Save vectors

Generates and saves vector records to a PostgreSQL database from text input.

Example
This flow reads files from OneDrive, converts their content to text, splits it into chunks, generates embedding vectors, and stores them in a PostgreSQL database for semantic search.

This action allows saving a text as a list of records in a PostgreSQL vector collection, in a specific format (Profitbase). This is done using the following steps:

1. Split - The input text is split into chunks using the Text splitter (link).
2. Embed - Each of the chunks resulting from the previous step is embedded using the Text embedder (link)
3. Build - A new collection record is built for each of the embedded chunk.
4. Upsert - The newly created record is inserted in the database collection.

Properties

Name	Type	Description
Title	Optional	The title or name of the action.
Connection	Required	Select or define your PostgreSQL connection.
Input text	Required	Text to vectorize and save. Can be a variable or static string.
Collection name	Required	The name of the table or collection in the database where vectors are saved.
Record definition	Optional	The definition of columns used in the vector search. Note: The vector column is not returned.
Connection timeout	Optional	Timeout duration for the connection (in seconds). Default is 60.
Description	Optional	Explanation of what this action does.

Collection Schema

The collection has the following columns:

• key (String):
  Contains the record's key. This is automatically generated by the node.

• content (String):
  Contains the plain-text of a chunk.

• vector (Vector):
  Contains the vector representation of the content column.

• context (String):
  Contains the context of the text inputted in the node. This can be a document name, a URL, or a generic ID representing the resource to which the text pertains.

• additional columns:
  For each custom column defined in the node, a new collection column will be created (including the specified data type).

Note

In order to use this action, make sure that your Postgres instance has the pgvector extension installed.