DEV Community: Shittu Olumide

Here’s how I would learn AI Agents as a total beginner

Shittu Olumide — Mon, 30 Mar 2026 10:23:33 +0000

Most people still use AI as a high-tech typewriter. They ask for an email draft or a summary of a meeting and call it a day. That approach is already becoming obsolete. We have moved past the point where AI just talks. Now, we are in the phase where AI acts.

Gartner predicts that 40% of enterprise software will have task-specific agents built into them by the end of 2026. To put that in perspective, that number was below 5% in 2024. This isn’t just a small update to how software works. It is a fundamental change in how we get work done.

An agent is different because it doesn’t just give you a response. It thinks through a goal, finds the tools it needs, and stays on the job until the task is complete. If I were starting today, I would not waste time on complex prompt engineering tricks. Instead, I would focus on the architecture of autonomy. This is the path I would take to go from zero to building agents that actually deliver value.

The market for this technology is expected to cross $196.6 billion in the coming years. The demand for people who can build these systems is far outstripping the number of people who actually understand how they work. Learning this now puts you ahead of the curve before the field becomes crowded.

1. Defining the “Agent” (It isn’t just a GPT with a fancy name)

Before you write a single line of code, you have to understand the difference between a Large Language Model (LLM) and an Agent. It is easy to confuse the two because agents rely on LLMs to function. However, the distinction is critical. An LLM is essentially a brain in a jar. It is brilliant, well-read, and capable of complex reasoning, but it cannot move or interact with the world on its own. It sits there waiting for you to ask it a question so it can provide a text-based answer.

“ An Agent is that same brain but with hands and a memory ”

In technical terms, an agent is a system that uses an LLM as its reasoning engine to achieve a specific goal. Instead of just answering a prompt, the agent looks at the goal, breaks it down into smaller steps, and chooses the right tools to execute those steps. If a chatbot is a librarian who tells you where the books are, an agent is the researcher who goes to the shelves, reads the books, and writes the report for you.

The shift toward this “agentic” workflow is driving massive efficiency gains. Organizations that have moved from simple chatbots to autonomous agents are seeing a 20% to 30% reduction in operational friction and support costs. This happens because the agent handles the “doing” part of the job, which previously required a human to copy and paste data between different tabs and applications.

When you build an agent, you are essentially creating a loop. The system “thinks” about the next step, “acts” by using a tool, “observes” the result of that action, and then starts the cycle again until the task is finished. This autonomy is what makes it an agent. If the system stops and waits for you to tell it what to do at every single step, it is just a complicated script, not an agent.

2. The Starter Pack: Three Foundations You Actually Need

You do not need a degree in advanced mathematics or a background in computer science to build an AI agent. Many people get intimidated by the “AI” label and assume they need to understand neural network weights or backpropagation. In reality, building agents is much more about logic and orchestration than it is about calculus.

If you are starting from zero, you only need to focus on three specific areas to get your first agent running.

Python: The Language of Automation

Python is the undisputed king of the AI world. As of 2026, it remains the most popular language for developers, holding a 29% share of the global programming market. You do not need to be an expert, but you must be comfortable with the basics:

Functions and Loops: This is how you tell the agent to repeat a task until it succeeds.
JSON Handling: This is the most important part. AI models communicate in a format called JSON. You need to know how to “parse” this data so your agent can read information from a weather API or a database and then use it.

The API Mental Model

An agent is only as good as the tools it can access. You need to understand how to use an API (Application Programming Interface), which is essentially a digital handshake between two programs. When you want your agent to send an email or check a stock price, it sends a request to an API and gets a response back.

Most beginners start with the OpenAI or Anthropic APIs. You will need to learn how to manage “API Keys” safely. Think of an API key like a credit card: if you leak it, anyone can use your account to run expensive AI models.

The Reasoning Loop: Think, Act, Observe

This is the “logic” foundation. Every agent follows a cycle often called the “ReAct” pattern. It is the same process a human uses to solve a problem:

Think: The agent looks at the user’s request and decides what it needs to do.
Act: The agent uses a tool, like a calculator or a web search.
Observe: The agent looks at the result of that action and asks, “Did this solve the problem?”

If the answer is no, the loop starts over. Understanding this flow is more important than memorizing code because it helps you debug why an agent is getting “stuck” in a loop or failing to complete a task.

By focusing only on these three foundations, you avoid the “tutorial hell” of learning things you will never use. Once you can write a basic Python script that talks to an API, you are already ahead of most people just playing with chat interfaces.

3. Phase One: The Single-Tool Agent

The most common mistake beginners make is trying to build a complex, multi-agent system on day one. Instead, you should start with a “Single-Tool Agent.” This is an AI that has one job and one specific tool to help it do that job. As of 2026, 81% of companies are taking this exact approach, using agents primarily for targeted lookups in third party software before moving on to more complex workflows.

The magic happens when you move from a simple prompt to a “ReAct” (Reason + Act) loop. In a standard chatbot, the AI just guesses the answer. In a ReAct loop, the AI evaluates if it has enough information. If it doesn’t, it pauses its text generation, calls a tool, and then uses the new data to finish the task. This pattern is highly effective. Data from early 2026 shows that single-tool agents handling tasks like travel planning or vendor comparisons achieve a completion success rate of 87%.

To build this, you don’t need a massive framework. You can see the logic in a few lines of Python. Below is a simplified example of how an agent “decides” to use a search tool.

Python:

# A simple representation of an agent reasoning loop
def simple_agent(user_query):
    # The 'Reasoning' step: The AI thinks about what it needs
    print(f"Thinking: I need to find the current price of {user_query}")

    # The 'Action' step: The AI chooses to call a specific tool
    # In a real setup, this would be an API call to Google or a Database
    raw_data = call_search_tool(user_query)

    # The 'Observation' step: The AI looks at the tool output
    print(f"Observing: The tool returned {raw_data}")

    # Final step: The AI provides the answer based on the new data
    return f"The current price of {user_query} is {raw_data}."

# This function simulates an external tool (like a web search)
def call_search_tool(query):
    # Simple mock data to represent a real API response
    return "$65,000"
# Running our beginner agent
print(simple_agent("Bitcoin"))

Code explanation:

In this script, the simple_agent function mimics the brain of the agent. Instead of just returning a hardcoded string, it follows a sequence. First, it identifies a gap in its knowledge. Second, it calls the call_search_tool function, which represents an external API. Finally, it takes that "observation" and incorporates it into the final response. This shift from "generating text" to "managing a process" is the core skill you are trying to learn.

Mastering this single-tool loop is your first milestone. Once you can consistently get an AI to use one tool correctly without getting confused, you have unlocked the foundation of autonomous AI. The goal here isn’t to build something fancy but to ensure your “handshake” between the AI and the external world is solid.

4. Phase Two: Give Your Agent a Memory

A large context window is often marketed as the solution to AI memory, but in a production environment, it is a trap. Just because a model can “read” a million tokens at once does not mean it remembers who you are when you come back two weeks later. A context window is like a whiteboard. It is great for the task happening right now, but once the session ends, the board is wiped clean. To build a serious agent, you need a filing cabinet.

This filing cabinet is what we call persistent memory. As of 2026, research shows that companies deploying agents with robust memory layers see a 30% reduction in operational costs because the AI doesn’t have to re-learn user preferences or project details every single session.

The Two Layers of Memory

If you want your agent to feel intelligent, you have to manage two distinct types of storage.

Short-term Memory: This is the immediate conversation history. It allows the agent to understand that when you say “Do it again,” you are referring to the last task it performed.
Long-term Memory: This is where you store facts that should last forever. If a user says they prefer Rust over Python, that should be moved from the whiteboard to the filing cabinet.

The most common way to handle long-term memory is through Retrieval-Augmented Generation (RAG). This technique currently dominates 51% of all enterprise AI implementations. RAG allows the agent to search through a massive database of past interactions or uploaded documents and pull only the relevant “memories” into the current conversation.

You can start practicing this by creating a simple “profile” system. Instead of just sending a prompt, you send the prompt along with a small snippet of data about the user.

Python:

# A simple way to simulate long-term memory
user_memory = {
    "user_123": {
        "preferred_language": "Rust",
        "experience_level": "Beginner"
    }
}

def personalized_agent(user_id, task):
    # Retrieve the 'memory' for this specific user
    memory = user_memory.get(user_id, {})
    language = memory.get("preferred_language", "Python")

    # The agent uses the memory to change its behavior
    print(f"Memory Check: User prefers {language}.")

    # Logic to execute the task based on memory
    return f"I am writing your {task} code in {language}."
# The agent now 'remembers' the user preference across different calls
print(personalized_agent("user_123", "web scraper"))

Code Explanation:
In this example, the user_memory dictionary acts as a mock database. When the personalized_agent function is called, the first thing it does is a "Memory Check." It looks up the user ID to see if there are any saved preferences. Because it finds that the user prefers Rust, it automatically adjusts its output without the user needing to specify the language again. In a real application, you would replace this dictionary with a vector database like Pinecone or Weaviate, but the logic remains exactly the same.

By implementing this, you move away from building a generic tool and start building a personalized assistant. This is the difference between a toy and a system that provides a 171% average ROI for businesses.

5. Phase Three: The Multi-Agent Leap

In the real world, a single person rarely handles an entire project from strategy to execution. You have managers, researchers, and writers. AI development is moving in the same direction. We have realized that asking one large model to do everything often leads to “cognitive overload,” where the AI starts losing track of instructions or hallucinating details. Specialization is the fix.

By the start of 2026, multi-agent orchestration captured a 66% share of the global agentic AI market. This shift happened because specialized teams of agents are more reliable than one “do-it-all” bot. When you break a task into parts, you can use smaller, faster models for simple steps and save the heavy, expensive models for high-level reasoning.

Choosing Your Framework: CrewAI vs. LangGraph

As you move into this phase, you will likely choose between two dominant tools.

CrewAI: This is the best choice if you want to get a project running quickly. It uses a “Role-Based” approach. You define an agent, give it a backstory, and assign it a task. It is intuitive because it mimics a human office. Community benchmarks show that developers can move from an idea to a working multi-agent prototype 40% faster using CrewAI compared to other frameworks.
LangGraph: If you need absolute control and “durable execution,” this is the industry standard. It models your agents as nodes in a graph. If an agent crashes halfway through a three-hour task, LangGraph can resume exactly where it left off. In early 2026, LangGraph surpassed CrewAI in total GitHub stars, largely driven by enterprise teams who need this level of “checkpointing” for production apps.

Building a Collaborative Team

The core idea here is delegation. You write a script where one agent is responsible for the “What” and another is responsible for the “How.”

Python:

# A conceptual look at multi-agent delegation
class Agent:
    def __init__(self, role, task):
        self.role = role
        self.task = task
def execute(self, input_data=None):
        # Simulating the agent performing its specific role
        return f"[{self.role}] processed: {self.task} with {input_data}"
def run_multi_agent_flow():
    # Step 1: The Researcher finds the data
    researcher = Agent("Researcher", "Find latest AI trends")
    found_data = researcher.execute()

    # Step 2: The Manager reviews and delegates to the Writer
    manager_decision = "This looks good. Summarize this for a blog."

