Metadata-Version: 2.1
Name: paperai
Version: 1.2.1
Summary: AI-powered literature discovery and review engine for medical/scientific papers
Home-page: https://github.com/neuml/paperai
Author: NeuML
License: Apache 2.0: http://www.apache.org/licenses/LICENSE-2.0
Project-URL: Documentation, https://github.com/neuml/paperai
Project-URL: Issue Tracker, https://github.com/neuml/paperai/issues
Project-URL: Source Code, https://github.com/neuml/paperai
Description: # paperai: AI-powered literature discovery and review engine for medical/scientific papers
        
        paperai builds an AI-powered index over sets of medical and scientific papers.
        
        ## Installation
        The easiest way to install is via pip and PyPI
        
            pip install paperai
        
        You can also install paperai directly from GitHub. Using a Python Virtual Environment is recommended.
        
            pip install git+https://github.com/neuml/paperai
        
        Python 3.6+ is supported
        
        If running on Windows, [check out this link for possible install issues](https://github.com/neuml/txtai#notes-for-windows).
        
        ## Building a model
        paperai indexes models previously built with [paperetl](https://github.com/neuml/paperetl). paperai currently supports querying SQLite databases.
        
        To build an index for a SQLite articles database:
        
            # Can optionally use pre-trained vectors
            # https://www.kaggle.com/davidmezzetti/cord19-fasttext-vectors#cord19-300d.magnitude
            # Default location: ~/.cord19/vectors/cord19-300d.magnitude
            python -m paperai.vectors
        
            # Build embeddings index
            python -m paperai.index
        
        The model will be stored in ~/.cord19
        
        ## Building a report file
        A report file is simply a markdown file created from a list of queries. An example report call:
        
            python -m paperai.report tasks/risk-factors.yml
        
        Once complete a file named tasks/risk-factors.md will be created.
        
        ## Running queries
        The fastest way to run queries is to start a paperai shell
        
            paperai
        
        A prompt will come up. Queries can be typed directly into the console.
        
        ## Tech Overview
        The tech stack is built on Python and creates a sentence embeddings index with FastText + BM25. Background on this method can be found in this [Medium article](https://towardsdatascience.com/building-a-sentence-embedding-index-with-fasttext-and-bm25-f07e7148d240) and an existing repository using this method [codequestion](https://github.com/neuml/codequestion).
        
        The model is a combination of the sentence embeddings index and a SQLite database with the articles. Each article is parsed into sentences and stored in SQLite along with the article metadata. FastText vectors are built over the full corpus. The sentence embeddings index only uses tagged articles, which helps produce most relevant results.
        
        Multiple entry points exist to interact with the model.
        
        - paperai.report - Builds a markdown report for a series of queries. For each query, the best articles are shown, top matches from those articles and a highlights section which shows the most relevant sections from the embeddings search for the query.
        - paperai.query - Runs a single query from the terminal
        - paperai.shell - Allows running multiple queries from the terminal
        
Keywords: search embedding machine-learning nlp covid-19 medical scientific papers
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown
