JabRef
v6
v6
  • JabRef Bibliography Management
  • Installation
  • Getting started
  • Collect
    • Add entry manually
    • Add entry using an ID
    • Add entry using reference text
    • Searching externally using Online Services
    • Add entry using PDFs
    • Add PDFs to an entry
    • Browser Extension
    • Import
      • Custom import filters
      • Import inspection window
  • Organize
    • Edit an entry
    • Groups
    • Keywords
    • Mark and grade
    • Comment on an entry
    • Searching within the library
    • Complete information using online databases
    • Manage associated files
    • Manage field names and their content
    • Best practices
    • Check consistency
    • Cleanup entries
    • Check integrity
    • Find duplicates
    • Merge entries
    • Save actions
  • Cite
    • BibTeX and biblatex
    • Pushing to external editor application
    • Export to Microsoft Word
    • OpenOffice/LibreOffice integration
  • Share
    • Sharing a Bib(la)TeX Library
    • Shared SQL Database
      • Migration of pre-3.6 SQL databases into a shared SQL database
    • Export
      • Custom export filters
    • Send as email
  • AI functionality
    • AI providers and API keys
    • AI preferences
    • AI troubleshooting
    • Running a local LLM model
  • Configuration
    • Customize the citation key generator
    • Customize entry types
    • Customize general fields
    • Customize key bindings
    • Library properties
    • Entry preview setup
    • Manage external file types
    • Manage protected terms
    • The string editor
  • Advanced information
    • Main Window
    • Entry Editor
      • Links to other entries
      • The Bibtex / Biblatex source tab
      • The 'owner' field
      • Time stamp field
      • LaTeX Citations Tab
    • About BibTeX and its fields
    • Strings
    • Field content selector
    • URL and DOI links in JabRef
    • Command line use and options
    • Automatic Backup (.sav and .bak) and Autosave
    • XMP metadata support in JabRef
    • Remote operation
    • Custom themes
    • Journal abbreviations
    • New subdatabase based on AUX file
    • How to expand first names of an entry
    • Debugging your library file
    • Resources
    • License
    • Knowledge
      • MS Office Bibliography XML format
      • Comparison of the Medline (txt), Medline (xml), and RIS format
      • EndNote Export Filter
  • Frequently Asked Questions
    • Linux
    • Mac OS X
    • Windows
  • JabKit
  • Discussion Forum
  • Contribute to JabRef
    • How to Improve the Help Page
    • How to translate the JabRef User Interface
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  • AI summary tab
  • AI chat tab
  • How does the AI functionality work?
  • More information

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AI functionality

PreviousSend as emailNextAI providers and API keys

Last updated 3 months ago

Was this helpful?

Since version 6, JabRef has AI functionality built in.

  • AI can generate a summary of a research paper

  • You can also chat with papers using a "smart" AI assistant

AI summary tab

When you activate this tab, AI will generate a quick overview of the paper for you.

The AI will mention the main objectives of the research, methods used, key findings, and conclusions.

AI chat tab

Here, you can ask questions, which are answered by the LLM.

In this window, you can see the following elements:

  • Chat history with your messages

  • Prompt for sending messages

  • A button for clearing the chat history (just in case)

How does the AI functionality work?

JabRef uses external AI providers to do the actual work. You can choose between various providers. They all run "Large Language Models" (LLMs) to process the requests and need chunks of text to work. For this, JabRef parses and indexes linked PDF files of entries: The file is split into parts of fixed-length (so-called chunks) and for each of them, an embedding is generated. An embedding itself is a representation of a part of text and in turn a vector that represents the meaning of the text. Each vector has a crucial property: texts with similar meaning have vectors that are close to (so-called vector similarity). As a result, whenever you ask AI a question, JabRef tries to find relevant pieces of text from the indexed files using vector similarity and provides those to the LLM system to be processed.

More information

https://github.com/JabRef/user-documentation/blob/main/en/ai/how-to-enable-ai-features.md
AI providers and API keys
AI troubleshooting
AI preferences
Running a local LLM model
AI summary tab screenshot
AI chat tab screenshot