search-names

search-names prepares lists of personal names and searches them in CSV text corpora. Its four-stage workflow cleans names, adds aliases and titles, creates search patterns, and runs deterministic exact or fuzzy matching.

Name matching is approximate record linkage, not identity verification. Common names, aliases, OCR errors, and incomplete source data can create false matches or missed matches. Review matches in context; do not use them alone for consequential decisions or to infer protected or sensitive traits.

Installation

pip install search-names

The installation includes the name parsers, spaCy interface, Sentence Transformers interface, and CLI.

Search a corpus

The search input is a CSV or .csv.gz file. The names are (identifier, name) pairs. Output contains one row per input row in the same order, fixed match slots, and a count column.

from search_names import search_names

stats = search_names(
    "articles.csv",
    [("person-1", "Jane Doe"), ("person-2", "John Smith")],
    "matches.csv",
    text_column="article_text",
    input_columns=("article_id", "article_text"),
    max_results=10,
    processes=4,
)

print(stats)

Fuzzy rules are (minimum name length, maximum edit distance) pairs. Later rules take precedence for longer names:

stats = search_names(
    "ocr_articles.csv.gz",
    [("person-1", "Jane Doe"), ("person-2", "John Smith")],
    "matches.csv",
    fuzzy_rules=[(8, 1), (15, 2)],
    processes=4,
)

To load a pattern table produced by the preprocessing stage:

from search_names.pipeline.step4_search import load_names_file

names = load_names_file(
    "preprocessed_names.csv",
    id_column="uniqid",
    name_column="search_name",
)

CSV and compressed CSV are supported import/export boundaries. The package does not ship schema-less CSV runtime assets or learned model weights in its wheel.

Prepare names

from search_names import augment_names, clean_names, preprocess

clean_names(
    input_file="raw_names.csv",
    output_file="clean_names.csv",
    name_column="Name",
    keep_duplicates=False,
)

augment_names(
    input_file="clean_names.csv",
    prefix_column="seat",
    name_column="FirstName",
    output_file="augmented_names.csv",
    prefix_file="prefixes.csv",
    nickname_file="nick_names.txt",
)

preprocess(
    input_file="augmented_names.csv",
    patterns=["FirstName LastName", "NickName LastName", "Prefix LastName"],
    output_file="preprocessed_names.csv",
    edit_length_thresholds=[10, 15],
    drop_patterns=["ambiguous pattern"],
)

The preprocessing stage’s edit-length thresholds are retained in its output. The search API uses the explicit fuzzy_rules pairs shown above.

Parse names

NameParser combines nameparser with parsernaam. The latter returns a model label (first, last, first_last, or last_first) and probability; search-names converts that record to explicit components. Probabilities below ml_threshold fall back to deterministic nameparser parsing. Deterministic parses have model_score=None; the package does not invent a confidence value.

from search_names.enhanced_name_parser import NameParser

parser = NameParser(parser_type="parsernaam", ml_threshold=0.8)
parsed = parser.parse("Nakamura Hiro")

print(parsed.first_name, parsed.last_name, parsed.model_score)

DataFrame parsing returns a copy, preserves the input index, and replaces any colliding parsed-output columns deliberately:

result = parser.parse_dataframe(frame, name_column="full_name")

NLP components

The NLP module exposes spaCy NER, semantic similarity, and entity linking:

from search_names.nlp_engine import NLPEngine

engine = NLPEngine(
    knowledge_base={"Jane Doe": {"aliases": ["J. Doe"]}},
    enable_ner=True,
    enable_similarity=True,
    enable_linking=True,
)
result = engine.process_text("Jane Doe spoke today.", link_entities=True)

Entity-linking results identify the lookup method (exact, normalized, or semantic). Only semantic matches have a numeric score, which is cosine similarity rather than a calibrated probability.

The default Sentence Transformer is downloaded from an immutable 40-character Hugging Face revision. The Hugging Face client automatically honors its normal authentication settings, including HF_TOKEN; public downloads do not require a token. The spaCy English model must be installed separately for NER:

python -m spacy download en_core_web_sm

Command line

search-names --help
search-names clean raw_names.csv --output clean_names.csv
search-names preprocess augmented_names.csv --output preprocessed_names.csv
search-names search articles.csv --names preprocessed_names.csv \
  --output matches.csv --text-column text --processes 4
search-names search ocr_articles.csv --names preprocessed_names.csv \
  --fuzzy-rule 8:1 --fuzzy-rule 15:2

Development

uv sync --all-groups
uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv build

The API documentation is generated from this README and the package docstrings, so usage and reference material do not drift into separate hand-maintained copies.

License

MIT. See the repository's LICENSE.