DSPy: Develop a RAG app using DSPy, Weaviate, and FastAPI
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.
More Info
![DSPy: Develop a RAG app using DSPy, Weaviate, and FastAPI]()
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.
More Info
