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Datasets

A Spicepod can contain one or more datasets referenced by relative path or defined inline.

Inline example:

spicepod.yaml

datasets:
  - from: spice.ai/spiceai/quickstart/datasets/taxi_trips
    name: taxi_trips
    acceleration:
      enabled: true
      mode: memory # / file
      engine: arrow # / cayenne / duckdb / sqlite / postgres / turso
      refresh_check_interval: 1h
      refresh_mode: full / append # / changes / caching

spicepod.yaml

datasets:
  - from: databricks:spiceai.datasets.specific_table
    name: uniswap_eth_usd
    params:
      environment: prod
    acceleration:
      enabled: true
      mode: memory # / file
      engine: arrow # / cayenne / duckdb / sqlite / postgres / turso
      refresh_check_interval: 1h
      refresh_mode: full / append # / changes / caching

Relative path example:

spicepod.yaml

datasets:
  - ref: datasets/taxi_trips

datasets/taxi_trips/dataset.yaml

from: spice.ai/spiceai/quickstart/datasets/taxi_trips
name: taxi_trips
acceleration:
  enabled: true
  refresh_check_interval: 1h

from​

The from field is a string that represents the Uniform Resource Identifier (URI) for the dataset. This URI is composed of two parts: a prefix indicating the Data Connector to use to connect to the dataset, a delimiter, and the path to the dataset within the source.

The syntax for the from field is as follows:

from: <data_connector>:<path>
# OR
from: <data_connector>/<path>
# OR
from: <data_connector>://<path>

Where:

  • <data_connector>: The Data Connector to use to connect to the dataset

    Currently supported data connectors:

    If the Data Connector is not explicitly specified, it defaults to spiceai.

  • <delimiter>: The delimiter between the Data Connector and the path. Currently supported delimiters are :, /, and ://. Some connectors place additional restrictions on the allowed delimiters to better conform to the expected syntax of the underlying data source, i.e. s3:// is the only supported delimiter for the s3 connector.

  • <path>: The path to the dataset within the source.

Identifier Case Sensitivity

Unquoted identifiers in the <path> are normalized to lowercase. To reference a table or schema with mixed-case or uppercase characters, wrap each case-sensitive part in double quotes:

datasets:
  # Case is preserved for "ActionExecutions"
  - from: postgres:my_schema."ActionExecutions"
    name: action_executions

  # Quote each part individually as needed
  - from: databricks:my_catalog."MySchema"."MyTable"
    name: my_table

This applies to all federated database connectors where <path> references a table identifier. Connectors that interpret <path> as a file path (e.g. s3, delta_lake, ftp) do not apply identifier normalization. See Identifier Case Sensitivity and Quoting for details.

ref​

An alternative to adding the dataset definition inline in the spicepod.yaml file. ref can be use to point to a directory with a dataset defined in a dataset.yaml file. For example, a dataset configured in a dataset.yaml in the "datasets/sample" directory can be referenced with the following:

dataset.yaml

from: spice.ai/spiceai/quickstart/datasets/taxi_trips
name: taxi_trips
acceleration:
  enabled: true
  refresh_check_interval: 1h

ref used in spicepod.yaml

version: v1
kind: Spicepod
name: duckdb
datasets:
  - ref: datasets/sample

name​

The name of the dataset. Used to reference the dataset in the pod manifest, as well as in external data sources. The name cannot be a reserved keyword.

Spice follows PostgreSQL SQL syntax conventions, which normalize unquoted identifiers to lowercase. A dataset named LINEITEM is accessible in queries as lineitem.

To preserve uppercase or mixed-case names, wrap the name in double quotes. In YAML, this requires an extra layer of quoting:

datasets:
  - from: snowflake:SNOWFLAKE_SAMPLE_DATA.TPCH_SF100.LINEITEM
    name: '"LINEITEM"'
    params:
      snowflake_account: JYFGIWYEFBW
      snowflake_warehouse: snowflake_wh
      snowflake_password: ${secrets:SNOWFLAKE_PASSWORD}
      snowflake_username: ${secrets:SNOWFLAKE_USERNAME}
-- Query using the preserved uppercase name
SELECT * FROM "LINEITEM";

Without the double quotes, the same dataset would be queryable only as lineitem.

