journal-rag

Source-control-friendly hybrid retrieval over team markdown journals. Heading-chunked BM25 + local vector embeddings fused via Reciprocal Rank Fusion (RRF), with regex as an escape hatch. Index built on startup with an optional gitignored JSON cache.

Embeddings run locally via @huggingface/transformers (default model: all-MiniLM-L6-v2) — no API keys, no external calls.

Each consuming repo commits journal-rag.config.json and markdown under docs/journal/ (or other configured folders). This package is the shared engine.

Per-repo config

Create journal-rag.config.json at the repo root:

{
  "sources": ["docs/journal"],
  "cachePath": ".journal-rag/index.json",
  "embeddingModel": "Xenova/all-MiniLM-L6-v2"
}

The vector cache (vectors.json) is stored in the same directory as cachePath.

Add to .gitignore:

.journal-rag/

Related MCP server: sage-mcp

Build & install (once per machine)

cd c:/repos/journal-rag
npm install          # runs prepare → build
npm link             # puts journal + journal-mcp on your PATH

npm link registers two global commands:

Re-run npm run build (or npm link again) after pulling server changes. Alternative to link: npm install -g . from this repo (same effect).

CLI (any teammate)

From a repo root with config:

journal search "HttpFacade singleton"        # hybrid BM25 + vector (default)
journal search "HttpFacade singleton" --bm25 # BM25-only (no embedding)
journal list --filter dialog
journal get docs/journal/2026-04-21_vapp-http-facade-and-singleton-sweep.md
journal index --rebuild

After npm link in this repo, journal search "..." works globally.

Set JOURNAL_RAG_WORKSPACE to an absolute repo root only when you must run the CLI from a subdirectory.

The first run downloads the embedding model (~80 MB) to the Hugging Face cache directory. Subsequent runs load from cache.

MCP tools

Editor setup

Use stdio — spawn Node with dist/server.js.

Put MCP config in the workspace, not your user profile

The server resolves journal-rag.config.json by walking up from its working directory. That file lives at each consuming repo's root (next to docs/journal/), not in journal-rag itself.

If you add the server to a global / user-level editor profile, the spawn cwd is usually wrong (home dir, editor install dir, last random folder, etc.) and the server cannot find config — even if you hardcode "cwd": "C:/repos/my-repo", that breaks the moment you open a second repo workspace.

Do this instead: commit workspace-level MCP config inside each repo that has journals. Teammates run npm link once (see above) so journal-mcp is on PATH — no machine-specific paths in the committed JSON.

Cursor

.cursor/mcp.json at the repo root (e.g. my-repo/.cursor/mcp.json) — safe to commit:

{
  "mcpServers": {
    "journal": {
      "command": "journal-mcp",
      "cwd": "${workspaceFolder}",
      "env": {
        "JOURNAL_RAG_WORKSPACE": "${workspaceFolder}"
      }
    }
  }
}

${workspaceFolder} resolves to the repo you opened. journal-mcp comes from npm link in the journal-rag repo.

VS Code (Copilot agent mode)

Same idea: .vscode/mcp.json in the repo, not User settings:

{
  "servers": {
    "journal": {
      "type": "stdio",
      "command": "journal-mcp",
      "cwd": "${workspaceFolder}"
    }
  }
}

JetBrains AI Assistant / Junie

Configure MCP at project scope (.idea / project settings), not the IDE default profile. Open the repo as the project root. Command: journal-mcp (after npm link).

If journal-mcp is not found

Ensure npm's global bin dir is on your PATH (npm bin -g). On Windows that is usually %APPDATA%\\npm. Then re-run npm link from journal-rag. Fallback for a single machine only: "command": "node", "args": ["<absolute-path>/journal-rag/dist/server.js"].

Fallback

If an editor cannot set cwd per workspace, set env JOURNAL_RAG_WORKSPACE to the absolute path of the consuming repo root in that workspace's MCP config.

Design notes

• Corpus is small (~tens of files); BM25 over heading chunks matches how journals are written.

• Vector embeddings (local, via Transformers.js) add semantic recall for paraphrased or conceptual queries.

• Reciprocal Rank Fusion (RRF, k=60) merges BM25 and vector rankings without needing score normalization.

• Index caches are optional and gitignored; markdown in git is the source of truth.

• Vector cache is incremental — only new/changed chunks are re-embedded on rebuild.