Merge pull request #264518 from HeidiSteen/heidist-proto

[azure search] prototype TOC concepts node

Author: v-regandowner

articles/search/TOC.yml

@@ -3,7 +3,7 @@
 - name: Overview
   expanded: true
   items:
-  - name: What's AI Search?
+  - name: What's Azure AI Search?
     href: search-what-is-azure-search.md
   - name: New in Search
     href: whats-new.md
@@ -13,11 +13,11 @@
     href: search-faq-frequently-asked-questions.yml
 - name: Quickstarts
   items:
-  - name: Vector search
+  - name: Vector store
     href: search-get-started-vector.md
   - name: Full text search
     href: search-get-started-text.md
-  - name: Semantic ranking
+  - name: Semantic reranking
     href: search-get-started-semantic.md
   - name: Retrieval Augmented Generation (RAG)
     href: search-get-started-retrieval-augmented-generation.md
@@ -127,7 +127,27 @@
     href: samples-rest.md
 - name: Concepts
   items:
-  - name: Retrieval (Search)
+  - name: Storage (indexes)
+    items:
+    - name: Search index
+      href: search-what-is-an-index.md
+    - name: Vector store
+      href: vector-search-overview.md
+    - name: Knowledge store 
+      href: knowledge-store-concept-intro.md
+    - name: Data import strategies
+      href: search-what-is-data-import.md
+  - name: Enrichment (skills)
+    items:
+    - name: Enrichment overview
+      href: cognitive-search-concept-intro.md
+    - name: Enrichment cache
+      href: cognitive-search-incremental-indexing-conceptual.md
+    - name: Skillsets
+      href: cognitive-search-working-with-skillsets.md
+    - name: Integrated vectorization (preview)
+      href: vector-search-integrated-vectorization.md
+  - name: Retrieval (queries)
     items:
     - name: Full-text search
       href: search-lucene-query-architecture.md
@@ -141,44 +161,14 @@
       href: search-query-overview.md
   - name: Relevance
     items:
+    - name: Semantic ranking
+      href: semantic-search-overview.md
     - name: Scoring in keyword queries (BM25)
       href: index-similarity-and-scoring.md
     - name: Scoring in vector queries
       href: vector-search-ranking.md
     - name: Scoring in hybrid queries (RRF)
       href: hybrid-search-ranking.md
-    - name: Semantic ranking
-      href: semantic-search-overview.md
-  - name: Indexing
-    items:
-    - name: Search indexes
-      href: search-what-is-an-index.md
-    - name: Import
-      href: search-what-is-data-import.md
-    - name: Import Data wizard
-      href: search-import-data-portal.md
-    - name: Analyzers
-      href: search-analyzers.md
-    - name: Indexers
-      href: search-indexer-overview.md
-  - name: Enrichment
-    items:
-    - name: Enrichment overview
-      href: cognitive-search-concept-intro.md
-    - name: Enrichment cache
-      href: cognitive-search-incremental-indexing-conceptual.md
-    - name: Skillsets
-      href: cognitive-search-working-with-skillsets.md
-    - name: Integrated vectorization (preview)
-      href: vector-search-integrated-vectorization.md
-    - name: Debug sessions
-      href: cognitive-search-debug-session.md
-    - name: Knowledge store 
-      href: knowledge-store-concept-intro.md
-    - name: Knowledge store projections
-      href: knowledge-store-projection-overview.md
-    - name: Index projections
-      href: index-projections-concept-intro.md
   - name: Security
     items:
     - name: Security overview
@@ -228,7 +218,7 @@
       href: search-dotnet-mgmt-sdk-migration.md
     - name: Upgrade the REST API
       href: search-api-migration.md
-  - name: Indexes
+  - name: Search indexes
     items:
     - name: Create a search index
       href: search-how-to-create-search-index.md
@@ -248,31 +238,50 @@
       href: search-howto-complex-data-types.md
     - name: Model relational data
       href: index-sql-relational-data.md
+    - name: Analyzers
+      items:
+      - name: Analyzers overview
+        href: search-analyzers.md
+      - name: Add a language analyzer
+        href: index-add-language-analyzers.md
+      - name: Add a custom analyzer
+        href: index-add-custom-analyzers.md
+    - name: Synonyms
+      items:
+      - name: Add synonyms
+        href: search-synonyms.md
+      - name: Synonyms C# example
+        href: search-synonyms-tutorial-sdk.md
   - name: Vector stores
     items:
-    - name: Create a vector index
+    - name: Create a vector store
       href: vector-search-how-to-create-index.md
     - name: Configure a vectorizer (preview)
