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Annotateai

Annotateai is an open-source Python library and Docker web application that uses large language models to read scientific papers, find important concepts and relevant sections, and generate concise contextual annotations with highlighted passages.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Annotateai — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Annotateai when a reader needs passage-level context while working through a paper, especially for medical or scientific literature. It is also suitable when a review workflow needs keyword-focused annotation across multiple papers.

Who it is for

  • Researchers reviewing scientific literature
  • Students reading academic papers
  • Medical and scientific literature teams
  • Developers building scripted or self-hosted paper-reading workflows

Documented uses

  • Add contextual explanations to passages in scientific papers
  • Highlight sections related to automatically identified concepts
  • Target a batch of papers with user-supplied keywords
  • Create a self-hosted web interface for paper annotation
  • Use hosted, Ollama, or local language models for annotation
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented interfaces are a Python package and a Docker web application. Python 3.10 or later is supported.

  • Choose the Python library for scripted use or batch-oriented workflows; choose the Docker application for the documented web interface.
  • Model choice is flexible: the project documents txtai-supported models, LLM API services, Ollama, and llama.cpp GGUF models.
  • User-supplied keywords can focus annotation on specific concepts instead of relying only on automatically generated concepts.
  • A virtual environment is recommended for Python installation.
  • The repository documents GPU-enabled Docker startup and model-related environment variables, but does not establish universal hardware requirements.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Annotateai and configure an LLM

Prepare the Python library with a supported language model for paper annotation.

Before you start

  • Python 3.10 or later
  • A Python virtual environment is recommended
  • An LLM supported by txtai
  • The annotateai package; txtai[pipeline-llm] is required for the documented LLM API examples
  • llama-cpp-python when using a llama.cpp model

Steps

  1. Install annotateai from PyPI with pip, or install directly from the GitHub repository for unreleased features.

  2. Create an Annotate instance by passing the path or identifier for a txtai-supported LLM.

  3. For an LLM API, Ollama endpoint, or llama.cpp GGUF model, use the corresponding documented model identifier; install txtai[pipeline-llm] for the API examples and llama-cpp-python for llama.cpp.

  4. Pass a paper URL or PDF path to the annotation instance, optionally supplying keywords for targeted concepts.

Expected outcome

A configured Python annotation workflow that can process a paper and generate contextual annotations.

Guide limitations

  • The documentation specifies supported model routes but does not provide a single model-independent hardware requirement.
  • The guide covers package and model setup; output quality depends on the selected language model and paper.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
4 October 2026
Original website title
neuml/annotateai · Official homepage title · retained in its source language
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