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📝 Automatically annotate papers using LLMs
| Date | Stars |
|---|---|
| 2026-07-24 | 422 |
| 2026-07-25 | 422 |
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| 2026-08-06 | 422 |
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<p align="center">
<img src="https://raw.githubusercontent.com/neuml/annotateai/master/logo.png"/>
</p>
<p align="center">
<b>Automatically annotate papers using LLMs</b>
</p>
<p align="center">
<a href="https://github.com/neuml/annotateai/releases">
<img src="https://img.shields.io/github/release/neuml/annotateai.svg?style=flat&color=success" alt="Version"/>
</a>
<a href="https://github.com/neuml/annotateai/releases">
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<a href="https://github.com/neuml/annotateai/issues">
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<a href="https://github.com/neuml/annotateai">
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`annotateai` automatically annotates papers using Large Language Models (LLMs). While LLMs can summarize papers, search papers and build generative text about papers, this project focuses on providing human readers with context as they read.
## Architecture
A one-line call does the following:
- Reads the paper
- Finds the title and important key concepts
- Goes through each page and finds sections that best emphasis the key concepts
- Reads the section and builds a concise short topic
- Annotates the paper and highlights those sections
## Installation
The easiest way to install is via pip and PyPI
```
pip install annotateai
```
Python 3.10+ is supported. Using a Python [virtual environment](https://docs.python.org/3/library/venv.html) is recommended.
`annotateai` can also be installed directly from GitHub to access the latest, unreleased features.
```
pip install git+https://github.com/neuml/annotateai
```
## Examples
`annotateai` can annotate any PDF but it works especially well for medical and scientific papers. The following shows a series of examples using papers from [arXiv](https://arxiv.org/).
This project also works well with papers from [PubMed](https://pubmed.ncbi.nlm.nih.gov/), [bioRxiv](https://www.biorxiv.org/) and [medRxiv](https://www.medrxiv.org/)!
### Setup
The primary input parameter is the path to the LLM. This project is backed by [txtai](https://github.com/neuml/txtai) and it supports any [txtai-supported LLM](https://neuml.github.io/txtai/pipeline/text/llm/).
```python
from annotateai import Annotate
# Lightweight but powerful default model
annotate = Annotate("Qwen/Qwen3-4B-Instruct-2507")
# Gemma 4
annotate = Annotate("google/gemma-4-E4B-it")
# llama.cpp version of the above model
# Run pip install llama-cpp-python to enable
annotate = Annotate(
"unsloth/gemma-4-E2B-it-GGUF/gemma-4-E2B-it-Q4_K_M.gguf"
)
```
### Annotate paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"
This paper proposed RAG before most of us knew we needed it.
```python
annotate("https://arxiv.org/pdf/2005.11401")
```
_Source: https://arxiv.org/pdf/2005.11401_
### Annotate paper "HunyuanVideo: A Systematic Framework For Large Video Generative Models"
This paper builds the largest open-source video generation model as of Dec 2024.
```python
annotate("https://arxiv.org/pdf/2412.03603v2")
```
_SourExcerpt of 6,366 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5f7febe7f92e03b3, topic:large-language-models, topic:llm
matched fp:5f7febe7f92e03b3, topic:vector-search
matched fp:5f7febe7f92e03b3, topic:nlp
matched fp:5f7febe7f92e03b3, topic:medical