Announcing the release of Spice v1.5.2! 🛠️
Spice v1.5.2 introduces a new Amazon Bedrock Models Provider for converse API (Nova) compatible models, AWS Redshift support using the Postgres data connector, and Hadoop Catalog Support for Iceberg tables along with several bug fixes and improvements.
Amazon Bedrock Models Provider: Adds a new Amazon Bedrock LLM Provider. Models compatible with the Converse API (Nova) are supported.
Amazon Bedrock provides access to a range of foundation models for generative AI. Spice supports using Bedrock-hosted models by specifying the bedrock prefix in the from field and configuring the required parameters.
Supported Model IDs:
amazon.nova-lite-v1:0amazon.nova-micro-v1:0amazon.nova-premier-v1:0amazon.nova-pro-v1:0Refer to the Amazon Bedrock documentation for details on available models and cross-region inference profiles.
Example Spicepod.yaml:
For more information, see the Amazon Bedrock Documentation.
AWS Redshift Support for Postgres Data Connector: Spice now supports connecting to Amazon Redshift using the PostgreSQL data connector. Redshift is a columnar OLAP database compatible with PostgreSQL, allowing you to use the same connector and configuration parameters.
To connect to Redshift, use the format postgres:schema.table in your Spicepod and set the connection parameters to match your Redshift cluster settings.
Example Spicepod.yaml:
Redshift types are mapped to PostgreSQL types. See the PostgreSQL connector documentation for details on supported types and configuration.
Hadoop Catalog Support for Iceberg: The Iceberg Data and Catalog connectors now support connecting to Hadoop catalogs on filesystem (file://) or S3 object storage (s3://, s3a://). This enables connecting to Iceberg catalogs without a separate catalog provider service.
Example Spicepod.yaml:
For more details, see the Iceberg Data Connector documentation and the Iceberg Catalog Connector documentation.
Parquet Reader: Optional Parquet Page Index: Fixed an issue where the Parquet reader, using arrow-rs and DataFusion, errored on files missing page indexes, despite the Parquet spec allowing optional indexes. The Spice team contributed optional page index support to arrow-rs (PR #6) and configurable handling in DataFusion (PR #93). A new runtime parameter, parquet_page_index, makes Parquet Page Indexes configurable in Spice:
This improves compatibility and query flexibility for Parquet datasets.
Amazon S3 Vectors Vector Engine: Amazon S3 Vectors is currently a preview AWS service. A recent update to the Amazon S3 Vectors service API introduced a breaking change that affects the integration when projecting (selecting) the embedding column. This results in the following error:
Json error: whilst decoding field 'data': expected [ got nullReceived only partial JSON payload from QueryVectors
The issue is expected to be resolved in the next release of Spice. A current workaround is to limit queries to non-embedding columns.
i.e. instead of:
Remove the *_embedding column from the projection. E.g.
This issue and workaround also applies to SELECT * FROM vector_search(..). E.g.
The Spice Cookbook includes 75 recipes to help you get started with Spice quickly and easily.
To upgrade to v1.5.2, use one of the following methods:
CLI:
Homebrew:
Docker:
Pull the spiceai/spiceai:1.5.2 image:
For available tags, see DockerHub.
Helm:
AWS Marketplace:
🎉 Spice is also now available in the AWS Marketplace!
No major dependency updates.