vectordb / Exports
vectordb
Table of contents
Enumerations
Classes
Interfaces
- AwsCredentials
- CleanupStats
- ColumnAlteration
- CompactionMetrics
- CompactionOptions
- Connection
- ConnectionOptions
- CreateTableOptions
- EmbeddingFunction
- IndexStats
- IvfPQIndexConfig
- MergeInsertArgs
- Table
- UpdateArgs
- UpdateSqlArgs
- VectorIndex
- WriteOptions
Type Aliases
Functions
Type Aliases
VectorIndexParams
ƬVectorIndexParams:IvfPQIndexConfig
Defined in
Functions
connect
▸connect(uri):Promise\<Connection>
Connect to a LanceDB instance at the given URI.
Accepted formats:
/path/to/database- local databases3://bucket/path/to/databaseorgs://bucket/path/to/database- database on cloud storagedb://host:port- remote database (LanceDB cloud)
Parameters
| Name | Type | Description |
|---|---|---|
uri | string | The uri of the database. If the database uri starts withdb:// then it connects to a remote database. |
Returns
Promise\<Connection>
See
ConnectionOptions for more details on the URI format.
Defined in
▸connect(opts):Promise\<Connection>
Connect to a LanceDB instance with connection options.
Parameters
| Name | Type | Description |
|---|---|---|
opts | Partial\<ConnectionOptions> | TheConnectionOptions to use when connecting to the database. |
Returns
Promise\<Connection>
Defined in
convertToTable
▸convertToTable\<T>(data,embeddings?,makeTableOptions?):Promise\<ArrowTable>
Type parameters
| Name |
|---|
T |
Parameters
| Name | Type |
|---|---|
data | Record\<string,unknown>[] |
embeddings? | EmbeddingFunction\<T> |
makeTableOptions? | Partial\<MakeArrowTableOptions> |
Returns
Promise\<ArrowTable>
Defined in
isWriteOptions
▸isWriteOptions(value): value is WriteOptions
Parameters
| Name | Type |
|---|---|
value | any |
Returns
value is WriteOptions
Defined in
makeArrowTable
▸makeArrowTable(data,options?):ArrowTable
An enhanced version of the makeTable function from Apache Arrowthat supports nested fields and embeddings columns.
This function converts an array of Record
Note that it currently does not support nulls.
If a schema is provided then it will be used to determine the resulting arraytypes. Fields will also be reordered to fit the order defined by the schema.
If a schema is not provided then the types will be inferred and the field orderwill be controlled by the order of properties in the first record.
If the input is empty then a schema must be provided to create an empty table.
When a schema is not specified then data types will be inferred. The inferencerules are as follows:
- boolean => Bool
- number => Float64
- String => Utf8
- Buffer => Binary
- Record
- Array => List
Parameters
| Name | Type | Description |
|---|---|---|
data | Record\<string,any>[] | input data |
options? | Partial\<MakeArrowTableOptions> | options to control the makeArrowTable call. |
Returns
ArrowTable
Example
import{fromTableToBuffer,makeArrowTable}from"../arrow";import{Field,FixedSizeList,Float16,Float32,Int32,Schema}from"apache-arrow";constschema=newSchema([newField("a",newInt32()),newField("b",newFloat32()),newField("c",newFixedSizeList(3,newField("item",newFloat16()))),]);consttable=makeArrowTable([{a:1,b:2,c:[1,2,3]},{a:4,b:5,c:[4,5,6]},{a:7,b:8,c:[7,8,9]},],{schema});By default it assumes that the column namedvector is a vector columnand it will be converted into a fixed size list array of type float32.ThevectorColumns option can be used to support other vector columnnames and data types.
constschema=newSchema([newField("a",newFloat64()),newField("b",newFloat64()),newField("vector",newFixedSizeList(3,newField("item",newFloat32()))),]);consttable=makeArrowTable([{a:1,b:2,vector:[1,2,3]},{a:4,b:5,vector:[4,5,6]},{a:7,b:8,vector:[7,8,9]},]);assert.deepEqual(table.schema,schema);You can specify the vector column types and names using the options as well
constschema=newSchema([newField('a',newFloat64()),newField('b',newFloat64()),newField('vec1',newFixedSizeList(3,newField('item',newFloat16()))),newField('vec2',newFixedSizeList(3,newField('item',newFloat16())))]);consttable=makeArrowTable([{a:1,b:2,vec1:[1,2,3],vec2:[2,4,6]},{a:4,b:5,vec1:[4,5,6],vec2:[8,10,12]},{a:7,b:8,vec1:[7,8,9],vec2:[14,16,18]}],{vectorColumns:{vec1:{type:newFloat16()},vec2:{type:newFloat16()}}}assert.deepEqual(table.schema,schema)