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

Overview

Learn to build an end-to-end RAG (Retrieval Augmented Generation) application in this first video of a three-part series focused on enabling conversations with unstructured data across multiple formats including audio, video, images, PDF, Excel, CSV, and HTML. Explore the integration of Langchain with various technologies, working with multiple Language Models including Gemini Pro Fast, Microsoft Phi3.5 Mini, and LLama 3.2 3B hosted on Nvidia NIM. Master the implementation of vector stores like Elastic, Chroma, Faiss, and Vilmus, while utilizing embedding models from Gemini and Nvidia. Gain hands-on experience with Streamlit for user interface development and application serving, along with Docker and Docker Compose for containerization. Access the complete project repository on GitHub to follow along with the practical implementation.

Syllabus

RAG: E2E Application. Speak with your Unstructured Data Part 1 #machinelearning #datascience

Taught by

The Machine Learning Engineer

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

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

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

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

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

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