From d58fcda123699622e3796817483ca036e647f52c Mon Sep 17 00:00:00 2001 From: davidastart <37092569+davidastart@users.noreply.github.com> Date: Fri, 18 Oct 2024 08:30:18 -0400 Subject: [PATCH] Updates --- ai-vector-rag/demo/demo.md | 8 ++++---- ai-vector-rag/introduction/introduction.md | 4 ++-- ai-vector-rag/rag/rag.md | 6 +++--- ai-vector-rag/sandbox/workshop/manifest.json | 2 +- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ai-vector-rag/demo/demo.md b/ai-vector-rag/demo/demo.md index f835715a..38511024 100644 --- a/ai-vector-rag/demo/demo.md +++ b/ai-vector-rag/demo/demo.md @@ -1,11 +1,11 @@ -# Lab 1: Run the RAG application using a User Interface +# Lab 1: Running a RAG Application -### **Introduction** +### Introduction Estimated Time: 10 Minutes -### **Objectives** +### Objectives -In this lab, you will run a RAG application interactively using a user-friendly interface. You’ll be able to choose and load several PDF documents and ask your own questions using a prompt.This setup will allow you to easily interact with the RAG system and observe how it processes and generates responses in real time. +In this lab, you will run a RAG application interactively using a user-friendly interface. You’ll be able to choose and load several PDF documents and ask your own questions using a prompt. This setup will allow you to easily interact with the RAG system and observe how it processes and generates responses in real time. ## Prerequisites This lab assumes you have: diff --git a/ai-vector-rag/introduction/introduction.md b/ai-vector-rag/introduction/introduction.md index ebc91c64..24b2fdad 100644 --- a/ai-vector-rag/introduction/introduction.md +++ b/ai-vector-rag/introduction/introduction.md @@ -20,14 +20,14 @@ Estimated Time: 30 Minutes ## About Oracle Ai Vector Search -Oracle AI Vector Search is a feature of Oracle Database 23AI that enables efficient searching of AI-generated vectors stored in the database. It supports fast search using various indexing strategies and can handle massive amounts of vector data. This makes it possible for Large Language Models (LLMs) to query private business data using a natural language interface, helping them provide more accurate and relevant results. Additionally, AI Vector Search allows developers to easily add semantic search capabilities to both new and existing applications. +Oracle AI Vector Search is a feature of Oracle Database 23ai that enables efficient searching of AI-generated vectors stored in the database. It supports fast search using various indexing strategies and can handle massive amounts of vector data. This makes it possible for Large Language Models (LLMs) to query private business data using a natural language interface, helping them provide more accurate and relevant results. Additionally, AI Vector Search allows developers to easily add semantic search capabilities to both new and existing applications. ### Objectives The labs in this workshop will guide you through the following tasks: - Getting familiar with the new Vector Datatype and PL/SQL packages for managing vector data and performing vector operations. - Using PL/SQL to develop applications that work with Large Language Models (LLMs). -- Implementing Oracle AI Vector Search to store and search vectors within Oracle Database 23AI. +- Implementing Oracle AI Vector Search to store and search vectors within Oracle Database 23ai. - Accessing popular LLMs and generating responses based on relevant data. - Running a complete sample application that integrates all these concepts and showcases how to build a practical RAG solution. diff --git a/ai-vector-rag/rag/rag.md b/ai-vector-rag/rag/rag.md index 84624f81..9a7a0a28 100644 --- a/ai-vector-rag/rag/rag.md +++ b/ai-vector-rag/rag/rag.md @@ -1,7 +1,7 @@ -# Lab 2: Build and Run the RAG Application +# Lab 2: Build your own RAG app! ### **Introduction** -A typical RAG (Retrieval Augmented Generation) application follows 7 key steps and requires a vector store. In this lab, you will learn how to build a RAG application using Oracle AI Vector Search. By the end of the lab, you will have stored, chunked, and vectorized data from a PDF document. You will then use a Large Language Model (LLM) to generate an answer to a user’s query based on the relevant context stored in Oracle Database 23AI. We’ll be working with documents (PDF) as the source data, but the same process can be applied to other data types like audio and video. +A typical RAG (Retrieval Augmented Generation) application follows 7 key steps and requires a vector store. In this lab, you will learn how to build a RAG application using Oracle AI Vector Search. By the end of the lab, you will have stored, chunked, and vectorized data from a PDF document. You will then use a Large Language Model (LLM) to generate an answer to a user’s query based on the relevant context stored in Oracle Database 23ai. We’ll be working with documents (PDF) as the source data, but the same process can be applied to other data types like audio and video. Here are the steps we will cover: @@ -10,7 +10,7 @@ Here are the steps we will cover: 3. Break the text into smaller chunks. 4. Use an embedding model to turn those chunks into vectors and store them in Oracle Database 23AI. 5. Ask a question in the prompt, and use the same embedding model to vectorize the question. -6. Perform a similarity search in Oracle Database 23AI using the question. +6. Perform a similarity search in Oracle Database 23ai using the question. 7. The search results and question are passed to the LLM to generate a final response. Each step will be explained in detail, guiding you through the process of interacting with models, embedding vectors, and using advanced search techniques. In the main workshop, additional information and documentation will be provided for further exploration and deeper understanding of the concepts covered. diff --git a/ai-vector-rag/sandbox/workshop/manifest.json b/ai-vector-rag/sandbox/workshop/manifest.json index d8e5d6b9..277536f1 100644 --- a/ai-vector-rag/sandbox/workshop/manifest.json +++ b/ai-vector-rag/sandbox/workshop/manifest.json @@ -20,7 +20,7 @@ "filename": "../../demo/demo.md" }, { - "title": "Lab 2: Exploring RAG ", + "title": "Lab 2: Build your own RAG app!", "description": "Exploring RAG", "type": "livelabs", "filename": "../../rag/rag.md"