diff --git a/src/manifest.json b/src/manifest.json
index 3c37234..fe0cccc 100644
--- a/src/manifest.json
+++ b/src/manifest.json
@@ -34,6 +34,26 @@
             "initialIsOpen": true,
             "path": "/uc/rag.md"
           }
+        },
+        {
+          "type": "internal-link",
+          "id": "ai_assistant",
+          "label": "Creating an AI Assistant",
+          "summary": "An assistant that temporarily retains conversations and understands by their meaning.",
+          "args": {
+            "initialIsOpen": true,
+            "path": "/uc/ai_assistant.md"
+          }
+        },
+        {
+          "type": "internal-link",
+          "id": "redis_use_cases_rag",
+          "label": "Building personalized recommendations",
+          "summary": "Not just keyword matching, but semantic understanding.",
+          "args": {
+            "initialIsOpen": true,
+            "path": "/uc/personalized_recommendations.md"
+          }
         }
       ]
     },
diff --git a/src/uc/ai_assistant.md b/src/uc/ai_assistant.md
new file mode 100644
index 0000000..baee798
--- /dev/null
+++ b/src/uc/ai_assistant.md
@@ -0,0 +1,150 @@
+This intelligent AI assistant is designed to support real-world, multi-session use with Redis as its memory core.
+
+What you get:
+    - **Smart Memory**: Ephemeral context that expires automatically, long-term facts retained forever
+    - **Semantic Search**: Recall relevant info by meaning using vector search
+    - **Zero Maintenance**: Auto-expiring short-term memory without a need to track timestamps manually
+    - **Multi-User**: Isolated memory per user
+    - **Learning**: Assistant can "understand" each user better the more it's used
+
+**Note**: Requires [Redis 8](https://hub.docker.com/_/redis/tags) for `HSETEX`, which adds per-field TTL for hashes, which is ideal for managing short-term memory with precision.
+
+### Architecture Overview
+| Layer | Description |
+| ---------- | ---------- |
+| `Working Memory`| `Short-term chat context (ephemeral)` |
+| `Knowledge Base` | `Persistent facts, user preferences` |
+| `Vector Search` | `Unified semantic recall across both layers` |
+
+### Working Memory (Ephemeral)
+Stores recent user messages with TTL based on importance. Automatically expires to prevent bloat.
+This uses `HSETEX`, a Redis 8 command that adds field-level expiration to hashes. It allows storing all temporary messages in a single key while managing TTLs per message, simplifying short-term memory management without needing multiple keys.
+
+```redis:[run_confirmation=true] Recent Conversations with TTL Based on Importance.
+// Quick exchanges (5 min)
+HSETEX user:alice:session:001 EX 300 FIELDS 1 msg:001 "What's the weather?"
+// Session context (30 min)  
+HSETEX user:alice:session:001 EX 1800 FIELDS 1 msg:002 "I need a dentist appointment"
+// Important decisions (2 hours)
+HSETEX user:alice:session:001 EX 7200 FIELDS 1 msg:003 "Book it for Tuesday 2 PM"
+```
+
+### Knowledge Base (Persistent)
+Long-term memory: stores important facts, user preferences, and context across sessions. These never expire.
+`embedding` is a binary-encoded `FLOAT32[]` used for vector similarity that can be generated using sentence-transformers or similar libraries. Demo uses 8-dim vectors; production models typically use 128–1536 dimensions.
+
+```redis:[run_confirmation=true] Important User Information That Never Expires.