See Identifier Case Sensitivity and Quoting for full details on quoting in both the from and name fields.

description​

The description of the dataset. Used as part of the Semantic Data Model.

access​

Optional. Specifies the access level for the dataset. Supported values are:

  • read (default): Read-only access.
  • read_write: Enables both read and write operations. Only supported for write-capable connectors.

To enable write operations, configure your dataset with read_write access:

datasets:
  - from: glue:my_catalog.my_schema.my_table
    name: my_table
    access: read_write
    params:
      # ... connector-specific parameters

time_column​

Optional. The name of the column that represents the temporal (time) ordering of the dataset.

Required to enable a retention policy on the dataset.

time_format​

Optional. The format of the time_column. The following values are supported:

  • timestamp - Default. Timestamp without a timezone. E.g. 2016-06-22 19:10:25 with data type timestamp.
  • timestamptz - Timestamp with a timezone. E.g. 2016-06-22 19:10:25-07 with data type timestamptz.
  • unix_seconds - Unix timestamp in seconds. E.g. 1718756687.
  • unix_millis - Unix timestamp in milliseconds. E.g. 1718756687000.
  • ISO8601 - ISO 8601 format.
  • date - Date in YYYY-MM-DD format. E.g. 2024-01-01.

Spice emits a warning if the time_column from the data source is incompatible with the time_format config.

Limitations
  • String-based columns are assumed to be ISO8601 format.

time_partition_column​

(Optional) Specify the column that represents the physical partitioning of the dataset when using append-based acceleration. When the defined time_column is a fine-grained timestamp and the dataset is physically partitioned by a coarser granularity (for example, by date), setting time_partition_column to the partition column (e.g. date_col) improves partition pruning, excludes irrelevant partitions during refreshes, and optimizes scan efficiency.

time_partition_format​

(Optional) Define the format of the time_partition_column. For instance, if the physical partitions follow a date format (YYYY-MM-DD), set this value to date. The same format options as time_format are supported for time_partition_column.

Schema Inference and Evolution​

Spice infers the dataset schema from the data source at startup. The inferred schema defines the column names, data types, and nullability used for the lifetime of that runtime process. Schema changes at the source are not applied at runtime — data refreshes will fail if the source schema drifts. Restart the runtime to re-infer the schema.

For connector-specific inference parameters, runtime schema change behavior, and recommendations, see Schema Inference.

unsupported_type_action​

Optional. Specifies the action to take when a data type that is not supported by the data connector is encountered.

The following values are supported:

  • error - Default. Return an error when an unsupported data type is encountered.
  • warn - Log a warning and ignore the column containing the unsupported data type.
  • ignore - Log nothing and ignore the column containing the unsupported data type.
  • string - Attempt to convert the unsupported data type to a string. Currently only supports converting the PostgreSQL JSONB type.
Limitations

Not all connectors support specifying an unsupported_type_action. When specified on a connector that does not support the option, the connector will fail to register. The following connectors support unsupported_type_action:

ready_state​

Supports one of two values:

  • on_registration: Mark the dataset as ready immediately, and queries on this table will fall back to the underlying source directly until the initial acceleration is complete
  • on_load: Mark the dataset as ready only after the initial acceleration. Queries against the dataset will return an error before the load has been completed.
datasets:
  - from: s3://my_bucket/my_dataset/
    name: my_dataset
    ready_state: on_registration # or on_load
    params: ...
    acceleration:
      enabled: true

check_availability​

Spice monitors the availability of non-accelerated datasets and emits metrics if a dataset becomes unavailable. Note that this monitoring process may trigger the startup of compute resources (for example, Databricks or Snowflake), potentially incurring additional costs. To disable availability monitoring, configure the check_availability parameter to disabled.

  • auto: Automatically check the availability monitor of the dataset. This is the default value. Accelerated datasets are not monitored.
  • disabled: Disable the availability monitor for the dataset.
datasets:
  - from: databricks:catalog.schema.table
    name: my_dataset
    check_availability: disabled
    params: ...

