theme-extractor 0.1.1

Parse a corpus, manage it and extract themes from a corpus.

theme-extractor

Python toolkit to compare theme/topic extraction strategies on the same corpus, with:

• one unified CLI
• one unified JSON output schema
• offline-first execution options
• Elasticsearch/OpenSearch backend support

Why this project

theme-extractor helps you answer one practical question: "Which extraction strategy works best for my corpus and constraints?"

It lets you run baseline lexical methods, embedding-based methods, and LLM-assisted methods with consistent outputs for easier comparison.

Core Commands

• theme-extractor ingest
• theme-extractor extract
• theme-extractor benchmark
• theme-extractor evaluate
• theme-extractor report
• theme-extractor doctor

Quickstart

Prerequisite: start one backend first (local Docker guide: /howto/docker-local.md).

uv sync --group elasticsearch
uv run theme-extractor doctor --output data/out/doctor.json
uv run theme-extractor ingest --input data/raw --output data/out/ingest.json
uv run python docker/index_corpus.py \
  --input data/raw \
  --backend-url http://localhost:9200 \
  --index theme_extractor \
  --cleaning-options all \
  --default-stopwords \
  --manual-stopwords "de,le,la,the,and,of" \
  --reset-index
uv run theme-extractor benchmark \
  --methods baseline_tfidf,terms,significant_terms,keybert,bertopic \
  --backend elasticsearch \
  --backend-url http://localhost:9200 \
  --index theme_extractor \
  --focus both \
  --output data/out/benchmark.json
uv run theme-extractor evaluate \
  --input data/out/benchmark.json \
  --output data/out/evaluation.json
uv run theme-extractor report \
  --input data/out/benchmark.json \
  --output data/out/report_benchmark.md

ingest generates a local JSON QA payload; extraction methods query backend indexes. Use docker/index_corpus.py to index cleaned/filtered text into Elasticsearch/OpenSearch.

Run significant_text separately with --agg-field content (see /howto/benchmark.md).

Methods Available

• Baselines:
  • baseline_tfidf
  • terms
  • significant_terms
  • significant_text
• Semantic:
  • keybert
  • bertopic (embedding on/off, reduction none/svd/nmf/umap, clustering kmeans/hdbscan)
• Generative:
  • llm (offline fallback behavior supported)

What You Get

• Unified extraction schema:
  • topic-first output (topics)
  • optional document-topic links (document_topics)
  • execution notes and metadata (notes, metadata)
• Benchmark output:
  • per-method outputs
  • comparison block
• Quantitative proxies via evaluate:
  • topic coherence proxy
  • inter-topic diversity
  • run-to-run stability

Documentation Map (How-To)

Detailed operations are intentionally kept in /howto:

• /howto/ingest.md: ingestion, cleaning, stopwords, streaming mode
• /howto/extract.md: single-method extraction and interpretation
• /howto/benchmark.md: multi-method comparison workflow
• /howto/report.md: markdown reporting workflow
• /howto/release.md: PyPI/TestPyPI release workflow
• /howto/docker-local.md: local Docker stacks (Elasticsearch/OpenSearch)
• /howto/troubleshooting.md: common failures and fixes

Sphinx documentation is available under /docs (includes README, how-to pages, and API docstrings).

Build locally:

uv run sphinx-build -b html docs docs/_build/html

Configuration

Use .env.template as bootstrap:

cp .env.template .env
set -a; source .env; set +a

Important variable groups:

• backend/runtime (THEME_EXTRACTOR_BACKEND*, THEME_EXTRACTOR_PROXY_URL)
• ingestion stopwords (THEME_EXTRACTOR_DEFAULT_STOPWORDS_ENABLED, THEME_EXTRACTOR_AUTO_STOPWORDS_*)
• PDF OCR fallback (THEME_EXTRACTOR_PDF_OCR_*) for scanned PDFs
• .msg extraction (THEME_EXTRACTOR_MSG_*) for metadata and attachment policy
• local model resolution (THEME_EXTRACTOR_LOCAL_MODELS_DIR)
• BERTopic embedding cache (THEME_EXTRACTOR_BERTOPIC_EMBEDDING_CACHE_*)

Project Governance

• AGENTS.md: operating rules
• plan.md: phased roadmap
• agent.md: agent charter

Community Standards

• CONTRIBUTING.md
• CODE_OF_CONDUCT.md
• SECURITY.md
• SUPPORT.md