rag-memory-epf-mcp

A project-local RAG memory MCP server — knowledge graph + multilingual vector search + FTS5 full-text search, all in a single SQLite file per project.

Key Features

• Project-local isolation — each project gets its own .memory/rag-memory.db. Multiple projects run simultaneously without interference.
• 3-signal hybrid search — vector similarity (bge-m3, 1024-dim) + FTS5 BM25 keyword matching + knowledge graph re-ranking, combined via Reciprocal Rank Fusion
• 100+ languages — Korean, Chinese, Japanese, Arabic, and more. Cross-lingual search works out of the box.
• Graph-aware scoring — per-entity geometric decay (0.5^i) with hard cap prevents any single document from dominating results
• 30 MCP tools — knowledge graph CRUD, document pipeline, hybrid search, multi-hop traversal, graph analytics (centrality / community detection / structure), export/import, temporal queries
• Codepoint-safe chunking — chunk offsets are Unicode codepoints, language-neutral across SQL substr, Python slicing, and JS [...str] iteration. Korean/CJK/emoji documents stay aligned. Verified by a publish-time invariant test.
• SQLite optimized — WAL mode, 32MB cache, 256MB mmap, FTS5 triggers, 7 indexes
• MCP SDK 1.27.1 — Tool Annotations (readOnly/destructive/idempotent), latest protocol 2025-11-25

Quick Start

{
  "mcpServers": {
    "rag-memory": {
      "command": "npx",
      "args": ["-y", "rag-memory-epf-mcp@latest"],
      "env": {
        "DB_FILE_PATH": "/path/to/your-project/.memory/rag-memory.db"
      }
    }
  }
}

Place this .mcp.json in each project folder with its own DB_FILE_PATH. Each project maintains completely isolated memory.

Tools (30)

Knowledge Graph (7)

Document Pipeline (8)

Search & Retrieval (9)

Migration (3)

Graph Analytics (3)

Document Processing Pipeline

storeDocument(id, content, metadata)
  → chunkDocument(documentId, maxTokens, overlap)
    → embedChunks(documentId)
       ├── generates vector embeddings for each chunk
       ├── auto-links entities to chunks (word boundary + CJK aware)
       └── returns { embeddedChunks, linkedEntities }

Architecture

┌─────────────────────────────────────────────┐
│  MCP Client (Claude Code, Gemini CLI, etc)  │
└──────────────────┬──────────────────────────┘
                   │ stdio (MCP SDK 1.27.1)
┌──────────────────▼──────────────────────────┐
│  rag-memory-epf-mcp                         │
│  ┌────────────┐ ┌─────────────┐ ┌────────┐  │
│  │ Knowledge  │ │ RAG Document│ │ Search │  │
│  │ Graph CRUD │ │ Pipeline    │ │ Engine │  │
│  └─────┬──────┘ └──────┬──────┘ └───┬────┘  │
│        │               │            │        │
│  ┌─────▼───────────────▼────────────▼─────┐  │
│  │  SQLite (WAL mode, per-project file)   │  │
│  │  ├── entities + relationships          │  │
│  │  ├── documents + chunk_metadata        │  │
│  │  ├── chunks (sqlite-vec, 1024-dim)     │  │
│  │  ├── entity_embeddings (sqlite-vec)    │  │
│  │  ├── entities_fts + chunks_fts (FTS5)  │  │
│  │  └── 11 migrations (auto-applied)      │  │
│  └────────────────────────────────────────┘  │
│                                              │
│  bge-m3 (ONNX, 100+ langs)                   │
└──────────────────────────────────────────────┘

Environment Variables

Changelog

v3.3.6

• 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.
• chunkText extracted to src/chunkText.ts — algorithm now testable in isolation. The class method is a thin wrapper. No user-facing API change.
• README accuracy — tool count corrected to 30, migration count to 11, Graph Analytics tools surfaced.

v3.3.5

• 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.
• 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.

v3.3.4

• 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.

v3.3.3

• 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.

v3.3.0

• Graph analytics (graphology) — three new MCP tools: getGraphMetrics (degree / betweenness / closeness / pagerank), detectCommunities (Louvain + modularity), analyzeGraphStructure (density / components / clustering). 27 → 30 tools.

v3.2.1

• 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().

v3.2.0

• 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.
• 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.

v3.0.0

• 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.
• Cross-lingual search — auto-detects non-English queries and performs dual-language search
• External dictionary — optional .memory/dictionary.json for custom translation pairs
• Modular tool system — tools extracted into src/tools/ with structured registry
• Migration system — extracted into src/migrations/ with versioned schema upgrades
• Dynamic version reporting — MCP server version now reads from package.json
• MIT LICENSE file — included in published package

v1.9.0

• Multi-hop graph traversal — getNeighbors tool with WITH RECURSIVE CTE, depth 1-5, cycle detection, bidirectional
• Embedding LRU cache — 500-entry in-memory cache, skips redundant re-computation
• Configurable model — EMBEDDING_MODEL env var to use alternative embedding models
• 27 tools total at this version (30 as of v3.3.0+)

v1.8.0

• MCP SDK 1.27.1 — protocol 2025-11-25, security fix GHSA-345p-7cg4-v4c7 (CVSS 7.1)
• Tool Annotations — all tools annotated (readOnlyHint, destructiveHint, idempotentHint)
• SIGTERM graceful shutdown — clean exit without ONNX mutex crash

v1.7.0

• SQLite optimization — WAL mode, 32MB cache, 256MB mmap, busy_timeout
• FTS5 full-text search — BM25 keyword matching + Reciprocal Rank Fusion with vector search
• updateRelations — update confidence scores and metadata without delete+recreate
• exportGraph / importGraph — JSON backup and restore (merge or replace)
• Batch embedding — embedAllEntities processes 32 entities in parallel
• Temporal filtering — searchNodes with since/until ISO 8601 date filters
• better-sqlite3 12.x — SQLite 3.51.3 with query planner improvements
• sqlite-vec 0.1.7 — DELETE space reclaim, KNN distance constraints
• DB indexes — entityType, relationType, chunk lookups
• SQL safety — safeRowid() validation for vec0 operations

v1.6.0

• Entity upsert — merges new observations into existing entities instead of ignoring duplicates
• Observation timestamps — auto [YYYY-MM-DD] prefix for staleness tracking
• Dedup by content — date-stripped comparison prevents duplicate observations

v1.5.0

• Chunk-level entity linking — precision linking to specific chunks, not all chunks
• Word boundary + CJK matching — Latin word boundaries, CJK substring matching
• Observation-derived aliases — file paths from observations matched against chunks

v1.4.x

• Switched to bge-m3 (1024-dim, 100+ languages)
• fp16 quantization, instruction prefix optimization

Development

git clone https://github.com/bripin123/rag-memory-epf-mcp.git
cd rag-memory-epf-mcp
npm install
npm run build
npm test                  # build + invariant verification
npm run verify:invariants # standalone invariant test (assumes dist/ built)

npm publish automatically runs prepublishOnly (build + verify:invariants); a chunk-offset regression blocks the publish at the source.

License

MIT License. See LICENSE.

Third-Party Model Licenses

Model weights are downloaded at runtime and not bundled in this package.

Built with: TypeScript, SQLite (WAL + FTS5 + sqlite-vec), bge-m3, MCP SDK 1.27.1