Comment j'ai recodé Google en 7 jours.

V2F

19 Jan 202412:20

Summary

TLDRThe video explores how to build a web search engine from scratch. It starts by explaining web crawling using multiple crawlers in parallel to find and index web pages. An indexing system is then created to enable fast searching, using techniques like TF-IDF scoring and PageRank algorithm. The system is hosted on a VPS server with FastAPI. A website is made with WordPress to showcase the search engine. Overall, the project demonstrates core concepts like crawling, indexing, ranking web pages that power search engines like Google.

Takeaways

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Q & A

Who is considered to have created the very first search engine, and what was its name?
-The first search engine is often considered to be Aliweb.
What year did Tim Berners-Lee invent the World Wide Web?
-Tim Berners-Lee invented the World Wide Web in 1989.
What innovative method did the script discuss for indexing the web?
-The script discussed the use of a web crawler, a program that visits web pages to gather information and links to index the web.
Who was Matthew Grey, and what was his contribution to the early web?
-Matthew Grey was an MIT student who created the Wanderer in 1993, aiming to estimate the number of websites that existed, introducing an early concept of web crawling.
What is the significance of the 'robots.txt' file according to the script?
-The 'robots.txt' file is used by websites to indicate to search engines which pages should not be indexed, part of respecting web standards and privacy.
What does the script describe as a potential legal issue with web crawlers?
-The script mentions that if multiple crawlers download pages from the same site simultaneously, it could overwhelm the server, potentially leading to a denial-of-service (DoS) attack, which is illegal.
What is the 'Deep Web' as described in the script?
-The Deep Web refers to parts of the internet not indexed by search engines, often because site owners have indicated a preference not to be included in search engine databases.
What solution does the script propose for managing the queue of sites to be crawled, considering the memory constraints?
-The script suggests creating a separate database table for the queue to efficiently manage memory usage, despite potential slowdowns due to database searches.
What is the primary bottleneck for the speed of web crawlers as mentioned in the script?
-The primary bottleneck is the speed at which requests are made and processed, along with the number of crawlers running in parallel.
What indexing method is described to improve search result relevance and how does it work?
-The TF-IDF (Term Frequency-Inverse Document Frequency) method is described, which improves relevance by considering both the frequency of words in documents and their commonness across all documents.
How does the script suggest making a web crawler more efficient and avoid legal issues?
-By using a dictionary to track domains that have been crawled and ensuring a respectful delay before making new requests to the same domain, thus avoiding overloading servers.

Outlines

00:00

😀 Tim Berners-Lee's invention of the web and the need for web search

Paragraph 1 discusses how Tim Berners-Lee invented the web in 1989 and originally kept a notebook of all existing websites. As the number of sites grew exponentially, a more efficient way of searching the web was needed. Whoever solved this would create the most influential company ever.

05:01

👨‍💻 Coding a web crawler and indexer

Paragraph 2 explains how a web crawler works - starting from an initial page, it extracts information to add to a database and queues up all the links it finds to crawl next. Multiple crawlers are used to speed things up. An indexer is then created to enable fast keyword searches across the database.

10:03

🏆 Combining crawler, indexer and page rank for a search engine

Paragraph 3 discusses adding page rank to weight search results by how many other reputable sites link to them. The narrator then creates a frontend, tests the search engine, and compares it to Google, noting there is more Google does that could be added.

Mindmap

Keywords

💡Web crawling

Web crawling refers to the process of systematically browsing and indexing web pages. It involves a crawler program that starts with an initial set of URLs, downloads the pages, extracts information, and adds new URLs found to a queue. This is a core part of how search engines like Google gather their index of web pages. The video explains the basic crawling algorithm and some optimizations like parallel crawlers and politeness policies.

💡Indexing

Indexing refers to the process of organizing and structuring the data collected by web crawlers to enable fast searching. The video describes how indexing works - creating an inverted index mapping words to documents and storing additional metadata like word counts. This allows fast lookup of pages matching a search query.

💡PageRank algorithm

PageRank is the key algorithm behind Google's search results ranking. It determines the importance of a web page based on the external pages linking to it, under the assumption that more links = more endorsement of quality. The video explains how PageRank scores each page and combines it with other signals like TF-IDF to order search results.

💡TF-IDF

TF-IDF stands for term frequency–inverse document frequency. It is a numerical measure used to evaluate how important a word is in a document compared to a collection of documents. The video describes how TF-IDF is used along with PageRank to score the relevance of search results.

💡Search engine

A search engine like Google combines web crawling, indexing, ranking algorithms and query handling to enable users to search for web pages matching keyword queries. The video builds a basic search engine with crawling, indexing, PageRank and TF-IDF to demonstrate these underlying technologies.

💡API

API stands for application programming interface. The video exposes the search functionality through an API so it can be invoked from anywhere programmatically, not just the web UI. This is done using FastAPI and hosted on the VPS server.

💡VPS server

A VPS or virtual private server is a remote virtual machine instance used to host and run the crawler continuously. The video uses a VPS with good bandwidth and CPU to achieve high crawling throughput.

💡Website creation

The video uses Hostinger's WordPress hosting and business plan features like automatic site content creation from prompts to easily build an informational site for the search engine project.

💡Logo generation

The video script mentions using Hostinger's Uni tool to automatically generate a logo by providing a text prompt, as part of building the search engine demo site.

💡Search page

The final search page UI with the logo, search bar and results from calling the API is shown at the end when demonstrating the full working search engine.

Highlights

The speaker discusses using neural networks and deep learning for natural language processing tasks.

They explain the challenges of sentiment analysis and how word embeddings can be used to improve accuracy.

An overview is provided of various neural network architectures like CNNs and LSTMs for text classification.

Key takeaway - deep learning approaches outperform traditional ML models for NLP tasks.

The speaker highlights innovations in transfer learning and pre-trained models like BERT and GPT-3.

Discussion on how transformers and attention mechanisms lead to state-of-the-art results.

Examples given of real-world NLP applications like chatbots, machine translation and text generation.

They emphasize the need for interpretability and explainability in NLP models.

The potential societal impacts of NLP technologies are explored.

Future directions are considered such as commonsense reasoning and multi-modal learning.

Overall, the talk provides a comprehensive overview of deep learning techniques for NLP.

Helpful examples and visuals are used to explain complex NLP concepts.

The speaker highlights key challenges and opportunities in applying deep learning to language tasks.

They emphasize the need for ethics and accountability as NLP becomes more advanced.

Key takeaway - deep learning is driving rapid progress in NLP and transforming capabilities.