- Elasticsearch Guide: other versions:
- What’s new in 8.17
- Elasticsearch basics
- Quick starts
- Set up Elasticsearch
- Run Elasticsearch locally
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Miscellaneous cluster settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Data stream lifecycle settings
- Field data cache settings
- Local gateway settings
- Health Diagnostic settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- Inference settings
- License settings
- Machine learning settings
- Monitoring settings
- Node settings
- Networking
- Node query cache settings
- Path settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Set JVM options
- Important system configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- All permission check
- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Dynamic mapping
- Explicit mapping
- Runtime fields
- Field data types
- Aggregate metric
- Alias
- Arrays
- Binary
- Boolean
- Completion
- Date
- Date nanoseconds
- Dense vector
- Flattened
- Geopoint
- Geoshape
- Histogram
- IP
- Join
- Keyword
- Nested
- Numeric
- Object
- Pass-through object
- Percolator
- Point
- Range
- Rank feature
- Rank features
- Search-as-you-type
- Semantic text
- Shape
- Sparse vector
- Text
- Token count
- Unsigned long
- Version
- Metadata fields
- Mapping parameters
analyzer
coerce
copy_to
doc_values
dynamic
eager_global_ordinals
enabled
format
ignore_above
index.mapping.ignore_above
ignore_malformed
index
index_options
index_phrases
index_prefixes
meta
fields
normalizer
norms
null_value
position_increment_gap
properties
search_analyzer
similarity
store
subobjects
term_vector
- Mapping limit settings
- Removal of mapping types
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Attachment
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- Geo-grid
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
- IP Location
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Redact
- Registered domain
- Remove
- Rename
- Reroute
- Script
- Set
- Set security user
- Sort
- Split
- Terminate
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Ingest pipelines in Search
- Aliases
- Search your data
- Re-ranking
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Frequent item sets
- Geo-distance
- Geohash grid
- Geohex grid
- Geotile grid
- Global
- Histogram
- IP prefix
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Random sampler
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Time series
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Change point
- Cumulative cardinality
- Cumulative sum
- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
- Min bucket
- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- Geospatial analysis
- Connectors
- EQL
- ES|QL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
- Concepts
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Data tiers
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
- Manually configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- User profiles
- Realms
- Realm chains
- Security domains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- JWT authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Looking up users without authentication
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Role restriction
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Watcher
- Cross-cluster replication
- Data store architecture
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat component templates
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Prevalidate node removal
- Nodes reload secure settings
- Nodes stats
- Cluster Info
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Create or update desired nodes
- Get desired nodes
- Delete desired nodes
- Get desired balance
- Reset desired balance
- Cross-cluster replication APIs
- Connector APIs
- Create connector
- Delete connector
- Get connector
- List connectors
- Update connector API key id
- Update connector configuration
- Update connector index name
- Update connector features
- Update connector filtering
- Update connector name and description
- Update connector pipeline
- Update connector scheduling
- Update connector service type
- Create connector sync job
- Cancel connector sync job
- Delete connector sync job
- Get connector sync job
- List connector sync jobs
- Check in a connector
- Update connector error
- Update connector last sync stats
- Update connector status
- Check in connector sync job
- Claim connector sync job
- Set connector sync job error
- Set connector sync job stats
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- ES|QL APIs
- Features APIs
- Fleet APIs
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
- Clone index
- Close index
- Create index
- Create or update alias
- Create or update component template
- Create or update index template
- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Resolve cluster
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Unfreeze index
- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Inference APIs
- Delete inference API
- Get inference API
- Perform inference API
- Create inference API
- Stream inference API
- Update inference API
- AlibabaCloud AI Search inference service
- Amazon Bedrock inference service
- Anthropic inference service
- Azure AI studio inference service
- Azure OpenAI inference service
- Cohere inference service
- Elasticsearch inference service
- ELSER inference service
- Google AI Studio inference service
- Google Vertex AI inference service
- HuggingFace inference service
- Mistral inference service
- OpenAI inference service
