Igor Lins e Silva
6aebf458ff
The pattern-matching detector had several systematic false positives that
crowded the init review with nonsense. Concrete fixes:
- CamelCase extraction: add `[A-Z][a-z]+(?:[A-Z][a-z]+|[A-Z]{2,})+` to
candidate patterns so `MemPalace`, `ChromaDB`, `OpenAI`, `ChatGPT` are
visible. Previously `MemPalace` fragmented into `Mem` + `Palace`.
- Dialogue `^NAME:\s` requires >=2 matches to count. A single metadata
line like `Created: 2026-04-21` was scoring as dialogue and classifying
`Created` as a person.
- Versioned/hyphenated pattern tightened to `\b{name}[-_]v?\d+(?:\.\d+)*\b`
(version-only). The previous `\b{name}[-v]\w+` matched `context-manager`,
`multi-word`, etc. - every hyphenated compound.
- Skip LICENSE/COPYING/NOTICE/AUTHORS/PATENTS files during scan. They
produce pure-English-prose noise (`Contributor`, `Software`, `Covered`,
`Before`).
- Extra SKIP_DIRS: `.terraform`, `vendor`, `target`.
- Expand stopword list with capitalized participles/descriptors that
commonly appear at sentence start: `created`, `updated`, `extracted`,
`processed`, `total`, `summary`, `auto`, `multi`, `hybrid`, `context`,
`bridge`, `batch`, `local`, `native`, `never`, `before`, `after`, etc.
- classify_entity: high-pronoun single-category signal now classifies as
person. A diary's main character gets referenced with pronouns, not
dialogue markers - requiring two signal categories demoted `Lu` (16
pronoun hits across 30 mentions) to uncertain. Gate on
`pronoun_hits >= 5 AND pronoun_hits / frequency >= 0.2` so common
sentence-start words (`Never`, `Before`) with incidental proximity
stay uncertain.
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.