Guide

See also the README file
and, above all, this series of articles.

The following Python and JavaScript libraries (python classes, unless otherwise specified) are being used – from lower to higher levels in the technology stack :

FOUNDATIONAL (CORE) LIBRARIES

Class GraphAccess

    (Formerly called "NeoAccess")

    High-level class to interface with the Neo4j graph database from Python.

    Conceptually, there are two parts to GraphAccess:
    
        1) "InterGraph": a thin wrapper around the Neo4j python connectivity library Neo4j Python Driver".
           This core library allows the execution of arbitrary Cypher (query language) commands, 
           and helps manage the complex data structures that they return.
           It may be used independently, or as the foundation of full "GraphAccess" library.
           This bottom layer is dependent on the specific graph database (for operations  such as connection, indexes, constraints), 
           and insulates the higher layers from it.
           Currently, 2 versions are available: one for use with the graph database Neo4j v4, and one for Neo4j v5.  
           More versions, also for Cypher-supporting graph databases other than Neo4j, are anticipated.
           
        2) "GraphAccess" : A layer above, providing higher-level functionality for common database operations,
           such as lookup, creation, deletion, modification, import, etc.

Background Information: Using Neo4j with Python : the Open-Source Library "GraphAccess"

Reference Guide

Source code

Tutorial 1 Tutorial 2 Tutorial 3 (Pandas import)

Unit tests (with pytest)

Class GraphSchema

    (Formerly called "NeoSchema")

    A layer above the class GraphAccess (or, in principle, another library providing a compatible interface),
    to provide an optional schema to the underlying database.

    Schemas may be used to either:
        1) acknowledge the existence of typical patterns in the data
        OR
        2) to enforce a mold for the data to conform to

    MOTIVATION

        Relational databases are suffocatingly strict for the real world.
        Neo4j by itself may be too anarchic.
        A schema (whether "lenient/lax/loose" or "strict") in conjunction with Neo4j may be the needed compromise.

    GOALS
        - Data integrity
        - Assist the User Interface
        - Infuse into Neo4j functionality that some people turn to RDF for.  However, carving out a new path
          rather than attempting to emulate RDF!

Background Information: Using Schema in Graph Databases such as Neo4j

User Guide

Reference Guide

Source code

Tutorial 1 : basic Schema operations (Classes, Properties, Data Nodes)
Tutorial 2 : set up a simple Schema (Classes, Properties) and perform a data import (Data Nodes and relationships among them)

Unit tests (with pytest) : Main set Extra tests, for data imports

SPECIAL-PURPOSE LIBRARIES

Class MediaManager

    Helper library for the management of media files (documents and images)

Background Information: A Technology Stack on Top of a (Neo4j) Graph Database

Reference Guide

Source code

Class UserManager

    Class for User Management in the database, including handling of encrypted passwords

Source code

Class FullTextIndexing

    Indexing-related methods, for full-text searching

Background Information: Full-Text Search with the Neo4j Graph Database

Reference Guide

Source code

Tutorial

Class Categories

    Library for Category-related operations.

    Categories have some attributes, such as "name" and "remarks",
    as well as "BA_subcategory" and "BA_see_also" relationships with other categories.

    The 1st part of this library handles the above entity.

    The 2nd part (possibly to be split off in the future) manages a (for now conflated)
    entity of "Category Page", to which a variety of nodes (e.g. representing records or media)
    are attached with positional attributes - for example to implement an ordered sequence
    of multimedia content on the page of a content-management UI.

Reference Guide

Source code

Class Collections

   
    An ordered sequence of Data Nodes.

    We define a "Collection" as an entity to which a variety of Data Nodes
    are attached, with positional attributes in their links.

    Example of use case: "pages" of "Content Items" attached to a "Category"

Reference Guide

Source code

Class NodeExplorer

    A largely experimental library to deal with management and visualization of data nodes

Source code

Class UploadHelper

    Helper class to manage file uploads with Flask 1.1 (using Werkzeug 2.1)

Source code

Class DocumentationGenerator

    To generate the HTML for a documentation page, from a python file.
    Used for the "Reference Guide" pages in this website!

