azure-functions-validation 0.7.1

Validation and serialization layer for Azure Functions Python v2 programming model

Azure Functions Validation

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Validation and serialization for the Azure Functions Python v2 programming model.

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Part of the Azure Functions Python DX Toolkit → Bring FastAPI-like developer experience to Azure Functions

Why this exists

Azure Functions Python v2 handlers often drift into the same repeated problems:

• Repeated manual parsing — every handler calls req.get_json(), req.params.get(), handles ValueError individually
• Inconsistent error responses — some handlers return 400, others 422, formats vary across the project
• Missing response contracts — response payloads silently diverge from the intended schema
• No type safety — request data flows through as untyped dicts, bugs surface only at runtime

What it does

• Typed validation — body, query, path, and header parameters validated via Pydantic v2
• Automatic error responses — invalid requests get consistent 400/422 JSON error bodies
• Response model enforcement — mismatches raise ResponseValidationError (HTTP 500)
• Decorator-first API — @validate_http wraps your handler, no boilerplate needed

Before / After

Without this package — manual parsing, manual errors, no contracts:

import json
import azure.functions as func

app = func.FunctionApp()


@app.route(route="users", methods=["POST"])
def create_user(req: func.HttpRequest) -> func.HttpResponse:
    try:
        body = req.get_json()
    except ValueError:
        return func.HttpResponse(
            json.dumps({"error": "Invalid JSON"}),
            status_code=400,
            mimetype="application/json",
        )

    name = body.get("name")
    email = body.get("email")
    if not name or not isinstance(name, str):
        return func.HttpResponse(
            json.dumps({"error": "name is required"}),
            status_code=400,
            mimetype="application/json",
        )
    if not email or not isinstance(email, str):
        return func.HttpResponse(
            json.dumps({"error": "email is required"}),
            status_code=400,
            mimetype="application/json",
        )

    return func.HttpResponse(
        json.dumps({"message": f"Hello {name}", "status": "success"}),
        mimetype="application/json",
    )

With @validate_http — typed, consistent, contract-enforced:

import azure.functions as func
from pydantic import BaseModel

from azure_functions_validation import validate_http

app = func.FunctionApp()


class CreateUserRequest(BaseModel):
    name: str
    email: str


class CreateUserResponse(BaseModel):
    message: str
    status: str = "success"


@app.route(route="users", methods=["POST"])
@validate_http(body=CreateUserRequest, response_model=CreateUserResponse)
def create_user(req: func.HttpRequest, body: CreateUserRequest) -> CreateUserResponse:
    return CreateUserResponse(message=f"Hello {body.name}")

Manual parsing and validation disappear from the handler. Error formatting and response contracts — handled.

What you get

Valid request → typed response:

$ curl -s -X POST http://localhost:7071/api/users \
    -H "Content-Type: application/json" \
    -d '{"name": "Alice", "email": "[e-mail]"}'

{"message": "Hello Alice", "status": "success"}

HTTP 200

Missing required field → automatic error response:

$ curl -s -X POST http://localhost:7071/api/users \
    -H "Content-Type: application/json" \
    -d '{"name": "Alice"}'

{
  "detail": [
    {
      "loc": ["email"],
      "msg": "Field required",
      "type": "missing"
    }
  ]
}

HTTP 422 — standardized error response, automatic

Invalid JSON → clear error:

$ curl -s -X POST http://localhost:7071/api/users \
    -H "Content-Type: application/json" \
    -d 'not json'

{"detail": [{"loc": [], "msg": "Invalid JSON", "type": "value_error"}]}

HTTP 400

FastAPI comparison

Feature	FastAPI	azure-functions-validation
Request body parsing	Built-in via type hints	@validate_http(body=Model)
Query/path/header validation	Query(), Path(), Header()	@validate_http(query=Model, path=Model, headers=Model)
Response model	response_model=	@validate_http(response_model=Model)
Validation errors	Automatic 422	Automatic 422 with {"detail": [...]}
Error customization	Exception handlers	ErrorFormatter callback

Scope

• Azure Functions Python v2 programming model
• HTTP-triggered functions registered on func.FunctionApp()
• Pydantic v2-based request and response validation

This package does not target the legacy function.json-based v1 programming model.

