The Knowledge Repository project is focused on facilitating the sharing of knowledge between data scientists and other technical roles using data formats and tools that make sense in these professions. It provides various data stores (and utilities to manage them) for "knowledge posts", with a particular focus on notebooks (R Markdown and Jupyter / iPython Notebook) to better promote reproducible research.
Check out this Medium Post for the inspiration for the project.
Note: The Knowledge Repository is a work in progress. There are lots of code cleanups and feature extensions TBD. Your assistance and involvement is more than encouraged.
1. Install the knowledge-repo tooling
pip install --upgrade knowledge-repo
2. Initialize a knowledge repository - your posts will get added here
knowledge_repo --repo ./example_repo init
3. Create a post template
for Rmd:
knowledge_repo --repo ./example_repo create Rmd example_post.Rmd
for ipynb
knowledge_repo --repo ./example_repo create ipynb example_post.ipynb
4. Edit the notebook file example_post.ipynb or example_post.Rmd as you normally would.
5. Add your post to the repo with path project/example
knowledge_repo --repo ./example_repo add example_post.Rmd -p project/example_rmd
knowledge_repo --repo ./example_repo add example_post.ipynb -p project/example_ipynb
6. Preview the added post
knowledge_repo --repo ./example_repo preview project/example_rmd
#or
knowledge_repo --repo ./example_repo preview project/example_ipynb
The Knowledge Repo is currently in a public beta, and we are rolling it out to more people to get feedback. In particular, we'd love to hear about the following:
- How easy is it to set up the git knowledge post repository?
- How easy is it to set up the web application, and make it live internally within your organization?
- Where are the gaps in our documentation that we should fill in to assist others in understanding the system?
- At a higher level, are there any blockers or barriers to setting up the Knowledge Repo in your organization?
Here's a running list of known issues we are working on:
- The in-app webeditor needs refactoring to:
- Rely completely on KnowledgePost objects instead of interacting with db records
- Trigger "save" actions when necessary
- Allow for image uploading
- The Python configuration for git knowledge repositories currently reads directly out of the
masterbranch, allowing (depending on your organization's git policy) a malicious user to commit arbitrary code into the master branch, which then gets run on client and server machines during interactions with the git repository using the inbuilt knowledge repository abstractions.
Knowledge posts are a general markdown format that is automatically generated from the following common formats:
- Jupyter/Ipython notebooks
- Rmd notebooks
- Markdown files
The Jupyter, Rmd, and Markdown files are required to have a specific set of yaml style headers which are used to organize and discover research:
---
title: I Found that Lemurs Do Funny Dances
authors:
- sally_smarts
- wesley_wisdom
tags:
- knowledge
- example
created_at: 2016-06-29
updated_at: 2016-06-30
tldr: This is short description of the content and findings of the post.
---
Users add these notebooks/files to the knowledge repository through the knowledge_repo tool, as described below; which allows them to be rendered and curated in the knowledge repository's web app.
If your favourite format is missing, we welcome contributions; and are happy to work with you to get it supported. See the "Contributing" section below to see how to add support for more formats.
Note that the web application can live on top of multiple Knowledge Repo backends. Supported types so far are:
- Git Repo + Remote Git Hosting Service (Primary Use Case)
- Web Application SQL db
There are two repositories associated with the Knowledge Repository project.
- This repository, which will be installed first. This is referred to as the knowledge repository tooling.
- A knowledge data repository, which is created second. This is where the knowledge posts are stored.
To install the knowledge repository tooling (and all its dependencies), simply run:
pip install --upgrade "knowledge-repo[all]"
You can also skip installing dependencies which are only required in special cases by replacing all with one or more of the following (separated by commas):
ipynb: Installs the dependencies required for adding/converting Jupyter notebook filespdf: Installs the dependencies required for uploading PDFs using the web editordev: Installs the dependencies required for doing development, including running the tests
The knowledge_repo script is the one that is used for all of the following actions. It requires the --repo flag to be passed to it, with the location of the knowledge data repository.
You can drop the --repo option by setting the $KNOWLEDGE_REPO environment variable with the location of the knowledge data repo in your bash/zsh/shell configuration. In bash, this would be done as such:
export $KNOWLEDGE_REPO=repo_path
There are two different ways to do this, depending on whether your organization already has a knowledge data repository or not:
If your organization already has a knowledge data repository setup, check it out onto your computer as you normally would; for example:
git clone git@example.com:example_data_repo.git
Running this same script if a repo already exists at <repo_path> will allow you to update it to be a knowledge data repository. This is useful if you are starting a repository on a remote service like GitHub, as this allows you to clone the remote repository as per normal; run this script; and then push the initialization back into the remote service using git push.
The following command will create a new repository at <repo_path>
knowledge_repo --repo <repo_path> init
If you are hosting this repository on a remote service like Github, and you've created the knowledge data repository using the init flag, you must push that to that remote service in order for the later commands to work. On Git, this can be done by creating the remote repository through Git and then running
git remote add origin url_of_the_repository
git push -u origin master
For more details about the structure of a knowledge repository, see the technical details section below.
