typeddict

Generate Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON/YAML/CSV.

Simple and powerful factories for mock data generation

Generate Python type definitions from a JSON sample (both Pydantic BaseModel and TypedDict are supported)

Mypy plugin for static type checking of TypedDict keys inspired by TypeScript's keyof type operator.

Dict to TypedDict generation and conversion utilities for Python 📖✨

Data specifications via type hints

Data contracts, built for modern Python

A Python library for merging dataclasses, TypedDicts, and Pydantic models seamlessly

Convert TypeScript Interfaces to Python TypedDict

Use `TypedDict` replace pydantic definitions.

Design robust structured outputs for LLM applications. Learn how to enforce schema-driven responses using TypedDict, Pydantic Output Parsers, and LangChain—especially for open-source LLMs that lack native structured output support.

OpenAPI SDK generator for humans. Type-safe, async-first, developer-friendly.

The DazzleLib bedrock: shared Protocols, TypedDict payload schemas, and exception root for the dazzle-* library stack. Stdlib-only by charter.

Examples of extracting and structuring LLM responses into a defined schema using TypedDict, Pydantic models, and JSON parsing in Python. Demonstrates schema enforcement for cleaner, more reliable AI output.

AI-powered resume analyzer using LangChain & Gemini to extract structured candidate data from bulk resumes and export it as CSV for efficient screening.

LangChain structured output 🔥🔗 forces language models to return responses matching your Pydantic schema instead of free-form text, automatically handling prompting and validation to ensure predictable, type-safe results.

Create TypedDict definitions for protobuf json

This repository demonstrates how to use LangChain’s with_structured_output to generate consistent, machine-readable responses. It includes examples with TypedDict, Pydantic, and JSON Schema, and how they help enforce reliable data formats in LLM applications.

Turn a GraphQL schema and your operations into a typed Python client with no imposed transport, no validation overhead, and no runtime dependencies

🧠 A practical collection of small projects and examples to explore LangChain, prompt templates, LLM chatbots, embeddings, JSON schema, and more — designed for hands-on learning and experimentation.