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Spice v1.7.1 (Sep 29, 2025)

· 6 min read
Kevin Zimmerman
Kevin Zimmerman
Principal Software Engineer at Spice AI

Announcing the release of Spice v1.7.1! 🔍

Spice v1.7.1 is a patch release focused on search improvements, bug fixes, and performance enhancements. This release introduces the Reciprocal Rank Fusion (RRF) user-defined table function (UDTF) for hybrid search, improves vector and text search reliability, and resolves several issues across the runtime, connectors, and query engine.

What's New in v1.7.1​

Reciprocal Rank Fusion (RRF) UDTF: Spice now supports Reciprocal Rank Fusion (RRF) as a user-defined table function, enabling advanced hybrid search scenarios that combine results from multiple search methods (e.g., vector and text search) for improved relevance ranking.

Features:

  • Multi-search fusion: Combine results from vector_search, text_search, and other search UDTFs in a single query.
  • Advanced tuning: Per-query ranking weights, recency boosting, and configurable decay functions.
  • Performance: Optional user-specified join key for optimal performance.
  • Automatic joining: Falls back to on-the-fly JOIN key computation when no explicit key is provided.

Example usage:

SELECT id, title, content, fused_score
FROM rrf(
  vector_search(documents, 'machine learning algorithms', rank_weight => 1.5),
  text_search(documents, 'neural networks deep learning', rank_weight => 1.2),
  join_key => 'id',    -- optional join key for optimal performance
  k => 60.0            -- optional smoothing factor
)
WHERE fused_score > 0.01
ORDER BY fused_score DESC;

Learn more in the RRF documentation.

Acceleration Refresh Metrics: Spice now exposes additional Prometheus metrics that provide detailed observability into dataset acceleration refreshes. These metrics help monitor data freshness and ingestion lag for accelerated datasets with a time column.

Reported metrics:

Metric NameDescription
dataset_acceleration_max_timestamp_before_refresh_msMaximum value of the dataset's time column before refresh (milliseconds).
dataset_acceleration_max_timestamp_after_refresh_msMaximum value of the dataset's time column after refresh (milliseconds).
dataset_acceleration_refresh_lag_msDifference between max timestamp after and before refresh (milliseconds).
dataset_acceleration_ingestion_lag_msLag between current wall-clock time and max timestamp after refresh (milliseconds).

These metrics are emitted during each acceleration refresh and can be scraped by Prometheus for monitoring and alerting. For more details, see the Observability documentation.

Bug Fixes & Improvements​

This release resolves several issues and improves reliability across search, connectors, and query planning:

  • Full-Text Search (FTS): Ensure FTS metadata columns can be used in projection, fix JOIN-level filters not having columns in schema, and adds support for persistent file-based FTS indexes. Default limit of 1000 results if no limit specified.
  • Vector Search: Default limit of 1000 results if no limit specified, and fix removing embedding column.
  • Databricks SQL Warehouse: Improved error handling and support for async queries.
  • Other: Fixes for Anthropic model regex validation, tweaked AI-model health checks, and improved error messages.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

  • Added Hybrid-Search using RRF - Combine results from multiple search methods (vector and text search) using Reciprocal Rank Fusion for improved relevance ranking.

The Spice Cookbook includes 78 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.7.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.7.1 image:

docker pull spiceai/spiceai:1.7.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is now available in the AWS Marketplace!

What's Changed​

Changelog​

  • ensure FTS metadata columns can be used in projection (#7282) by @Jeadie in #7282
  • Fix JOIN level filters not having columns in schema (#7287) by @Jeadie in #7287
  • Use file-based fts index (#7024) by @Jeadie in #7024
  • Remove 'PostApplyCandidateGeneration' (#7288) by @Jeadie in #7288
  • RRF: Rank and recency boosting (#7294) by @mach-kernel in #7294
  • RRF: Preserve base ranking when results differ -> FULL OUTER JOIN does not produce time column (#7300) by @mach-kernel in #7300
  • fix removing embedding column (#7302) by @Jeadie in #7302
  • RRF: Fix decay for disjoint result sets (#7305) by @mach-kernel in #7305
  • RRF: Project top scores, do not yield duplicate results (#7306) by @mach-kernel in #7306
  • RRF: Case sensitive column/ident handling (#7309) by @mach-kernel in #7309
  • For vector_search, use a default limit of 1000 if no limit specified (#7311) by @lukekim in #7311
  • Fix Anthropic model regex and add validation tests (#7319) by @ewgenius in #7319
  • Enhancement: Implement before/after/lag metrics for acceleration refresh (#7310) by @krinart in #7310
  • Refactor chat model health check to lower tokens usage for reasoning models (#7317) by @ewgenius in #7317
  • Enable chunking in SearchIndex (#7143) by @Jeadie in #7143
  • Use logical plan in SearchQueryProvider. (#7314) by @Jeadie in #7314
  • FTS max search results 100 -> 1000 (#7331) by @Jeadie in #7331
  • Improve Databricks SQL Warehouse Error Handling (#7332) by @sgrebnov in #7332
  • use spicepod embedding model name for 'model_name' (#7333) by @Jeadie in #7333
  • Handle async queries for Databricks SQL Warehouse API (#7335) by @phillipleblanc in #7335
  • RRF: Fix ident resolution for struct fields, autohashed join key for varying types (#7339) by @mach-kernel in #7339

