sintezi 0.2.0

Synthetic data generation that actually doesn't hurt.

sintezi

Synthetic data generation that actually doesn't hurt.

A type-safe Python library for generating synthetic data using LLMs. Built with structured outputs, automatic retry policies, and support for multiple response formats (JSON, XML).

Why sintezi? Unlike general-purpose LLM frameworks (LangChain, LlamaIndex), sintezi is focused on bulk synthetic data generation with explicit developer control:

• Bulk generation first — optimized for creating large synthetic datasets, not building chatbots or agents
• Explicit control — you define formats, parsers, and retry logic; no hidden prompt engineering or magic
• Simple by design — no memory systems, RAG pipelines, or high-level abstractions; just clean, predictable data generation

If you need agentic workflows, memory, or RAG, use LangChain. If you need to generate 10,000 structured examples with full control, use sintezi.

Features

• Type-safe — Pydantic models for requests and responses with full type hints
• Multiple formats — JSON, XML, plain text, or custom formatters
• Smart retry — Separate retry policies for network errors and validation failures
• Auto-parsing — Automatic format selection based on Pydantic models
• LLM-agnostic — Works with any OpenAI-compatible API

Installation

pip install sintezi

Requirements: Python 3.11+

Quick start

from pydantic import BaseModel
from openai import AsyncOpenAI
from sintezi.ai.context import ai_context_from_openai
from sintezi.ai.executor import StructuredAiCall, StructuredAiCallConfig, AiCallParameters
from sintezi.ai.formatter import auto_formatter_for_type
from sintezi.ai.parser import auto_parser_for_type

class ProductInfo(BaseModel):
    name: str
    category: str

class ProductDescription(BaseModel):
    description: str

# Setup
client = AsyncOpenAI(api_key="your-api-key")
ctx = ai_context_from_openai(client)

config = StructuredAiCallConfig(
    system_message="Generate product descriptions.",
    parameters=AiCallParameters(model="gpt-4o-mini"),
)

ai_call = StructuredAiCall(
    ctx=ctx,
    config=config,
    formatter=auto_formatter_for_type(ProductInfo),
    parser=auto_parser_for_type(ProductDescription),
)

# Generate
product = ProductInfo(name="Laptop", category="Electronics")
result = await ai_call(product)
print(result.description)

See the quick start guide for a complete walkthrough.

Documentation

Full documentation: https://mrapplexz.github.io/sintezi/

• Quick start guide — complete walkthrough with examples
• Executors — available AI call executors
• Formatters — JSON, XML, custom formats
• Parsers — response parsing and validation
• Retry policies — network and validation retry configuration
• API Reference — complete API documentation

License

MIT