Merge branch 'main' into DOC-5172

Author: cmilesb

.github/workflows/redisvl_docs_sync.yaml

@@ -61,11 +61,16 @@ jobs:
                   linkTitle=$(awk '{ $1=$1; for (i=2; i<=NF; i++) if ($i != "RedisVL" && $i != "API" && $i != "CLI" && $i != "LLMs" && $i != "JSON") $i=tolower($i); print }' <<< "${linkTitle}")
               fi
 
+              # Get alias
+              alias=$(echo ${src} | sed -E 's|./redis_vl_hugo/|/integrate/redisvl/| ; s|\.md$|| ; s|\/_index$||')
+
               # Inject frontmatter in destination file
               cat >_tmp <<EOL
           ---
           linkTitle: ${linkTitle}
           title: ${title}
+          aliases:
+          - ${alias}
           EOL
 
               # Inject weight property for index pages to preserve order
@@ -106,7 +111,6 @@ jobs:
 
           # Convert jupyter notebooks to markdown
           jupyter nbconvert --to markdown build/jupyter_execute/user_guide/*.ipynb --output-dir redis_vl_hugo/user_guide/ 2>/dev/null
-          jupyter nbconvert --to markdown build/jupyter_execute/user_guide/release_guide/*.ipynb --output-dir redis_vl_hugo/user_guide/release_guide/ 2>/dev/null
           jupyter nbconvert --to markdown build/jupyter_execute/overview/cli.ipynb --output-dir redis_vl_hugo/overview/ 2>/dev/null
 
           # Prepare markdown files
@@ -209,13 +213,11 @@ jobs:
           # Format _index.md pages
           cp ./build/markdown/api/index.md ./redis_vl_hugo/api/_index.md
           cp ./build/markdown/user_guide/index.md ./redis_vl_hugo/user_guide/_index.md
-          cp ./build/markdown/user_guide/release_guide/index.md ./redis_vl_hugo/user_guide/release_guide/_index.md
           cp ./build/markdown/overview/index.md ./redis_vl_hugo/overview/_index.md
 
           index_markdown_pages=(
               ./redis_vl_hugo/api/_index.md
               ./redis_vl_hugo/user_guide/_index.md
-              ./redis_vl_hugo/user_guide/release_guide/_index.md
               ./redis_vl_hugo/overview/_index.md
           )
 

content/commands/command.md

@@ -101,7 +101,7 @@ Command flags are an array. It can contain the following simple strings (status
 * **no_auth:** executing the command doesn't require authentication.
 * **no_async_loading:** the command is denied during asynchronous loading (that is when a replica uses disk-less `SWAPDB SYNC`, and allows access to the old dataset).
 * **no_mandatory_keys:** the command may accept key name arguments, but these aren't mandatory.
-* **no_multi:** the command isn't allowed inside the context of a [transaction]({{< relref "/develop/reference/transactions" >}}).
+* **no_multi:** the command isn't allowed inside the context of a [transaction]({{< relref "develop/using-commands/transactions" >}}).
 * **noscript:** the command can't be called from [scripts]({{< relref "/develop/programmability/eval-intro" >}}) or [functions]({{< relref "/develop/programmability/functions-intro" >}}).
 * **pubsub:** the command is related to [Redis Pub/Sub]({{< relref "/develop/pubsub" >}}).
 * **random**: the command returns random results, which is a concern with verbatim script replication.

content/commands/expire.md

@@ -266,6 +266,10 @@ still take the full state of the expires existing in the dataset, so when a
 replica is elected to master it will be able to expire the keys independently,
 fully acting as a master.
 
+### Redis Query Engine and expiration
+
+Starting with Redis 8, the Redis Query Engine has enhanced behavior when handling expiring keys. For detailed information about how [`FT.SEARCH`]({{< relref "/commands/ft.search" >}}) and [`FT.AGGREGATE`]({{< relref "/commands/ft.aggregate" >}}) commands interact with expiring keys, see [Key and field expiration behavior]({{< relref "/develop/ai/search-and-query/advanced-concepts/expiration" >}}).
+
 ## Return information
 
 {{< multitabs id="expire-return-info" 

content/commands/ft.aggregate.md

@@ -496,5 +496,6 @@ Next, count GitHub events by user (actor), to produce the most active users.
 ## Related topics
 
 - [Aggregations]({{< relref "/develop/ai/search-and-query/advanced-concepts/aggregations" >}})
+- [Key and field expiration behavior]({{< relref "/develop/ai/search-and-query/advanced-concepts/expiration" >}})
 - [RediSearch]({{< relref "/develop/ai/search-and-query" >}})
 

content/commands/ft.search.md

@@ -860,5 +860,6 @@ Query with `CONTAINS` operator:
 
