Igor Lins e Silva
cd98d6674e
- Resolve the EF inside the two reopen branches that actually call `client.get_collection` / `client.create_collection`, so warm-cache reads stay zero-cost (no `MempalaceConfig()` / `_resolve_providers` on every tool call). - Reuse `ChromaBackend._resolve_embedding_function()` instead of duplicating its try/except + log message + None-fallback. - Reword the inline + CHANGELOG explanation to clarify that ChromaDB 1.x persists the EF *identity* (its `name()`) but not the *instance/ configuration* — `mempalace.embedding` documents this and spoofs `name()` to `"default"` precisely so the identity check passes; the bug was the *provider list* (lazy ONNX selection) silently differing.
mempalace/ — Core Package
The Python package that powers MemPalace. All modules, all logic.
Modules
| Module | What it does |
|---|---|
cli.py |
CLI entry point — routes to mine, search, init, compress, wake-up |
config.py |
Configuration loading — ~/.mempalace/config.json, env vars, defaults |
normalize.py |
Converts 5 chat formats (Claude Code JSONL, Claude.ai JSON, ChatGPT JSON, Slack JSON, plain text) to standard transcript format |
miner.py |
Project file ingest — scans directories, chunks by paragraph, stores to ChromaDB |
convo_miner.py |
Conversation ingest — chunks by exchange pair (Q+A), detects rooms from content |
searcher.py |
Semantic search via ChromaDB vectors — filters by wing/room, returns verbatim + scores |
layers.py |
4-layer memory stack: L0 (identity), L1 (critical facts), L2 (room recall), L3 (deep search) |
dialect.py |
AAAK compression — entity codes, emotion markers, 30x lossless ratio |
knowledge_graph.py |
Temporal entity-relationship graph — SQLite, time-filtered queries, fact invalidation |
palace_graph.py |
Room-based navigation graph — BFS traversal, tunnel detection across wings |
mcp_server.py |
MCP server — 19 tools, AAAK auto-teach, Palace Protocol, agent diary |
onboarding.py |
Guided first-run setup — asks about people/projects, generates AAAK bootstrap + wing config |
entity_registry.py |
Entity code registry — maps names to AAAK codes, handles ambiguous names |
entity_detector.py |
Auto-detect people and projects from file content |
general_extractor.py |
Classifies text into 5 memory types (decision, preference, milestone, problem, emotional) |
room_detector_local.py |
Maps folders to room names using 70+ patterns — no API |
spellcheck.py |
Name-aware spellcheck — won't "correct" proper nouns in your entity registry |
split_mega_files.py |
Splits concatenated transcript files into per-session files |
Architecture
User → CLI → miner/convo_miner → ChromaDB (palace)
↕
knowledge_graph (SQLite)
↕
User → MCP Server → searcher → results
→ kg_query → entity facts
→ diary → agent journal
The palace (ChromaDB) stores verbatim content. The knowledge graph (SQLite) stores structured relationships. The MCP server exposes both to any AI tool.