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Per-project isolation, 38 MCP tools, codepoint-safe chunking (Korean/CJK/emoji).","maintainers":[{"name":"bripin123","email":"heesong.koh@hotmail.com"}],"readme":"# rag-memory-epf-mcp\n\n[![npm version](https://img.shields.io/npm/v/rag-memory-epf-mcp)](https://www.npmjs.com/package/rag-memory-epf-mcp)\n[![npm downloads](https://img.shields.io/npm/dm/rag-memory-epf-mcp)](https://www.npmjs.com/package/rag-memory-epf-mcp)\n[![GitHub license](https://img.shields.io/github/license/bripin123/rag-memory-epf-mcp)](https://github.com/bripin123/rag-memory-epf-mcp/blob/main/LICENSE)\n[![Platforms](https://img.shields.io/badge/Platform-Windows%20%7C%20macOS%20%7C%20Linux-blue)](https://github.com/bripin123/rag-memory-epf-mcp)\n\nA **project-local RAG memory** MCP server — knowledge graph + multilingual vector search + FTS5 full-text search, all in a single SQLite file per project.\n\n## Key Features\n\n- **Project-local isolation** — each project gets its own `.memory/rag-memory.db`. Multiple projects run simultaneously without interference.\n- **Hybrid search** — vector similarity (bge-m3, 1024-dim) + FTS5 BM25 keyword matching (RRF-fused). Knowledge-graph re-ranking is an **opt-in legacy/experimental** signal since v5.3.0 (measured to hurt known-item retrieval)\n- **100+ languages** — Korean, Chinese, Japanese, Arabic, and more. Cross-lingual search works out of the box.\n- **Graph re-ranker (opt-in)** — per-entity geometric decay (0.5^i) with a hard cap 0.4; the cap bounds the boost but was measured (2026-08-17) to let heavily-linked chunks saturate it and outrank the exact chunk — hence off by default\n- **38 MCP tools** — knowledge graph CRUD, observation lifecycle (correct / retract / history), document pipeline, hybrid search, multi-hop traversal, graph analytics (centrality / community detection / structure), export/import, temporal queries\n- **Observations that hold their history** — corrections supersede instead of overwrite, search returns only current facts, and every revision keeps its provenance\n- **Structure-anchored chunking (c1, v5)** — boundaries anchor to markdown structure (fence-aware, H1–H4 first, block-greedy, exact-token fallback), so editing the top of a file no longer re-embeds the whole document: unchanged text reuses its stored vectors at sync time. Chunk offsets are Unicode codepoints, language-neutral across SQL `substr`, Python slicing, and JS `[...str]` iteration; a publish-time invariant gate locks the gap-free partition. `overlap` is retired (omit or 0).\n- **SQLite optimized** — WAL mode, 32MB cache, FTS5 triggers, 7 indexes; mmap is opt-in (`RAG_MEMORY_MMAP_SIZE`, default 0 since 6.3.0 — see Environment Variables)\n- **MCP SDK 1.27.1** — Tool Annotations (readOnly/destructive/idempotent), latest protocol 2025-11-25\n\n## Quick Start\n\n```json\n{\n  \"mcpServers\": {\n    \"rag-memory\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"rag-memory-epf-mcp@latest\"],\n      \"env\": {\n        \"DB_FILE_PATH\": \"/path/to/your-project/.memory/rag-memory.db\"\n      }\n    }\n  }\n}\n```\n\nPlace this `.mcp.json` in each project folder with its own `DB_FILE_PATH`. Each project maintains completely isolated memory.\n\n## Tools (38)\n\n### Knowledge Graph (7)\n| Tool | Description | Annotation |\n|------|------------|------------|\n| `createEntities` | Create entities with observations and types (upsert) | idempotent |\n| `createRelations` | Establish relationships between entities | idempotent |\n| `addObservations` | Add contextual information to entities (dedup) | idempotent |\n| `updateRelations` | Update relationship confidence and metadata | idempotent |\n| `deleteEntities` | Remove entities and relationships | destructive |\n| `deleteRelations` | Remove specific relationships | destructive |\n| `deleteObservations` | **Deprecated** soft-retract shim — see Observation Lifecycle | destructive |\n\n### Observation Lifecycle (7)\n| Tool | Description | Annotation |\n|------|------------|------------|\n| `correctObservation` | Supersede a revision with corrected text, keeping the old one | |\n| `retractObservation` | `active` → `retracted` (hidden from search, kept in history) | |\n| `restoreObservation` | `retracted` → `active` | |\n| `approveObservation` | `provisional` → `active` | |\n| `declineObservation` | `provisional` → `retracted` (reason required) | |\n| `purgeObservation` | Physically delete a revision and its successors (`confirm='PURGE'`) | destructive |\n| `getObservationHistory` | Every revision, status, provenance and event | read-only |\n\n### Document Pipeline (9)\n| Tool | Description | Annotation |\n|------|------------|------------|\n| `storeDocument` | Store documents with metadata. Replacing an existing document reports what it destroyed: `{ replaced, deletedChunks }` | idempotent |\n| `chunkDocument` | Create text chunks with configurable parameters | — |\n| `embedChunks` | Generate 1024-dim embeddings + auto-link entities | idempotent |\n| `embedAllEntities` | Batch embed all entities (32 parallel) | idempotent |\n| `extractTerms` | Extract potential entity terms | — |\n| `linkEntitiesToDocument` | Link entities to chunks where they actually appear (text-matched) | idempotent |\n| `deleteDocuments` | Remove documents and associated data | destructive |\n| `listDocuments` | View all stored documents | readOnly |\n| `syncDocumentFromFile` | One-call server-side sync: reads file + delete/store/chunk/embed/link, content stays off model context. Atomic (embed-first transaction swap) + `content_hash` dedup (skips unchanged files). `excludePattern` strips regions before indexing, and the hash follows the stripped text so changing the pattern re-indexes | idempotent |\n\n### Search & Retrieval (9)\n| Tool | Description | Annotation |\n|------|------------|------------|\n| `hybridSearch` | Vector + FTS5 BM25, plus an **opt-in** graph re-ranker (`useGraph: true`; default off since v5.3.0). Degrades to FTS5-only (`search_mode`) when the embedding model is down | readOnly |\n| `searchNodes` | Semantic entity search with `since`/`until` temporal filtering | readOnly |\n| `openNodes` | Retrieve specific entities by name | readOnly |\n| `readGraph` | Get complete knowledge graph | readOnly |\n| `getNeighbors` | Multi-hop graph traversal (depth 1-5, cycle detection) | readOnly |\n| `getDetailedContext` | Get full context for a chunk | readOnly |\n| `exportGraph` | Export full graph as JSON (backup) | readOnly |\n| `importGraph` | Import graph from JSON (merge or replace) | destructive |\n| `getKnowledgeGraphStats` | Knowledge base statistics | readOnly |\n\n### Migration (3)\n| Tool | Description | Annotation |\n|------|------------|------------|\n| `getMigrationStatus` | Check database schema version | readOnly |\n| `runMigrations` | Apply pending migrations | idempotent |\n| `rollbackMigration` | Revert to a previous schema version | destructive |\n\n### Graph Analytics (3)\n| Tool | Description | Annotation |\n|------|------------|------------|\n| `getGraphMetrics` | Per-entity centrality (degree, betweenness, closeness, pagerank) | readOnly |\n| `detectCommunities` | Louvain community detection + modularity score | readOnly |\n| `analyzeGraphStructure` | Density, connected components, clustering coefficient | readOnly |\n\n## Document Processing Pipeline\n\n```\nstoreDocument(id, content, metadata)\n  → chunkDocument(documentId, maxTokens)   # overlap retired in v5 (omit or 0)\n    → embedChunks(documentId)\n       ├── generates vector embeddings for each chunk\n       ├── auto-links entities to chunks (word boundary + CJK aware)\n       └── returns { embeddedChunks, linkedEntities }\n```\n\n## Architecture\n\n```\n┌─────────────────────────────────────────────┐\n│  MCP Client (Claude Code, Gemini CLI, etc)  │\n└──────────────────┬──────────────────────────┘\n                   │ stdio (MCP SDK 1.27.1)\n┌──────────────────▼──────────────────────────┐\n│  rag-memory-epf-mcp                         │\n│  ┌────────────┐ ┌─────────────┐ ┌────────┐  │\n│  │ Knowledge  │ │ RAG Document│ │ Search │  │\n│  │ Graph CRUD │ │ Pipeline    │ │ Engine │  │\n│  └─────┬──────┘ └──────┬──────┘ └───┬────┘  │\n│        │               │            │        │\n│  ┌─────▼───────────────▼────────────▼─────┐  │\n│  │  SQLite (WAL mode, per-project file)   │  │\n│  │  ├── entities + relationships          │  │\n│  │  ├── documents + chunk_metadata        │  │\n│  │  ├── chunks (sqlite-vec, 1024-dim)     │  │\n│  │  ├── entity_embeddings (sqlite-vec)    │  │\n│  │  ├── entities_fts + chunks_fts (FTS5)  │  │\n│  │  ├── observation lifecycle (4 tables)  │  │\n│  │  └── 13 migrations (auto-applied)      │  │\n│  └────────────────────────────────────────┘  │\n│                                              │\n│  bge-m3 (ONNX, 100+ langs)                   │\n└──────────────────────────────────────────────┘\n```\n\n## Environment Variables\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `DB_FILE_PATH` | `rag-memory.db` (server dir) | Path to project-local SQLite database. A **relative** path resolves against the server's working directory (since 6.3.0; before that it resolved against the package install directory, which under `npx` is the npm cache) |\n| `RAG_MEMORY_MMAP_SIZE` | `0` | SQLite `mmap_size` in bytes. **Off by default