DSPy - Develop a RAG app using DSPy, Weaviate, and FastAPI

[dkmdkm]

Free Download DSPy - Develop a RAG app using DSPy, Weaviate, and FastAPI
Published 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 51m | Size: 1.12 GB
Master Full-Stack RAG App Development with FastAPI, Weaviate, DSPy, and React

What you'll learn
Build and Deploy a Full-Stack RAG Application
Efficient Data Management with Weaviate
Document Parsing and File Handling
Implement Advanced Backend Features with FastAPI
Requirements
Basic Knowledge of Python
Familiarity with REST APIs
Understanding of Frontend Development
Development Environment Setup
Description
Learn to build a comprehensive full-stack Retrieval Augmented Generation (RAG) application from scratch using cutting-edge technologies like FastAPI, Weaviate, DSPy, and React. In this hands-on course, you will master the process of developing a robust backend with FastAPI, handling document uploads and parsing with DSPy, and managing vector data storage using Weaviate. You'll also create a responsive React frontend to provide users with an interactive interface. By the end of the course, you'll have the practical skills to develop and deploy AI-powered applications that leverage retrieval-augmented generation techniques for smarter data handling and response generation.Here's the structured outline of your course with sections and lectures:Section 1: IntroductionLecture 1: IntroductionLecture 2: Extra: Learn to Build an Audio AI AssistantLecture 3: Building the API with FastAPISection 2: File UploadLecture 4: Basic File Upload RouteLecture 5: Improved Upload RouteSection 3: Parsing DocumentsLecture 6: Parsing Text DocumentsLecture 7: Parsing PDF Documents with OCRSection 4: Vector Database, Background Tasks, and FrontendLecture 8: Setting Up a Weaviate Vector StoreLecture 9: Adding Background TasksLecture 10: The Frontend, Finally!Section 5: Extra - Build an Audio AI AssistantLecture 11: What You Will BuildLecture 12: The FrontendLecture 13: The BackendLecture 14: The End
Who this course is for
Backend Developers wanting to learn how to build APIs with FastAPI and integrate AI-driven features like document parsing and vector search.
Full-Stack Developers seeking to gain practical experience in combining a React frontend with an AI-powered backend.
Data Scientists and AI Practitioners who want to explore new ways to implement retrieval-augmented generation models for real-world applications.
AI Enthusiasts curious about vector databases like Weaviate and the emerging field of RAG, with the motivation to learn and build AI-based apps from scratch.
Homepage

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

No Password - Links are Interchangeable

 

[0dayddl]

DSPy: Develop a RAG app using DSPy, Weaviate, and FastAPI
Published 9/2024
Duration: 1h51m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.12 GB
Genre: eLearning | Language: English​

Master Full-Stack RAG App Development with FastAPI, Weaviate, DSPy, and React

What you'll learn
Build and Deploy a Full-Stack RAG Application
Efficient Data Management with Weaviate
Document Parsing and File Handling
Implement Advanced Backend Features with FastAPI

Requirements
Basic Knowledge of Python
Familiarity with REST APIs
Understanding of Frontend Development
Development Environment Setup

Description
Learn to build a comprehensive full-stack
Retrieval Augmented Generation (RAG) application
from scratch using cutting-edge technologies like
FastAPI, Weaviate, DSPy, and React
. In this hands-on course, you will master the process of developing a robust backend with FastAPI, handling document uploads and parsing with DSPy, and managing vector data storage using Weaviate. You'll also create a responsive React frontend to provide users with an interactive interface. By the end of the course, you'll have the practical skills to develop and deploy AI-powered applications that leverage retrieval-augmented generation techniques for smarter data handling and response generation.
Here's the structured outline of your course with sections and lectures:
Section 1: Introduction
Lecture 1: Introduction
Lecture 2: Extra: Learn to Build an Audio AI Assistant
Lecture 3: Building the API with FastAPI
Section 2: File Upload
Lecture 4: Basic File Upload Route
Lecture 5: Improved Upload Route
Section 3: Parsing Documents
Lecture 6: Parsing Text Documents
Lecture 7: Parsing PDF Documents with OCR
Section 4: Vector Database, Background Tasks, and Frontend
Lecture 8: Setting Up a Weaviate Vector Store
Lecture 9: Adding Background Tasks
Lecture 10: The Frontend, Finally!
Section 5: Extra - Build an Audio AI Assistant
Lecture 11: What You Will Build
Lecture 12: The Frontend
Lecture 13: The Backend
Lecture 14: The End
Who this course is for:
Backend Developers wanting to learn how to build APIs with FastAPI and integrate AI-driven features like document parsing and vector search.
Full-Stack Developers seeking to gain practical experience in combining a React frontend with an AI-powered backend.
Data Scientists and AI Practitioners who want to explore new ways to implement retrieval-augmented generation models for real-world applications.
AI Enthusiasts curious about vector databases like Weaviate and the emerging field of RAG, with the motivation to learn and build AI-based apps from scratch.

Bitte Anmelden oder Registrieren um Links zu sehen.

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!