameworks may be more suitable for scalable solutions.

• Complexity: Evaluate the complexity of the tasks your agent needs to perform. Single-agent frameworks might be sufficient for simpler tasks, while multi-agent frameworks can handle more intricate workflows.

• Integration: Determine the tools and APIs your agent will need to interact with. Some frameworks offer better support for specific integrations, which can streamline development.

• Performance: Consider the performance requirements of your LLM agent application. Efficient indexing, retrieval, and response times are crucial for a smooth user experience.

• Community and Support: Look into the community and support available for the framework. A strong community can provide valuable resources, plugins, and troubleshooting assistance.

By thoroughly assessing these factors, you can select a framework that aligns with your project’s goals and ensures a successful implementation.

Next, we will explore the steps to set up your development environment, including the installation of necessary packages and configuration of essential tools.

LLM Agent Build: Setting Up Environment

Setting up your development environment correctly is crucial to building a robust and efficient LLM agent. This involves installing the necessary Python packages, configuring essential tools like custom search engines and APIs, and fine-tuning your models to meet specific needs.

This section will walk you through each step to ensure your environment is ready for development to understand how to build LLM agent.

Python Package Installation

Installing the required Python packages is the first step in setting up your development environment. These packages provide the tools and libraries needed to build, train, and deploy your LLM agent.

Essential packages include libraries for interacting with large language models, performing calculations, and integrating search functionality.

The complete code for setting up the development environment for your LLM agent application is provided below.

# Install the necessary Python packages
!pip install openai py_expression_eval google-api-python-client

import openai
from py_expression_eval import Parser
from googleapiclient.discovery import build
import os

Configuring Custom Search Engine and Google API Key

You need to configure a custom search engine using Google's API to enable your LLM agent to perform web searches. This setup allows your agent to retrieve information from the web, providing up-to-date and relevant responses.

Set Up a Custom Search Engine

• Go to Google Custom Search and create a new search engine.

• Obtain your Custom Search Engine ID (CSE ID).

Get a Google API Key

• Go to the Google Cloud Console.

• Create a new project and enable the Custom Search API.

• Generate an API key for your project.

Configure Environment Variables

• Set the API Key and CSE ID as environment variables in your development environment.

# Set environment variables for API Key and Custom Search Engine ID
os.environ["GOOGLE_API_KEY"] = "your_google_api_key"
os.environ["GOOGLE_CSE_ID"] = "your_custom_search_engine_id"

# Function to perform a search query using Google Custom Search
def search(query):
    service = build("customsearch", "v1",
                    developerKey=os.environ["GOOGLE_API_KEY"])
    result = service.cse().list(q=query, cx=os.environ["GOOGLE_CSE_ID"], num=10).execute()
    return [item['snippet'] for item in result.get('items', [])]

# Example search query
query = "latest AI trends"
search_results = search(query)
print(search_results)

Guide to Fine-Tuning: Creating/Modifying .yml

Fine-tuning your LLM involves adjusting the model to better suit your specific needs. This can be achieved by creating or modifying '.yml' configuration files and using tools like Axolotl for training. Fine-tuning ensures your model performs optimally for the tasks you require.

Step 1: Install Axolotl

Axolotl is a tool that simplifies the fine-tuning process. To get started, you must clone the Axolotl repository and install the necessary packages. This setup provides you with the environment required for fine-tuning your LLM agent.

• Clone the Axolotl repository:

git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl

• Install the required packages:

pip3 install packaging
pip3 install -e .
pip3 install -U git+https://github.com/huggingface/peft.git

Step 2: Create or Modify .yml Configuration Files

The .yml configuration files define the parameters for fine-tuning your model. These files include settings for the base model, tokenizer, datasets, training parameters, and more. You can create a new .yml file or modify an existing one to suit your needs.

