Merge branch 'main' into DOC-4928

Author: rrelledge

.github/workflows/autocomment.yaml

@@ -51,9 +51,8 @@ jobs:
             $FILES_URL \
               | jq -r --arg prefix $BRANCH_NAME/ '.[] | select(((.filename | test("content\/.+\\.md")) and .status != "removed")) | ($prefix + .filename)' \
               | sed -E -e 's|(^[^/]+/)([^/]+/)|\1|' -e 's|^|https://redis.io/docs/staging/|' -e 's|(_?index)?\.md||' \
-              | uniq \
-              | xargs \
-              | sed 's/ /<br>/g')
+              | sort \
+              | uniq)
 
           # Get all changed image files
           CHANGED_IMAGE_FILES=$(curl -Ls \
@@ -70,14 +69,19 @@ jobs:
             for CHANGED_IMAGE_FILE in $CHANGED_IMAGE_FILES; do
               MD_FILE_WITH_IMAGE=$(grep -ro "$CHANGED_IMAGE_FILE" content \
                 | sed -E -e 's|:.+||' -e "s|^content/|https://redis.io/docs/staging/$BRANCH_NAME/|" -e 's|(_?index)?\.md||' \
+                | sort \
                 | uniq)
               MD_FILES_WITH_IMAGE+=($MD_FILE_WITH_IMAGE)
             done
-              CHANGED_MD_FILES_WITH_IMAGE=$(printf "%s\n" "${MD_FILES_WITH_IMAGE[@]}" \
+              CHANGED_MD_FILES=$(printf "%s\n" "${CHANGED_MD_FILES}" "${MD_FILES_WITH_IMAGE[@]}" \
+                | sort \
                 | uniq \
                 | xargs \
                 | sed 's/ /<br>/g')
-              CHANGED_MD_FILES="${CHANGED_MD_FILES}<br>${CHANGED_MD_FILES_WITH_IMAGE}"
+          else
+            CHANGED_MD_FILES=$(printf "%s\n" "${CHANGED_MD_FILES}" \
+                | xargs \
+                | sed 's/ /<br>/g')
           fi
 
           if [[ -z "$CHANGED_MD_FILES" ]]

