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Production use of this feature is available for specific editions only. Contact our sales team for more information.
Postgres Vector Upsert is an orchestration component that lets you convert text data stored in your cloud data warehouse into embeddings and then store these embeddings as vectors in your Postgres vector database.

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Video example

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Properties

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Name
string
required
A human-readable name for the component.
Select your cloud data warehouse.
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Database
drop-down
required
The Snowflake database. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
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Schema
drop-down
required
The Snowflake schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
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Table
string
required
The Snowflake table that holds your source data.
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Key Column
drop-down
required
Set a column as the primary key.
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Text Column
drop-down
required
The column of data to convert into embeddings to then be upserted into your Postgres vector database.
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Limit
integer
Set a limit for the numbers of rows from the table to load. The default is 1000.
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Embedding Provider
drop-down
required
The embedding provider is the API service used to convert the search term into a vector. Choose either OpenAI or Amazon Bedrock. The embedding provider receives a search term (e.g. “How do I log in?”) and returns a vector.Choose your provider:
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OpenAI API Key
drop-down
required
Use the drop-down menu to select the corresponding secret definition that denotes the value of your OpenAI API key.Read Secrets and secret definitions to learn how to create a new secret definition.To create a new OpenAI API key:
  1. Log in to OpenAI.
  2. Click your avatar in the top-right of the UI.
  3. Click View API keys.
  4. Click + Create new secret key.
  5. Give a name for your new secret key and click Create secret key.
  6. Copy your new secret key and save it. Then click Done.
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Embedding Model
drop-down
required
Select an embedding model.Currently supports:
ModelDimension
text-embedding-ada-0021536
text-embedding-3-small1536
text-embedding-3-large3072
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API Batch Size
integer
required
Set the size of array of data per API call. The default size is 10. When set to 10, 1000 rows would therefore require 100 API calls.You may wish to reduce this number if a row contains a high volume of data; and conversely, increase this number for rows with low data volume.
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Host
string
required
Your Postgres hostname.
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Port
string
required
The TCP port number the Postgres server listens on. The default is 5432.
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Database
string
required
The name of your Postgres database.
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Username
string
required
Your Postgres username.
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Password
drop-down
required
Use the drop-down menu to select the corresponding secret definition that denotes the value of your Postgres password.Read Secrets and secret definitions to learn how to create a new secret definition.
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Schema
drop-down
required
The Postgres schema. The available schemas are determined by the Postgres database you have provided.
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Table
drop-down
required
The table to load data from. The available tables are determined by the Postgres schema you have selected.
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Key Column Name
drop-down
required
The column in your table to use as the key column.
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Text Column Name
drop-down
required
The column in your table with your original text data.
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Embedding Column Name
drop-down
required
The column in your table used to store your embeddings.
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Connection Options
column editor
required
  • Parameter: A JDBC Postgres parameter supported by the database driver.
  • Value: A value for the given parameter.