Example phi-finetune.yml:

base_model: "teknium/Puffin-PHi-v2"
base_model_config: "Teknium/Puffin-PHi-v2"
model_type: "AutoModelForCausalLM"
tokenizer_type: "AutoTokenizer"
is_llama_derived_model: false
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true

datasets:
  - path: "h2oai/h2ogpt4-instruct-gm_20k"

system_prompt: "The assistant gives helpful, detailed, and polite answers to the user's questions."
val_set_size: 0.1

output_dir: "./puffin-finetuned"

sequence_len: 1024
sample_packing: false
pad_to_sequence_len: false

adapter: lora
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - query_key_value

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: linear
learning_rate: 0.0002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true

gradient_checkpointing: false
recompute_last_layers: null
resume_from_checkpoint: null
local_rank: -1
logging_steps: 1
flash_attention: false

Step 3: Fine-Tune the Model

With your configuration file ready, you can now proceed to fine-tune your model using Axolotl. The following command launches the fine-tuning process based on the parameters specified in your '.yml' file.

• Start fine-tuning:

accelerate launch -m axolotl.cli.train --config phi-finetune.yml

Detailed Explanation of Configuration Parameters

Base Model and Tokenizer:

• 'base_model' and 'base_model_config': Specifies the pre-trained model to be used as the base for fine-tuning.

• 'tokenizer_type': Defines the tokenizer compatible with the base model.

• 'is_llama_derived_model': Indicates if the model is derived from Llama, affecting how the model is handled.

Dataset Configuration:

'datasets': Defines the datasets to be used for fine-tuning. Each dataset entry includes the path to the dataset file and its type.

Training Parameters:

'sequence_len': Maximum length of input sequences.

'sample_packing': Whether to pack multiple samples into a single input sequence.

'pad_to_sequence_len': Specifies if inputs should be padded to the maximum sequence length.

'gradient_accumulation_steps': Number of steps to accumulate gradients before updating model weights.

'micro_batch_size': Size of each mini-batch for training.

'num_epochs': Number of epochs to train the model.

'optimizer': Specifies the optimization algorithm to use (e.g., 'adamw_torch').

'learning_rate, lr_scheduler', 'max_grad_norm': Learning rate and scheduler settings.

Output Configuration:

'output_dir': Directory to save the fine-tuned model and other outputs.

'logging_steps': Frequency of logging training progress.

Model Adapter Settings:

'adapter': Specifies the type of adapter to use (e.g., 'qlora').

'lora_r', 'lora_alpha', 'lora_dropout': Parameters for the LoRA (Low-Rank Adaptation) method, which fine-tunes a small subset of model parameters.

Monitoring and Evaluating Fine-Tuning

Axolotl will log the training progress during the fine-tuning process, including loss values, accuracy, and other relevant metrics. You can monitor these logs to ensure the model is training correctly and adjust as needed.

Once fine-tuning is complete, the resulting model will be saved in the specified 'output_dir'. You can then use this fine-tuned model for inference, applying it to your tasks and evaluating its performance.

By following this guide, you can fine-tune your LLM to meet your application's unique requirements, ensuring optimal performance and accuracy. With the development environment set up and the model fine-tuned, you are now ready to implement and test the core functionalities of your LLM agent.

LLM Agent Build: Implementing Essential Tool

Integrating essential tools is crucial to maximizing the utility and functionality of your LLM agent. These tools enable the agent to perform various tasks beyond simple text generation, such as retrieving information from the web, executing mathematical calculations, and providing recommendations or classifications.

This section covers implementing these essential tools, ensuring your LLM agent is well-equipped to handle diverse tasks efficiently.

Implementation of Search Functionality

Integrating a search functionality allows your LLM agent to access up-to-date information from the web, providing relevant and accurate responses to user queries. Google's Custom Search Engine (CSE) is an excellent tool.

Set up the Custom Search Engine:

Obtain the API Key and Custom Search Engine ID as described in the previous section.

Implement the Search Function:

Use the 'google-api-python-client' library to interact with Google's Custom Search API.

from googleapiclient.discovery import build
import os

# Set environment variables for API Key and Custom Search Engine ID
os.environ["GOOGLE_API_KEY"] = "your_google_api_key"
os.environ["GOOGLE_CSE_ID"] = "your_custom_search_engine_id"

# Function to perform a search query using Google Custom Search
def search(query):
    service = build("customsearch", "v1",
                    developerKey=os.environ["GOOGLE_API_KEY"])
    result = service.cse().list(q=query, cx=os.environ["GOOGLE_CSE_ID"], num=10).execute()
    return [item['snippet'] for item in result.get('items', [])]

# Example search query
query = "latest AI trends"
search_results = search(query)
print(search_results)

This function takes a query string, sends it to Google’s Custom Search Engine, and returns a list of snippets from the search results.