+// User preferences - need vector fields for search
+HSET user:alice:knowledge:pref:001 user_id "alice" memory_type "knowledge" content "prefers mornings before 10 AM" importance 9 timestamp 1717935000 embedding "\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x3f\x40\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x00\x00\x00\x40\x80\x00\x00"
+HSET user:alice:knowledge:pref:002 user_id "alice" memory_type "knowledge" content "likes detailed explanations" importance 8 timestamp 1717935000 embedding "\x3f\x40\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+// Personal facts
+HSET user:alice:knowledge:personal:001 user_id "alice" memory_type "knowledge" content "allergic to shellfish" importance 10 timestamp 1717935000 embedding "\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x60\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+HSET user:alice:knowledge:personal:002 user_id "alice" memory_type "knowledge" content "golden retriever named Max" importance 7 timestamp 1717935000 embedding "\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x00\x00\x00\x40\x60\x00\x00"
+// Work context
+HSET user:alice:knowledge:work:001 user_id "alice" memory_type "knowledge" content "Senior PM at TechCorp" importance 8 timestamp 1717935000 embedding "\x40\x40\x00\x00\x3f\x00\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00"
+HSET user:alice:knowledge:work:002 user_id "alice" memory_type "knowledge" content "leading Project Apollo" importance 9 timestamp 1717935000 embedding "\x40\x60\x00\x00\x40\x80\x00\x00\x3f\x40\x00\x00\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x3f\x00\x00\x00"
+```
+
+### Vector Search: Semantic Memory Recall
+Unify working + knowledge memory into a vector index for semantically meaningful search.
+
+```redis:[run_confirmation=true] Create a Vector Index
+FT.CREATE idx:memory
+    ON HASH
+    PREFIX 2 vmemory: user:
+    SCHEMA
+        user_id TAG
+        memory_type TAG
+        content TEXT
+        importance NUMERIC
+        timestamp NUMERIC
+        embedding VECTOR HNSW 6
+            TYPE FLOAT32
+            DIM 8 // DIM = embedding size; 8 used here for simplicity — in production, use 128 to 1536
+            DISTANCE_METRIC COSINE // COSINE = measures semantic closeness
+```
+
+### Add Indexed Memory
+Populate the vector index with memory items from both ephemeral and persistent layers.
+
+```redis:[run_confirmation=true] Add entries for the chatbot
+// Working memory (expires from Redis, stays in search until rebuild)
+HSET vmemory:alice:001 user_id "alice" memory_type "working" content "Book dentist for Tuesday 2 PM" importance 8 timestamp 1717935310 embedding "\x3f\x80\x00\x00\x40\x00\x00\x00\x40\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00\x40\x20\x00\x00\x40\x60\x00\x00\x3f\x40\x00\x00"
+
+// Knowledge base (persistent)
+HSET vmemory:alice:kb:001 user_id "alice" memory_type "knowledge" content "allergic to shellfish" importance 10 timestamp 1717935000 embedding "\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x60\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+```
+
+### Full Memory Search
+Search across all memories to recall relevant data.
+
+```redis:[run_confirmation=false] Find Top 5 Related Messages By Meaning
+FT.SEARCH idx:memory 
+    "(@user_id:{alice}) => [KNN 5 @embedding $vec AS score]" 
+    PARAMS 2 vec "\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x60\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+    RETURN 4 content memory_type importance score
+    SORTBY score ASC
+    DIALECT 2
+```
+
+### Session-Only Search
+Limit results to current conversation context (ephemeral memory).
+
+```redis:[run_confirmation=false] Session-Only Search
+FT.SEARCH idx:memory 
+    "(@user_id:{alice} @memory_type:{working}) => [KNN 5 @embedding $vec AS score]" 
+    PARAMS 2 vec "\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x00\x00\x00\x40\x60\x00\x00"
+    RETURN 4 content score timestamp memory_type
+    SORTBY score ASC
+    DIALECT 2
+```
+
+### Knowledge-Only Search
+Focus only on persistent memory: facts, preferences, decisions.
+
+```redis:[run_confirmation=false] Knowledge-Only Search
+FT.SEARCH idx:memory 
+    "(@user_id:{alice} @memory_type:{knowledge}) => [KNN 8 @embedding $vec AS score]" 
+    PARAMS 2 vec "\x40\x40\x00\x00\x3f\x00\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00"
+    RETURN 4 content importance score timestamp
+    SORTBY score ASC
+    DIALECT 2
+```
+
+### Monitoring Memory State
+Use these queries to inspect what’s stored in memory.