    # Step 3: The Writer takes the research and the manager's note
    writer = Agent("Writer", "Write a summary")
    final_output = writer.execute(input_data=f"{found_data} and {manager_decision}")

    return final_output
# Running the collaborative workflow
print(run_multi_agent_flow())

Code Explanation:

This code demonstrates a “Sequential” workflow. The Agent class is a template that can be customized with different roles. In the run_multi_agent_flow function, we create a researcher and a writer. The output of the researcher is passed directly to the writer. This "handoff" is the foundation of multi-agent systems. In a production setting, you would use a framework like CrewAI to handle these handoffs automatically, allowing agents to "talk" to each other until the manager agent is satisfied with the result.

6. Building the “Proof of Concept” Project

Theory is a comfortable place to stay, but it won’t teach you how to handle an agent that starts hallucinating or getting stuck in a logic loop. The fastest way to move from a beginner to someone who actually understands this tech is to build a project that solves a recurring problem. Research shows that developers using AI agents to assist in their workflows can complete tasks 126% faster than those working manually.

For your first build, I recommend an Automated Newsletter Researcher. This is a project that actually saves you time. Instead of you spending thirty minutes every morning scouring the web for news, your agent does it for you.

The Tools of the Trade

You don’t need a heavy enterprise stack for this. Stick to the “minimalist” path to keep your code clean and easy to debug.

Python: The backbone of your script.
OpenAI Agents SDK: This is a lightweight toolkit released recently that focuses on simple agent-to-agent handoffs and tool use without the bloat of larger frameworks. It is currently the production leader in terms of adoption because of its stability.
Tavily: A search engine built specifically for AI agents that returns clean, LLM-ready content instead of messy HTML.

The Success Metric

Your project is “finished” when your agent can autonomously find three relevant articles on a specific topic, summarize them into three bullet points each, and save that summary to a local file without you touching your keyboard. This isn’t just a coding exercise. It is a functional piece of automation that delivers a measurable result. Early data from 2026 suggests that even simple personal automation projects like this can reclaim up to 20% of a professional’s daily schedule.

The Implementation

Here is a structured look at how you would set up the core logic for this researcher.

Python:

import os
# Assuming the use of a simplified Agents SDK pattern
from agents_sdk import Agent, Runner

# Step 1: Define the search tool
def search_the_web(query):
    # This would typically call the Tavily API
    # For this example, we return a mock list of data
    return [
        {"title": "AI Agents in 2026", "content": "Agents are now 40% of apps."},
        {"title": "Python vs Rust", "content": "Rust is gaining ground in AI."},
        {"title": "OpenAI SDK", "content": "Minimalism is the new standard."}
    ]
# Step 2: Create the Researcher Agent
researcher = Agent(
    name="News Scout",
    instructions="Find the top 3 news stories about AI Agents and summarize them.",
    tools=[search_the_web]
)
# Step 3: Run the workflow
def main():
    # The runner manages the 'Think-Act-Observe' loop automatically
    runner = Runner()
    print("Starting the research process...")

    result = runner.run(
        agent=researcher,
        user_input="What are the biggest trends in AI Agents this week?"
    )

    # Step 4: Save the output to a file
    with open("newsletter_draft.txt", "w") as f:
        f.write(result.output)

    print("Newsletter draft saved successfully.")
if __name__ == "__main__":
    main()

Code Explanation:

In this setup, we use a Runner object to handle the heavy lifting of the reasoning loop. The researcher agent is given a specific identity and a single tool: the search_the_web function. When the runner.run() command is executed, the AI realizes it doesn't know the latest news. It triggers the search tool, observes the results we provided in the mock list, and then uses its internal logic to summarize those results. Finally, the script takes that summary and writes it to a physical file on your hard drive.

This project proves that you can bridge the gap between “chatting” and “doing.” Once you see that text file appear on your desktop, you have officially moved past the beginner phase.

Conclusion

The AI agent market is moving faster than the current talent pool can keep up. According to research from Research and Markets, the global AI agents market is expected to reach $12.06 billion by the end of 2026, representing a growth rate of 45.5% from the previous year. This growth is not just a theoretical spike. It is a direct result of companies moving away from simple chatbots toward autonomous systems that can actually finish complex work.

While roughly 62% of organizations are currently experimenting with these systems, only about 6% have managed to become “high performers” who are effectively scaling agents across their business. This creates a massive opening for anyone who can move past the beginner stage. Most people are still stuck in the cycle of just asking an AI for summaries. By building the single-tool and multi-agent projects we covered in this guide, you are positioning yourself in that top 6% of the workforce.

The reality of 2026 is that proficiency in building autonomous workflows is becoming a baseline requirement. Gartner suggests that by 2027, 75% of all hiring processes will include some form of certification or testing for workplace AI proficiency. The goal for you right now should not be to just use an agent. Your goal should be to understand how to build, maintain, and orchestrate them.

Do not wait for a perfect certification or a formal university course to tell you that you are ready. The hands-on experience of building a proof of concept is worth more than any theory you could read. Tools like the OpenAI SDK and Tavily have made the entry barrier lower than ever. Start with one tool, one logic loop, and one goal. The market is waiting for the people who actually know how to build the “hands” for the brain.

Functional Programming in Python: Leveraging Lambda Functions and Higher-Order Functions

Shittu Olumide — Fri, 18 Apr 2025 13:14:11 +0000

Writing several lines of code for a computer program instructs the computer on how to solve a problem or achieve a particular task by executing the code. Without this instruction, the computer is clueless about how to solve the problem.

It’s relieving to know we can solve problems with a computer by writing several lines of code, sometimes hundreds. Now, imagine that you have been saddled with the responsibility of solving repetitive programming tasks several times a day.

Don’t tell me you intend to keep writing the several lines of code that make up the program every time to solve your tasks. Come on, be a smart programmer, modularize your code, and use functions!

Functional programming is a programming paradigm that involves organizing your code into functions. When programs built with this paradigm run, they are treated as a chain or connection of several other functions.

This programming paradigm treats functions as first-class citizens — this implies that functions can be passed as arguments, returned from other functions, and, lastly, assigned to a variable.

In this article, we’ll explore the principles of functional programming in Python, focusing on lambda functions and higher-order functions to simplify your code and boost productivity.

Prerequisite

This article is suitable for beginners in Python programming who have basic knowledge and wish to learn in-depth about functions.

What are Functions

Functions in programming refer to a block of code that encapsulates or hides the inner implementation of the solution to a specific task or problem. A function is designed to solve a specific task; there is no one-size-fits-all approach. There has to be one function for a specific task.

A function takes in an argument, processes it, and may return a result or perform an action without returning any value. With that being said, let’s move on to the different types of functions available in Python.

Types of Functions in Python

There are several types of functions in Python, but this article will focus on Lambda, user-defined, and higher-order functions.

User-defined functions: These are functions created by the programmer to perform a specific operation. They usually follow the standard function paradigm structure.

def say_hello(name):
 return f"Hello, {name}!, welcome."

Built-in functions: They are functions that come with Python, you don’t have to define them because there is already an abstracted implementation. You just go ahead and use it in your code. Example: print(), len(), sum().
Special (Magic/Dunder) Functions: These are special functions that are built into Python; they have double underscores (__) in their names, and they are used in Python to perform special operations.
Lambda functions: They are also known as anonymous functions. They are single-expression functions created using the lambda keyword. They are used for simple operations.
Higher-order functions: These are functions that take other functions as arguments or return other functions. Examples are: filter(), reduce(), and map().

Anatomy of a typical Python function

"""
Module
Docstring
"""

def function_name(parameter):
    """
    Function Docstring
    """
    return f"you passed {parameter}, this function is used for demonstration purposes."

The code snippet above shows the various parts of a typical Python function; let’s go into every detail:

Module Docstring: According to the PEP (Python Enhancement Proposals) 257, a module docstring should be placed at the top of your Python file where your function is written. The module docstring should ideally contain information about the file’s contents. Always ensure that it is contained inside the multi-line quotes.
def Keyword: This keyword marks the beginning of the function being defined. It always precedes the function name.
function_name: Python functions are named based on the task they perform. Appropriately naming your functions in your code helps ensure a readable code that some other programmer can easily make meaning from. The function_name comes after the def keyword and precedes a bracket.
parameter: The parameter is a placeholder for the actual value or argument that will be passed to the function when it is called or used. Not all functions have a parameter; sometimes, it is just empty brackets after the function_name. Such functions don’t expect the user to pass any value to them; they perform the task they are defined for when called, regardless.
Function Docstring: Slightly different from the Module Docstring, the Function Docstring contains precise information about the function and what it does, the type of arguments it receives, and the value it returns. Refer to PEP 257 for more details on how to use docstrings in Python.
return statement: The return statement in the Python function primarily sends the resultant value or values from the function to the caller. It marks the end of the function execution. From the code snippet above, the return statement sends the string in quotes from the function after replacing the right value for the parameter. Functions can still work without a return statement, but there wouldn’t be any value or values that can be collected and stored in a variable.

Let’s look deeper into Lambda and Higher-Order functions

Lambda functions:

Lambda functions, as previously discussed, are anonymous because they are not defined by a name like typical Python functions. See the code snippet below:

answer = lambda a, b: a + b
print(answer(3, 2))

The variable answer is merely a reference to the function and not the name, a lambda function can still be written without any variable.

print((lambda a, b: a + b)(2, 3)) # outputs 5

Now you see from the code snippet above that it is without any reference variable, but still works fine.

Higher-order functions:

Earlier, we discussed that Higher-Order Functions can:

Take other function(s) as arguments
Return other functions.

You will see this in action in a bit.

# Function that takes another function as an argument
def apply_function(func, value):
 """Applies the passed function to the given value."""
 return func(value)

# Example function to pass
def square(x):
 return x ** 2

# Using the apply_function with the square function
result = apply_function(square, 5)
print(result) # Output: 25

From the code snippet above, apply_function is the Higher-Order Function. It takes the square function as one of its arguments, uses it to perform the squaring operation, and finally returns the result.

One of the benefits of using Higher-Order Functions is that they promote code modularity; different functions handle the separation of logic. This is how to pass a function as an argument to a Higher-Order function.

Let’s now look at how to return functions from a Higher-Order Function.

# Function that returns another function
def create_multiplier(multiplier_factor):
 """ Returns a function that multiplies its input by a given multiplier factor."""
 def multiply_with_input(number):
 return number * multiplier_factor
 return multiply_with_input

# Using the function
double_value = create_multiplier(2) # Returns a function that multiplies by 2
triple_value = create_multiplier(3) # Returns a function that multiplies by 3

# Calling the returned functions
print(double_value(5)) # Output: 10
print(triple_value(5)) # Output: 15

From the code shown above, the Higher-Order function is the create_multiplier, and the function defined inside it is the multiply_with_input function. If you look keenly, you will see that both functions take in arguments. How do we pass arguments on to the outer and inner functions during usage? You will see that in a bit.

# Using the function
double_value = create_multiplier(2) # Returns a function that multiplies by 2

# Calling the returned functions
print(double_value(5)) # Output: 10

From the example above, you can see that the create_multiplier is first called, and an argument is passed to it. The multiply_with_input function is returned to the double_value variable; it can now be accessed by passing an argument to it.

This is how a function can be returned from a Higher-Order Function.

Conclusion

In this article, you have learned about functional programming and how we can leverage it for a cleaner and modular code; you also learned about Lambda and Higher-Order Functions — the lambda keyword enables creating functions using a single expression without giving it a name, making it suitable for handling simple operations even inside another function. Higher-order functions are a slightly advanced type of Python functions — they can take in functions(s) as arguments and can also return a function.

Note: Always ensure that no brackets are added to a higher-order function when passing a function as an argument. By adding the brackets to it, you are calling it already, which will result in an error. Passing the function without brackets, which is the right thing, implies that you are passing a reference to the function.