.github/workflows/cleanup_pr_folders.yaml

@@ -0,0 +1,79 @@
+name: cleanup_pr_folders
+
+on:
+  pull_request:
+    types:
+      - closed
+
+jobs:
+  staging:
+    if: github.event.pull_request.merged == true
+    runs-on: ubuntu-latest
+    permissions:
+      contents: 'read'
+      id-token: 'write'
+    env:
+      STAGING_BUCKET: docs-staging-learn-redis-com
+      STAGING_PROJECT_ID: ${{ secrets.GCP_PROJECT_STAGING }}
+      STAGING_SERVICE_ACCOUNT: ${{ secrets.STAGING_SERVICE_ACCOUNT }}
+      STAGING_WORKLOAD_IDENTITY_PROVIDER: ${{ secrets.STAGING_WORKLOAD_IDENTITY_PROVIDER }}
+
+    steps:
+    - name: 'Google auth'
+      uses: 'google-github-actions/auth@v2'
+      with:
+        project_id: '${{ env.STAGING_PROJECT_ID }}'
+        service_account: '${{ env.STAGING_SERVICE_ACCOUNT }}'
+        workload_identity_provider: '${{ env.STAGING_WORKLOAD_IDENTITY_PROVIDER }}'
+
+    - name: 'Set up Cloud SDK'
+      uses: 'google-github-actions/setup-gcloud@v2'
+      with:
+        project_id: '${{ env.STAGING_PROJECT_ID }}'
+        version: '>= 363.0.0'
+
+    - name: Remove pull request staging folder 
+      run: |
+        BRANCH_TO_CLEANUP="${{ github.event.pull_request.head.ref }}"
+        STAGING_BUCKET="${{ env.STAGING_BUCKET }}"
+
+        if gsutil -q stat gs://${STAGING_BUCKET}/staging/${BRANCH_TO_CLEANUP}/*; then
+          gsutil -q -m rm -r gs://${STAGING_BUCKET}/staging/${BRANCH_TO_CLEANUP}/*
+          echo "Removed gs://${STAGING_BUCKET}/staging/${BRANCH_TO_CLEANUP}"
+        fi
+
+  prod:
+    if: github.event.pull_request.merged == true
+    runs-on: ubuntu-latest
+    permissions:
+      contents: 'read'
+      id-token: 'write'
+    env:
+      PROD_BUCKET: docs-prod-learn-redis-com
+      PROD_PROJECT_ID: ${{ secrets.GCP_PROJECT_PROD }}
+      PROD_SERVICE_ACCOUNT: ${{ secrets.PROD_SERVICE_ACCOUNT }}
+      PROD_WORKLOAD_IDENTITY_PROVIDER: ${{ secrets.PROD_WORKLOAD_IDENTITY_PROVIDER }}
+
+    steps:
+    - name: 'Google auth'
+      uses: 'google-github-actions/auth@v2'
+      with:
+        project_id: '${{ env.PROD_PROJECT_ID }}'
+        service_account: '${{ env.PROD_SERVICE_ACCOUNT }}'
+        workload_identity_provider: '${{ env.PROD_WORKLOAD_IDENTITY_PROVIDER }}'
+
+    - name: 'Set up Cloud SDK'
+      uses: 'google-github-actions/setup-gcloud@v2'
+      with:
+        project_id: '${{ env.PROD_PROJECT_ID }}'
+        version: '>= 363.0.0'
+
+    - name: Remove pull request staging folder
+      run: |
+        BRANCH_TO_CLEANUP="${{ github.event.pull_request.head.ref }}"
+        PROD_BUCKET="${{ env.PROD_BUCKET }}"
+
+        if gsutil -q stat gs://${PROD_BUCKET}/staging/${BRANCH_TO_CLEANUP}/*; then
+          gsutil -q -m rm -r gs://${PROD_BUCKET}/staging/${BRANCH_TO_CLEANUP}/*
+          echo "Removed gs://${PROD_BUCKET}/staging/${BRANCH_TO_CLEANUP}"
+        fi

config.toml

@@ -55,7 +55,7 @@ rdi_redis_gears_version = "1.2.6"
 rdi_debezium_server_version = "2.3.0.Final"
 rdi_db_types = "cassandra|mysql|oracle|postgresql|sqlserver"
 rdi_cli_latest = "latest"
-rdi_current_version = "v1.6.4"
+rdi_current_version = "v1.6.5"
 
 [params.clientsConfig]
 "Python"={quickstartSlug="redis-py"}

content/commands/type/index.md

@@ -47,7 +47,7 @@ title: TYPE
 ---
 Returns the string representation of the type of the value stored at `key`.
 The different types that can be returned are: `string`, `list`, `set`, `zset`,
-`hash` and `stream`.
+`hash`, `stream`, and `vectorset`.
 