+
+```redis:[run_confirmation=false] Check Memory State
+// Check active session memory
+HGETALL user:alice:knowledge:pref:001  // Example for one preference item
+
+// View user knowledge
+HGETALL user:alice:knowledge:pref:001
+
+// Search user's memories
+FT.SEARCH idx:memory
+    "@user_id:{alice}"
+    RETURN 3 content memory_type importance
+```
+
+### Data Cleanup
+
+For privacy compliance, delete all user-related keys.
+
+```redis:[run_confirmation=true] Complete user removal
+DEL user:alice:knowledge:pref:001
+DEL user:alice:knowledge:pref:002
+DEL user:alice:knowledge:personal:001
+DEL user:alice:knowledge:personal:002
+DEL user:alice:knowledge:work:001
+DEL user:alice:knowledge:work:002
+DEL vmemory:alice:001
+DEL vmemory:alice:kb:001
+```
+
+### Next Steps
+Now that your assistant has memory and meaning, you can:
+    - Combine with RAG Pipelines
+    - Use sentence-transformers to generate high-dimensional vectors
+    - Add [Redis Flex](https://redis.io/solutions/flex/?utm_source=redisinsight&utm_medium=app&utm_campaign=tutorials) for fallback persistence
+    - Use Redis ACLs to isolate users, enforce quotas, and monitor usage
\ No newline at end of file
diff --git a/src/uc/personalized_recommendations.md b/src/uc/personalized_recommendations.md
new file mode 100644
index 0000000..fccbae4
--- /dev/null
+++ b/src/uc/personalized_recommendations.md
@@ -0,0 +1,164 @@
+Imagine building a movie recommendation app that goes beyond keyword matching. Your users get intuitive, meaningful suggestions based on the true meaning of movie plots — powered by semantic understanding.
+
+### Store Movie Documents with Vector Embeddings
+Semantic search uses vector embeddings — numeric representations of text that capture meaning, enabling search by intent rather than keywords.
+
+We'll import a dataset of plot summaries, each paired with an embedding vector.
+
+Each movie is stored as a JSON document with:
+  - `title`, `genres`, `year`, `plot`
+  - `embedding`: a binary-encoded `FLOAT32[]` for vector similarity, generated via sentence-transformer models or similar.
+
+```redis:[run_confirmation=true] Upload Movies
+// Demo uses 8 DIM embeddings; production typically uses 128–1536D.
+JSON.SET movie:001 $ '{"title":"Toy Story","genres":["Animation","Comedy","Family"],"plot":"Toys come to life when humans arent around.","year":1995,"embedding":[0.22,0.04,0.33,0.12,-0.02,0.17,0.09,0.01]}'
+JSON.SET movie:002 $ '{"title":"Inside Out","genres":["Animation","Comedy","Drama"],"plot":"Emotions guide a young girl through change.","year":2015,"embedding":[0.20,0.03,0.31,0.11,-0.03,0.16,0.08,0.02]}'
+JSON.SET movie:003 $ '{"title":"Whiplash","genres":["Drama","Music"],"plot":"A young drummer is pushed to greatness.","year":2014,"embedding":[0.14,0.01,0.22,0.08,-0.07,0.10,0.04,0.00]}'
+JSON.SET movie:004 $ '{"title":"La La Land","genres":["Drama","Music","Romance"],"plot":"A jazz musician falls in love in LA.","year":2016,"embedding":[0.15,0.03,0.23,0.09,-0.08,0.14,0.06,0.01]}'