Overview of Statsmodels

Shittu Olumide — Thu, 13 Mar 2025 16:12:58 +0000

Having data, no matter how quality it may be, without being able or knowing how to properly analyze and draw crucial statistical insights from it I am talking about key pointers and indicators that can help inform your next big business decision could be very frustrating, this is like having a gold mine but not having the capacity to tap into it, even though it can change your life tremendously, for the better.

Statistical modeling is a crucial framework used by statisticians, researchers, and data professionals to draw valuable insights from data. It largely involves the use of mathematical and statistical techniques and methodologies to analyze, represent, predict, and interpret data. It gives sufficient information that aids proper understanding of given data, leading to informed decision-making. It is to this end that different tools were developed for conducting statistical modeling. To name only a few tools used for this purpose, we have:

Python libraries (statsmodel, sci-kit-learn, pandas).
R programming language (Specifically designed for scientific computing tasks).
SAS/SPSS (A commercial software for advanced statistical analysis).
Excel (Used for simple statistical modeling and visualization)

In this article, we will be focusing on statsmodel; we will learn about its various tools and how it is used for statistical modeling with a simple code illustration.

Prerequisites

Prior knowledge of Python programming and statistical operations on datasets is useful, as it would ease the understanding of this article’s content.

What is statsmodel ?

statsmodel contains methods and classes used for statistical modeling. According to the official website of statsmodel, “statsmodel is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration.”

The ability of statsmodel to integrate easily with pandas and numpy libraries, which are very popular and commonly used tools for data operations, such as loading, exploring, and manipulating datasets, makes it favorable to Data Professionals.

statsmodel contains various statistical tools often used by professionals to conduct statistical operations on data. Listed below are some of the predominantly used statsmodel tools for performing various tasks:

Time series-analysis: When performing time series tasks (analyzing and forecasting time series data) with statsmodel, ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables) models are commonly used.
Data Visualization: statsmodel contains plotting functions that can be used to create visual representations of statistical data and also visualize model diagnostics.
Linear Regression Model: Ordinary Least Squares (OLS), Generalized Least Squares (GLS), and Weighted Least Squares (WLS) are all tools available in statsmodel, they are used for analyzing Linear Regression models. They all have their various use cases.
Statistical Tests: statsmodel provides numerous testing frameworks for conducting statistical tests on data, these tools can be used for both hypothesis testing and diagnosis. Here are some of the tests that statsmodel provides: t-tests (used to compare the means of two distinct groups of data), chi-squared tests (Used for testing the relationships that exist between categorical data, that is, text data), ANOVA (Analysis of Variance), unlike the t-test that compares the means of two data groups, ANOVA is used to compare the mean of multiple data groups.
Datasets: In-built datasets are available in statsmodel and can be used to practice and test operations.
Nonparametric methods: These are tools provided by the statsmodel module for analyzing data without assuming a specific distribution. To achieve this, the Kernel Density Estimation (KDE) approach is predominantly used.

Applications of statsmodel

statsmodel has numerous real-world applications, and it is used in various industries to carry out niche tasks.

Here are some key areas where statsmodel is utilized:

Finance: statsmodel is used in finance for forecasting and risk analysis. Time series can be used to monitor stock prices and returns.
Econometrics: Economic data can be analyzed, and economic theories can be tested using the statsmodel.
Marketing: E-commerce companies use statsmodel to analyze and understand customer behavior while interacting with their business to predict demand and strategize for better profitability.
Energy: In the energy sector, the statsmodel is used to forecast energy consumption and pricing at a given time for electricity and gas markets.
Manufacturing: In the manufacturing industry, the statsmodel is used for predictive maintenance; it predicts potential failure from analyzing previous data.

How to install and use statsmodel

So far in this article, we have learned a lot of theories about statsmodel, laying a good foundation for building more advanced knowledge. Let’s take it a little bit further to the coding side.

Installing statsmodel

To install statsmodel on your machine, follow the steps below:

Step 1

Create and activate a virtual environment.

On Linux/Mac OS:

python3 -m venv venv
source venv/bin/activate

On Windows OS:

python -m venv venv
venv\scripts\activate

Step 2

Install the statsmodel module on your machine by entering this code in your terminal window:

pip install statsmodels

Step 3

Confirm the installation was successful. Create a new Python file, and write the following code inside it:

import statsmodels.api as sm
print(sm.__version__)

After running this code, the version of statsmodel you installed should show on your terminal.

We just finished installing statsmodel; now, let us do a Linear Regression Analysis using statsmodel. Before that, let us install a dependency that we will use in our code. Run this code in your terminal window:

pip install pandas

This installs the pandas library into our created virtual environment, which is useful for loading and manipulating data.

In the Python file you created earlier, write the following code:

import statsmodels.api as sm
import pandas as pd

# Example dataset
data = sm.datasets.get_rdataset("mtcars").data

# Define variables
X = data[['hp', 'wt']] # Independent variables
X = sm.add_constant(X) # Add intercept
y = data['mpg'] # Dependent variable

# Fit the model
model = sm.OLS(y, X)
results = model.fit()

# Print the summary
print(results.summary())

In this code snippet, the statsmodel.api module was imported, which happens to be the main module that provides tools for statistical modeling. The pandas library was also imported to handle the data frame data type returned from the mtcars dataset.
The dataset is stored as a pandas data frame in the data variable.
The dependent and independent variables were extracted and stored in the y and X variables, respectively.
The OLS (Ordinary Least Square) method was used to create an OLS regression model.
The model was fitted to the data.
Lastly, the summary of the model was printed, which shows a detailed overview of the regression results.

When you run this code, you should get an output similar to what is shown in the image below.

Conclusion

In this article, we’ve explored the power of statsmodel and its role in statistical modeling and conducted linear regression analysis. statsmodel provides a comprehensive collection of tools for data professionals, researchers, and analysts.

By integrating seamlessly with pandas and numpy, statsmodel makes statistical modeling in Python intuitive and accessible. Whether you’re working in finance, healthcare, manufacturing, or marketing, this library offers powerful capabilities for extracting meaningful insights from data.

As you’ve seen from our example, installing and using statsmodel is straightforward, and with a solid grasp of its tools, you can unlock new possibilities in data analysis. I recommend that you dive even deeper — experiment with other datasets and explore more advanced statistical models- to cement what you are learning and grow your expertise even more.

Build a RAG-Powered Research Paper Assistant

Shittu Olumide — Tue, 11 Mar 2025 12:28:24 +0000

Have you ever spent hours sifting through academic papers only to feel overwhelmed by the sheer amount of information? Finding, analyzing, and synthesizing relevant research can be daunting. But what if there was a tool that could do the heavy lifting for you?

Retrieval-Augmented Generation, a state-of-the-art AI framework that puts together the accuracy of retrieval systems with the creative problem-solving capabilities of Large Language Models. In this article, we will explain RAG in detail, show how it works, and take you through a step-by-step process to build a research assistant powered by OpenAI.

Why Does It Matter?

Let’s break it down before we get too technical:

Retrieval: This part retrieves the most relevant documents from a large dataset, such as academic papers or books, based on a user’s query.
Generation: It takes that information and synthesizes it with the knowledge to generate responses through a language model, such as OpenAI’s GPT-4.

By combining these two capabilities, RAG generates highly accurate and contextually rich outputs. It’s like having an AI librarian who will not only recommend the best books but also summarize and explain them to you.

What Will You Build?

In this tutorial, we’ll create a RAG-powered Research Paper Assistant capable of searching a database of academic papers, summarizing key points from relevant studies, and answering user queries with accurate, well-cited information.

We will be using:

OpenAI GPT-4 to generate the text.
Pinecone or Weaviate for vector search.
LangChain to orchestrate the RAG pipeline.

Step 1: Setup Your Environment

Okay, first things first: prepare the tools and libraries that will be used for this project.

Prerequisites:

Basic programming skills in any language. In this case, Python will be used.
Research paper dataset Arxiv Open Access or Semantic Scholar
OpenAI API key
Environment Setup: You can run this application locally on your system or in a cloud-based Jupyter Notebook environment like Google Colab, which is beginner-friendly and free for basic usage.

Accessing OpenAI for Free

If you don’t already have an OpenAI account, follow these steps to get started:

Sign Up: Visit OpenAI’s website and sign up for an account.
Free Credits: New users receive free credits, which you can use to practice along with this tutorial.
Educational Discounts: If you’re a student, check for OpenAI’s educational programs or grants to access additional credits.

Installation of Required Libraries

The following are the commands to install our required libraries:

pip install openai langchain pinecone-client sentence-transformers

Note: If using Google Colab, add the ! prefix before each command to execute it in the notebook.

Step 2: Prepare the Data

A good assistant requires a well-prepared dataset. The preparation of your dataset can be done through the following steps:

1. Gather Your Data

Download a dataset of research papers or use APIs like Semantic Scholar to fetch the abstracts and metadata. Pro tip: Stick to domains you are interested in unless you want to start analyzing the migratory patterns of penguins when your interests actually lie in studying neural networks.

In this tutorial, we will make use of the ArXiv Open Access Dataset available in Kaggle. The following is how you can access and load it into your environment:

Log in to Kaggle or sign up, then follow the below steps:
Follow the dataset page.
Download the dataset and upload the papers.csv file to your Google Colab or your local directory.
Use the following code snippet to load the file:

import pandas as pd
# Load dataset
from google.colab import files
# Upload CSV
uploaded = files.upload()  # Select 'papers.csv'
# Load dataset into a DataFrame e.g (papers.csv)
df = pd.read_csv("papers.csv")

If running locally, ensure the papers.csv file is in the same directory as your script and load it as follows:

df = pd.read_csv("papers.csv")

2. Preprocess the Data

We need to clean up the data by removing duplicates, irrelevant information, and formatting issues. Here’s how we’ll do it:

from sentence_transformers import SentenceTransformer
# Extract abstracts and titles
data = df[["title", "abstract"]].dropna()
# Generate embeddings
model = SentenceTransformer("all-MiniLM-L6-v2")
data["embeddings"] = data["abstract"].apply(lambda x: model.encode(x).tolist())
# Save preprocessed data
data.to_json("preprocessed_data.json", orient="records")

Step 3: Set Up Vector Search

A vector database enables fast and efficient retrieval. We’ll use Pinecone for this step.

Create a Pinecone Index

Metric: Cosine similarity.
Dimension: Match the embedding size of your model (e.g., 384 for MiniLM).

Upload Data to Pinecone

import pinecone
pinecone.init(api_key="YOUR_API_KEY", environment="us-west1-gcp")
index = pinecone.Index("research-assistant")
# Upload data
for idx, record in data.iterrows():
    index.upsert([(str(idx), record["embeddings"])])

Step 4: Build the RAG Pipeline

Now comes the fun part: combining retrieval with generation.

Define the Retrieval Function

This function queries the Pinecone index:

def retrieve_relevant_docs(query, top_k=5):
    query_embedding = model.encode(query).tolist()
    results = index.query(query_embedding, top_k=top_k, include_metadata=True)
    return [res["metadata"] for res in results["matches"]]

Generate Responses

Use OpenAI to synthesize responses:

import openai
def generate_response(query, documents):
    context = "\n".join([doc["abstract"] for doc in documents])
    prompt = f"Use the following context to answer the query:\n{context}\n\nQuery: {query}\nAnswer:"
     response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=150
   )
   return response["choices"][0]["text"].strip()

Step 5: Create the Interactive Assistant

Let’s tie everything together with a simple interface.

