KDB.AI Python API

This page contains the references for KDB.AI's Python API. For example usage, see the Quickstart Guide.

Note

Before you start, ensure you have the following installed on your machine:

Note

The argument index_option of function search() is the index specific options for similarity search. For example, efSearch can be specified for HNSW indexes, while clusters can be specified for IVF/IVFPQ indexes.

For details of the usage of index_option, see the How to use an Index in KDB.AI page.

Session

Session represents a connection to a KDB.AI instance. To interact with KDBAI Cloud or Server, you first need to create a session. This section summarizes how to create and close a session.

Create session

kdbai_client.Session

Session represents a connection to a KDB.AI instance.

Input parameters:

Name

Type

Description

Required

Default

api_key

str

API Key to be used for authentication.

No

None

endpoint

str

Server endpoint to connect to.

No

'http://localhost:8081'

host

str

Hostname of the KDB.AI server.

No

None

port

int

Port number on the server.

No

- 8081 if mode='rest'
- 8082 if mode='qipc'

mode

str

Implementation method used for the session. Possible values: rest and qipc

No

None

Tip

  1. If you don't provide the mode parameter:

    • A REST-based session is created if the endpoint starts with https://cloud.kdb.ai.

    • Otherwise, a qIPC-based session is created.

  2. Note that the REST-based implementation:

    • has worse performance due to payload serialization and deserialization.

    • has a 10MB limit on payload size for the train and insert methods.

Example

Python

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import kdbai_client as kdbai
### local server
session = kdbai.Session(endpoint='http://localhost:8082')
session = kdbai.Session(endpoint='http://localhost:8082', mode='qipc')
### local server using REST
session = kdbai.Session(endpoint='http://localhost:8081', mode='rest')
### local server using TLS
session = kdbai.Session(endpoint='http://localhost:8082', options={'tls': True})
### cloud instance
session = kdbai.Session(api_key="abc" endpoint="https://...", mode="rest")

Error handling

Description

Message

Troubleshooting

Success: Session is created and KDB.AI instance can be interacted with.

True

N/A

Fail: Incorrect API Key is provided when attempting to connect to a KDB.AI Cloud.

KDBAIException with appropriate error message:

  • qIPC: Error during creating connection, make sure KDB.AI server is running and accepts qIPC connection on port {port}: {e}“ where e is the original underlying error.

  • REST: Failed to open a session on {self.endpoint} using API key with prefix {tmp}. Please double check your endpoint and api_key.

Check endpoint (host/port), credentials, and mode parameter. Check port forwarding in your environment and what port rules are allowed/denied.

Fail: No API Key is provided when attempting to connect to a KDB.AI Cloud.

KDBAIException with appropriate error message:

  • qIPC: “Error during creating connection, make sure KDB.AI server is running and accepts qIPC connection on port {port}: {e}“ where e is the original underlying error.

  • REST: Failed to open a session on {self.endpoint} using API key with prefix {tmp}. Please double check your endpoint and api_key.

Check endpoint (host/port), credentials, and mode parameter. Check port forwarding in your environment and what port rules are allowed/denied.

Fail: Server and client versions are incompatible.

Your KDB.AI server is not compatible with this client (kdbai_client=={version}). Use kdbai_client >={versions['clientMinVersion']} and <={versions['clientMaxVersion']}

Upgrade/downgrade either Server or client.

Error: Session cannot be created because KDB.AI is not available.

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Close session

session.close()

WARNING

You cannot execute any client-server interaction after this call.

Example

Python

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session.close()

Error handling

Description

Message

Troubleshooting

Success: Session is closed and KDB.AI instance can no longer be interacted with.

True

N/A

Get version

session.version()

Retrieve version info from server and compatible client min/max version.

Example

Python

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session.version()

Error handling

Description

Message

Troubleshooting

Success. version info is returned.

{'serverVersion': '1.4.0','clientMinVersion': '1.4.0' ,'clientMaxVersion': 'latest'}

N/A

Error: KDBAI is not available.