Building a Calculator Tool

You can integrate a calculator tool using the py_expression_eval library to enable your LLM agent to handle mathematical queries. This tool allows the agent to evaluate and solve mathematical expressions accurately.

Install the py_expression_eval library:

pip install py_expression_eval

Implement the Calculator Function:

Use the 'Parser' class from 'py_expression_eval' to parse and evaluate mathematical expressions.

from py_expression_eval import Parser

# Initialize the parser
parser = Parser()

# Function to evaluate a mathematical expression
def calculate(expression):
    try:
        result = parser.parse(expression).evaluate({})
        return result
    except Exception as e:
        return str(e)

# Example calculation
expression = "3 * (2 + 7)"
result = calculate(expression)
print(f"The result of the expression '{expression}' is: {result}")

This function takes a mathematical expression as input, evaluates it, and returns the result.

LLM Agent Build: Explorating of Additional Tools

Beyond search and calculation, your LLM agent can integrate additional tools to enhance its functionality. These tools include recommendation engines, classification algorithms, and summarization tools, each serving different purposes.

Recommendation Engines: Recommendation engines suggest products or content based on user preferences and behavior. Implementing a recommendation engine involves collecting user data, analyzing preferences, and generating recommendations.

Example of a simple recommendation function:

def recommend(items, user_preferences):
    # Dummy implementation: Recommend the first item that matches user preferences
    for item in items:
        if any(pref in item for pref in user_preferences):
            return item
    return "No matching items found."

items = ["AI book", "ML course", "Data Science workshop"]
user_preferences = ["ML"]
recommendation = recommend(items, user_preferences)
print(f"Recommended item: {recommendation}")

Classification Algorithms: Classification involves categorizing text or data into predefined groups. This can be useful for sorting customer queries, tagging content, or organizing information.

Example of a simple text classification function:

def classify(text, categories):
    # Dummy implementation: Classify based on keyword presence
    for category, keywords in categories.items():
        if any(keyword in text for keyword in keywords):
            return category
    return "Uncategorized"

text = "I need help with my AI project."
categories = {
    "Support": ["help", "support", "assist"],
    "Sales": ["buy", "purchase", "order"],
    "General": ["project", "general", "info"]
}

category = classify(text, categories)
print(f"Text classified as: {category}")

Summarization Tools: Summarization tools condense long documents or articles into concise summaries, extracting key information for quick consumption.

Example of a simple summarization function:

from transformers import pipeline

# Load a pre-trained summarization model
summarizer = pipeline("summarization")

# Function to summarize a text
def summarize(text):
    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
    return summary[0]['summary_text']

text = ("Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to "
        "the natural intelligence displayed by humans and animals. Leading AI textbooks define "
        "the field as the study of 'intelligent agents': any device that perceives its environment "
        "and takes actions that maximize its chance of successfully achieving its goals.")
summary = summarize(text)
print(f"Summary: {summary}")

These additional tools greatly enhance the versatility and capability of your LLM agent, enabling it to perform a wide range of tasks efficiently.

By implementing these essential tools, your LLM agent will be well-equipped to handle diverse queries and provide valuable assistance across various domains. Next, we will explore the functionality and loop process of the agent, ensuring it can interact continuously and execute actions effectively.

LLM Agent Build: Agent Functionality and Loop Process

To ensure your LLM agent can handle continuous interactions and execute actions effectively, it's essential to implement a loop process, set up the system prompt, and manage input and actions through streaming functionality. This section outlines these critical components.

Loop Process for Continuous Interaction

The loop process is vital for maintaining an ongoing interaction with the user. It allows the agent to receive input, process it, and provide appropriate responses seamlessly.

