Merge pull request #1885 from redis/DOC-5506

Add 8 new notebook links to AI notebook collection

Author: mich-elle-luna

content/develop/ai/notebook-collection.md

@@ -20,18 +20,26 @@ weight: 40
 | Implementing hybrid search with Redis | Hybrid and Vector Search | Combines vector similarity with keyword filters. | [Open in Colab](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 | Hybrid and Vector Search | Demonstrates pure vector search using the Redis Python client. | [Open in Colab](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 | Hybrid and Vector Search | Uses RedisVL for advanced vector indexing and querying. | [Open in Colab](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 float32 index to float16 or integer data types | Hybrid and Vector Search | Demonstrates data type optimization for vector indices. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/03_dtype_support.ipynb) |
 | RAG from scratch with Redis Vector Library | RAG | Basic RAG implementation using RedisVL. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb) |
 | RAG using Redis and LangChain | RAG | Shows integration between Redis and LangChain for RAG. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/02_langchain.ipynb) |
 | RAG using Redis and LlamaIndex | RAG | Walkthrough of RAG with Redis and LlamaIndex. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/03_llamaindex.ipynb) |
 | Advanced RAG with RedisVL | RAG | Advanced concepts and techniques using RedisVL. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) |
 | RAG using Redis and Nvidia | RAG | NVIDIA + Redis for LLM context retrieval. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) |
 | Utilize RAGAS framework to evaluate RAG performance | RAG | Evaluation of RAG apps using the RAGAS framework. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/06_ragas_evaluation.ipynb) |
+| Implement a simple RBAC policy with vector search using Redis | RAG | Role-based access control implementation for RAG systems. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/07_user_role_based_rag.ipynb) |
 | LangGraph and agents | Agents | Getting started with agent workflows. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) |
 | Movie recommendation system | Agents | Collaborative agent-based movie recommender. | [Open in Colab](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 | Agents | Comprehensive agent implementation with advanced features. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/02_full_featured_agent.ipynb) |
+| Optimize semantic cache threshold with RedisVL | Semantic Cache | Performance optimization for semantic caching systems. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb) |
+| Simple examples of how to build an allow/block list router in addition to a multi-topic router | Semantic Router | Basic routing patterns and access control mechanisms. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/00_semantic_routing.ipynb) |
+| Use `RouterThresholdOptimizer` from RedisVL to setup best router config | Semantic Router | Router configuration optimization using RedisVL. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/01_routing_optimization.ipynb) |
 | Facial recognition | Computer Vision | Face matching using Facenet and RedisVL. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) |
 | Content filtering with RedisVL | Recommendation Systems | Introduction to content-based filtering. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb) |
 | Collaborative filtering with RedisVL | Recommendation Systems | Intro to collaborative filtering with RedisVL. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) |
-| Advanced RAG example | Best Practices | Demonstrates mature RAG implementation patterns. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) |
+| Intro deep learning two tower example with RedisVL | Recommendation Systems | Deep learning approach to recommendation systems. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/02_two_towers.ipynb) |
+| Credit scoring system using Feast with Redis as the online store | Feature Store | Feature store implementation for ML model serving. | [Open in Colab](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/00_feast_credit_score.ipynb) |
+
 
 ## Additional resources