The monitoring works by executing a query that selects one row and all columns from the dataset. i.e.:

SELECT
  "p_partkey",
  "p_name",
  "p_mfgr",
  "p_brand",
  "p_type",
  "p_size",
  "p_container",
  "p_retailprice",
  "p_comment"
FROM
  spiceai_sandbox.tpch.part
LIMIT 1

If the monitoring query fails a warning is emitted in the logs, an error is propagated to the task_history table and the dataset_unavailable_time_ms metric is incremented for the failing dataset.

acceleration​

Optional. Accelerate queries to the dataset by caching data locally.

acceleration.enabled​

Enable or disable acceleration, defaults to true.

acceleration.engine​

The acceleration engine to use, defaults to arrow. The following engines are supported:

  • arrow - Accelerated in-memory backed by Apache Arrow DataTables.
  • cayenne - Accelerated by Spice Cayenne (Vortex) engine (Alpha, v1.9.0-rc.1+).
  • duckdb - Accelerated by an embedded DuckDB database.
  • postgres - Accelerated by a Postgres database.
  • sqlite - Accelerated by an embedded SQLite database.
  • turso - Accelerated by an embedded Turso (libSQL) database (Beta).

acceleration.mode​

Optional. The mode of acceleration. The following values are supported:

  • memory - Store acceleration data in-memory. Not supported for Spice Cayenne (cayenne).
  • file - Store acceleration data in a file. Reuses any existing file on startup. Supported for Spice Cayenne (cayenne), duckdb and sqlite acceleration engines.
  • file_create - Always create a new acceleration file on startup, removing any existing file. When snapshots are enabled, the existing file is snapshotted before deletion. Supported for Spice Cayenne (cayenne), duckdb and sqlite acceleration engines.
  • file_update - Open an existing acceleration file if it exists, then check schema compatibility on refresh. If the source schema change is additive (new columns only), the existing file is kept. If the schema change is incompatible (columns removed, renamed, or type changed), the file is snapshotted (if snapshots are enabled) and recreated from scratch. Supported for Spice Cayenne (cayenne), duckdb and sqlite acceleration engines.

acceleration.snapshots​

Optional. Controls how this dataset participates in managed acceleration snapshots. Requires the Spicepod to configure the top-level snapshots block, the acceleration engine to be duckdb or sqlite, and mode: file with a dataset-specific file path (for example acceleration.params.duckdb_file: /nvme/my_dataset.db).

Supported values:

  • enabled – Download the newest snapshot on startup when the acceleration file is missing and write a fresh snapshot after each refresh.
  • bootstrap_only – Download snapshots on startup but never write new ones.
  • create_only – Write snapshots after refreshes but never download them on startup.
  • disabled (default) – Do not use snapshots for this dataset.

Snapshots are written beneath the configured snapshot location using Hive-style partitioning (month=YYYY-MM/day=YYYY-MM-DD/dataset=<dataset>). For more background, see Acceleration snapshots.

acceleration.snapshots_trigger​

Optional. Controls when Spice creates new snapshots. The available triggers depend on the dataset's refresh mode.

For batch-based datasets (refresh_mode: full, refresh_mode: caching, or refresh_mode: append with time_column):

  • refresh_complete (default) – Create a snapshot after each data refresh completes.
  • time_interval – Create snapshots at a fixed time interval specified by snapshots_trigger_threshold.

For stream-based datasets (refresh_mode: changes, or refresh_mode: append without time_column):

  • time_interval (default) – Create snapshots at a fixed time interval. Defaults to 10m if snapshots_trigger_threshold is not specified.
  • stream_batches – Create a snapshot after a specified number of batches are processed.

See Acceleration snapshots for more details.

acceleration.snapshots_trigger_threshold​

Optional. The threshold value for snapshot creation, interpreted based on the configured snapshots_trigger:

  • When snapshots_trigger: time_interval – A duration specifying how often to create snapshots (e.g., 10m, 1h). Defaults to 10m for stream-based datasets.
  • When snapshots_trigger: stream_batches – An integer specifying the number of batch updates after which to create a snapshot.

Not applicable when snapshots_trigger: refresh_complete.

acceleration.snapshots_compaction​

Optional. Enable database compaction before uploading snapshots. Only supported for the duckdb acceleration engine. Defaults to disabled.

When enabled, Spice uses DuckDB's internal compaction mechanism (COPY DATABASE) to optimize the database file before uploading, reducing snapshot size and improving bootstrap performance.

Supported values:

  • enabled – Compact the database before creating each snapshot.
  • disabled (default) – Upload snapshots without compaction.

acceleration.refresh_mode​

Optional. How to refresh the dataset. The following values are supported:

  • full - Refresh the entire dataset.
  • append - Append new data to the dataset. When time_column is specified, new records are fetched from the latest timestamp in the accelerated data at the acceleration.refresh_check_interval.
  • changes - Apply change data capture (CDC) events to incrementally update the dataset.
  • caching - Cache data based on request metadata (HTTP requests). Uses row-level replacement based on cache keys. See Caching Mode for details.

acceleration.refresh_check_interval​

Optional. How often data should be refreshed. For append datasets without a specific time_column, this config is not used. If not defined, the accelerator will not refresh after it initially loads data. Cannot be specified in conjunction with a refresh_cron.

See Duration

acceleration.refresh_cron​

Optional. Specifies a cron schedule which controls how often data is refreshed. For append datasets without a specific time_column, this config is not used. If not defined, the accelerator will not refresh after it initially loads data.

See the cron schedule reference.

acceleration.params.caching_ttl​

Optional. The time-to-live (TTL) for cached data before it is considered stale. Only applicable when refresh_mode: caching. Defaults to 30s.

When cached data exceeds this age (measured from the fetched_at timestamp), it becomes stale. If caching_stale_while_revalidate_ttl is also configured, stale data is immediately served to queries (no delay) while a background refresh is triggered to update the cache, implementing the Stale-While-Revalidate (SWR) pattern. If caching_stale_while_revalidate_ttl is not set, queries wait for fresh data once the TTL expires.

Example:

datasets:
  - from: https://api.tvmaze.com
    name: tv_shows
    acceleration:
      enabled: true
      refresh_mode: caching
      engine: duckdb
      mode: file # Persist cache to disk
      params:
        caching_ttl: 15s # Cache data is fresh for 15 seconds
      refresh_check_interval: 30s # Periodic background refresh

See Caching Mode for detailed TTL configuration and behavior.

See Duration

acceleration.params.caching_stale_while_revalidate_ttl​

Optional. The duration after caching_ttl expires during which stale data is served while refreshing in the background. Only applicable when refresh_mode: caching. Defaults to none (stale data is not served).

When caching_ttl expires and data becomes stale, this parameter controls how long stale data continues to be served immediately while a background refresh occurs. After the combined caching_ttl + caching_stale_while_revalidate_ttl period, queries wait for fresh data instead of returning stale results.

If omitted, cached data becomes "rotten" immediately after caching_ttl expires, and queries will wait for fresh data rather than returning stale results.

Example:

datasets:
  - from: https://api.tvmaze.com
    name: tv_shows
    acceleration:
      enabled: true
      refresh_mode: caching
      engine: duckdb
      mode: file
      params:
        caching_ttl: 15s # Cache data is fresh for 15 seconds
        caching_stale_while_revalidate_ttl: 30s # Serve stale data for 30 seconds while refreshing
      refresh_check_interval: 60s

See Caching Mode for detailed TTL configuration and behavior.

See Duration

acceleration.params.caching_stale_if_error​

Optional. Controls whether expired cached data is served when the upstream data source returns an error. Only applicable when refresh_mode: caching. Defaults to disabled.

When set to enabled, queries return expired cached data instead of failing if the upstream source returns an error during a refresh attempt. This provides fault tolerance for APIs with intermittent availability or rate limits.