Initialize the Conversation:

The loop starts by receiving the user's input.

The agent processes the input and decides on the next action (e.g., using a tool or responding directly).

Continuous Interaction:

The agent listens for new inputs, processes them, and responds accordingly, maintaining the flow of conversation.

def agent_loop():
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'exit':
            break
        response = process_input(user_input)
        print(f"Agent: {response}")

def process_input(user_input):
    # Dummy implementation: Echo the user input
    return f"Processed: {user_input}"

agent_loop()

Setting Up the System Prompt

The system prompt sets the initial context and instructions for the agent, guiding its behavior and responses. This prompt is crucial for defining the agent's role and the scope of its capabilities.

system_prompt = """
You are an advanced AI assistant. Your tasks include searching the web, performing calculations, and providing concise summaries.
When you need to perform a search, use the format:
Action: Search
Action Input: "your search query here"
When you need to perform a calculation, use the format:
Action: Calculate
Action Input: "your mathematical expression here"
When you need to summarize a text, use the format:
Action: Summarize
Action Input: "your text here"
""

Streaming Agent Functionality

Streaming functionality allows the agent to handle input in real time and execute actions based on the input received. This ensures a responsive and interactive user experience.

Handling Input:

• The agent listens for input from the user, processes it, and determines the appropriate action.

Executing Actions:

• The agent uses the relevant tool (e.g., search, calculate, summarize) and generates a response based on the action determined.

def stream_agent(prompt):
    import re
    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}]

    while True:
        response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
        response_text = response.choices[0].message['content']
        print(f"Agent: {response_text}")

        action, action_input = extract_action_and_input(response_text)
        if action == "Search":
            result = search(action_input)
        elif action == "Calculate":
            result = calculate(action_input)
        elif action == "Summarize":
            result = summarize(action_input)
        else:
            result = "Unknown action"

        print(f"Result: {result}")
        messages.append({"role": "user", "content": f"Observation: {result}"})

def extract_action_and_input(response_text):
    action_pattern = r"Action: (.+?)\n"
    input_pattern = r"Action Input: \"(.+?)\""
    action = re.findall(action_pattern, response_text)[0]
    action_input = re.findall(input_pattern, response_text)[0]
    return action, action_input

LLM Agent Build: Core Logic Development

The core logic of an LLM agent involves decision-making processes that determine the following action based on the LLM's output. This section covers how the agent decides on actions, extracts necessary inputs, and includes code snippets to illustrate these mechanics.

Agent Logic: Deciding the Next Action

The agent uses the LLM's output to determine the following action. This involves analyzing the response and identifying whether to perform a search, calculation or another task.

• Analyze LLM Output: The LLM generates a response based on the user's input. The agent parses this response to identify the required action.

• Decision-Making Process: The agent decides on the following action (e.g., Search, Calculate, Summarize) based on the parsed output.

Agent Logic: Extraction of Action and Input

The agent must extract the specific command and relevant input from the LLM's response to perform the required action.

Parse the Response: Use regular expressions to identify the action and its input from the response text.

Prepare for Execution: Extracted inputs are then used to execute the corresponding tool function.

import re

def extract_action_and_input(response_text):
    action_pattern = r"Action: (.+?)\n"
    input_pattern = r"Action Input: \"(.+?)\""
    action = re.findall(action_pattern, response_text)[0]
    action_input = re.findall(input_pattern, response_text)[0]
    return action, action_input

Agent Responses: Code Snippets

Here’s how the agent integrates decision-making and response handling:

Generate Response and Extract Action: The agent processes user input, generates a response, and extracts the action and input.

Execute the Action: The agent performs the action (e.g., search, calculate) and provides the result.

def process_input(user_input):
    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": user_input}]
    response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
    response_text = response.choices[0].message['content']
    action, action_input = extract_action_and_input(response_text)

    if action == "Search":
        result = search(action_input)
    elif action == "Calculate":
        result = calculate(action_input)
    elif action == "Summarize":
        result = summarize(action_input)
    else:
        result = "Unknown action"

    return result

# Example usage
user_input = "Find the latest AI research papers."
result = process_input(user_input)
print(f"Agent Result: {result}")

LLM Agent Build: Testing and Evaluation

Ensuring your LLM agent functions correctly and efficiently requires thorough testing and evaluation. This involves conducting tests with specific queries, evaluating the agent's thought process and decision accuracy, and addressing potential pitfalls.