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Database

Create, delete, and retrieve databases.

In KDB.AI, a database is a collection of tables which store related data.

Tip

To simplify database design/management and prevent naming conflicts, follow the principles below:

  • Unique database names: Each database must have a unique name and can contain multiple tables.

  • Unique table names within a database: Tables within a database must have unique names, but different databases can contain tables with the same name. This is similar to the concept of namespaces.

  • Cascade deletion: When deleting a database, all child entities (tables) will also be deleted.

  • Default database: You don't need to create a database to create tables. If you create a table without specifying a database, it will be placed in a default, undeletable database.

Create database

session.create_database

Input parameters:

Name

Type

Description

Required

Default

database

str

Name of the database to create.

Yes

None

Note

Database name rules

  • Max length is 128 characters

  • Must contain only alphanumeric characters and underscore

  • Must start with an alpha character

Example

Python

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session.create_database("myDatabase")

Error handling

Description

Message

Troubleshooting

Success: Database is created and returned

database instance

N/A

Fail: Database name is not unique

Raise exception

A database with the given name already exists. Create a database with another name.

Fail: Database name is not a valid name

Raise exception

Provide a valid str for the database name.

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Get database

session.database

Retrieve database with a given name.

Input parameters:

Name

Type

Description

Required

database

str

Name of the database to be retrieved

Yes

Example

Python

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session.database("myDatabase")

Error handling

Description

Message

Troubleshooting

Success: Database with given name is found

Database instance.

N/A

Fail: Database with given name is not found

KDB.AI Exception: database {name} does not exist

Check the name of the database you are searching for as it does not seem to exist.

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Refresh database

database.refresh()

This method ensures that the list of tables associated with the loaded database is current. If the list is not up-to-date, it updates it. This is particularly useful if tables have been added to the database after the getDatabase function was called.

Example

Python

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database.refresh()

Error handling

Description

Message

Troubleshooting

Success: Database is refreshed

None

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

List databases

session.databases

Retrieve list of databases in ascending order.

Example

Python

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session.databases()

Error handling

Description

Message

Troubleshooting

Success: Returns list of database names and default database included

list of database names

N/A

Error: Databases cannot be listed because KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Delete database

database.drop

Delete database with a given name and all associated tables.

Input parameters:

Name

Type

Description

Required

database

str

Name of the database to be deleted.

Yes

Example

Python

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db=session.database("myDatabase")
db.drop()

Error handling

Description

Message

Troubleshooting

Success: Database with given name has been deleted

N/A

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Table

Create, delete, update, and retrieve tables.

Create table

database.create_table

Input parameters:

Name

Type

Description

Required

database

instance name

Name of the database.

Yes

table

str

Name of the table to create.

Yes

external_data_references

dict

Should contain the keys:

  • path (path to the existing kdb+ table mounted in our Docker container)

  • provider (set to kx)

WARNING

The name of the table should match the name of the target table in the existing kdb+ database.

No

schema

dict

Schema details for the table.

Yes - if external_data_references is not specified.

indexes

list of dict

List of index definitions

No

partitionColumn

str

Column name to partition on

No

embeddingConfigurations

dict

Should be keyed by embedding column name

No

Note

Table name rules

  • Max length is 128 characters

  • Must contain only alphanumeric characters and underscore

  • Must start with an alpha character

Example

Python

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schema = [{'name': 'id', 'type': 'int16'},
{'name': 'tag', 'type': 'bool'},
{'name': 'author', 'type': 'str'},
{'name': 'length', 'type': 'int32'},
{'name': 'content', 'type': 'str'},
{'name': 'createdDate', 'type': 'datetime64[D]'},
{'name': 'embeddings', 'type': 'float64s'}]
indexes = [
  {'type': 'flat', 'name': 'flat', 'column': 'embeddings',  'params': {'dims': 1536}},
  {'type': 'hnsw', 'name': 'fast_hnsw', 'column': 'embeddings', 'params': {'dims': 1536,'M': 8, 'efConstruction': 8}},
  {'type': 'hnsw', 'name': 'accurate_hnsw','column': 'embeddings', 'params': {'dims': 1536,'M': 64, 'efConstruction':256}} 
]
db = session.database("default")
db.create_table(table="myTable", schema=schema, indexes=indexes)
# create partitioned table
db.create_table(table="myPartitionedTable", schema=schema, indexes=indexes, partition_column='createdDate')

schema

Attributes:

