Connect ChatBotKit and Neo4j to Build Intelligent Automations
Enable Integrations or automations with these events of ChatBotKit and Neo4j
Enable Integrations or automations with these events of ChatBotKit and Neo4j
Actions
Add Dataset Record
Add a new text record to the selected dataset.
Create Conversation
Create a new conversation with initial messages; optionally add a contact, task, space, or bot.
Create dataset
Create a dataset to train or update your chatbot
Fetch DataSet
This action fetch dataset detail.
Delete dataset
Delete a dataset from ChatBotKit
Create Record
This action create record By datasetID.
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Frequently Asked Questions
To start, connect both your ChatBotKit and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in ChatBotKit triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how ChatBotKit data is recorded in Neo4j. This includes choosing which data fields go into which fields of Neo4j, setting up custom formats, and filtering out unwanted information.
The data sync between ChatBotKit and Neo4j typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between ChatBotKit and Neo4j. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About ChatBotKit
ChatBotKit is a comprehensive platform designed to help businesses and developers create, manage, and deploy chatbots with ease. It offers a range of tools and features to build intelligent conversational agents that can enhance customer interaction and automate support tasks.
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About Neo4j
Neo4j is a leading graph database platform that enables organizations to unlock the value of connections, influences, and relationships in data. It is designed to handle highly connected data and is used for a variety of applications such as fraud detection, real-time recommendations, and network management.
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