Connect Timeneye and Pinecone to Build Intelligent Automations
Enable Integrations or automations with these events of Timeneye and Pinecone
Enable Integrations or automations with these events of Timeneye and Pinecone
Actions
Find a Project
Get an existing project details by its project name
Create project
Create an project
Create an index
Create an index for vectors created with an external embedding model.
Generate vectors
Generate vector embeddings for input data.
Delete an Index
Removes a specified index by its ID or name.
Fetch Index by Name
Returns details and structure of a specified index.
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Frequently Asked Questions
To start, connect both your Timeneye and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in Timeneye triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how Timeneye 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 Timeneye 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 Timeneye 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 Timeneye
Timeneye is a time tracking and project management tool designed to help teams and individuals efficiently manage their time and projects. With features like time tracking, reporting, and integrations with other productivity tools, Timeneye aims to streamline workflows and improve productivity.
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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