Toolkit to normalize text to UMLS / ontologies
Project description
ClickHouse backend
The DuckDB builder remains the source of truth. Build a DuckDB file with
build_merged_duckdb, then upload its canonical tables into ClickHouse:
uv run python scripts/upload_clickhouse.py data/dbs_final/SmallMolecule.duckdb --database normalization
The upload shows a progress bar for each copied table; pass --no-progress to
silence it.
Connection settings are read from .env with python-dotenv and use the
official clickhouse-connect client. Set CH_HTTP, for example
http://host:8123/normalization; CH_USER and CH_PASSWORD may be supplied
separately and override URL credentials.
Use the ClickHouse backend from Python:
from norm_toolkit import ClickHouseNormalizer
normalizer = ClickHouseNormalizer(database="normalization")
result = normalizer.normalize(["aspirin"], top_k=5)
You can also pass a DSN in code:
normalizer = ClickHouseNormalizer(
dsn="http://host:8123/normalization",
database="normalization",
)
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