 - [Extensions]({{< relref "/develop/ai/search-and-query/administration/extensions" >}})
 - [Highlighting]({{< relref "/develop/ai/search-and-query/advanced-concepts/highlight" >}})
+- [Key and field expiration behavior]({{< relref "/develop/ai/search-and-query/advanced-concepts/expiration" >}})
 - [Query syntax]({{< relref "/develop/ai/search-and-query/query/" >}})
 - [RediSearch]({{< relref "/develop/ai/search-and-query/" >}})

content/commands/hexpire.md

@@ -113,6 +113,10 @@ You can call `HEXPIRE` using as argument a field that already has an
 existing TTL set.
 In this case, the time to live is _updated_ to the new value.
 
+## Redis Query Engine and field expiration
+
+Starting with Redis 8, the Redis Query Engine has enhanced behavior when handling expiring hash fields. For detailed information about how [`FT.SEARCH`]({{< relref "/commands/ft.search" >}}) and [`FT.AGGREGATE`]({{< relref "/commands/ft.aggregate" >}}) commands interact with expiring hash fields, see [Key and field expiration behavior]({{< relref "/develop/ai/search-and-query/advanced-concepts/expiration" >}}).
+
 ## Example
 
 ```

content/commands/scan.md

@@ -286,7 +286,7 @@ Also note that this behavior is specific of [`SSCAN`]({{< relref "/commands/ssca
 
 ## Further reading
 
-For more information about managing keys, please refer to the [The Redis Keyspace]({{< relref "/develop/keyspace" >}}) tutorial.
+For more information about managing keys, please refer to the [The Redis Keyspace]({{< relref "/develop/using-commands/keyspace" >}}) tutorial.
 
 ## Additional examples
 

content/develop/ai/ai-videos.md

@@ -20,6 +20,8 @@ Explore our collection of video tutorials and demonstrations showcasing how Redi
 | What if you could skip redundant LLM calls and make your AI app faster, cheaper, and smarter? This video breaks down semantic caching and shows how it can transform your AI applications. | Unlock the Power of Retrieval-Augmented Generation (RAG) with RedisVL. This tutorial will show you how to build a complete RAG pipeline from scratch using Redis as your vector database. | Vector databases have been trending recently as they power modern search, recommendations, and AI-driven applications. Learn what vector databases are and how they work. |
 | [**Building the future Architecting AI Agents with AWS, LlamaIndex and Redis**](https://www.youtube.com/watch?v=SFWroqAbBM4) | [**Building AI Apps using LangChain**](https://www.youtube.com/watch?v=YhxksXfgsp0) | [**Resources to Learn AI with Redis**](https://www.youtube.com/watch?v=M_WU_fN_lrs) |
 | The ins and outs of AI agents: understand their role in breaking down tasks into manageable components for better performance. Learn how to architect AI agents using AWS, LlamaIndex, and Redis. | This series of videos dives into the integration between LangChain and Redis to power AI applications that need runtime speed, scalability, and intelligent data management. | This video shows which resources you can use to learn AI with Redis and build powerful AI applications. |
+| [**What Is RAG? Retrieval-Augmented Generation Explained Simply**](https://www.youtube.com/watch?v=xPMQ2cVbUTI) | [**Chunking Strategies Explained**](https://www.youtube.com/watch?v=ZTOtxiWb2bE) | [**What is an embedding model?**](https://www.youtube.com/watch?v=0U1S0WSsPuE) |
+| Retrieval-Augmented Generation (RAG) is one of the most powerful architectural patterns in GenAI today—combining the strengths of large language models (LLMs) with real-time, external context from your own data. In this session, learn why it matters and how each component—from query rewriting to dense retrieval to semantic chunking—works behind the scenes to power more accurate, grounded, and up-to-date responses. | Are you interested in building LLM applications that actually work? Your chunking strategy makes all the difference. In this video, get a  break down of the science of text chunking so your embeddings can start answering the right questions to your users. | Everyone’s talking about embedding models lately—but what do they actually do, and why does it matter? This video breaks it down in simple terms and shows how embeddings power search, recommendations, and AI features behind the scenes. |
 
 ### Additional Resources
 

content/develop/ai/search-and-query/_index.md

@@ -1,6 +1,7 @@
 ---
 aliases:
 - /develop/interact/search-and-query
+- /develop/interact/search-and-query/
 categories:
 - docs
 - develop

content/develop/ai/search-and-query/administration/_index.md

@@ -1,6 +1,7 @@
 ---
 aliases:
-- /develop/interact/search-and-query/administration/_index
+- /develop/interact/search-and-query/administration
+- /develop/interact/search-and-query/administration/
 categories:
 - docs
 - develop