Migrate awsbedrock to new search terminology

awsbedrock/query_based/frontmatter.md

@@ -0,0 +1,24 @@
+---
+# frontmatter
+path: "/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-or-composite-vector-index"
+title: RAG with Bedrock using Couchbase Hyperscale and Composite Vector Index
+short_title: Amazon Bedrock RAG with Hyperscale/Composite Index
+description:
+  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using Hyperscale and Composite Vector Index.
+  - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Nova Pro language model.
+  - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
+content_type: tutorial
+filter: sdk
+technology:
+  - vector search
+tags:
+  - Hyperscale Vector Index
+  - Composite Vector Index
+  - Artificial Intelligence
+  - LangChain
+  - Amazon Bedrock
+sdk_language:
+  - python
+length: 60 Mins
+alt_paths: ["/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-vector-index", "/tutorial-aws-bedrock-couchbase-rag-with-composite-vector-index"]
+---

awsbedrock/search_based/aws_index.json

@@ -0,0 +1,73 @@
+{
+    "type": "fulltext-index",
+    "name": "vector_search_bedrock",
+    "uuid": "",
+    "sourceType": "gocbcore",
+    "sourceName": "vector-search-testing",
+    "planParams": {
+      "maxPartitionsPerPIndex": 64,
+      "indexPartitions": 16
+    },
+    "params": {
+      "doc_config": {
+        "docid_prefix_delim": "",
+        "docid_regexp": "",
+        "mode": "scope.collection.type_field",
+        "type_field": "type"
+      },
+      "mapping": {
+        "analysis": {},
+        "default_analyzer": "standard",
+        "default_datetime_parser": "dateTimeOptional",
+        "default_field": "_all",
+        "default_mapping": {
+          "dynamic": true,
+          "enabled": false
+        },
+        "default_type": "_default",
+        "docvalues_dynamic": false,
+        "index_dynamic": true,
+        "store_dynamic": false,
+        "type_field": "_type",
+        "types": {
+          "shared.bedrock": {
+            "dynamic": true,
+            "enabled": true,
+            "properties": {
+              "embedding": {
+                "dynamic": false,
+                "enabled": true,
+                "fields": [
+                  {
+                    "dims": 1024,
+                    "index": true,
+                    "name": "embedding",
+                    "similarity": "dot_product",
+                    "type": "vector",
+                    "vector_index_optimized_for": "recall"
+                  }
+                ]
+              },
+              "text": {
+                "dynamic": false,
+                "enabled": true,
+                "fields": [
+                  {
+                    "index": true,
+                    "name": "text",
+                    "store": true,
+                    "type": "text"
+                  }
+                ]
+              }
+            }
+          }
+        }
+      },
+      "store": {
+        "indexType": "scorch",
+        "segmentVersion": 16
+      }
+    },
+    "sourceParams": {}
+}

awsbedrock/gsi/frontmatter.md → awsbedrock/search_based/frontmatter.md

@@ -1,18 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-aws-bedrock-couchbase-rag-with-global-secondary-index"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock using GSI index
-short_title: RAG with Couchbase and Amazon Bedrock using GSI index
+path: "/tutorial-aws-bedrock-couchbase-rag-with-search-vector-index"
+title: RAG with Amazon Bedrock using Couchbase Search Vector Index
+short_title: RAG with Bedrock using Couchbase Search Vector Index
 description:
-  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using GSI.
-  - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Claude language model.
+  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using Search Vector Index.
+  - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Nova Pro language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - GSI
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - Amazon Bedrock