Vectors

Learn how to use vector fields and vector similarity queries

Vector fields allow you to use vector similarity queries in the FT.SEARCH command. Vector similarity enables you to load, index, and query vectors stored as fields in Redis hashes or in JSON documents (via integration with the JSON module)

Vector similarity provides these functionalities:

  • Realtime vector indexing supporting two indexing methods:

  • Realtime vector update/delete, triggering an update of the index.

  • K-nearest neighbors (KNN) search and range filter (from v2.6.1) supporting three distance metrics to measure the degree of similarity between two vectors $u$, $v$ $\in \mathbb{R}^n$ where $n$ is the length of the vectors:

    • L2 - Euclidean distance between two vectors

      $d(u, v) = \sqrt{ \displaystyle\sum_{i=1}^n{(u_i - v_i)^2}}$

    • IP - Inner product of two vectors

      $d(u, v) = 1 -u\cdot v$

    • COSINE - Cosine distance of two vectors

      $d(u, v) = 1 -\frac{u \cdot v}{\lVert u \rVert \lVert v \rVert}$

Create a vector field

You can add vector fields to the schema in FT.CREATE using this syntax:

FT.CREATE ... SCHEMA ... {field_name} VECTOR {algorithm} {count} [{attribute_name} {attribute_value} ...]

Where:

  • {algorithm} must be specified and be a supported vector similarity index algorithm. The supported algorithms are:

    • FLAT - Brute force algorithm.

    • HNSW - Hierarchical Navigable Small World algorithm.

The {algorithm} attribute specifies the algorithm to use when searching k most similar vectors in the index or filtering vectors by range.

  • {count} specifies the number of attributes for the index. Must be specified. Notice that {count} counts the total number of attributes passed for the index in the command, although algorithm parameters should be submitted as named arguments.

For example:

FT.CREATE my_idx SCHEMA vec_field VECTOR FLAT 6 TYPE FLOAT32 DIM 128 DISTANCE_METRIC L2

Here, three parameters are passed for the index (TYPE, DIM, DISTANCE_METRIC), and count counts the total number of attributes (6).

  • {attribute_name} {attribute_value} are algorithm attributes for the creation of the vector index. Every algorithm has its own mandatory and optional attributes.

Creation attributes per algorithm

FLAT

Mandatory parameters are:

  • TYPE - Vector type. Current supported types are FLOAT32 and FLOAT64.

  • DIM - Vector dimension specified as a positive integer.

  • DISTANCE_METRIC - Supported distance metric, one of {L2, IP, COSINE}.

Optional parameters are:

  • INITIAL_CAP - Initial vector capacity in the index affecting memory allocation size of the index.

  • BLOCK_SIZE - Block size to hold BLOCK_SIZE amount of vectors in a contiguous array. This is useful when the index is dynamic with respect to addition and deletion. Defaults to 1024.

Example

FT.CREATE my_index1 
SCHEMA vector_field VECTOR 
FLAT 
10 
TYPE FLOAT32 
DIM 128 
DISTANCE_METRIC L2 
INITIAL_CAP 1000000 
BLOCK_SIZE 1000

HNSW

Mandatory parameters are:

  • TYPE - Vector type. Current supported types are FLOAT32 and FLOAT64.

  • DIM - Vector dimension, specified as a positive integer.

  • DISTANCE_METRIC - Supported distance metric, one of {L2, IP, COSINE}.

Optional parameters are:

  • INITIAL_CAP - Initial vector capacity in the index affecting memory allocation size of the index.

  • M - Number of maximum allowed outgoing edges for each node in the graph in each layer. on layer zero the maximal number of outgoing edges will be 2M. Default is 16.

  • EF_CONSTRUCTION - Number of maximum allowed potential outgoing edges candidates for each node in the graph, during the graph building. Default is 200.

  • EF_RUNTIME - Number of maximum top candidates to hold during the KNN search. Higher values of EF_RUNTIME lead to more accurate results at the expense of a longer runtime. Default is 10.

  • EPSILON - Relative factor that sets the boundaries in which a range query may search for candidates. That is, vector candidates whose distance from the query vector is radius*(1 + EPSILON) are potentially scanned, allowing more extensive search and more accurate results (on the expense of runtime). Default is 0.01.

