from typing import Annotated
from uuid import UUID, uuid4
from pydantic import Field
from coodie import Document, PrimaryKey, ClusteringKey, Indexed

class Product(Document):
    id: Annotated[UUID, PrimaryKey()] = Field(default_factory=uuid4)
    category: Annotated[str, ClusteringKey()] = "general"
    name: str
    brand: Annotated[str, Indexed()] = "Unknown"
    price: float = 0.0

    class Settings:
        name = "products"      # table name (defaults to snake_case class name)
        keyspace = "my_ks"

import asyncio
from coodie import init_coodie
# Product defined above — same field definitions, using coodie.aio.Document

async def main():
    await init_coodie(hosts=["127.0.0.1"], keyspace="my_ks")
    await Product.sync_table()

    p = Product(name="Gadget", brand="Acme", price=9.99)
    await p.save()

    results = await Product.find(brand="Acme").limit(10).all()
    for product in results:
        print(product.name, product.price)

    gadget = await Product.get(id=p.id)
    await gadget.delete()

asyncio.run(main())

from coodie.sync import Document, init_coodie

class Product(Document):
    ...  # same field definitions

init_coodie(hosts=["127.0.0.1"], keyspace="my_ks")
Product.sync_table()

p = Product(name="Widget", price=4.99)
p.save()

results = Product.find(brand="Acme").allow_filtering().all()
one = Product.find_one(name="Widget")

# Filter, sort, and limit
products = (
    await Product.find()
    .filter(brand="Acme")
    .order_by("price")
    .limit(20)
    .all()
)

# Count
n = await Product.find(brand="Acme").allow_filtering().count()

# Async iteration
async for p in Product.find(brand="Acme"):
    print(p)

# Delete matching rows
await Product.find(brand="Discontinued").allow_filtering().delete()

coodie supports ScyllaDB's vector<float, N> column type and ANN similarity search via VectorIndex.

class ProductEmbedding(Document):
    product_id: Annotated[UUID, PrimaryKey()] = Field(default_factory=uuid4)
    embedding: Annotated[
        list[float],
        Vector(dimensions=384),
        VectorIndex(similarity_function="COSINE"),
    ]
    ...

results = await ProductEmbedding.find().order_by_ann("embedding", query_vector).limit(10).all()

📖 Full guide: Vector Search (ANN)