Connect Nibo and Pinecone to Build Intelligent Automations
Enable Integrations or automations with these events of Nibo and Pinecone
Enable Integrations or automations with these events of Nibo and Pinecone
Actions
Create Bank Account
Add a company bank account with name, bank, agency, account number, verification digit, and opening balance/date.
Create Employee
Create a new employee record with contact, address, bank, and company details.
Create Cost Center
Create a cost center using the provided description.
View Account Statement
Shows the account statement for a selected account and date range.
Create an index
Create an index for vectors created with an external embedding model.
Generate vectors
Generate vector embeddings for input data.
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Frequently Asked Questions
To start, connect both your Nibo and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in Nibo triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how Nibo data is recorded in Pinecone. This includes choosing which data fields go into which fields of Pinecone, setting up custom formats, and filtering out unwanted information.
The data sync between Nibo and Pinecone 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 Nibo and Pinecone. 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 Nibo
Nibo is a comprehensive platform designed to streamline financial management and accounting processes for businesses. It offers tools for bookkeeping, financial reporting, and tax management, making it easier for companies to maintain accurate financial records and ensure compliance.
Learn MoreAbout Pinecone
Pinecone is a vector database designed to provide fast and scalable similarity search and machine learning applications. It enables developers to build high-performance applications that require real-time vector search and retrieval, making it ideal for AI-driven solutions.
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