There are two types of configuration files, one for knowledge-data git repos that holds posts, and another for the web application.
When running knowledge_repo init to make a folder a knowledge-data git repo, a .knowledge_repo_config file will be created in the folder. The file will be a copy of the default repo configuration file located here.
This configuration file will allow you to add postprocessors to post contributions from the repo, add rules for which subdirectories posts can be added to, and check the format of author names at contribution time. See the file itself for more detail.
Specify a configuration file when running the web application by adding the flag --config path/to/config_file.py. An example configuration file is provided here.
This configuration file lets you specify details specific to the web server. For instance, one can specify the database connection string or the request header that contains usernames. See the file itself for more detail.
If you have already set up your system as described below, here is a snapshot of the commands you need to run to upload your knowledge post stored in ~/Documents/my_post.Rmd. For Jupyter / iPython Notebooks, the commands are the same, replacing all instances of Rmd with ipynb. It assumes you have configured the KNOWLEDGE_REPO environment variable to point to your local copy of the knowledge repository. The code is written for producing and contributing an ipynb file to make the examples clear, R Markdown files are run by using Rmd in place of ipynb in each command.
knowledge_repo create Rmd ~/Documents/my_post.Rmd, which creates a template with required yaml headers. Templates can also be downloaded by clicking "Write a Post!" the web application. Make sure your post has these headers with correct values for your post- Do your work in the generated my_post.Rmd file. Make sure the post runs through from start to finish before attempting to add to the Knowledge Repo!
knowledge_repo add ~/Documents/my_post.Rmd [-p projects/test_project] [--update]knowledge_repo preview projects/test_projectknowledge_repo submit projects/test_project- From your remote git hosting service, request a review for merging the post. (ie. open a pull request on Github)
- After it has been reviewed, merge it in to the master branch.
Once the knowledge data repository has been initialized, it is possible to start adding posts. Each post in the knowledge repository requires a specific header format, used for metadata formatting. To create a new post using a provided template, which has both the header information and example content, run the following command:
knowledge_repo --repo <repo_path> create {ipynb, Rmd, md} filename
The first argument indicates the type of the file that you want created, while the second argument indicates where the file should be created.
If the knowledge data repository is created at knowledge_data_repo, running
knowledge_repo --repo knowledge_data_repo create md ~/Documents/my_first_knowledge_post.md
will create a file, ~/Documents/my_first_knowledge_post.md, the contents of which will be the boilerplate template of the knowledge post.
The help menu for this command (and all following commands) can be reached by adding the -h flag, knowledge_repo --repo <repo_path> create -h.
Alternatively, by going to the /create route in the webapp, you can click the button for whichever template you would like to have,
and that will download the correct template.
Once you've finished writing a post, the next step is to add it to the knowledge data repository. To do this, run the following command:
knowledge_repo --repo <repo_path> add <file with format {ipynb, Rmd, md}> [-p <location in knowledge repo>]
Using the example from above, if we wanted to add the post ~/Documents/my_first_knowledge_post.md to knowledge_data_repo,
we would run:
knowledge_repo --repo knowledge_data_repo add ~/Documents/my_first_knowledge_post.md -p projects/test_knowledge
The -p flag specifies the location of the post in the knowledge data repository - in this case, knowledge_data_repo/projects/test_knowledge.
The -p flag does not need to be specified if path is included in the header of the knowledge post.
To update an existing knowledge post, pass the --update flag to the add command. This will allow the add operation to override exiting knowledge posts.
knowledge_repo --repo <repo_path> add --update <file with format {ipynb, Rmd, md}> <location in knowledge repo>
If you would like to see how the post would render on the web app before submitting the post for review, run the following command:
knowledge_repo --repo <repo_path> preview <path of knowledge post to preview>
In the case from above, we would run:
knowledge_repo --repo knowledge_data_repo preview projects/test_knowledge
There are other arguments that can be passed to this command, adding the -h flag shows them all along with further information about them.
After running the add command, two things should have happened:
- A new folder should have been created at the path specified in the add command, which ends in
.kp. This is added automatically to indicate that the folder is a knowledge post. - This folder will have been committed to the repository on the branch named
<repo_path>/path_in_add_command
Running the example command: knowledge_repo --repo knowledge_data_repo add ~/Documents/my_first_knowledge_post.md -p projects/test_knowledge, we would have seen:
- A new folder:
knowledge_data_repo/projects/test_knowledge.kpwhich was committed on - A branch (that you are now on), called
knowledge_data_repo/projects/test_knowledge
To submit this post for review, simply run the command:
knowledge_repo --repo <repo_path> submit <the path of the knowledge post>
In this case, we would run:
knowledge_repo --repo knowledge_data_repo submit knowledge_data_repo/projects/test_knowledge.kp
The knowledge repo's default behavior is to add the markdown's contents as is to your knowledge post git repository. If you do not have git LFS set up, it may be in your interest to have these images hosted on some type of cloud storage, so that pulling the repo locally isn't cumbersome.