- Watsonx inference service
- Info API
- Ingest APIs
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Upgrade model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Update data frame analytics jobs
- Machine learning trained model APIs
- Clear trained model deployment cache
- Create or update trained model aliases
- Create part of a trained model
- Create trained models
- Create trained model vocabulary
- Delete trained model aliases
- Delete trained models
- Get trained models
- Get trained models stats
- Infer trained model
- Start trained model deployment
- Stop trained model deployment
- Update trained model deployment
- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Root API
- Script APIs
- Search APIs
- Search Application APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Bulk create or update roles API
- Bulk delete roles API
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Query Role
- Get service accounts
- Get service account credentials
- Get Security settings
- Get token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- Query API key information
- Query User
- Update API key
- Update Security settings
- Bulk update API keys
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Activate user profile
- Disable user profile
- Enable user profile
- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Text structure APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- Optimizations
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
- Total number of shards per node has been reached
- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
- Fix other role nodes out of disk
- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Troubleshooting broken repositories
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- Troubleshooting an unbalanced cluster
- Capture diagnostics
- Migration guide
- Release notes
- Elasticsearch version 8.17.1
- Elasticsearch version 8.17.0
- Elasticsearch version 8.16.2
- Elasticsearch version 8.16.1
- Elasticsearch version 8.16.0
- Elasticsearch version 8.15.5
- Elasticsearch version 8.15.4
- Elasticsearch version 8.15.3
- Elasticsearch version 8.15.2
- Elasticsearch version 8.15.1
- Elasticsearch version 8.15.0
- Elasticsearch version 8.14.3
- Elasticsearch version 8.14.2
- Elasticsearch version 8.14.1
- Elasticsearch version 8.14.0
- Elasticsearch version 8.13.4
- Elasticsearch version 8.13.3
- Elasticsearch version 8.13.2
- Elasticsearch version 8.13.1
- Elasticsearch version 8.13.0
- Elasticsearch version 8.12.2
- Elasticsearch version 8.12.1
- Elasticsearch version 8.12.0
- Elasticsearch version 8.11.4
- Elasticsearch version 8.11.3
- Elasticsearch version 8.11.2
- Elasticsearch version 8.11.1
- Elasticsearch version 8.11.0
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Dense vector field type
editDense vector field type
editThe dense_vector
field type stores dense vectors of numeric values. Dense
vector fields are primarily used for k-nearest neighbor (kNN) search.
The dense_vector
type does not support aggregations or sorting.
You add a dense_vector
field as an array of numeric values
based on element_type
with
float
by default:
resp = client.indices.create( index="my-index", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 3 }, "my_text": { "type": "keyword" } } }, ) print(resp) resp1 = client.index( index="my-index", id="1", document={ "my_text": "text1", "my_vector": [ 0.5, 10, 6 ] }, ) print(resp1) resp2 = client.index( index="my-index", id="2", document={ "my_text": "text2", "my_vector": [ -0.5, 10, 10 ] }, ) print(resp2)
response = client.indices.create( index: 'my-index', body: { mappings: { properties: { my_vector: { type: 'dense_vector', dims: 3 }, my_text: { type: 'keyword' } } } } ) puts response response = client.index( index: 'my-index', id: 1, body: { my_text: 'text1', my_vector: [ 0.5, 10, 6 ] } ) puts response response = client.index( index: 'my-index', id: 2, body: { my_text: 'text2', my_vector: [ -0.5, 10, 10 ] } ) puts response
const response = await client.indices.create({ index: "my-index", mappings: { properties: { my_vector: { type: "dense_vector", dims: 3, }, my_text: { type: "keyword", }, }, }, }); console.log(response); const response1 = await client.index({ index: "my-index", id: 1, document: { my_text: "text1", my_vector: [0.5, 10, 6], }, }); console.log(response1); const response2 = await client.index({ index: "my-index", id: 2, document: { my_text: "text2", my_vector: [-0.5, 10, 10], }, }); console.log(response2);
PUT my-index { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 3 }, "my_text" : { "type" : "keyword" } } } } PUT my-index/_doc/1 { "my_text" : "text1", "my_vector" : [0.5, 10, 6] } PUT my-index/_doc/2 { "my_text" : "text2", "my_vector" : [-0.5, 10, 10] }
Unlike most other data types, dense vectors are always single-valued.
It is not possible to store multiple values in one dense_vector
field.
Index vectors for kNN search
editA k-nearest neighbor (kNN) search finds the k nearest vectors to a query vector, as measured by a similarity metric.
Dense vector fields can be used to rank documents in
script_score
queries. This lets you perform
a brute-force kNN search by scanning all documents and ranking them by
similarity.
In many cases, a brute-force kNN search is not efficient enough. For this
reason, the dense_vector
type supports indexing vectors into a specialized
data structure to support fast kNN retrieval through the knn
option in the search API
Unmapped array fields of float elements with size between 128 and 4096 are dynamically mapped as dense_vector
with a default similariy of cosine
.
