title: 'Search in SQL' sidebar_label: 'Search' description: 'Reference for search functions and filtering in Spice SQL.' sidebar_position: 20

This section documents search capabilities in Spice SQL, including vector search, full-text search, and lexical filtering methods. These features help retrieve relevant data using semantic similarity, keyword matching, and pattern-based filtering.

Table of Contents

• Table of Contents (opens in a new tab)
• Vector Search (vector_search) (opens in a new tab)
  • Usage (opens in a new tab)
    • Example (opens in a new tab)
  • Multi-Query (Late-Interaction) Form (opens in a new tab)
• Full-Text Search (text_search) (opens in a new tab)
  • Usage (opens in a new tab)
    • Example (opens in a new tab)
• Reciprocal Rank Fusion (rrf) (opens in a new tab)
  • Usage (opens in a new tab)
    • Examples (opens in a new tab)
• Reranking (rerank) (opens in a new tab)
• Lexical Search: LIKE, =, and Regex (opens in a new tab)
  • LIKE (Pattern Matching) (opens in a new tab)
  • = (Keyword/Exact Match) (opens in a new tab)
  • Regex Filtering (opens in a new tab)
    • Example (opens in a new tab)

Vector Search (vector_search)

Vector search retrieves records by semantic similarity using embeddings. It is ideal for finding related content even when exact keywords differ.

Usage

SELECT id, score
FROM vector_search(table, 'search query')
ORDER BY score DESC
LIMIT 5;

SELECT id, score
FROM vector_search(table, 'search query')
ORDER BY score DESC
LIMIT 5;

• table: Dataset name (required)
• query: Search text, or an array of strings for multi-query (opens in a new tab) search (required)
• column: Column name (optional if only one embedding column; required when the table has multiple embedded columns)
• limit: Maximum results (optional). When omitted, the engine-defined maximum is used.
• include_score: Include relevance scores (optional, default TRUE)
• distance_metric: Similarity metric used to rank candidate vectors (optional, named argument). Supported values: 'cosine' (default) and 'l2' (negated Euclidean distance). 'dot' is parsed but not yet wired through the scan path.
• rank_weight: Per-query ranking weight (optional, named argument). Only meaningful when vector_search is passed as a subquery to rrf (opens in a new tab).

Filter Pushdown

WHERE predicates on base table columns (e.g., created_at, product_category) are pushed down as pre-filters — they are applied before the similarity ranking, so only matching rows are scored and returned. This means results reflect the top-K within the filtered set, not the top-K of the entire table filtered afterward.

Predicates on computed columns like score are applied as post-filters after ranking.

Example

-- Filters on created_at are pushed down before ranking
SELECT review_id, rating, customer_id, body, score
FROM vector_search(reviews, 'issues with same day shipping', 1500)
WHERE created_at >= to_unixtime(now() - INTERVAL '7 days')
ORDER BY score DESC
LIMIT 2;

-- Filters on created_at are pushed down before ranking
SELECT review_id, rating, customer_id, body, score
FROM vector_search(reviews, 'issues with same day shipping', 1500)
WHERE created_at >= to_unixtime(now() - INTERVAL '7 days')
ORDER BY score DESC
LIMIT 2;

To override the similarity metric, pass distance_metric as a named argument:

SELECT id, body, score
FROM vector_search(reviews, 'issues with shipping', distance_metric => 'l2')
ORDER BY score DESC
LIMIT 10;

SELECT id, body, score
FROM vector_search(reviews, 'issues with shipping', distance_metric => 'l2')
ORDER BY score DESC
LIMIT 10;

See Vector-Based Search (opens in a new tab) for configuration and advanced usage.

Multi-Query (Late-Interaction) Form

When the target column is a multi-vector column (opens in a new tab), vector_search also accepts an array of query strings. Each query is embedded independently and the per-row score is Σ_q max_e cos(q, e) — ColBERT-style late interaction. Passing an array to a scalar or chunked column returns an error. At most 32 query strings are accepted per call.

SELECT product_id, name, score
FROM vector_search(products, ['hiking', 'waterproof', 'lightweight'], tags)
ORDER BY score DESC
LIMIT 10;

SELECT product_id, name, score
FROM vector_search(products, ['hiking', 'waterproof', 'lightweight'], tags)
ORDER BY score DESC
LIMIT 10;

Full-Text Search (text_search)

Full-text search uses BM25 scoring to retrieve records matching keywords in indexed columns.