Guidance on Conducting Tests

Testing your agent with a variety of queries is crucial to assess its performance and robustness. Use both simple and complex queries to evaluate how well the agent handles different scenarios.

• Simple Queries: Test basic functionality, such as retrieving information or performing simple calculations.

test_query_1 = "What is the capital of France?"
print(process_input(test_query_1))  # Expected output: "The capital of France is Paris."

• Complex Queries: Challenge the agent with multi-step tasks or queries requiring context understanding.

test_query_2 = "Calculate the square root of the sum of 25 and 144."
print(process_input(test_query_2))  # Expected output: "The square root of the sum of 25 and 144 is 13."

Evaluating the Agent's Thought Process and Accuracy of Decisions

• Trace Decision Steps: Review the agent's intermediate steps and actions taken during the query processing. Ensure the agent follows a logical and expected path.

def trace_decision(query):
    response = process_input(query)
    print(f"Trace for '{query}': {response}")

trace_decision("Summarize the latest trends in AI.")

Accuracy Assessment:

Verify the correctness of the results produced by the agent.

Compare the agent's output with known correct answers or trusted sources.

Handling Potential Pitfalls

Addressing potential issues is crucial for maintaining the reliability and efficiency of your agent.

• Data Quality: Ensure the training data is accurate, relevant, and free of biases. Regularly update the dataset to include new and diverse examples.

• Model Selection: Choose the appropriate model that fits your use case and performance requirements. Consider models like GPT-4 for advanced capabilities or smaller models for specific tasks.

• Training Optimization: Fine-tune the model with sufficient epochs and appropriate hyperparameters. Monitor training progress and adjust parameters to avoid overfitting or underfitting.

You can ensure your LLM agent performs effectively and reliably by conducting thorough tests, evaluating the thought process and accuracy, and addressing potential pitfalls. With these steps, your agent will be ready to handle various tasks and provide valuable assistance across different scenarios.

Resources for Further Learning

Exploring various resources, communities, and projects is essential to deepening your understanding of LLM agents and staying updated with the latest advancements.

This section concisely guides recommended readings, collaborative communities, and continuous learning resources.

Recommended Reading List for Developing LLM Agents

• AutoGPT: Explore the foundational concepts and architecture behind one of the first true autonomous agents. GitHub Repository

• Voyager by NVIDIA Research: Delve into self-improving agents that learn to use new tools or build tools without external intervention. Voyager Project

Communities and Projects for Insights and Collaboration

• Auto-GPT: Join discussions and contribute to the Auto-GPT project, a community-driven initiative for developing autonomous agents. Auto-GPT GitHub

• MetaGPT: Participate in the MetaGPT project to collaborate on creating advanced LLM agents. MetaGPT GitHub

Resources for Continuous Learning and Development in Generative AI and LLM Fields

• Online Courses and Tutorials: Platforms like Coursera, edX, and Udemy offer comprehensive courses on Generative AI and LLMs.

• Research Papers and Publications: Follow publications on arXiv and attend AI conferences such as NeurIPS and ACL to stay updated with the latest research.

• Professional Networks: Engage with AI professionals and researchers through LinkedIn groups, forums like AI Alignment Forum, and specialized communities on Reddit.

By leveraging these resources, you can enhance your knowledge, collaborate with peers, and stay ahead in the rapidly evolving field of Generative AI and LLMs.

Conclusion and Next Steps

Creating an LLM agent from scratch involves setting up the development environment, implementing essential tools, building core logic, and conducting thorough testing and evaluation. These unique advantages make them powerful tools for various applications. As you continue developing your agent, keep experimenting with new features and learning from each iteration to enhance its performance and versatility.

RAGA AI's comprehensive testing platform streamlines this process, ensuring high-quality results and reducing development time. Keep experimenting and learning to enhance your agent's performance.

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