opentaxonomy 0.1.7

LLM-powered semantic taxonomy generator for raw categorical data

OpenTaxonomy

LLM-powered semantic taxonomy generator for raw categorical data.

What it does

OpenTaxonomy takes a column of messy raw values (bank transactions, product names, survey responses — anything) and generates a structured semantic taxonomy from it using Claude as the reasoning engine.

Output for each value:

• {column}_normalized — cleaned entity name (e.g. "REWE SAGT DANKE 46654184/..." → "REWE")
• canonical_id — taxonomy path (e.g. ft.expenditure.variable.groceries)

The taxonomy itself is a set of YAML files — a seed.yaml capturing the domain structure, one node.yaml per tree node (each an ontological contract with inclusion/exclusion criteria and a decision record), and a placement_map.yaml that is the source of truth for all mappings.

Architecture

The core is the Prima Seed — a universal questioning protocol that generates domain-specific taxonomic trees from any categorical data:

• Q0 Identify the Form: what unifies all values?
• Q0b Establish context: what operational realm governs classification?
• Q1 Primary differentiation: the most essential splitting criterion
• Q2 Recursive differentiation: applied per branch at each level
• Q3 Dialectical check: values that resist placement expose flawed criteria

Installation

pip install opentaxonomy

Requires Python 3.11+ and an Anthropic API key.

Usage

export ANTHROPIC_API_KEY=sk-ant-...

# Generate a new taxonomy from raw data
opentaxonomy create -i transactions.csv -c description -o ./taxonomy

# Place new/unseen values into an existing taxonomy
opentaxonomy run -i new_data.csv -c description -s ./taxonomy

Supported input formats

Format	Example
CSV / TSV	data.csv, data.tsv
JSON	data.json
Excel	data.xlsx
Parquet	data.parquet
Database	postgresql://user:pass@host/db + --db-table

Options

opentaxonomy create
  -i, --input        Input file or database connection string  [required]
  -c, --column       Column containing raw values to classify  [required]
  -o, --output-dir   Where to write taxonomy files  [default: ./taxonomy]
  --domain-hint      Optional hint to guide the LLM (e.g. "German grocery products")
  --model            Claude model  [default: claude-sonnet-4-6]
  --api-key          Anthropic API key (or set ANTHROPIC_API_KEY)

opentaxonomy run
  -i, --input        Input file or database connection string  [required]
  -c, --column       Column containing raw values to classify  [required]
  -s, --seed-dir     Directory with seed.yaml and placement_map.yaml  [default: ./taxonomy]
  --model            Claude model  [default: claude-sonnet-4-6]
  --api-key          Anthropic API key (or set ANTHROPIC_API_KEY)

Output structure

taxonomy/
├── seed.yaml                  # Domain seed: context, levels, edge cases
├── placement_map.yaml         # Raw values → canonical IDs (source of truth)
└── nodes/
    ├── root.yaml              # Root node
    ├── expenditure.yaml       # Internal node with decision record
    ├── groceries.yaml         # Leaf node with inclusion/exclusion criteria
    └── ...

Each node file is an ontological contract:

node: Groceries
canonical_id: ft.expenditure.variable.groceries
question: Is this a purchase of food or household staples from a supermarket or grocery store?
criteria:
  includes:
    - Supermarket purchases (REWE, LIDL, EDEKA, etc.)
    - Organic/bio market purchases
  excludes:
    - Restaurant meals
    - Drugstore purchases unless food items
edge_cases:
  - term: REWE TO GO
    resolution: Included — still a grocery/convenience purchase
    decided: true
parent: Variable Necessities
children: []
version: 1.0.0

License

MIT