6 Free Practical LLMs Notebooks to Get Your Hands Started with LLM Applications
Embark on an exhilarating journey into the limitless possibilities of Large Language Models (LLMs) with this article, offering an exclusive lineup of six free and hands-on notebooks.
Dive into the fascinating universe of LLM applications, from crafting apps with visual acuity, auditory recognition, and speech generation, to developing a cutting-edge movie recommendation system. Navigate the intricacies of vector data manipulation and master the art of semantic search using OpenAI Embedding Creation and Question-Answering capabilities.
Take your skills a step further by launching open-source applications with LangChain, gaining insights into collaborative development. Cap off your adventure by constructing a Resume Evaluator using OpenAI, showcasing the practical prowess of LLMs in professional settings.
This curated collection of free notebooks is your gateway to unlocking the immense potential of Large Language Models across diverse real-world applications. Get ready for an engaging exploration that combines practicality with innovation!
Table of Contents:
Build LLM Apps that Can See Hear Speak
Building Movie Recommendation
Working with Vector Data
Semantic Search with OpenAI Embedding Creation
Semantic Search with OpenAI QA
Launch Open-Source Apps with LangChain
Building Resume Evaluator using OpenAI
1. Buid LLM Apps that Can See Hear Speak
OpenAI added new features to ChatGPT so that it can now see and hear. If you would like to harness the power of these new features and build LLM applications that can now see, hear, and speak you can start with this hands-on notebook.
This hands-on demo will showcase how to build a seamless interaction with your database through a user-friendly UI using voice recognition and OpenAI embeddings.
Here are the points covered in this notebook:
Techniques to fetch relevant company news articles using the requests library.
The art of embedding questions and answers for enhanced interaction.
The power of voice recognition in database interaction.
An introduction to OpenAI’s new voice and image capabilities.
How to utilize the new text-to-speech model for generating human-like audio.
2. Working with Vector Data
To be able to build LLM applications you will have to use a vector database. Traditional databases let you perform fast search and retrieval on structured data. Vector databases on the other hand do the same thing but for text embedding vectors with a very high speed.
If you would like to learn more about it, you can go through this practical notebook to learn more about it.
In this hands-on notebook you will learn the following:
What are vector databases? Their role in modern AI architecture
Building and integrating efficient AI pipelines with vector search
Choosing the best vector databases for scalability
Architectural and performance nuances of leading vector databases in the market.
3. Semantic Search with OpenAI Embedding Creation
In this notebook, we will demonstrate an example of conducting a semantic search on SingleStoreDB with SQL! Unlike traditional keyword-based search methods, semantic search algorithms take into account the relationships between words and their meanings, enabling them to deliver more accurate and relevant results — even when search terms are vague or ambiguous.
In this example, you will use Open AI embeddings API to create embeddings for the dataset and run semantic_search using the dot_product vector matching function!
4. Semantic Search with OpenAI QA
In this Notebook, you will use a combination of Semantic Search and a Large Langauge Model (LLM) to build a basic Retrieval Augmented Generation (RAG) application.
5. Launch Open-Source Apps with LangChain
In this notebook, you will learn how to launch an open-source application with LangChain. This notebook covers:
Dive deep into building a GPT App using LangChain, with hands-on examples and live coding.
Uncover LangChain’s native support for efficient vector functions to power Generative AI with simple SQL queries.
Absorb practical techniques and strategies for building intelligent GPT applications.
Delve into the power of LangChain’s scalable, distributed architecture and OpenAI’s advanced machine-learning models for GPT.
6. Building Resume Evaluator using OpenAI
If you are looking to build a cool LLM application. Here is one build an automated HR / Recruiter Resume Scan and Assessment system using OpenAI. This will improve your technical skills and you will be better at job searching.
If you would like to get a starting point you try this notebook in which you will learn the following:
Enhance your hiring process with AI-powered tools.
Automate resume screening and reduce unconscious bias.
Implement AI-driven recruitment analytics.
Improve candidate experience with efficient assessments.
Integrate OpenAI with HR systems for better talent acquisition.
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