Basic Command-Line Interface

if __name__ == "__main__":
    print("Welcome to the RAG-Powered Research Assistant!")
    while True:
        query = input("Enter your research question (or 'exit' to quit): ")
        if query.lower() == "exit":
            break
        docs = retrieve_relevant_docs(query)
            if not docs:
                print("No relevant documents found.")
            else:
                answer = generate_response(query, docs)
                print(f"Answer: {answer}\n")

Step 6: Enhance the Experience

To make your assistant even more powerful, add the following:

Add Citations: Add paper titles and authors to give credibility to the output.
Web Interface: Provide a nice UI created in React or Next.js.
Summarization: Allow summarizing of whole papers.

Advanced Features for Your RAG Assistant

To add more capabilities to your research assistant, the following capabilities can be implemented for it:

Citation Generation

The inclusion of citations or references for the retrieved research papers will make your assistant more reliable and useful for academic purposes. You can extract metadata such as authors, titles, and publication years to create properly formatted citations.

def format_citation(doc):
   return f"{doc['title']} by {doc['authors']} ({doc['year']})"

Overview of Findings

Summarizing long documents or multiple papers into concise, digestible insights helps streamline the research process. Use OpenAI or specialized summarization models like bart-large-cnn.

def summarize_docs(documents):
    summaries = [doc["abstract"][:500] for doc in documents] # Limit the text length
    combined_summary = " ".join(summaries)
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"Summarize the following research abstracts:\n{combined_summary}",
 max_tokens=200
 )
   return response["choices"][0]["text"].strip()

Real-Time Data Updates

If your dataset is updated regularly, consider periodic updates with a scheduler to keep the assistant updated. This can be automated using cron jobs or Celery.

Multilingual

If your audience includes researchers all over the world, include translation capabilities. Libraries like transformers with models like Helsinki-NLP can help.

Deploying Your Assistant

Once your RAG-powered assistant is built, deploy it to make it accessible. Here’s how:

API Deployment with Flask or FastAPI

Create an API endpoint to handle user queries and return results.

from fastapi import FastAPI
app = FastAPI()
@app.post("/query")
async def query_research_assistant(query: str):
    docs = retrieve_relevant_docs(query)
    if not docs:
    return {"answer": "No relevant documents found."}
    answer = generate_response(query, docs)
    return {"answer": answer}

Run the API on the local system or deploy it to Heroku, AWS, Google Cloud, etc.

Web Interface

Create a user-friendly interface using frameworks such as React, Vue.js, or even Streamlit for fast, interactive web application development.

Dockerize for Portability

Package your application in a Docker container for easy deployment across environments.

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "app:app", " - host", "0.0.0.0", " - port", "8000"]

Integration with Slack or Discord

Create bots that respond to user queries in Slack or Discord channels to make the assistant accessible in collaboration tools.

Testing and Iteration

Prior to the release of your assistant, quality needs to be controlled:

Accuracy Testing: The accuracy of both retrieval and generation components should be tested. Test real-world queries and check response relevance.
Performance Testing: Measure response times, especially when your assistant processes large amounts of data. If needed, improve the retrieval and generation steps.
User Feedback: Ask researchers or target users for feedback and implement their suggestions to improve the user-friendliness and functionality of your assistant.

Future Possibilities with RAG

Retrieval-augmented generation is not limited to research assistance alone. Here are a few other applications:

Legal Research: Quickly find and summarize legal documents or case law.
Healthcare: Assist doctors by retrieving relevant medical studies and summarizing patient cases.
E-commerce: Improve customer support by combining product information retrieval with personalized recommendations.

Conclusion

By building a RAG-powered research assistant, you will be entering the future of intelligent tools that mix precision with creativity. This will not only assist academic researchers in doing their work more effectively but also open doors to endless possibilities in other domains. With frameworks like LangChain, powerful language models like GPT-4, and scalable vector databases like Pinecone, creating your assistant has never been more accessible.

So, why wait? Dive in and change the way research is done in your field. The possibilities are endless, and your journey has just begun.

Call-to-Action

Want to build your RAG-powered assistant? Begin coding now and join the AI revolution in academic research. Share your experience and let us know how it transforms your workflow!

References

OpenAI API Documentation Learn more about how to use the OpenAI API for text generation: https://platform.openai.com/docs/
LangChain Documentation Official guide to orchestrating RAG pipelines with LangChain: https://www.langchain.com/
Pinecone Documentation A comprehensive resource for setting up and using Pinecone for vector search: https://docs.pinecone.io/
ArXiv Dataset on Kaggle Access the ArXiv Open Access Dataset for research papers: https://www.kaggle.com/datasets/Cornell-University/arxiv
Sentence Transformers Learn about the sentence-transformers library and its usage for embeddings: https://www.sbert.net/
Semantic Scholar API Explore the Semantic Scholar API to fetch academic papers: https://www.semanticscholar.org/product/api
Pinecone Blog: Building with RAG A detailed tutorial on Retrieval-Augmented Generation with Pinecone: https://www.pinecone.io/learn/rag/
Google Colab Documentation Beginner-friendly documentation to get started with Google Colab: https://colab.research.google.com/

Prompt Engineering Patterns for Successful RAG Implementations

Shittu Olumide — Tue, 25 Feb 2025 15:06:56 +0000

RAG has been the magic sauce behind the scenes, empowering many AI-driven applications to transcend the divide from static knowledge toward dynamic and real-time information. But getting exactly the right responses- precise, relevant, and of high value- is both a science and an art. Herein comes your guide on implementing prompt engineering patterns to make any implementation of RAG more effective and efficient.

Why Prompt Engineering Matters in RAG

Now, imagine setting a request to the AI assistant for today’s stock market trends, and it gives information from a finance book from ten years ago. This is what happens when your prompts are not clear, specified, or structured.

RAG retrieves information from outside and builds informed responses, but its capability identifies highly with how the prompt is set. Well-structured and clearly defined prompts ensure the following:

High retrieval accuracy
Less hallucination and misinformation
More context-aware responses

Prerequisites

Before diving into the deep end, one should have:

A high-level understanding of Large Language Models (LLMs)
Understanding of RAG architecture
Some Python experience (we are going to write a bit of code)
A sense of humor- Trust me, it helps.

1. Direct Retrieval Pattern

“Retrieve only, no guessing.”

On questions requiring factual accuracy, forcing the model to rely on the retrieved documents minimizes hallucinations.

Example:

prompt = "Using only the provided retrieved documents, answer the following question. Do not add any external knowledge."

Why it works:

Keeps answers grounded in retrieved data
Less speculation or incorrect responses

Pitfall:

If too restrictive, the AI becomes overly cautious with many “I don’t know” responses.

2. Chain of Thought (CoT) Prompting

“Think like a detective.”

For complicated reasoning, the process of leading the AI through logical steps amplifies response quality.

Example:

prompt = "Break down the following problem into logical steps and solve it step by step using the retrieved data.""

Why it works:

Improves reasoning and transparency
Improves explainability in responses

Pitfall:

Increases response time and token usage

3. Context Enrichment Pattern

“More context, fewer errors.”

Extra context in the prompt provides for more accurate responses.

Example:

context = "You are a cybersecurity expert analyzing a recent data breach."
prompt = f"{context} Based on the retrieved documents, explain the breach's impact and potential solutions."

Why it works:

Tailor responses to domain-specific needs
Reduces ambiguity in AI output

Pitfall:

Too much context can overwhelm the model

4. Instruction-Tuning Pattern

“Be clear, be direct.”

LLMs perform better when instructions are precise and structured.

Example:

prompt = "Summarize the following document in three bullet points, each under 20 words."

Why this works:

Guides the model towards structured output
Avoids excessive verbosity

Pitfall:

Rigid formats may limit nuanced responses

5. Persona-Based Prompting

“Personalize responses for target groups.”

If your RAG model serves heterogeneous end users, say novice vs. expert, response personalization will enrich participation.

Example:

user_type = "Beginner"
prompt = f"Explain blockchain technology as if I were a {user_type}, using simple language and real-world examples."

Why it works:

Increased accessibility
Enhances personalization

Common mistake:

Oversimplification could be missing information relevant to an expert

6. Error Handling Pattern

“What if AI gets it wrong?”

Prompts have to include a reflection of the outcome so AI can flag any uncertainties.

Example:

prompt = "If your response contains conflicting information, state your confidence level and suggest areas for further research."

Why it works:

More transparent responses
Less risk of misinformation

Pitfall:

AI may always give low-confidence answers, even when the answer is correct.

7. Multi-Pass Query Refinement

“Iterate until the answer is perfect.”

Instead of providing a single-shot response, this approach iterates queries to refine accuracy.

Example:

prompt = "Generate an initial answer, then refine it based on retrieved documents to improve accuracy."

Why it works:

Helps AI self-correct mistakes
Improves factual consistency

Pitfall:

Requires more processing time

8. Hybrid Prompting with Few-Shot Examples

“Show, don’t tell.”

Few-shot learning reinforces the results in consistency, supported with examples.

Example:

prompt = "Here are two examples of well-structured financial reports. Follow this pattern when summarizing the retrieved data."

Why it works:

Gives reference structure
Develops coherence and quality

Pitfall:

Requires selected examples of curation

Implementing RAG for Song Recommendations

import torch
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration

# Load the RAG model, tokenizer, and retriever
model_name = "facebook/rag-sequence-nq"
tokenizer = RagTokenizer.from_pretrained(model_name)
retriever = RagRetriever.from_pretrained(model_name)
model = RagSequenceForGeneration.from_pretrained(model_name, retriever=retriever)

# Define user input: Mood for song recommendation
user_mood = "I'm feeling happy and energetic. Recommend some songs to match my vibe."

# Tokenize the query
input_ids = tokenizer(user_mood, return_tensors="pt").input_ids

# Generate a response using RAG
with torch.no_grad():
 output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)

# Decode and print the response
recommendation = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print("🎵 Song Recommendations:", recommendation[0])

Additional Considerations

There are a few things you need to also consider, namely handling long queries, optimizing retrieval quality, and evaluate and refine prompts.

Handling Long Queries

Break complicated queries into subqueries.
Summarize inputs before giving them to the model.
Order retrievals based on keyword relevance.

Optimising Retrieval Quality

Use of embeddings for superior similarity search
Fine-tuning of retriever models on the domain-specific task
Hybrid search: experimentation with BM 25 + Embeddings.

Evaluate and Refine Prompts

Response quality could be monitored via human feedback.
A/B Testing of Prompts for their efficacy
Iteration on prompts will need to be modified based on various metrics.

Conclusion: How to Master Prompt Engineering in RAG

Mastery of RAG requires not only a powerful LLM but also precision in crafting the prompt. The right patterns help considerably increase response accuracy, relevance to the context, and swiftness. Be it finance, healthcare, cybersecurity, or any other domain, structured prompt engineering will ensure your AI delivers value-driven insight.

Final Tip: Iterate. The best prompts evolve, much like the finest AI applications. A well-engineered prompt today may need to be adjusted tomorrow as your use cases expand and AI capabilities improve. Stay adaptive, experiment, and refine for optimal performance.