       href: vector-search-how-to-configure-vectorizer.md
     - name: Chunk documents
       href: vector-search-how-to-chunk-documents.md
     - name: Generate embeddings
       href: vector-search-how-to-generate-embeddings.md
-
-  - name: Analyzers
-    items:
-    - name: Add a language analyzer
-      href: index-add-language-analyzers.md
-    - name: Add a custom analyzer
-      href: index-add-custom-analyzers.md
-  - name: Synonyms
+  - name: Knowledge stores
     items:
-    - name: Add synonyms
-      href: search-synonyms.md
-    - name: Synonyms C# example
-      href: search-synonyms-tutorial-sdk.md
-  - name: Indexers
+    - name: Knowledge store overview
+      href: knowledge-store-concept-intro.md
+    - name: Knowledge store projections overview
+      href: knowledge-store-projection-overview.md
+    - name: Create using REST
+      href: knowledge-store-create-rest.md
+    - name: Shape data
+      href: knowledge-store-projection-shape.md
+    - name: Define projections
+      href: knowledge-store-projections-examples.md 
+    - name: Projection example
+      href: knowledge-store-projection-example-long.md
+    - name: Connect with Power BI
+      href: knowledge-store-connect-power-bi.md
+  - name: Indexers and skills
     items:
+    - name: Indexer overview
+      href: search-indexer-overview.md
     - name: Create an indexer
       href: search-howto-create-indexers.md
     - name: Run or reset indexers
@@ -281,6 +290,10 @@
       href: search-howto-schedule-indexers.md
     - name: Define field mappings
       href: search-indexer-field-mappings.md
+    - name: Portal indexing
+      items:
+      - name: Import data wizard
+        href: search-import-data-portal.md
     - name: Indexing whole files
       items:
       - name: Content metadata properties
@@ -307,82 +320,74 @@
       href: search-indexer-troubleshooting.md
     - name: Troubleshoot indexer errors and warnings
       href: cognitive-search-common-errors-warnings.md
-  - name: Data sources (indexers)
-    items:
-      - name: Data sources gallery
-        href: search-data-sources-gallery.md
-      - name: Azure Storage
-        items:
-        - name: Search over blobs
-          href: search-blob-storage-integration.md
-        - name: ADLS Gen2
-          href: search-howto-index-azure-data-lake-storage.md
-        - name: Blobs
-          href: search-howto-indexing-azure-blob-storage.md
-        - name: Files
-          href: search-file-storage-integration.md
-        - name: Tables
-          href: search-howto-indexing-azure-tables.md
-        - name: Index changed and deleted content
-          href: search-howto-index-changed-deleted-blobs.md
-      - name: Azure Cosmos DB
-        items:
-        - name: Azure Cosmos DB for NoSQL
-          href: search-howto-index-cosmosdb.md
-        - name: Azure Cosmos DB for MongoDB
-          href: search-howto-index-cosmosdb-mongodb.md
-        - name: Azure Cosmos DB for Apache Gremlin
-          href: search-howto-index-cosmosdb-gremlin.md
-      - name: Azure DB for MySQL
-        href: search-howto-index-mysql.md
-      - name: Azure SQL
-        items:
-        - name: Azure SQL Databases
-          href: search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md
-        - name: Azure SQL Managed Instances
-          href: search-howto-connecting-azure-sql-mi-to-azure-search-using-indexers.md
-        - name: Azure SQL Server VMs
-          href: search-howto-connecting-azure-sql-iaas-to-azure-search-using-indexers.md
-      - name: SharePoint in Microsoft 365
-        href: search-howto-index-sharepoint-online.md
-  - name: Skillsets (indexers)
-    items:
-    - name: Attach an Azure AI multi-service resource
-      href: cognitive-search-attach-cognitive-services.md
-    - name: Create a skillset
-      href: cognitive-search-defining-skillset.md
-    - name: Debug a skillset
-      href: cognitive-search-how-to-debug-skillset.md
-    - name: Reference an annotation    
-      href: cognitive-search-concept-annotations-syntax.md
-    - name: Map to index fields
-      href: cognitive-search-output-field-mapping.md
-    - name: Process image files
-      href: cognitive-search-concept-image-scenarios.md
-    - name: Cache (incremental) enrichment
-      href: search-howto-incremental-index.md
-    - name: Design tips
-      href: cognitive-search-concept-troubleshooting.md
-    - name: Custom skills
+    - name: Data sources (indexers)
       items:
-      - name: Integrate custom skills
