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Spice v1.5.0-rc.2 (July 14, 2025)

This is the second release candidate for v1.5.0, which introduces SQL-integrated vector and full-text search, partitioning for DuckDB acceleration, and automated refreshes for search indexes and views. It adds a new AWS Bedrock Embeddings Model Provider, a new Oracle Database connector, and promotes the Spice.ai Cloud Data Connector to stable, alongside multi-column vector search for expanded search. This release also upgrades DuckDB from v1.1.3 to v1.3.2, accelerating Spice.ai datasets with improved indexes, query performance, and internal storage optimizations.

What's New in v1.5.0-rc.2

SQL-integrated Search: Vector and full-text search capabilities are now natively available in SQL queries, extending the power of the POST v1/search endpoint to all SQL workflows.

Example Vector-Similarity-Search (VSS) using the new vector_search UDTF on the table reviews for the search term "Cricket bats".

:::sql
SELECT review_id, review_text, review_date, score
FROM vector_search(reviews, "Cricket bats")
WHERE country_code="AUS"
LIMIT 3

Example Full-Text-Search (FTS) using the new text_search UDTF on the table reviews for the search term "Cricket bats".

:::sql
SELECT review_id, review_text, review_date, score
FROM text_search(reviews, "Cricket bats")
LIMIT 3

DuckDB v1.3.2 Upgrade: Upgraded DuckDB engine from v1.1.3 to v1.3.2. Key improvements include support for adding primary keys to existing tables, resolution of over-eager unique constraint checking for smoother inserts, and 13% reduced runtime on TPC-H SF100 queries through extensive optimizer refinements. The v1.2.x release of DuckDB was skipped due to a regression in indexes.

Partitioned Acceleration: DuckDB file-based accelerations now support partition_by expressions, enabling queries to scale to large datasets through automatic data partitioning and query predicate pruning. New UDFs, bucket and truncate, simplify partition logic.

New UDFs useful for partition_by expressions:

  • bucket(num_buckets, col): Partitions a column into a specified number of buckets based on a hash of the column value.
  • truncate(width, col): Truncates a column to a specified width, aligning values to the nearest lower multiple (e.g., truncate(10, 101) = 100).

Example Spicepod.yml configuration:

:::yaml
datasets:
  - from: s3://my_bucket/some_large_table/
    name: my_table
    params:
      file_format: parquet
    acceleration:
      enabled: true
      engine: duckdb
      mode: file
      partition_by: bucket(100, account_id) # Partition account_id into 100 buckets

Full-Text-Search (FTS) Index Refresh: Accelerated datasets with search indexes maintain up-to-date results with configurable refresh intervals.

Example refreshing search indexes on body every 10 seconds (based on acceleration.refresh_check_interval).

:::yaml
datasets:
  - from: github:github.com/spiceai/docs/pulls
    name: spiceai.doc.pulls
    params:
      github_token: ${secrets:GITHUB_TOKEN}
    acceleration:
      enabled: true
      refresh_mode: full
      refresh_check_interval: 10s
    columns:
      - name: body
        full_text_search:
          enabled: true
          row_id:
            - id

Scheduled View Refresh: Accelerated Views now support cron-based refresh schedules using refresh_cron, automating updates for accelerated data.

Example Spicepod.yml configuration:

:::yaml
views:
  - name: my_view
    sql: SELECT 1
    acceleration:
      enabled: true
      refresh_cron: '0 * * * *' # Every hour

For more details, refer to Scheduled Refreshes.

  • Multi-column Vector Search: For datasets configured with embeddings on more than one column, POST v1/search and similarity_search will perform parallel vector search on each column, and aggregate results using a reciprocal rank fusion scoring method.

Example Spicepod.yml where search results will consider both the Github issue's title and the content of its body.

:::yaml
datasets:
  - from: github:github.com/apache/datafusion/issues
    name: datafusion.issues
    params:
      github_token: ${secrets:GITHUB_TOKEN}
    columns:
      - name: title
        embeddings:
          - from: hf_minilm
      - name: body
        embeddings:
          - from: openai_embeddings

AWS Bedrock Embeddings Model Provider: Added support for AWS Bedrock embedding models, including Amazon Titan Text Embeddings and Cohere Text Embeddings.

Example Spicepod.yaml:

:::yaml
embeddings:
  - from: bedrock:cohere.embed-english-v3
    name: cohere-embeddings
    params:
      aws_region: us-east-1
      input_type: search_document
      truncate: END

  - from: bedrock:amazon.titan-embed-text-v2:0
    name: titan-embeddings
    params:
      aws_region: us-east-1
      dimensions: '256'

For more details, refer to the AWS Bedrock Embedding Models Documentation.

Oracle Data Connector: Use from: oracle: to access and accelerate data stored in Oracle databases, deployed on-premises or in the cloud.

Example Spicepod.yml:

:::yaml
datasets:
  - from: oracle:"SH"."PRODUCTS"
    name: products
    params:
      oracle_host: 127.0.0.1
      oracle_username: scott
      oracle_password: tiger

See the Oracle Data Connector documentation for details.

Spice.ai Cloud Data Connector: Graduated to Stable.

Contributors

Breaking Changes

  • Search HTTP API Response: POST v1/search response payload has changed. See the new API documentation for details.

  • Model Provider Parameter Prefixes: Model Provider parameters use provider-specific prefixes instead of openai_ prefixes (e.g., hf_temperature instead of openai_temperature for HuggingFace, anthropic_max_completion_tokens for Anthropic, perplexity_tool_choice for Perplexity). The openai_ prefix remains supported for backward compatibility but is now deprecated will be removed in a future release.

Cookbook Updates

The Spice Cookbook now includes 72 recipes to help you get started with Spice quickly and easily.

Upgrading

To upgrade to v1.5.0-rc.2, download and install the specific binary from github.com/spiceai/spiceai/releases/tag/v1.5.0-rc.2 or pull the v1.5.0-rc.2 Docker image (spiceai/spiceai:1.5.0-rc.2).

What's Changed

Dependencies

Changelog

Source: README.md, updated 2025-07-15