Spice v1.7.0 (Sep 23, 2025)

· 21 min read
Sergei Grebnov
Sergei Grebnov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.7.0!⚡

Spice v1.7.0 upgrades to DataFusion v49 for improved performance and query optimization, introduces real-time full-text search indexing for CDC streams, EmbeddingGemma support for high-quality embeddings, new search table functions powering the /v1/search API, embedding request caching for faster and cost-efficient search and indexing, and OpenAI Responses API tool calls with streaming. This release also includes numerous bug fixes across CDC streams, vector search, the Kafka Data Connector, and error reporting.

What's New in v1.7.0​

DataFusion v49 Highlights​

DataFusion Clickbench Performance Graph Source: DataFusion 49.0.0 Release Blog.

Performance Improvements 🚀

  • Equivalence System Upgrade: Faster planning for queries with many columns, enabling more sophisticated sort-based optimizations.
  • Dynamic Filters & TopK Pushdown: Queries with ORDER BY and LIMIT now use dynamic filters and physical filter pushdown, skipping unnecessary data reads for much faster top-k queries.
  • Compressed Spill Files: Intermediate files written during sort/group spill to disk are now compressed, reducing disk usage and improving performance.
  • WITHIN GROUP for Ordered-Set Aggregates: Support for ordered-set aggregate functions (e.g., percentile_disc) with WITHIN GROUP.
  • REGEXP_INSTR Function: Find regex match positions in strings.

Spice Runtime Highlights​

EmbeddingGemma Support: Spice now supports EmbeddingGemma, Google's state-of-the-art embedding model for text and documents. EmbeddingGemma provides high-quality, efficient embeddings for semantic search, retrieval, and recommendation tasks. You can use EmbeddingGemma via HuggingFace in your Spicepod configuration:

Example spicepod.yml snippet:

embeddings:
  - from: huggingface:huggingface.co/google/embeddinggemma-300m
    name: embeddinggemma
    params:
      hf_token: ${secrets:HUGGINGFACE_TOKEN}

Learn more about EmbeddingGemma in the official documentation.

POST /v1/search API Use Search Table Functions: The /v1/search API now uses the new text_search and vector_search Table Functions for improved performance.

Embedding Request Caching: The runtime now supports caching embedding requests, reducing latency and cost for repeated content and search requests.

Example spicepod.yml snippet:

runtime:
  caching:
    embeddings:
      enabled: true
      max_size: 128mb
      item_ttl: 5s

See the Caching documentation for details.

Real-Time Indexing for Full Text Search: Full Text search indexing is now supported for connectors that enable real-time changes, such as Debezium CDC streams. Adding a full-text index on a column with refresh_mode: changes works as it does for full/append-mode refreshes, enabling instant search on new data.

Example spicepod.yml snippet:

datasets:
  - from: debezium:cdc.public.question
    name: questions
    acceleration:
      enabled: true
      engine: duckdb
      primary_key: id
      refresh_mode: changes # Use 'changes'
    params: *kafka_params
    columns:
      - name: title
        full_text_search:
          enabled: true # Enable full-text-search indexing
          row_id:
            - id

OpenAI Responses API Tool Calls with Streaming: The OpenAI Responses API now supports tool calls with streaming, enabling advanced model interactions such as web_search and code_interpreter with real-time response streaming. This allows you to invoke OpenAI-hosted tools and receive results as they are generated.

Learn more in the OpenAI Model Provider documentation.

Runtime Output Level Configuration: You can now set the output_level parameter in the Spicepod runtime configuration to control logging verbosity in addition to the existing CLI and environment variable support. Supported values are info, verbose, and very_verbose. The value is applied in the following priority: CLI, environment variables, then YAML configuration.

Example spicepod.yml snippet:

runtime:
  output_level: info # or verbose, very_verbose

For more details on configuring output level, see the Troubleshooting documentation.

