Title

RAG example with Oracle AI Vector Search

About Or Title

About This Workshop

Desc Long

Retrieval-augmented generation (RAG) is a relatively new artificial intelligence technique that can improve the quality of OCI Generative AI service by allowing large language models (LLMs) to tap additional data resources without retraining the model. AI Vector Search enables searching both structured and unstructured data by semantics or meaning and values, enabling ultra-sophisticated AI search applications.
This simple hands-on workshop can be executed in 90 minutes and consists of a basic example of AI Vector Search and the integration with OCI Generative AI service for RAG execution. It runs on Oracle Autonomous Database 23ai free tier or paid account. You can test any SQL statements and PL/SQL code in APEX tools and SQL Developer Web.
You will learn how to run OCR on scanned documents and images containing text graphics to extract data for the embeddings. Besides PDF and Word documents, you can use JSON documents, such as GeoJSON shapes with geographical spatial information, which can be edited from the APEX demo application's GUI.

Workshop Length

1 hour, 30 minutes

Outline

• Introduction
• Prepare cloud components and import demo app
• Import documents and generate embeddings
• Use markdown format for free text extracted
• Identify text lines and apply labels for classification
• Run AI Vector Search and RAG
• Test multiple prompts and LLMs for inference tasks
• Create Oracle Text thesaurus and define tokens
• Convert documents using PDF and Word formats
• Extract text from scanned documents and images with OCR
• Create and edit GeoJSON documents for semantic analysis

Prerequisites

This lab assumes you have the following:
• Access to Oracle Cloud Infrastructure (OCI), paid account or free tier.
• Basic experience with OCI Cloud Console and standard components.
• Experience with Oracle Database features, SQL, and PL/SQL code.
• Experience with Oracle Application Express (APEX) low-code development.

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