Introduction to Paperai
Paperai is a cutting-edge tool designed to enhance the way we search and organize medical and scientific papers. It provides semantic search capabilities and workflow solutions for these types of documents, facilitating easier and more efficient information retrieval and analysis.
Key Features
Semantic Search
Paperai offers a robust semantic search engine that helps users find precise matches for medical and scientific queries. This capability is powered by sophisticated machine learning algorithms, allowing researchers and professionals to locate relevant information quickly from a vast collection of papers.
Workflow Applications
The tool also supports the development of comprehensive reporting applications which integrate machine learning. These applications help users create detailed reports by processing and analyzing the available data efficiently.
Recognition
Paperai and its developer, NeuML, have been noteworthy in the tech and scientific communities. They have been referenced in popular articles, highlighting their contribution to understanding the coronavirus via machine-learning analysis of an extensive database of papers. Some notable mentions include:
- An analysis of 47,000 papers on the coronavirus by machine-learning experts, featured in the Wall Street Journal.
- Contributions to COVID-19 research challenges and data analysis.
Installation Guide
Via Pip
Installing paperai can be effortlessly done using pip, a Python package manager. This method is suitable for users with Python 3.8 or newer. To ensure a clean setup, a Python virtual environment is recommended.
Use the following command to install via pip:
pip install paperai
Using Docker
For those who prefer using Docker, paperai can be integrated into a Docker image. This approach simplifies the deployment of dependencies.
Here is a basic command sequence to get started with Docker:
wget https://raw.githubusercontent.com/neuml/paperai/master/docker/Dockerfile
docker build -t paperai .
docker run --name paperai --rm -it paperai
For a more integrated solution, the paperetl tool can be included, providing a unified environment for indexing and querying content.
Hands-On Examples
Paperai provides several examples to demonstrate its capabilities:
Notebooks
An introductory notebook, Introducing Paperai, showcases the tool's functionalities with an option to try it on Google Colab.
Applications
One key application is the search functionality. It allows users to set search parameters, perform queries, and view results from the paperai index, making it an essential feature for many researchers.
Model Building
Creating an Index
Paperai builds its indexes based on databases generated with the paperetl tool. Users have the option to customize the index configuration through an index.yml file, adjusting settings similar to a txtai embeddings instance.
To build an index, the following command is used:
python -m paperai.index <path to input data> <optional index configuration>
Running Queries
The quickest way to execute queries is through the paperai shell, which users can launch with:
paperai <path to model directory>
Generating Reports
Paperai supports creating reports in multiple formats such as Markdown, CSV, and even annotated PDFs. The reports extract data directly from articles, presenting it in a user-friendly format. To generate a report, the following command can be used:
python -m paperai.report report.yml 50 md <path to model directory>
Technical Overview
At its core, paperai is built on a combination of a txtai embeddings index and a SQLite database. Each article is broken down into sentences and stored along with metadata, providing a rich, searchable dataset. Users can interact with this data through several interfaces, including reporting, querying from a terminal, or an interactive shell for multi-query execution.