What this package does not do

This package does not own:

• API documentation and spec generation — use azure-functions-openapi
• Runtime exposure or graph deployment — use azure-functions-langgraph
• Project scaffolding — use azure-functions-scaffold

Features

• Typed body, query, path, and header validation via @validate_http
• Automatic 400 / 422 responses with {"detail": [...]} envelope
• Response model validation — mismatches raise ResponseValidationError (HTTP 500)
• Custom per-handler error formatting via ErrorFormatter

Installation

pip install azure-functions-validation

Your Azure Functions app should also include:

azure-functions
azure-functions-validation

For local development:

git clone https://github.com/yeongseon/azure-functions-validation.git
cd azure-functions-validation
pip install -e .[dev]

Quick Start

import azure.functions as func
from pydantic import BaseModel

from azure_functions_validation import validate_http


class CreateUserRequest(BaseModel):
    name: str
    email: str


class CreateUserResponse(BaseModel):
    message: str
    status: str = "success"


app = func.FunctionApp()


@app.function_name(name="create_user")
@app.route(route="users", methods=["POST"], auth_level=func.AuthLevel.ANONYMOUS)
@validate_http(body=CreateUserRequest, response_model=CreateUserResponse)
def create_user(req: func.HttpRequest, body: CreateUserRequest) -> CreateUserResponse:
    return CreateUserResponse(message=f"Hello {body.name}")

Start the Functions host locally:

func start

Verify locally and on Azure

After deploying (see docs/deployment.md), the same request produces the same response in both environments.

Local

curl -s http://localhost:7071/api/users \
  -H "Content-Type: application/json" \
  -d '{"name": "Alice", "email": "[e-mail]"}'

{"message": "Hello Alice", "status": "success"}

Azure

curl -s "https://<your-app>.azurewebsites.net/api/users" \
  -H "Content-Type: application/json" \
  -d '{"name": "Alice", "email": "[e-mail]"}'

{"message": "Hello Alice", "status": "success"}

Invalid requests return the same 400 error in both environments:

Local

curl -s http://localhost:7071/api/users \
  -H "Content-Type: application/json" \
  -d 'not json'

{"detail": [{"loc": [], "msg": "Invalid JSON", "type": "value_error"}]}

HTTP 400

Azure

curl -s "https://<your-app>.azurewebsites.net/api/users" \
  -H "Content-Type: application/json" \
  -d 'not json'

{"detail": [{"loc": [], "msg": "Invalid JSON", "type": "value_error"}]}

HTTP 400

Verified against a temporary Azure Functions deployment in koreacentral (Python 3.12, Consumption plan). Response captured and URL anonymized.

When to use

• You have HTTP-triggered Azure Functions that accept JSON request bodies
• You want Pydantic-based validation without writing manual parsing code
• You need consistent error response formats across handlers
• You want response schema enforcement to catch contract drift

Documentation

• Project docs live under docs/
• Smoke-tested examples live under examples/
• Product requirements: PRD.md
• Design principles: DESIGN.md

Ecosystem

This package is part of the Azure Functions Python DX Toolkit.

Design principle: azure-functions-validation owns request/response validation and serialization. azure-functions-openapi owns API documentation and spec generation. azure-functions-langgraph owns LangGraph runtime exposure.

Package	Role
azure-functions-openapi	OpenAPI spec generation and Swagger UI
azure-functions-validation	Request/response validation and serialization
azure-functions-db	Database bindings for SQL, PostgreSQL, MySQL, SQLite, and Cosmos DB
azure-functions-langgraph	LangGraph deployment adapter for Azure Functions
azure-functions-scaffold	Project scaffolding CLI
azure-functions-logging	Structured logging and observability
azure-functions-doctor	Pre-deploy diagnostic CLI
azure-functions-durable-graph	Manifest-first graph runtime with Durable Functions (experimental)
azure-functions-python-cookbook	Recipes and examples

For AI Coding Assistants

When integrating with LLM-powered coding assistants, provide these files for context:

• llms.txt — Concise index with quick start and API overview
• llms-full.txt — Expanded reference with full signatures and patterns

Reference the files at repository root:

• https://github.com/yeongseon/azure-functions-validation/blob/main/llms.txt
• https://github.com/yeongseon/azure-functions-validation/blob/main/llms-full.txt

Disclaimer

This project is an independent community project and is not affiliated with, endorsed by, or maintained by Microsoft.

Azure and Azure Functions are trademarks of Microsoft Corporation.

License

MIT