zoldics-service-utils 0.1.13

A most utile package for the governance of pythonic micro-services

PythonServiceUtil

PythonServiceUtil is a utility library designed to streamline Python-based microservice development. It offers robust support for:

• REST Inter-service Calls: Simplifies HTTP communication between services.
• AWS SQS Integration: Enhances message queue operations.
• Redis Support: Provides efficient caching and data storage.
• MongoDB Integration: Simplifies database interactions, including transactions (mongoengine object data mapper).
• Context Management: Facilitates seamless context management in multithreaded and multiprocessing environments.

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Features

Validation Workflow

The validation process is structured as follows:

1. Access Token Validation: Validates the access token as the primary step.
2. Fallback to X-API-KEY Validation: If the access token validation fails, X-API-KEY validation is used as a fallback.

Note: The JWKS (JSON Web Key Set) must follow the format List[Jwk_TH]. Specify the symmetric or asymmetric algorithm to use via AUTH_TOKEN_ALGORITHM.

class Jwk_TH(TypedDict, total=False):
    alg: Required[str]
    e: str
    kid: Required[str]
    kty: str
    n: str
    use: str

def __load_jwks() -> List[Jwk_TH]:
    return json.loads(str(config("JWKS")))

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Configuration

Configure the library using an .env file with the following keys:

ENVIRONMENT=develop
LOGGING_FILENAME=service.log
AUTH_TOKEN_ALGORITHM=RS256
BEDROCK_AWS_REGION_NAME=region_name
AWS_REGION_NAME=region_name
AWS_ACCESS_KEY=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_access_key
X_API_KEY_EMBEDDING_SERVICE_1=access_key
X_API_KEY_EMBEDDING_SERVICE_2=access_key
JWKS=JWKS

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Usage

Headers Validation Model

The headers validation model supports both inter-service calls and client-to-backend communication. It first checks cookie validation and then falls back to header-based authorization.

import uuid
from pydantic import BaseModel, Field
from typing import Any, Dict

class Headers_PM(BaseModel):
    correlationid: str = Field(default_factory=lambda: str(uuid.uuid4()))
    username: str = "not_applicable"
    authorization: str = Field(default="")

    def model_dump(self, exclude_fields={}, **kwargs) -> Dict[str, Any]:
        return super().model_dump(**kwargs, exclude=exclude_fields)

Setting Up REST Middlewares

Here is an example of setting up REST middlewares in a FastAPI app:

app.add_middleware(
    HeaderValidationMiddleware,
    x_api_key_1=cast(str, config("X_API_KEY_EMBEDDING_SERVICE_1")),
    x_api_key_2=cast(str, config("X_API_KEY_EMBEDDING_SERVICE_2")),
    authexpiryignore_paths=frozenset([
        ServicePaths.CONTEXT_PATH.value + "/encoders",
        ServicePaths.CONTEXT_PATH.value + "/llm",
    ]),
)
app.add_middleware(ExceptionMiddleware)

Key Fields in Headers_PM

• correlationid: A unique identifier for tracing requests, generated using uuid4().
• username: The username associated with the request; defaults to "not_applicable".
• authorization: The authorization token, defaulting to an empty string.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add feature'.
4. Push to the branch: git push origin feature-name.
5. Open a pull request.

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Publishing to PyPI

To upload the library to PyPI, follow these steps:

pip install -r requirements.dev.txt
python setup.py bdist_wheel sdist
twine check dist/*
twine upload dist/*