Vector search
Vector search in a PostgreSQL database.
Example 
This flow processes a user's chat question by first receiving it through a Chat completion trigger, then converting it into a vector using a Text embedding generator, performing a Vector search in a postgreSQL database for relevant context, and finally passing the user input and retrieved context to Azure OpenAI Chat completion, which generates a response that is returned to the client via the Return node.
Properties
| Name | Type | Description |
|---|---|---|
| Title | Optional | The title or name of the action. |
| Connection | Required | The PostgreSQL database connection that will be used for the search. |
| Table | Required | The name of the table where the vector search will be performed. |
| Search text | Required | The input text string used to perform the vector search, can be provided by a trigger (see below). |
| Record definitions | Required | The definition of columns used in vector search. Note! The vector column is not part of the returned data. |
| Filter | Optional | A filter expression to narrow down the records (e.g., category == cats ). |
| Top | Optional | The maximum number of top results to return. |
| Skip | Optional | The number of top results to skip (default: 0). |
| Distance function | Optional | The method for calculating vector similarity, e.g., Cosine Distance (default). |
| Score limit | Optional | A threshold value that limits results to those with a distance score at or below this score (for e.g. CosineDistance). |
| Search result variable name | Optional | The name of the variable to store results. |
| Description | Optional | Any additional notes or information relevant to the search configuration. |
Returns
The Vector Search action returns an object containing a ToPrompt() function that is used by the Chat completion Context property (in the example above). The resultObject also contains a set of records containing the key, content and vector columns that the user has specified as Record Definitions. 
