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QS fix for vectors array
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‎articles/search/search-get-started-vector.md

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@@ -7,7 +7,7 @@ author: HeidiSteen
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ms.author: heidist
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ms.service: cognitive-search
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ms.topic: quickstart
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ms.date: 06/29/2023
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ms.date: 07/07/2023
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---
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# Quickstart: Use preview REST APIs for vector search queries
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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],
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"fields": "contentVector",
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"k": 5
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}
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"vectors": [
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{
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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],
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"fields": "contentVector",
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"k": 5
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}
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]
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}
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```
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"select": "title, content, category"
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"k": 10
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},
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"vector": [
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{
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"select": "title, content, category"
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"k": 10
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},
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],
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"filter": "category eq 'Databases'"
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}
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```
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### Cross-field vector search
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Cross-field vector search allows you to send a single query across multiple vector fields in your vector index. For this example, I want to calculate the similarity across both `titleVector` and `contentVector`:
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```http
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POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version={{api-version}}
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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],
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"fields": "titleVector, contentVector",
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"k": 5
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}
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"vector": [
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{
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"value": [
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0.018708462,
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. . .
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],
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"fields": "titleVector, contentVector",
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"k": 5
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}
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]
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}
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```
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### Multi-query vector search
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Multi-query vector search allows you to send a multiple queries across multiple vector fields in your vector index. For this example, I want to calculate the similarity across both `titleVector` and `contentVector` but will send in two different query embeddings respectively. This scenario is ideal for multi-modal use cases where you want to search over a `textVector` field and an `imageVector` field. You can also use this scenario if you have different embedding models with different dimensions in your search index.
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```http
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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},
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"vectors": [
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{
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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}
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],
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"search": "what azure services support full text search",
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"top": "10"
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}
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**Single Vector Search**: Results ordered by cosine similarity (default vector similarity distance function)
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```
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```json
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{
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"@search.score": 0.8851871,
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"title": "Azure Cognitive Search",
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**Hybrid Search**: Combined keyword and vector search results using Reciprocal Rank Fusion.
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```
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```json
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{
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"@search.score": 0.03333333507180214,
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"title": "Azure Cognitive Search",
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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},
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"vectors": [
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{
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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}
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],
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"search": "what azure services support full text search",
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"filter": "category eq 'Databases'",
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"top": "10"
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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},
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"vectors": [
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{
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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}
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],
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"search": "what azure services support full text search",
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"select": "title, content, category",
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"queryType": "semantic",
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Content-Type: application/json
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api-key: {{admin-api-key}}
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{
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"vector": {
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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},
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"vectors": [
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{
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"value": [
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-0.009154141,
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0.018708462,
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. . .
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-0.02178128,
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-0.00086512347
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],
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"fields": "contentVector",
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"k": 10
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}
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],
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"search": "what azure services support full text search",
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"select": "title, content, category",
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"queryType": "semantic",
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## Next steps
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As a next step, we recommend reviewing the demo code for [Python](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-python), or [C#](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-dotnet).
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