-        href: cognitive-search-custom-skill-interface.md
-      - name: Scale out custom skills
-        href: cognitive-search-custom-skill-scale.md
-      - name: Example - Bing Entity Search
-        href: cognitive-search-create-custom-skill-example.md
-  - name: Knowledge stores
-    items:
-    - name: Create using REST
-      href: knowledge-store-create-rest.md
-    - name: Shaping data
-      href: knowledge-store-projection-shape.md
-    - name: Define projections
-      href: knowledge-store-projections-examples.md 
-    - name: Projection example
-      href: knowledge-store-projection-example-long.md
-    - name: Connect with Power BI
-      href: knowledge-store-connect-power-bi.md
+        - name: Data sources gallery
+          href: search-data-sources-gallery.md
+        - name: Azure Storage
+          items:
+          - name: Search over blobs
+            href: search-blob-storage-integration.md
+          - name: ADLS Gen2
+            href: search-howto-index-azure-data-lake-storage.md
+          - name: Blobs
+            href: search-howto-indexing-azure-blob-storage.md
+          - name: Files
+            href: search-file-storage-integration.md
+          - name: Tables
+            href: search-howto-indexing-azure-tables.md
+          - name: Index changed and deleted content
+            href: search-howto-index-changed-deleted-blobs.md
+        - name: Azure Cosmos DB
+          items:
+          - name: Azure Cosmos DB for NoSQL
+            href: search-howto-index-cosmosdb.md
+          - name: Azure Cosmos DB for MongoDB
+            href: search-howto-index-cosmosdb-mongodb.md
+          - name: Azure Cosmos DB for Apache Gremlin
+            href: search-howto-index-cosmosdb-gremlin.md
+        - name: Azure DB for MySQL
+          href: search-howto-index-mysql.md
+        - name: Azure SQL
+          items:
+          - name: Azure SQL Databases
+            href: search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md
+          - name: Azure SQL Managed Instances
+            href: search-howto-connecting-azure-sql-mi-to-azure-search-using-indexers.md
+          - name: Azure SQL Server VMs
+            href: search-howto-connecting-azure-sql-iaas-to-azure-search-using-indexers.md
+        - name: SharePoint in Microsoft 365
+          href: search-howto-index-sharepoint-online.md
+    - name: Skillsets (indexers)
+      items:
+      - name: Attach an Azure AI multi-service resource
+        href: cognitive-search-attach-cognitive-services.md
+      - name: Create a skillset
+        href: cognitive-search-defining-skillset.md
+      - name: Create an index projection for a secondary index
+        href: index-projections-concept-intro.md
+      - name: Debug sessions overview
+        href: cognitive-search-debug-session.md
+      - name: Debug a skillset
+        href: cognitive-search-how-to-debug-skillset.md
+      - name: Reference an annotation    
+        href: cognitive-search-concept-annotations-syntax.md
+      - name: Map to index fields
+        href: cognitive-search-output-field-mapping.md
+      - name: Process image files
+        href: cognitive-search-concept-image-scenarios.md
+      - name: Cache (incremental) enrichment
+        href: search-howto-incremental-index.md
+      - name: Design tips
+        href: cognitive-search-concept-troubleshooting.md
+      - name: Custom skills
+        items:
+        - name: Integrate custom skills
+          href: cognitive-search-custom-skill-interface.md
+        - name: Scale out custom skills
+          href: cognitive-search-custom-skill-scale.md
+        - name: Example - Bing Entity Search
+          href: cognitive-search-create-custom-skill-example.md
   - name: Queries (text and vector)
     items:
     - name: Full text query
@@ -427,14 +432,14 @@
         href: search-query-fuzzy.md
   - name: Relevance
     items:
-    - name: Configure BM25 ranking
-      href: index-ranking-similarity.md
-    - name: Add a scoring profile
-      href: index-add-scoring-profiles.md
     - name: Enable or disable semantic ranking
       href: semantic-how-to-enable-disable.md      
     - name: Configure semantic ranking
       href: semantic-how-to-query-request.md
+    - name: Configure BM25 ranking
+      href: index-ranking-similarity.md
+    - name: Add a scoring profile
+      href: index-add-scoring-profiles.md
   - name: Performance and monitoring
     items:
     - name: Performance analysis

