Unverified50% confidenceFactExact time
Pydantic reads the type annotations of data models at runtime, automatically generates validation logic, and rejects data that does not meet expectations before it enters the business logic, throwing clear errors.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA common practice for output validation is to use Pydantic to define the expected JSON Schema, combined with retry logic (such as the Tenacity library) to automatically re-request when the model's output format is non-compliant.73% similarUnverifiedIf the deserialization logic fails to correctly identify an already-rejected terminal state, the system may treat it as a pending state, thereby re-initiating the approval or executing the tool call.70% similarUnverifiedWhen parsing model output, the parsing logic should be wrapped with try...except, and an empty list should be returned if parsing fails, to ensure the program does not crash.70% similarUnverifiedPydanticOutputParser 是 LangChain 提供的输出解析器，将 LLM 回复解析为符合指定 Pydantic 模型的 Python 对象，其优势是模型无关、约束表达力强，能无缝接入自动重试循环。69% similarUnverifiedConstructing isomorphic variants of test sets (same logical structure but with surface symbols replaced) can force models to rely on genuine logical reasoning rather than memorization to answer.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1018873API
curl https://kongchang.com/api/v1/knowledge/claims/1018873MCP
get_claim(id=1018873)