Bug Fixes​

Several bugs and issues have been resolved in this release, including:

  • CDC Streams: Fixed issues where refresh_mode: changes could prevent the Spice runtime from becoming Ready, and improved support for full-text indexing on CDC streams.
  • Vector Search: Fixed bugs where vector search HTTP pipeline could not find more than one IndexedTableProvider, and resolved errors with field mismatches in vector_search UDTF.
  • Kafka Integration: Improved Kafka schema inference with configurable sample size, improved consumer group persistence for SQLite and Postgres accelerations, and added cooperative mode support.
  • Perplexity Web Search: Fixed bug where Perplexity web search sometimes used incorrect query schema (limit).
  • Databricks: Fixed issue with unparsing embedded columns.
  • Error Reporting: ThrottlingException is now reported correctly instead of as InternalError.
  • Iceberg Data Connector: Added support for LIMIT pushdown.
  • Amazon S3 Vectors: Fixed ingestion issues with zero-vectors and improved handling when vector index is full.
  • Tracing: Fixed vector search tracing to correctly report SQL status.

Contributors​

New Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

The Spice Cookbook includes 78 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.7.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.7.0 image:

docker pull spiceai/spiceai:1.7.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is now available in the AWS Marketplace!

What's Changed​

Dependencies​

Changelog​

Spice v1.6.1 (Sep 1, 2025)

· 3 min read
Jack Eadie
Jack Eadie
Token Plumber at Spice AI

Announcing the release of Spice v1.6.1! ⚡

Spice 1.6.1 is a patch release that provides improved Kafka type inference and JSON flattening support, alongside several bug fixes.

What's New in v1.6.1​

Improved Kafka Type Inference: Improve Kafka type inference by configuring the number of Kafka messages sampled during schema inference. Increasing the sample size can improve the robustness and reliability of inferred schemas, especially in cases where data contains optional fields or varying structures.

Example spicepod.yml:

dataset:
  - from: kafka:orders_events
    name: orders
    params:
      schema_infer_max_records: 100 # Default 1.

For details, see the Kafka Data Connector Documentation.

Improved Kafka JSON Support: Enable nested JSON Kafka messages to be represented in flattened JSON format for the dataset schema.

Example spicepod.yml:

dataset:
  - from: kafka:orders_events
    name: orders
    params:
      flatten_json: true # default false

For example, the object:

{
  "order_id": "a1f2c3d4-1111-2222-3333-444455556666",
  "customer": {
    "id": 101,
    "name": "Alice",
    "premium": true,
    "contact": {
      "email": "[email protected]",
      "phone": "555-1234"
    }
  },
  "discount": 5.0,
  "shipped": false
}

With flatten_json: true the result is:

+------------------------+-----------+-------------+
| column_name            | data_type | is_nullable |
+------------------------+-----------+-------------+
| order_id               | Utf8      | YES         |
| customer.id            | Int64     | YES         |
| customer.name          | Utf8      | YES         |
| customer.premium       | Boolean   | YES         |
| customer.contact.email | Utf8      | YES         |
| customer.contact.phone | Utf8      | YES         |
| discount               | Float64   | YES         |
| shipped                | Boolean   | YES         |
+------------------------+-----------+-------------+

With flatten_json: false or ommitted the result is:

+-------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+
| column_name | data_type                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | is_nullable |
+-------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+
| order_id    | Utf8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | YES         |
| customer    | Struct([Field { name: "id", data_type: Int64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "name", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "premium", data_type: Boolean, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "contact", data_type: Struct([Field { name: "email", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "phone", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]) | YES         |
| discount    | Float64                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | YES         |
| shipped     | Boolean                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | YES         |
+-------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------+

For details, see the Kafka Data Connector Documentation.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No new cookbook recipes added in this release.

The Spice Cookbook includes 77 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.6.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.6.1 image:

docker pull spiceai/spiceai:1.6.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is now available in the AWS Marketplace!

What's Changed​

Changelog​

  • Fix metadata field issue by @Advayp in #6957
  • Update datafusion and datafusion-table-providers crates (#6985) by @Jeadie in #6985
  • Add flatten_json param support for Kafka connector (#6976) by @sgrebnov in #6976
  • Add schema_inference_sample_count param support for Kafka connector (#6969) by @sgrebnov in #6969
  • Add integration test for Kafka connector (#6965) by @sgrebnov in #6965
  • Skip dataset health check for IcebergTableProvider datasets by @phillipleblanc in #6995

Spice v1.6.0 (Aug 26, 2025)

· 22 min read
Sergei Grebnov
Sergei Grebnov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.6.0! 🔥

Spice 1.6.0 upgrades DataFusion to v48, reducing expressions memory footprint by ~50% for faster planning and lower memory usage, eliminating unnecessary projections in queries, optimizing string functions like ascii and character_length for up to 3x speedup, and accelerating unbounded aggregate window functions by 5.6x. The release adds Kafka and MongoDB connectors for real-time streaming and NoSQL data acceleration, supports OpenAI Responses API for advanced model interactions including OpenAI-hosted tools like web_search and code_interpreter, improves the OpenAI Embeddings Connector with usage tier configuration for higher throughput via increased concurrent requests, introduces Model2Vec embeddings for ultra-low-latency encoding, and improves the Amazon S3 Vectors engine to support multi-column primary keys.

