From 40c1a07a6ee3a95572dcb094e3391614e1d768b0 Mon Sep 17 00:00:00 2001 From: Shyam Venkat Date: Tue, 4 Nov 2025 10:43:58 +0530 Subject: [PATCH] haystack and llamaindex title change --- haystack/fts/frontmatter.md | 6 +++--- haystack/gsi/frontmatter.md | 6 +++--- lamaindex/fts/frontmatter.md | 6 +++--- lamaindex/gsi/frontmatter.md | 6 +++--- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/haystack/fts/frontmatter.md b/haystack/fts/frontmatter.md index f4560c85..d29ba8fe 100644 --- a/haystack/fts/frontmatter.md +++ b/haystack/fts/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter path: "/tutorial-openai-haystack-rag-with-fts" -title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack" -short_title: "RAG with Openai and Haystack" +title: "Retrieval-Augmented Generation (RAG) with OpenAI, Haystack and Couchbase Search Vector Index" +short_title: "RAG with OpenAI, Haystack and Couchbase Search Vector Index" description: - - Learn how to build a semantic search engine using Couchbase's Search vector index. + - Learn how to build a semantic search engine using Couchbase's Search Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with the embeddings generated by OpenAI Services. - You will understand how to perform Retrieval-Augmented Generation (RAG) using Haystack, Couchbase and OpenAI services. content_type: tutorial diff --git a/haystack/gsi/frontmatter.md b/haystack/gsi/frontmatter.md index 4a0ba94a..23fab296 100644 --- a/haystack/gsi/frontmatter.md +++ b/haystack/gsi/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter path: "/tutorial-openai-haystack-rag-with-gsi" -title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack" -short_title: "RAG with Openai and Haystack" +title: "Retrieval-Augmented Generation (RAG) with OpenAI, Haystack and Couchbase Hyperscale and Composite Vector Indexes" +short_title: "RAG with OpenAI Haystack and Couchbase Hyperscale and Composite Vector Indexes" description: - - Learn how to build a semantic search engine using Couchbase's GSI vector index. + - Learn how to build a semantic search engine using Couchbase's Hyperscale and Composite Vector Indexes. - This tutorial demonstrates how to integrate Couchbase's GSI vector search capabilities with OpenAI embeddings. - You will understand how to perform Retrieval-Augmented Generation (RAG) using Haystack, Couchbase and OpenAI services. content_type: tutorial diff --git a/lamaindex/fts/frontmatter.md b/lamaindex/fts/frontmatter.md index f7c7c0f2..59917eb5 100644 --- a/lamaindex/fts/frontmatter.md +++ b/lamaindex/fts/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter path: "/tutorial-openai-llamaindex-rag-with-fts" -title: "Retrieval-Augmented Generation (RAG) with OpenAI and LlamaIndex" -short_title: "RAG with Openai and LlamaIndex" +title: "Retrieval-Augmented Generation (RAG) with OpenAI, LlamaIndex and Couchbase Search Vector Index" +short_title: "RAG with Openai, LlamaIndex and Couchbase Search Vector Index" description: - - Learn how to build a semantic search engine using Couchbase's Search vector index. + - Learn how to build a semantic search engine using Couchbase's Search Vector Index. - This tutorial demonstrates how to integrate Couchbase's search vector search capabilities with the embeddings generated by OpenAI Services. - You will understand how to perform Retrieval-Augmented Generation (RAG) using Llamaindex, Couchbase and OpenAI services. content_type: tutorial diff --git a/lamaindex/gsi/frontmatter.md b/lamaindex/gsi/frontmatter.md index 203abd5d..48715c7b 100644 --- a/lamaindex/gsi/frontmatter.md +++ b/lamaindex/gsi/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter path: "/tutorial-openai-llamaindex-rag-with-gsi" -title: "Retrieval-Augmented Generation (RAG) with OpenAI and LlamaIndex" -short_title: "RAG with Openai and LlamaIndex" +title: "Retrieval-Augmented Generation (RAG) with OpenAI, LlamaIndex and Couchbase Hyperscale and Composite Vector Indexes" +short_title: "RAG with Openai, LlamaIndex and Couchbase Hyperscale and Composite Vector Indexes" description: - - Learn how to build a semantic search engine using Couchbase's GSI vector search. + - Learn how to build a semantic search engine using Couchbase's Hyperscale and Composite Vector Indexes. - This tutorial demonstrates how to integrate Couchbase's GSI vector search capabilities with OpenAI embeddings. - You will understand how to perform Retrieval-Augmented Generation (RAG) using LlamaIndex and GSI vector indexes. content_type: tutorial