Memory Architecture
Three memory systems powering intelligent responses
Memory Architecture Overview
Vector Search Index
GitLab • Confluence • Databricks • Slack] --> D[RAG Response Generation
Context + Retrieved Docs] B[Short Term Memory
Thread Context
Conversation History • User Context] --> D C[Episodic Memory
User Patterns
Interaction History • Resolution Tracking
Future Implementation] -.-> D classDef semantic fill:#1e40af,stroke:#3b82f6,stroke-width:2px,color:#dbeafe classDef shortTerm fill:#166534,stroke:#16a34a,stroke-width:2px,color:#dcfce7 classDef episodic fill:#581c87,stroke:#8b5cf6,stroke-width:2px,color:#f3e8ff,opacity:0.7 classDef rag fill:#dc2626,stroke:#ef4444,stroke-width:3px,color:#fecaca class A semantic class B shortTerm class C episodic class D rag
Three complementary memory systems work together to provide contextual, intelligent responses by combining documentation knowledge, conversation context, and user interaction patterns.
Semantic Memory
Vector Search Index
Comprehensive knowledge base containing documentation and code from four knowledge sources
Data Sources
GitLab
Documentation and MRs Summaries
Confluence
Pages under "Interested in Data? Start here!"
Databricks
Knowledge Base Articles and Official Documentation
Slack
Past conversations from support channels
Example Use Cases
📚 Documentation Discovery
"How do I set up a DAB for a lakehouse app?"
→ Retrieves relevant developer guides from data-atlas-terraform GitLab repo and Databricks pages
🔍 Cross-Platform Search
"What's the process for onboarding new data scientists?"
→ Finds procedures across multiple knowledge sources
⚡ Instant Access
"Show me the latest API changes for MLflow integration"
→ Surfaces recent updates from Databricks documentation
Short Term Memory
Thread Context Management
Maintains conversation continuity for coherent multi-turn dialogue
Context Components
Conversation History
Previous questions and answers in the current thread
User Context
Role, team, channel context
Session State
Current workflow, active troubleshooting steps
Example Use Cases
🔄 Follow-up Questions
"Can you explain that last step in more detail?"
→ References previous response context
🎯 Contextual Refinement
"Actually, I'm working with the staging environment"
→ Adjusts all subsequent responses for staging context
📋 Multi-step Workflows
"What's the next step after configuring the cluster?"
→ Continues from where the conversation left off
Episodic Memory
User Pattern Recognition
Tracks interaction patterns and resolution history for personalized support
Planned Capabilities
Interaction History
Long-term patterns of user questions and preferences
Resolution Tracking
Success rates of different solution approaches per user
Learning Preferences
Preferred communication styles and detail levels
Example Use Cases
🎯 Personalized Responses
"Based on your previous questions, you might also want to know..."
→ Proactive suggestions based on user patterns
📈 Learning Adaptation
Adjusts explanation depth based on user expertise level
→ More technical details for experienced users
🔄 Proactive Support
"You typically encounter this issue after cluster updates"
→ Anticipates needs based on historical patterns