+JSON.SET movie:005 $ '{"title":"The Matrix","genres":["Action","Sci-Fi"],"plot":"A hacker discovers reality is a simulation.","year":1999,"embedding":[0.12,-0.03,0.25,0.04,-0.10,0.09,0.05,-0.02]}'
+JSON.SET movie:006 $ '{"title":"Inception","genres":["Action","Adventure","Sci-Fi"],"plot":"A thief steals information through dreams.","year":2010,"embedding":[0.14,-0.01,0.27,0.06,-0.09,0.10,0.04,-0.03]}'
+JSON.SET movie:007 $ '{"title":"Tenet","genres":["Action","Sci-Fi","Thriller"],"plot":"Time-inversion to prevent World War III.","year":2020,"embedding":[0.13,-0.06,0.29,0.05,-0.11,0.12,0.06,-0.01]}'
+JSON.SET movie:008 $ '{"title":"Finding Nemo","genres":["Animation","Adventure","Family"],"plot":"A clownfish searches for his son.","year":2003,"embedding":[0.18,0.02,0.30,0.10,-0.05,0.15,0.07,0.01]}'
+JSON.SET movie:009 $ '{"title":"Coco","genres":["Animation","Family","Music"],"plot":"A boy enters the Land of the Dead.","year":2017,"embedding":[0.21,0.04,0.34,0.13,-0.02,0.19,0.10,0.02]}'
+JSON.SET movie:010 $ '{"title":"Soul","genres":["Animation","Adventure","Comedy"],"plot":"A jazz musician explores the afterlife.","year":2020,"embedding":[0.16,0.02,0.28,0.10,-0.06,0.13,0.07,0.00]}'
+JSON.SET movie:011 $ '{"title":"The Dark Knight","genres":["Action","Crime","Drama"],"plot":"Batman fights the Joker.","year":2008,"embedding":[0.12,-0.03,0.25,0.04,-0.09,0.10,0.05,-0.02]}'
+JSON.SET movie:012 $ '{"title":"Frozen","genres":["Animation","Adventure","Comedy"],"plot":"A princess sets off to find her sister.","year":2013,"embedding":[0.22,0.04,0.33,0.12,-0.03,0.18,0.10,0.02]}'
+JSON.SET movie:013 $ '{"title":"The Lion King","genres":["Animation","Adventure","Drama"],"plot":"A lion prince flees and returns.","year":1994,"embedding":[0.19,0.02,0.32,0.10,-0.04,0.18,0.09,0.03]}'
+JSON.SET movie:014 $ '{"title":"Shrek","genres":["Animation","Adventure","Comedy"],"plot":"An ogre rescues a princess.","year":2001,"embedding":[0.21,0.03,0.32,0.11,-0.04,0.17,0.09,0.01]}'
+JSON.SET movie:015 $ '{"title":"The Social Network","genres":["Biography","Drama"],"plot":"The rise of Facebook and its creator.","year":2010,"embedding":[0.10,-0.01,0.21,0.05,-0.07,0.06,0.03,-0.02]}'
+JSON.SET movie:016 $ '{"title":"Guardians of the Galaxy","genres":["Action","Adventure","Sci-Fi"],"plot":"A group of intergalactic criminals must save the universe.","year":2014,"embedding":[0.13,0.00,0.28,0.07,-0.08,0.11,0.05,-0.01]}'
+JSON.SET movie:017 $ '{"title":"Moana","genres":["Animation","Adventure","Family"],"plot":"A young girl sets sail to save her island.","year":2016,"embedding":[0.20,0.03,0.33,0.12,-0.03,0.17,0.09,0.02]}'
+JSON.SET movie:018 $ '{"title":"Whale Rider","genres":["Drama","Family"],"plot":"A girl fights tradition to become chief.","year":2002,"embedding":[0.15,0.01,0.25,0.09,-0.05,0.12,0.06,0.00]}'
+JSON.SET movie:019 $ '{"title":"Rocketman","genres":["Biography","Drama","Music"],"plot":"The story of Elton Johns breakthrough years.","year":2019,"embedding":[0.14,0.01,0.22,0.07,-0.06,0.11,0.05,0.01]}'
