Obsidian Hybrid Search

Your Obsidian vault already contains your best thinking. Obsidian Hybrid Search makes that thinking easier to find, reuse, and bring into AI-assisted work.

It gives your vault one retrieval engine and three practical ways to use it. The native Obsidian plugin gives you fast search, previews, similar notes, link discovery, and graph views while you write. The MCP server lets AI agents search and read your notes as tool calls. The CLI gives power users the same engine for indexing, filtering, reranking, reading, and scripting.

The search understands how real vaults are built. It combines semantic search, BM25 full text, fuzzy title and alias matching, tags, folders, frontmatter, wikilinks, backlinks, and similar-note lookup. You can search by idea, phrase, title, relationship, or metadata without remembering the exact words you wrote.

That turns Obsidian into a stronger personal knowledge system and a better starting point for AI work. Agents can begin from your own notes, pull cited context from source files, follow related material, and work with knowledge you already trust. OHS runs locally by default with SQLite, FTS5, sqlite-vec, RRF ranking, and optional OpenAI-compatible embedding APIs.

Search quality

Evaluated on the Obsidian Help vault (171 notes, 58 queries, local model):

¹ CPU (Apple Silicon), hybrid mode, no rerank. ² GPU (Apple Silicon Metal), LLM query expansion + reranking.

OHS uses Xenova/multilingual-e5-small. How to reproduce → · Full benchmark →

Real knowledge-vault benchmark

OHS is also evaluated on Andy Matuschak’s public evergreen notes, converted into an Obsidian vault with title-based note filenames, source URLs in frontmatter, local attachments, and 5,000+ internal note links across 1,357 notes.

The curated golden set includes 78 hand-judged queries across known-item lookup, paraphrases, quote fragments, ambiguous topics, citation lookup, and multi-note evidence.

Using the default local embedding model, OHS performs strongly on this dense note network.

The benchmark exercises retrieval over a highly connected real-world knowledge vault, including queries that do not simply repeat note titles.

Result JSON · Reproduce and interpret →

Large memory benchmark

To test retrieval on a larger public dataset, LongMemEval-S was converted into a 22,419-note Obsidian-style vault with 470 retrieval queries. Using baai/bge-m3 embeddings, OHS ranked the answer-bearing notes strongly:

For this benchmark, each query uses the LongMemEval-provided haystack as its search scope. That makes the result reproducible and easy to inspect query by query, while still exercising retrieval over a large generated memory vault.

Result JSON · Reproduce and interpret →

Features

• Hybrid search
  • BM25 + fuzzy title + semantic embeddings, fused with RRF
• Alias search
  • notes with aliases: in frontmatter are indexed and searchable by any alias; alias matches are boosted in BM25 (weight 5×) and fuzzy title scoring
• Four search modes
  • hybrid, semantic, fulltext, title (for text queries)
• Similar note lookup
  • pass --path to find semantically related notes using stored chunk embeddings, with a title + content fallback
• Graph traversal
  • --path --related shows linked notes at configurable depth; filter by --direction outgoing|backlinks|both
• Links & backlinks
  • every result includes outgoing links and backlinks
• Scope filtering
  • restrict to subfolder(s); supports multiple values and exclusions (-notes/dev/)
• Tag filtering
  • filter by tag(s); supports multiple values and exclusions (-category/cs)
• Snippet control
  • --snippet-length sets the context window; empty snippets always fall back to note content
• Extended output
  • --extended adds a TAGS/ALIASES column to the CLI table showing frontmatter tags (#tag) and aliases
• Incremental indexing
  • only re-indexes changed files; watches for edits in real time
• Multi-query fan-out
  • pass multiple queries at once (ohs "q1" "q2" or queries[] in MCP); results are merged via RRF — a note that ranks well in any one query floats to the top; useful when the note may use different vocabulary than the query
• Cross-encoder reranking
  • --rerank re-scores results with bge-reranker-v2-m3 (ONNX int8, ~570 MB download once); improves precision for conceptual and multilingual queries; applied after multi-query merge
• Local embeddings
  • works offline via @huggingface/transformers (no API key required); default model: Xenova/multilingual-e5-small, 100+ languages
• Remote embeddings
  • OpenAI-compatible API (OpenRouter, Ollama, etc.)
• Note reading
  • read fetches one or more notes by vault-relative path; returns full content with title, aliases, tags, links, and backlinks; on path miss returns top-3 fuzzy suggestions
• Ignore patterns
  • exclude folders, extensions, or specific files
• Obsidian plugin
  • native search modal inside Obsidian powered by the same CLI — see obsidian-hybrid-search-plugin

Installation

npm install -g obsidian-hybrid-search
# or run directly without installing:
npx obsidian-hybrid-search

CLI usage

Quick start

Option A — recommended: set OBSIDIAN_VAULT_PATH once in your shell profile.