Usage

SELECT id, score
FROM text_search(table, 'search terms', col)
ORDER BY score DESC
LIMIT 5;

SELECT id, score
FROM text_search(table, 'search terms', col)
ORDER BY score DESC
LIMIT 5;

• table: Dataset name (required)
• query: Keyword or phrase (required)
• column: Column to search (optional if the table has a single full-text index; required when multiple columns are indexed)
• limit: Maximum results (optional). Defaults to 1000, which is the maximum supported.
• include_score: Include relevance scores (optional, default TRUE)
• rank_weight: Per-query ranking weight (optional, named argument). Only meaningful when text_search is passed as a subquery to rrf (opens in a new tab).

By default, text_search retrieves up to 1000 results. To request fewer, specify a smaller limit.

Filter Pushdown {#full-text-filter-pushdown}

With the built-in Tantivy engine, WHERE predicates on columns carried in the full-text index are pushed into the index scan as pre-filters — they are applied before the top-K limit, so the results are the top-K within the filtered set rather than the filtered remainder of an unfiltered top-K. A predicate the index cannot apply is left to Spice's query engine above the scan, and one the index applies only approximately is re-checked there, so results are the same either way; only how many rows survive the limit changes.

-- state and additions are applied inside the index, before the limit of 5
SELECT id, title, score
FROM text_search(doc.pulls, 'search keywords', body, 5)
WHERE state = 'open' AND additions > 100;

-- state and additions are applied inside the index, before the limit of 5
SELECT id, title, score
FROM text_search(doc.pulls, 'search keywords', body, 5)
WHERE state = 'open' AND additions > 100;

Filterable columns are the dataset's primary key (or full_text_search.row_id (opens in a new tab)) and any column declared with metadata.vectors (opens in a new tab), whose values are carried into the index alongside the searched text. Columns of a type the index cannot represent — dates and timestamps — are skipped, with a warning logged at startup naming the column.

Predicates that push down: =, !=, <, <=, >, >=, BETWEEN, IN, a prefix LIKE 'x%' on a string column, and AND / OR / NOT combinations of them. Predicates that do not, and are applied above the scan instead: anything on the searched text column itself (it is tokenized), on a floating-point column, or on a binary column; a case-insensitive or negated LIKE; an IN list containing NULL; and any comparison whose operands are not a column and a literal.

The Tantivy warm tier used with engine: elasticsearch (opens in a new tab) is built without those extra columns — its schema is the primary key and _score alone — so only primary-key predicates push into it.

Example

SELECT id, title, score
FROM text_search(doc.pulls, 'search keywords', body)
ORDER BY score DESC
LIMIT 5;

SELECT id, title, score
FROM text_search(doc.pulls, 'search keywords', body)
ORDER BY score DESC
LIMIT 5;

See Full-Text Search (opens in a new tab) for configuration and details.

Reciprocal Rank Fusion (rrf)

Reciprocal Rank Fusion (RRF) combines results from multiple search queries to improve relevance by merging rankings from different search methods. Advanced features include per-query ranking weights, recency boosting, and flexible decay functions.

Usage

rrf is variadic and takes two or more search UDTF calls as arguments. Named parameters provide advanced control over ranking, recency, and fusion behavior.

:::info The rrf function automatically adds a fused_score column to the result set, which contains the combined relevance score from all input search queries. Results are sorted by fused_score DESC by default when no explicit ORDER BY clause is specified. :::

SELECT id, content, fused_score
FROM rrf(
    vector_search(table, 'search query', rank_weight => 20),
    text_search(table, 'search terms', column),
    join_key => 'id',    -- explicit join key for performance
    k => 60.0            -- smoothing parameter
)
ORDER BY fused_score DESC
LIMIT 10;

SELECT id, content, fused_score
FROM rrf(
    vector_search(table, 'search query', rank_weight => 20),
    text_search(table, 'search terms', column),
    join_key => 'id',    -- explicit join key for performance
    k => 60.0            -- smoothing parameter
)
ORDER BY fused_score DESC
LIMIT 10;

Arguments:

Note that rank_weight is specified as the last argument to either a text_search or vector_search UDTF call (as shown above). All other arguments can be specified in any order after the search calls (within an rrf invocation).

