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‎src/vss/ai_assistant.md

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This intelligent AI assistant is designed to support real-world, multi-session use with Redis as its memory core.
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What you get:
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- **Smart Memory**: Ephemeral context that expires automatically, long-term facts retained forever
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- **Semantic Search**: Recall relevant info by meaning using vector search
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- **Zero Maintenance**: Auto-expiring short-term memory without a need to track timestamps manually
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- **Multi-User**: Isolated memory per user
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- **Learning**: Assistant can "understand" each user better the more it's used
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**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.
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### Architecture overview
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| Layer | Description |
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| ---------- | ---------- |
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| `Working Memory`| `Short-term chat context (ephemeral)` |
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| `Knowledge Base` | `Persistent facts, user preferences` |
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| `Vector Search` | `Unified semantic recall across both layers` |
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### Working memory (ephemeral)
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Stores recent user messages with TTL based on importance. Automatically expires to prevent bloat.
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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.
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```redis:[run_confirmation=true] Recent Conversations with TTL Based on Importance.
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// Quick exchanges (5 min)
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HSETEX user:alice:session:001 EX 300 FIELDS 1 msg:001 "What's the weather?"
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// Session context (30 min)
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HSETEX user:alice:session:001 EX 1800 FIELDS 1 msg:002 "I need a dentist appointment"
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// Important decisions (2 hours)
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HSETEX user:alice:session:001 EX 7200 FIELDS 1 msg:003 "Book it for Tuesday 2 PM"
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```
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### Knowledge base (persistent)
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Long-term memory: stores important facts, user preferences, and context across sessions. These never expire.
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`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.
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```redis:[run_confirmation=true] Important User Information That Never Expires.
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// User preferences - need vector fields for search
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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"
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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"
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// Personal facts
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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"
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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"
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// Work context
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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"
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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"
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```
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### Vector search: semantic memory recall
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Unify working + knowledge memory into a vector index for semantically meaningful search.
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```redis:[run_confirmation=true] Create a Vector Index
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FT.CREATE idx:memory
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ON HASH
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PREFIX 2 vmemory: user:
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SCHEMA
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user_id TAG
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memory_type TAG
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content TEXT
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importance NUMERIC
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timestamp NUMERIC
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embedding VECTOR HNSW 6
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TYPE FLOAT32
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DIM 8 // DIM = embedding size; 8 used here for simplicity — in production, use 128 to 1536
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DISTANCE_METRIC COSINE // COSINE = measures semantic closeness
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```
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### Add indexed memory
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Populate the vector index with memory items from both ephemeral and persistent layers.
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```redis:[run_confirmation=true] Add entries for the chatbot
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// Working memory (expires from Redis, stays in search until rebuild)
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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"
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// Knowledge base (persistent)
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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"
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```
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### Full memory search
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Search across all memories to recall relevant data.
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```redis:[run_confirmation=false] Find Top 5 Related Messages By Meaning
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FT.SEARCH idx:memory
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"(@user_id:{alice}) => [KNN 5 @embedding $vec AS score]"
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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"
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RETURN 4 content memory_type importance score
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SORTBY score ASC
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DIALECT 2
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```
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### Session-only search
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Limit results to current conversation context (ephemeral memory).
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```redis:[run_confirmation=false] Session-Only Search
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FT.SEARCH idx:memory
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"(@user_id:{alice} @memory_type:{working}) => [KNN 5 @embedding $vec AS score]"
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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"
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RETURN 4 content score timestamp memory_type
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SORTBY score ASC
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DIALECT 2
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```
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### Knowledge-only search
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Focus only on persistent memory: facts, preferences, decisions.
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```redis:[run_confirmation=false] Knowledge-Only Search
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FT.SEARCH idx:memory
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"(@user_id:{alice} @memory_type:{knowledge}) => [KNN 8 @embedding $vec AS score]"
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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"
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RETURN 4 content importance score timestamp
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SORTBY score ASC
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DIALECT 2
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```
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### Monitoring memory state
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Use these queries to inspect what’s stored in memory.
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```redis:[run_confirmation=false] Check Memory State
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// Check active session memory
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HGETALL user:alice:knowledge:pref:001 // Example for one preference item
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// View user knowledge
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HGETALL user:alice:knowledge:pref:001
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// Search user's memories
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FT.SEARCH idx:memory
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"@user_id:{alice}"
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RETURN 3 content memory_type importance
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```
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### Data cleanup
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For privacy compliance, delete all user-related keys.
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```redis:[run_confirmation=true] Complete user removal
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DEL user:alice:knowledge:pref:001
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DEL user:alice:knowledge:pref:002
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DEL user:alice:knowledge:personal:001
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DEL user:alice:knowledge:personal:002
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DEL user:alice:knowledge:work:001
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DEL user:alice:knowledge:work:002
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DEL vmemory:alice:001
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DEL vmemory:alice:kb:001
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```
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### Next steps
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Now that your assistant has memory and meaning, you can:
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- Combine with RAG Pipelines
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- Use sentence-transformers to generate high-dimensional vectors
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- Add [Redis Flex](https://redis.io/solutions/flex/?utm_source=redisinsight&utm_medium=app&utm_campaign=tutorials) for fallback persistence
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- Use Redis ACLs to isolate users, enforce quotas, and monitor usage

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