PostgreSQL - Vector Search
In this article
Overvie
Snap Type
The PostgreSQL - Vector Search Snap is a Read-type Snap.
Prerequisites
A valid account with the required permissions.
Support for Ultra Pipelines
Works in Ultra Pipelines.Â
Limitations and Known Issues
None.
Snap Views
Type | Format | Number of Views | Examples of Upstream and Downstream Snaps | Description |
---|---|---|---|---|
Input | Document   |
|
|
|
Output | Document  |
|
| For each input document, all results are grouped in a single output document. |
Error | Error handling is a generic way to handle errors without losing data or failing the Snap execution. You can handle the errors that the Snap might encounter when running the pipeline by choosing one of the following options from the When errors occur list under the Views tab:
Learn more about Error handling in Pipelines. |
Snap Settings
Asterisk ( * ): Indicates a mandatory field.
Suggestion icon (): Indicates a list that is dynamically populated based on the configuration.
Expression icon ( ): Indicates the value is an expression (if enabled) or a static value (if disabled). Learn more about Using Expressions in SnapLogic.
Add icon ( ): Indicates that you can add fields in the field set.
Remove icon ( ): Indicates that you can remove fields from the field set.
Upload icon ( ): Indicates that you can upload files.
Field Name | Field Type | Description |
---|---|---|
Label* Â Default Value:Â PostgreSQL Vector Search | String | Specify a name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snaps in your pipeline. Â |
Schema name  Default Value: N/A | String/Expression/Suggestion | Specify the schema name for searching for a vector. |
Table Name* Â Default Value: N/AÂ | String/Expression/Suggestion | Specify the table name for searching for a vector. |
Vector Column* Â Default Value:Â N/A | String/Expression/Suggestion | Specify the vector column name to search. |
Where Clause  Default Value: N/A | String/Expression/Suggestion | Specify the where clause to use in the vector search query statement. Because of the limitation of theSQL standard, you cannot use the |
Limit Rows  Default Value: 4 | Integer/Expression | Specify the number of rows the query must return.  |
Distance Function* Â Default Value:Â L2 | Dropdown List | Choose the similarity function to compare vectors. The available options are:
Learn more about the Vector Similarity Functions. |
Include vector values  Default Value: Deselected | Checkbox/Expression | Select this checkbox to include vector values in the response. This field does not support input schema from the upstream Snaps. |
Include scores  Default Value: Selected | Checkbox/Expression | Select this checkbox to include similarity scores in the response. |
Ignore empty result Default Value: Deselected | Checkbox | Select this checkbox to ignore the empty results and not write a document to the output view when a search operation returns no results.. |
Number of retries  Default Value: 0 | Integer/Expression | Specify the maximum number of retry attempts the Snap must make if a network failure occurs. |
Retry interval (seconds) Â Default Value:Â 0 | Integer/Expression | Specify the time period between two successive retry requests. |
Snap execution Default Value:Â Validate & Execute | Dropdown list | Select one of the following three modes in which the Snap executes:
|
Example
Use Cosine Distance to Find Similar Vectors
The example pipeline below demonstrates how to use the PostgreSQL - Vector Search Snap to find similar vectors using the Cosine distance function.
Step 1: Configure the Mapper Snap with a vector to find similar vectors in the PostgreSQL database.
Step 2: Configure the PostgreSQL - Vector Search Snap as shown below:
Step 3: Validate the pipeline. On validation, the Snap fetches similar vectors based on the following criteria:
The match vectors have cosine similarity distances, indicating their similarity to the input vector.
The cosine similarity distances measure how close the match vectors are to the input vector, with values closer to 0, indicating higher similarity.
The first match has the highest similarity (lowest distance), followed by the second match.
Related Content
Have feedback? Email documentation@snaplogic.com | Ask a question in the SnapLogic Community
© 2017-2024 SnapLogic, Inc.