+JSON.SET movie:020 $ '{"title":"Amadeus","genres":["Biography","Drama","Music"],"plot":"The rivalry between Mozart and Salieri.","year":1984,"embedding":[0.13,0.00,0.20,0.06,-0.07,0.10,0.04,0.00]}'
+JSON.SET movie:021 $ '{"title":"The Sound of Music","genres":["Biography","Drama","Music"],"plot":"A governess brings music to a family.","year":1965,"embedding":[0.14,0.02,0.21,0.07,-0.06,0.11,0.05,0.01]}'
+JSON.SET movie:022 $ '{"title":"Les Miserables","genres":["Drama","Music","Romance"],"plot":"The struggles of ex-convict Jean Valjean.","year":2012,"embedding":[0.13,0.01,0.23,0.08,-0.07,0.12,0.05,0.01]}'
+JSON.SET movie:023 $ '{"title":"The Greatest Showman","genres":["Biography","Drama","Music"],"plot":"The story of P.T. Barnum and his circus.","year":2017,"embedding":[0.15,0.03,0.25,0.09,-0.05,0.13,0.06,0.02]}'
+JSON.SET movie:024 $ '{"title":"A Star Is Born","genres":["Drama","Music","Romance"],"plot":"A musician helps a young singer find fame.","year":2018,"embedding":[0.14,0.02,0.24,0.08,-0.06,0.12,0.05,0.01]}'
+JSON.SET movie:025 $ '{"title":"Mad Max: Fury Road","genres":["Action","Adventure","Sci-Fi"],"plot":"In a post-apocalyptic wasteland, Max helps rebels escape.","year":2015,"embedding":[0.11,-0.02,0.26,0.05,-0.10,0.08,0.05,-0.02]}'
+JSON.SET movie:026 $ '{"title":"Blade Runner 2049","genres":["Sci-Fi","Thriller"],"plot":"A new blade runner uncovers secrets.","year":2017,"embedding":[0.12,-0.03,0.27,0.06,-0.09,0.09,0.06,-0.01]}'
+JSON.SET movie:027 $ '{"title":"Arrival","genres":["Drama","Sci-Fi","Thriller"],"plot":"A linguist communicates with aliens.","year":2016,"embedding":[0.13,-0.01,0.28,0.07,-0.08,0.11,0.05,-0.01]}'
+JSON.SET movie:028 $ '{"title":"Interstellar","genres":["Adventure","Drama","Sci-Fi"],"plot":"Explorers travel through a wormhole in space.","year":2014,"embedding":[0.14,-0.02,0.29,0.08,-0.09,0.12,0.06,-0.02]}'
+JSON.SET movie:029 $ '{"title":"E.T. the Extra-Terrestrial","genres":["Family","Sci-Fi"],"plot":"A boy befriends an alien.","year":1982,"embedding":[0.17,0.01,0.31,0.10,-0.06,0.15,0.07,0.01]}'
+JSON.SET movie:030 $ '{"title":"The Avengers","genres":["Action","Adventure","Sci-Fi"],"plot":"Superheroes team up to save the world.","year":2012,"embedding":[0.13,0.00,0.27,0.07,-0.08,0.11,0.06,-0.01]}'
+JSON.SET movie:031 $ '{"title":"Guardians of the Galaxy Vol. 2","genres":["Action","Adventure","Comedy"],"plot":"The Guardians fight to protect the galaxy.","year":2017,"embedding":[0.15,0.01,0.28,0.09,-0.07,0.13,0.07,0.01]}'
+JSON.SET movie:032 $ '{"title":"Up","genres":["Animation","Adventure","Comedy"],"plot":"An old man goes on an adventure in his flying house.","year":2009,"embedding":[0.21,0.04,0.32,0.11,-0.04,0.16,0.09,0.02]}'
+JSON.SET movie:033 $ '{"title":"Zootopia","genres":["Animation","Adventure","Comedy"],"plot":"A bunny cop solves a mystery in a city of animals.","year":2016,"embedding":[0.20,0.03,0.31,0.10,-0.05,0.15,0.08,0.01]}'
+JSON.SET movie:034 $ '{"title":"Big Hero 6","genres":["Animation","Action","Comedy"],"plot":"A robotics prodigy teams with friends to fight crime.","year":2014,"embedding":[0.19,0.02,0.30,0.09,-0.05,0.14,0.08,0.01]}'
