Semantic Search Api With S-Bert And Search Api With Rag/Llm

Using Artificial Intelligence (NLP) to build a semantic text query API with BERT and RAG (LangChain/LLM)

What you'll learn

Implement semantic text search engine API using S-BERT.

Implement a search engine API using Retrieval-Augmented Generation (RAG) and LLM.

Bootcamp for building an artificial intelligence API with resources used in companies like Google.

Acquisition of knowledge in Natural Language Processing (NLP) for text processing with Machine Learning.

Using NLP tools like NLTK, Spacy, Sentence Transformers for building search engine.

Using NLP tools like NLTK, Spacy, Sentence Transformers for building search engine.

Hands on (practical project) in building a complete Artificial Intelligence / Machine Learning project in Python.

Develop an LLM agent using LangChain.

Requirements

Python knowledge

Pandas Knowledge

Description

In a rich Artificial Intelligence Bootcamp, learn S-BERT and RAG(LLM) through Natural Processing Language (NLP) with Python, and develop a semantic text search engine API by solving a real problem of a purchasing analysis system.As content:Fundamentals.Learn the fundamentals of Data Science;Learn the fundamentals of Machine Learning;Learn the fundamentals of Natural Language Processing;Learn the fundamentals of Data Cleaning, Word Embendings, Stopwords, and Lemmatization;Learn the fundamentals of text search by keywords and semantic text search;Practice Data Science to understand the problem, prepare the database and statistical analysis;Practical project.This course is divided into two modules where you will learn concepts and build a text search application in a practical way.BERT In this module, you will work with:Python to develop the application;Data cleaning techniques to prepare the database;Using the SpaCy library for Natural Language Processing;Generating Word Embeddings and calculating similarity for data recovery;Transformers model for data recovery by context;S-BERT as a semantic text search tool;Flask and Flassger for developing APIs.Retrieval-augmented generation (RAG)In this module you will work with:Python to develop the application;Large Language Models (LLMs). Advanced AI models that understand and generate natural language;Using the OpenAI API to build AI productsLangChain to build applications that use LLMs;Flask and Flassger for developing APIs.Optional module: learn how to develop API with Flask.Welcome and have fun.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Hello Everyone

Lecture 3 Business Understanding and Application Architecture

Lecture 4 Artificial Intelligence, Machine Learning and Natural Language Processing

Lecture 5 Preconditions and Tech Stacks

Lecture 6 Development Tools and Project Organization

Lecture 7 Creating the Project

Lecture 8 Python Virtual Environment (venv) - (Optional Module)

Lecture 9 Libraries and Dependencies - (Optional Module)

Section 2: Collecting Data

Lecture 10 Data Collection

Lecture 11 Data Files

Lecture 12 Repository: Dataset Reading

Lecture 13 Understanding the Dataset (Optional)

Section 3: Data Preprocessing

Lecture 14 Data Cleaning

Lecture 15 Organizing the Dataset

Lecture 16 Removing Missing Data Values

Lecture 17 Removing Anomalies

Lecture 18 Removing Outliers

Lecture 19 Removing Stopwords

Lecture 20 What's spaCy?

Lecture 21 Data Lemmatization

Lecture 22 Data Capitalization

Lecture 23 Other Data Cleanups

Lecture 24 Refactoring Preprocessing Code

Lecture 25 Saving the Results with Apache Parquet

Lecture 26 Search Term Preprocessing

Section 4: Flask Essentials (Optional Module)

Lecture 27 What is Flask?

Lecture 28 Flask API: First Application and Debug Mode

Lecture 29 URI and Rest API

Lecture 30 Flask API: APP Routing, Get REST method and Swagger

Lecture 31 Flask API: APP Routing, Get and Post REST method and Swagger

Section 5: Word Embeddings

Lecture 32 Vector Embeddings and Word Embeddings

Lecture 33 Calculating the Similarity of Embeddings

Lecture 34 Evolution of Vector Embeddings

Lecture 35 Word Embeddings to Semantic Search

Lecture 36 Generating the Word Embeddings

Lecture 37 Dimensionality Reduction

Section 6: Semantic Text Search with S-BERT

Lecture 38 Search Engine

Lecture 39 What is Semantic Search?

Lecture 40 Transformers

Lecture 41 Reading and Transforming Embeddings File

Lecture 42 BERT and S-BERT

Lecture 43 Semantic Search Engine with S-BERT

Lecture 44 Refactoring Code to Use APIs: Data Preprocessing

Lecture 45 Refactoring Code to Use APIs: Word Embeddings

Lecture 46 Refactoring Code to Use APIs: Semantic Search

Lecture 47 PyTorch and Hungin Faces

Lecture 48 S-BERT - Results Assessment

Section 7: RAG and LLM

Lecture 49 Generative AI

Lecture 50 LLM

Lecture 51 LLMs Agents, OpenAI and ChatGPT API

Lecture 52 RAG, LangChain and Fine Tuning

Lecture 53 Develop an API to Search Text with RAG - Load Datasource and Embeddings

Lecture 54 Develop an API to Search Text with RAG - Retrive documents and Environment Files

Lecture 55 Develop an API to Search Text with RAG - Template, RAG and API

Section 8: RAG vs Semantic Search

Lecture 56 RAG vs Semantic Search

Section 9: Course Closure

Lecture 57 Project Available for Download

Lecture 58 Credits and Acknowledgements

Interested in innovation in the latest and most valuable Data Science and Artificial Intelligence technologies.,Interested in deepening Natural Language Processing (NLP) techniques,Interested in building a semantic text search engine that evaluates synonyms in search terms.