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Semantic Search API with S-BERT and Search API with RAG/LLM
Rating: 4.6 out of 5(6 ratings)
1,456 students

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)
Created byAndré Vieira de Lima
Last updated 3/2025
English

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.

Course content

9 sections • 58 lectures • 7h 21m total length

  • Introduction2:04

    Course Introduction.

  • Hello Everyone0:38

    Hello.

    My name is André Vieira and I will guide you on this learning journey.

    You can find me on Linkedin André Vieira de Lima

  • Business Understanding and Application Architecture4:21

    Understand business needs through Storytelling.

  • Artificial Intelligence, Machine Learning and Natural Language Processing6:17

    Learn basic concepts of Artificial Intelligence, Machine Learning, Data Science and Natural Language Processing.

  • Preconditions and Tech Stacks1:05

    Preconditions for learning and technologies used.

  • Development Tools and Project Organization5:55

    Discover which development tools are used in this training. Understand how our project will be structured.

  • Creating the Project5:25

    Creating the project in Pycharm.

  • Python Virtual Environment (venv) - (Optional Module)1:03

    This module is optional. If you do not intend to use Pycharm and want to install the libraries and dependencies manually, just follow the guide provided.

  • Libraries and Dependencies - (Optional Module)0:52

    This module is optional. Libraries and dependencies will be installed during the development of the project. If you are interested in installing it in advance, just follow the guide provided.

  • Introduction Test

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:

  1. 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;

  2. Practice Data Science to understand the problem, prepare the database and statistical analysis;

  3. Practical project.
    This course is divided into two modules where you will learn concepts and build a text search application in a practical way.

    1. 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.

    2. 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 products

      • LangChain to build applications that use LLMs;

      • Flask and Flassger for developing APIs.

  4. Optional module: learn how to develop API with Flask.

Welcome and have fun.

Who this course is for:

  • 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.

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