+JSON.SET movie:035 $ '{"title":"The Prestige","genres":["Drama","Mystery","Sci-Fi"],"plot":"Two magicians engage in a deadly rivalry.","year":2006,"embedding":[0.12,-0.02,0.24,0.06,-0.08,0.10,0.05,-0.01]}'
+JSON.SET movie:036 $ '{"title":"Dunkirk","genres":["Action","Drama","History"],"plot":"Allied soldiers are evacuated during WWII.","year":2017,"embedding":[0.10,-0.03,0.22,0.05,-0.09,0.07,0.04,-0.02]}'
+JSON.SET movie:037 $ '{"title":"Jumanji: Welcome to the Jungle","genres":["Action","Adventure","Comedy"],"plot":"Teens trapped in a video game jungle.","year":2017,"embedding":[0.16,0.01,0.27,0.08,-0.06,0.12,0.06,0.01]}'
+JSON.SET movie:038 $ '{"title":"Cinderella","genres":["Animation","Family","Fantasy"],"plot":"A young girl overcomes her cruel stepmother.","year":1950,"embedding":[0.19,0.03,0.31,0.11,-0.04,0.16,0.08,0.02]}'
+JSON.SET movie:039 $ '{"title":"Mulan","genres":["Animation","Adventure","Drama"],"plot":"A young woman disguises as a soldier.","year":1998,"embedding":[0.20,0.03,0.32,0.11,-0.04,0.17,0.09,0.02]}'
+JSON.SET movie:040 $ '{"title":"Beauty and the Beast","genres":["Animation","Family","Fantasy"],"plot":"A young woman falls in love with a beast.","year":1991,"embedding":[0.18,0.02,0.30,0.10,-0.05,0.15,0.08,0.01]}'
+JSON.SET movie:041 $ '{"title":"The Godfather","genres":["Crime","Drama"],"plot":"The aging patriarch of an organized crime dynasty transfers control to his son.","year":1972,"embedding":[0.11,-0.04,0.24,0.06,-0.10,0.07,0.05,-0.03]}'
+JSON.SET movie:042 $ '{"title":"Pulp Fiction","genres":["Crime","Drama"],"plot":"The lives of two mob hitmen, a boxer, and others intertwine.","year":1994,"embedding":[0.12,-0.03,0.23,0.07,-0.09,0.09,0.04,-0.01]}'
+JSON.SET movie:043 $ '{"title":"Forrest Gump","genres":["Drama","Romance"],"plot":"The presidencies of Kennedy and Johnson through the eyes of Forrest.","year":1994,"embedding":[0.14,0.01,0.26,0.08,-0.07,0.11,0.06,0.01]}'
+JSON.SET movie:044 $ '{"title":"Gladiator","genres":["Action","Drama"],"plot":"A former Roman General seeks revenge.","year":2000,"embedding":[0.13,0.00,0.25,0.07,-0.08,0.10,0.05,0.00]}'
+JSON.SET movie:045 $ '{"title":"Titanic","genres":["Drama","Romance"],"plot":"A seventeen-year-old aristocrat falls in love with a kind but poor artist.","year":1997,"embedding":[0.15,0.02,0.28,0.09,-0.06,0.13,0.06,0.01]}'
+JSON.SET movie:046 $ '{"title":"Jurassic Park","genres":["Adventure","Sci-Fi"],"plot":"Scientists clone dinosaurs for a theme park.","year":1993,"embedding":[0.14,-0.01,0.26,0.08,-0.07,0.11,0.06,0.00]}'
+JSON.SET movie:047 $ '{"title":"The Shawshank Redemption","genres":["Drama"],"plot":"Two imprisoned men bond over a number of years.","year":1994,"embedding":[0.15,0.00,0.27,0.09,-0.06,0.12,0.07,0.01]}'
+JSON.SET movie:048 $ '{"title":"Fight Club","genres":["Drama"],"plot":"An insomniac and a soap maker form an underground fight club.","year":1999,"embedding":[0.13,-0.02,0.24,0.07,-0.08,0.10,0.05,-0.01]}'
