ZotSeek | AI-Powered Semantic Search & MCP Server for Zotero

Find similar papers by meaning, not just keywords. 100% local, no data leaves your machine. Now with a built-in MCP server for AI agents.

Status: ✅ Stable release · Zotero 8, 9 & 10 · Transformers.js running locally

New: 🤖 MCP server built in — Claude Code, Codex, and any MCP client can search your library and cite papers with links that open straight to the matched PDF page. Fully local, read-only, opt-in. Set it up in one line →

New from the same author: 🔎 citefact — audit your manuscript against your Zotero library: citations exist, quotes are verbatim, claims are supported.

Features

• 🔒 100% Local - No data sent to cloud, works completely offline
• 🧠 True Semantic Search - Find papers by meaning, not just keywords
• 🤖 AI Agent Access (MCP) - Let Claude Code and other MCP clients search your library, fully local and opt-in (docs)
• 🔍 Find Similar Documents - Right-click any paper → discover related research
• 📖 Search from PDF Selection - Select text while reading → right-click → find documents about that concept
• 🔎 Natural Language Search - Search with queries like "machine learning in healthcare"
• 🔀 Multi-Query Search - Combine up to 4 queries with AND/OR logic to find topic intersections
• 🔗 Hybrid Search - Combines AI + keyword search for best results
• ⚡ Lightning Fast - Searches complete in <100ms
• 📑 Section-Aware - See which section matched (Abstract, Methods, Results)
• 📄 Matched-Passage Preview - Hover a result to read the exact passage that matched, with query terms highlighted
• 📍 Passage-Level Location - Jump to exact page & paragraph in Full Document mode
• ✅ Multi-Select in Results - Select multiple search results, right-click to add to collections
• 📁 Save Results as Collection - One click saves the full result set into a new Zotero collection so you can revisit the list later without re-running the search
• 🧩 Selectable Embedding Models - Choose from 4 curated local models, including multilingual options; non-bundled models download once from Hugging Face to your machine
• 🖥️ Local Inference Server (optional) - Serve the embedding model from LM Studio, Ollama, llama.cpp or vLLM on the same machine for native GPU speed: OpenAI-compatible API, localhost only
• 🔄 Auto-Index - Automatically index new papers when you add them to your library
• 👥 Group Libraries (opt-in) - Extend indexing and search to your Zotero group libraries with the Index scope setting
• 🗑️ Auto-Cleanup - Embeddings automatically removed when items are deleted or trashed
• 🚫 Tag-Based Exclusion - Tag items with zotseek-exclude to skip them during indexing
• 📊 Indexing Status Column - "ZotSeek" column in the item list shows whether each paper is fully indexed, partially indexed (chunk limit hit), out of date, excluded, or not indexed
• ⏸️ Pause & Cancel - Pause or cancel long-running indexing operations at any time
• 💾 Crash-Resilient - Checkpoint saving every 10 items, auto-resume on next startup if a bulk run was interrupted, worker recovery on sleep/wake, skips problematic chunks automatically
• 🔌 Plugin API - Other Zotero plugins can call ZotSeek's search programmatically
• ⚙️ Configurable - Customize via Zotero Settings → ZotSeek (also accessible from search dialog)
• 🌐 Localized - UI available in English and Chinese (zh-CN)

Privacy & Security

ZotSeek is designed with privacy as a core principle:

What this means:

• Your research never leaves your machine
• No cloud services, no API keys, no subscriptions
• No telemetry or usage tracking
• Uninstalling the plugin removes all ZotSeek data

Optional local inference server: the default is the fully in-process engine described above, which makes no network requests at all. If you choose to add a local inference server (LM Studio, Ollama, llama.cpp or vLLM), the guarantee shifts from "no network code exists" to "network code provably cannot leave the machine": every request is validated at request time against an allow-list of 127.0.0.1, localhost and [::1], there is no preference to override this, redirects to a non-loopback address are refused rather than followed, and cloud or remote endpoints are unsupported by design. This is opt-in and off by default.

More Screenshots

Find Similar Documents

Context Menu

PDF Selection Context Menu

Settings Panel

Indexing Progress

How It Works

The Big Picture

flowchart TD
    subgraph INDEX["1️⃣ INDEX"]
        A[📄 Paper] --> B[🤖 AI Model] --> C[768 numbers]
    end
    
    subgraph SEARCH["2️⃣ SEARCH"]
        D[🔍 Query] --> E[Query → 768 numbers]
        E --> F{Compare all papers}
        F --> G[📊 Ranked results]
    end
    
    C -.->|stored| F

How it works: Each paper becomes 768 numbers capturing its meaning. To search, we convert your query to numbers and find papers with similar numbers.

Step-by-Step Process

1️⃣ Indexing Your Library

When you use "Index Current Collection" or "Update Library Index":

For each paper:
  1. Extract title + abstract (Abstract mode)
     — OR —
     Extract PDF text page-by-page with exact page numbers (Full Document mode)
  2. Split into paragraphs, filter out References/Bibliography
  3. Send to local AI model (nomic-embed-text-v1.5)
  4. Model outputs 768 numbers per chunk (the "embedding")
  5. Save embeddings + location metadata to local database (zotseek.sqlite)

Time: ~3 seconds per chunk

2️⃣ Finding Similar Documents

When you right-click → "Find Similar Documents":

  1. Load the selected paper's embedding
  2. Compare against all indexed papers (cached in memory)
  3. Rank by semantic similarity
  4. Show top results

Time: ~70ms (with cache)

Hybrid Search

The plugin combines semantic search (AI embeddings) with Zotero's keyword search using Reciprocal Rank Fusion (RRF) for optimal results.

Search Modes

Why Hybrid Search?

Result Indicators

Section-Aware Results

The Source column shows which section of the paper matched your query:

Matched-Passage Preview

Hover any result row to see a tooltip with the exact passage that matched your query, along with its location (page & paragraph), section type, and match score. This lets you judge whether a result is relevant without opening the paper. In Keyword and Hybrid searches the query terms are highlighted inside the passage, and the preview is centered on the first match so the relevant text is always in view. (Pure semantic search has no literal terms to highlight, so the passage is shown without highlighting.)

Result Granularity (Full Document Mode)

When using Full Document indexing mode, you can toggle between two result views:

By Section - Aggregates all chunks per paper and shows the highest-scoring match. The Location column shows where that best match was found (page & paragraph), so you get one diverse result per paper without losing the exact location:

By Location - Returns every matching paragraph individually with its own score:

In By Location mode, clicking a result opens the PDF to the exact page where the match was found.

Multi-Qu