since 6.3.0** — with mmap on, one writer + two readers on a Google Drive folder (Windows) produced `database disk image is malformed` reads; set e.g. `268435456` to restore the previous behaviour. The applied value is printed in the boot banner (`| mmap <n>`) |\n| `EMBEDDING_MODEL` | `Xenova/bge-m3` | HuggingFace model ID for embeddings |\n| `RAG_MEMORY_EMBEDDINGS` | `lazy` | Boot mode: `lazy` (connect instantly, model loads in background), `eager` (wait for model + reconciliation, pre-3.6 behavior), `off` (never load the model — FTS5-only, zero download) |\n| `RAG_MEMORY_MODEL_CACHE_DIR` | OS user cache | Version-independent model cache location (see `docs/UPDATING.md`) |\n| `RAG_MEMORY_TRUST_LEGACY_VECTORS` | unset | Set `1` to grandfather pre-existing vectors under a **custom** `EMBEDDING_MODEL` (default model configs grandfather automatically) |\n\n## Changelog\n\n### v6.4.0 (2026-10-03)\n\n- (Published first as `6.4.0-rc.1` on the `next` dist-tag; promoted to `latest` after a canary of the published\n  artifact against a backup copy of a real project database: version and model as expected, `hybrid` mode on all\n  probes, the identifier probe's answer at rank 4 and the known-item probe's at rank 1, every score <= 2/61,\n  query-cold median 445 ms, `query_prefix_version` 2, entity/relation/document/chunk counts unchanged, no errors.)\n\n- **Changed** `hybridSearch` default ranking = reciprocal rank fusion of the vector and FTS5 candidate lists\n  (k=60). A chunk that only FTS5 finds — typically an exact identifier, filename or setting key — can now reach\n  the top; before, it scored at most 0.0164 and sat under every vector candidate in the measured corpora.\n  `relevance_score` on the default call is a rank score (at most 2/61, comparable only within one call). Measured on frozen snapshots: exact-identifier hit@10 7/20 → 18/20; worse on 0 of\n  203 queries across three corpora.\n- **Changed** the per-result summary path is opt-in (`RAG_MEMORY_SEARCH_SUMMARIES=on`). It embedded every\n  sentence fragment of every candidate (median 121 s per search) and its context boost pushed vector-0 chunks\n  to rank 1. `off` keeps working. Legacy paths (`summaries=on`, `useGraph: true`) keep the 6.3.2 formula.\n- **Changed** with the default model (bge-m3) queries are embedded as raw text: the bge-v1.5 instruction the loader\n  prepended to every query is gone (bge-m3 needs none; stored vectors never had it). A custom `EMBEDDING_MODEL` keeps\n  the previous query instruction. Paraphrase hit@10 7 → 9 of 20, identifier hit@1 7 → 8,\n  document known-item (`hybridSearch`) unchanged or +1; entity known-item (`searchNodes`) hit@10 unchanged,\n  hit@1 -1 / -1 / +1 (hub / uap / hal). No re-index.\n- Details and rollout: `docs/UPDATING.md`, `specs/changes/search-fusion-rrf/proposal.md`.\n\n### v6.3.2 (2026-09-25)\n\n- **Changed — on macOS the engine takes `-wal` and `-shm` out of cloud sync.** It sets\n  `com.apple.fileprovider.ignore#P = 1` on both files after every open (SQLite recreates them after the\n  last connection closes, so once is not enough). Measured with Google Drive for desktop: the client\n  treated `-wal` as a synced item and put a new file at the path while the engine held the old one open\n  (`lsof -p` showed the descriptor inside `FileProvider/…/wharf/delete/`); from then on every write\n  failed with `SQLITE_IOERR` (\"disk I/O error\") until the engine restarted — 10 s to 3 min after start,\n  with one engine attached. With the attribute set, the same live database kept the same `-wal` for\n  25 min of use. The main database file still syncs. Other platforms: no-op.\n- **Changed — a swapped sidecar is detected and the connection reopened.** The idle tick compares the\n  `-wal`/`-shm` inodes with the ones recorded at open (two consecutive mismatches) and reopens with the\n  same configuration; an `SQLITE_IOERR*` from a checkpoint or a tool call reopens too. A tool that hit\n  the I/O error is retried once only if repeating it changes nothing (reads, `createEntities`,\n  `createRelations`, `addObservations`, `deleteEntities`, `deleteRelations`); other tools get the\n  error back after the reopen. A reopen waits for running tool calls, is not done while shutting\n  down, and is braked after 3 in 10 s. A failed reopen retries with backoff and, after 10 failures,\n  shuts the server down so the host shows it as failed. A reconciliation cut by a reopen reruns.\n- Known limit: writes acknowledged in the last ~1 s before a swap can be lost if the old file is no\n  longer readable (the engine tries one checkpoint on the old handle first and logs `walsz-before=`).