Merge branch 'main' into DOC-4570

Author: cmilesb

content/commands/json.arrappend/index.md

@@ -67,7 +67,7 @@ is JSONPath to specify. Default is root `$`.
 
 ## Return value 
 
-`JSON.ARRAPEND` returns an [array]({{< relref "develop/reference/protocol-spec#resp-arrays" >}}) of integer replies for each path, the array's new size, or `nil`, if the matching JSON value is not an array. 
+`JSON.ARRAPPEND` returns an [array]({{< relref "develop/reference/protocol-spec#resp-arrays" >}}) of integer replies for each path, the array's new size, or `nil`, if the matching JSON value is not an array. 
 For more information about replies, see [Redis serialization protocol specification]({{< relref "/develop/reference/protocol-spec" >}}). 
 
 ## Examples
@@ -82,7 +82,7 @@ redis> JSON.SET item:1 $ '{"name":"Noise-cancelling Bluetooth headphones","descr
 OK
 {{< / highlight >}}
 
-Add color `blue` to the end of the `colors` array. `JSON.ARRAPEND` returns the array's new size.
+Add color `blue` to the end of the `colors` array. `JSON.ARRAPPEND` returns the array's new size.
 
 {{< highlight bash >}}
 redis> JSON.ARRAPPEND item:1 $.colors '"blue"'

content/commands/json.debug-memory/index.md

@@ -72,7 +72,7 @@ Get the values' memory usage in bytes.
 
 {{< highlight bash >}}
 redis> JSON.DEBUG MEMORY item:2
-(integer) 253
+(integer) 573
 {{< / highlight >}}
 </details>
 
@@ -84,4 +84,3 @@ redis> JSON.DEBUG MEMORY item:2
 
 * [RedisJSON]({{< relref "/develop/data-types/json/" >}})
 * [Index and search JSON documents]({{< relref "/develop/interact/search-and-query/indexing/" >}})
-

content/develop/ai/index.md

@@ -44,7 +44,7 @@ Learn to perform vector search and use gateways and semantic caching in your AI/
 
 ## Quickstarts
 
-Quickstarts or recipes are useful when you are trying to build specific functionality. For example, you might want to do RAG with LangChain or set up LLM memory for you AI agent. Get started with the following Redis Python notebooks.
+Quickstarts or recipes are useful when you are trying to build specific functionality. For example, you might want to do RAG with LangChain or set up LLM memory for your AI agent. Get started with the following Redis Python notebooks.
 
 * [The place to start if you are brand new to Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb)
 
@@ -53,6 +53,7 @@ Vector search retrieves results based on the similarity of high-dimensional nume
 * [Implementing hybrid search with Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb)
 * [Vector search with Redis Python client](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) 
 * [Vector search with Redis Vector Library](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb)
+* [Shows how to convert a float 32 index to float16 or integer data types](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/03_dtype_support.ipynb)
 
 #### RAG  
 Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability of an LLM to respond to user queries. The retrieval part of RAG is supported by a vector database, which can return semantically relevant results to a user’s query, serving as contextual information to augment the generative capabilities of an LLM.
@@ -65,12 +66,14 @@ Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability o
 * [Vector search with Azure](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059)
 * [RAG with Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/)
 * [RAG with Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main)
-* [Notebook for additional tips and techniques to improve RAG quality](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) 
+* [Notebook for additional tips and techniques to improve RAG quality](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb)
+* [Implement a simple RBAC policy with vector search using Redis](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/07_user_role_based_rag.ipynb) 
 
 #### Agents
 AI agents can act autonomously to plan and execute tasks for the user.
 * [Notebook to get started with LangGraph and agents](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) 
 * [Build a collaborative movie recommendation system using Redis for data storage, CrewAI for agent-based task execution, and LangGraph for workflow management.](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb)
+* [Full-Featured Agent Architecture](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/02_full_featured_agent.ipynb)
 
