AI Engine
Current engine active
Jobe™
Recursive Intelligence Loop
Every cross-document connection discovered by the 3-tier search is analyzed by the LLM
and persisted back into the knowledge base as a first-class document. Future queries
find these insights, compound them, and generate deeper connections —
the system literally gets smarter with every conversation.
Query ──► 3-Tier Search ──► Graph Cross-Refs
│
LLM Insight Generation
(Who / What / Why / When)
│
┌───────┴───────┐
▼ ▼
Qdrant FAST Neo4j Graph
(embedding) (entities)
│ │
└───────┬───────┘
▼
Intelligence Folder (DIG)
│
◄───────┘
Available for next query
Storage Pipeline
Insights follow the same pipeline as imported PDFs:
SQLite chunk record → Titan v2 1024d embedding →
Qdrant FAST vector store → Neo4j entity graph.
Managed in the Intelligence/ folder in DIG with full lifecycle
(view, re-extract, delete).
Trigger Conditions
Insight generation activates when Neo4j returns
cross_reference type
connections — shared entities between different documents.
Uses the currently selected AI Engine (Balanced = Haiku, Advanced = Sonnet).
Prompt: Who/What/Why/When + Hidden Insight framework, max 600 tokens.
Lifecycle
Deleting a conversation shows an option to also remove its generated insight
from the knowledge base (Qdrant + Neo4j + SQLite). Keeping insights after
deletion preserves the compounding intelligence effect.
-- insights
--
Qdrant FAST 1024d