ElasticV7DocIndex
docarray.index.backends.elasticv7.ElasticV7DocIndex
Bases: ElasticDocIndex
Source code in docarray/index/backends/elasticv7.py
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DBConfig
dataclass
Bases: ElasticDocIndex.DBConfig
Dataclass that contains all "static" configurations of ElasticDocIndex.
Source code in docarray/index/backends/elasticv7.py
QueryBuilder
Bases: ElasticDocIndex.QueryBuilder
Source code in docarray/index/backends/elasticv7.py
build(*args, **kwargs)
Build the elastic search v7 query object.
Source code in docarray/index/backends/elasticv7.py
find(query, search_field='embedding', limit=10, num_candidates=None)
Find k-nearest neighbors of the query.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query |
Union[AnyTensor, BaseDoc]
|
query vector for KNN/ANN search. Has single axis. |
required |
search_field |
str
|
name of the field to search on |
'embedding'
|
limit |
int
|
maximum number of documents to return per query |
10
|
Returns:
Type | Description |
---|---|
self |
Source code in docarray/index/backends/elasticv7.py
RuntimeConfig
dataclass
Bases: ElasticDocIndex.RuntimeConfig
Dataclass that contains all "dynamic" configurations of ElasticDocIndex.
Source code in docarray/index/backends/elasticv7.py
__init__(db_config=None, **kwargs)
Initialize ElasticV7DocIndex
Source code in docarray/index/backends/elasticv7.py
execute_query(query, *args, **kwargs)
Execute a query on the ElasticDocIndex.
Can take two kinds of inputs:
- A native query of the underlying database. This is meant as a passthrough so that you can enjoy any functionality that is not available through the Document index API.
- The output of this Document index'
QueryBuilder.build()
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query |
Dict[str, Any]
|
the query to execute |
required |
Returns:
Type | Description |
---|---|
Any
|
the result of the query |