Semantic Search Api With S-Bert And Search Api With Rag llm

Semantic Search Api With S-Bert And Search Api With Rag/llm
Published 3/2025
Created by André Vieira de Lima
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 58 Lectures ( 7h 21m ) | Size: 3.55 GB

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.
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.
Homepage

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