\n- The 6.3.1 `wal_checkpoint(TRUNCATE)` is unchanged. `RESTART` (± `journal_size_limit`) was measured and\n  rejected: after SIGKILL its WAL replayed onto a foreign main file 3/3 times.\n- No tool, argument, return shape or schema changed. Spec: `specs/changes/wal-sidecar-guard/`.\n\n### v6.3.1\n\n- **Changed — the engine empties the WAL while it is idle.** It runs `wal_checkpoint(TRUNCATE)` itself:\n  on a 1 s tick whenever the `-wal` file has bytes in it, ~200 ms after a tool call, first thing in the\n  signal handler, and before closing. SQLite folds the WAL only when the *last* connection closes and\n  only if that close gets to run; with several engines on one file, or a host that kills the engine\n  (measured: codex-cli 0.155.1 sends SIGTERM and SIGKILLs ~185 ms later; Windows terminates children\n  outright), frames stayed in the WAL — on 6.3.0 a fresh database sat at 4 KB main + 663 KB WAL for as\n  long as the engine ran. In a cloud-synced folder such a WAL can be replayed onto a main file written\n  by another machine. A write made in the last ~1 s before a hard kill can still be in the WAL; a 0-byte\n  `-wal` and a `-shm` left behind by a hard kill are harmless. Running engines on two machines against\n  the same synced file at the same time is still unsafe.\n- **Changed — SIGHUP and SIGBREAK shut the engine down cleanly.** Closing the terminal or multiplexer\n  pane used to kill the process by default signal action, before the database was closed.\n- No tool, argument, return shape or schema changed.\n\n### v6.3.0 (2026-09-16)\n\n- **Changed — a relative `DB_FILE_PATH` now resolves against the server's working directory.** It used\n  to resolve against the package install directory, which under `npx` is the npm cache: a relative\n  value opened a database in a place nobody looks, with no error. Absolute paths and the unset default\n  are unchanged. (Framework spec 2026-09-14 §11-1.) If you relied on the old install-directory\n  resolution of a relative path, set an absolute path.\n- **Changed — SQLite `mmap_size` is opt-in, default 0.** Measured 2026-09-15 on a Google Drive folder\n  (Windows, WAL): with mmap 256 MB, one writer + two readers produced 373,684 `database disk image is\n  malformed` reads in 20 s; with mmap 0, none. `RAG_MEMORY_MMAP_SIZE=<bytes>` restores it; the boot\n  banner reports the applied value. (Spec §11-2.)\n\n### v6.1.0\n\n**Versioning note.** This content was tagged `v6.0.2` locally before release, but npm never received\na 6.0.2 — it published as **6.1.0**. Coming from any 6.0.x, upgrading to 6.1.0 brings exactly this\nfix.\n\n- **Fixed — a failure mid-deletion could leave partial state.** Deleting an entity ran its cleanup\n  steps (embeddings → chunk associations → relationships → the entity row) with no wrapping\n  transaction, so a failure in the middle committed the earlier steps: embeddings and links purged\n  while the entity itself survived. Each entity's deletion is now one transaction — any step fails,\n  that entity's whole sequence rolls back. Batch semantics are unchanged: other entities still\n  proceed when one fails.\n- **Fixed — deleting an entity left its knowledge-graph chunks behind.** Two gaps compounded: the\n  delete path never invoked the entity-chunk sweeper, and KG relationship chunks are keyed by\n  `relationship_id`, so once the relationship rows were deleted their ids could no longer be found\n  and those chunks dangled forever — still vector-searchable after both of their endpoints were\n  gone. Deletion now captures relationship ids *before* removing the rows and sweeps both: stale\n  entity chunks via the existing `deleteStaleKgChunks`, captured relationship chunks via the new\n  `deleteKgRelationshipChunks` projection helper. Practical exposure today is bounded — the chunk\n  generation path is dormant (not tool-exposed) — but once seeded these chunks stay retrievable\n  unless swept here.\n- Regression: `test/delete-entities-kg-hygiene.test.mjs` (registered in `verify:engine`) covers both,\n  verified RED before the fix; a mutation check confirms that removing the transaction reproduces the\n  partial state (`entities_alive=1` with `relationships_left=0`). Full suite green (`npm test`,\n  46-call chain).\n\n### v6.0.1\n\n**Versioning note.** Both changes below stop links from being created that should never have been\ncreated, and restore links that should have been. No tool signature, return shape or schema\nchanges, so this is a patch: **derived-link density is not part of the compatibility contract**.\nRead the entries as \"linking got more precise\", not as an API break.\n\n- **An observation alias must now also appear in the entity's own name.