What's New in v1.6.0​

DataFusion v48 Highlights​

Spice.ai is built on the DataFusion query engine. The v48 release brings:

Performance & Size Improvements 🚀: Expressions memory footprint was reduced by ~50% resulting in faster planning and lower memory usage, with planning times improved by 10-20%. There are now fewer unnecessary projections in queries. The string functions, ascii and character_length were optimized for improved performance, with character_length achieving up to 3x speedup. Queries with unbounded aggregate window functions have improved performance by 5.6 times via avoided unnecessary computation for constant results across partitions. The Expr struct size was reduced from 272 to 144 bytes.

New Features & Enhancements ✨: Support was added for ORDER BY ALL for easy ordering of all columns in a query.

See the Apache DataFusion 48.0.0 Blog for details.

Runtime Highlights​

Amazon S3 Vectors Multi-Column Primary Keys: The Amazon S3 Vectors engine now supports datasets with multi-column primary keys. This enables vector indexes for datasets where more than one column forms the primary key, such as those splitting documents into chunks for retrieval contexts. For multi-column keys, Spice serializes the keys using arrow-json format, storing them as single string keys in the vector index.

Model2Vec Embeddings: Spice now supports model2vec static embeddings with a new model2vec embeddings provider, for sentence transformers up to 500x faster and 15x smaller, enabling scenarios requiring low latency and high-throughput encoding.

embeddings:
  - from: model2vec:minishlab/potion-base-8M # HuggingFace model
    name: potion
  - from: model2vec:path/to/my/local/model # local model
    name: local

Learn more in the Model2Dev Embeddings documentation.

Kafka Data Connector: Use from: kafka:<topic> to ingest data directly from Kafka topics for integration with existing Kafka-based event streaming infrastructure, providing real-time data acceleration and query without additional middleware.

Example Spicepod.yml:

- from: kafka:orders_events
  name: orders
  acceleration:
    enabled: true
    refresh_mode: append
  params:
    kafka_bootstrap_servers: server:9092

Learn more in the Kafka Data Connector documentation.

MongoDB Data Connector: Use from: mongodb:<dataset> to access and accelerate data stored in MongoDB, deployed on-premises or in the cloud.

Example spicepod.yml:

datasets:
  - from: mongodb:my_dataset
    name: my_dataset
    params:
      mongodb_host: localhost
      mongodb_db: my_database
      mongodb_user: my_user
      mongodb_pass: password

Learn more in the MongoDB Data Connector documentation.

OpenAI Responses API Support: The OpenAI Responses API (/v1/responses) is now supported, which is OpenAI's most advanced interface for generating model responses.

To enable the /v1/responses HTTP endpoint, set the responses_api parameter to enabled:

Example spicepod.yml:

models:
  - name: openai_model_using_responses_api
    from: openai:gpt-4.1
    params:
      openai_api_key: ${ secrets:OPENAI_API_KEY }
      responses_api: enabled # Enable the /v1/responses endpoint for this model

Example curl request:

curl http://localhost:8090/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1",
    "input": "Tell me a three sentence bedtime story about Spice AI."
  }'

To use responses in spice chat, use the --responses flag.

Example:

spice chat --responses # Use the `/v1/responses` endpoint for all completions instead of `/v1/chat/completions`

Use OpenAI-hosted tools supported by Open AI's Responses API by specifying the openai_responses_tools parameter:

Example spicepod.yml:

models:
  - name: test
    from: openai:gpt-4.1
    params:
      openai_api_key: ${ secrets:SPICE_OPENAI_API_KEY }
      tools: sql, list_datasets
      responses_api: enabled
      openai_responses_tools: web_search, code_interpreter #  'code_interpreter' or 'web_search'

These OpenAI-specific tools are only available from the /v1/responses endpoint. Any other tools specified via the tools parameter are available from both the /v1/chat/completions and /v1/responses endpoints.

Learn more in the OpenAI Model Provider documentation.

OpenAI Embeddings & Models Connectors Usage Tier: The OpenAI Embeddings and Models Connectors now supports specifying account usage tier for embeddings and model requests, improving the performance of generating text embeddings or calling models during dataset load and search by increasing concurrent requests.

Example spicepod.yml:

embeddings:
  - from: openai:text-embedding-3-small
    name: openai_embed
    params:
      openai_usage_tier: tier1

By setting the usage tier to the matching usage tier for your OpenAI account, the Embeddings and Models Connector will increase the maximum number of concurrent requests to match the specified tier.

Learn more in the OpenAI Model Provider documentation.