Name

Type

Description

Required

name

str

Column name

Yes

type

str

Column type

Yes

Note: Column name rules

  • Max length is 128 characters

  • Must contain only alphanumeric characters and underscore

  • Must start with an alpha character

Example

Python

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schema = [ { 'name': 'id', 'type': 'int32'}, { 'name': 'isValid', 'type': 'bool'},
{ 'name': 'embeddings', 'type': 'float32s' }, { 'name': 'sparse_col', 'type': 'general' } ]

indexes

Attributes:

Name

Type

Description

Required

name

str

Index name

Yes

type

str

Index type, for example: flat, qFlat, hsnw, ivf, ivfpq, qhsnw

Yes

column

str

kdb+ column name to apply index

Yes

params

dict

Index parameters containing index-specific attributes for Flat, qFlat, HNSW, ivf, ivfpq, qHNSW

Yes

Example

Python

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indexes = [
  {'type': 'flat', 'name': 'flat', 'column': 'embeddings',  'params': {'dims': 1536}},
  {'type': 'hnsw', 'name': 'fast_hnsw', 'column': 'embeddings', 'params': {'dims': 1536, 'M': 8, 'efConstruction': 8}},
  {'type': 'hnsw', 'name': 'accurate_hnsw','column': 'embeddings', 'params': {'dims': 1536, 'M': 64, 'efConstruction':256}}  
]
flat

Index-specific attributes (params) for type = flat

Attribute

Description

Type

Required

Default

dims

Dimension of vector space

int

Yes

N/A

metric

Distance metric

str

No

L2
qFlat

Index-specific attributes (params) for type = qFlat

Attribute

Description

Type

Required

Default

dims

Dimension of vector space

int

Yes

N/A

metric

Distance metric

str

No

L2
hnsw

Index-specific attributes (params) for type = hnsw

Attribute

Description

Type

Required

Default

dims

Dimension of vector space

int

Yes

N/A

M

Graph valency

int

No

8

efConstruction

Search depth at construction

int

No

8

metric

Distance metric

str

No

L2
qHnsw

Index-specific attributes (params) for type = qHnsw

Attribute

Description

Type

Required

Default

dims

Dimension of vector space

int

Yes

N/A

M

Graph valency

int

No

8

efConstruction

Search depth at construction

int

No

8

metric

Distance metric

str

No

L2

mmapLevel

Level of memory mapping. Accepted values:

  • 0 for both vectors and node connection in memory;

  • 1 for memory-mapped vectors and in-memory nodes ;

  • 2 for both vectors and node connections memory mapped.

int

No

1

Note

An index consists of vectors and nodes. Vectors represent the data points in the vector space, while nodes are part of the graph structure used to organize and search through these vectors efficiently. Nodes connect vectors based on their similarity, forming a graph that facilitates fast nearest-neighbor searches.

ivf

Index-specific attributes (params) for type = ivf

Attribute

Description

Type

Required

Default

nclusters

Number of clusters

long

No

8

metric

Distance metric

str

No

L2
ivfpq

Index-specific attributes (params) for type = ivfpq

Attribute

Description

Type

Required

Default

nclusters

Number of clusters

long

No

8

nbits

Number of bits to quantize

long

No

8

nsplits

Number of vectors to split

long

No

8

metric

Distance metric

str

No

L2

external_data_references

Attributes:

Name

Type

Description

Required

path

byte str

Path to external table, for instance the existing kdb+ table mounted in our Docker container.

