IntegrationsBotpressTimescaleDB
Botpress + TimescaleDB

Connect Botpress and TimescaleDB to Build Intelligent Automations

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TimescaleDB

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Frequently Asked Questions

How do I start an integration between Botpress and TimescaleDB?

To start, connect both your Botpress and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Botpress triggers actions in TimescaleDB (or vice versa).

Can we customize how data from Botpress is recorded in TimescaleDB?

Absolutely. You can customize how Botpress data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.

How often does the data sync between Botpress and TimescaleDB?

The data sync between Botpress and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.

Can I filter or transform data before sending it from Botpress to TimescaleDB?

Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.

Is it possible to add conditions to the integration between Botpress and TimescaleDB?

Yes, you can set conditional logic to control the flow of data between Botpress and TimescaleDB. 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.

Botpress

About Botpress

Botpress is a leading open-source platform designed for creating, managing, and deploying chatbots. It offers a comprehensive suite of tools for developers to build intelligent conversational agents that can be integrated into various applications and services. With its modular architecture and user-friendly interface, Botpress simplifies the process of developing chatbots, making it accessible for both technical and non-technical users.

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TimescaleDB

About TimescaleDB

TimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.

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