Filter Pushdown

WHERE predicates on base table columns (e.g., review_date, product_category) are pushed down into each nested search subquery as pre-filters — they are applied before ranking and fusion, so each subquery only considers matching rows. This means the fused results reflect the top-K within the filtered set, not a post-filtered slice of unfiltered rankings.

Predicates on computed columns like fused_score are applied as post-filters after fusion.

-- review_date and product_category are pushed into each vector_search before ranking
SELECT review_id, review_headline
FROM rrf(
    vector_search(amazon_reviews, 'cannot exit the app', rank_weight => 20),
    vector_search(amazon_reviews, 'app not working', rank_weight => 10),
    join_key => 'review_id',
    k => 60.0
)
WHERE review_date > '2015-06-15' AND product_category = 'Mobile_Apps'
LIMIT 10;

-- review_date and product_category are pushed into each vector_search before ranking
SELECT review_id, review_headline
FROM rrf(
    vector_search(amazon_reviews, 'cannot exit the app', rank_weight => 20),
    vector_search(amazon_reviews, 'app not working', rank_weight => 10),
    join_key => 'review_id',
    k => 60.0
)
WHERE review_date > '2015-06-15' AND product_category = 'Mobile_Apps'
LIMIT 10;

Examples

Basic Hybrid Search:

-- Combine vector and text search for enhanced relevance
SELECT id, title, content, fused_score
FROM rrf(
    vector_search(documents, 'machine learning algorithms'),
    text_search(documents, 'neural networks deep learning', content),
    join_key => 'id'  -- explicit join key for performance
)
WHERE fused_score > 0.01
ORDER BY fused_score DESC
LIMIT 5;

-- Combine vector and text search for enhanced relevance
SELECT id, title, content, fused_score
FROM rrf(
    vector_search(documents, 'machine learning algorithms'),
    text_search(documents, 'neural networks deep learning', content),
    join_key => 'id'  -- explicit join key for performance
)
WHERE fused_score > 0.01
ORDER BY fused_score DESC
LIMIT 5;

Weighted Ranking:

-- Boost semantic search over exact text matching
SELECT fused_score, title, content
FROM rrf(
    text_search(posts, 'artificial intelligence', rank_weight => 50.0),
    vector_search(posts, 'AI machine learning', rank_weight => 200.0)
)
ORDER BY fused_score DESC
LIMIT 10;

-- Boost semantic search over exact text matching
SELECT fused_score, title, content
FROM rrf(
    text_search(posts, 'artificial intelligence', rank_weight => 50.0),
    vector_search(posts, 'AI machine learning', rank_weight => 200.0)
)
ORDER BY fused_score DESC
LIMIT 10;

Recency-Boosted Search:

-- Exponential decay favoring recent content
SELECT fused_score, title, created_at
FROM rrf(
    text_search(news, 'breaking news'),
    vector_search(news, 'latest updates'),
    time_column => 'created_at',
    recency_decay => 'exponential',
    decay_constant => 0.05,
    decay_scale_secs => 3600  -- 1 hour scale
)
ORDER BY fused_score DESC
LIMIT 10;

-- Exponential decay favoring recent content
SELECT fused_score, title, created_at
FROM rrf(
    text_search(news, 'breaking news'),
    vector_search(news, 'latest updates'),
    time_column => 'created_at',
    recency_decay => 'exponential',
    decay_constant => 0.05,
    decay_scale_secs => 3600  -- 1 hour scale
)
ORDER BY fused_score DESC
LIMIT 10;

Linear Decay:

-- Linear decay over 24 hours
SELECT fused_score, content
FROM rrf(
    text_search(posts, 'trending'),
    vector_search(posts, 'viral popular'),
    time_column => 'created_at',
    recency_decay => 'linear',
    decay_window_secs => 86400
)
ORDER BY fused_score DESC;

-- Linear decay over 24 hours
SELECT fused_score, content
FROM rrf(
    text_search(posts, 'trending'),
    vector_search(posts, 'viral popular'),
    time_column => 'created_at',
    recency_decay => 'linear',
    decay_window_secs => 86400
)
ORDER BY fused_score DESC;

How RRF works:

• Each input query is ranked independently by score
• Rankings are combined using the formula: RRF Score = Σ(rank_weight / (k + rank))
• Documents appearing in multiple result sets receive higher scores
• The k parameter controls ranking sensitivity (lower = more sensitive to rank position)

Advanced query tuning:

• Rank weighting: Individual queries can be weighted using rank_weight parameter
• Recency boosting: When time_column is specified, scores are multiplied by a decay factor
  • Exponential decay: e^(-decay_constant * age_in_units) where age is in decay_scale_secs
  • Linear decay: max(0, 1 - (age_in_units / decay_window_secs))
• Auto-join: When no join_key is specified, rrf infers the primary key from the underlying tables; if none is available, rows are joined by an auto-generated row identifier

Reranking (rerank)

Reranking reorders candidate results using a dedicated reranker model or an LLM-as-reranker for improved relevance. The input can be any search UDTF (vector_search, text_search, rrf) or a plain table.