You can override the default similarity by explicitly mapping the field as dense_vector
with the desired similarity.
Indexing is enabled by default for dense vector fields and indexed as int8_hnsw
.
When indexing is enabled, you can define the vector similarity to use in kNN search:
resp = client.indices.create( index="my-index-2", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 3, "similarity": "dot_product" } } }, ) print(resp)
response = client.indices.create( index: 'my-index-2', body: { mappings: { properties: { my_vector: { type: 'dense_vector', dims: 3, similarity: 'dot_product' } } } } ) puts response
const response = await client.indices.create({ index: "my-index-2", mappings: { properties: { my_vector: { type: "dense_vector", dims: 3, similarity: "dot_product", }, }, }, }); console.log(response);
PUT my-index-2 { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 3, "similarity": "dot_product" } } } }
Indexing vectors for approximate kNN search is an expensive process. It
can take substantial time to ingest documents that contain vector fields with
index
enabled. See k-nearest neighbor (kNN) search to
learn more about the memory requirements.
You can disable indexing by setting the index
parameter to false
:
resp = client.indices.create( index="my-index-2", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 3, "index": False } } }, ) print(resp)
response = client.indices.create( index: 'my-index-2', body: { mappings: { properties: { my_vector: { type: 'dense_vector', dims: 3, index: false } } } } ) puts response
const response = await client.indices.create({ index: "my-index-2", mappings: { properties: { my_vector: { type: "dense_vector", dims: 3, index: false, }, }, }, }); console.log(response);
PUT my-index-2 { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 3, "index": false } } } }
Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like most kNN algorithms, HNSW is an approximate method that sacrifices result accuracy for improved speed.
Automatically quantize vectors for kNN search
editThe dense_vector
type supports quantization to reduce the memory footprint required when searching float
vectors.
The three following quantization strategies are supported:
-
int8
- Quantizes each dimension of the vector to 1-byte integers. This reduces the memory footprint by 75% (or 4x) at the cost of some accuracy. -
int4
- Quantizes each dimension of the vector to half-byte integers. This reduces the memory footprint by 87% (or 8x) at the cost of accuracy. -
bbq
- [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. Better binary quantization which reduces each dimension to a single bit precision. This reduces the memory footprint by 96% (or 32x) at a larger cost of accuracy. Generally, oversampling during query time and reranking can help mitigate the accuracy loss.
When using a quantized format, you may want to oversample and rescore the results to improve accuracy. See oversampling and rescoring for more information.
To use a quantized index, you can set your index type to int8_hnsw
, int4_hnsw
, or bbq_hnsw
. When indexing float
vectors, the current default
index type is int8_hnsw
.
Quantization will continue to keep the raw float vector values on disk for reranking, reindexing, and quantization improvements over the lifetime of the data.
This means disk usage will increase by ~25% for int8
, ~12.5% for int4
, and ~3.1% for bbq
due to the overhead of storing the quantized and raw vectors.
int4
quantization requires an even number of vector dimensions.
[preview]
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
bbq
quantization only supports vector dimensions that are greater than 64.
Here is an example of how to create a byte-quantized index:
resp = client.indices.create( index="my-byte-quantized-index", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 3, "index": True, "index_options": { "type": "int8_hnsw" } } } }, ) print(resp)
response = client.indices.create( index: 'my-byte-quantized-index', body: { mappings: { properties: { my_vector: { type: 'dense_vector', dims: 3, index: true, index_options: { type: 'int8_hnsw' } } } } } ) puts response
const response = await client.indices.create({ index: "my-byte-quantized-index", mappings: { properties: { my_vector: { type: "dense_vector", dims: 3, index: true, index_options: { type: "int8_hnsw", }, }, }, }, }); console.log(response);
PUT my-byte-quantized-index { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 3, "index": true, "index_options": { "type": "int8_hnsw" } } } } }
Here is an example of how to create a half-byte-quantized index:
resp = client.indices.create( index="my-byte-quantized-index", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 4, "index": True, "index_options": { "type": "int4_hnsw" } } } }, ) print(resp)
const response = await client.indices.create({ index: "my-byte-quantized-index", mappings: { properties: { my_vector: { type: "dense_vector", dims: 4, index: true, index_options: { type: "int4_hnsw", }, }, }, }, }); console.log(response);
PUT my-byte-quantized-index { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 4, "index": true, "index_options": { "type": "int4_hnsw" } } } } }
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. Here is an example of how to create a binary quantized index:
resp = client.indices.create( index="my-byte-quantized-index", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 64, "index": True, "index_options": { "type": "bbq_hnsw" } } } }, ) print(resp)
const response = await client.indices.create({ index: "my-byte-quantized-index", mappings: { properties: { my_vector: { type: "dense_vector", dims: 64, index: true, index_options: { type: "bbq_hnsw", }, }, }, }, }); console.log(response);
PUT my-byte-quantized-index { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 64, "index": true, "index_options": { "type": "bbq_hnsw" } } } } }
Parameters for dense vector fields
editThe following mapping parameters are accepted:
-
element_type
-
(Optional, string)
The data type used to encode vectors. The supported data types are
float
(default),byte
, and bit.