References

Lewis, P., et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” NeurIPS, 2020.
Brown, T., et al. “Language Models are Few-Shot Learners.” NeurIPS, 2020.
OpenAI. “GPT-4 Technical Report.” 2023.
Google AI. “Understanding Prompt Engineering for LLMs.” Blog post, 2023.
Borgeaud, S., et al. “Improving Language Models by Retrieving from Trillions of Tokens.” DeepMind, 2022.
Radford, A., et al. “Learning Transferable Visual Models From Natural Language Supervision.” OpenAI, 2021.

Machine Learning Basics: Building Your First Predictive Model in R

Shittu Olumide — Wed, 11 Dec 2024 13:38:18 +0000

Machine learning models used for prediction purposes have become one of the most adopted technologies by different organizations. These models are capable of predicting future occurrences/outcomes giving valuable insights for making key decisions, hence leading to growth and increased productivity.

In light of this, the demand for professionals capable of building high-performance predictive models continues to soar at a rapid rate. This has seen myriads of people with transferable skill sets move towards becoming machine learning engineers; I am talking about data analysts conversant with R or even statisticians or other researchers alike who utilize R, as R is a powerful language for building machine learning models.

In the course of this article, you will learn how to build a predictive model using the R programming language.

Prerequisites

This tutorial is suitable for people familiar with the R programming language, Visual Studio Code (as a code editor), and who already know a thing or two about machine learning (just a little is enough)

Objectives

By going through this article, you should be able to:

Learn how to build a simple machine-learning model in R using the Logistic Regression algorithm.
Evaluate the performance of your model.

Machine learning (ML) can be likened to teaching a computer to learn from experience instead of being explicitly programmed for every task. Imagine showing a kid hundreds of pictures of cats and dogs and asking them to identify which is which - they'll eventually figure it out. That's essentially how ML works but for computers.

It all started with the idea of creating systems that could mimic human intelligence. Over the years, it evolved from simple rule-based systems to sophisticated algorithms that can handle vast amounts of data. Today, ML powers everything from voice assistants like Siri to personalized recommendations on Netflix.

At its core, ML involves feeding data into algorithms, training them to recognize patterns, and making predictions or decisions without constant human guidance.

Building a Predictive Model in R

1.Install and load the necessary packages

I assume you already have R set up on your computer. If you don't, feel free to check out this resource. Install and load the necessary packages you will be using for training the model, as shown below.

install.packages("tidyverse")  # For data manipulation and visualization
install.packages("caret")      # For model training and evaluation
library(tidyverse)             # loading the tidyverse library
library(caret)                 # loading the caret library

2.Load your preferred dataset
In this step, you choose the dataset you will use to train your model. It can be an externally prepared dataset in CSV format or a built-in dataset. For this article, we will be using the built-in mtcars dataset.

data <- mtcars

3.Exploratory data analysis
This step involves understanding the dataset you are working with and checking its suitability for use. It encompasses a series of processes, such as checking for null values in the dataset and filling them appropriately if there are.

View the dataset:

glimpse(data)

Check for missing values in the mtcars built-in dataset.

# Check for missing values
sum(is. na(mtcars))

View the statistical summary of the dataset.

# Summary statistics
summary(mtcars)

4.Split the dataset for testing and training the model

This involves splitting your dataset into a particular proportion. In this example, we are using the 80:20 proportion, that is, 80% for training and the remaining 20% for testing the model.

Ensure reproducibility and split the dataset

set.seed(123)  # For reproducibility

# Split the data into 80% training and 20% testing
train_index <- createDataPartition(data$mpg, p = 0.8, list = FALSE)
train_data <- data[train_index, ]
test_data <- data[-train_index, ]

5.Build the Linear Regression model

Use the train() function from the caret package to build your model

# Train the model
model <- train(
  mpg ~ wt + hp,  # Formula: mpg as a function of wt and hp
  data = train_data,
  method = "lm",  # Linear regression method
  trControl = trainControl(method = "cv", number = 5)  # 5-fold cross-validation
)

# View model summary
print(model)

In this step, we have successfully built our linear regression model. The next step is to evaluate its performance using a suitable metric. For this article, we are going to use the RMSE metric.

6.Model evaluation

Use the model you have built in the previous step to make predictions on the test dataset and evaluate the predicted values using the RMSE metric.

# Make predictions
predictions <- predict(model, newdata = test_data)

# Evaluate performance
results <- data.frame(
  Actual = test_data$mpg,
  Predicted = predictions
)

# Calculate RMSE and R-squared
rmse <- sqrt(mean((results$Actual - results$Predicted)^2))

print(paste("RMSE:", rmse))

7.Model visualization

This is an optional step as it seeks to provide a visual expression of the predicted values and the actual values, showing the accuracy of the model.

# Plot actual vs predicted
plot(test_data$mpg, predictions, main = "Actual vs Predicted",
     xlab = "Actual mpg", ylab = "Predicted mpg")
abline(0, 1, col = "red")  # Add diagonal line for reference

Congrats on making it this far. We have successfully built a linear regression machine learning model using the R programming language.

Let's combine all the code snippets for a better understanding.

install.packages("tidyverse")  # For data manipulation and visualization
install.packages("caret")      # For model training and evaluation
library(tidyverse)             # loading the tidyverse library
library(caret) 

data <- mtcars

glimpse(data)

# Check for missing values
sum(is. na(mtcars))

# Summary statistics
summary(mtcars)

set.seed(123)  # For reproducibility

# Split the data into 80% training and 20% testing
train_index <- createDataPartition(data$mpg, p = 0.8, list = FALSE)
train_data <- data[train_index, ]
test_data <- data[-train_index, ]

 # Train the model
model <- train(
  mpg ~ wt + hp,  # Formula: mpg as a function of wt and hp
  data = train_data,
  method = "lm",  # Linear regression method
  trControl = trainControl(method = "cv", number = 5)  # 5-fold cross-validation
)

# View model summary
print(model)

# Make predictions
predictions <- predict(model, newdata = test_data)

# Evaluate performance
results <- data.frame(
  Actual = test_data$mpg,
  Predicted = predictions
)

# Calculate RMSE and R-squared
rmse <- sqrt(mean((results$Actual - results$Predicted)^2))

print(paste("RMSE:", rmse))

# Plot actual vs predicted
plot(test_data$mpg, predictions, main = "Actual vs Predicted",
     xlab = "Actual mpg", ylab = "Predicted mpg")
abline(0, 1, col = "red")  # Add diagonal line for reference

Conclusion

In this article, we have built a Machine Learning model using the built-in mtcars dataset and the linear regression algorithm. Still, for your implementation when working on projects, you can use any other external dataset and algorithm you deem fit for the particular task.

It is recommended that you conduct proper exploratory data analysis on your dataset and experiment with different algorithms before selecting the one that best suits the task.

How to Install PySpark on Your Local Machine

Shittu Olumide — Mon, 09 Dec 2024 13:13:47 +0000

If you’re stepping into the world of Big Data, you have likely heard of Apache Spark, a powerful distributed computing system. PySpark, the Python library for Apache Spark, is a favorite among data enthusiasts for its combination of speed, scalability, and ease of use. But setting it up on your local machine can feel a bit intimidating at first.

Fear not — this article walks you through the entire process, addressing common questions and making the journey as straightforward as possible.

What is PySpark, and Why Should You Care?

Before going into installation, let’s understand what PySpark is. PySpark allows you to leverage the massive computational power of Apache Spark using Python. Whether you’re analyzing terabytes of data, building machine learning models, or running ETL (Extract, Transform, Load) pipelines, PySpark allows you to work with data more efficiently than ever.

Now that you understand PySpark, let’s go through the installation process.

Step 1: Ensure Your System Meets the Requirements

PySpark runs on various machines, including Windows, macOS, and Linux. Here’s what you need to install it successfully:

Java Development Kit (JDK): PySpark requires Java (version 8 or 11 is recommended).
Python: Ensure you have Python 3.6 or later.
Apache Spark Binary: You’ll download this during the installation process.

To check your system readiness:

Open your terminal or command prompt.
Type java -version and python —version to confirm Java and Python installations.

If you don’t have Java or Python installed, follow these steps:

For Java: Download it from Oracle’s official website.
For Python: Visit Python’s download page.

Step 2: Install Java

Java is the backbone of Apache Spark. To install it:

1.Download Java: Visit the Java SE Development Kit download page. Choose the appropriate version for your operating system.

2.Install Java: Run the installer and follow the prompts. On Windows, you’ll need to set the JAVA_HOME environment variable. To do this:

Copy the path variable, go to the local disk on your machine, select program files, look for the java folder open it you will see jdk-17 (your own version may not be 17). Open it, and you will be able to see your path and copy like below

Search for Environment Variables in the Windows search bar.
Under System Variables, click New and set the variable name as JAVA_HOME and the value as your Java installation path you copied above (e.g., C:\Program Files\Java\jdk-17).

3.Verify Installation: Open a terminal or command prompt and type java-version.

Step 3: Install Apache Spark

1.Download Spark: Visit Apache Spark’s website and select the version compatible with your needs. Use the pre-built package for Hadoop (a common pairing with Spark).

2.Extract the Files:

On Windows, use a tool like WinRAR or 7-Zip to extract the file.
On macOS/Linux, use the command tar -xvf spark-.tgz

3.Set Environment Variables:

For Windows: Add Spark’s bin directory to your system’s PATH variable.
For macOS/Linux: Add the following lines to your .bashrc or .zshrc file:

export SPARK_HOME=/path/to/spark
export PATH=$SPARK_HOME/bin:$PATH

4.Verify Installation: Open a terminal and type spark-shell. You should see Spark’s interactive shell start.

Step 4: Install Hadoop (Optional but Recommended)

While Spark doesn’t strictly require Hadoop, many users install it for its HDFS (Hadoop Distributed File System) support. To install Hadoop:

Download Hadoop binaries from Apache Hadoop’s website.
Extract the files and set up the HADOOP_HOME environment variable.

Step 5: Install PySpark via `pip`

Installing PySpark is a breeze with Python’s pip tool. Simply run:

pip install pyspark

To verify, open a Python shell and type:

pip install pysparkark.__version__)

If you see a version number, congratulations! PySpark is installed 🎉

Step 6: Test Your PySpark Installation

Here’s where the fun begins. Let’s ensure everything is working smoothly:

Create a Simple Script:
Open a text editor and paste the following code:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("PySparkTest").getOrCreate()
data = [("Alice", 25), ("Bob", 30), ("Cathy", 29)]
columns = ["Name", "Age"]
df = spark.createDataFrame(data, columns)
df.show()

Save it as test_pyspark.py

Run the Script:
In your terminal, navigate to the script’s directory and type:

python test_pyspark.py

You should see a neatly formatted table displaying the names and ages.

Troubleshooting Common Issues

Even with the best instructions, hiccups happen. Here are some common problems and solutions:

Issue: java.lang.NoClassDefFoundError
Solution: Double-check your JAVA_HOME and PATH variables.
Issue: PySpark installation succeeded, but the test script failed.
Solution: Ensure you’re using the correct Python version. Sometimes, virtual environments can cause conflicts.
Issue: The spark-shell command doesn’t work.
Solution: Verify that the Spark directory is correctly added to your PATH.

Why Use PySpark Locally?

Many users wonder why they should bother installing PySpark on their local machine when it’s primarily used in distributed systems. Here’s why:

Learning: Experiment and learn Spark concepts without requiring a cluster.
Prototyping: Test small data jobs locally before deploying them to a larger environment.
Convenience: Debug issues and develop applications with ease.