Yes

provider

str

Provider of external table, for example #!python kx.

Yes

Example

Launch the KDB.AI Server container with the -v flag to mount an existing kdb+ DB in the container, for example:

Shell

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docker run -it --rm -e NUM_WRK=1                        \
                    -e THREADS=16                       \
                    -e KDB_LICENSE_B64                  \
                    -v $PWD/vecdb/data:/tmp/kx/data/vdb \
                    -v $PWD/taq/db:/tmp/kx/remote:ro    \   <= mount a local ./taq/db under /tmp/kx/remote in the container as read-only
                    -p 8082:8082                        \
                    kdbai-db:local

Then:

Python

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database.create_table("tq", external_data_references=[{'path': b'/tmp/kx/remote', 'provider': 'kx'}])

Note

How to set up threads and the number of workers

For optimal performance, make sure to read about setting up both NUM_WRKand THREADS environment variables.

WARNING

The name of the table (tq) should match the name of the target table in the existing kdb+ db.

Error handling

Description

Message

Troubleshooting

Success: Table is created and returned

successresult`error!True;table_dictionary;"

N/A

Fail: Table name is not unique

Raise exception

Specify a different table name as it appears a table with this name already exists.

Fail: Table name is not valid

Raise exception

Use a valid string for the table name.

Fail: Any of the input parameters are of wrong type

ValueError: "invalid arguments types: " ...

Provide the correct type of input parameters required.

Fail: Any of the input parameters are missing

ValueError: "missing arguments: " ...

Provide required input parameters.

Fail: Any of the input parameters are invalid

ValueError: "invalid arguments: " ...

Provide known or valid input parameters.

Fail: Schema individual attributes are not valid

ValueError: "invalid table attributes: " ...

Provide valid attributes in the schema.

Fail: Schema individual types are not valid

ValueError: "invalid column types: " ...

Provide valid column types in the schema.

Fail: Index individual parameters are not valid

ValueError: "invalid index parameters: " ...

Double check the parameters of one of the specified indexes.

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Get Table

database.table

Retrieve a table from a database with a given name.

Example

Python

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db=session.database("default")
db.table("myTable")

Error handling

Description

Message

Troubleshooting

Success: Table with given name is found

Table meta dictionary as Pandas DataFrame

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Refresh table

table.refresh()

This method ensures that the table index and schema information associated with the table is current and calls getTable function.

Example

Python

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table.refresh()

Error handling

Description

Message

Troubleshooting

Success: Table is refreshed

None

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

List tables

database.tables

Retrieve a list of tables from a database with a given name.

Note

Tables are cached on the database instance. As a result, the data might have changed since the last get or refresh.

Example

Python

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db = session.database("myDatabase")
db.tables

Error handling

Description

Message

Troubleshooting

Success: Tables found

List of table names

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Load external table

table.load

Load external/reference table.

Input parameters:

Name

Type

Description

Required

database

str

Name of the database to be loaded.

Yes

table

str

Name of the table to be loaded.

Yes

Example

Python

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db=session.database("myDatabase")
tbl = db.table("myTable")
tbl.load()

Error handling

Description

Message

Troubleshooting

Success: Table is loaded

N/A

N/A

Fail: Table does not exist

`fail` Table does not exist

Table does not exist in database. Try get table/database endpoint to verify.

Error: KDBAI is not available.

`Error during request.` "Make sure KDB.AI server is running."

Check your connection and if your server is running.

Delete Table

table.drop()

Delete a table with a given name and all associated indexes.

Example

Python

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db = session.database("default")
table = db.table("myTable")
table.drop()

Error handling

Description

Message

Troubleshooting

Success: Table with given name has been deleted

N/A

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Index

Retrieve and list indexes.

Get index

table.index

Retrieve an index from a table.

Input parameters:

Name

Type

Description

Required

name

str

Name of the index to be retrieved

Yes

Example

Python

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table.index('trade_flat_index')

Error handling

Description

Message

Troubleshooting

Success: Index with given name is found and returned

dictionary

N/A

Fail: Index name is not valid

ValueError: Index name is invalid

Provide a valid string for the index name.

