Spice v1.5.0-rc.3 (July 16, 2025)

This is the third release candidate for v1.5.0, building on the capabilities introduced in v1.5.0-rc.2. This release introduces native support for Amazon S3 Vectors, enabling petabyte scale vector search directly from S3 vector buckets, alongside SQL-integrated vector and full-text search, partitioning for DuckDB acceleration, and automated refreshes for search indexes and views. It includes the AWS Bedrock Embeddings Model Provider, the Oracle Database connector, and the now-stable Spice.ai Cloud Data Connector, and the upgrade to DuckDB v1.3.2.

What's New in v1.5.0-rc.3

Amazon S3 Vectors Support: Spice.ai now integrates with Amazon S3 Vectors, launched in public preview on July 15, 2025, enabling vector-native object storage with built-in indexing and querying. This integration supports semantic search, recommendation systems, and retrieval-augmented generation (RAG) at petabyte scale with S3’s durability and elasticity. Spice.ai manages the vector lifecycle—ingesting data, embedding it with models like Amazon Titan or Cohere via AWS Bedrock, or MiniLM L6 available from HuggingFace, and storing it in S3 Vector buckets.

Example Spicepod.yml configuration for S3 Vectors:

:::yaml
datasets:
  - from: s3://my_vector_bucket/data/
    name: my_vectors
    params:
      file_format: parquet
    acceleration:
      enabled: true
    vectors:
      engine: s3_vectors
      params:
        s3_vectors_aws_region: us-east-2
        s3_vectors_bucket: my-s3-vectors-bucket
    columns:
      - name: content
        embeddings:
          - from: bedrock_titan
            row_id:
              - id

Example SQL query using S3 Vectors:

:::sql
SELECT *
FROM vector_search(my_vectors, 'Cricket bats', 10)
WHERE price < 100
ORDER BY score

For more details, refer to the S3 Vectors Documentation.

Highlights in v1.5.0-rc.3

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 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 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.

• Read the DuckDB v1.2.0 announcement.
• Read the DuckDB v1.3.0 announcement.

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:

:::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 perform parallel vector search on each column, aggregating results using reciprocal rank fusion.

Example Spicepod.yml for multi-column search:

:::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.yml:

:::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.

Spice.ai Cloud Data Connector: Graduated to Stable.

Contributors

• @Jeadie
• @peasee
• @sgrebnov
• @Sevenannn
• @kczimm
• @phillipleblanc
• @Advayp
• @lukekim
• @ewgenius

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 for HuggingFace, anthropic_max_completion_tokens for Anthropic, perplexity_tool_choice for Perplexity). The openai_ prefix remains supported for backward compatibility but is deprecated and will be removed in a future release.

Cookbook Updates

• Added Oracle Data Connector cookbook: Connect to tables in Oracle databases.
• Added Hashed Partitioning with DuckDB cookbook: Accelerate data on large datasets by partitioning data into a fixed number of buckets.

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.3, download and install the specific binary from github.com/spiceai/spiceai/releases/tag/v1.5.0-rc.3 or pull the v1.5.0-rc.3 Docker image (spiceai/spiceai:1.5.0-rc.3).

What's Changed

Dependencies

• delta_kernel: Upgraded to v0.12.1
• DuckDB: Upgraded from v1.1.3 to v1.3.2
• iceberg-rust: Upgraded from v0.4.0 to v0.5.1

Changelog

• v1.5.0-rc.2 release notes (#6440) by @lukekim in #6440
• Amazon S3 Vectors support by @lukekim in #6468