It has never been easier to create a chatbot that can chat with your PDF documents.

Even with beginner-level Python skills and no prior UI/UX experience, you can achieve this feat.

Simply use @LangChainAI and @chainlit_io.

Here's how 👇 First, import the necessary libraries.

Although the list of imports for this chatbot may be quite long, don't let it discourage you.

The actual implementation is quite easy.

Just found a great free alternative to Midjourney: @LeonardoAi_ .

Blown away by its power and simplicity 🤯

Here's why it's so great 👇 Creating high-quality images requires detailed descriptions.

But if you are like me who just can't be bothered, you can get the help of their prompt generation feature.

Use this feature to brainstorm powerful prompts and create vivid images easily.

Chatbot Speedrun!⌚️

Watch me build a fully functional chatbot in just 1 minute and 54 seconds!

I often say that creating your own chatbot with @LangChainAI and @chainlit_io is simple, but now I'll prove it.

Let me show you how easy it is.

Demo and code below👇 This is the chatbot we created. Fully functional and with a nice user interface.

Since we didn't engineer any prompts, it's still kind of basic. But it's fully functional nonetheless.

So simply copy the code below, add your own prompts, and you're good to go.

As an AI developer, it is challenging to determine where your skills are in demand and what skill stacks you should focus on developing.

Today, I mostly rely on two websites that allow me to keep a pulse on the market. 👇 1. Product Hunt

Product Hunt enables me to keep an eye on products being developed by individual creators, startups, and founders.

Excellent way to stimulate your creativity, discover cutting-edge products being launched, and see for yourself where the tech is headed.

Most people think that you have to be a programming expert to create an AI app.

Not true at all, beginner Python skills are already enough to get you started.

You can easily build the backend of your AI app with just a few lines of code using @LangChainAI Chains.

Here's how 👇 Chains are essential to Langchain as they handle most of the backend code.

They deal with the LLM APIs, process responses, return output, and ensure that everything runs smoothly to create a single, coherent application.

All of that in just 3 or 4 lines of code on your part.

The way your chatbot remembers your past interactions can make or break its performance.

A simple tweak can take it from answering useless gibberish to answering highly contextual and practical responses.

Here’s how memories work in @LangChainAI 👇 As in humans, so in chatbots.

Memory is simply the way your chatbot remembers its past interactions.

You can choose to have it remember everything or only the most important details.

Each option has its advantages and disadvantages.

In just under 3 minutes, I was able to create my own personalized chatbot using LangChain and @chainlit_io .

The process required minimal coding and no prior UI/UX experience. Ideal for all AI developers and AI enthusiasts.

Here's a short guide to help you get started 👇 PS: For the sake of this tutorial, I plugged my personal content idea generator AI agent to the chatbot that was already pre-built with langchain.

To begin, install the Chainlit library on your machine by using the command "pip install chainlit".

Creating a custom AI agent is the easiest thing in the world when you use the @LangChainAI library.

Here's a short guide on how to create your custom agent from scratch and get it up and running 👇 The prompt template is literally the prompt template your agent will follow.

Feel free to place multiple placeholders to simplify the process of creating your prompt and offering you easy customization.

To create a prompt for your agent, follow the code below.