Igor Lins e Silva
dfb22f5345
- 22 content pages across Guide, Concepts, and Reference sections - Custom indigo/cyan theme with Lucide icons and Mermaid diagrams - GitHub Actions workflow for GitHub Pages deployment - Live preview: https://mempalace-docs.netlify.app/
105 lines
3.0 KiB
Markdown
105 lines
3.0 KiB
Markdown
# Memory Stack
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MemPalace uses a 4-layer memory stack. Each layer loads progressively more data only when needed.
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## The Layers
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| Layer | What | Size | When |
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|-------|------|------|------|
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| **L0** | Identity — who is this AI? | ~50-100 tokens | Always loaded |
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| **L1** | Essential Story — top moments | ~500-800 tokens | Always loaded |
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| **L2** | Room Recall — filtered retrieval | ~200–500 each | When topic comes up |
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| **L3** | Deep Search — full semantic query | Variable | When explicitly asked |
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In the current implementation, a typical wake-up is roughly **~600-900 tokens** for L0 + L1. Searches only fire when needed.
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## Layer 0: Identity
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A plain text file at `~/.mempalace/identity.txt`. Always loaded as the AI's self-concept.
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```text
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I am Atlas, a personal AI assistant for Alice.
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Traits: warm, direct, remembers everything.
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People: Alice (creator), Bob (Alice's partner).
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Project: A journaling app that helps people process emotions.
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```
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~50 tokens. Tells the AI who it is and who it works with.
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## Layer 1: Essential Story
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Auto-generated from the highest-importance drawers in the palace. Groups by room, picks the top moments, and keeps the output bounded.
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The generation process:
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1. Reads all drawers from ChromaDB
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2. Scores each by importance/emotional weight
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3. Takes the top 15 moments
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4. Groups by room for readability
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5. Truncates to fit within 3,200 characters
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```
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## L1 — ESSENTIAL STORY
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[auth-migration]
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- Team decided to migrate from Auth0 to Clerk — pricing + DX (session_2026-01-15.md)
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- Kai debugged the OAuth token refresh issue (session_2026-01-20.md)
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[deploy-process]
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- Switched to blue-green deploys after the January outage (session_2026-02-01.md)
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```
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## Layer 2: On-Demand Recall
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Loaded when a specific topic or wing comes up in conversation. Retrieves drawers filtered by wing and/or room — typically ~200–500 tokens.
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```python
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stack = MemoryStack()
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stack.recall(wing="driftwood", room="auth")
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# → returns recent drawers about auth in the driftwood project
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```
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## Layer 3: Deep Search
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Full semantic search against the entire palace. This is what fires when you or the AI explicitly asks a question.
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```python
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stack.search("why did we switch to GraphQL")
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# → returns top-5 matching drawers with similarity scores
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```
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## Wake-Up Budget
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The point of the stack is bounded startup context, not a fixed universal token count. The exact size depends on your identity file and what Layer 1 selects, but the implementation keeps wake-up meaningfully smaller than loading the full corpus into the prompt.
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## Using the Stack
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### CLI
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```bash
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# Wake-up context (L0 + L1)
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mempalace wake-up
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# Project-specific wake-up
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mempalace wake-up --wing driftwood
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```
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### Python API
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```python
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from mempalace.layers import MemoryStack
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stack = MemoryStack()
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# L0 + L1: wake-up (~600-900 tokens in typical use)
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print(stack.wake_up())
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# L2: on-demand recall
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print(stack.recall(wing="myapp"))
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# L3: deep search
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print(stack.search("pricing change"))
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# Status
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print(stack.status())
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```
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