This lets you run the tool from any directory. Add to ~/.zshrc or ~/.bashrc:

export OBSIDIAN_VAULT_PATH="/path/to/your/vault"

Then reload (source ~/.zshrc) and index your vault once:

obsidian-hybrid-search reindex

After that you can search from any directory:

obsidian-hybrid-search "zettelkasten"

Option B — no env var: run from inside your vault.

The tool detects the vault root by looking for the .obsidian/ folder, walking up from the current directory. cd into your vault (or any subfolder) and run:

cd /path/to/your/vault
obsidian-hybrid-search reindex   # detects vault root, creates DB, indexes everything
obsidian-hybrid-search "zettelkasten"

Commands work from any directory inside the vault tree. From outside the vault (e.g. via shell aliases called from ~), use Option A or pass --db /path/to/vault/.obsidian-hybrid-search.db explicitly.

Optional: remote embedding API instead of local model.

By default the local Xenova/multilingual-e5-small model is used — works offline, no API key needed. Downloads ~117 MB on first run. Supports 100+ languages including Russian, Chinese, Japanese, and more.

To use a remote API instead, add to your shell profile:

export OPENAI_API_KEY="sk-..."

# Default API base is https://api.openai.com/v1 — override for other providers:
# export OPENAI_BASE_URL="https://openrouter.ai/api/v1"  # OpenRouter
# export OPENAI_BASE_URL="http://localhost:11434/v1"     # Ollama (no key needed)
# export OPENAI_BASE_URL="http://localhost:1234/v1"      # LM Studio (no key needed)

# Optional: override the embedding model (default: text-embedding-3-small)
# export OPENAI_EMBEDDING_MODEL="text-embedding-3-small"

Search modes

--mode only affects text queries. When --path is given without --related, search uses semantic similarity regardless of --mode; --path --related traverses links/backlinks instead.

# Hybrid search (default)
obsidian-hybrid-search "zettelkasten atomic notes"

# Fulltext BM25 search
obsidian-hybrid-search "permanent notes" --mode fulltext

# Fuzzy title search (fast, typo-tolerant)
obsidian-hybrid-search "zettleksten" --mode title

# Semantic / vector search
obsidian-hybrid-search "how to build a knowledge graph" --mode semantic

# Limit results and set a score threshold
obsidian-hybrid-search "productivity systems" --limit 5 --threshold 0.3

# Restrict to a subfolder
obsidian-hybrid-search "daily review" --scope notes/periodic/
obsidian-hybrid-search "daily review" --folder notes/periodic/    # alias for --scope

# Restrict to multiple subfolders (OR)
obsidian-hybrid-search "productivity" --scope notes/pkm/ --scope notes/2024/

# Exclude a subfolder
obsidian-hybrid-search "programming" --scope notes/ --scope -notes/archive/

# Filter by tag
obsidian-hybrid-search "productivity" --tag pkm
obsidian-hybrid-search "machine learning" --tag note/basic/primary

# Filter by multiple tags (AND include, exclude with -)
obsidian-hybrid-search "learning" --tag pkm --tag work

# Filter by frontmatter / properties (exact match, case-insensitive)
obsidian-hybrid-search "notes" --frontmatter status:todo
obsidian-hybrid-search "notes" --prop priority:high          # --prop is alias for --frontmatter

# Filter by multiple frontmatter fields (AND)
obsidian-hybrid-search "notes" --frontmatter status:todo --frontmatter priority:high

# Exclude by frontmatter value
obsidian-hybrid-search "notes" --frontmatter -status:done

# Filter-only mode: no query, just filters (returns all matching notes sorted by title)
obsidian-hybrid-search --frontmatter status:todo
obsidian-hybrid-search --folder notes/2024/
obsidian-hybrid-search --tag pkm
obsidian-hybrid-search --frontmatter status:done --tag archived

# Unlimited results in filter-only mode (default limit is 10)
obsidian-hybrid-search --folder notes/ --limit 0

# Find semantically similar notes
obsidian-hybrid-search --path notes/pkm/zettelkasten.md

# Graph traversal: show notes linked to/from this note
# Results show depth: -1/-2 = backlinks, 0 = source, +1/+2 = outgoing links
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related --depth 2

# Only outgoing links (what this note references)
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related --direction outgoing

# Only backlinks (who references this note)
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related --direction backlinks

# Traverse standard Markdown note links instead of Obsidian wikilinks
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related --link-type markdown

# Traverse both wikilinks and standard Markdown note links
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related --link-type all

# Longer context around each link
obsidian-hybrid-search --path notes/pkm/zettelkasten.md --related --snippet-length 500

# Rerank results with a cross-encoder model (improves precision, ~1-3s extra latency)
# Downloads bge-reranker-v2-m3 ONNX (~570 MB) on first use, cached in ~/.cache/huggingface/
obsidian-hybrid-search "zettelkasten atomic notes" --rerank

