Query reference - IBM Cloud

query=bees

filter=bees

natural_language_query="How do bees fly" aggregation=term(enriched_tex t.entities.type)

result

count=15 offset=100

result return=title,url sort=enriched_text.sentiment. document.score passages=true&passages.fields =text,abstract,conclusion passages=true&passages.count= 6 passages=true&passages.charac ters=144 highlight=true deduplicate=true

deduplicate.field=title collection_ids={1},{2},{3} similar=true similar.document_ids={id1}, {id2} similar.fields=text,title

0 - 9 _ +

-

. ,

:

extracted-content2 field_name field_name

+extracted-content new:extracted-content

enriched_text.concepts.text text:computer title::"Query building" text:!computer text::!winter title::"Dorothy said: \"There's no place like home"\" enriched_text.concepts.text:"IBM Watson" filter-entities:(text:Turkey,type:Location) |

query-enriched.entities.text:Google|IBM query-enriched.entities.text:Google,IBM enriched_text.sentiment.document.score>0.679

text:IBM^3 query-enriched_text.concepts.text:pre* query-enriched_text.entities.text:cat~1

term(enriched_text.concepts.text,cou nt:10) filter(enriched_text.concepts.text:c loud computing) nested(enriched_text.entities) histogram(product.price,interval:1) timeslice(last_modified,2day,America /New York) term(enriched_text.concepts.text).to p_hits(10) unique_count(enriched_text.entities. type) max(product.price)

min(product.price)

average(product.price)

sum(product.price)