Long-term Memories0
Graph Entities0
Raw Vectors Ledger0
Cognitive Insights0
Trigger Cognitive reflection Audit
Triggering the reflection engine prompts Gemini to analyze all your stored memories. It extracts behavioral trends, finds where you deviate from your goals, and writes a cognitive insight record that is fed back into your memory vector database.
MongoDB Collections Inspector
Here is the active schema representation inside your MongoDB database layer:
db.users0 docs
Default user Profile.
Fields: id, name, email, createdAt
db.conversations0 docs
Chat session headers.
Fields: id, userId, title, updatedAt
db.memories0 docs
Cognitive embeddings store.
Fields: id, userId, content, category, embedding (vector), confidence
db.entities0 docs
Graph node elements.
Fields: id, userId, name, type, description
db.relationships0 docs
Subject-predicate-object links.
Fields: id, userId, sourceEntityId, targetEntityId, type
db.embeddings0 docs
Raw vector ledger ledger.
Fields: id, userId, text, model, dimensions, vector
db.goals0 docs
Long-term milestones.
Fields: id, userId, title, progress, status
db.tasks0 docs
Daily action items.
Fields: id, userId, title, status, dueDate
db.reflections0 docs
Behavioral audit outcomes.
Fields: id, userId, insight, evidence