roam-semantic-search

Fully local semantic search over a Roam Research graph: fetch clear-text content through the Roam Local API, embed it with a locally hosted model, store vectors in a single SQLite file, and answer meaning-based queries from a CLI or an MCP server. Nothing about the graph's content ever leaves the machine — that constraint is the project's founding requirement, and it is enforced in code: the embedding client refuses any non-loopback server URL.

Roam Desktop ──(Local API, localhost HTTP)──► fetch ──► normalize ──► embed ──► store
                                                                        ▲          │
                                                          Ollama (localhost)   SQLite (FTS5 + vector blobs)
                                                                                   │
                                              MCP server (stdio) ◄── query ◄───────┘
                                              CLI (roam-semantic-search search)

Full design, phase results, and decision log: docs/design-plan.md.

How it works

• Fetch — one flat Datalog pull of every entity carrying a :block/uid (pages and blocks alike) through the Roam Local API; ~1 s for a 10k-entity graph. For an encrypted graph, the running Roam Desktop client is the only clear-text doorway, so the indexer runs on the same machine.

• Normalize — each block embeds with its breadcrumb: the page title plus ancestor block texts, root-first (ordered by ancestor count, never by wire order, which is creation order and diverges from depth on ~12% of nested blocks). Roam markup is cleaned to prose ([[refs]] → text, ((uid)) references resolve to their target's text one level deep); roam/js and roam/css pages are skipped, and daily-note pages are indexed (skippable with --no-daily-notes). Each record also carries retrieval emphasis in three weight tiers: the page names its own text references ([[Page]] and #tag alike — its concepts, highest), its direct-child tags:: values (its tags, middle), and its plain words plus its whole subtree's folded text (base). The keyword leg realizes the tiers as per-column BM25 weights (4/2/1); the vector leg by embed-input composition (labeled concept/tag segments, descendant text truncated first).

• Embed — a local Ollama server running nomic-embed-text (768-dim), with the model's search_document: / search_query: retrieval prefixes. Loopback-only, enforced.

• Store — one SQLite file (default ~/.cache/roam-semantic-search/<graph>.db): records + float32 embedding blobs, an FTS5 keyword mirror, and provenance meta. No SQLite extensions; vector KNN is a brute-force numpy matrix product (milliseconds at this scale).

• Query — hybrid retrieval: cosine KNN and BM25 rankings fused by reciprocal rank fusion, so paraphrase ("where do I argue…") and exact identifiers both rank.

• Refresh — incremental: re-fetch + re-normalize everything (cheap), then re-embed only records whose content hash changed and delete vanished uids. Selection is by content hash alone — an edit changes descendants' breadcrumbs and referrers' resolved text, which no per-entity timestamp can see. A no-change refresh takes ~2 s.

Related MCP server: ragi

Requirements

• Roam Desktop running locally with the Local API enabled (port, graph name, and a bearer token from Roam → Settings)

• Ollama with the embedding model pulled: ollama pull nomic-embed-text (brew services start ollama keeps it running at login)

• Python ≥ 3.14 and a sibling checkout of guffin (the Local API transport layer)

Install

python3.14 -m venv .venv
.venv/bin/pip install -e ../guffin
.venv/bin/pip install -e ".[dev]"

Configuration

The CLI and MCP server read the same environment the guffin tools use:

CLI

roam-semantic-search build              # full fetch → normalize → embed → store (~100 s for ~8k records)
roam-semantic-search refresh            # incremental: re-embed only what changed (~2 s when idle)
roam-semantic-search search "why the human must stay responsible" -k 5
roam-semantic-search stats              # store provenance: model, counts, build/refresh moments

A hit shows the Roam uid (usable as a ((ref))), the fused score, each ranking's position (v: vector, k: keyword), the breadcrumb, and the text:

 1. ((9KMmmo5aH))  [block  score 0.0323  v:2 k:2]
    The new Programmer (in the age of AI assistants) › The human Programmer/engineer
    The human also remains the accountability boundary. The assistant can propose; ...

MCP server

roam-semantic-search-mcp serves the index over stdio to any MCP client, with three tools: semantic_search (hits plus index meta, so a caller can judge staleness), refresh_index, and index_stats. Register with Claude Code:

claude mcp add --scope user roam-semantic-search --env GUFFIN_ROAM_GRAPH_NAME=<graph> -- $(pwd)/.venv/bin/roam-semantic-search-mcp

The Local API port and token are inherited from the shell environment rather than stored in the client's config; without them refresh_index fails cleanly while search keeps working.

Development

.venv/bin/black .
.venv/bin/ruff check --fix src/ tests/
.venv/bin/pyright          # strict
.venv/bin/pytest

Conventions follow guffin's (Python 3.14, src layout, pyright strict, @validate_call, regex not re, 120-char lines). The index DB contains the graph's text in clear form — treat it like an export, and keep it out of anything synced or shared.