Boost Your PySpark Productivity

To get the most out of PySpark, consider these tips:

Set Up a Virtual Environment: Use tools like venv or conda to isolate your PySpark installation.
Integrate with IDEs: Tools like PyCharm and Jupyter Notebook make PySpark development more interactive.
Leverage PySpark Documentation: Visit Apache Spark’s documentation for in-depth guidance.

Engage with the PySpark Community

Getting stuck is normal, especially with a powerful tool like PySpark. Engage with the vibrant PySpark community for help:

Join Forums: Websites like Stack Overflow have dedicated Spark tags.
Attend Meetups: Spark and Python communities often host events where you can learn and network.
Follow Blogs: Many data professionals share their experiences and tutorials online.

Conclusion

Installing PySpark on your local machine may seem daunting at first, but following these steps makes it manageable and rewarding. Whether you’re just starting your data journey or sharpening your skills, PySpark equips you with the tools to tackle real-world data problems.

PySpark, the Python API for Apache Spark, is a game-changer for data analysis and processing. While its potential is immense, setting it up on your local machine can feel challenging. This article breaks down the process step-by-step, covering everything from installing Java and downloading Spark to testing your setup with a simple script.

With PySpark installed locally, you can prototype data workflows, learn Spark’s features, and test small-scale projects without needing a full cluster.

How to Use PySpark for Machine Learning

Shittu Olumide — Wed, 04 Dec 2024 16:48:00 +0000

Since the release of Apache Spark (an open-source framework for processing Big Data), it has become one of the most widely used technologies for processing large amounts of data in parallel across multiple containers — it prides itself on efficiency and speed compared to similar software that existed before it.

Working with this amazing technology in Python is feasible through PySpark, a Python API that allows you to interact with and tap into ApacheSpark’s amazing potential using the Python programming language.

In this article, you will learn and get started with using PySpark to build a machine-learning model using the Linear Regression algorithm.

Note: Having prior knowledge of Python, an IDE like VSCode, how to use a command prompt/terminal and familiarity with Machine Learning concepts is essential for proper understanding of the concepts contained in this article.

By going through this article, you should be able to:

Understand what ApacheSpark is.
Learn about PySpark and how to use it for Machine Learning.

What’s PySpark all about?

According to the Apache Spark official website, PySpark lets you utilize the combined strengths of ApacheSpark (simplicity, speed, scalability, versatility) and Python (rich ecosystem, matured libraries, simplicity) for “data engineering, data science, and machine learning on single-node machines or clusters.”

Image source

PySpark is the Python API for ApacheSpark, which means it serves as an interface that lets your code written in Python communicate with the ApacheSpark technology written in Scala. This way, professionals already familiar with the Python ecosystem can quickly utilize the ApacheSpark technology. This also ensures that existing libraries used in Python remain relevant.

Detailed Guide on how to use PySpark for Machine Learning

In the ensuing steps, we will build a machine-learning model using the Linear Regression algorithm:

Install project dependencies: I’m assuming that you already have Python installed on your machine. If not, install it before moving to the next step. Open your terminal or command prompt and enter the code below to install the PySpark library.

pip install pyspark

You can install these additional Python libraries if you do not have them.

pip install pandas numpy

Create a file and import the necessary libraries: Open VSCode, and in your chosen project directory, create a file for your project, e.g pyspart_model.py. Open the file and import the necessary libraries for the project.

from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
import pandas as pd

Create a spark session: Start a spark session for the project by entering this code under the imports.

spark = SparkSession.builder.appName("LogisticRegressionExample").getOrCreate()

Read the CSV file (the dataset you will be working with): If you already have your dataset named data.csv in your project directory/folder, load it using the code below.

data = spark.read.csv("data.csv", header=True, inferSchema=True)

Exploratory data analysis: This step helps you understand the dataset you are working with. Check for null values and decide on the cleansing approach to use.

# Display the schema my
 data.printSchema() 
# Show the first ten rows 
data.show(10)
# Count null values in each column
missing_values = df.select(
    [count(when(isnull(c), c)).alias(c) for c in df.columns]
)

# Show the result
missing_values.show()

Optionally, if you are working with a small dataset, you can convert it to a Python data frame and directory and use Python to check for missing values.

pandas_df = data.toPandas()
# Use Pandas to check missing values
print(pandas_df.isna().sum())

Data preprocessing: This step involves converting the columns/features in the dataset into a format that PySpark’s machine-learning library can easily understand or is compatible with.

Use VectorAssembler to combine all features into a single vector column.

# Combine feature columns into a single vector column
feature_columns = [col for col in data.columns if col != "label"]
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")

# Transform the data
data = assembler.transform(data)

# Select only the 'features' and 'label' columns for training
final_data = data.select("features", "label")

# Show the transformed data
final_data.show(5)

Split the dataset: Split the dataset in a proportion that is convenient for you. Here, we are using 70% to 30%: 70% for training and 30% for testing the model.

train_data, test_data = final_data.randomSplit([0.7, 0.3], seed=42)

Train your model: We are using the Logistic Regression algorithm for training our model.

Create an instance of the LogisticRegression class and fit the model.

lr = LogisticRegression(featuresCol="features", labelCol="label")

# Train the model
lr_model = lr.fit(train_data)

Make predictions with your trained model: Use the model we have trained in the previous step to make predictions

predictions = lr_model.transform(test_data)
# Show predictions
predictions.select("features", "label", "prediction", "probability").show(5)

Model Evaluation: Here, the model is being evaluated to determine its predictive performance or its level of correctness. We achieve this by using a suitable evaluation metric.

Evaluate the model using the AUC metric

evaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction", labelCol="label", metricName="areaUnderROC")

# Compute the AUC
auc = evaluator.evaluate(predictions)
print(f"Area Under ROC: {auc}")

The end-to-end code used for this article is shown below:

from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Start Spark session
spark = SparkSession.builder.appName("LogisticRegressionExample").getOrCreate()

# Load and preprocess data
data = spark.read.csv("data.csv", header=True, inferSchema=True)
assembler = VectorAssembler(inputCols=[col for col in data.columns if col != "label"], outputCol="features")
data = assembler.transform(data).select("features", "label")

# Split the data
train_data, test_data = data.randomSplit([0.7, 0.3], seed=42)

# Train the model
lr = LogisticRegression(featuresCol="features", labelCol="label")
lr_model = lr.fit(train_data)

# Make predictions
predictions = lr_model.transform(test_data)
predictions.select("features", "label", "prediction", "probability").show(5)

# Evaluate the model
evaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction", labelCol="label", metricName="areaUnderROC")
auc = evaluator.evaluate(predictions)
print(f"Area Under ROC: {auc}")

Next steps 🤔

We have reached the end of this article. By following the steps above, you have built your machine-learning model using PySpark.

Always ensure that your dataset is clean and free of null values before proceeding to the next steps. Lastly, make sure your features all contain numerical values before going ahead to train your model.

Unveiling CodiumAI PR-Agent: A Comparative Analysis Against GitHub Copilot for Pull Requests

Shittu Olumide — Mon, 18 Dec 2023 14:25:21 +0000

Comparing CodiumAI PR-Agent to Copilot for Pull Request

Are you drowning in pull requests? Are endless revisions slowing you down? Well, trust me you're not alone. This is because traditional workflows lack speed. All thanks to the advent of AI-powered pull request assistants a game-changer in efficiency and accuracy, reshaping how you work.

As you must have guessed there are so many solutions out there but the two prominent solutions are CodiumAI's PR-Agent and GitHub Copilot for Pull Requests. These titans promise enhanced workflows, superior code quality, and seamless collaboration.

Before we dive in, it's important to let you know what you will learn in this article. This article is more than just a comparison article between CodiumAI PR-Agent and GitHub Copilot for Pull Requests, we will build a muscle memory from the ground up by looking at a series of important and key concepts before diving into the comparisons and their importance. Sounds good?

This article is aimed at developers and project managers who want to elevate their efficiency, collaboration, and code brilliance in team-based software development.

What is a PR?

A Pull Request (PR) is a fundamental concept in version control and collaborative software development, primarily used in platforms like GitHub, GitLab, or Bitbucket. It's a mechanism that enables developers to propose changes to a codebase hosted in a repository.

How does this work?

A developer makes changes in their local copy of a repository, such as fixing a bug or adding a new feature.
To incorporate these changes into the main codebase (often referred to as the "master" branch), they create a Pull Request.
Creating a Pull Request: The developer creates a branch in the repository to work on their changes, separating them from the main codebase. Once the changes are complete, they initiate a Pull Request, signaling that they want their changes reviewed and potentially merged into the main branch.
Review Process: Other team members or collaborators are invited to review the proposed changes within the Pull Request. Discussions, comments, and feedback occur within the PR, allowing for collaboration and refinement of the code.
Merge or Close: After the changes in the PR have been reviewed, tested, and approved, the changes are merged into the main branch. If there are issues or the changes are not yet ready, the PR can be closed without merging.

What are PR-Agents?

Pull Request Agents, are AI-powered assistants designed to enhance the process of reviewing and managing code changes within software development teams. These agents operate within version control platforms, analyzing code modifications proposed by developers before merging them into the main codebase.

In simpler terms, they're like smart collaborators that help developers improve the quality of their code changes and ensure smooth integration into the larger project. PR-Agents leverage machine learning and natural language processing to suggest improvements, detect potential issues, and assist with code reviews.

PR-Agent offers extensive pull request functionalities across various git providers: GitHub, GitLab, or Bitbucket, CodeCommit, Azure DevOps, Gerrit.

The diagram below illustrates PR-Agent tools and their flow:

Why do streamlined review procedures matter?

It is a known fact that effective code reviews are the backbone of code quality, consistency, and maintainability, streamlined review processes ensure:

Timely Releases: Prevent delays in releasing new features or updates.
Faster Development Workflow: Avoid bottlenecks that slow down progress.
Enhanced Code Quality: Minimize bugs and errors for a more robust product.
Positive Developer Experience: Avoid frustration and dissatisfaction among team members.

Now that you understand the basics, let's move on to talk about these two technologies in detail.

CodiumAI PR-Agent

CodiumAI PR-Agent is an open-source tool aiming to help developers review pull requests faster and more efficiently. This tool is built by AI experts Itamar Friedman, Dedy Kredo, and other team members at CodiumAI. The PR-Agent gives developers and repo maintainers information to expedite the PR approval process. It also provides code suggestions that help improve the PR’s integrity, from finding bugs to (soon) providing meaningful tests. This seamless integration allows developers to see the effects of their work, without having to leave the git provider (GitHub, Gitlab, etc.) environment.

Using the PR-Agent, you can cut review time in half, write cleaner, and more efficient code. It automatically analyzes the commits and the PR and can provide several types of feedback:

PR Review: Feedback about the PR main theme, type, relevant tests, security issues, focused PR, and various suggestions for the PR content. 
Code Suggestion: Committable code suggestions for improving the PR. 
Auto-Description: Automatically generating PR description - name, type, summary, and code walkthrough.
Question Answering: Answering free-text questions about the PR.

Check out CodiumAI PR-Agent on GitHub.

Installation
Follow these steps to install and run the CodiumAI PR-Agent tool on different git platforms.

CodiumAI PR-Agent automatically analyzes the pull request and provides several types of commands:

Just mention @CodiumAI-Agent and add the desired command in any PR comment. The agent will generate a response based on your command e.g @CodiumAI-Agent /review

/describe

The describe tool scans the PR code changes and automatically generates PR description - title, type, summary, walkthrough, and labels. It can be invoked manually by commenting on any PR.

/review

The review tool scans the PR code changes, and automatically generates a PR review. It can be invoked manually by commenting on any PR.