Contributors​

New Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

The Spice Cookbook includes 77 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.6.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.6.0 image:

docker pull spiceai/spiceai:1.6.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is also now available in the AWS Marketplace!

What's Changed​

Dependencies​

Changelog​

  • Support Streaming with Tool Calls (#6941) by @Advayp in #6941
  • Fix parameterized query planning in DataFusion (#6942) by @Jeadie in #6942
  • Update the UnableToLoadCredentials error with a pointer to docs (#6937) by @phillipleblanc in #6937
  • Fix spicecloud benchmark (#6935) by @krinart in #6935
  • [Debezium] Support for VariableScaleDecimal (#6934) by @krinart in #6934
  • Update to DF 48 (#6665) by @mach-kernel and @kczimm in #6665
  • Mark append-stream and CDC datasets as ready after first message (#6914) by @sgrebnov in #6914
  • Model2Vec embedding model support (#6846) by @mach-kernel in #6846
  • Update snapshot for S3 vector search test (#6920) by @Jeadie in #6920
  • remove [] from queryset in spicepod path for CI (#6919) by @Jeadie in #6919
  • Remove verbose tracing (#6915) by @Jeadie in #6915
  • Refactor how models supporting the Responses API are loaded (#6912) by @Advayp in #6912
  • Write tests for truncate formatting in arrow_tools and fix bug. (#6900) by @Jeadie in #6900
  • Support using the Responses API from spice chat (#6894) by @Advayp in #6894
  • Include GPT-5 into Text-To-SQL and Financebench benchmarks (#6907) by @sgrebnov in #6907
  • Better error message when credentials aren't loaded for S3 Vectors (#6910) by @phillipleblanc in #6910
  • Add tracing and system prompt support for the Responses API (#6893) by @Advayp in #6893
  • Constraint violation check is improved to control behavior when violations occur within a batch (#6897) by @phillipleblanc in #6897
  • fix: Multi-column text search with v1/search (#6905) by @peasee in #6905
  • fix: Correctly project text search primary keys to underlying projection (#6904) by @peasee in #6904
  • fix: Update benchmark snapshots (#6901) by @app/github-actions in #6901
  • In S3vector, do not pushdown on non-filterable columns (#6884) by @Jeadie in #6884
  • Run E2E Test CI macOS build on bigger runners (#6896) by @phillipleblanc in #6896
  • Enable configuration of the Responses API for the Azure model provider (#6891) by @Advayp in #6891
  • fix: Update benchmark snapshots (#6888) by @app/github-actions in #6888
  • Update OpenAPI specification for /v1/responses (#6889) by @Advayp in #6889
  • Add test to ensure tools are injected correctly in the Responses API (#6886) by @Advayp in #6886
  • Enable embeddings for append streams (#6878) by @sgrebnov in #6878
  • Show correct limit for EXPLAIN plans in S3VectorsQueryExec (#6852) by @Jeadie in #6852
  • Responses API support for Azure Open AI (#6879) by @Advayp in #6879
  • fix: Update search test case structure (#6865) by @peasee in #6865
  • Fix mongodb benchmark (#6883) by @phillipleblanc in #6883
  • Support multiple column primary keys for S3 vectors. (#6775) by @Jeadie in #6775
  • Kafka Data Connector: persist consumer between restarts (#6870) by @sgrebnov in #6870
  • Fix newlines in errors added in recent PRs (#6877) by @phillipleblanc in #6877
  • Add override parameter to force support for the Responses API (#6871) by @Advayp in #6871
  • Don't use metadata columns in VectorScanTableProvider (#6854) by @Jeadie in #6854
  • Add non-streaming tool call support (hosted and Spice tools) via the Responses API (#6869) by @Advayp in #6869
  • Update error guideline to remove newlines + remove newlines from error messages. (#6866) by @phillipleblanc in #6866
  • Remove void acceleration engine + optional table behaviors (#6868) by @phillipleblanc in #6868
  • Kafka Data Connector basic support (#6856) by @sgrebnov in #6856
  • Federated+Accelerated TPCH Benchmarks for MongoDB (#6788) by @krinart in #6788
  • Pass embeddings calculated in compute_index to the acceleration (#6792) by @phillipleblanc in #6792