 #### LLM memory
 LLMs are stateless. To maintain context within a conversation chat sessions must be stored and resent to the LLM. Redis manages the storage and retrieval of chat sessions to maintain context and conversational relevance.
@@ -81,14 +84,24 @@ LLMs are stateless. To maintain context within a conversation chat sessions must
 An estimated 31% of LLM queries are potentially redundant. Redis enables semantic caching to help cut down on LLM costs quickly.
 * [Build a semantic cache using the Doc2Cache framework and Llama3.1](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/doc2cache_llama3_1.ipynb) 
 * [Build a semantic cache with Redis and Google Gemini](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/semantic_caching_gemini.ipynb)
+* [Optimize semantic cache threshold with RedisVL](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb)
+
+#### Semantic routing  
+Routing is a simple and effective way of preventing misuses with your AI application or for creating branching logic between data sources etc.
+* [Simple examples of how to build an allow/block list router in addition to a multi-topic router](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/00_semantic_routing.ipynb) 
+* [Use RouterThresholdOptimizer from redisvl to setup best router config](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/01_routing_optimization.ipynb)
 
 #### Computer vision
 Build a facial recognition system using the Facenet embedding model and RedisVL.
 * [Facial recognition](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/computer-vision/00_facial_recognition_facenet.ipynb)
 
 #### Recommendation systems
 * [Intro content filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb) 
-* [Intro collaborative filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) 
+* [Intro collaborative filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb)
+* [Intro deep learning two tower example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/02_two_towers.ipynb)
+
+#### Feature store
+* [Credit scoring system using Feast with Redis as the online store](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/00_feast_credit_score.ipynb)
 
 ## Tutorials
 Need a deeper-dive through different use cases and topics?
@@ -97,10 +110,12 @@ Need a deeper-dive through different use cases and topics?
 * [Agentic RAG](https://github.com/redis-developer/agentic-rag) - A tutorial focused on agentic RAG with LlamaIndex and Amazon Bedrock
 * [RAG on Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) - A RAG tutorial featuring Redis with Vertex AI
 * [RAG workbench](https://github.com/redis-developer/redis-rag-workbench) - A development playground for exploring RAG techniques with Redis
+* [ArXiv Chat](https://github.com/redis-developer/ArxivChatGuru) - Streamlit demo of RAG over ArXiv documents with Redis & OpenAI
 
-#### Recommendation system
+#### Recommendations and search
 * [Recommendation systems w/ NVIDIA Merlin & Redis](https://github.com/redis-developer/redis-nvidia-recsys) - Three examples, each escalating in complexity, showcasing the process of building a realtime recsys with NVIDIA and Redis
 * [Redis product search](https://github.com/redis-developer/redis-product-search) - Build a real-time product search engine using features like full-text search, vector similarity, and real-time data updates
+* [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search) - Full stack implementation of Redis with React FE
 
 ## Ecosystem integrations
 
@@ -113,6 +128,8 @@ Need a deeper-dive through different use cases and topics?
 * [Deploy GenAI apps faster with Redis and NVIDIA NIM](https://redis.io/blog/use-redis-with-nvidia-nim-to-deploy-genai-apps-faster/)
 * [Building LLM Applications with Kernel Memory and Redis](https://redis.io/blog/building-llm-applications-with-kernel-memory-and-redis/)
 * [DocArray integration of Redis as a vector database by Jina AI](https://docs.docarray.org/user_guide/storing/index_redis/)
+* [Semantic Kernel: A popular library by Microsoft to integrate LLMs with plugins](https://learn.microsoft.com/en-us/semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/redis-connector?pivots=programming-language-csharp)
+* [LiteLLM integration](https://docs.litellm.ai/docs/caching/all_caches#initialize-cache---in-memory-redis-s3-bucket-redis-semantic-disk-cache-qdrant-semantic)
 
 ## Benchmarks
 See how we stack up against the competition.

content/develop/clients/php/connect.md

@@ -12,7 +12,7 @@ categories:
 description: Connect your PHP application to a Redis database
 linkTitle: Connect
 title: Connect to the server
-weight: 2
+weight: 10
 ---
 
 ## Basic connection

content/develop/clients/php/queryjson.md

@@ -12,7 +12,7 @@ categories:
 description: Learn how to use the Redis query engine with JSON
 linkTitle: Index and query JSON
 title: Example - Index and query JSON documents
-weight: 2
+weight: 20
 ---
 
 This example shows how to index and query Redis JSON data using `predis`.