Example

FT.CREATE my_index2 
SCHEMA vector_field VECTOR 
HNSW 
16 
TYPE FLOAT64 
DIM 128 
DISTANCE_METRIC L2 
INITIAL_CAP 1000000 
M 40 
EF_CONSTRUCTION 250 
EF_RUNTIME 20
EPSILON 0.8

Indexing vectors

Storing vectors in hashes

Storing vectors in Redis hashes is done by sending a binary blob of vector data. A common way of doing so is by using python numpy with redis-py client:

import numpy as np
from redis import Redis

redis_conn = Redis(host = 'localhost', port = 6379)
vector_field = "vector"
dim = 128

# Store a blob of a random vector of type float32 under a field named 'vector' in Redis hash.
np_vector = np.random.rand(dim).astype(np.float32)
redis_conn.hset('key', mapping = {vector_field: np_vector.tobytes()})

Note that the vector blob size must match the vector field dimension and type specified in the schema, otherwise the indexing will fail in the background.

Storing vectors in JSON

Vector fields are supported upon indexing fields of JSON documents as well:

FT.CREATE my_index ON JSON SCHEMA $.vec as vector VECTOR FLAT 6 TYPE FLOAT32 DIM 4 DISTANCE_METRIC L2

Unlike in hashes, vectors are stored in JSON documents as arrays (not as blobs).

Example

JSON.SET 1 $ '{"vec":[1,2,3,4]}'

As of v2.6.1, JSON supports multi-value indexing. This capability accounts for vectors as well. Thus, it is possible to index multiple vectors under the same JSONPath. Additional information is available in the Indexing JSON documents section.

Example

JSON.SET 1 $ '{"vec":[[1,2,3,4], [5,6,7,8]]}'
JSON.SET 1 $ '{"foo":{"vec":[1,2,3,4]}, "bar":{"vec":[5,6,7,8]}}'

Querying vector fields

You can use vector similarity queries in the FT.SEARCH query command. To use a vector similarity query, you must specify the option DIALECT 2 or greater in the command itself, or set the DEFAULT_DIALECT option to 2 or greater, by either using the command FT.CONFIG SET or when loading the redisearch module and passing it the argument DEFAULT_DIALECT 2.

There are two types of vector queries: KNN and range:

The syntax for vector similarity KNN queries is *=>[<vector_similarity_query>] for running the query on an entire vector field, or <primary_filter_query>=>[<vector_similarity_query>] for running similarity query on the result of the primary filter query.

As of version 2.4, you can use vector_similarity_query only per query and over the entire query filter.

Invalid example

"(@title:Matrix)=>[KNN 10 @v $B] @year:[2020 2022]"

Valid example

"(@title:Matrix @year:[2020 2022])=>[KNN 10 @v $B]"

The <vector_similarity_query> part inside the square brackets needs to be in the following format:

KNN (<number> | $<number_attribute>) @<vector_field> $<blob_attribute> [<vector_query_param_name> <value>|$<value_attribute>] [...]] [ AS <dist_field_name> | $<dist_field_name_attribute>]

Every *_attribute parameter should refer to an attribute in the PARAMS section.

  • <number> | $<number_attribute> - Number of requested results ("K").

  • @<vector_field> - vector_field should be the name of a vector field in the index.

  • $<blob_attribute> - An attribute that holds the query vector as blob and must be passed through the PARAMS section. The blob's byte size should match the vector field dimension and type.

  • [<vector_query_param_name> <value>|$<value_attribute> [...]] - An optional part for passing one or more vector similarity query parameters. Parameters should come in key-value pairs and should be valid parameters for the query. See which runtime parameters are valid for each algorithm.

  • [AS <dist_field_name> | $<dist_field_name_attribute>] - An optional part for specifying a distance field name, for later sorting by the similarity metric and/or returning it. By default, the distance field name is "__<vector_field>_score" and it can be used for sorting without using AS <dist_field_name> in the query.

Note: As of v2.6, vector query params and distance field name can be specified in query attributes like syntax as well. Thus, the following format is also supported:

<primary_filter_query>=>[<vector_similarity_query>]=>{$<param>: (<value> | $<value_attribute>); ... }

where every valid <vector_query_param_name> can be sent as a $<param>, and $yield_distance_as is the equivalent for AS with respect to specifying the optional <dist_field_name> (see examples below).

Range query

Range queries is a way of filtering query results by the distance between a vector field value and a query vector, in terms of the relevant vector field distance metric.
The syntax for range query is @<vector_field>: [VECTOR_RANGE (<radius> | $<radius_attribute>) $<blob_attribute>]. Range queries can appear multiple times in a query, similarly to NUMERIC and GEO clauses, and in particular they can be a part of the <primary_filter_query> in KNN Hybrid search.

  • @<vector_field> - vector_field should be the name of a vector field in the index.

  • <radius> | $<radius_attribute> - A positive number that indicates the maximum distance allowed between the query vector and the vector field value.