  • Add non-streaming and streaming support for OpenAI Responses API endpoint (#6830) by @Advayp in #6830
  • Use latest version of OpenAI crate to resolve issues with Service Tier deserialization (#6853) by @Advayp in #6853
  • Update openapi.json (#6799) by @app/github-actions in #6799
  • Improve management message (#6850) by @lukekim in #6850
  • fix: Include FTS search column if it is the PK (#6836) by @peasee in #6836
  • Refactor Health Checks (#6848) by @Advayp in #6848
  • Introduce a Responses trait and LLM registry for model providers that support the OpenAI Responses API (#6798) by @Advayp in #6798
  • fix: Update datafusion-table-providers to include constraints (#6837) by @peasee in #6837
  • Bump postcard from 1.1.2 to 1.1.3 (#6841) by @app/dependabot in #6841
  • Bump governor from 0.10.0 to 0.10.1 (#6835) by @app/dependabot in #6835
  • Bump ctor from 0.2.9 to 0.5.0 (#6827) by @app/dependabot in #6827
  • Bump azure_core from 0.26.0 to 0.27.0 (#6826) by @app/dependabot in #6826
  • Bump rstest from 0.25.0 to 0.26.1 (#6825) by @app/dependabot in #6825
  • Use latest commit in our fork of async-openai (#6829) by @Advayp in #6829
  • Bump rustls from 0.23.27 to 0.23.31 (#6824) by @app/dependabot in #6824
  • Bump async-trait from 0.1.88 to 0.1.89 (#6823) by @app/dependabot in #6823
  • Bump hyper from 1.6.0 to 1.7.0 (#6814) by @app/dependabot in #6814
  • Bump serde_json from 1.0.140 to 1.0.142 (#6812) by @app/dependabot in #6812
  • Add s3 vector test retrieving vectors (#6786) by @Jeadie in #6786
  • fix: Allow v1/search with only FTS (#6811) by @peasee in #6811
  • Bump tantivy from 0.24.1 to 0.24.2 (#6806) by @app/dependabot in #6806
  • Bump tokio-util from 0.7.15 to 0.7.16 (#6810) by @app/dependabot in #6810
  • fix: Improve FTS index primary key handling (#6809) by @peasee in #6809
  • Bump logos from 0.15.0 to 0.15.1 (#6808) by @app/dependabot in #6808
  • Bump hf-hub from 0.4.2 to 0.4.3 (#6807) by @app/dependabot in #6807
  • Bump odbc-api from 13.0.1 to 13.1.0 (#6803) by @app/dependabot in #6803
  • fix: Spice search CLI with FTS supports string or slice unmarshalling (#6805) by @peasee in #6805
  • Bump uuid from 1.17.0 to 1.18.0 (#6797) by @app/dependabot in #6797
  • Bump reqwest from 0.12.22 to 0.12.23 (#6796) by @app/dependabot in #6796
  • Bump anyhow from 1.0.98 to 1.0.99 (#6795) by @app/dependabot in #6795
  • Bump clap from 4.5.41 to 4.5.45 (#6794) by @app/dependabot in #6794
  • Respect default MAX_DECODING_MESSAGE_SIZE (100MB) in Flight API (#6802) by @sgrebnov in #6802
  • Fix compilation errors caused by upgrading async-openai (#6793) by @Advayp in #6793
  • Remove outdated vector search benchmark (replaced with testoperator) (#6791) by @sgrebnov in #6791
  • Handle errors in vector ingestion pipeline (#6782) by @phillipleblanc in #6782
  • fix: Explicitly error when chunking is defined for vector engines (#6787) by @peasee in #6787
  • Make VectorScanTableProvider and VectorQueryTableProvider support multi-column primary keys (#6757) by @Jeadie in #6757
  • Use megascience/megascience Q+A dataset for text search testing. (#6702) by @Jeadie in #6702
  • Flight REPL autocomplete (#6589) by @krinart in #6589
  • use ref: github.event.pull_request.head.sha in integration_models.yml (#6780) by @Jeadie in #6780
  • fix: Move search telemetry calls in UDTF to scan (#6778) by @peasee in #6778
  • Fix Hugging Face models and embeddings loading in Docker (#6777) by @ewgenius in #6777
  • feat: Migrate bedrock rate limiter (#6773) by @peasee in #6773
  • Run the PR checks on the DEV runners (#6769) by @phillipleblanc in #6769
  • feat: add OpenAI models rate controller (#6767) by @peasee in #6767
  • Implement MongoDB data connector (#6594) by @krinart in #6594
  • fix: Use head ref for concurrency group (#6770) by @peasee in #6770
  • fix: Run enforce pulls with spice on pull_request_target (#6768) by @peasee in #6768
  • feat: Add OpenAI Embeddings Rate Controller (#6764) by @peasee in #6764
