Pydantic v2 & TypedDict Generation for Modern Python
FastAPI, LangChain, LlamaIndex, and modern Python web frameworks rely heavily on Pydantic models for runtime schema validation, data parsing, and auto-generating OpenAPI specifications.
FormJson analyzes incoming JSON payloads, generates snake_case field names adhering to PEP 8 conventions, preserves camelCase JSON keys via Field(alias="..."), and produces ready-to-use schemas.
JSON to Python Type Annotations
| JSON Value | Python Type Hint | Pydantic v2 Field Representation |
|---|---|---|
| "Jane Doe" | str | name: str |
| 42 | int | user_id: int |
| 3.14 | float | score: float |
| true / false | bool | is_active: bool |
| null | Optional[Any] | metadata: Optional[Any] |
| ["a", "b"] | List[str] | tags: List[str] |
FastAPI & Pydantic Request Body Integration
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
# Paste your FormJson generated model here:
class UserModel(BaseModel):
user_id: int
display_name: str
is_admin: bool = False
@app.post("/users/")
async def create_user(payload: UserModel):
# Payload is 100% validated and typed!
return {"status": "success", "id": payload.user_id}