Pydantic is a Python library that uses standard type hints to define data models and enforce data schemas at run-time. Originally, type annotations were added to Python for static analysis, but their growing versatility has led to broader uses, including run-time validation. Built on a fast Rust core, it provides efficient data validation, parsing and serialization.
While it’s best known for web API development, Pydantic has also become essential in LLM applications. We typically use the structured output from LLMs technique to manage the unpredictable nature of LLMs. By defining a strict data schema, it acts as a safety net for the unpredictable nature of model output — converting free-form text responses into deterministic, type-safe Python objects (e.g., JSON). This approach, often implemented through Pydantic AI or LangChain, turns potentially brittle LLM interactions into reliable, machine-readable data contracts. Our teams have successfully used Pydantic in production to extract structured representations from unstructured documents, ensuring the output conforms to a valid structure. Given its maturity, performance and reliability, Pydantic is now our default choice for production-level Python AI applications.
起初，Python 添加了类型注解用以支持静态分析。然而，考虑到类型注解和一般注解在其他编程语言中的广泛使用，开发者开始将 Python 的类型注解用于其他目的只是时间问题。pydantic 就是其中一种，用类型注解进行运行时数据验证和设置管理。当接收到JSON数据，并需要将其解析为复杂的 Python 结构时，pydantic 确保传入的数据与预期类型匹配，或在不匹配时报告错误。虽然 pydantic 可以被直接使用，许多开发者将它作为最流行的 Python web 框架之一—— FastAPI 的一部分来使用。事实上，在 FastAPI 中使用 pydantic 被认为非常有必要，以至于最近有一个 Python 更改提议——旨在降低加载带注解的代码到内存中的成本——被重新考虑， 因为它会在运行时破坏类型注解的使用。