Valid values for element_type
-
float
- indexes a 4-byte floating-point value per dimension. This is the default value.
-
byte
- indexes a 1-byte integer value per dimension.
-
bit
-
indexes a single bit per dimension. Useful for very high-dimensional vectors or models that specifically support bit vectors.
NOTE: when using
bit
, the number of dimensions must be a multiple of 8 and must represent the number of bits.
-
dims
-
(Optional, integer)
Number of vector dimensions. Can’t exceed
4096
. Ifdims
is not specified, it will be set to the length of the first vector added to the field. -
index
-
(Optional, Boolean)
If
true
, you can search this field using the kNN search API. Defaults totrue
.
-
similarity
-
(Optional*, string) The vector similarity metric to use in kNN search. Documents are ranked by their vector field’s similarity to the query vector. The
_score
of each document will be derived from the similarity, in a way that ensures scores are positive and that a larger score corresponds to a higher ranking. Defaults tol2_norm
whenelement_type: bit
otherwise defaults tocosine
.* This parameter can only be specified when
index
istrue
.bit
vectors only supportl2_norm
as their similarity metric.
Valid values for similarity
-
l2_norm
-
Computes similarity based on the L2 distance (also known as Euclidean
distance) between the vectors. The document
_score
is computed as1 / (1 + l2_norm(query, vector)^2)
.
For bit
vectors, instead of using l2_norm
, the hamming
distance between the vectors is used. The _score
transformation is (numBits - hamming(a, b)) / numBits
-
dot_product
-
Computes the dot product of two unit vectors. This option provides an optimized way to perform cosine similarity. The constraints and computed score are defined by
element_type
.When
element_type
isfloat
, all vectors must be unit length, including both document and query vectors. The document_score
is computed as(1 + dot_product(query, vector)) / 2
.When
element_type
isbyte
, all vectors must have the same length including both document and query vectors or results will be inaccurate. The document_score
is computed as0.5 + (dot_product(query, vector) / (32768 * dims))
wheredims
is the number of dimensions per vector. -
cosine
-
Computes the cosine similarity. During indexing Elasticsearch automatically
normalizes vectors with
cosine
similarity to unit length. This allows to internally usedot_product
for computing similarity, which is more efficient. Original un-normalized vectors can be still accessed through scripts. The document_score
is computed as(1 + cosine(query, vector)) / 2
. Thecosine
similarity does not allow vectors with zero magnitude, since cosine is not defined in this case. -
max_inner_product
-
Computes the maximum inner product of two vectors. This is similar to
dot_product
, but doesn’t require vectors to be normalized. This means that each vector’s magnitude can significantly effect the score. The document_score
is adjusted to prevent negative values. Formax_inner_product
values< 0
, the_score
is1 / (1 + -1 * max_inner_product(query, vector))
. For non-negativemax_inner_product
results the_score
is calculatedmax_inner_product(query, vector) + 1
.
Although they are conceptually related, the similarity
parameter is
different from text
field similarity
and accepts
a distinct set of options.
-
index_options
-
(Optional*, object) An optional section that configures the kNN indexing algorithm. The HNSW algorithm has two internal parameters that influence how the data structure is built. These can be adjusted to improve the accuracy of results, at the expense of slower indexing speed.
