Our great sponsors
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Thymeleaf
Thymeleaf is a modern server-side Java template engine for both web and standalone environments.
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SurveyJS
Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.
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nlp.js
An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more
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PostgreSQL
Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
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WooCommerce
A customizable, open-source ecommerce platform built on WordPress. Build any commerce solution you can imagine.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Foundation
The most advanced responsive front-end framework in the world. Quickly create prototypes and production code for sites that work on any kind of device.
While we wait for these tools to pop up, any tech question on the internals of Xatkit you'd like to know? And if you want to read more about the technologies we have listed above, this twitter thread gives some pointers to good tutorials for them:
Our eCommerce dashboard is a Spring application relying on Thymeleaf as server-side Java template engine and foundation as responsive front-end framework.
To determine which chatbot intent is the best match for the user textual input, we rely on nlp.js (in JS) though we are in the process of moving to our new Python NLP server for better optimization of the needs of eCommerce conversations. A preprocessor language model is also used to improve the chances of a matching.
As the bot is automatically adapted to the data of the eCommerce shop hosting the bot, the bot logic communicates with the WooCommerce API and stores some shop data into a SQL PostgreSQL database.
To determine which chatbot intent is the best match for the user textual input, we rely on nlp.js (in JS) though we are in the process of moving to our new Python NLP server for better optimization of the needs of eCommerce conversations. A preprocessor language model is also used to improve the chances of a matching.
The eCommerce chatbot is implemented as a WordPress plugin in PHP whose mission is to simply embed in the proper PHP WP pages the Xatkit widget displaying the bot. The widget itself is a react component that talks with the server component managing the core chatbot logic via a websocket.
As the bot is automatically adapted to the data of the eCommerce shop hosting the bot, the bot logic communicates with the WooCommerce API and stores some shop data into a SQL PostgreSQL database.
Our eCommerce dashboard is a Spring application relying on Thymeleaf as server-side Java template engine and foundation as responsive front-end framework.
The eCommerce chatbot is implemented as a WordPress plugin in PHP whose mission is to simply embed in the proper PHP WP pages the Xatkit widget displaying the bot. The widget itself is a react component that talks with the server component managing the core chatbot logic via a websocket.
The core elements of the ecommerce bot (the intents, training sentences and business logic to execute for every matched intent) are implemented with our Java Fluent API that wraps a set of EMF-based core classes modeling our chatbot specification primitives and the help of Lombok to simplify the writing of the bot specification.
Our eCommerce dashboard is a Spring application relying on Thymeleaf as server-side Java template engine and foundation as responsive front-end framework.