infermodel 0.1.0

Rust-backed schema inference from Sequence[Mapping] with Pydantic model emission

infermodel

Rust-backed schema inference from Sequence[Mapping] data (e.g. list of dicts) with Pydantic model emission.

Infer a schema from a sequence of mappings (e.g. list or tuple of dicts), then convert that inferred schema into a Pydantic model on the Python side—without hardcoding schema logic in application code.

Features

• Rust core: Performance-critical traversal, merge logic, and required/nullable tracking
• Python ergonomics: Pydantic v2 model creation via infer_model(...)
• Conservative defaults: Strings stay strings; int+float promotes to float; incompatible mixes become Any
• Required vs nullable: Tracks presence (missing key → optional) and explicit None (nullable) separately

Installation

From the project root (with a virtualenv activated):

pip install -e .
# or: maturin develop

Requirements: Python 3.10+, Pydantic v2. Build requires Rust (for the extension).

Quick start

from infermodel import infer_schema, infer_model

data = [
    {"id": 1, "name": "Alice"},
    {"id": 2, "name": None},
    {"id": 3},  # missing "name" -> optional
]

# Get introspectable schema dict
schema = infer_schema(data)
# {"type": "model", "fields": {"id": {"type": "int", "required": True, "nullable": False}, ...}}

# Get a Pydantic model class
Model = infer_model(data, model_name="Record")
instance = Model(id=1, name="Alice")

API

• infer_schema(data, config=None)
  Returns a nested dict with type, fields, and per-field type, required, nullable.

• infer_model(data, model_name="InferredModel", config=None)
  Infers the schema and returns a dynamic Pydantic model class.

• InferConfig
  Dataclass for policy options (e.g. incompatible_scalar_policy, string_date_policy). V1 uses built-in policies only.

• model_from_schema(schema, model_name="InferredModel")
  Build a Pydantic model from an existing schema dict (e.g. from infer_schema).

Required vs nullable

• Required: field present in every row.
• Optional: field missing in at least one row.
• Nullable: at least one row had explicit None for that field.

These are independent: a field can be required and nullable, or optional and not nullable.

Development

# Create venv and install in editable mode with Rust extension
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
maturin develop

# Run tests
pytest

License

MIT