  • $<blob_attribute> - An attribute that holds the query vector as blob and must be passed through the PARAMS section. The blob's byte size should match the vector field dimension and type.

  • Range query params: range query clause can be followed by a query attributes section as following: @<vector_field>: [VECTOR_RANGE (<radius> | $<radius_attribute>) $<blob_attribute>]=>{$<param>: (<value> | $<value_attribute>); ... }, where the relevant params in that case are $yield_distance_as and $epsilon. Note that there is no default distance field name in range queries.

Support for hybrid queries

The definition of hybrid query combine different conditions and predicates with scoring methods from multiple fields in a overall compound score. These queries are executed against an index that encompasses both searchable field content and generated embeddings. With a single query, you can employ document selection and diverse scoring choices, consolidating the resulting scores using a chosen function to influence the relevance and importance of the results for a given use case.

Currently, Redis don't support Hybrid Queries with mulitple scoring options. Redis approach combines pre-filtering documents against an index containing searchable fields (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR) and generated embeddings, finding similarity between them according with chosen algorithm (for example, KNN).

Pre-filter with a Vector similarity KNN query syntax have a form of <primary_filter_query>=>[<vector_similarity_query>]. The first part defines the document selection and filterying, and the second it's represented by a Query Modifiers Attributes that define with similarity should be applied, in this case KNN. Redis Stack has an internal mechanism for optimizing the computation of such queries. Two modes in which pre-filter queries are executed are:

  1. Batches mode - In this mode, a batch of the high-scoring documents from the vector index are retrieved. These documents are returned ONLY if <primary_filter_query> is satisfied. In other words, the document contains a similar vector and meets the filter criteria. The procedure terminates when k documents that pass the <primary_filter_query> are returned or after every vector in the index was obtained and processed.

The batch size is determined by a heuristics that is based on k, and the ratio between the expected number of documents in the index that pass the <primary_filter_query> and the vector index size. The goal of the heuristics is to minimize the total number of batches required to get the k results, while preserving a small batch size as possible. Note that the batch size may change dynamically in each iteration, based on the number of results that passed the filter in previous batches.

  1. Ad-hoc brute-force mode - In general, this approach is preferable when the number of documents that pass the <primary_filter_query> part of the query is relatively small. Here, the score of every vector which corresponds to a document that passes the filter is computed, and the top k results are selected and returned. Note that the results of the KNN query will always be accurate in this mode, even if the underline vector index algorithm is an approximate one.

The specific execution mode of a pre-filtered query is determined by a heuristics that aims to minimize the query runtime, and is based on several factors that derive from the query and the index.

Moreover, the execution mode may change from batches to ad-hoc BF during the run, based on estimations of some relevant factors, that are being updated from one batch to another.

Runtime attributes

Pre-filter query attributes (Hybrid approach)

These optional attributes allow overriding the default auto-selected policy in which a pre-filter query is executed:

  • HYBRID_POLICY - The policy to run the pre-filter query (hybrid approach) in. Possible values are BATCHES and ADHOC_BF (not case sensitive). Note that the batch size will be auto selected dynamically in BATCHES mode, unless the BATCH_SIZE attribute is given.
  • BATCH_SIZE - A fixed batch size to use in every iteration, when the BATCHES policy is auto-selected or requested.

Algorithm-specific attributes

FLAT

Currently, no runtime parameters are available for FLAT indexes.

HNSW

Optional parameters are:

  • EF_RUNTIME - The number of maximum top candidates to hold during the KNN search. Higher values of EF_RUNTIME will lead to a more accurate results on the expense of a longer runtime. Defaults to the EF_RUNTIME value passed on creation (which defaults to 10).

  • EPSILON - Relative factor that sets the boundaries in which a range query may search for candidates. That is, vector candidates whose distance from the query vector is radius*(1 + EPSILON) are potentially scanned, allowing more extensive search and more accurate results (on the expense of runtime). Defaults to the EPSILON value passed on creation (which defaults to 0.01).

Important notes:

  1. Although specifying K requested results in KNN search, the default LIMIT is 10, to get all the returned results, specify LIMIT 0 <K> in your command.

  2. By default, the results are sorted by their document's score. To sort by some vector similarity score, use SORTBY <dist_field_name>. See examples below.

  3. It is recommended to adjust the <radius> parameter in range queries to the corresponding vector field distance metric and to the data itself. In particular, recall that the distance between the vectors in an index whose distance metric is Cosine is bounded by 2, while L2 distance between the vectors is not bounded. Hence, it is better to consider the distance between the vectors that are considered similar and choose <radius> accordingly.

