Production use of this feature is available for specific editions only. Contact our sales team for more information.
SQL queries will need to be used rather than the create table component to create a destination table. Take care to make sure the vector dimensions match the vector dimensions of the model you intend to use.Example SQL query to create a table with a vector column:
CREATE TABLE "destination-table" ("id" NUMBER, "text" TEXT, "embedding_result" VECTOR(float, 768));The vector dimension is set to a fixed value for each embedding model. To find the value, see the Model property, below.Properties
A human-readable name for the component.
The Snowflake database. The special value
[Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.The Snowflake schema. The special value
[Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.The Snowflake table that holds your source data.
This column is used to uniquely identify each row in the table. It is used to ensure that the data is not duplicated when it is loaded into the destination. An example use case would be a column of product IDs that you want to use to identify each product in the table.
This column is used to generate vectors for the text data in the table, which are then upserted as embeddings to your Snowflake vector database. An example use case of this column would be a column of product reviews that you want to convert into vectors for semantic search or to perform sentiment analysis on.
Set a limit for the maximum number of rows to load from the table. The default is 1000.
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:
- OpenAI
- Amazon Bedrock
Select the secret definition that represents your OpenAI API key.Choose the secret definition that represents your credentials for this connector.If you have not already saved your credentials for this connector as a secret definition, click Add secret to create a secret definition representing these credentials. Read Secrets and secret definitions for details about creating a secret definition.To create a new OpenAI API key:
- Log in to OpenAI.
- Click your avatar in the top-right of the UI.
- Click View API keys.
- Click + Create new secret key.
- Give a name for your new secret key and click Create secret key.
- Copy your new secret key and save it. Then click Done.
Select an embedding model.Currently supports:
| Model | Dimension |
|---|---|
| text-embedding-ada-002 | 1536 |
| text-embedding-3-small | 1536 |
| text-embedding-3-large | 3072 |
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.
The Snowflake destination database. The special value
[Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.The Snowflake destination schema. The special value
[Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.Select the destination table.
The column in the destination table to use as the key column.
The column in the destination table that will hold the copied source data.
The column in the destination table that will hold the vector embeddings.