💻Inserting with API
Inserting with the Vector AI API
If you are looking for more flexibility when you are inserting your documents via the API or Python SDK, then the following is for you:
When inserting your data into Vector AI, you will need a way to encode vectors as you insert. As a result, we have built a few ways to allow users to flexibly encode vectors.
There is no right endpoint for inserting. Different users will have different insertion and encoding preferences based on technical requirements. A few examples of such situations:
Shirley (data scientist) needs to insert her data into Vector AI. However, her model is not yet deployed and she needs to test the results before deploying to ensure her vectors work as intended. So she encodes all her documents prior to inserting and searches using her locally saved model.
Krissy (data engineer) has just deployed her model. However, over time, she realizes she can't keep encoding her models locally as she is not always at her computer. Instead, she ploys her model and ensures that she is able to encode while inserting to ensure that her stakeholders are using the right data.
Tom (machine learning engineer) has a serious amount of data. He realizes he needs to run an encoding job later and try out different vector searches. For this, he quickly inserts them all into Vector AI and then runs multiple different encoding jobs that adds new vectors afterwards.
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