Valid values:

  • enabled - Serve expired cached data when upstream errors occur
  • disabled (default) - Propagate upstream errors to queries

Example:

datasets:
  - from: https://api.tvmaze.com
    name: tv_shows
    acceleration:
      enabled: true
      refresh_mode: caching
      engine: duckdb
      mode: file
      params:
        caching_ttl: 15s
        caching_stale_while_revalidate_ttl: 30s
        caching_stale_if_error: enabled # Serve stale data on upstream errors
      refresh_check_interval: 60s

See Caching Mode for detailed behavior.

acceleration.refresh_sql​

Optional. Filters the data fetched from the source to be stored in the accelerator engine. Supported for full and append refresh mode datasets.

Must be of the form SELECT * FROM {name} WHERE {refresh_filter}. {name} is the dataset name declared above, {refresh_filter} is any SQL expression that can be used to filter the data, i.e. WHERE city = 'Seattle' to reduce the working set of data that is accelerated within Spice from the data source.

Limitations
  • The refresh SQL only supports filtering data from the current dataset - joining across other datasets is not supported.
  • Queries for data that have been filtered out will not fall back to querying against the federated table.

acceleration.refresh_data_window​

Optional. A duration to filter dataset refresh source queries to recent data (duration into past from now). Requires time_column and time_format to also be configured. Supported for full and append refresh mode datasets.

For example, refresh_data_window: 24h will include only records with a timestamp within the last 24 hours.

See Duration

acceleration.refresh_append_overlap​

Optional. A duration to specify how far back to include records based on the most recent timestamp found in the accelerated data. Requires time_column to also be configured. Only supported for append refresh mode datasets.

This setting can help mitigate missing data issues caused by late arriving data.

Example: If the latest timestamp in the accelerated data table is 2020-01-01T02:00:00Z, setting refresh_append_overlap: 1h will include records starting from 2020-01-01T01:00:00Z.

See Duration

acceleration.refresh_retry_enabled​

Optional. Specifies whether an accelerated dataset should retry data refresh in the event of transient errors. The default setting is true.

Retries follow a Fibonacci backoff strategy. To disable refresh retries, set refresh_retry_enabled: false.

acceleration.refresh_retry_max_attempts​

Optional. Defines the maximum number of retry attempts when refresh retries are enabled. The default is undefined, with no upper limit on attempts.

acceleration.refresh_on_startup​

Optional. Controls the refresh behavior of an accelerated dataset across restarts. Defaults to auto.

Supported Values​

  • auto (Default) – Maintains refresh state across restarts:
    • With refresh_check_interval: Schedules next refresh based on last successful refresh time, triggering immediately if interval has already elapsed
    • Without refresh_check_interval: No refresh (on-demand only)
  • always – Forces a dataset refresh on every startup, regardless of the existing acceleration state.

Setting refresh_on_startup: always ensures that accelerated data is always refreshed to match the source when the service restarts. This is useful in development environments or when data consistency is critical after deployment.

acceleration.params​

Optional. Parameters to pass to the acceleration engine. The parameters are specific to the acceleration engine used.

acceleration.retention_check_enabled​

Optional. Enable or disable retention policy check, defaults to false.

acceleration.retention_period​

Optional. The retention period for the dataset. Combine with time_column and time_format to determine if the data should be retained or not.

retention_period or retention_sql must be specified when acceleration.retention_check_enabled is true. When both retention_period and retention_sql are configured, both retention policies will be applied during each retention check.

See Duration

acceleration.retention_sql​

Optional. Custom SQL statement to define data retention logic. Takes the form of a DELETE FROM <table> WHERE <predicates> statement.

This parameter is useful for scenarios like soft-deleting rows in append-only datasets or removing data based on complex business logic that goes beyond simple time-based retention.

retention_period or retention_sql must be specified when acceleration.retention_check_enabled is true. When both retention_period and retention_sql are configured, both retention policies will be applied during each retention check.

acceleration.retention_check_interval​

Optional. How often the retention policy should be checked.

Required when acceleration.retention_check_enabled is true.

See Duration

acceleration.refresh_jitter_enabled​

Optional. Enable or disable refresh jitter, defaults to false. The refresh jitter adds/substracts a randomized time period from the refresh_check_interval.

acceleration.refresh_jitter_max​

Optional. The maximum amount of jitter to add to the refresh interval. The jitter is a random value between 0 and refresh_jitter_max. Defaults to 10% of refresh_check_interval.

metrics​

Optional. Enable component-specific metrics for the dataset. Each component can expose its own set of metrics that can be enabled selectively to monitor specific aspects of its operation.