/ask "..."

The ask tool answers questions about the PR, based on the PR code changes. It can be invoked manually by commenting on any PR.

/improve

The improve tool scans the PR code changes, and automatically generates committable suggestions for improving the PR code. It can be invoked manually by commenting on any PR.

/update_changelog

The update_changelog tool automatically updates the CHANGELOG.md file with the PR changes. It can be invoked manually by commenting on any PR.

/similar_issue

The similar issue tool retrieves the most similar issues to the current issue. It can be invoked manually by commenting on any PR.

/add_docs

The add_docs tool scans the PR code changes and automatically suggests documentation for the undocumented code components (functions, classes, etc.). It can be invoked manually by commenting on any PR.

/generate_labels

The generate_labels tool scans the PR code changes and given a list of labels and their descriptions, it automatically suggests labels that match the PR code changes. It can be invoked manually by commenting on any PR.

Check out the usage guide for running the PR-Agent commands via different interfaces here.

Why use CodiumAI PR-Agent?

It emphasizes real-life practical usage with each tool (review, improve, ask, etc.) having a single GPT-4 call, no more.
The time taken to obtain an answer is less than 30 seconds.
CodiumAI PR-Agent supports multiple git providers (GitHub, Gitlab, Bitbucket, CodeCommit), there are multiple ways to use the tool (CLI, GitHub Action, GitHub App, Docker, etc.), and multiple models (GPT-4, GPT-3.5, Anthropic, Cohere, Llama2).
It is open-source, and contributions are welcome from the community.
The JSON prompting strategy enables to have modular, customizable tools. For example, the /review tool categories can be controlled via the configuration file. Adding additional categories is easy and accessible.
CodiumAI PR-Agent's compression strategy has an ability that enables it to effectively tackle both short and long PRs.

GitHub Copilot for Pull Requests

GitHub Copilot for Pull Requests is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It integrates directly into the code editor, providing real-time suggestions and completions as developers write code. Leveraging OpenAI's GPT (G*enerative Pre-trained Transformer*) technology, Copilot analyzes the context of the code being written and generates suggestions for entire lines or chunks of code.

Copilot helps developers to quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on sites like Stack Overflow and across the internet, - says Friedman (GitHub CEO).

It aims to enhance developer productivity by offering intelligent code suggestions, reducing repetitive coding tasks, and providing assistance in various aspects of software development, ultimately aiming to expedite the coding process and improve code quality.

How GitHub Copilot for Pull Requests works is shown in the image below.

Key features of GitHub Copilot for Pull Requests include:

AI-Powered Code Suggestions: Copilot assists developers by offering suggestions for code completion, including function calls, variable assignments, loops, and more. It provides intelligent hints based on the current context and the desired functionality.
Natural Language Understanding: Developers can write code in plain English-like descriptions or comments, and Copilot interprets these and generates corresponding code snippets. This allows for a more natural and expressive way of coding.
Support for Multiple Languages: GitHub Copilot for Pull Requests supports various programming languages, enabling developers to receive contextually relevant code suggestions across a wide range of programming paradigms.
Autocompletion and Refactoring: Copilot not only helps in writing new code but also assists in refactoring existing code by providing suggestions for optimizations, cleaner implementations, and more efficient solutions.
Learning from User Interactions: As developers use Copilot, the tool learns from the interactions and feedback, improving its suggestions and adapting to developers' coding styles over time.
Collaborative Development: Copilot facilitates collaborative coding by suggesting shared code snippets, enabling faster and more efficient teamwork.
Integration with GitHub: Seamlessly integrated into the GitHub ecosystem, Copilot augments the coding experience for developers using GitHub's repositories and version control systems.

GitHub Copilot for Pull Requests commands

GitHub Copilot for Pull Requests provides various commands, referred to as copilot commands, that allow developers to interact with the tool and get specific code suggestions or functionalities. Here's a list and explanation of some common Copilot commands:

@copilot

This command serves as a marker tag inserted in the pull request description or code comments to invoke Copilot's functionality. When used in a comment or description, Copilot reads these tags to understand the developer's intent and provide relevant code suggestions or actions.

@copilot:all

A command that showcases all different kinds of content in one go. It's used to request diverse content suggestions from Copilot within a single command.

@copilot:summary

This command expands to provide a concise, one-paragraph summary of the changes or functionality outlined in the pull request. It's useful for quickly grasping the essence of the modifications.

@copilot:walkthrough

When used, this command expands to offer a detailed list of changes, including links to relevant pieces of code. It provides a more comprehensive overview of the modifications made in the pull request.

@copilot:poem

An interesting command that requests Copilot to generate a poem related to the changes in the pull request. It's more of a creative or fun way to summarize modifications.

CodiumAI PR-Agent vs. GitHub Copilot for Pull Requests

Feature	CodiumAI PR-Agent	GitHub Copilot for Pull Requests
Availability	Readily available	Waitlist required
Open Source	Yes	No
Pricing	Free for individual developers	Paid
Supported commands	Supports many command	Supports only one command
Git platforms support	All (GitHub, GitLab, Bitbucket, CodeCommit)	GitHub only
IDE integration	Available on Visual Studio Code, Vim, JetBrains IDEs	Available on Visual Studio Code, Vim, Neovim, JetBrains IDEs, Azure Data Studio
Multiple language support	Yes	Yes
Powered by GPT	Yes	Yes
Test Generation	Yes	Yes
Code Completion	Yes	Yes
Code Analysis	Yes	Yes
AI-powered Features	Auto-description, code review, Q&A, code suggestion,documentation, etc.	Pull request summaries, review enhancements, auto-completion

Some limitations of Codium PR-Agent

The PR-Agent is very resourceful too but it also comes with its limitations, let's have a look at some of these.

Quality and Security of Generated Code

PR-Agent generates code based on patterns and examples from its training data. The quality and security of the suggested code may vary, potentially leading to suboptimal or insecure implementations. It's crucial to review and validate the generated code thoroughly.

Biases and Inaccuracies

Like any AI model, PR-Agent might exhibit biases or inaccuracies present in its training data. It might generate code that reflects biases or includes inaccuracies, requiring developers to critically assess and validate the suggestions.

Open Source Collaboration

The transparent and community-backed nature of open-source tools offers great visibility and support from a diverse pool of contributors. However, this collaborative approach might result in a slower pace of development and features that might lack the refined polish found in proprietary tools such as GitHub Copilot for Pull Requests.

Limited Understanding of Context

Explanation: While PR-Agent excels at providing suggestions, it might lack a deep understanding of specific project contexts, business requirements, or domain-specific intricacies. Developers might need to fine-tune or adapt the suggestions to fit their particular use cases.

Conclusion

Exploring AI-driven pull request tools has been enlightening. CodiumAI's PR-Agent and GitHub Copilot for Pull Requests offer valuable support, each with its distinct strengths. Those valuing customization, adaptability, and immediate accessibility might favor CodiumAI's PR-Agent.

PR-Agent's extensive command toolkit empowers users to tailor the tool to their preferences, in contrast to Copilot's single-command approach. This granularity facilitates personalized code analysis, pull request descriptions, and interactive functions, fostering a closer bond with the tool and amplifying productivity.

PR-Agent's platform flexibility enables diverse teams to standardize workflows across platforms, ensuring uniform code quality. Its adaptable pricing structure democratizes AI access, making it inclusive for all. With scalable paid tiers offering advanced features, it caters to varied team needs—ready for instant use, no waiting, no subscriptions.

Building a PDF Summarizer with LangChain

Shittu Olumide — Mon, 11 Dec 2023 23:59:43 +0000

We are in an era inundated with information, and the ability to distill complex documents into concise, digestible summaries is a skill in high demand. Imagine a world where lengthy PDFs, often dense with data, could be swiftly summarized with precision.

There are many paid and free tools that can help summarize documents such as PDFs out there, but you can build your custom PDF summarizer tailored to your taste using tools powered by LLMs.

In this article, you will learn how to build a PDF summarizer using LangChain, Gradio and you will be able to see your project live, so you if are looking to get started with LangChain or build an LLM-powered application for your portfolio, this tutorial is for you.

Prerequisites

LangChain: This is one of the most useful libraries to help developers build apps powered by LLMs. LangChain enables LLM models to generate responses based on the most up-to-date information available online and also simplifies the process of organizing large volumes of data so that it can be easily accessed by LLMs.

Install using pip.

pip install langchain

Gradio: Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere!

Installation using pip.

pip install gradio

To build this LLM-powered application, we will break it down into three simple and easy steps.

Step 1:

Import the important libraries, for this tutorial we will import LangChain and Gradio.

from langchain.document_loaders import PyPDFLoader
import gradio as gr

loader = PyPDFLoader("whitepaper.pdf") 
documents = loader.load()

From the code above:

from langchain.document_loaders import PyPDFLoader: Imports the PyPDFLoader module from LangChain, enabling PDF document loading ("whitepaper.pdf") which is in the same directory as our Python script.
import gradio as gr: Imports Gradio, a Python library for creating customizable UI components for machine learning models and functions.

Step 2:

Create a summarize function to make the summarization. This function will define the PDF file path and an optional custom prompt as input.

def summarize_pdf (pdf_file_path, custom_prompt=""):
    loader = PyPDFLoader(pdf_file_path)
    docs = loader.load_and_split()
    chain = load_summarize_chain(llm, chain_type="map_reduce")
    summary = chain.run(docs)

    return summary

We define a function named summarize_pdf that takes a PDF file path and an optional custom prompt.
Then we use the PyPDFLoader to load and split the PDF document into separate sections.
Utilizing the LangChain's summarization capabilities through the load_summarize_chain function to generate a summary based on the loaded document.
Return the generated summary.

Step 3:

This is the step where we need to set the Gradio Interface.

def main():
    input_pdf_path = gr.inputs.Textbox(label="Enter the PDF file path")
    output_summary = gr.outputs.Textbox(label="Summary")

    interface = gr.Interface(
        fn = summarize_pdf,
        inputs = input_pdf_path,
        outputs = output_summary,
        title = "PDF Summarizer",
        description = "This app allows you to summarize your PDF files.",
    ).launch(share=True)

Sets up Gradio UI components for user interaction.
We define an input field (input_pdf_path) for users to enter the PDF file path.
Then we specify an output field (output_summary) to display the summarized text.
We create a Gradio interface (interface) that utilizes the summarize_pdf function as the core functionality.
Finally, we configure the interface with a title, description, input/output components, and launch the UI for user interaction.

Launch/ Testing

We can now call the main() function to run the application.

main()

From the screenshot, you will see that Gradio launched the application on a public and private URL.

Here is what the finished project looks like:

Conclusion

By harnessing LangChain’s capabilities alongside Gradio’s intuitive interface, we’ve demystified the process of converting lengthy PDF documents into concise, informative summaries.

This isn’t just about building a tool; it’s about embracing the potential of technology to enhance information accessibility. Through this article, we’ve bridged the gap between complex data and user-friendly insights, empowering individuals to navigate through vast volumes of information effortlessly.

Happy summarizing! 😎

Resources

Full GitHub code: https://github.com/zenUnicorn/PDF-Summarizer-Using-LangChain

LangChain documentation: https://python.langchain.com/docs/get_started/introduction

Your LLM hallucinates, Why?

Shittu Olumide — Tue, 28 Nov 2023 23:35:32 +0000

It is a fact that large language models (LLMs) have really made life easier for the whole world, and there has really been a huge adoption of AI in all areas of life. But as much as this new trend is interesting and helpful, it also has its limitations.

