incorporator 1.0.5

The Dynamic Class Building and Zero-Boilerplate Universal Data Gateway.

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🌌 Incorporator (v1.0.5)

The Dynamic Class Building and Zero-Boilerplate Universal Data Gateway.

Stop writing boilerplate models, manual HTTP connection loops, pagination state-trackers, and fragile data-cleaning lambda functions.

Incorporator is an elite Python framework that transforms raw JSON, CSV, and XML APIs into fully typed, relational Python Object Graphs in a single line of code. Trade away pages of unrelated code for an easy, prebuilt engine.

This is a framework that handles dynamic Pydantic metaprogramming, graph relational mapping, asynchronous connection pooling, and declarative ETL in less than 30KB.

🚀 Installation

pip install incorporator

⚡ The "Zero-Boilerplate" Philosophy

The Old Way: Define a rigid BaseModel, write an httpx loop, handle 429 retries, write a custom paginator, manually link foreign keys, catch KeyErrors, and hope the API schema doesn't change.

The Incorporator Way:

class Crypto(Incorporator): pass

async def crypto_coins():
    coins = await Crypto.incorp(
        inc_url="https://api.coingecko.com/api/v3/coins/markets?vs_currency=usd",
        inc_code="id",
        inc_name="name",
    )

    # Returns a dynamically compiled Pydantic list wrapper with an O(1) memory registry!
    bitcoin = coins.inc_dict['bitcoin']
    print(bitcoin.circulating_supply)       # 20021206.0
    print(bitcoin.current_price)            # 78321

asyncio.run(crypto_coins())

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🛠️ The Core Architectural Pillars

1. The Holy Trinity API & Dynamic Registries

• incorp(): Extracts raw data, compiles dynamic Pydantic schemas natively, and loads data into intelligent IncorporatorList wrappers.
• refresh(): Hydrates existing instances seamlessly with new data (perfect for live feeds).
• export(): Dumps stateful object graphs back into sanitized JSON, XML, or CSV files.
• The inc_dict: Every object automatically registers itself into a memory-safe WeakValueDictionary. Look up any object instantly: coins.inc_dict.get('bitcoin').

2. Declarative ETL & Null-Safe Converters

Data is messy. Incorporator's built-in conv_dict tools intercept bad data before Pydantic validation, shielding you from crashes with beautiful, readable syntax.

• inc(type): Automatically ranks fallbacks. inc(float) safely converts API garbage like "unknown" or "n/a" into 0.0.
• calc(func, *keys): Multi-column row calculations. calc(len, 'residents', default=0).
• link_to & link_to_list: Zero-boilerplate Graph Relational Mapping.

3. Native Concurrency & The State Carrier

Pass parent objects into inc_parent and declare an inc_child path. Incorporator caches the path state, drills into the nested objects, automatically spins up an asyncio.Semaphore, and batches concurrent deep-drills across a single shared httpx pool.

4. Advanced Asynchronous Pagination

Isolated OOP strategies gracefully handle pagination without infinite loops. Includes NextUrlPaginator, CursorPaginator, OffsetPaginator, PageNumberPaginator, and natively supports POST-body cursor overrides.

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📖 Real-World Showcases

Showcase 1: Graph Relational Mapping (Star Wars API)

Turn disconnected flat APIs into deeply nested, traversable object graphs using split_and_get and link_to.

from incorporator.methods.converters import split_and_get, link_to, link_to_list

class Planet(Incorporator): pass
class Film(Incorporator): pass
class Person(Incorporator): pass

async def far_far_away():
    BASE_URL = "https://swapi.dev/api"
    
    # 0. Build a reusable, highly efficient ID extractor
    get_id = split_and_get('/', -1, int)
    
    # 1. Build the foundational Graph Nodes
    planets = await Planet.incorp(
        inc_url=f"{BASE_URL}/planets/", rec_path="results",
        inc_code="id", inc_name="name", inc_page=NextUrlPaginator("next"),
        conv_dict={"url": get_id}, name_chg=[("url", "id")]
    )

    films = await Film.incorp(
        inc_url=f"{BASE_URL}/films/", rec_path="results",
        inc_code="id", inc_name="title", inc_page=NextUrlPaginator("next"),
        conv_dict={"url": get_id}, name_chg=[("url", "id")]
    )

