zotero topic modeling

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pyproject.toml

[project]
name = ""
version = ""
description = ""
authors = [
    {name = "Guilherme Pires", email = "gpires@altius.org"},
]
dependencies = [
    "pyzotero>=1.5.18",
    "bertopic>=0.16.0",
    "safetensors>=0.4.1",
    "python-dotenv>=1.0.0",
]
requires-python = ">=3.10"
readme = "README.md"
license = {text = "MIT"}

zotero-topics.py

import os
import logging
import json
import argparse

from pyzotero import zotero

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

from dotenv import load_dotenv
from tqdm import tqdm


logger = logging.getLogger(__name__)


def main(args):
    library_id = args.library_id
    api_key = args.api_key
    bertopic_model = args.bertopic_model

    library_type = "user"
    zot = zotero.Zotero(library_id, library_type, api_key)

    items = zot.everything(zot.items())
    if args.verbose:
        items = tqdm(items)
        logging.basicConfig(encoding='utf-8', level=logging.DEBUG)

    logger.info("Extracting abstracts and titles from Zotero library")
    abstracts = [item['data'].get('abstractNote', '') for item in items]
    titles = [item['data'].get('title', '') for item in items]

    to_model = [abstract + title for abstract, title in zip(abstracts, titles)]

    if args.bertopic_model:
        logger.info("Using BERTopic model")

        from bertopic import BERTopic
        topic_model = BERTopic.load(bertopic_model)

        logger.info("Transforming abstracts into topics")
        topics, _ = topic_model.transform(to_model)

        if args.tag_items:
            # Tag items with topics
            for idx, item in enumerate(items):
                if 'abstractNote' in item['data']:
                    topic = topics[idx]
                    if topic != -1:
                        tags = topic_model.get_topic(topic)[0]
                        zot.add_tags(item, [tag[0] for tag in tags])

        table = topic_model.get_topic_info()
        tags = table.set_index("Topic").loc[topics].Representation.values

    else:
        logger.info("Using LDA model")

        vectorizer = CountVectorizer(max_df=0.85, min_df=2, stop_words='english', lowercase=True)
        X = vectorizer.fit_transform(to_model)
        lda = LatentDirichletAllocation(n_components=100, random_state=0)
        topics = lda.fit_transform(X).argmax(axis=1)
        topic_words = np.array(vectorizer.get_feature_names_out())
        top_feature_idx = lda.components_.argsort(axis=1)[topics, -20:]
        tags = topic_words[top_feature_idx].tolist()

        if args.tag_items:
            for i, item in enumerate(items):
                if 'abstractNote' in item['data']:
                    topic = lda.transform(X[i]).argmax()
                    top_feature_idx = lda.components_[topic].argsort()[-20:]
                    tags = topic_words[top_feature_idx].tolist()
                    zot.add_tags(item, tags)

    topics = dict(zip(titles, tags))
    print(json.dumps(topics, indent=4))


if __name__ == "__main__":
    load_dotenv()

    parser = argparse.ArgumentParser()
    parser.add_argument("--library-id", help="Zotero library ID", default=os.getenv("ZOTERO_LIBRARY_ID"))
    parser.add_argument("--api-key", help="Zotero API key", default=os.getenv("ZOTERO_API_KEY"))
    parser.add_argument("--bertopic-model", help="BERTopic model", default=None)
    #parser.add_argument("--bertopic-model", help="BERTopic model", default="MaartenGr/BERTopic_Wikipedia")
    parser.add_argument("--tag-items", help="Tag items with topics", action="store_true")
    parser.add_argument("--verbose", help="Verbose logging", action="store_true")

    args = parser.parse_args()
    main(args)