** 6.0.0 capped how many\n  entities may share a token. That answers \"does this token point at one entity?\" and says nothing\n  about the other direction, so an entity that mentions a common filename *once* still attached to\n  every chunk containing it — and, being the sole owner, sailed through the cap. The cap stopped\n  the explosion, not the magnet. Measured after the 6.0.0 cleanup on a live corpus: 2,014\n  alias-only links remained and **34 entities held 68.1%** of them; the largest had the entity name\n  in **zero** of its chunks. Requiring the token (or its stem) to appear in the name brings that to\n  6 entities / 42.1%, and what remains are entities that really are about that file. Expect far\n  fewer alias links on new ingests; existing rows are not rewritten.\n- **A chunk-frequency cap was measured and rejected on the evidence, not on cost.** The full-corpus\n  scan is 585ms (731 tokens x 2,913 chunks). It was rejected because the sweep has no knee and every\n  cut also removed legitimate links — `log_coverage.py` occurs in 64 chunks and belongs to an entity\n  about exactly that file. The name condition is structural: no threshold, same meaning at any\n  corpus size.\n- **Fixed — entity names whose own edge is punctuation never linked.** The Latin matcher used\n  `\\b<name>\\b`. `\\b` asserts a transition between a word character and a non-word character, so when\n  the name itself starts or ends with punctuation — `Widget Review (2026-05-27)`, `--build-flag` —\n  there is no transition to assert and the pattern cannot match however the text reads. Measured on\n  a live 2,913-chunk / 312-name corpus: **23 standalone occurrences across 13 names** were invisible.\n  Both the chunk matcher and the document range finder now accept an occurrence whose neighbours on\n  both sides are non-word. **Not a widening**: both sides must still be non-word, so `Data` continues\n  not to match inside `Database`. The scan runs on the original text rather than a lowercased copy,\n  because folding can change length (`İ` becomes two units) and the range finder converts these\n  indices to codepoints.\n- Regressions: `test/alias-link-gate.test.mjs` (magnet case) and `test/entity-name-boundary.test.mjs`,\n  both registered in `verify:engine` and both verified to fail without their fix.\n\n### v6.0.0\n\n- **Breaking — observation-derived alias links are now gated.** `autoLinkEntities` used to take every\n  `stem.ext` token appearing anywhere in an entity's observations and link that entity to every chunk\n  containing the token as a substring. Measured on a live 2,891-chunk / 604-entity corpus: 65,388 of\n  66,841 `chunk_entities` rows (97.8%) existed only because of such a hit, and one token — `agents.md`,\n  held by 88 entities — accounted for 39,248 of the 80,096 distinct (chunk, entity) pairs any alias\n  could reach (49.0%). The regex also accepted things that are not filenames at all (`v3.3`,\n  `gpt-5.6`, `1.7mb`, `github.com`, `os.path`).\n  A token now has to (a) look like a filename — extension whitelist, stem ≥ 3 chars, non-numeric\n  stem — and (b) be held by at most 3 entities, and it must match on token boundaries so `foo.py` no\n  longer matches inside `notfoo.pyc`. Effect on the same corpus: alias links 100% → 4.0%. Filtering by\n  extension alone leaves 92.3%, so the owner cap is what does the work. The cap is a judgement, not a\n  discovered boundary — the sweep is smooth (`owners<=1` 1.6% … `<=10` 19.9%) — and it is a cap, not a\n  ban: a filename named by one to three records still links, which is what the alias path was for.\n- **What this means for existing databases.** Nothing is rewritten on upgrade: rows already in\n  `chunk_entities` stay, and new ingests simply link far less. Links have always been a function of\n  when a document was last processed (nothing re-links older documents when entities are added), and\n  `chunk_entities` has no provenance column, so old alias rows cannot be told apart from name matches\n  after the fact. If you want the old noise gone you have to clean it offline, before or after\n  upgrading. Callers that assumed dense `chunk_entities` coverage will see sparser graphs.\n- Regression: `test/alias-link-gate.test.mjs` (registered in `verify:engine`), verified to fail\n  without the gate.\n\n### v5.3.0\n- (Published first as `5.3.0-rc.1` on the `next` dist-tag; promoted to `latest` after a canary run of the published artifact against a real project database: default call carries no `graph_boost` and equals explicit `useGraph:false`, the known-item probe from the 2026-08-17 measurement returns the correct gotcha at rank 1, opt-in `true` still exposes `graph_boost`, schema/MCP defaults read `false`.)\n- **Behavior change — `hybridSearch` graph re-ranking is now opt-in** (`useGraph` default `true` → `false`; tool schema, MCP exposure and the manager signature agree). Omitting the argument now means \"no graph re-ranking\" — a behavior change for callers that relied on the old default, hence a release-candidate first (`next` dist-tag, fleet canary) before stable. Measured 2026-08-17 on three real corpora (self-retrieval, usable samples 120/117/120, summaries off): with the additive graph boost on, the known-item chunk got worse in 46/49/52 samples and better in 3/2/0 (sign test p < 7e-11 per corpus), 106 targets left the top-10 entirely; reproduced on the summaries-on product path (HAL, 20 paired samples: hit@1 10→7, hit@5 18→13). Mechanism: only query-matched/connected entities score, but the per-entity boost saturates the cap quickly, so heavily-linked chunks can outrank the exact chunk even at `vector_similarity` 0. This is a harm-reduced default, not a validated graph improvement: the boost path is unchanged for `useGraph: true` (legacy/experimental re-ranker for back-compat and evaluation; the graph does not generate candidates — for relationship exploration use `openNodes` → `getNeighbors`). Regression lock: `test/search-graph-default.test.mjs`.\n- (v5.0.0–v5.2.0 notes live in the git tags / `docs/UPDATING.md`.)\n\n### v4.0.0\n\n**Observation lifecycle (schema v13).** Observations used to be a JSON array of strings on the\nentity row. A correction overwrote a string, so the fact that it *was* a correction disappeared —\nand if you deleted the wrong duplicate, nothing recorded that either. Observations now have stable\nids, provenance, and a status, and `entities.observations` becomes a projection synthesised from\nthe `active` revisions.\n\n- **Corrections keep the previous revision.** `correctObservation(observation_id, content, change_kind, reason)`\n  marks the old revision `superseded` and inserts a new one that inherits its position in the array,\n  so a correction does not reorder anything.\n- **Search returns `active` revisions only.** A retracted or superseded fact stops coming back from\n  `openNodes` / `searchNodes` / `readGraph` / `getNeighbors` without being destroyed.\n- **`getObservationHistory({entity_name | observation_id | root_id})` is the only history surface.**\n  It always returns `{ roots: [...] }`, one root per logical observation, revisions oldest-first.\n- **State transitions are a table, not a guess**: `retract` / `restore` / `approve` / `decline`.\n  `superseded` is terminal. Anything outside the table is rejected.\n- **Provenance**: `addObservations` and `createEntities` accept `sources: [{source_kind, source_ref, source_hash?}]`.\n  Repeated content from a *new* source adds evidence to the existing revision instead of a duplicate.\n  Unknown provenance is zero source rows — the engine does not invent one.\n- **`observation_ids`**: `addObservations` and `createEntities` return ids aligned 1:1 with the input,\n  `null` where no revision was created (dedup or source-only).\n- **`purgeObservation(observation_id, 'PURGE')`** physically deletes, as a suffix purge from the\n  target to the newest revision of that root. It is separate, explicit, and almost never what you want.\n- **⚠️ BREAKING**: `deleteObservations` is deprecated. It now performs a soft **retract** instead of\n  a delete, and a batch where any item matches two or more active revisions **aborts with zero\n  mutations** — v3.6 deleted every duplicate and carried on, but a machine cannot tell which\n  revision was meant. Use `retractObservation(observation_id)` to say which one.\n- Migration to v13 writes a recovery point first (`<db>.v12.bak`) using the SQLite Online Backup API,\n  and verifies it — `quick_check` plus FTS5's own `integrity-check`, because a snapshot can be\n  structurally valid and still have a broken full-text index. **An existing one is never\n  overwritten**: the next attempt writes `.bak.1`, then `.bak.2`. Every file in that rotation was\n  taken before any schema change, so each is a valid pre-migration snapshot on its own and nothing\n  has to prove which matches the live database. Slots are bounded and a full set refuses to migrate.\n  Conversion runs in one transaction with two gates: `PRAGMA foreign_key_check`, then a byte-exact\n  comparison of the rebuilt projection against the original array. `foreign_keys` is checked at boot\n  and the server refuses to migrate without it. Restore and slot-exhaustion runbook:\n  `docs/UPDATING.md`.