Usage

SELECT *
FROM rerank(
    <input>,
    document => 'column_name',
    model    => 'reranker_name',
    limit    => 10
)

SELECT *
FROM rerank(
    <input>,
    document => 'column_name',
    model    => 'reranker_name',
    limit    => 10
)

Arguments:

Query Auto-Propagation

When the input is a search UDTF (vector_search, text_search, or rrf wrapping search UDTFs), the query string is automatically extracted from the nested call. Single-string, make_array(...), and ARRAY[...] query forms are all supported. For multi-query inputs, the first query string is used.

For bare-table inputs, query must be provided explicitly.

Examples

Rerank hybrid search results:

SELECT * FROM rerank(
    rrf(
        vector_search(docs, 'delta lake time travel', limit => 50),
        text_search(docs, 'delta lake time travel', limit => 50)
    ),
    document => 'content',
    model    => 'cohere_rr',
    limit    => 10
);

SELECT * FROM rerank(
    rrf(
        vector_search(docs, 'delta lake time travel', limit => 50),
        text_search(docs, 'delta lake time travel', limit => 50)
    ),
    document => 'content',
    model    => 'cohere_rr',
    limit    => 10
);

Rerank a plain table with an explicit query:

SELECT * FROM rerank(
    tickets,
    query    => 'auth failures',
    document => 'body',
    model    => 'voyage_rr',
    limit    => 5
);

SELECT * FROM rerank(
    tickets,
    query    => 'auth failures',
    document => 'body',
    model    => 'voyage_rr',
    limit    => 5
);

LLM-as-reranker with custom prompt:

SELECT * FROM rerank(
    vector_search(kb, 'onboarding checklist', limit => 40),
    document        => 'content',
    model           => 'gpt_mini',
    strategy        => 'pointwise',
    prompt_template => 'Rate 0-1: is this useful for a new hire?\nQuery: {query}\nDoc: {document}',
    limit           => 10
);

SELECT * FROM rerank(
    vector_search(kb, 'onboarding checklist', limit => 40),
    document        => 'content',
    model           => 'gpt_mini',
    strategy        => 'pointwise',
    prompt_template => 'Rate 0-1: is this useful for a new hire?\nQuery: {query}\nDoc: {document}',
    limit           => 10
);

See Reranking (opens in a new tab) for configuration, provider setup, and additional examples.

Lexical Search: LIKE, =, and Regex

Spice SQL supports traditional filtering for exact and pattern-based matches:

LIKE (Pattern Matching)

SELECT * FROM my_table WHERE column LIKE '%substring%';

SELECT * FROM my_table WHERE column LIKE '%substring%';

• % matches any sequence of characters.
• _ matches a single character.

= (Keyword/Exact Match)

SELECT * FROM my_table WHERE column = 'exact value';

SELECT * FROM my_table WHERE column = 'exact value';

Returns rows where the column exactly matches the value.

Regex Filtering

Spice SQL supports the PostgreSQL regex operators ~ (match), ~* (case-insensitive match), !~ (not match), and !~* (case-insensitive not match) — see Operators (opens in a new tab). Alternatively, use scalar functions such as regexp_like, regexp_match, and regexp_replace. For details and examples, see the Scalar Functions documentation (opens in a new tab).

Example

SELECT * FROM my_table WHERE column ~ '^spice.*ai$';
-- Or, equivalently:
SELECT * FROM my_table WHERE regexp_like(column, '^spice.*ai$');

SELECT * FROM my_table WHERE column ~ '^spice.*ai$';
-- Or, equivalently:
SELECT * FROM my_table WHERE regexp_like(column, '^spice.*ai$');

For more on hybrid and advanced search, see Search Functionality (opens in a new tab) and Vector-Based Search (opens in a new tab)