# Show tags and aliases alongside results
obsidian-hybrid-search "zettelkasten" --extended

# JSON output (for scripting)
obsidian-hybrid-search "spaced repetition" --json

# Output only paths (one per line) — useful for piping into read
obsidian-hybrid-search --frontmatter id:OHS-4 --only-paths
ohs read ${(f)"$(ohs search --frontmatter status:todo --only-paths)"}  # zsh: read all matching notes

# Output absolute filesystem paths
obsidian-hybrid-search "zettelkasten" --only-absolute-paths

# Open results in Obsidian (each in a new tab)
obsidian-hybrid-search "zettelkasten" --open

# Reindex the vault
obsidian-hybrid-search reindex

# Force full reindex
obsidian-hybrid-search reindex --force

# Reindex a single file
obsidian-hybrid-search reindex notes/pkm/zettelkasten.md

# Show indexing status
obsidian-hybrid-search status

# Show recent indexing activity
obsidian-hybrid-search status --recent

# Show chunks that failed to embed
obsidian-hybrid-search status --errors

# Read a note by path (outputs body content without frontmatter)
obsidian-hybrid-search read notes/pkm/zettelkasten.md

# Read raw file from vault (with frontmatter, like cat)
obsidian-hybrid-search read notes/pkm/zettelkasten.md --raw

# Read multiple notes (separator between each)
obsidian-hybrid-search read notes/pkm/zettelkasten.md notes/pkm/evergreen-notes.md

# Cap content length
obsidian-hybrid-search read notes/pkm/zettelkasten.md --snippet-length 2000

# Structured output with all metadata
obsidian-hybrid-search read notes/pkm/zettelkasten.md --json

Shell aliases

Add to your ~/.zshrc or ~/.bashrc for quick access:

alias ohs='obsidian-hybrid-search'
alias ohss='obsidian-hybrid-search --mode semantic'
alias ohst='obsidian-hybrid-search --mode title'
alias ohsf='obsidian-hybrid-search --mode fulltext'
alias ohsr='obsidian-hybrid-search read'
alias ohsi='obsidian-hybrid-search reindex'
alias ohsst='obsidian-hybrid-search status'

Then reload (source ~/.zshrc) and use:

ohs "zettelkasten"                        # hybrid search
ohss "how to build a knowledge graph"     # semantic
ohst "zettelkasten"                       # fuzzy title (typo-tolerant)
ohsf "permanent notes"                    # fulltext BM25
ohsr "notes/pkm/zettelkasten.md"          # read note by path
ohsi                                      # reindex vault
ohsst                                     # show status
ohsst --recent                            # show recent indexing activity
ohsst --errors                            # show chunks that failed to embed

Output example

Hybrid search returns a table with scores and snippets. Scores are color-coded by relevance:

┌───────┬───────────────────────────────┬────────────────────────────────────────────┐
│ SCORE │ PATH                          │ SNIPPET                                    │
├───────┼───────────────────────────────┼────────────────────────────────────────────┤
│  0.98 │ notes/pkm/zettelkasten.md     │ A note-taking method developed by Niklas   │
│       │                               │ Luhmann. Each note contains one atomic...  │
├───────┼───────────────────────────────┼────────────────────────────────────────────┤
│  0.72 │ notes/pkm/evergreen-notes.md  │ Evergreen notes are written to evolve over │
│       │                               │ time. Unlike fleeting notes, they are...   │
└───────┴───────────────────────────────┴────────────────────────────────────────────┘

With --extended, a TAGS/ALIASES column is added. Tags are prefixed with #, aliases are shown as-is:

┌───────┬───────────────────────────────┬──────────────────┬──────────────────────────────┐
│ SCORE │ PATH                          │ TAGS/ALIASES     │ SNIPPET                      │
├───────┼───────────────────────────────┼──────────────────┼──────────────────────────────┤
│  0.98 │ notes/pkm/zettelkasten.md     │ #pkm             │ A note-taking method...      │
│       │                               │ ЗК               │                              │
│       │                               │ slip-box         │                              │
├───────┼───────────────────────────────┼──────────────────┼──────────────────────────────┤
│  0.72 │ notes/pkm/evergreen-notes.md  │ #pkm             │ Evergreen notes are written  │
│       │                               │ #writing         │ to evolve over time...       │
└───────┴───────────────────────────────┴──────────────────┴──────────────────────────────┘

Title mode omits the snippet column automatically.

MCP server

Most AI assistants operate without access to your personal knowledge — they can only work with what you paste into the conversation. Adding this server gives any MCP-compatible assistant a persistent, searchable index of your entire vault. It becomes a tool call, not a copy-paste session: the assistant queries your notes the same way it calls any other tool, gets ranked results with snippets and links, and can navigate your knowledge graph on request.