Fail: Index with given name is not found

ValueError: Index name is not found

Provide correct index name.

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

List indexes

table.indexes

List all indexes for a table.

Example

 

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db = session.database("default")
table = db.table("myTable")
table.indexes

Error handling

Description

Message

Troubleshooting

Success: Indexes found and returned

list of dictionaries

N/A

Error: KDBAI is not available

Cannot write to handle ...

Check your connection and if your server is running.

Update indexes

table.update_indexes

Build one or more indexes.

Tip

Allows to build indexes from scratch. Only supported for kdb+ HDB tables.

Input parameters:

Name

Type

Description

Required

indexes

list

List of index names to build.

Yes

parts

list

Partitions list to build index in case of partition database. If not given, then indexes will be built on all partitions.

Yes

Example

Python

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db = session.database("default")
table = db.table("SEC")
table.update_indexes(indexes=["flat_index"], parts=[1,2,3]) #assuming we have a partition column with integer type

Error handling

Description

Message

Troubleshooting

Success: Index(es) with given name(s) updated successfully

None

N/A

Fail: Operation called on a table managed by kdbai

KDBAIException: feature not supported: build index is only allowed on reference database

Use build index only on reference tables.

Fail: Index name is not valid

ValueError: Index name is invalid

Provide a valid string for the index name

Fail: Index with given name is not found

KDBAIException: index not found: invalid

Provide correct index name.

Fail: Update operation is not valid

ValueError: Update operation is not valid

Error: KDBAI is not available

Cannot write to handle ...

Check your connection and if your server is running.

Data

Insert, query, and search data.

Insert data

table.insert

Add rows to a table.

Input parameters:

Name

Type

Description

Required

payload

dataframe

Data to insert.

No - not required when using external database.

Example

Python

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db = session.database("default")
table = db.table("myTable")
table.insert(data)

Error handling

Description

Message

Troubleshooting

Success: Data inserted successfully.

dictionary

N/A

Fail: Data table does not match with table schema.

KDBAIException: "data has wrong types: cols provided <columns sent> - expecting <columns expected>"

Check data schema and expected table schema.

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Train data

table.train

Train data.

Input parameters:

Name

Type

Description

Required

payload

table

Data to insert.

Yes

Example

Python

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db = session.database("default")
table = db.table("myTable")
table.train(payload=data)

Error handling

Description

Message

Troubleshooting

Success: Index(es) with given name(s) updated successfully

True

N/A

Fail: Index name is not valid

ValueError: Index name is invalid

Provide a valid string for the index name.

Fail: Index with given name is not found

ValueError: Index name is not found

Provide correct index name.

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Query data

table.query

Query data from a table.

Input parameters:

Name

Type

Description

Required

filter

list of tuples

List of filter conditions, parse tree style.

No

sort_columns

list of str

The columns by which to sort the results.

No

group_by

list of str

The column values by which to group the results.

No

aggs

dictionary

Aggregation rules. Dictionary structure: - Key → new column name - Value → old column name or parse tree style aggregation rule

No

limit

int

Number of rows to return.

No

Example

Python

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db = session.database("default")
table = db.table("myTable")
table.query()  #returns all rows in the table

Error handling

Description

Message

Troubleshooting

Success: Successful query

Pandas DataFrame

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Search data

table.search

Perform a similarity search.

Input parameters:

Name

Type

Description

Required

type

str

Specify the type of search (tss or otherwise).

No

vectors

dictionary

Indexes to query with query vectors.

Yes

n

int

Number of neighbors to return.

No

range

float

Range within which the nearest neighbors are returned. (only for qFlat)

No

index_params

dictionary (key is index name and value is dictionary of parameters for that index)

Weights required for multi index search.