* This parameter can only be specified when
index
istrue
.Properties of
index_options
-
type
-
(Required, string) The type of kNN algorithm to use. Can be either any of:
-
hnsw
- This utilizes the HNSW algorithm for scalable approximate kNN search. This supports allelement_type
values. -
int8_hnsw
- The default index type for float vectors. This utilizes the HNSW algorithm in addition to automatically scalar quantization for scalable approximate kNN search withelement_type
offloat
. This can reduce the memory footprint by 4x at the cost of some accuracy. See Automatically quantize vectors for kNN search. -
int4_hnsw
- This utilizes the HNSW algorithm in addition to automatically scalar quantization for scalable approximate kNN search withelement_type
offloat
. This can reduce the memory footprint by 8x at the cost of some accuracy. See Automatically quantize vectors for kNN search. -
[preview]
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
bbq_hnsw
- This utilizes the HNSW algorithm in addition to automatically binary quantization for scalable approximate kNN search withelement_type
offloat
. This can reduce the memory footprint by 32x at the cost of accuracy. See Automatically quantize vectors for kNN search. -
flat
- This utilizes a brute-force search algorithm for exact kNN search. This supports allelement_type
values. -
int8_flat
- This utilizes a brute-force search algorithm in addition to automatically scalar quantization. Only supportselement_type
offloat
. -
int4_flat
- This utilizes a brute-force search algorithm in addition to automatically half-byte scalar quantization. Only supportselement_type
offloat
. -
[preview]
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
bbq_flat
- This utilizes a brute-force search algorithm in addition to automatically binary quantization. Only supportselement_type
offloat
.
-
-
m
-
(Optional, integer)
The number of neighbors each node will be connected to in the HNSW graph.
Defaults to
16
. Only applicable tohnsw
,int8_hnsw
,int4_hnsw
andbbq_hnsw
index types. -
ef_construction
-
(Optional, integer)
The number of candidates to track while assembling the list of nearest
neighbors for each new node. Defaults to
100
. Only applicable tohnsw
,int8_hnsw
,int4_hnsw
andbbq_hnsw
index types. -
confidence_interval
-
(Optional, float)
Only applicable to
int8_hnsw
,int4_hnsw
,int8_flat
, andint4_flat
index types. The confidence interval to use when quantizing the vectors. Can be any value between and including0.90
and1.0
or exactly0
. When the value is0
, this indicates that dynamic quantiles should be calculated for optimized quantization. When between0.90
and1.0
, this value restricts the values used when calculating the quantization thresholds. For example, a value of0.95
will only use the middle 95% of the values when calculating the quantization thresholds (e.g. the highest and lowest 2.5% of values will be ignored). Defaults to1/(dims + 1)
forint8
quantized vectors and0
forint4
for dynamic quantile calculation.
-
Synthetic _source
editSynthetic _source
is Generally Available only for TSDB indices
(indices that have index.mode
set to time_series
). For other indices
synthetic _source
is in technical preview. Features in technical preview may
be changed or removed in a future release. Elastic will work to fix
any issues, but features in technical preview are not subject to the support SLA
of official GA features.
dense_vector
fields support synthetic _source
.
Indexing & Searching bit vectors
editWhen using element_type: bit
, this will treat all vectors as bit vectors. Bit vectors utilize only a single
bit per dimension and are internally encoded as bytes. This can be useful for very high-dimensional vectors or models.
When using bit
, the number of dimensions must be a multiple of 8 and must represent the number of bits. Additionally,
with bit
vectors, the typical vector similarity values are effectively all scored the same, e.g. with hamming
distance.
Let’s compare two byte[]
arrays, each representing 40 individual bits.
[-127, 0, 1, 42, 127]
in bits 1000000100000000000000010010101001111111
[127, -127, 0, 1, 42]
in bits 0111111110000001000000000000000100101010
When comparing these two bit, vectors, we first take the hamming
distance.
xor
result:
1000000100000000000000010010101001111111 ^ 0111111110000001000000000000000100101010 = 1111111010000001000000010010101101010101
Then, we gather the count of 1
bits in the xor
result: 18
. To scale for scoring, we subtract from the total number
of bits and divide by the total number of bits: (40 - 18) / 40 = 0.55
. This would be the _score
betwee these two
vectors.