Component metrics are disabled by default and can be enabled by adding a metrics section to the dataset configuration. Each metric can be enabled individually by specifying its name in the metrics list.

Example Configuration​

datasets:
  - from: mysql:my_table
    name: my_dataset
    metrics:
      - name: connection_count
        enabled: true
      - name: connections_in_pool
        enabled: true
      - name: active_wait_requests
        enabled: true
    params:
      mysql_host: localhost
      mysql_tcp_port: 3306
      mysql_user: root
      mysql_pass: ${secrets:MYSQL_PASS}

For detailed information about metrics available for specific components, see the component metrics documentation.

acceleration.indexes​

Optional. Specify which indexes should be applied to the locally accelerated table. Not supported for in-memory Arrow acceleration engine.

The indexes field is a map where the key is the column reference and the value is the index type.

A column reference can be a single column name or a multicolumn key. The column reference must be enclosed in parentheses if it is a multicolumn key.

See Indexes

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      indexes:
        number: enabled # Index the `number` column
        '(hash, timestamp)': unique # Add a unique index with a multicolumn key comprised of the `hash` and `timestamp` columns

acceleration.primary_key​

Optional. Specify the primary key constraint on the locally accelerated table. Not supported for in-memory Arrow acceleration engine.

The primary_key field is a string that represents the column reference that should be used as the primary key. The column reference can be a single column name or a multicolumn key. The column reference must be enclosed in parentheses if it is a multicolumn key.

See Constraints

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      primary_key: hash # Define a primary key on the `hash` column

acceleration.on_conflict​

Optional. Specify what should happen when a constraint is violated. Not supported for in-memory Arrow acceleration engine.

The on_conflict field is a map where the key is the column reference and the value is the conflict resolution strategy.

A column reference can be a single column name or a multicolumn key. The column reference must be enclosed in parentheses if it is a multicolumn key.

Only a single on_conflict target can be specified, unless all on_conflict targets are specified with drop.

The possible conflict resolution strategies are:

  • upsert - Upsert the incoming data when the primary key constraint is violated.
  • upsert_dedup - Same as upsert, but also deduplicates the data if there are duplicate rows that trigger a violation constraint within a single update. See Advanced upsert behavior.
  • upsert_dedup_by_row_id - Same as upsert, but resolves any violations by arbitrarily choosing the row with the highest row id. See Advanced upsert behavior.
  • drop - Drop the data when the primary key constraint is violated.

See Constraints

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      primary_key: hash
      indexes:
        '(number, timestamp)': unique
      on_conflict:
        # Upsert the incoming data when the primary key constraint on "hash" is violated,
        # alternatively "drop" can be used instead of "upsert" to drop the data update.
        hash: upsert

acceleration.on_zero_results​

Optional. Controls the behavior when an accelerated query returns zero results. Defaults to return_empty.

The following values are supported:

  • return_empty - Default. Return an empty result set when the accelerated query returns no rows.
  • use_source - Fall back to querying the original data source when the accelerated query returns no rows.
datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      on_zero_results: use_source

acceleration.partition_by​

Optional. Specifies columns to partition the accelerated data by, enabling partition-level operations and optimized storage. Defaults to no partitioning (empty).

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      partition_by: block_date

columns​

Optional. Define metadata, semantic details and features (e.g. embeddings, or table indexes) for specific columns in the dataset.

datasets:
  - from: file:sales_data.parquet
    name: sales
    columns:
      - name: address_line1
        description: The first line of the address.
        embeddings:
          - from: hf_minilm
            row_id: order_number
            chunking:
              enabled: true
              target_chunk_size: 256
              overlap_size: 32
        full_text_search:
          enabled: true

columns[*].name​

The name of the column in the table schema.

columns[*].description​

Optional. A description of the column's contents and purpose. Used as part of the Semantic Data Model.

columns[*].embeddings​

Optional. Create vector embeddings for this column.

columns[*].embeddings[*].from​

The embedding model to use, specify the component name.

columns[*].embeddings[*].row_id​

Optional. For datasets without a primary key, used to explicitly specify column(s) that uniquely identify a row.