 ## Examples
 

content/develop/clients/nodejs/transpipe.md

@@ -152,3 +152,29 @@ const updatedPath = await client.get('shellpath');
 console.log(updatedPath);
 // >>> /usr/syscmds/:/usr/mycmds/
 ```
+
+In an environment where multiple concurrent requests are sharing a connection
+(such as a web server), you must wrap the above transaction logic in a call to
+`client.executeIsolated` to get an isolated connection, like so:
+
+```js
+await client.executeIsolated(async (client) => {
+   await client.watch('shellpath');
+   // ...
+})
+```
+
+This is important because the server tracks the state of the WATCH on a
+per-connection basis, and concurrent WATCH and MULTI/EXEC calls on the same
+connection will interfere with one another.
+
+You can configure the size of the isolation pool when calling `createClient`:
+
+```js
+const client = createClient({
+    isolationPoolOptions: {
+        min: 1,
+        max: 100,
+    },
+})
+```

content/develop/data-types/json/_index.md

@@ -19,7 +19,9 @@ weight: 11
 [![Discord](https://img.shields.io/discord/697882427875393627?style=flat-square)](https://discord.gg/QUkjSsk)
 [![Github](https://img.shields.io/static/v1?label=&message=repository&color=5961FF&logo=github)](https://github.com/RedisJSON/RedisJSON/)
 
-The JSON capability of Redis Community Edition provides JavaScript Object Notation (JSON) support for Redis. It lets you store, update, and retrieve JSON values in a Redis database, similar to any other Redis data type. Redis JSON also works seamlessly with the [Redis Query Engine]({{< relref "/develop/interact/search-and-query/" >}}) to let you [index and query JSON documents]({{< relref "/develop/interact/search-and-query/indexing/" >}}).
+The JSON data type provides JavaScript Object Notation (JSON) support for Redis. It lets you store, retrieve, and update JSON documents. 
+A JSON document can be queried and manipulated using JSONPath expressions.
+JSON also works seamlessly with the [Redis Query Engine]({{< relref "/develop/interact/search-and-query/" >}}) to let you [index and query JSON documents]({{< relref "/develop/interact/search-and-query/indexing/" >}}).
 
 ## Primary features
 
@@ -156,4 +158,4 @@ A JSON value passed to a command can have a depth of up to 128. If you pass to a
 
 ## Further information
 
-Read the other pages in this section to learn more about Redis JSON
+Read the other pages in this section to learn more about Redis JSON

content/develop/data-types/vector-sets/_index.md

@@ -38,3 +38,166 @@ The following commands are available for vector sets:
 - [VREM]({{< relref "/commands/vrem" >}}) - remove an element from a vector set.
 - [VSETATTR]({{< relref "/commands/vsetattr" >}}) - set or replace attributes on a vector set element.
 - [VSIM]({{< relref "/commands/vsim" >}}) - retrieve elements similar to a given vector or element with optional filtering.
+
+## Examples
+
+The following examples give an overview of how to use vector sets. For clarity,
+we will use a set of two-dimensional vectors that represent points in the
+Cartesian coordinate plane. However, in real use cases, the vectors will typically
+represent *text embeddings* and have hundreds of dimensions. See
+[Redis for AI]({{< relref "/develop/ai" >}}) for more information about using text
+embeddings.
+
+The points we will use are A: (1.0, 1.0), B: (-1.0, -1.0), C: (-1.0, 1.0), D: (1.0. -1.0), and
+E: (1.0, 0), shown in the diagram below.
+
+{{<image filename="images/vecsets/VecSetExamplePoints.drawio.svg" alt="Example points on the coordinate plane." width="400px">}}
+
+### Basic operations
+
+Start by adding the point vectors to a set called `points` using
+[`VADD`]({{< relref "/commands/vadd" >}}). This also creates the vector set object.
+The [`TYPE`]({{< relref "/commands/type" >}}) command returns a type of `vectorset`
+for this object.
+
+{{< clients-example vecset_tutorial vadd >}}
+> VADD points VALUES 2 1.0 1.0 pt:A
+(integer) 1
+> VADD points VALUES 2 -1.0 -1.0 pt:B
+(integer) 1