+JSON.SET movie:049 $ '{"title":"The Silence of the Lambs","genres":["Crime","Drama","Thriller"],"plot":"A young FBI cadet seeks help from an imprisoned cannibal.","year":1991,"embedding":[0.11,-0.03,0.22,0.06,-0.09,0.08,0.04,-0.02]}'
+JSON.SET movie:050 $ '{"title":"The Departed","genres":["Crime","Drama","Thriller"],"plot":"An undercover cop and a mole in the police attempt to identify each other.","year":2006,"embedding":[0.12,-0.02,0.23,0.07,-0.08,0.09,0.05,-0.01]}'
+JSON.SET movie:51 $ '{"title":"Saturday Night Fever","year":1977,"genres":["Drama","Music"],"plot":"A young man finds escape from his mundane life through disco dancing.","embedding":[0.154,-0.050,0.300,0.150,-0.150,0.154,0.034,-0.118]}'
+JSON.SET movie:52 $ '{"title":"The Rose","year":1979,"genres":["Music","Drama"],"plot":"A rock star struggles with fame, addiction, and love.","embedding":[0.144,-0.055,0.295,0.145,-0.160,0.150,0.030,-0.120]}'
+JSON.SET movie:53 $ '{"title":"Cabaret","year":1972,"genres":["Drama","Music"],"plot":"A performer and a writer navigate love and politics in pre-WWII Berlin.","embedding":[0.151,-0.052,0.297,0.148,-0.152,0.150,0.031,-0.121]}'
+JSON.SET movie:54 $ '{"title":"Tommy","year":1975,"genres":["Drama","Music","Fantasy"],"plot":"A deaf and blind boy becomes a pinball champion and religious figure.","embedding":[0.149,-0.051,0.301,0.149,-0.151,0.153,0.033,-0.119]}'
+JSON.SET movie:55 $ '{"title":"All That Jazz","year":1979,"genres":["Drama","Music"],"plot":"A choreographer reflects on his life and art while facing death.","embedding":[0.153,-0.049,0.299,0.151,-0.149,0.152,0.032,-0.117]}'
+```
+
+### Create a Vector-Enabled Search Index
+Redis stores movie data as JSON documents with text fields (title, genres, plot) and vector embeddings. Indexing enables fast filtering and approximate nearest neighbor (ANN) searches on embeddings.
+
+```redis:[run_confirmation=true] Create a Vector Index
+FT.CREATE idx:movies ON JSON PREFIX 1 "movie:" SCHEMA
+  $.title AS title TEXT
+  $.genres[*] AS genres TAG
+  $.plot AS plot TEXT
+  $.year AS year NUMERIC
+  $.embedding AS embedding VECTOR FLAT
+    6
+        TYPE FLOAT32
+        DIM 8 // DIM = embedding size, DIM 8 is just for demo purposes. In real use, embeddings are usually 128–1536 dimensions.
+        DISTANCE_METRIC COSINE // COSINE = measures semantic closeness
+ ```
+
+This sets the stage for combined textual and semantic search.
+
+### Semantic Search by Query Embedding
+When users search “I want a fun animated movie about toys and friendship”, sentence-transformers models can convert the text into a vector. You can store and query this vector in Redis for semantic search.
+
+```redis:[run_confirmation=false] Search Per Plot
+FT.SEARCH idx:movies "*=>[KNN 3 @embedding $vec AS score]"
+  PARAMS 2 vec "\x9a\x99\x19\x3f\xcd\xcc\xcc\x3d\x9a\x99\x4c\x3f\x9a\x99\x33\x3e\x9a\x99\x33\x3f\xcd\xcc\x66\x3e\xcd\xcc\xcc\x3d\xcd\xcc\x4c\x3e"
+  SORTBY score
+  RETURN 3 title plot score
+  DIALECT 2
+```
+
+Redis returns top movies with embeddings close to the query vector - Toy Story ranks first, even if keywords don’t exactly match.
