How I Built My Own Personalized Google: A Step-by-Step Guide to AI Mastery

Meet My Google: Your Own Simple, Personalized AI Search, Tailor-Made

Full Article

What is This Article About?
○ This article provides a step-by-step guide on building a personalized Google-like search engine using AI technology.
○ It covers the process of crawling websites, selecting relevant sites, indexing their content, and creating a natural language search interface powered by a retrieval-augmented generation (RAG) model.
○ The focus is on the steps after web crawling, such as filtering and indexing the crawled data, and building the search interface.

Why Read It?
○ Understand how to leverage AI and natural language processing (NLP) technologies to build a powerful search tool that can understand and respond to natural language queries.
○ Discover the power of combining different technologies like text embedding, vector search, and language models to create a sophisticated and personalized search experience.

Let's Design
The article describes the design of various components, including:
Index Selector, Indexer and Vector Database: Indexes the content from the selected URLs using techniques like text splitting, embedding generation, and vector storage (Chroma vector db).
Large Language Model (LLM): Integrates a large language model for understanding and generating natural language responses.
User Interface (UI)
Retrieval and Response Generation: Leverages the LLM to retrieve relevant information from the indexed data and generate coherent responses.

Let's Get Cooking!
○ The article provides a GitHub repository link with the code for the project. ○ It explains that the code is concise and modular, making it an excellent learning experience for exploring practical applications of AI technologies.
○ The article then breaks down the code into three main modules:
Index Selector Module: Filters active URLs from a JSON file containing web crawler data.
Indexer Module: Indexes the content from the active URLs using techniques like text splitting, embedding generation, and vector storage.
Aoogle Module: user-friendly interface

Closing thoughts
○ Building a personalized search engine is no longer limited to tech giants like Google, thanks to advancements in AI and NLP technologies.
○ By following the steps outlined in the article, readers can gain a deeper understanding of how search engines work and acquire practical skills in combining different AI technologies to solve complex problems.