\n\n### v3.6.0\n- **Lite install / lazy boot**: the MCP server connects immediately — FTS5 search, knowledge graph and CRUD work from the first second, while the bge-m3 model (~1.2GB) loads or downloads in the background. Hybrid search switches on automatically. Requires Node **>= 24**.\n- **Version-independent model cache** with a cross-process download lock: engine version bumps no longer re-download the model, and concurrent servers on one machine never corrupt a download. Cleaning the npx cache no longer deletes the model.\n- **Embedding provenance + automatic backfill**: every vector records its input hash and model profile; anything missing or stale (including rows written while the model was unavailable) is re-embedded automatically with a per-target retry cap. Fixes a long-standing defect where `deleteObservations` left stale entity vectors behind.\n- **Search state transparency**: responses report `search_mode` (`hybrid` / `hybrid-partial` / `fts-only`), model state, provenance coverage and a `degradation_reason`; `searchNodes` gains a lexical FTS fallback so entities never disappear from search while embeddings catch up. `getKnowledgeGraphStats` gains a `server` block (version, node, states, coverage) for update-reliability checks.\n- **⚠️ BREAKING**: (1) `hybridSearch` now returns an envelope `{results, search_mode, model_state, coverage, degradation_reason?}` instead of a bare array (per-item `search_mode` removed). (2) `deleteObservations` returns `{results: [{entityName, deleted, embedding_status}], total_deleted}` instead of a success string. Error responses now set `isError: true`. Migration/rollback and fleet-rollout guidance: `docs/UPDATING.md`.\n\n### v3.5.0\n- **Atomic `syncDocumentFromFile`**: embeddings are computed before any DB write, then applied in a single synchronous transaction, so a failed embedding (e.g. model still loading) leaves the existing document fully intact instead of a half-deleted or partially-embedded state.\n- **`content_hash` dedup**: unchanged files short-circuit the delete/chunk/embed pipeline (`skipped: true`), with a completeness gate that still re-processes a partially-embedded document.\n- **FTS5-only graceful degradation**: when the embedding model is unavailable, `hybridSearch` returns BM25 (full-text) results tagged `search_mode: 'fts-only'` instead of failing the whole query.\n\n### v3.4.0\n- **`syncDocumentFromFile`: one-call server-side document sync** - reads a file on the server and runs the full pipeline (`deleteDocuments` → `storeDocument` → `chunkDocument` → `embedChunks` → `linkEntitiesToDocument`) in a single call, returning only a terse summary `{ documentId, bytes, chunks, embeddedChunks, linkedEntities, warning? }`. File content is read server-side and never routed through the model context, collapsing the usual 5 tool calls per document into one and keeping conversation context flat (the dominant cost of large sync runs). 30 → 31 tools.\n\n### v3.3.6\n- **Publish-time invariant test** — `npm run verify:invariants` (wired as `prepublishOnly`) catches `chunkText` offset regressions before they ship. Tests ASCII / Korean / emoji-heavy / mixed CJK + supplementary plane / pure supplementary inputs against the codepoint-slice contract.\n- **`chunkText` extracted to `src/chunkText.ts`** — algorithm now testable in isolation. The class method is a thin wrapper. No user-facing API change.\n- **README accuracy** — tool count corrected to 30, migration count to 11, Graph Analytics tools surfaced.\n\n### v3.3.5\n- **Fix: chunk offsets stored as JS UTF-16 units instead of Unicode codepoints** — Korean/CJK/emoji documents had `start_pos`/`end_pos` that disagreed with SQL `substr` and Python slicing for any chunk crossing a supplementary character. `chunkText` now maintains parallel UTF-16 + codepoint cursors and reports codepoint offsets.\n- **Migration v11** — converts existing `chunk_metadata.start_pos`/`end_pos` from UTF-16 units to codepoints by re-locating each chunk via `indexOf` and counting codepoints.\n\n### v3.3.4\n- **Migration version 9 → 10 jump + `ALTER TABLE` idempotency guards** — some databases from early v3.x experiments (Ollama dimension swap) had recorded a migration at version 9, causing the new v9 migration to silently no-op. Bumped to version 10 and added `PRAGMA table_info` guards so the column-add is safe to re-run.\n\n### v3.3.3\n- **Separate token-space and char-space offsets in `chunk_metadata`** — added `start_token`/`end_token` columns. Existing `start_pos`/`end_pos` are reinterpreted as character offsets into `documents.content`. Backfill migration recomputes char offsets via `indexOf` with a per-document cursor; misses leave NULL so callers can re-chunk to repair.