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spiceai/docs/README.md

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
Vector Search (vector_search)
- Usage
  - Example
Full-Text Search (text_search)
- Usage
  - Example
Reciprocal Rank Fusion (rrf)
- Usage
  - Examples
Lexical Search: LIKE, =, and Regex

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

table: Dataset name (required)
query: Search text (required)
col: Column name (optional if only one embedding column)
limit: Maximum results (optional, default: 1000)
include_score: Include relevance scores (optional, default TRUE)
rank_weight: Result rank weight (optional, named argument, default score * 1, only when specified as an argument in RRF)

By default, vector_search retrieves up to 1000 results. To change this, specify a limit parameter in the function call.

Example

See Vector-Based Search for configuration and advanced usage.

Full-Text Search (`text_search`)

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

Usage

table: Dataset name (required)
query: Keyword or phrase (required)
col: Column to search (required if multiple indexed columns)
limit: Maximum results (optional, default: 1000)
include_score: Include relevance scores (optional, default TRUE)
rank_weight: Result rank weight (optional, named argument, default score * 1, only when specified as an argument in RRF)

By default, text_search retrieves up to 1000 results. To change this, specify a limit parameter in the function call.

Example

See Full-Text Search 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. :::

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).

Parameter	Type	Required	Description
`query_1`	Search UDTF call	Yes	First search query (e.g., `vector_search`, `text_search`)
`query_2`	Search UDTF call	Yes	Second search query
`...`	Search UDTF call	No	Additional search queries (variadic)
`join_key`	String	No	Column name to use for joining results (default: auto-hash)
`k`	Float	No	Smoothing parameter for RRF scoring (default: 60.0)
`time_column`	String	No	Column name containing timestamps for recency boosting
`recency_decay`	String	No	Decay function: 'linear' or 'exponential' (default: 'exponential')
`decay_constant`	Float	No	Decay rate for exponential decay (default: 0.01)
`decay_scale_secs`	Float	No	Time scale in seconds for decay (default: 86400)

Examples

Basic Hybrid Search:

Weighted Ranking:

Recency-Boosted Search:

Linear Decay:

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, rows are automatically hashed for joining

Lexical Search: LIKE, =, and Regex

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

LIKE (Pattern Matching)

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

= (Keyword/Exact Match)

Returns rows where the column exactly matches the value.

Regex Filtering

Spice SQL does not support the ~ or !~ operators for regular expression matching. Instead, use scalar functions such as regexp_like, regexp_match, and regexp_replace for regex-based filtering. For details and examples, see the Scalar Functions documentation.

Example

For more on hybrid and advanced search, see Search Functionality and Vector-Based Search

spiceai/docs/README.md

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

Table of Contents
Vector Search (vector_search)
- Usage
  - Example
Full-Text Search (text_search)
- Usage
  - Example
Reciprocal Rank Fusion (rrf)
- Usage
  - Examples
Lexical Search: LIKE, =, and Regex

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

table: Dataset name (required)
query: Search text (required)
col: Column name (optional if only one embedding column)
limit: Maximum results (optional, default: 1000)
include_score: Include relevance scores (optional, default TRUE)
rank_weight: Result rank weight (optional, named argument, default score * 1, only when specified as an argument in RRF)

By default, vector_search retrieves up to 1000 results. To change this, specify a limit parameter in the function call.

Example

See Vector-Based Search for configuration and advanced usage.

Full-Text Search (`text_search`)

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

Usage

table: Dataset name (required)
query: Keyword or phrase (required)
col: Column to search (required if multiple indexed columns)
limit: Maximum results (optional, default: 1000)
include_score: Include relevance scores (optional, default TRUE)
rank_weight: Result rank weight (optional, named argument, default score * 1, only when specified as an argument in RRF)

By default, text_search retrieves up to 1000 results. To change this, specify a limit parameter in the function call.

Example

See Full-Text Search for configuration and details.

Reciprocal Rank Fusion (`rrf`)

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.

Arguments:

Parameter	Type	Required	Description
`query_1`	Search UDTF call	Yes	First search query (e.g., `vector_search`, `text_search`)
`query_2`	Search UDTF call	Yes	Second search query
`...`	Search UDTF call	No	Additional search queries (variadic)
`join_key`	String	No	Column name to use for joining results (default: auto-hash)
`k`	Float	No	Smoothing parameter for RRF scoring (default: 60.0)
`time_column`	String	No	Column name containing timestamps for recency boosting
`recency_decay`	String	No	Decay function: 'linear' or 'exponential' (default: 'exponential')
`decay_constant`	Float	No	Decay rate for exponential decay (default: 0.01)
`decay_scale_secs`	Float	No	Time scale in seconds for decay (default: 86400)

Examples

Basic Hybrid Search:

Weighted Ranking:

Recency-Boosted Search:

Linear Decay:

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, rows are automatically hashed for joining

Lexical Search: LIKE, =, and Regex

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

LIKE (Pattern Matching)

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

= (Keyword/Exact Match)

Returns rows where the column exactly matches the value.

Regex Filtering

Example

For more on hybrid and advanced search, see Search Functionality and Vector-Based Search

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;

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;

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;

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;

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;

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;

-- 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;

-- 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;

-- 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 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;

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

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

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

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

SELECT * FROM my_table WHERE regexp_like(column, '^spice.*ai$');

SELECT * FROM my_table WHERE regexp_like(column, '^spice.*ai$');

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

Table of Contents

Vector Search (vector_search)

Usage

Example

Full-Text Search (text_search)

Usage

Example

Reciprocal Rank Fusion (rrf)

Usage

Examples

Lexical Search: LIKE, =, and Regex

LIKE (Pattern Matching)

= (Keyword/Exact Match)

Regex Filtering

Example

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

Table of Contents

Vector Search (vector_search)

Usage

Example

Full-Text Search (text_search)

Usage

Example

Reciprocal Rank Fusion (rrf)

Usage

Examples

Lexical Search: LIKE, =, and Regex

LIKE (Pattern Matching)

= (Keyword/Exact Match)

Regex Filtering

Example

Vector Search (`vector_search`)

Full-Text Search (`text_search`)

Reciprocal Rank Fusion (`rrf`)

Vector Search (`vector_search`)

Full-Text Search (`text_search`)

Reciprocal Rank Fusion (`rrf`)