Add to your MCP config (.mcp.json, claude_desktop_config.json, or equivalent for your client).

Minimal config (local embeddings, no API key)

Uses the built-in Xenova/multilingual-e5-small model — works fully offline, supports 100+ languages. Downloads ~117 MB on first run.

{
  "mcpServers": {
    "obsidian-hybrid-search": {
      "command": "npx",
      "args": ["-y", "-p", "obsidian-hybrid-search@latest", "obsidian-hybrid-search-mcp"],
      "env": {
        "OBSIDIAN_VAULT_PATH": "/path/to/your/vault"
      }
    }
  }
}

Full config (OpenRouter)

{
  "mcpServers": {
    "obsidian-hybrid-search": {
      "command": "npx",
      "args": ["-y", "-p", "obsidian-hybrid-search@latest", "obsidian-hybrid-search-mcp"],
      "env": {
        "OBSIDIAN_VAULT_PATH": "/path/to/your/vault",
        "OBSIDIAN_PREFIX": "myvault_",
        "OBSIDIAN_RESPECT_GITIGNORE": "true",
        "OBSIDIAN_IGNORE_PATTERNS": ".obsidian/**,templates/**,*.canvas",
        "OBSIDIAN_INCLUDE_PATTERNS": "private/notes/**",
        "OPENAI_API_KEY": "sk-or-v1-...",
        "OPENAI_BASE_URL": "https://openrouter.ai/api/v1",
        "OPENAI_EMBEDDING_MODEL": "openai/text-embedding-3-small"
      }
    }
  }
}

Note: On first run, npx will install the package automatically. Ignore patterns are persisted in the database and restored on every subsequent startup even if the env var is missing.

Shared HTTP server

Use this when multiple MCP clients should share one long-lived search/indexing process.

Start or reuse the background server:

OBSIDIAN_VAULT_PATH="/path/to/your/vault" obsidian-hybrid-search serve

serve starts the MCP server over HTTP by default; serve --http is the explicit equivalent. The command prints the server URL, PID, log path, and a client config snippet. The default bind address is 127.0.0.1:3939.

Then add this to a URL-based MCP client config (.mcp.json, claude_desktop_config.json, or equivalent):

{
  "mcpServers": {
    "obsidian-hybrid-search": {
      "url": "http://127.0.0.1:3939/mcp"
    }
  }
}

Manage the server:

obsidian-hybrid-search serve status
obsidian-hybrid-search serve stop
obsidian-hybrid-search serve --foreground
obsidian-hybrid-search serve --http --foreground

HTTP mode uses MCP Streamable HTTP. If port 3939 is already in use, the command exits with an error instead of choosing another port automatically. Use --port for separate vaults.

When binding beyond localhost, add the client-facing Host header with --allowed-host <host[:port]> or OBSIDIAN_MCP_ALLOWED_HOSTS; --allow-any-host disables Host-header protection for trusted networks.

Available MCP tools

The MCP server exposes four tools:

If OBSIDIAN_PREFIX is set, tool names are prefixed in the MCP list (for example myvault_search, myvault_read). By default OBSIDIAN_PREFIX is empty, so tool names remain search, read, reindex, status.

Configuration

Ignore patterns

• folder/** — ignore a directory and all its contents
• *.canvas — ignore by extension
• exact/path.md — ignore a specific file

Root and nested .gitignore files are respected by default. Set OBSIDIAN_RESPECT_GITIGNORE=false to disable this. Use OBSIDIAN_INCLUDE_PATTERNS to re-include Markdown notes that are ignored only by .gitignore; include patterns do not override OBSIDIAN_IGNORE_PATTERNS or internal exclusions.

The ignore configuration is persisted in the database, so it is restored automatically even if the environment variable is missing on restart.

How it works

1. Indexing — notes are chunked by headings (with sliding-window fallback), embedded, and stored in SQLite with FTS5 and sqlite-vec.
2. Search — BM25 (with column weights: title 10×, aliases 5×, content 1×), fuzzy trigram title/alias search, and vector KNN search run in parallel; results are fused with RRF and scored 0–1 (higher = more relevant).
3. Links — wikilinks ([[note]]) are resolved to note paths and stored; every search result includes links and backlinks arrays.
4. Watcher — chokidar watches for file changes and incrementally re-indexes in the background.

Development

npm install
npm test          # run test suite
npm run build     # compile TypeScript

Tests use fake embeddings (no API key required) and run against a temporary vault. All tests cover chunking, BM25 scoring, fuzzy search, links/backlinks, tag filtering, scope filtering, related-mode traversal, direction/score logic, snippet fallback, and ignore pattern matching.

License

MIT