+> VADD points VALUES 2 -1.0 1.0 pt:C
+(integer) 1
+> VADD points VALUES 2 1.0 -1.0 pt:D
+(integer) 1
+> VADD points VALUES 2 1.0 0 pt:E
+(integer) 1
+> TYPE points
+vectorset
+{{< /clients-example >}}
+
+
+Get the number of elements in the set (also known as the *cardinality* of the set)
+using [`VCARD`]({{< relref "/commands/vcard" >}}) and the number of dimensions of
+the vectors using [`VDIM`]({{< relref "/commands/vdim" >}}):
+
+{{< clients-example vecset_tutorial vcardvdim >}}
+> VCARD points
+(integer) 5
+> VDIM points
+(integer) 2
+{{< /clients-example >}}
+
+Get the coordinate values from the elements using [`VEMB`]({{< relref "/commands/vemb" >}}).
+Note that the values will not typically be the exact values you supplied when you added
+the vector because
+[quantization]({{< relref "/develop/data-types/vector-sets/performance#quantization-effects" >}})
+is applied to improve performance.
+
+{{< clients-example vecset_tutorial vemb >}}
+> VEMB points pt:A
+1) "0.9999999403953552"
+2) "0.9999999403953552"
+9> VEMB points pt:B
+1) "-0.9999999403953552"
+2) "-0.9999999403953552"
+> VEMB points pt:C
+1) "-0.9999999403953552"
+2) "0.9999999403953552"
+> VEMB points pt:D
+1) "0.9999999403953552"
+2) "-0.9999999403953552"
+> VEMB points pt:E
+1) "1"
+2) "0"
+{{< /clients-example >}}
+
+Set and retrieve an element's JSON attribute data using
+[`VSETATTR`]({{< relref "/commands/vsetattr" >}})
+and [`VGETATTR`]({{< relref "/commands/vgetattr" >}}). You can also pass an empty string
+to `VSETATTR` to delete the attribute data:
+
+{{< clients-example vecset_tutorial attr >}}
+> VSETATTR points pt:A "{\"name\": \"Point A\", \"description\": \"First point added\"}" 
+(integer) 1
+> VGETATTR points pt:A
+"{\"name\": \"Point A\", \"description\": \"First point added\"}"
+> VSETATTR points pt:A "" 
+(integer) 1
+> VGETATTR points pt:A
+(nil)
+{{< /clients-example >}}
+
+Remove an unwanted element with [`VREM`]({{< relref "/commands/vrem" >}})
+
+{{< clients-example vecset_tutorial vrem >}}
+> VADD points VALUES 2 0 0 pt:F
+(integer) 1
+127.0.0.1:6379> VCARD points
+(integer) 6
+127.0.0.1:6379> VREM points pt:F
+(integer) 1
+127.0.0.1:6379> VCARD points
+(integer) 5
+{{< /clients-example >}}
+
+### Vector similarity search
+
+Use [`VSIM`]({{< relref "/commands/vsim" >}}) to rank the points in order of their vector distance from a sample point:
+
+{{< clients-example vecset_tutorial vsim_basic >}}
+> VSIM points values 2 0.9 0.1
+1) "pt:E"
+2) "pt:A"
+3) "pt:D"
+4) "pt:C"
+5) "pt:B"
+{{< /clients-example >}}
+
+Find the four elements that are closest to point A and show their distance "scores":
+
+{{< clients-example vecset_tutorial vsim_options >}}
+> VSIM points ELE pt:A WITHSCORES COUNT 4
+1) "pt:A"
+2) "1"
+3) "pt:E"
+4) "0.8535534143447876"
+5) "pt:C"
+6) "0.5"
+7) "pt:D"
+8) "0.5"
+{{< /clients-example >}}
+
+Add some JSON attributes and use
+[filter expressions]({{< relref "/develop/data-types/vector-sets/filtered-search" >}})
+to include them in the search:
+
+{{< clients-example vecset_tutorial vsim_filter >}}
+> VSETATTR points pt:A "{\"size\":\"large\",\"price\": 18.99}"
+(integer) 1
+> VSETATTR points pt:B "{\"size\":\"large\",\"price\": 35.99}"
+(integer) 1
+> VSETATTR points pt:C "{\"size\":\"large\",\"price\": 25.99}"
+(integer) 1
+> VSETATTR points pt:D "{\"size\":\"small\",\"price\": 21.00}"
+(integer) 1
+> VSETATTR points pt:E "{\"size\":\"small\",\"price\": 17.75}"
+(integer) 1
+
+# Return elements in order of distance from point A whose
+# `size` attribute is `large`.
+> VSIM points ELE pt:A FILTER '.size == "large"'
+1) "pt:A"
+2) "pt:C"
+3) "pt:B"
+
+# Return elements in order of distance from point A whose size is
+# `large` and whose price is greater than 20.00.
+> VSIM points ELE pt:A FILTER '.size == "large" && .price > 20.00'
+1) "pt:C"
+2) "pt:B"
+{{< /clients-example >}}
+
+## More information
+
+See the other pages in this section to learn more about the features
+and performance parameters of vector sets.