Vector search examples

"Pure" KNN queries

Return the 10 documents for which the vector stored under its vec field is the closest to the vector represented by the following 4-bytes blob:

FT.SEARCH idx "*=>[KNN 10 @vec $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 2

Now, sort the results by their distance from the query vector:

FT.SEARCH idx "*=>[KNN 10 @vec $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY __vec_score DIALECT 2

Return the top 10 similar documents, use query params (see "params" section in FT.SEARCH command) for specifying K and EF_RUNTIME parameter, and set EF_RUNTIME value to 150 (assuming vec is an HNSW index):

FT.SEARCH idx "*=>[KNN $K @vec $BLOB EF_RUNTIME $EF]" PARAMS 6 BLOB "\x12\xa9\xf5\x6c" K 10 EF 150 DIALECT 2

Similar to the previous queries, this time use a custom distance field name to sort by it:

FT.SEARCH idx "*=>[KNN $K @vec $BLOB AS my_scores]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" K 10 SORTBY my_scores DIALECT 2

Use query attributes syntax to specify optional parameters and the distance field name:

FT.SEARCH idx "*=>[KNN 10 @vec $BLOB]=>{$EF_RUNTIME: $EF; $YIELD_DISTANCE_AS: my_scores}" PARAMS 4 EF 150 BLOB "\x12\xa9\xf5\x6c" SORTBY my_scores DIALECT 2

Pre-filter KNN queries (hybrid approach)

Among documents that have 'Dune' in their title field and their num value is in the range [2020, 2022], return the top 10 for which the vector stored in the vec field is the closest to the vector represented by the following 4-bytes blob:

FT.SEARCH idx "(@title:Dune @num:[2020 2022])=>[KNN $K @vec $BLOB AS my_scores]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" K 10 SORTBY my_scores DIALECT 2

Use a different filter for the hybrid approach: this time, return the top 10 results from the documents that contain a 'shirt' tag in the type field and optionally a 'blue' tag in their color field. Here, the results are sorted by the full-text scorer.

FT.SEARCH idx "(@type:{shirt} ~@color:{blue})=>[KNN $K @vec $BLOB]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" K 10 DIALECT 2

And, here's a pre-filter with KNN query in which the hybrid policy is set explicitly to "ad-hoc brute force" (rather than auto-selected):

FT.SEARCH idx "(@type:{shirt})=>[KNN $K @vec $BLOB HYBRID_POLICY ADHOC_BF]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" K 10 SORTBY __vec_scores DIALECT 2

And, now, here's a pre-filter with KNN query in which the hybrid policy is set explicitly to "batches", and the batch size is set explicitly to be 50 using a query parameter:

FT.SEARCH idx "(@type:{shirt})=>[KNN $K @vec $BLOB HYBRID_POLICY BATCHES BATCH_SIZE $B_SIZE]" PARAMS 6 BLOB "\x12\xa9\xf5\x6c" K 10 B_SIZE 50 DIALECT 2

Run the same query as above and use the query attributes syntax to specify optional parameters:

FT.SEARCH idx "(@type:{shirt})=>[KNN 10 @vec $BLOB]=>{$HYBRID_POLICY: BATCHES; $BATCH_SIZE: 50}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 2

See additional Python examples in this Jupyter notebook

Range queries

Return 100 documents for which the distance between its vector stored under the vec field to the specified query vector blob is at most 5 (in terms of vec field DISTANCE_METRIC):

FT.SEARCH idx "@vec:[VECTOR_RANGE $r $BLOB]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" r 5 LIMIT 0 100 DIALECT 2

Run the same query as above and set EPSILON parameter to 0.5 (assuming vec is HNSW index), yield the vector distance between vec and the query result in a field named my_scores, and sort the results by that distance.

FT.SEARCH idx "@vec:[VECTOR_RANGE 5 $BLOB]=>{$EPSILON:0.5; $YIELD_DISTANCE_AS: my_scores}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY my_scores LIMIT 0 100 DIALECT 2

Use the vector range query in a complex query: return all the documents that contain either 'shirt' in their type tag with their num value in the range [2020, 2022] OR a vector stored in vec whose distance from the query vector is no more than 0.8, then sort results by their vector distance, if it is in the range:

FT.SEARCH idx "(@type:{shirt} @num:[2020 2022]) | @vec:[VECTOR_RANGE 0.8 $BLOB]=>{$YIELD_DISTANCE_AS: my_scores}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY my_scores DIALECT 2

See additional Python examples in this Jupyter notebook

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