Specifying a row_id enables unique identifier lookups for datasets from external systems that may not have a primary key.

columns[*].embeddings[*].chunking​

Optional. The configuration to enable and define the chunking strategy for the embedding column.

columns:
  - name: description
    embeddings:
      - from: hf_minilm
        chunking:
          enabled: true
          target_chunk_size: 512
          overlap_size: 128
          trim_whitespace: false

See embeddings[*].chunking for details.

columns[*].embeddings[*].vector_size​

Optional. Specifies the size (number of dimensions) of the embedding vector for use in federated queries to databases that do not support arrays with fixed lengths.

columns:
  - name: review_body
    embeddings:
      - from: embed-static-retrieval
        vector_size: 1024

columns[*].full_text_search​

columns[*].full_text_search.enabled​

Optional. Enable or disable full text search support for specific column in the dataset. Default false.

columns[*].full_text_search.row_id​

Optional. For datasets without a primary key, used to explicitly specify column(s) that uniquely identify a row.

Specifying a row_id enables unique identifier lookups for datasets from external systems that may not have a primary key.

columns[*].metadata​

Optional. Specific metadata associated to the column.

columns[*].metadata.vectors​

Optional. If provided, a vector engine (see below) should store this column for a particular use, determined by the value, which is one of:

  • non-filterable: Store the column in the vector engine.
  • filterable: Store the column in the vector engine, and ensure the engine can filter on the column (if possible in the engine).

Only applicable if vectors.enabled is both defined and true.

embeddings​

Optional. Create vector embeddings for specific columns of the dataset.

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    embeddings:
      - column: extra_data
        use: hf_minilm

embeddings[*].column​

The column name to create an embedding for.

embeddings[*].use​

The embedding model to use, specific the component name embeddings[*].name.

embeddings[*].column_pk​

Optional. For datasets without a primary key, explicitly specify column(s) that uniquely identify a row.

embeddings[*].chunking​

Optional. The configuration to enable and define the chunking strategy for the embedding column.

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    embeddings:
      - column: extra_data
        use: hf_minilm
        chunking:
          enabled: true
          target_chunk_size: 512
          overlap_size: 128
          trim_whitespace: false

embeddings[*].chunking.enabled​

Optional. Enable or disable chunking for the embedding column. Defaults to false.

embeddings[*].chunking.target_chunk_size​

The desired size of each chunk, in tokens.

If the desired chunk size is larger than the maximum size of the embedding model, the maximum size will be used.

embeddings[*].chunking.overlap_size​

Optional. The number of tokens to overlap between chunks. Defaults to 0.

embeddings[*].chunking.trim_whitespace​

Optional. If enabled, the content of each chunk will be trimmed to remove leading and trailing whitespace. Defaults to true.

metadata​

Optional. Additional key-value metadata for the dataset.

The metadata field serves two purposes:

  1. Semantic metadata — Arbitrary key-value pairs used as part of the Semantic Data Model.

    datasets:
      - from: spice.ai/eth.recent_blocks
        name: eth.recent_blocks
        metadata:
          instructions: The last 128 blocks.

  2. File metadata columns — For file-based connectors (S3, ABFS, File, FTP, SFTP, SMB, NFS, HTTP/HTTPS), the following reserved keys enable virtual columns that expose per-file object store metadata in query results:

    KeyValueColumn TypeDescription
    _locationenabledUtf8Full URI of the source file
    _last_modifiedenabledTimestamp(µs, "UTC")When the file was last modified
    _sizeenabledUInt64File size in bytes
    datasets:
      - from: s3://bucket/data/
        name: my_data
        params:
          file_format: parquet
        metadata:
          _location: enabled
          _last_modified: enabled
          _size: enabled

    If a data file already contains a column with the same name as a metadata column, the metadata column is not added.

vectors​

vectors.enabled​

Enable or disable vector storage, defaults to true.

vectors.engine​

The vector engine to use. The following engines are supported:

vectors.params​

Optional. Parameters to pass to the vector engine. The parameters are specific to the vector engine used.