No

options

dictionary

Use this dictionary:

  • to rename the distance column with distanceColumn=newname

  • to not return metadata columns with indexOnly=True

  • to return TSS matched patterns with returnMatches=True

  • to force a TSS search on a partitioned tables with failing partitions with force=True

Yes

filter

list of tuples

List of filter conditions, parse tree style.

No

searchBy

str or list of str

(Non Transformed TSS only) Perform a TSS search on each group inferred from the specified columns (not to be confused with groupBy which is used for final aggregation of the results)

No

group_by

list of str

The column values by which to group the results.

No

aggs

dictionary

Aggregation rules.

No

sort_columns

list of str

The columns by which to sort the results.

No

Example

Python

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db = session.database("default")
table = db.table("myTable")
table.search(vectors={"indexName":v},n=10)

# Filter the data using 'range' (only for qFlat indexes)
table.search(vectors={"indexName":v}, range=5.5)

options

Attribute

Description

Type

Required

Default

distanceColumn

Rename distance column to this.

str

No

None

indexOnly

Return only index information

bool

No

None

returnMatches

(Non Transformed TSS only) Return the full detected pattern for each match

boolean

No

None

force

(Non Transformed TSS only) Force the TSS search even some searchBy group or table partition is failing, ex: when a partition has less data points than the searched pattern

boolean

No

None

index_params

index_params is a dictionary where key is index name and value is a dictionary with the arguments below .

Attribute

Description

Type

Required

Default

weight

Weight for each index.

float

Required for multi index input.

None

Note

For multi-index searches, you have to allocate a weight to each index. The sum of all weights must be equal to 1.

Error handling

Description

Message

Troubleshooting

Success: Successful query

list of Pandas DataFrames

N/A

Error: KDBAI is not available

RuntimeError('Error during request, make sure KDB.AI server running')

Check your connection and if your server is running.

Example

Python

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db = session.database('default')
table = db.table('myTable')
# search vectors are either list of vectors or list of dictionaries (for sparse search)
v = [[1.0, 2.3, 6.7, 2.3, 2.3], [1.0, 2.3, 6.7, 2.3, 2.3]]
s = [{1: 2.3, 43: 0.4, 324: 4.6}, {1: 2.3, 43: 0.4, 324: 4.6}]

# simple dense search
table.search(vectors={'indexName': v}, n=10)

# simple sparse search
table.search(vectors={'sparseIndexName': s}, n=10)

# filtered search
table.search(vectors={'indexName': v}, n=10, filter=[['in', 'sym', [['AA', 'ABC']], ['<', 'num', 250]])

# TSS
table.search(vectors={'columnToSearch': v}, n=10, type='tss')
table.search(vectors={'columnToSearch': v}, n=10, type='tss', options={'returnMatches': True, 'force': True})

# use index_params
table.search(vectors={'indexName': v}, n=10, index_params={'indexName': {'efSearch': 64}})

# hybrid search
index_params = {
    'indexName': {'weight': 0.6},
    'sparseIndexName': {'weight': 0.4},
}
table.search(vectors={'indexName': v, 'sparseIndexName': s}, n=10, index_params=index_params)

# override __nn_distance column name
table.search(vectors={'indexName': v}, n=10, options={"distanceColumn": 'myDist'})

# override sort columns
table.search(vectors={'indexName': v}, n=10, sort_columns=['price', 'quantity'])

# group by
table.search(vectors={'indexName': v}, n=10, group_by=['sym'])

# aggs
table.search(vectors={'indexName': v}, n=10, aggs={'max_price': ['max', 'price']})
table.search(vectors={'indexName': v}, n=10, group_by=['sym'], aggs={'max_price': ['max', 'price']})

# rename a column
table.search(vectors={'indexName': v}, n=10, aggs={'new_col_name': 'existing_col_name'})

# change the result type
table.search(vectors={'indexName': v}, n=10, result_type='pd')
table.search(vectors={'indexName': v}, n=10, result_type='q')
table.search(vectors={'indexName': v}, n=10, result_type='py')