Semantic Search API with S-BERT and Search API with RAG/LLM

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).

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