  • Move AWS SDK credential bridge integration test to the existing AWS SDK integration test run (#6766) by @phillipleblanc in #6766
  • Use Spice specific errors instead of OpenAIError in embedding module (#6748) by @kczimm in #6748
  • Use context in Glue Catalog Provider (#6763) by @Advayp in #6763
  • pin cargo-deny to previous version (#6762) by @kczimm in #6762
  • Bump actions/download-artifact from 4 to 5 (#6720) by @app/dependabot in #6720
  • Upgrade dependabot dependencies (#6754) by @phillipleblanc in #6754
  • Set E2E Test CI models build to 90 minute timeout (#6756) by @phillipleblanc in #6756
  • chore: upgrade to Rust 1.87.0 (#6614) by @kczimm in #6614
  • feat: Add initial runtime-rate-limiter crate (#6753) by @peasee in #6753
  • feat: Add more embedding traces, add MiniLM MTEB spicepod (#6742) by @peasee in #6742
  • Update QA analytics for release (#6740) by @Advayp in #6740
  • Always use 'returnData: true' for s3 vector query index (#6741) by @Jeadie in #6741
  • feat: Add Embedding and Search anonymous telemetry (#6737) by @peasee in #6737
  • Add 1.5.2 to SECURITY.md (#6739) by @ewgenius in #6739
  • Combine the Iceberg and Object Store AWS SDK bridges into one crate (#6718) by @Advayp in #6718
  • Updates to v1.5.2 release notes (#6736) by @lukekim in #6736
  • Update end game template - move glue catalog to catalogs section (#6732) by @ewgenius in #6732
  • Update v1.5.2.md (#6735) by @kczimm in #6735
  • Add note about S3 Vectors workaround (#6734) by @phillipleblanc in #6734
  • feat: Avoid joining for VectorScanTableProvider if the index is sufficient (#6714) by @peasee in #6714
  • update changelog (#6729) by @kczimm in #6729
  • remove unneeded autogenerated s3 vector code (#6715) by @Jeadie in #6715
  • fix: Set S3 vectors default limit to 30, add more tracing (#6712) by @peasee in #6712
  • docs: Add Hadoop cookbook to endgame template (#6708) by @peasee in #6708
  • Fix testoperator append mode compilation error (#6706) by @phillipleblanc in #6706
  • test: Add VectorScanTableProvider snapshot tests (#6701) by @peasee in #6701
  • feat: Add Hadoop catalog-mode benchmark (#6684) by @peasee in #6684
  • Move shared AWS crates used in bridges to workspace (#6705) by @Advayp in #6705
  • Use installation id to group connections (#6703) by @Advayp in #6703
  • Add Guardrails for AWS bedrock models (#6692) by @Jeadie in #6692
  • Update bedrock keys for CI. (#6693) by @Jeadie in #6693
  • Update acknowledgements (#6690) by @app/github-actions in #6690
  • ROADMAP updates Aug 1, 2025 (#6667) by @lukekim in #6667
  • Add retry logic for OpenAI embeddings creation (#6656) by @sgrebnov in #6656
  • Make models E2E chat test more robust (#6657) by @sgrebnov in #6657
  • Update Search GH Workflow to use Test Operator (#6650) by @sgrebnov in #6650
  • Score and P95 latency calculation for MTEB Quora-based vector search tests in Test Operator (#6640) by @sgrebnov in #6640
  • Fix multiple query error being classified as an internal error (#6635) by @Advayp in #6635
  • Add Support for S3 Table Buckets (#6573) by krinart in #6573
  • set MISTRALRS_METAL_PRECOMPILE=0 for metal (#6652) by @kczimm in #6652
  • Vector search to push down udtf limit argument into logical sort plan (#6636) by @mach-kernel in #6636
  • docs: Update qa_analytics.csv (#6643) by @peasee in #6643
  • Update SECURITY.md (#6642) by @Jeadie in #6642
  • docs: Update qa_analytics.csv (#6641) by @peasee in #6641
  • Separate token usage (#6619) by @Advayp in #6619
  • Fix typo in release notes (#6634) by @Advayp in #6634
  • Add environment variable for org token (#6633) by @Advayp in #6633
  • CDC: Compute embeddings on ingest (#6612) by @mach-kernel in #6612
  • Add view name to view creation errors (#6611) by @lukekim in #6611
  • Add core logic for running MTEB Quora-based vector search tests in Test Operator (#6607) by @sgrebnov in #6607
  • Revert "Update generate-openapi.yml (#6584)" (#6620) by @Jeadie in #6620
  • Non-accelerated views should report as ready only after all dependent datasets are ready (#6617) by @sgrebnov in #6617