To add support for pushing images to cloud storage, we provide a postprocessor. This file needs one line to be configured for your organization's cloud storage. Once configured, the postprocessor's registry key can be added to the knowledge git repository's configuration file as a postprocessor.
Running the web app allows you to locally view all the knowledge posts in the repository, or to serve it for others to view. It is also useful when developing on the web app.
Running the web app in development/local/private mode is as simple as running:
knowledge_repo --repo <repo_path> runserver
Supported options are --port and --dburi which respectively change the local port on which the server is running, and the sqlalchemy uri where the database can be found and/or initiated. The default port is 7000, and the default dburi is sqlite:////tmp/knowledge.db. If the database does not exist, it is created (if that is possible) and initialised. Database migrations are automatic (unless disabled to prevent accidental data loss), but can be performed manually using:
knowledge_repo --repo <repo_path> db_upgrade --dburi <db>
The web application can be run on top of multiple knowledge repo backends. To do this, include each repo with a name and path, prefixed by --repo. For example:
knowledge_repo --repo {git}/path/to/git/repo --repo {webposts}sqlite:////tmp/dbrepo.db:mypostreftable runserver
If including a dbrepo, add the name of the dbrepo to the WEB_EDITOR_PREFIXES in the server config, and add it as config when running the app:
knowledge_repo --repo {git}/path/to/git/repo --repo {webposts}sqlite:////tmp/dbrepo.db:mypostreftable runserver --config resources/server_config.py
Note that this is required for the web application's internal post writing UI.
Deploying the web app is much like running the development server, except that the web app is deployed on top of gunicorn. It also allows for enabling server-side components such as sending emails to subscribed users.
Deploying is as simple as:
knowledge_repo --repo <repo_path> deploy
or if using multiple repos:
knowledge_repo --repo {git}/path/to/git/repo --repo {webposts}sqlite:////tmp/dbrepo.db:mypostreftable deploy --config resources/server_config.py
Supported options are --port, --dburi,--workers, --timeout and --config. The --config option allows you to specify a python config file from which to load the extended configuration. A template config file is provided in resources/server_config.py. The --port and --dburi options are as before, with the --workers and --timeout options specifying the number of threads to use when serving through gunicorn, and the timeout after which the threads are presumed to have died, and will be restarted.
We would love to work with you to create the best knowledge repository software possible. If you have ideas or would like to have your own code included, add an issue or pull request and we will review it.
Support for conversion of a particular filetype to a knowledge post is added by writing a new KnowledgePostConverter object. Each converter should live in its own file in knowledge_repo/converters. Refer to the implementation for ipynb, Rmd, and md for more details. If your conversion is site-specific, you can define these subclasses in .knowledge_repo_config, whereupon they will be picked up by the conversion code.
When a KnowledgePost is constructed by converting from support filetypes, the resulting post is then passed through a series of postprocessors (defined in knowledge_repo/postprocessors). This allows one to modify the knowledge post, upload images to remote storage facilities (such as S3), and/or verify some additional structure of the knowledge posts. As above, defining or importing these classes in .knowledge_repo_config.py allows for postprocessors to be used locally.
Is the Knowledge Repository missing something else that you would like to see? Let us know, and we'll see if we cannot help you.
A knowledge repository is a virtual filesystem (such as a git repository or database). A GitKnowledgeRepository, for example, has the following structure:
<repo>
+ .git # The git repository metadata
+ .resources # A folder into which the knowledge_repo repository is checked out (as a git submodule)
- .knowledge_repo_config.py # Local configuration for this knowledge repository
- <knowledge posts>
The use of a git submodule to checkout the knowledge_repo into .resources allows use to ensure that the client and server are using the same version of the code. When one uses the knowledge_repo script, it actually passes the options to the version of the knowledge_repo script in .resources/scripts/knowledge_repo. Thus, updating the version of knowledge_repo used by client and server alike is as simple as changing which revision is checked out by git submodule in the usual way. That is:
pushd .resources
git pull
git checkout <revision>/<branch>
popd
git commit -a -m 'Updated version of the knowledge_repo'
git push
Then, all users and servers associated with this repository will be updated to the new version. This prevents version mismatches between client and server, and all users of the repository.
In development, it is often useful to disable this chaining. To use the local code instead of the code in the checked out knowledge repository, pass the --dev option as:
knowledge_repo --repo <repo_path> --dev <action> ...
A knowledge post is a directory, with the following structure:
<knowledge_post>
- knowledge.md
+ images/* [Optional]
+ orig_src/* [Optional; stores the original converted file]
Images are automatically extracted from the local paths on your computer, and placed into images. orig_src contains the file(s) from which the knowledge post was converted from.