Generative AI mini projects using Python

Welcome to the exciting world of Generative AI mini projects using Python (GAI)! This innovative field is transforming content creation, spanning from lifelike images to captivating music and even engaging narratives. As early career professionals, grasping Gen AI development holds enormous promise for your future pursuits, with the applications of tomorrow likely to be enriched with Generative AI mini projects using Python features.

This blog emphasizes project-based learning, a practical method for mastering GAI concepts, particularly RAG (Retrieval-Augmented Generation) Application development. We’ll explore three exciting projects that will arm you with the skills to navigate the intriguing world of Large Language Models (LLMs).

Here are three Generative AI mini projects using Python and RAG development projects that will set you on a course to learn the concepts through hands-on experience:

Top 3 Generative AI mini projects using Python

Project: Building a Financial Analysis Software App Using GPT Chat API, SERP API, and Yahoo Finance API

Problem Statement:

The project aims to develop a Financial Analysis software application for beginners to provide investment thesis and recommendations on any given company. By leveraging the OpenAI GPT Chat API, SERP API, and Yahoo Finance API, the goal is to create a comprehensive tool for data analytics projects. The focus is on gathering company performance information using financial data and offering valuable insights for making investment decisions.

Solution Steps:

1. Extract publicly available news items about the company using the SERP API.
2. Utilize the Yahoo Finance API to gather stock price data and financial performance information.
3. Write the collected data into a text file for use in the RAG Application.
4. Implement a detailed prompt to extract investment thesis and recommendations based on the data.

Technologies Used:

• OpenAI GPT Turbo 3.5 Chat Completion API: for chat capabilities and generating responses.
• SERP API: for extracting information from search engine result pages.
• Yahoo Finance API: for obtaining stock price and financial performance data.

Learning Benefits:

Participants in this project will gain hands-on experience in developing a financial analysis software application using cutting-edge technologies. By working with APIs and external data sources, learners will enhance their skills in data analytics and project development. Building a RAG App will provide a deeper understanding of how to leverage AI models for financial analysis and decision-making.

Conclusion:

In conclusion, this project offers a valuable opportunity for beginners to explore the intersection of finance and data analytics. By building a Financial Analysis Software App using the GPT Chat API, SERP API, and Yahoo Finance API, participants can enhance their technical skills and gain practical experience in the field. The project equips learners with the tools and knowledge needed to analyze company performance and make informed investment recommendations. Overall, it is a rewarding learning experience for students looking to delve into the world of financial data analysis.

Project Name: LangChain + OpenAI GPT: Document Summarisation Project

Problem Statement: In today’s fast-paced world, the ability to quickly and efficiently summarize large or complex texts is crucial. Students, researchers, and professionals often find themselves overwhelmed by the amount of information they need to process. Traditional methods of summarization can be time-consuming and inaccurate, leading to a need for a more advanced solution.

Solution Steps:

• Learn how to use the LangChain framework and OpenAI language model, GPT, to build a text summarization tool.
• Utilize three distinct LangChain techniques of text summarization to read content from various formats and sources.
• Harness the summarization and information extraction capabilities of GPT through the OpenAI API.
• Develop a Streamlit UI demonstration of the LangChain Text Summarization tool for a user-friendly experience.

Technologies Used:

• LangChain framework
• OpenAI language model, GPT
• Streamlit UI library

Learning Benefits: By enrolling in this course, learners will:

• Gain hands-on experience in building a powerful text summarization tool using cutting-edge technologies.
• Develop skills in summarizing content from PDFs, DOCX files, and web pages.
• Learn to interpret diverse formats and condense information effectively.
• Understand how to leverage large language models like GPT for text summarization and information extraction.
• Enhance their knowledge of app creation and UI design through the integration with the Streamlit library.

Conclusion: The LangChain + OpenAI GPT: Document Summarisation Project offers learners a unique opportunity to delve into the world of advanced text summarization tools. By mastering the techniques taught in this course, participants can equip themselves with valuable skills that are in high demand across various industries. Whether you are a student looking to enhance your project portfolio or a professional seeking to streamline your workflow, this project provides a solid foundation for building innovative solutions in the realm of text summarization.

Project Name: LangChain + Vector DB + GPT 3.5 Turbo Semantic Search Chat App

The LangChain + Vector DB + GPT 3.5 Turbo Semantic Search Chat App project aims to develop a robust Semantic Search application that can provide precise answers to user queries. By leveraging the SQuAD 2.0 dataset, Pinecone vector database, LangChain tools and agents, and the OpenAI GPT Chat API, this project creates a searchable space for the dataset and enables interactive querying using natural language processing capabilities.

Solution Steps:

Data Preparation and Indexing:

• Load the SQuAD 2.0 dataset and preprocess it to extract questions, answers, and contextual paragraphs.
• Use OpenAI’s embedding models to convert textual data into high-dimensional vectors.
• Upsert the vectorized data into a Pinecone index for semantic searches.

Semantic Search Agent Development in LangChain:

• Create a LangChain Tool for querying from the Pinecone DB and a LangChain Agent using the Q&A Tool.
• Process the query using LangChain’s conversational agents to generate a prompt for the OpenAI GPT model.
• Convert the processed query into a vector using OpenAI embeddings and perform a similarity search in the Pinecone index.
• Pass the context passages to the OpenAI GPT Chat API to generate a natural language answer.
• Use LangChain’s conversational memory to maintain context for follow-up questions and clarifications.

Technologies Used: SQuAD 2.0 Dataset, Pinecone Vector Database, LangChain Framework, OpenAI GPT Chat API.

Learning Benefits: By enrolling in this project, learners will gain hands-on experience in building a cutting-edge Semantic Search Chat App using the latest technologies in natural language processing and vector databases. They will learn to create a conversational agent, understand the use of vector databases for semantic search, and develop a deep understanding of integrating various tools and APIs for advanced chat applications.

Conclusion: The LangChain + Vector DB + GPT 3.5 Turbo Semantic Search Chat App project offers an exciting opportunity for beginners to delve into the world of Generation AI projects. By completing this course, learners will enhance their skills in natural language processing, conversational AI, and data indexing, setting a strong foundation for further exploration in the field of AI and machine learning.

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