Merge branch 'main' into open_source_bp_2

Author: bobpeulen

ai-and-app-modernisation/ai-services/generative-ai-service/lowcode-rag-genai/README.md

@@ -1,12 +1,12 @@
-# Low Code Modular RAG-based Knowledge Search Engine using OCI Generative AI, OCI Vector Search, and Oracle Integration Cloud
+# Modular GenAI-based Knowledge Search Engine using OCI Generative AI, OCI DB 23ai, and Oracle Integration Cloud
 
 In this article, we'll explore how to enable an enterprise-grade RAG-based Knowledge Search Engine with a low-code approach.
 
 You’ll learn how to use Oracle Integration Cloud to integrate and orchestrate business chanels like a Web Application built in Oracle Visual Builder, productivity channels like OCI Object Storage, local large and small language models (LLMs), and vector databases to ingest live data into the RAG-based Knowledge Search Engine store. 
 
-You'll use Oracle Cloud Infrastructure (OCI) Document Understanding to extract information from different document types. Leverage OCI Generative AI for document summarization, generation and synthesis of answers to questions on documents. Use OCI DB Cloud Service 23AI for Document Extraction, Vector Search and Embedding (using ONNX local models to the DB) capabilities , and apply local OCI Data Science models for better answers from advanced RAG.
+You'll use Oracle Cloud Infrastructure (OCI) Document Understanding to extract information from different document types. Leverage OCI Generative AI for document summarization, generation and synthesis of answers to questions on documents. Use OCI DB Cloud Service 23ai for Document Extraction, Vector Search and Embedding (using ONNX local models to the DB) capabilities , and apply local OCI Data Science models for better answers from advanced RAG.
 
-Reviewed: 30.05.2024
+Reviewed: 10.06.2024
 
 # When to use this asset?