Spice v1.5.2 (Aug 11, 2025)

· 7 min read
Kevin Zimmerman
Kevin Zimmerman
Principal Software Engineer at Spice AI

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.

What's New in v1.5.2​

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-v1:0
  • amazon.nova-micro-v1:0
  • amazon.nova-premier-v1:0
  • amazon.nova-pro-v1:0

Refer to the Amazon Bedrock documentation for details on available models and cross-region inference profiles.

Example Spicepod.yaml:

models:
  - from: bedrock:us.amazon.nova-lite-v1:0
    name: novash
    params:
      aws_region: us-east-1
      aws_access_key_id: ${ secrets:AWS_ACCESS_KEY_ID }
      aws_secret_access_key: ${ secrets:AWS_SECRET_ACCESS_KEY }
      bedrock_guardrail_identifier: arn:aws:bedrock:abcdefg012927:0123456789876:guardrail/hello
      bedrock_guardrail_version: DRAFT
      bedrock_trace: enabled
      bedrock_temperature: 42

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:

# Example datasets for Redshift TPCH tables
datasets:
  - from: postgres:public.customer
    name: customer
    params:
      pg_host: ${secrets:PG_HOST}
      pg_port: 5439
      pg_sslmode: prefer
      pg_db: dev
      pg_user: ${secrets:PG_USER}
      pg_pass: ${secrets:PG_PASS}
  - from: postgres:public.lineitem
    name: lineitem
    params:
      pg_host: ${secrets:PG_HOST}
      pg_port: 5439
      pg_sslmode: prefer
      pg_db: dev
      pg_user: ${secrets:PG_USER}
      pg_pass: ${secrets:PG_PASS}

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:

catalogs:
  - from: iceberg:file:///tmp/hadoop_warehouse/
    name: local_hadoop
  - from: iceberg:s3://my-bucket/hadoop_warehouse/
    name: s3_hadoop

  # Example datasets
  - from: iceberg:file:///data/hadoop_warehouse/test/my_table_1
    name: local_hadoop
  - from: iceberg:s3://my-bucket/hadoop_warehouse/test/my_table_2
    name: s3_hadoop

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:

runtime:
  params:
    parquet_page_index: required # Options: required, skip, auto
  • required: (Default) Errors if page indexes are absent.
  • skip: Ignores page indexes, potentially reducing query performance.
  • auto: Uses page indexes if available; skips otherwise.

This improves compatibility and query flexibility for Parquet datasets.

Contributors​

Breaking Changes​

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:

SELECT url, title, scored, body_embedding
FROM vector_search(pulls, 'bugs in DuckDB', 4)
WHERE state = 'OPEN'
ORDER BY score DESC
LIMIT 4;

Remove the *_embedding column from the projection. E.g.

SELECT url, title, scored
FROM vector_search(pulls, 'bugs in DuckDB', 4)
WHERE state = 'OPEN'
ORDER BY score DESC
LIMIT 4;

This issue and workaround also applies to SELECT * FROM vector_search(..). E.g.

SELECT *
FROM vector_search(pulls, 'bugs in DuckDB', 4)
WHERE state = 'OPEN'
ORDER BY score DESC
LIMIT 4;

Cookbook Updates​

The Spice Cookbook includes 75 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.5.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.5.2 image:

docker pull spiceai/spiceai:1.5.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is also now available in the AWS Marketplace!

What's Changed​

Dependencies​

No major dependency updates.

Changelog​

  • fixes for databricks OpenAI compatibility (#6629) by @Jeadie in #6629
  • Update spicepod.schema.json (#6632) by @app/github-actions in #6632
  • Remove 'stream_options' from databricks LLMs (#6637) by @Jeadie in #6637
  • Move retry and rate limiting logic for Amazon bedrock out of embeddings. (#6626) by @Jeadie in #6626
  • Disable Metal precomplation in integration_llms.yml (#6649) by @Jeadie in #6649
  • fix: Hadoop integration test (#6660) by @peasee in #6660
  • feat: Add Hadoop Catalog Data Component (#6658) by @peasee in #6658
  • update datafusion-table-providers to latest spiceai tag (#6661) by @mach-kernel in #6661
  • feat: Add Hadoop Catalog connectors for Iceberg (#6659) by @peasee in #6659
  • Make FullTextSearchExec robust to RecordBatch column ordering. (#6675) by @Jeadie in #6675
  • Make 'runtime-object-store' crate (#6674) by @Jeadie in #6674
  • fix: Support include for Iceberg (#6663) by @peasee in #6663
  • feat: Add Hadoop TPCH benchmark (#6678) by @peasee in #6678
  • feat: Add Hadoop metadata_path parameter (#6680) by @peasee in #6680
  • fix: Automatically infer Hadoop warehouse scheme (#6681) by @peasee in #6681
  • Amazon Bedrock, specifically Nova models (#6673) by @Jeadie in [#6673](https://github.com/spiceai/spiceai/pull/6673
  • fix perplexity_auth_token parameters for web_search (#6685) by @Jeadie in #6685
  • Fix AWS Auth issue (#6699) by @Advayp in #6699
  • Limit Concurrent Requests for GitHub (#6672) by @Advayp in #6672
  • Add runtime parameter to enable more permissive parquet reading when page indexes are missing (#6716) by @phillipleblanc in #6716
  • Improve Flight REPL error messages (#6696) by @lukekim in #6696
  • Fixes from search tests (#6710) by @Jeadie in #6710