WikiNest

Video Demo: https://youtu.be/JnTWxK1tgcs

Description:

Over View:

WikiNest is an advanced wiki platform designed to harness the power of semantic search and graph databases. Unlike traditional wikis that rely solely on keyword matching, WikiNest provides users with a richer, more contextually relevant search experience.

Key Features:

1. User Registration and Authentication: Users can securely register and log in to WikiNest, ensuring personalized experiences and data security.
2. Interactive Dashboard: Accessible via the navigation bar, the dashboard empowers users to seamlessly add documents of interest directly to the database.
3. Semantic Search Capability: At the heart of WikiNest is its semantic search engine. When users enter a query, the system doesn't just match keywords; it understands the context and meaning behind the query. This ensures more accurate and contextually relevant search results.
4. Graph Database Abstraction: WikiNest abstracts SQL behind a graph interface to mimic graph databases in order to establish relationships between documents. When a user accesses a particular document, WikiNest showcases related documents, providing a comprehensive view of interconnected information.

Benefits:

1. Knowledge Exploration: The graph database integration promotes exploration by presenting related documents, allowing users to delve deeper into topics of interest.
2. Personalized Interaction: With the ability to add documents to the database, users can curate content tailored to their needs, enhancing their overall experience.

Project File Structure and Description:

1. database:

   • Uses SQLAlchemy to interact with and store models in the database.

2. graph_sql:

   • A wrapper around the database that abstracts it as a graph database.

3. graph:

   • Implements a graph data structure for performing algorithms. It's capable of handling weighted graphs and automatically dropping edges below a specified threshold.

4. fast search:

   • Implements or uses TF-IDF and Sentence BERT for document retrieval.
   • Contains classes: FastSearchMethod for individual methods and MixedFastSearchMethod that combines TF-IDF and Sentence BERT.
   • Overcame challenges with long document searches by adjusting the weight of TF-IDF based on the target document's size.
   • Wraps these methods in a FastSearch class.
   • Capable of preprocessing and computing similarity matrices of documents, as well as finding top-relevant documents to a given query.

5. app.py:

   • Manages web app route redirections, including functionalities for user registration, login, adding new documents, and navigation.

6. Other files for production and libraries:

   • scripts/dev.py: Runs yarn dev.
   • static folder: Contains generated CSS and JS file bundles.
   • templates folder: Includes HTML files:
     • 404
     • apology
     • dashboard: Allows users to add new documents with title, primary, and secondary content.
     • index: Home page where users can submit search queries.
     • layout
     • login
     • register
     • result: Page displaying individual documents with related documents listed at the bottom.
     • results: Displays top search results.
   • Other miscellaneous files:
     • imports.js
     • imports.styles.js
     • main.css
     • package.json
     • Pipfile
     • postcss.config.cjs
     • rollup.config.js
     • search_engine.py
     • tailwind.config.js