Sphinx Extensions API¶
Since many projects will need special features in their documentation, Sphinx is designed to be extensible on several levels.
Here are a few things you can do in an extension:
Add new builders to support new output formats or actions on the parsed documents.
Register custom reStructuredText roles and directives, extending the markup using the Docutils markup API.
Add custom code to so-called «hook points» at strategic places throughout the build process, allowing you to register a hook and run specialized code. For example, see the Sphinx core events.
An extension is simply a Python module with a
setup() function. A user
activates the extension by placing the extension’s module name
(or a sub-module) in their
extensions configuration value.
When sphinx-build is executed, Sphinx will attempt to import each
module that is listed, and execute
function is used to prepare the extension (e.g., by executing Python code),
linking resources that Sphinx uses in the build process (like CSS or HTML
files), and notifying Sphinx of everything the extension offers (such
as directive or role definitions). The
app argument is an instance of
Sphinx and gives you control over most aspects of the Sphinx build.
The configuration file itself can be treated as an extension if it
setup() function. All other extensions to load must be
listed in the
extensions configuration value.
The rest of this page describes some high-level aspects of developing extensions and various parts of Sphinx’s behavior that you can control. For some examples of how extensions can be built and used to control different parts of Sphinx, see the Extension tutorials.
There are several key objects whose API you will use while writing an extension. These are:
The application object (usually called
app) is an instance of
Sphinx. It controls most high-level functionality, such as the setup of extensions, event dispatching and producing output (logging).
If you have the environment object, the application is available as
The build environment object (usually called
env) is an instance of
BuildEnvironment. It is responsible for parsing the source documents, stores all metadata about the document collection and is serialized to disk after each build.
Its API provides methods to do with access to metadata, resolving references, etc. It can also be used by extensions to cache information that should persist for incremental rebuilds.
If you have the application or builder object, the environment is available as
The builder object (usually called
builder) is an instance of a specific subclass of
Builder. Each builder class knows how to convert the parsed documents into an output format, or otherwise process them (e.g. check external links).
If you have the application object, the builder is available as
The config object (usually called
config) provides the values of configuration values set in
conf.pyas attributes. It is an instance of
The config is available as
To see an example of use of these objects, refer to Extension tutorials.
One thing that is vital in order to understand extension mechanisms is the way in which a Sphinx project is built: this works in several phases.
Phase 0: Initialization
In this phase, almost nothing of interest to us happens. The source directory is searched for source files, and extensions are initialized. Should a stored build environment exist, it is loaded, otherwise a new one is created.
Phase 1: Reading
In Phase 1, all source files (and on subsequent builds, those that are new or changed) are read and parsed. This is the phase where directives and roles are encountered by docutils, and the corresponding code is executed. The output of this phase is a doctree for each source file; that is a tree of docutils nodes. For document elements that aren’t fully known until all existing files are read, temporary nodes are created.
There are nodes provided by docutils, which are documented in the docutils documentation. Additional nodes are provided by Sphinx and documented here.
During reading, the build environment is updated with all meta- and cross reference data of the read documents, such as labels, the names of headings, described Python objects and index entries. This will later be used to replace the temporary nodes.
The parsed doctrees are stored on the disk, because it is not possible to hold all of them in memory.
Phase 2: Consistency checks
Some checking is done to ensure no surprises in the built documents.
Phase 3: Resolving
Now that the metadata and cross-reference data of all existing documents is known, all temporary nodes are replaced by nodes that can be converted into output using components called transforms. For example, links are created for object references that exist, and simple literal nodes are created for those that don’t.
Phase 4: Writing
This phase converts the resolved doctrees to the desired output format, such as HTML or LaTeX. This happens via a so-called docutils writer that visits the individual nodes of each doctree and produces some output in the process.
Some builders deviate from this general build plan, for example, the builder that checks external links does not need anything more than the parsed doctrees and therefore does not have phases 2–4.
To see an example of application, refer to Developing a «TODO» extension.
Nuevo en la versión 1.3.
setup() function can return a dictionary. This is treated by Sphinx
as metadata of the extension. Metadata keys currently recognized are:
'version': a string that identifies the extension version. It is used for extension version requirement checking (see
needs_extensions) and informational purposes. If not given,
"unknown version"is substituted.
'env_version': an integer that identifies the version of env data structure if the extension stores any data to environment. It is used to detect the data structure has been changed from last build. The extensions have to increment the version when data structure has changed. If not given, Sphinx considers the extension does not stores any data to environment.
'parallel_read_safe': a boolean that specifies if parallel reading of source files can be used when the extension is loaded. It defaults to
False, i.e. you have to explicitly specify your extension to be parallel-read-safe after checking that it is.
The parallel-read-safe extension must satisfy the following conditions:
The core logic of the extension is parallelly executable during the reading phase.
It has event handlers for
env-purge-docevents if it stores data to the build environment object (env) during the reading phase.
'parallel_write_safe': a boolean that specifies if parallel writing of output files can be used when the extension is loaded. Since extensions usually don’t negatively influence the process, this defaults to
The parallel-write-safe extension must satisfy the following conditions:
The core logic of the extension is parallelly executable during the writing phase.
APIs used for writing extensions¶
These sections provide a more complete description of the tools at your disposal when developing Sphinx extensions. Some are core to Sphinx (such as the Application API) while others trigger specific behavior (such as the API i18n)
- Application API
- Project API
- Build environment API
- Builder API
- Environment Collector API
- Docutils markup API
- Domain API
- Parser API
- Doctree node classes added by Sphinx
- Logging API
- API i18n
- Deprecated APIs