laydata 0.1.1

LayData Python client SDK

LayData Python Client SDK

🚀 An async Python SDK for interacting with LayData — an API-first database platform similar to Airtable, but built for developer speed and flexibility.

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📦 Installation

pip install laydata

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⚡ Quickstart

A minimal example showing the full high-level flow: connect, navigate the structure, work with records, and close the session.

import asyncio from laydata import Data

async def main(): # 1. Connect to LayData data = Data(endpoint="http://127.0.0.1:8077")

# 2. Navigate the structure (use PascalCase!)
MyCompany = await data.space("MyCompany")
SalesCRM = await MyCompany.base("SalesCRM")
Customers = await SalesCRM.table("Customers")

# 3. Work with records
NewCustomer = await Customers.add({
    "CustomerName": "Alice",
    "Email": "[e-mail]",
    "IsActive": True
})

AllCustomers = await Customers.records(take=10)
await Customers.delete_record(NewCustomer["id"])

# 4. Close the connection
await data.close()

asyncio.run(main())

💡 Tip: Always use PascalCase for Space, Base, Table, and field names. It keeps your data model clean, predictable, and less error-prone.

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🧠 Core Concepts

LayData organizes your data in a simple hierarchy:

Space → Base → Table → Record

Entity Example Description Space MyCompany Top-level workspace (e.g. a company or project) Base SalesCRM A database within a Space Table Customers A table containing records Record Customer A single row inside a table

📌 All operations are async and follow the same pattern: space → base → table → record

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🪄 Common Workflows

✏️ Create and Update Records

Create a new record

Customer = await Customers.add({ "CustomerName": "Alice", "Email": "[e-mail]" })

Find a record and edit it

PlumberJob = await Jobs.get_by("JobName", "Plumber") await PlumberJob.edit({"JobName": "Plumba"})

Get a specific field value

salary = PlumberJob.field("Salary") print(salary)

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🔍 Query and Filter Data

Simple filtering

HighValueCustomers = await Customers.where("Value", ">=", 10000).all()

Get the top record

TopCustomer = await Customers.desc("Value").first()

Find by field

SpecificCustomer = await Customers.get_by("Email", "[e-mail]")

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🔗 Chained Queries

TopElectronics = await ( Products .contains("Category", "Electronics") .gte("Price", 200) .is_not_empty("Description") .desc("Price") .take(10) .all() )

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⚙️ Configuration

Create a .env file:

LAYDATA_BASE_URL=http://127.0.0.1:8077 LAYDATA_ALLOW_ATTACHMENTS=1 # for local development only

Load it automatically:

from dotenv import load_dotenv load_dotenv()

data = Data() # uses LAYDATA_BASE_URL from .env

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🧰 Requirements • Python >= 3.10 • httpx – async HTTP client • python-dotenv (optional)

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🧪 Advanced Usage

💡 These features are powerful but not essential for getting started.

📁 Special Field Types

from laydata import SingleSelect, MultiSelect, Date, Attachment from datetime import datetime

Employee = await Employees.add({ "Department": SingleSelect("Engineering"), "Skills": MultiSelect(["Python", "React"]), "HireDate": Date(datetime(2023, 1, 15)), "ProfilePhoto": Attachment("https://example.com/photo.jpg") })

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🛠 Table Metadata Management

Tasks = await ProjectBase.table("Tasks", icon="📋", description="Task tracking") await Tasks.update_icon("✅") await Tasks.update_description("Updated description")

AllTables = await ProjectBase.tables()

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📦 Batch Operations & Error Handling

BatchData = [{"Name": f"Item {i}", "Price": 10 + i} for i in range(10)]

for item in BatchData: try: await Items.add(item) except Exception as e: print(f"Failed: {e}")

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💡 Best Practices

✅ Always use PascalCase for Space, Base, Table, and field names. ✅ Treat records as objects — record.edit() and record.field() are the preferred ways to work with them. ✅ Start with simple queries (where().all(), get_by()) and build up to more complex filters as needed. ✅ Keep risky or infrequent operations (bulk deletes, update_icon) in dedicated functions or scripts.

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📚 Next Steps • 📘 Explore Advanced Usage • 🛠 Use LayData as a backend for admin panels, CRMs, or internal tools • 🌐 Watch for new releases on GitHub

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