AI MEMORY LAYER

Real-time retrieval, processing, and storage workflow

How It Works

When a user asks a question requiring historical context, the system performs a dual-mode search: vector embeddings find semantically similar past conversations while SQL queries filter by time and keywords. Retrieved memories are injected into the AI prompt, enabling contextually-aware responses. After each interaction, the system extracts key facts and stores them with embeddings for future retrieval. This write-read cycle happens in under 2 seconds, creating the illusion of continuous memory across conversations. The architecture combines fast indexed lookups with semantic understanding to scale context retrieval for millions of users.

1
User Question
Query requiring context
💬 "What infrastructure tasks were completed last quarter?"
2
Memory Query
Parallel search operations

🔍 Vector

Semantic similarity

15ms

⚡ SQL

Structured lookup

12ms
3
Retrieved Data
Memories from database

💾 Mem 1

Database performance tuning

💾 Mem 2

Microservice deployment

💾 Mem 3

API integration

~30ms total
4
Context Injection
Memories added to prompt
System: Context from Jan...
User: "What cloud projects..."
1,250 tokens
5
AI Response
Model generates answer
🤖: "Last quarter the team completed database performance tuning, microservice deployment optimization, and API integration work."
~1.8s
6
Fact Extraction
Identify new info

🧠 Extract

"User queried Q4 infrastructure tasks"

0.92 confidence
7
Store Memory
Write to database
📝 INSERT:
INSERT INTO memories...
VALUES ('user_123', ...)
~20ms
8
System Ready
Memory available

✅ Database

Queryable

✅ Vector

Searchable

✅ Cache

Fast access

Total: ~2.1s