seerai is an intelligent research assistant plugin for Zotero 7 that integrates AI-powered chat, advanced search, and data extraction capabilities directly into your research workflow. Chat with your papers, extract structured data, and accelerate your literature review with a local-first, privacy-focused design.

• Contextual Conversations: Chat with AI about your selected papers with full context awareness.
• Smart Context Priority: Automatically prioritizes content sources:
  1. Zotero Notes (OCR note, and other notes for highest priority)
  2. Indexed PDF Text (Fast, efficient, howver consumes alot of tokens, and would cause limit issues)
  3. OCR (Fallback for scanned documents with no indexed text)
• Multi-paper Support: Add multiple papers to a single conversation for comparative analysis.
• Streaming Responses: Real-time, token-by-token response rendering.
• Markdown & Math: Responses are formatted with syntax highlighting and LaTeX math support.
• Vision Support: Paste images directly into chat for multimodal analysis.
• Enhanced Keybindings:
  • Enter: Insert new line
  • Shift+Enter: Send message

• Web Search: Integrated Firecrawl support for finding full-text content.
• Semantic Scholar Agent: Advanced paper search with filtering (Year, Venue, Citation Count).
• Smart Import:
  • PDF Discovery: Automatically finds and attaches PDFs during import.
  • Source Link: Fallback to source links if PDFs are unavailable.
  • Status Indicators: Clear feedback on import status (⬇️ Importing, ✅ Imported, ⚠️ Failed).

• Structured Extraction: Extract specific data points from multiple papers into a comparative table.
• AI-Powered Columns: Define custom columns with AI prompts (e.g., "Methodology", "Sample Size").
• Inline Editing: innovative inline editor for column titles and prompts.
• One-Click Generation: Generate data for individual cells or entire columns instantly.
• Side Strip Actions: Unified controls for adding, removing columns, generating triggers, and settings.

• Flexible OCR Options:
  • Mistral OCR: High-quality cloud OCR (Recommended).
  • DataLab.to: Reliable cloud-based extraction.
  • Local Marker: Run your own local OCR server for free, private processing.
• Auto-Processing: Automatically processes unindexed PDFs when needed.

• Model Presets: Pre-configured settings for popular providers:
  • OpenAI (GPT-5, o3)
  • Anthropic (Claude Sonnet 4.5)
  • Google (Gemini 3 Pro)
  • DeepSeek, Mistral, Groq, OpenRouter
  • Local Models (Openai compatible endpoint, Ollama, LM Studio)
    • 12-16g Vram - Qwen3-4B-Thinking-2507
    • 24-32g Vram - gpt-oss-20b
    • 48-64g Vram - QwQ-32B
    • 96-128g Vram - Qwen3-Next-80B-A3B-Instruct
• Per-Conversation Models: Switch models dynamically based on the task complexity.

1. Download the latest release (.xpi file) from Releases.
2. In Zotero, go to Tools → Add-ons.
3. Click the gear icon ⚙️ and select Install Add-on From File....
4. Select the downloaded .xpi file.
5. Restart Zotero.

# Clone the repository
git clone https://github.com/dralkh/seerai.git
cd seerai

# Install dependencies
npm install

# Build the plugin
npm run build

# The .xpi file will be generated in the root directory

Go to Zotero → Settings → seerai to configure your AI providers and services.

Use the Add Configuration button to set up your AI models.

• Presets: Select from built-in presets (OpenAI, Anthropic, Ollama, etc.) for quick setup.
• Custom: Manually configure API URL, Key, and Model ID for any OpenAI-compatible provider.
• Default: Set a preferred model as your default for new conversations.

Choose your preferred text extraction engine:

• Mistral OCR: Requires Mistral API Key. Best for accuracy.
• Cloud (DataLab.to): Requires DataLab API Key.
• Local Marker Server: Requires running a local Python server.
  • URL: http://localhost:8001 (Default)
  • See Marker Project for setup.

• Semantic Scholar: Add your API Key for higher rate limits and faster searches.
• Firecrawl: Add API Key to enable deep web search capabilities - local instance with (GitHub).

1. Select a paper (or multiple) in your library.
2. Open the SeerAI sidebar tab.
3. (Optional) Customize context inclusions via the settings icon (Abstracts, Notes).
4. Type your question or use templates from the Prompt Library (Book icon).

1. Open the Tables tab in the main view.
2. Click + on the side strip to add a new column.
3. Define the column header and AI prompt (e.g., "What is the sample size?").
4. Drag and drop papers into the table.
5. Click Generate on cells to extract data.

• Access via the Book Icon 📖 in chat.
• Use built-in templates (Summarize, Critique, Compare).
• Create custom templates with placeholders:
  • !: Saved Prompts
  • /: Papers
  • ^: Folders
  • ~: Tags
  • @: Authors
  • #: Topics

Propose several advanced features to enhance seerai's capabilities. These are currently in the just idea board.

Enhanced search functionality to help users find relevant literature more effectively.

• Autocomplete: Intelligent suggestions for tags, creators, and collections as you type.
• Complex Queries: Support for boolean logic (AND/OR) and nested search conditions (e.g., "Title contains X AND Year > 2020").
• Field-Specific Search: Dedicated filters for titles, authors, years, and tags.

• Voice, Transcription, Embedding Integration: Support for OpenAI-compatible embedding, voice, transcription models (e.g., text-embedding-3-small, local Ollama embeddings).
• Contextual Retrieval: Find papers based on conceptual similarity rather than just exact text matches.
• In-Memory Vector Store — Fast, local indexing of session-relevant papers for semantic analysis.
• RAG - used when 80% limit reached to context size

• Verifier Button — One-click verification to check all extracted data against source text.
• Confidence Scores — AI-generated confidence ratings for each extracted data point.
• Source Highlighting — Click a cell to see the exact passage in the paper where the data came from.

• URL Discovery — Usage Firecrawl API for pdf discovery

Citations referencing within tables and chat on generation - MCP Connectors UI revamp

• Node.js 18+
• Zotero 7

The codebase follows a modular architecture:

seerai/
├── addon/                 # Zotero integration files (XUL/XHTML)
├── src/
│   ├── modules/           # Core feature modules
│   │   ├── chat/          # Chat engine & state
│   │   ├── assistant.ts   # Main assistant logic
│   │   ├── firecrawl.ts   # Firecrawl integration
│   │   ├── ocr.ts         # OCR implementation
│   │   ├── openai.ts      # LLM client implementation
│   │   ├── semanticScholar.ts # Semantic Scholar integration
│   │   └── preferenceScript.ts # Preferences logic
│   ├── utils/             # Utility functions
│   └── hooks.ts           # Zotero event listeners
└── package.json

npm start       # Start dev server with hot reload
npm run build   # Build for production
npm run lint:fix # Fix code style issues

Contributions are welcome!

1. Fork the repo.
2. Create a feature branch (git checkout -b feature/MyFeature).
3. Commit changes (git commit -m 'Add MyFeature').
4. Push to branch (git push origin feature/MyFeature).
5. Open a Pull Request.

MIT License - see LICENSE for details.

• Zotero
• Mistral AI
• Semantic Scholar
• Firecrawl
• Marker