Environment Data Structure
Each environment has all their data contained within their own, isolated database structure
Environment schema
Answer
The answer table stores the settings for the response that are created in the virtual agent. The answer can be identified by a name (for example “Welcome”), a description and tags related to the answer subject. It also can be configured to be evaluable and/or transactional.
This table is used on Virtual Agents where Automated Learning is not used; if you want the AL's counterpart, go to question.
Answer Template
The answer_template is the content of an answer that was created in either the answer table or the question table, but never in both. Either can have many records in the answer_template table, since each channel that a particular answer is delivered to may have a custom template. The content is the answer that will be sent to the user. For example, the name of the answer is “Welcome” and the content is “Hello, I’m a virtual assistant”
API Key
The api_key table stores API keys used to validate Syntphony CAI’s service calls.
Automated Test
The automated_tests stores general information about automated tests. To execute an automated test, the user must fill a spreadsheet and insert it in the Cockpit. Once executed, the results are stored in the automated_tests_execution table.
Automated Tests Execution
The automated tests execution table stores the results of an automated tests, by attempt. In previous versions, this data was located within the automated test itself and was overwritten on execution.
Automated Test Utterance
An automated_test_utterance is a single instance of exemple provided for an automated test. They belong to automated_tests.
Automated Test Utterance Execution
The automated_test_utterance_execution table stores the individual execution results per utterance.
Bot
This table stores all information from a virtual agent(VA), internally named bot. There is a homonym table in the admin data structure, which stores the bot's permission access and envorinment relations, while this one stores Cockpit data and it's logical functions.
Bulk Training
Bulk trainings contains bulk-inserted intents and utterances. This table refers to the bulk as a whole and it's execution status. Each individual intent and utterance are stored in bulk_training_item.
Bulk Training Item
The bulk_training_item represents one line of a provided file in a bulk_training.
Channel
The channel table stores channel data creation. It is possible to identify to which virtual agent the channel is associated, the channel name and type.
Channel Type
The channel_type table stores the types of channels. Within each classification, there are of channels types, such as:
Smart Speakers & Social Robots: Amazon Echo e Google Home
Smart Assistants: Alexa, Cortana e Siri
Messaging Platform: Facebook, Twitter e Skype
Synthetic Reality: ARCore e Samsung Gear VR
Mobile/Tablet/Desktop: Andriod, iOS e Web
Cognitive Contact Center: IVR e VR
Channel Classification
The channel_classification table stores the classifications of channels, each one identified by an ID. These groups are:
Smart Speakers & Social Robots
Smart Assistants
Messaging Platform
Synthetic Reality
Mobile/Tablet/Desktop
Cognitive Contact Center
Configuration
The configuration tables stores environment's configuration keys. Those may be bot-specific, if a bot_uuid is provided, otherwise they behave as global configurations for the whole environment.
Entity
The entity table stores configurations for entities created in the virtual agent. An example would be the entity “sport”.
Document
The document table stores documents supplied by a user to properly configure the Automated Learning functionality. This structure stores data regarding a document and their path to your private bucket.
Entity Value
The entity_value table stores the entity content. So, in the entity “sport”, the values would be “football”, “basketball” or “tennis”.
Entity Sample
The entity_sample table stores words that has a similar meaning to the entity value. For example, if the entity value is “football”, it could store “soccer” or “association football”.
Entity System
The entity_system table contains a map of all existing system entities.
Facebook Configuration
The facebook_configuration stores the chat configuration in a Facebook page.
Facebook User
The facebook_user table stores the data of the facebook user who interacted with the virtual agent.
Input
The input table contains possible Wait input cells within a dialog flow and their call to actions.
Input Type
A map of all existing Input types.
Intents
The intents table stores the configuration of intents created in the virtual agent.
To learn more how to use Intents in Syntphony CAI
NLP Engine
The nlp_engine stores NLP integration data.
Question
The question table stores questions related to the Automated Learning functionality. It relates to a document in which it should have it's text consulted for replies. This table has fields akin to Answer and is it's counterpart for AL using agents.
Question Variable
The question table stores variables associated to questions. A question may have more than on variable assigned to it. Variables are freeform samples of queries for a question such as "What is a Virtual Agent?" or "Where can i find documentations about Virtual Agents?".
Satisfaction
The satisfaction table stores the user's assessment of virtual agent attendance. At the end of the session, the user is asked to evaluate the virtual agent service and comment their experience. This table can generate the following reports:
Satisfaction survey result: shows the result of the satisfaction survey, that allows to analyse the level of user satisfaction with a grading system.
Volume of answer: total amount of responses from the satisfaction survey
Solved questions: number of users who had their questions answered
Session
The session table has all the services made by the virtual agent. No matter how many questions the user has asked during a session, only one record will appear for each session. This table can generate reports such as:
Average daily sessions: how many sessions are run per day.
Volume of session: total sessions. It can be broke down in different time frames, such as month or timeframe/total sessions.
Channel/session evolution: a line graph showing the evolution of the number of sessions, making possible to see if there was a increase or decrease.
Tags
The tags table stores the tags created in the virtual agent. Tags helps to identify objects, and they are shared between all objects with same-named tags in a bot.
Tag Use
The tag_uses table stores tag data and which repository it is related to, allowing to identify the tag, repository type and which repository ID the tag is related to. They must connect to an object among entity, answer, intent, service or flow; but may not connect to more than one.
Technical Log
The technical_log table stores technical information from user questions, such as response times and service calls, information about calls and execution errors on a non-volatile fashion. When the response from the Syntphony CAI chat service returns an error, it also sends a UUID for troubleshooting, found within this table.
This table is currently disabled at Syntphony CAI 4.1.0.
The structure is present, but no logs will be registered until further notice.
Training
The training table stores a virtual agent training proccess whenever it is not using Automated Learning. When a user trains a virtual agent in the Cockpit, the ongoing and resulting data are stored in this table.
Training AL
The training_al table stores a virtual agent training proccess whenever it is using Automated Learning. When a user trains a virtual agent in the Cockpit, the ongoing and resulting data are stored in this table.
Training AL Document
The training_al_documents table stores a virtual agent's training proccess detail document-wise, describing each's document's status regarding this one training proccess.
Transactional Service
The transactional_service table stores the transactional calls performed during a session. One may identify which service was called and the answer content by the webhook field.
User Interaction
The user_interaction table stores each one of the users interactions, be the question asked or the answer sent by the virtual agent. The interactions between user and virtual agent are stored and identified by the session code. It is also possible to identify whether it was a user or a virtual agent interaction. This table can generate the following reports:
Volume of questions: total amount of questions asked by users
Number of questions per channel: total number of user questions per channel
Question evolution: a graph showing the number of questions from the users evolution.
Average question per user: shows the average number of questions per user.
Top 10 words: ranking with the 10 most typed words by users
Utterance
The utterances table stores intent examples. When a user creates an intent in the virtual agent, he must add sentences that can appear in a conversation with the virtual agent. Those are these sentences.
Whatsapp User
This table stores data regarding the 'user' your infobip whatsapp channel is using.
Relationship tables
Bot-Nlp Engine Relationship
The bot_nlp_engine table stores information connecting a bot to an instance of a chosen nlp_engine.
Nlp Engine-Entity System Relationship
The nlp_engine_entity_system table connects System Entities to Nlp Engines. When the Cockpit user configures the usage of a System Entity on a given bot, this connection is created. When connected, an System Entity is enabled for the NLP, while it's absence means it is disabled.
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