Gen AI Course | Gen AI Tutorial For Beginners
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Introduction
Welcome to this comprehensive guide on Generative AI (Gen AI). In this article, we will cover the foundational concepts of Gen AI, delve into the LangChain framework used for building generative applications, and culminate with two hands-on projects: an equity news research tool using a commercial GPT model and a Q&A tool for the retail industry using an open-source language model.
Understanding Generative AI Fundamentals
Generative AI can be broadly categorized into two types: generative and non-generative AI. Non-generative AI applies to solutions where decisions are made based on available data, like diagnosing pneumonia from a chest X-ray or evaluating credit history for loan approval. In contrast, Generative AI focuses on content creation, such as ChatGPT, which allows users to compose resumes, plan trips, or generate images.
Evolution of Generative AI
The evolution of AI has seen various paradigms, starting from statistical machine learning to deep learning, recurrent neural networks, and ultimately, the introduction of transformer architecture. Transformers, as illustrated in the paper “Attention is All You Need,” have been pivotal for creating large language models (LLMs) like GPT-4, capable of understanding context and generating coherent text based on input prompts.
The Language Model Analogy
An analogy is made using a “stochastic parrot” concept to elucidate how language models operate. Just like a parrot repeats what it hears with probabilistic correctness, language models predict words based on vast training datasets. Large language models are trained using a vast array of texts, enabling them to generate text outputs that reflect the patterns of natural language.
LangChain: A Framework for Building Gen AI Applications
LangChain is a powerful Python framework designed for developing applications on top of LLMs. It streamlines the process of using various models (commercial or open-source) to create generative applications. Essential to its utility is the concept of embeddings and vector databases, which help in efficiently retrieving meaningful data to respond to user queries.
Key Concepts in LangChain
- Embeddings: Numerical representations of textual data that help in understanding and retrieving relevant information.
- Vector Databases: Structures that allow for efficient searching and retrieval based on embeddings.
- Retrieval-Augmented Generation (RAG): A method of utilizing existing datasets to improve the responses generated by LLMs, particularly useful when dealing with private organizational data.
Hands-On Projects
Project 1: Equity News Research Tool
In our first project, we'll create a news research tool that allows users to input URLs of news articles and ask questions related to the content. By utilizing LangChain and a commercial GPT model, the tool retrieves relevant information and provides accurate answers.
We will cover all the technical aspects, including setting up a server, embedding text, configuring a vector database, and creating a user-friendly interface using Streamlit.
Project 2: Retail Q&A Tool
In the second project, we will develop a Q&A tool specifically for the retail industry. This tool will allow users to inquire about inventory and pricing, translating their questions into SQL queries that can be executed against a MySQL database. This project emphasizes the real-world applicability of generative AI in improving operational efficiencies in retail.
Conclusion and Resources
This mini-course aims to provide learners with a robust understanding of generative AI principles and practical experience with real-world projects using LangChain and various language models. The knowledge gained can be invaluable for anyone looking to leverage AI technologies.
For more in-depth understanding, consider checking additional resources, including tutorials on embedding techniques, vector databases, and LangChain functionalities.
Keywords
Generative AI, LangChain, commercial GPT model, open-source language model, equity news research tool, retail Q&A tool, embeddings, vector database, retrieval-augmented generation.
FAQ
What is Generative AI?
Generative AI refers to a category of AI that can create new content, including text, images, videos, and more.
What is LangChain?
LangChain is a Python framework that simplifies building applications on top of large language models.
What are embeddings?
Embeddings are numerical representations of text data used in machine learning to capture semantic meaning.
How does the equity news research tool work?
The tool takes URLs of news articles as input and uses a generative AI model to analyze and respond to user queries about the articles.
What challenges might arise when using an LLM?
Common issues include incorrect assumptions about data structure and irrelevant outputs if the model is not properly trained with examples specific to the task at hand.
