RAG: End-to-End Application - Speaking with Unstructured Data Part 2 - Spanish

Overview

Learn to build an end-to-end RAG (Retrieval Augmented Generation) application in this Spanish language video tutorial, part 2 of a 3-part series. Explore the code implementation and architecture for creating a RAG component capable of interacting with various unstructured data types including audio, video, images, PDF, Excel, and HTML. Master the integration of multiple technologies including Langchain for the core functionality, multiple LLMs (Gemini Pro Fast, Microsoft Phi3.5 Mini, and LLama 3.2 3B hosted on Nvidia NIM), various vector stores (Elastic, Choma, Faiss, and Vilmus), and embedding models from Gemini and Nvidia. Gain hands-on experience with Streamlit for UX and application serving, while learning to containerize the application using Docker and Docker Compose. Access the complete source code through the provided GitHub repository to follow along with the implementation.

Syllabus

RAG: E2E App. Habla con tu data no estructurada Parte 2 (Español) #machinelearning #datascience

Taught by

The Machine Learning Engineer

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Taught by

RAG: End-to-End Application - Speaking with Unstructured Data - Part 1 (Spanish)

RAG: End-to-End Application - Hablando con Datos No Estructurados - Parte 3

Building RAG Applications: Speaking with Unstructured Data Using LangChain - Part 1

Building RAG Applications: Speaking with Unstructured Data Using LangChain - Part 2

Building End-to-End RAG Applications with Unstructured Data - Part 3: Docker Integration

Master LLMs with LangChain

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