krita 0.1.2

Krita (कृत): Create synthetic datasets using LLMs from schemas

Krita (कृत)

Sanskrit: "made, created, formed" - the root of "Sanskrit" itself

Generate synthetic datasets using LLMs from schemas and upload to Hugging Face.

Quick Start

pip install krita

# Create a schema
krita init-schema schema.yaml

# Generate data
krita generate schema.yaml --output dataset.json

# Upload to Hugging Face
krita upload dataset.json username/my-dataset

Features

• Schema-driven generation: Define your data structure with field types, constraints, and examples
• Multiple LLM providers: OpenAI GPT, Anthropic Claude, and custom OpenAI-compatible endpoints
• Custom endpoint support: Use any OpenAI-compatible API endpoint
• Automatic validation: Ensures generated data matches your schema
• Hugging Face integration: Direct upload to Hugging Face Hub with metadata
• Multiple formats: JSON, JSONL, CSV, Parquet output
• CLI and Python API: Use from command line or integrate into your code

Installation

pip install krita

Python API

Note: Install as krita, import as synthetica

from synthetica import SyntheticDataGenerator, DataSchema, FieldType

# Define schema
schema = DataSchema(
    name="customer_reviews",
    description="Product reviews dataset",
    num_samples=1000,
    fields=[
        {"name": "product", "type": FieldType.TITLE, "required": True},
        {"name": "rating", "type": FieldType.NUMBER, "constraints": {"min": 1, "max": 5}},
        {"name": "review", "type": FieldType.REVIEW, "required": True},
        {"name": "reviewer", "type": FieldType.NAME, "required": True}
    ]
)

# Generate data
generator = SyntheticDataGenerator(llm_provider="openai")
data = generator.generate(schema)

# Upload to Hugging Face
from synthetica import HuggingFaceUploader
uploader = HuggingFaceUploader()
uploader.upload_dataset(data, "username/customer-reviews")

Custom AI Endpoints

Use any OpenAI-compatible endpoint (Ollama, vLLM, custom deployments):

from synthetica import SyntheticDataGenerator

# Use your custom endpoint directly
generator = SyntheticDataGenerator(
    llm_provider="openai",  # Use OpenAI-compatible interface
    base_url="https://your-api.com/v1",  # Your custom endpoint
    llm_model="your-model-name",
    api_key="your-api-key"  # Optional, if required
)

data = generator.generate(schema)

Using Custom Types

from synthetica import SyntheticDataGenerator, DataSchema, FieldSchema, FieldType

# Define schema with custom types
schema = DataSchema(
    name="healthcare_records",
    description="Patient healthcare records",
    num_samples=50,
    fields=[
        FieldSchema(name="patient_id", type=FieldType.UUID, required=True),
        FieldSchema(name="name", type=FieldType.NAME, required=True),
        FieldSchema(
            name="diagnosis",
            type="icd_diagnosis",  # Custom type
            description="Primary diagnosis",
            custom_type_definition="ICD-10 diagnosis with code and description",
            examples=["E11.9 - Type 2 diabetes mellitus"],
            required=True
        ),
        FieldSchema(
            name="medication",
            type=FieldType.CUSTOM,  # Using CUSTOM enum
            description="Current medication",
            custom_type_definition="Medication name, dosage, and frequency",
            examples=["Metformin 500mg twice daily"],
            required=False
        )
    ]
)

# Generate data with custom types
generator = SyntheticDataGenerator(llm_provider="openai")
data = generator.generate(schema)

Schema Format

name: "user_profiles"
description: "User profile data"
num_samples: 500
context: "Generate diverse, realistic user profiles"
fields:
  - name: "id"
    type: "uuid"
    required: true
  - name: "name"
    type: "name"
    required: true
    examples: ["John Doe", "Jane Smith"]
  - name: "email"
    type: "email"
    required: true
  - name: "age"
    type: "number"
    constraints:
      min: 18
      max: 80
  - name: "bio"
    type: "description"
    required: false

Supported Field Types

Built-in Types

• text, name, email, phone, address
• date, number, boolean, uuid
• category, url, json
• title, description, review

Custom Types

Define your own field types for specialized domains:

fields:
  - name: "medical_diagnosis"
    type: "icd_diagnosis"  # Custom type name
    description: "Medical diagnosis"
    custom_type_definition: "ICD-10 diagnosis code with description (e.g., 'E11.9 - Type 2 diabetes')"
    examples:
      - "I10 - Essential hypertension"
      - "E78.5 - Hyperlipidemia"

  - name: "certification"
    type: "custom"  # Use 'custom' enum value
    description: "Professional certification"
    custom_type_definition: "Professional certification with issuing body and expiration date"
    examples:
      - "AWS Solutions Architect - Valid until 2025-12-31"

CLI Commands

# Initialize schema
krita init-schema schema.yaml

# Generate data
krita generate schema.yaml --provider openai --output data.json

# Upload to Hugging Face
krita upload data.json username/dataset-name --description "My dataset"

# List providers
krita list-providers

Configuration

Set environment variables:

export OPENAI_API_KEY="your-key"
export ANTHROPIC_API_KEY="your-key"
export HF_TOKEN="your-token"

Custom Endpoint Examples

Ollama (local):

generator = SyntheticDataGenerator(
    llm_provider="openai",
    base_url="http://localhost:11434/v1",
    llm_model="llama3.1"
)

vLLM deployment:

generator = SyntheticDataGenerator(
    llm_provider="openai",
    base_url="https://your-vllm-server.com/v1",
    llm_model="meta-llama/Llama-3.1-8B-Instruct",
    api_key="your-api-key"
)

Internal enterprise endpoint:

generator = SyntheticDataGenerator(
    llm_provider="openai",
    base_url="https://internal-ai.company.com/v1",
    llm_model="company-model-v1",
    api_key="your-api-key"
)

License

MIT