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How do I configure the FeatureBase 6 Vector Store node?#

Vector stores are specially designed databases used to store a one-dimensional array of numerical data used to map bias and weight in machine learning models.

The FeatureBase 6 Vector Store node requires authenticates with a FeatureBase database and requires a table containing at least one VECTOR column.

The Vector Store node is designed to:

  • GET ranked documents
  • INSERT documents
  • RETRIEVE documents for use with AI nodes

Before you begin#

Step 1 - create FeatureBase Vector Store table#

Note

The Vector dimension must match in the node and the FeatureBase table

Create a destination table in FeatureBase:

Step 2 - add the node#

  • Click + to open the nodes panel
  • Search for FeatureBase 6 Vector Store in the node search field.
  • Click the node to add it to the canvas.
  • Choose an appropriate FeatureBase credential.

Step 3 - set parameters#

There are three ways to add parameters to Fixed or Expression fields in a given node:

Method Description Additional information
Load from previous nodes Click Execute previous nodes in the Input panel to load parameters from connected and configured nodes Not available for Triggers
Add a fixed value Click Fixed on available fields to enter plain-text or JSON values Learn how to edit fixed responses
Add a JSON expression Click Expression on available fields to enter a JSON expression Learn how to edit expressions

Note

The Expression editor loads all possible parameters from connected nodes. These can then be added to fields as required

Note

Click Test to load values from input nodes you can drag and drop into fields to map to your output values

| Parameter | Description | Additional information | |---|---|---|---| | Credential | FeatureBase database ID and API key | FeatureBase Credential | | FeatureBase Table | Vector table in your FeatureBase database | |

Step 3 - set operation mode#

  • Set an Operation Mode parameter:
Parameter Description Additional information
Get many Retrieve vector values from a connected vector based on the numerical similarity to a given prompt Learn about vector stores
Insert documents Insert vector values to a connected vector store Learn about vector stores
Retrieve documents (for Agent/Chain) Retrieve vector values from a vector store, then supply them to a Vector retriever connected to an Agent or Chain node * Learn about Agent nodes
* Learn about chain nodes
  • Set additional parameters based on the Operation Mode:
Parameter Description Used with
Memory key Unique identifier that takes the form <workflow-id><vector-store-key> * Get Many
* Insert documents * Retrieve documents
Prompt Search query Get Many
Limit Number of results to retrieve from the Vector store Get Many
Clear store Delete data at a specified memory key before inserting new data

Next step#