Search

InsightMaker.search(query, dataset=None, fields=None, size=100, raw=False)

Parameters:

Name	Type	Description
query	String, dict or insight_maker.api. query_builders. SubQuery	InsightMaker Query, can either be a Lucene Syntax query string, Elasticsearch Query DSL as a python dictionary (starting at bool), or a SubQuery (or decedent) object from insight_maker.api.query_builders (wrappers for Query DSL)
dataset	string, Dataset or list	Optional - Dataset object, dataset name string, or list of either
fields	list	Optional – List of field ids of Field objects
size	integer	Number of results to return (default 100)
raw	boolean	Default False, if true, returns the raw REST JSON response as a python dictionary.

Response:

A DataSample object, properties:

• DataSample.df A pandas DataFrame object containing all results

• DataSample.scroll_id The scroll ID required for a scroll search

Example:

Below is an example of a simple string query, these use Lucene Syntax or more simply, they behave in same way as the search box in the enterprise search app. An asterisk will return all documents.

results = im.search("*", dataset = "sharepoint")
print(results.df)

Alternatively, a python dictionary can be used to store an Elastic Query DSL JSON query. This is the most complex, but also most flexible way to query InsightMaker. For example:

query = {
    "bool": {
        "must":
            [
                {
                    "range": {"size": {"gte": 1000, "lt": 2000}}
                },
                {
                    "bool":
                        {
                            "must_not": [{"regexp": {"name": {"value": ".*\\.txt"}}}]
                        }
                }
            ]
    }
}

results = im.search(query, dataset = "documents", size = 10)
print(results.df)