+
+### Adding Filters for Hybrid Search
+
+Now, let’s find music movies:
+“A feel-good film about music and students.”
+
+You can combine a genre filter with vector similarity:
+
+```redis:[run_confirmation=false] Search Per Genre
+FT.SEARCH idx:movies "@genres:{Music} =>[KNN 5 @embedding $vec AS score]"
+  PARAMS 2 vec "\x9a\x99\x1d\x3e\xcd\xcc\x4c\xbd\x9a\x99\x99\x3e\x9a\x99\x19\x3e\x9a\x99\x19\xbe\x9a\x99\x1d\x3e\xcd\xcc\x0c\x3e\x9a\x99\xf1\xbc"
+  SORTBY score
+  RETURN 3 title genres score
+  DIALECT 2
+```
+
+This hybrid query uses Redis’s tagging system plus vector search, improving relevance.
+
+### Using Embeddings of Existing Movies for Recommendations
+
+Let’s say you love Inception and want similar movies. Let’s retrieve the Inception plot embedding and use it as the query vector:
+```redis:[run_confirmation=false] Get the Embedding From the Movie Document
+JSON.GET movie:006 $.embedding
+```
+Now let’s run vector similarity search using that embedding as a binary blob (`FLOAT32`-packed):
+
+```redis:[run_confirmation=false] Search for Similar Movies
+FT.SEARCH idx:movies "*=>[KNN 5 @embedding $vec AS score]" 
+    PARAMS 2 vec "\xCD\xCC\x56\x3E\x9A\x99\xF3\xBC\xCD\xCC\x00\x3F\x66\x66\x34\x3E\xC6\xF5\x1B\xBE\x9A\x99\x4D\x3E\x9A\x99\x99\x3D\x9A\x99\xB5\xBD" 
+    SORTBY score 
+    RETURN 2 title score 
+    DIALECT 2
+```
+
+Redis finds movies like Arrival and The Departed, showcasing content-based recommendation with semantic similarity.
+
+### Combining Metadata Filters with Vector Search
+Let’s find classic musical rebellion films from the 90s:
+```redis:[run_confirmation=false] Search for Classical Musical Rebellion Films
+FT.SEARCH idx:movies "(@genres:{Music} @year:[1970 1979]) =>[KNN 5 @embedding $vec AS score]"
+  PARAMS 2 vec "\x9a\x99\x1d\x3e\xcd\xcc\x4c\xbd\x9a\x99\x99\x3e\x9a\x99\x19\x3e\x9a\x99\x19\xbe\x9a\x99\x1d\x3e\xcd\xcc\x0c\x3e\x9a\x99\xf1\xbc"
+  SORTBY score
+  RETURN 4 title year genres score
+  DIALECT 2
+```
+This shows how Redis vector search works seamlessly with numeric and tag filters.
+
+### Personalizing Recommendations
+Say we like Animated and Sci-Fi movies. You can personalize results by filtering the vector search:
+```redis:[run_confirmation=false] Search Per Genres
+FT.SEARCH idx:movies '@genres:{"Animated"|"Sci-Fi"} =>[KNN 5 @embedding $vec AS score]'
+   PARAMS 2 vec "\x9a\x99\x1d\x3e\xcd\xcc\x4c\xbd\x9a\x99\x99\x3e\x9a\x99\x19\x3e\x9a\x99\x19\xbe\x9a\x99\x1d\x3e\xcd\xcc\x0c\x3e\x9a\x99\xf1\xbc"
+   SORTBY score
+   RETURN 3 title genres score
+   DIALECT 2
+```
+
+This makes Redis recommendations responsive to evolving user preferences without retraining embeddings.
+
+### Next Steps
+  - Learn more about building personalized recommendations with this [workshop](https://github.com/redis-developer/redis-movies-searcher/tree/springio-2025-workshop).
+  - Build a UI for natural language queries that delivers instant semantic recommendations.
+  - Add personalization by merging user preferences with semantic search.
+  - Explore advanced vector search methods like HNSW indexing for large datasets.
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