One of the limitations of LLM is "hallucination", others include:

Contextual understanding
Domain-Specific Knowledge
Lack of Common Sense Reasoning
Continual learning constraints.

What does it really mean for an LLM to hallucinate?

Hallucination refers to instances where the model generates information or outputs that are factually incorrect, nonsensical, or not grounded in the provided context.

We can also say:

A hallucination occurs when the model generates text or responses that seem plausible on the surface but lack factual accuracy or logical consistency.

Things to consider if your model is hallucinating:

Token Limit

The token limit is the maximum number of tokens or words, that the model can process in a single sequence. Tokens can include words, subwords, or characters, each represented as a unit in the model's input.

This limit is imposed due to computational constraints and memory limitations within the model architecture. It affects both the input and output of the model. When the input text exceeds this limit, the model cannot process the entire sequence at once, potentially leading to truncation, where only a portion of the input is considered. Similarly, for text generation tasks, the output length is also capped by this token limit.

The token window limit is how many words an LLM can absorb while generating an answer. Your LLM (e.g. ChatGPT) is a sponge - what does this mean?

Too much much water (words) = no more absorption.

GPT-3 has a limit of 2048 tokens (gpt-3.5-turbo has up 16,385 tokens).
GPT-4 has a token limit of 128,000 tokens.

For instance, if an LLM has a token limit of 2048 tokens, any input longer than that would need to be split into smaller segments for processing, potentially affecting the context and coherence of the information being processed.

This token limit poses challenges when dealing with lengthy texts, complex documents, or tasks that require processing a large amount of information in a single sequence and this can lead to model hallucinations.

Token isn't everything

Claude 2.1 now has a 200,000 token limit and you might think it must be the best LLM yet. Well, not really. An academic paper shows the opposite, they tested Cluade 2.1 and GPT-4. They performed a "needle in a haystack" scenario and both LLM had to find the information. The longer the text, the harder. Makes sense?

The study also shows other factors:

How attentive to details the LLM is.
It's ability to discern relevance.

So bigger does not necessarily mean better, it challenges the notion that a larger token limit in LLMs = better performance.

The data training bias

What you feed the LLM = the quality of the LLM, so this means that the quality of the training matters. When the training data is bad, there can be a biased data reflection, potentially perpetuating or amplifying societal biases.

Wrap up

Recognizing these limitations helps in using LLMs effectively while considering their strengths and weaknesses. Addressing and minimizing hallucination in LLMs involves ongoing research and development to enhance the model's contextual understanding, improve fact-checking capabilities, and refine its ability to generate accurate and contextually appropriate responses.

Synergy in AI: Bridging the Gap Between Data Science and Business

Shittu Olumide — Tue, 24 Oct 2023 09:23:57 +0000

A 2023 report by McKinsey & Company found that companies that are data-driven are more likely to outperform their peers by up to 5%. In 2021, a study by Havard Business Review found that companies that have a strong data culture are more likely to be innovative and successful.

These statistics show that data science is becoming increasingly important for businesses of all sizes. However, there is still a gap between data scientists and business users. This gap can lead to a number of problems, such as:

Data scientists not understanding the needs of the business.
Business users not understanding the technical capabilities of data science.
Data science projects not being aligned with business goals.
Data science insights not being used to make business decisions.

At its core, synergy in AI represents the harmonious integration of data science and business operations. It’s the science of combining data-driven insights with strategic decision-making, creating a dynamic partnership that enhances the overall performance and competitiveness of an organization. In essence, it’s about leveraging the incredible potential of AI and data analytics to fuel business growth, improve efficiency, and stay ahead in an increasingly data-centric world.

Real-world examples of successful synergy

The concept of synergy between data science and business is not theoretical; it has already yielded remarkable results in real-world scenarios. Several organizations have harnessed the power of this synergy to achieve outstanding outcomes:

Netflix: The streaming giant employs data science to understand user preferences, recommend content, and personalize the user experience. By seamlessly integrating data science into their business model, Netflix has become a content powerhouse, creating hit shows and retaining millions of subscribers worldwide.
Amazon: Amazon’s recommendation engine, powered by advanced data analytics, has significantly boosted its sales. This collaborative approach between data scientists and business teams enables them to offer personalized product suggestions, improving customer satisfaction and driving revenue.
Uber: Uber utilizes data science not only for optimizing ride routes but also for surge pricing, matching drivers with riders, and predicting demand patterns. This collaborative approach has not only made ride-sharing convenient for customers but also enhanced drivers’ earnings and the company’s overall profitability.

Why bridge the gap?

Why is bridging the gap between data science and business so crucial in today’s landscape? The answer lies in the transformative power of data. Data has evolved from being merely a byproduct of business operations to a strategic asset that, when harnessed effectively, can unlock a world of possibilities.

On one side of the spectrum, data scientists possess the expertise to mine, analyze, and derive insights from vast datasets. On the other side, business leaders have the vision and acumen to steer their organizations toward success. The synergy between these two worlds not only bridges the gap but also unlocks the full potential of data, transforming it into actionable intelligence that drives innovation and growth.

Here are some other reasons why there is a need to bridge the gap:

Breaking down silos

One of the most pressing needs is to break down the silos that traditionally separate these two domains. Silos often develop when data scientists and business professionals work in isolation, each with their own set of objectives, tools, and language. These silos can lead to inefficiencies, miscommunication, and missed opportunities.

Breaking down silos means fostering a culture of collaboration and communication between data science and business teams. It involves creating an environment where data scientists and business professionals can interact seamlessly, share insights, and align their efforts towards common goals. When silos are dismantled, data scientists gain a deeper understanding of business needs, and business professionals become more data-savvy, resulting in a powerful synergy that drives innovation and growth.

Collaborative approaches

Synergy in AI is not merely about coexistence; it’s about active collaboration. Collaborative approaches entail bringing data scientists and business professionals together as equal partners in the decision-making process. By involving data scientists early on in business discussions and vice versa, organizations can ensure that data-driven insights are integrated into strategic planning and day-to-day operations.

Collaboration also means fostering cross-functional teams where individuals with diverse skills and expertise work together to solve complex problems. Data scientists and business experts can combine their strengths to formulate hypotheses, analyze data, and develop actionable strategies. This collaborative spirit encourages creativity, adaptability, and a holistic perspective, all of which are vital in navigating the data-driven landscape.

Bridging the gap

To bridge the gap between these two domains effectively, organizations must employ a multi-faceted approach that encompasses cross-training and education, integrated teams, and the utilization of cutting-edge tools and technologies for collaboration.

Cross-training and education

To foster collaboration and understanding between data scientists and business professionals, organizations should invest in cross-training and education initiatives. This approach involves:

Educational workshops: Conduct workshops and training programs that help data scientists grasp the intricacies of business operations and enable business professionals to understand the fundamentals of data science. These sessions should be interactive and encourage knowledge sharing.
Interdisciplinary courses: Encourage employees to pursue interdisciplinary courses or certifications that combine data science and business topics. This not only enhances their skill sets but also breaks down barriers between departments.
Mentorship programs: Implement mentorship programs where experienced data scientists mentor business professionals (and vice versa). This one-on-one guidance fosters a deeper understanding of each other’s roles and responsibilities.

Integrated teams

Breaking down the silos between data science and business departments is paramount for achieving synergy. Building integrated teams involves:

Cross-functional teams: Form cross-functional teams that comprise data scientists, business analysts, marketing experts, and other relevant roles. These teams work together on projects, bringing diverse perspectives to the table.
Shared goals: Define shared objectives that encourage collaboration. When teams have common goals and KPIs, they are more likely to work together seamlessly to achieve them.
Regular meetings: Hold regular meetings where both data science and business professionals come together to discuss progress, challenges, and insights. These meetings promote open communication and knowledge exchange.

Tools and technologies for collaboration

To enable effective collaboration, organizations should leverage tools and technologies tailored to the needs of both data science and business teams:

Data visualization tools: Invest in data visualization platforms that allow business professionals to easily interpret complex data and gain actionable insights. These tools bridge the gap between raw data and decision-making.
Collaborative analytics platforms: Implement collaborative analytics platforms that facilitate real-time collaboration between data scientists and business analysts. These platforms enable joint data exploration and modeling.
Communication and documentation tools: Use communication and documentation tools that ensure all team members have access to project updates, data sources, and analysis documentation. Transparency is key to successful collaboration.

Benefits of Synergy in AI

In this section, we explore the benefits of this synergy in AI in depth:

Enhanced Decision-Making

Data-driven insights: Synergy in AI ensures that business decisions are grounded in data-backed insights. Data scientists can analyze vast datasets, identify patterns, and provide actionable recommendations to inform strategic choices.
Predictive analytics: By leveraging AI and machine learning models, businesses can predict future trends, customer behavior, and market dynamics. This foresight empowers organizations to make proactive decisions and stay ahead of the competition.

Improved Efficiency and Productivity

Automation: AI-powered tools and algorithms can automate repetitive tasks, reducing manual workloads and allowing employees to focus on higher-value tasks. This leads to increased productivity and cost savings.
Optimized operations: Data-driven insights can help streamline processes and optimize resource allocation, ensuring that businesses operate more efficiently and effectively.

Innovation and Competitive Advantage

Market responsiveness: Synergy in AI enables organizations to adapt swiftly to changing market conditions. They can respond to customer preferences and market shifts in real-time, gaining a competitive edge.
New revenue streams: Collaborative AI initiatives often lead to the development of innovative products or services, opening up new revenue streams and business opportunities.

Customer-Centric Strategies

Personalization: AI-driven customer segmentation and personalization strategies allow businesses to tailor their offerings to individual customer preferences. This leads to higher customer satisfaction and loyalty.
Enhanced customer experience: With AI-powered chatbots, recommendation engines, and sentiment analysis, organizations can deliver exceptional customer experiences, leading to increased customer retention.

Risk Mitigation

Anomaly detection: AI models can identify unusual patterns or outliers in data, helping organizations detect fraud, security breaches, or operational anomalies early, and minimizing potential risks.
Compliance and regulation: By using AI to ensure data compliance and monitor regulatory changes, businesses can avoid legal and financial penalties.

Data Monetization

Data-driven products: Synergy between data science and business can lead to the creation of data-driven products or services that can be monetized, diversifying revenue streams.
Partnerships and collaborations: Organizations can leverage their data assets to establish partnerships or collaborations with other businesses, expanding their market reach.

Measurable ROI

Quantifiable outcomes: Synergy in AI often yields measurable results, making it easier to calculate the return on investment (ROI) of data science initiatives and justifying ongoing investments.

Employee Satisfaction and Development

Cross-training: Encouraging collaboration between data scientists and business teams fosters a culture of continuous learning and skill development. Employees become more versatile and adaptable in their roles.

Wrap up

Throughout this article, we’ve emphasized the critical importance of synergy between data science and business. As we look ahead, businesses are encouraged to proactively foster collaboration between their data science and business teams. Breaking down the silos that have traditionally separated these two domains is not just a strategic advantage; it’s a necessity in today’s data-driven era. Encouraging cross-training, investing in integrated technologies, and cultivating a culture of cooperation will be essential steps toward realizing the full potential of synergy in AI.

References:

McKinsey & Company: The Value of Data: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/new-years-resolutions-for-tech-in-2023
Harvard Business Review: Why Data Science Is More Important Than Ever: https://hbr.org/2019/01/data-science-and-the-art-of-persuasion