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/
71 lines
2.0 KiB
Markdown
71 lines
2.0 KiB
Markdown
# Local Models
|
|
|
|
MemPalace works with any local LLM — Llama, Mistral, or any offline model. Since local models generally don't speak MCP yet, there are two approaches.
|
|
|
|
## Wake-Up Command
|
|
|
|
Load your world into the model's context:
|
|
|
|
```bash
|
|
mempalace wake-up > context.txt
|
|
# Paste context.txt into your local model's system prompt
|
|
```
|
|
|
|
This gives your local model a bounded wake-up context, typically around **~600-900 tokens** in the current implementation. It includes:
|
|
- **Layer 0**: Your identity — who you are, what you work on
|
|
- **Layer 1**: Top moments from the palace — key decisions, recent work
|
|
|
|
For project-specific context:
|
|
```bash
|
|
mempalace wake-up --wing driftwood > context.txt
|
|
```
|
|
|
|
## CLI Search
|
|
|
|
Query on demand, feed results into your prompt:
|
|
|
|
```bash
|
|
mempalace search "auth decisions" > results.txt
|
|
# Include results.txt in your prompt
|
|
```
|
|
|
|
## Python API
|
|
|
|
For programmatic integration with your local model pipeline:
|
|
|
|
```python
|
|
from mempalace.searcher import search_memories
|
|
|
|
results = search_memories(
|
|
"auth decisions",
|
|
palace_path="~/.mempalace/palace",
|
|
)
|
|
|
|
# Format results for your model's context
|
|
context = "\n".join(
|
|
f"[{r['wing']}/{r['room']}] {r['text']}"
|
|
for r in results["results"]
|
|
)
|
|
|
|
# Inject into your local model's prompt
|
|
prompt = f"Context from memory:\n{context}\n\nUser: What did we decide about auth?"
|
|
```
|
|
|
|
## AAAK for Compression
|
|
|
|
Use [AAAK dialect](/concepts/aaak-dialect) to compress wake-up context further:
|
|
|
|
```bash
|
|
mempalace compress --wing myapp --dry-run
|
|
```
|
|
|
|
AAAK is readable by any LLM that reads text — Claude, GPT, Gemini, Llama, Mistral — without a decoder.
|
|
|
|
## Full Offline Stack
|
|
|
|
The core memory stack can run offline:
|
|
- **ChromaDB** on your machine — vector storage and search
|
|
- **Local model** on your machine — reasoning and responses
|
|
- **AAAK** for compression — optional, no cloud dependency
|
|
- **Optional reranking or external model integrations** may introduce cloud calls, depending on how you configure the system
|