Pydantic
Pydantic is a Python library for defining typed data models and schemas, validating and parsing data against them, and serializing validated values.
Build Financial Software with Generative AI (From Scratch)
Pydantic is a Python library for declaring data models and validating and serializing data using Python type annotations.
- How it is used
- Defines validated, documented API and database-facing schemas for an ACH application.
- How deep it goes
- Builds models, field metadata, pattern constraints, exception handling, tests, and database-backed field validation.
- Examples
- Uses AchFile and AchFilesResponse models, file-ID patterns, validation-error tests, and bank-routing-number checks.
- What you can do after
- Apply it via Pydantic to define documented response models and enforce structural or database-backed validation rules.
Read how Build Financial Software with Generative AI (From Scratch) explains Pydantic →
Build Python Web Apps with Streamlit
pydantic is a Python data-validation library that uses type annotations to validate and serialize data against declared schemas.
- How it is used
- Structures and validates LLM responses so applications can parse quiz evaluations reliably.
- How deep it goes
- Defines BaseModel schemas and application usage while deferring inheritance and implementation internals.
- Examples
- Uses an AnswerEvaluation model to validate quiz correctness and return the correct answer.
- What you can do after
- Build typed response schemas and consume parsed fields in an OpenAI-powered Python application.
Read how Build Python Web Apps with Streamlit explains Pydantic →
Django in Action
Pydantic is a Python library for declaring data schemas with type annotations and validating or serializing structured data.
- How it is used
- Supports Django Ninja API endpoints that expose Django ORM objects as JSON.
- How deep it goes
- Explains typed schema declarations and ModelSchema configuration, stopping short of Pydantic internals.
- Examples
- Uses a promoters endpoint that serializes identifiers, names, dates, and a null death value.
- What you can do after
- Build it from scratch by defining a ModelSchema and returning a Django queryset from a typed API response.
AI Agents in Action, Second Edition
Pydantic is a Python library for defining typed data models and validating, parsing, and serializing data.
- How it is used
- Stabilizes agent handoffs by constraining language-model inputs and outputs to inspectable data structures.
- How deep it goes
- Demonstrates model definitions, nested task structures, strict JSON-schema failures, and a repair using explicit fields.
- Examples
- Uses research-plan models, list and dictionary task formats, TypedDict task records, and extra='forbid'.
- What you can do after
- Build structured agent outputs and diagnose strict-schema failures without relying on brittle string parsing.
Read how AI Agents in Action, Second Edition explains Pydantic →
Related concepts
- BaseModelused by · Pydantic BaseModel is the base class for declaring typed data models with structured-field validation, parsing, serialization, and schema support.
- FastAPIused by · FastAPI is a Python web framework for building APIs, with type-annotation-based request handling, validation, and automatic OpenAPI documentation.
- schemauses · A data schema is a formal specification of the structure, fields, types, relationships, and constraints of data.
- LangServeused by · LangServe is a LangChain library for deploying runnable components and workflows as network-accessible APIs.
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