\n\n### v3.3.0\n- **Graph analytics (graphology)** — three new MCP tools: `getGraphMetrics` (degree / betweenness / closeness / pagerank), `detectCommunities` (Louvain + modularity), `analyzeGraphStructure` (density / components / clustering). 27 → 30 tools.\n\n### v3.2.1\n- **Fix: `autoLinkEntities` silent failure** — was JOINing a non-existent `observations` table (observations are stored as JSON array column in `entities`). Changed to direct column select + `JSON.parse()`.\n\n### v3.2.0\n- **Chunk-level entity linking in `linkEntitiesToDocument`** — entities are now linked only to chunks where they actually appear (using `buildEntityMatcher` word-boundary/CJK matching), instead of blanket-linking to all chunks. Fixes search result domination by heavily-linked documents.\n- **Graph boost decay + hard cap** — per-entity scores are sorted descending and decayed geometrically (0.5^i): 1st entity 100%, 2nd 50%, 3rd 25%, etc. Hard cap at 0.4 prevents graph signal from overwhelming vector similarity.\n\n### v3.0.0\n- **Back to self-contained embeddings** — reverted from Ollama dependency (v2.x) to built-in `@huggingface/transformers` with bge-m3 (1024-dim). No external services required.\n- **Cross-lingual search** — auto-detects non-English queries and performs dual-language search\n- **External dictionary** — optional `.memory/dictionary.json` for custom translation pairs\n- **Modular tool system** — tools extracted into `src/tools/` with structured registry\n- **Migration system** — extracted into `src/migrations/` with versioned schema upgrades\n- **Dynamic version reporting** — MCP server version now reads from package.json\n- **MIT LICENSE file** — included in published package\n\n### v1.9.0\n- **Multi-hop graph traversal** — `getNeighbors` tool with `WITH RECURSIVE` CTE, depth 1-5, cycle detection, bidirectional\n- **Embedding LRU cache** — 500-entry in-memory cache, skips redundant re-computation\n- **Configurable model** — `EMBEDDING_MODEL` env var to use alternative embedding models\n- 27 tools total at this version (30 as of v3.3.0+)\n\n### v1.8.0\n- **MCP SDK 1.27.1** — protocol 2025-11-25, security fix GHSA-345p-7cg4-v4c7 (CVSS 7.1)\n- **Tool Annotations** — all tools annotated (readOnlyHint, destructiveHint, idempotentHint)\n- **SIGTERM graceful shutdown** — clean exit without ONNX mutex crash\n\n### v1.7.0\n- **SQLite optimization** — WAL mode, 32MB cache, 256MB mmap, busy_timeout\n- **FTS5 full-text search** — BM25 keyword matching + Reciprocal Rank Fusion with vector search\n- **updateRelations** — update confidence scores and metadata without delete+recreate\n- **exportGraph / importGraph** — JSON backup and restore (merge or replace)\n- **Batch embedding** — `embedAllEntities` processes 32 entities in parallel\n- **Temporal filtering** — `searchNodes` with `since`/`until` ISO 8601 date filters\n- **better-sqlite3 12.x** — SQLite 3.51.3 with query planner improvements\n- **sqlite-vec 0.1.7** — DELETE space reclaim, KNN distance constraints\n- **DB indexes** — entityType, relationType, chunk lookups\n- **SQL safety** — `safeRowid()` validation for vec0 operations\n\n### v1.6.0\n- **Entity upsert** — merges new observations into existing entities instead of ignoring duplicates\n- **Observation timestamps** — auto `[YYYY-MM-DD]` prefix for staleness tracking\n- **Dedup by content** — date-stripped comparison prevents duplicate observations\n\n### v1.5.0\n- **Chunk-level entity linking** — precision linking to specific chunks, not all chunks\n- **Word boundary + CJK matching** — Latin word boundaries, CJK substring matching\n- **Observation-derived aliases** — file paths from observations matched against chunks\n\n### v1.4.x\n- Switched to **bge-m3** (1024-dim, 100+ languages)\n- fp16 quantization, instruction prefix optimization\n\n## Development\n\n```bash\ngit clone https://github.com/bripin123/rag-memory-epf-mcp.git\ncd rag-memory-epf-mcp\nnpm install\nnpm run build\nnpm test                  # build + invariant verification\nnpm run verify:invariants # standalone invariant test (assumes dist/ built)\n```\n\n`npm publish` automatically runs `prepublishOnly` (`build` + `verify:invariants`); a chunk-offset regression blocks the publish at the source.\n\n## License\n\nMIT License. See [LICENSE](LICENSE).\n\n### Third-Party Model Licenses\n\n| Component | License | Details |\n|-----------|---------|---------|\n| **bge-m3** | MIT | [Model card](https://huggingface.co/BAAI/bge-m3) |\n| **@huggingface/transformers** | Apache 2.0 | JS inference runtime |\n\nModel weights are downloaded at runtime and not bundled in this package.\n\n---\n\n**Built with**: TypeScript, SQLite (WAL + FTS5 + sqlite-vec), bge-m3, MCP SDK 1.27.1\n","readmeFilename":"README.md"}