Added schema excerpt for generative AI scenario

Author: HeidiSteen

articles/search/vector-store.md

@@ -29,21 +29,19 @@ Considerations for vector storage include the following points:
 
 In Azure AI Search, there are two patterns for working with search results. 
 
-+ Generative search. Language models formulate a response to the user's query using data from Azure AI Search. This pattern usually includes an orchestration layer to coordinate prompts and maintain context. In this pattern, results are fed into prompt flows and chat models like GPT and Text-Davinci. This approach is based on [**Retrieval augmented generation (RAG)**](retrieval-augmented-generation-overview.md) architecture, where the search index provides the grounding data.
++ Generative search. Language models formulate a response to the user's query using data from Azure AI Search. This pattern usually includes an orchestration layer to coordinate prompts and maintain context. In this pattern, results are fed into prompt flows, received by chat models like GPT and Text-Davinci. This approach is based on [**Retrieval augmented generation (RAG)**](retrieval-augmented-generation-overview.md) architecture, where the search index provides the grounding data.
 
-+ Classic search. The search engine formulates a response based on content in your index, and you render those results in a client app. In a direct response from the search engine, results are returned in a flattened row set, and you can choose which fields are passed to the client app. It's expected that you would populate the vector store (search index) with nonvector content that's human readable in your response. The search engine matches on vectors, but can return nonvector values from the same search document. [**Vector queries**](vector-search-how-to-query.md) and [**hybrid queries**](hybrid-search-how-to-query.md) cover the types of requests.
++ Classic search. Search engine formulates a response based on content in your index, and you render those results in a client app. In a direct response from the search engine, results are returned in a flattened row set, and you can choose which fields are passed to the client app. It's expected that you would populate the vector store (search index) with nonvector content that's human readable in your response. The search engine matches on vectors, but can return nonvector values from the same search document. [**Vector queries**](vector-search-how-to-query.md) and [**hybrid queries**](hybrid-search-how-to-query.md) cover the types of requests.
 
-Your index schema should reflect your primary use case.
+Your index schema should reflect your primary use case. The following section highlights the differences in field composition for solutions built for generative AI or classic search.
 
 ## Schema of a vector store
 
-The following examples highlight the differences in field composition for solutions build for generative AI or classic search.
-
 An index schema for a vector store requires a name, a key field (string), one or more vector fields, and a vector configuration. Nonvector fields are recommended for hybrid queries, or for returning verbatim human readable content that doesn't have to go through a language model. For instructions about vector configuration, see [Create a vector store](vector-search-how-to-create-index.md).
 
 ### Basic vector field configuration
 
-A vector field, such as `"content_vector"` in the following example, is of type `Collection(Edm.Single)`. It must be searchable and retrievable. It can't be filterable, facetable, or sortable, and it can't have analyzers, normalizers, or synonym map assignments. It must have dimensions set to the number of embeddings generated by the embedding model. For instance, if you're using text-embedding-ada-002, it generates 1,536 embeddings. A vector search profile is specified in a separate vector search configuration and assigned to a vector field using a profile name.
+A vector field, such as `"content_vector"` in the following example, is of type `Collection(Edm.Single)`. It must be searchable and retrievable. It can't be filterable, facetable, or sortable, and it can't have analyzers, normalizers, or synonym map assignments. It must have dimensions set to the number of embeddings generated by the embedding model. For instance, if you're using text-embedding-ada-002, it generates 1,536 embeddings. A vector search profile is specified in a separate [vector search configuration](vector-search-how-to-create-index.md) and assigned to a vector field using a profile name.
 
 ```json
 {
@@ -80,7 +78,7 @@ The bias of this schema is that search documents are built around data chunks. I
 
 Data chunking is necessary for staying within the input limits of language models, but it also improves precision in similarity search when queries can be matched against smaller chunks of content pulled from multiple parent documents. Finally, if you're using semantic ranking, the semantic ranker also has token limits, which are more easily met if data chunking is part of your approach.
 
-In the following example, for each search document, there's one chunk ID, chunk, title, and vector field. The chunkID and parent ID are populated by the wizard, using base 64 encoding of blob metadata (path). Chunk and title are derived from blob content and blob name. Only the vector field is fully generated. It calls an Azure OpenAI resource that you provide.
+In the following example, for each search document, there's one chunk ID, chunk, title, and vector field. The chunkID and parent ID are populated by the wizard, using base 64 encoding of blob metadata (path). Chunk and title are derived from blob content and blob name. Only the vector field is fully generated. It's the vectorized version of the chunk field. Embeddings are generated by calling an Azure OpenAI embedding model that you provide.
 
 ```json
 "name": "example-index-from-import-wizard",
@@ -115,6 +113,23 @@ Fields from the chat index that support generative search experience:
 ]
 ```
 
+Fields from the conversations index that supports orchestration and chat history:
+
+```json
+"fields": [
+    { "name": "id", "type": "Edm.String", "key": true, "searchable": false, "filterable": true, "retrievable": true, "sortable": false, "facetable": false },
+    { "name": "conversation_id", "type": "Edm.String", "searchable": false, "filterable": true, "retrievable": true, "sortable": false, "facetable": true },
+    { "name": "content", "type": "Edm.String", "searchable": true, "filterable": false, "retrievable": true },
+    { "name": "content_vector", "type": "Collection(Edm.Single)", "searchable": true, "retrievable": true, "dimensions": 1536, "vectorSearchProfile": "default-profile" },
+    { "name": "metadata", "type": "Edm.String", "searchable": true, "filterable": false, "retrievable": true },
+    { "name": "type", "type": "Edm.String", "searchable": false, "filterable": true, "retrievable": true, "sortable": false, "facetable": true },
+    { "name": "user_id", "type": "Edm.String", "searchable": false, "filterable": true, "retrievable": true, "sortable": false, "facetable": true },
+    { "name": "sources", "type": "Collection(Edm.String)", "searchable": false, "filterable": true, "retrievable": true, "sortable": false, "facetable": true },
+    { "name": "created_at", "type": "Edm.DateTimeOffset", "searchable": false, "filterable": true, "retrievable": true },
+    { "name": "updated_at", "type": "Edm.DateTimeOffset", "searchable": false, "filterable": true, "retrievable": true }
+]
+```
+
 Here's a screenshot showing search results in [Search Explorer](search-explorer.md) for the conversations index. The search score is 1.00 because the search was unqualified. Notice the fields that exist to support orchestration and prompt flows. A conversation ID identifies a specific chat. `"type"` indicates whether the content is from the user or the assistant. Dates are used to age out chats from the history.
 
 :::image type="content" source="media/vector-search-overview/vector-schema-search-results.png" alt-text="Screenshot of Search Explorer with results from an index designed for RAG apps.":::