articles/search/hybrid-search-overview.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 11/01/2023
+ms.date: 01/29/2024
 ---
 
 # Hybrid search using vectors and full text in Azure AI Search
@@ -24,9 +24,9 @@ This article explains the concepts, benefits, and limitations of hybrid search.
 
 ## How does hybrid search work?
 
-In Azure AI Search, vector indexes containing embeddings can live alongside textual and numerical fields allowing you to issue hybrid full text and vector queries. Hybrid queries can take advantage of existing functionality like filtering, faceting, sorting, scoring profiles, and [semantic ranking](semantic-search-overview.md) in a single search request.
+In Azure AI Search, vector fields containing embeddings can live alongside textual and numerical fields, allowing you to formulate hybrid queries that execute in parallel. Hybrid queries can take advantage of existing functionality like filtering, faceting, sorting, scoring profiles, and [semantic ranking](semantic-search-overview.md) in a single search request.
 
-Hybrid search combines results from both full text and vector queries, which use different ranking functions such as BM25 and HNSW. A [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md) algorithm is used to merge results. The query response provides just one result set, using RRF to determine which matches are included.
+Hybrid search combines results from both full text and vector queries, which use different ranking functions such as BM25 and HNSW. A [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md) algorithm merges the results. The query response provides just one result set, using RRF to pick the most relevant matches from each query.
 
 ## Structure of a hybrid query
 

articles/search/index.yml

@@ -64,7 +64,7 @@ landingContent:
     linkLists:
       - linkListType: how-to-guide
         links:
-          - text: Create a vector index in AI Studio
+          - text: Create a vector store in AI Studio
             url: /azure/ai-studio/how-to/index-add
           - text: Build a question and answer copilot
             url: /azure/ai-studio/tutorials/deploy-copilot-ai-studio

articles/search/media/vector-search-how-to-chunk-documents/langchain-example-tiktoken-histogram.png

@@ -104,7 +104,7 @@ sections:
       - question: |
           How does vector search work in Azure AI Search?
         answer: |
-          With standalone vector search, you first use a deep neural network (DNN), such as a large language model (LLM), to transform content into a vector representation within an embedding space. You can then provide these vectors in a document payload to the search index for indexing. To serve search requests, you use the same DNN from indexing to transform the search query into a vector representation, and vector search finds the most similar vectors and return the corresponding documents.
+          With standalone vector search, you first use an embedding model to transform content into a vector representation within an embedding space. You can then provide these vectors in a document payload to the search index for indexing. To serve search requests, you use the same DNN from indexing to transform the search query into a vector representation, and vector search finds the most similar vectors and return the corresponding documents.
           
           In Azure AI Search, you can index vector data as fields in documents alongside textual and other types of content. The data type for a vector field is `Collection(Edm.Single)`.