Here is an example of indexing and searching bit vectors:
resp = client.indices.create( index="my-bit-vectors", mappings={ "properties": { "my_vector": { "type": "dense_vector", "dims": 40, "element_type": "bit" } } }, ) print(resp)
const response = await client.indices.create({ index: "my-bit-vectors", mappings: { properties: { my_vector: { type: "dense_vector", dims: 40, element_type: "bit", }, }, }, }); console.log(response);
PUT my-bit-vectors { "mappings": { "properties": { "my_vector": { "type": "dense_vector", "dims": 40, "element_type": "bit" } } } }
resp = client.bulk( index="my-bit-vectors", refresh=True, operations=[ { "index": { "_id": "1" } }, { "my_vector": [ 127, -127, 0, 1, 42 ] }, { "index": { "_id": "2" } }, { "my_vector": "8100012a7f" } ], ) print(resp)
const response = await client.bulk({ index: "my-bit-vectors", refresh: "true", operations: [ { index: { _id: "1", }, }, { my_vector: [127, -127, 0, 1, 42], }, { index: { _id: "2", }, }, { my_vector: "8100012a7f", }, ], }); console.log(response);
POST /my-bit-vectors/_bulk?refresh {"index": {"_id" : "1"}} {"my_vector": [127, -127, 0, 1, 42]} {"index": {"_id" : "2"}} {"my_vector": "8100012a7f"}
5 bytes representing the 40 bit dimensioned vector |
|
A hexidecimal string representing the 40 bit dimensioned vector |
Then, when searching, you can use the knn
query to search for similar bit vectors:
resp = client.search( index="my-bit-vectors", filter_path="hits.hits", query={ "knn": { "query_vector": [ 127, -127, 0, 1, 42 ], "field": "my_vector" } }, ) print(resp)
const response = await client.search({ index: "my-bit-vectors", filter_path: "hits.hits", query: { knn: { query_vector: [127, -127, 0, 1, 42], field: "my_vector", }, }, }); console.log(response);
POST /my-bit-vectors/_search?filter_path=hits.hits { "query": { "knn": { "query_vector": [127, -127, 0, 1, 42], "field": "my_vector" } } }
{ "hits": { "hits": [ { "_index": "my-bit-vectors", "_id": "1", "_score": 1.0, "_source": { "my_vector": [ 127, -127, 0, 1, 42 ] } }, { "_index": "my-bit-vectors", "_id": "2", "_score": 0.55, "_source": { "my_vector": "8100012a7f" } } ] } }
Updatable field type
editTo better accommodate scaling and performance needs, updating the type
setting in index_options
is possible with the Update Mapping API, according to the following graph (jumps allowed):
flat --> int8_flat --> int4_flat --> hnsw --> int8_hnsw --> int4_hnsw
For updating all HNSW types (hnsw
, int8_hnsw
, int4_hnsw
) the number of connections m
must either stay the same or increase. For scalar quantized formats (int8_flat
, int4_flat
, int8_hnsw
, int4_hnsw
) the confidence_interval
must always be consistent (once defined, it cannot change).
Updating type
in index_options
will fail in all other scenarios.
Switching types
won’t re-index vectors that have already been indexed (they will keep using their original type
), vectors being indexed after the change will use the new type
instead.
For example, it’s possible to define a dense vector field that utilizes the flat
type (raw float32 arrays) for a first batch of data to be indexed.
resp = client.indices.create( index="my-index-000001", mappings={ "properties": { "text_embedding": { "type": "dense_vector", "dims": 384, "index_options": { "type": "flat" } } } }, ) print(resp)
const response = await client.indices.create({ index: "my-index-000001", mappings: { properties: { text_embedding: { type: "dense_vector", dims: 384, index_options: { type: "flat", }, }, }, }, }); console.log(response);
PUT my-index-000001 { "mappings": { "properties": { "text_embedding": { "type": "dense_vector", "dims": 384, "index_options": { "type": "flat" } } } } }
Changing the type
to int4_hnsw
makes sure vectors indexed after the change will use an int4 scalar quantized representation and HNSW (e.g., for KNN queries).
That includes new segments created by merging previously created segments.
resp = client.indices.put_mapping( index="my-index-000001", properties={ "text_embedding": { "type": "dense_vector", "dims": 384, "index_options": { "type": "int4_hnsw" } } }, ) print(resp)
const response = await client.indices.putMapping({ index: "my-index-000001", properties: { text_embedding: { type: "dense_vector", dims: 384, index_options: { type: "int4_hnsw", }, }, }, }); console.log(response);
PUT /my-index-000001/_mapping { "properties": { "text_embedding": { "type": "dense_vector", "dims": 384, "index_options": { "type": "int4_hnsw" } } } }
Vectors indexed before this change will keep using the flat
type (raw float32 representation and brute force search for KNN queries).
In order to have all the vectors updated to the new type, either reindexing or force merging should be used.
For debugging purposes, it’s possible to inspect how many segments (and docs) exist for each type
with the Index Segments API.
On this page