Source code

Utilities

    General utilities for comparisons and pandas

Source code

PLUGIN-provided Handlers

    Each registered plugin may opt to provide python methods for its specific needs.

Source code

DATA-MANAGER LAYER

Class DataManager

    For general, high-level database-interaction operations.
    Used by the UI for Page Generation,
    as well as by the web API to produce data for the endpoints.

    This library is primarily a common entry point for data requests: 
    many specific operations get delegated to other, more specialized, libraries.

    This class does NOT get instantiated.

Background Information: A Technology Stack on Top of a (Neo4j) Graph Database

Reference Guide

Source code

FLASK LAYER

Flask serves 2 roles: the implementation of a Web API, and the generation of pages for a web app.

The "Flask" object is instantiated in main.py

The top-level Flask modules that specify how to dispatch the URL's are called "blueprints", and get initialized in main.py, by calls to several BrainAnnex class methods:

CLASS METHOD	Prefix for all URL's handled by this module
HomeRouting.setup()	(none, i.e. top-level)
Navigation.setup()	/navigation
PagesRouting.setup()	/BA/pages
ApiRouting.setup()	/BA/api
SamplePagesRouting.setup()	/sample/pages
SampleApiRouting.setup()	/sample/api

All Flask-related files, except for main.py, are under the flask_modules folder, at the top level.

That includes Pyhon files, Jinja (Flask) templates, and static files (such as CSS, images, JavaScript and Vue.js components)

Flask Layer : WEB API

Class ApiRouting

    Setup, routing and endpoints for all the web pages served by this module.
    Note that this class does not directly interact with the Neo4j database.

    SECTIONS:
        - UTILITY methods
        - For DEBUGGING
        - ROUTING:
            * SCHEMA-related (reading)
            * SCHEMA-related (creating)
            * CONTENT-ITEM MANAGEMENT
                VIEWING CONTENT ITEMS
                MODIFYING EXISTING CONTENT ITEMS
            * CATEGORY-RELATED (incl. adding new Content Items)
                POSITIONING WITHIN CATEGORIES
            * FILTERS
            * IMPORT-EXPORT  (upload/download)
            * EXPERIMENTAL

Source code

Class ServerCommunication (JavaScript)

    Static JavaScript class for the front-end to communicate with the server using the fetch() API.

    Small wrapper library that simplifies the server communication, 
    and helps support 2 web API standards.

User Guide

Source code

Flask Layer : WEB-APP PAGES

Class PagesRouting

    Setup and routing for all the Flask-based web pages served by this module

Source code

Flask Templates (HTML)

    HTML Flask templates for the various pages of the Brain-Annex provided site (including administrative pages and category-viewer pages)

Source code

Static Assets (CSS and JavaScript, including Vue.js components)

    For the various site pages

Source code

Flask Layer : NAVIGATION

Class Navigation

    Router/generator for navigation pages:
        CSS static pages, and HTML pages that get included into other Flask files
        
    Setup, and routing, for all the web pages

Source code

Function get_site_pages()

    Definition of the Navigation Bar entries for the overall site navigation
    (both Brain Annex and, possibly, co-hosted sites)

Source code

TOP LEVEL

MAIN PROGRAM

    It starts up a server for web User Interface and an API
    Run this file, and then set the browser to http://localhost:5000/some_url
    (the actual port number is configurable; the URL's are specified in the various modules)

    IMPORTANT: first change the config.ini file as needed (for instructions, see the install page)
    
    Note: this main program may also be started from the CLI with the "flask run" command

Source code

Class InitializeBrainAnnex

    INITIALIZATION of various static classes

Source code

CONFIGURATION FILES

    The installation comes with a file of default configurations: config.defaults.ini

    This file needs to be duplicated, renamed config.ini , and personalized.  For instructions, see the install page

    Data from this file can be accessed in other modules (such as main.py) by using:   from configparser import ConfigParser

Source code