Update index.md to include updates from redis-ai-resources repo

content/develop/ai/index.md

@@ -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.