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    3. DSPy: Develop a RAG app using DSPy, Weaviate, and FastAPI

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

    Posted By: lucky_aut
    Date: 20 Sep 2024 09:47:58
    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
    UPWARDS