pydantypes 1.0.0

The missing types for Pydantic — cloud, DevOps, web, and data engineering.

pydantypes

The missing types for Pydantic — cloud, DevOps, web, data, and AI engineering.

pydantypes provides validated, constrained Pydantic types for identifiers, ARNs, URIs, resource names, and classification labels that appear everywhere in modern infrastructure and AI code. Catch invalid values at parse time, not at deploy time.

Installation

pip install pydantypes
# or
uv add pydantypes

Quick Example

Every type validates and parses — you get structured properties, not just accept/reject.

from pydantic import BaseModel
from pydantypes.cloud.aws import S3Uri, IamRoleArn

class PipelineConfig(BaseModel):
    source: S3Uri
    execution_role: IamRoleArn

config = PipelineConfig(
    source="s3://my-bucket/data/input.parquet",
    execution_role="arn:aws:iam::123456789012:role/pipeline-role",
)
config.source.bucket             # "my-bucket"
config.source.key                # "data/input.parquet"
config.execution_role.role_name  # "pipeline-role"

Domains

Domain	Package	What You Get
AWS	pydantypes.cloud.aws	S3 URIs, IAM ARNs, Lambda names, EC2/ECS/EKS IDs, account IDs, regions
Azure	pydantypes.cloud.azure	Blob Storage URIs, resource IDs, Key Vault names, subscription IDs
GCP	pydantypes.cloud.gcp	GCS URIs, project IDs, service account emails, Cloud Run services
DevOps	pydantypes.devops	Docker image refs, Git SHAs/refs/URLs, K8s names/labels, Helm charts, Terraform addresses
Web	pydantypes.web	Hosts, URNs, slugs, JWTs, MIME types, FQDNs, port ranges, Bearer tokens, hashes
Data	pydantypes.data	SQL identifiers, Kafka topics, connection strings, column names
AI	pydantypes.ai	Classification labels with lifecycle, deprecation, alias resolution

Types You Won't Find Anywhere Else

Parse cloud resources into structured components

from pydantypes.cloud.aws import Arn
from pydantypes.cloud.azure import ResourceId

arn = Arn("arn:aws:iam::123456789012:role/pipeline-role")
arn.service      # "iam"
arn.account_id   # "123456789012"
arn.resource     # "role/pipeline-role"

rid = ResourceId(
    "/subscriptions/12345678-1234-1234-1234-123456789012"
    "/resourceGroups/myRG/providers/Microsoft.Compute/virtualMachines/myVM"
)
rid.subscription_id     # "12345678-1234-1234-1234-123456789012"
rid.resource_group      # "myRG"
rid.provider_namespace  # "Microsoft.Compute"
rid.resource_type       # "virtualMachines"
rid.resource_name       # "myVM"

Decompose Docker refs and Git URLs

from pydantypes.devops import DockerImageRef, GitSshUrl

img = DockerImageRef("ghcr.io/owner/app:v2.1@sha256:abcdef1234567890")
img.registry    # "ghcr.io"
img.repository  # "owner/app"
img.tag         # "v2.1"
img.digest      # "sha256:abcdef1234567890"

url = GitSshUrl("[e-mail]:torvalds/linux.git")
url.host   # "github.com"
url.owner  # "torvalds"
url.repo   # "linux"

Validate web identifiers with RFC-compliant parsing

from pydantypes.web import Host, Urn, Jwt, MimeType

host = Host("[2001:db8::1]")
host.host_type  # "ipv6"

host = Host("api.example.com")
host.host_type  # "domain"

urn = Urn("urn:isbn:0451450523")
urn.nid  # "isbn"
urn.nss  # "0451450523"

jwt = Jwt("eyJhbGciOiJIUzI1NiJ9.eyJzdWIiOiIxMjM0NSJ9.signature")
jwt.header   # {"alg": "HS256"}
jwt.payload  # {"sub": "12345"}

mime = MimeType("application/json;charset=utf-8")
mime.type        # "application"
mime.subtype     # "json"
mime.parameters  # {"charset": "utf-8"}

Manage classification label lifecycles

Nothing like this exists in the Pydantic ecosystem. LabelEnum gives you typed classification labels for LLM and ML projects — with descriptions, deprecation warnings, retirement enforcement, and alias resolution built in. Compatible with OpenAI structured outputs, LangChain with_structured_output, and all Pydantic-based LLM frameworks.

from pydantic import BaseModel
from pydantypes.ai import LabelEnum, Label

class Sentiment(LabelEnum):
    POSITIVE = Label("positive", description="Expresses approval or satisfaction")
    NEGATIVE = Label("negative", description="Expresses disapproval or frustration")
    NEUTRAL  = Label("neutral",  description="No clear emotional signal")

    # Taxonomy evolves — deprecate old labels without breaking existing data
    MIXED = Label(
        "mixed",
        deprecated=True,
        successor="NEUTRAL",
        description="Contradictory signals",
    )

    # Retired labels are rejected outright, but aliases still resolve
    AMBIGUOUS = Label(
        "ambiguous",
        retired=True,
        successor="NEUTRAL",
        aliases=["unclear", "unknown"],
    )

class Result(BaseModel):
    sentiment: Sentiment

# Active labels work normally
result = Result(sentiment="positive")
result.sentiment.description  # "Expresses approval or satisfaction"

# Deprecated labels still parse but emit a DeprecationWarning
result = Result(sentiment="mixed")  # warns: "Label 'mixed' is deprecated. Use 'NEUTRAL' instead."

# Retired labels are rejected — forces migration
Result(sentiment="ambiguous")  # -> ValidationError

# Aliases silently resolve to their target
result = Result(sentiment="unclear")  # resolves to AMBIGUOUS's alias -> handled

In any classification project, your label taxonomy will change. Labels get merged, split, renamed. Without lifecycle management you end up with dead labels in your schema, silent data quality regressions, and no migration path. LabelEnum makes taxonomy evolution a first-class concern.

# Introspect your taxonomy programmatically
Sentiment.active_labels()      # [POSITIVE, NEGATIVE, NEUTRAL]
Sentiment.deprecated_labels()  # [MIXED]
Sentiment.retired_labels()     # [AMBIGUOUS]
Sentiment.schema_values()      # ["positive", "negative", "neutral", "mixed"] (excludes retired)
Sentiment.alias_map()          # {"unclear": AMBIGUOUS, "unknown": AMBIGUOUS}

Compatibility

pydantypes is designed as a complement to pydantic-extra-types. While pydantic-extra-types covers general-purpose types (colors, phone numbers, payment cards), pydantypes focuses on infrastructure and engineering identifiers.

For IBAN and BIC (banking identifiers), use schwifty which provides native Pydantic v2 support with __get_pydantic_core_schema__.

• Requires Pydantic v2.5+
• Supports Python 3.10–3.13

Architecture

See ARCHITECTURE.md for type patterns, conventions, and design decisions.

Development

# Clone and set up
git clone https://github.com/oborchers/pydantypes.git
cd pydantypes
make init

# Run checks
make check        # lint + typecheck + test
make format       # auto-format code
make test-cov     # tests with coverage report

Disclaimer

pydantypes is an independent, community-maintained package and is not affiliated with or endorsed by Pydantic Services Inc.

License

MIT