    # 2. Fetch People and map relations natively
    people = await Person.incorp(
        inc_url=f"{BASE_URL}/people/", rec_path="results",
        inc_code="id", inc_name="name", inc_page=NextUrlPaginator("next"),
        conv_dict={
            "url": get_id,
            "homeworld": link_to(planets, extractor=get_id),
            "films": link_to_list(films, extractor=get_id)
        },
        name_chg=[("url", "id")]
    )

    # Find Boba Fett at O(1) speed with graph mapping already built natively!
    boba_fett = people.inc_dict.get(22)
    print(boba_fett.homeworld.inc_name)  # "Kamino"
    print(boba_fett.films[0].inc_name)   # "The Empire Strikes Back"

asyncio.run(far_far_away())

Showcase 2: Explicit Parent-Based Enrichment (PokéAPI)

Pass shallow objects into inc_parent and explicitly declare inc_child to trigger automatic concurrent bulk scraping. No for loops required.

class Nav(Incorporator): pass
class Pokemon(Incorporator): pass

async def inc_pokedex():
    BASE_URL = "https://pokeapi.co/api/v2"

    # 1. SHALLOW DISCOVERY: Fetch 150 navigation objects.
    # We explicitly tell the framework that the next URLs live in the "url" key.
    pokemon_nav = await Nav.incorp(
        inc_url=f"{BASE_URL}/pokemon/?limit=50&offset=0",
        rec_path="results",
        inc_name="name",
        inc_child="url",  # <--- The State Carrier saves this path!
        inc_page=NextUrlPaginator("next"),
        call_lim=3
    )

    # 2. DEEP ENRICHMENT: The framework reads the cached state from Phase 1,
    # drills into the 150 objects, extracts the URLs, and fires 150 concurrent requests!
    enriched_pokemon = await Pokemon.incorp(
        inc_parent=pokemon_nav,
        inc_code="id",
        inc_name="name",
        excl_lst=["sprites", "moves", "game_indices", "held_items"]
    )

    # Deep objects are fully built.
    for pokemon in enriched_pokemon[:3]:
        print(pokemon.inc_name, pokemon.abilities[0].ability.name)

asyncio.run(inc_pokedex())

Showcase 3: XML Parsing to Live Bulk POSTs (NHTSA API)

Seamlessly bridge deep local XML data with live JSON REST APIs using Declarative POST tokens.

from incorporator.methods.converters import join_all

class JimmyInvoice(Incorporator): pass
class NHTSARecord(Incorporator): pass

async def audit_jimmys():
    # 1. Extract nested data from a local XML file into the Pydantic engine.
    # We set `inc_child` to cache the dot-notation path to the VINs.
    invoices = await JimmyInvoice.incorp(
        inc_file="shady_jimmy.xml",
        rec_path="Dealership.AuditFile.Invoices.Invoice",
        inc_child="Vehicle.VIN" 
    )

    # 2. Hit a live JSON Bulk Endpoint using a Declarative POST payload.
    # The `join_all` token automatically extracts the VINs and joins them 
    # into a single, highly-optimized Batch POST Request!
    live_records = await NHTSARecord.incorp(
        inc_url="https://vpic.nhtsa.dot.gov/api/vehicles/DecodeVINValuesBatch/",
        inc_parent=invoices,
        method="POST",
        payload_type="form",
        form_payload={
            "format": "json", 
            "data": join_all(";") # <--- Zero-boilerplate batching!
        },
        rec_path="Results",
        inc_code="VIN"
    )

    # 3. Audit instantly via the memory-safe registry
    for inv in invoices:
        vin = inv.Vehicle.VIN
        actual_car = live_records.inc_dict.get(vin)
        if actual_car and actual_car.ModelYear != int(inv.Vehicle.Year):
            print("Fraud Detected!", inv.inc_code, inv.Vehicle.Model)

asyncio.run(audit_jimmys())

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🕵️ Non-Blocking Observability

Need production logs without starving your async event loop?

from incorporator import LoggedIncorporator

class WebAPI(LoggedIncorporator): pass

# Configures background multithreaded queue logging automatically
instance = await WebAPI.incorp(
    inc_url="https://api.example.com/data",
    enable_logging=True
)

instance.log_info("Standard trace")
instance.log_error("API Offline", exc_info=True)
instance.log_api("Web traffic trace") # Routes to isolated api.log

🤝 Contributing

1. Let's go!

Built for data engineers who want to sleep at night.