Awesome
<!-- markdownlint-disable MD033 MD041 --> <p align="center"> <img src="https://github.com/radiantearth/stac-site/raw/master/images/logo/stac-030-long.png" width=400> <p align="center">FastAPI implemention of the STAC API spec.</p> </p> <p align="center"> <a href="https://github.com/stac-utils/stac-fastapi/actions?query=workflow%3Acicd" target="_blank"> <img src="https://github.com/stac-utils/stac-fastapi/workflows/stac-fastapi/badge.svg" alt="Test"> </a> <a href="https://github.com/stac-utils/stac-fastapi/blob/main/LICENSE" target="_blank"> <img src="https://img.shields.io/github/license/stac-utils/stac-fastapi.svg" alt="License"> </a> </p>Documentation: https://stac-utils.github.io/stac-fastapi/
Source Code: https://github.com/stac-utils/stac-fastapi
Python library for building a STAC compliant FastAPI application. The project is split up into several namespace packages:
| Package | Description | Version |
|---|---|---|
| stac_fastapi.api | An API layer which enforces the stac-api-spec. |  |
| stac_fastapi.extensions | Abstract base classes for STAC API extensions and third-party extensions. |  |
| stac_fastapi.types | Shared types and abstract base classes used by the library. |  |
Backends
Backends are hosted in their own repositories:
- stac-fastapi-pgstac: Postgres backend implementation with PgSTAC.
- stac-fastapi-elasticsearch: Backend implementation with Elasticsearch.
- stac-fastapi-sqlalchemy: Postgres backend implementation with sqlalchemy.
stac-fastapi was initially developed by arturo-ai.
Response Model Validation
A common question when using this package is how request and response types are validated?
This package uses stac-pydantic to validate and document STAC objects. However, by default, validation of response types is turned off and the API will simply forward responses without validating them against the Pydantic model first. This decision was made with the assumption that responses usually come from a (typed) database and can be considered safe. Extra validation would only increase latency, in particular for large payloads.
To turn on response validation, set ENABLE_RESPONSE_MODELS to True. Either as an environment variable or directly in the ApiSettings.
With the introduction of Pydantic 2, the extra time it takes to validate models became negatable. While ENABLE_RESPONSE_MODELS still defaults to False there should be no penalty for users to turn on this feature but users discretion is advised.
Installation
# Install from PyPI
python -m pip install stac-fastapi.types stac-fastapi.api stac-fastapi.extensions
# Install a backend of your choice
python -m pip install stac-fastapi.sqlalchemy
# or
python -m pip install stac-fastapi.pgstac
Other backends may be available from other sources, search PyPI for more.
Development
Install the packages in editable mode:
python -m pip install -e \
'stac_fastapi/types[dev]' \
'stac_fastapi/api[dev]' \
'stac_fastapi/extensions[dev]'
To run the tests:
python -m pytest