Performs a search on a table
The method expects an object with the following mandatory properties:
- the name of the table to search
- the match query object For details, see the documentation on SearchRequest The method returns an object with the following properties:
- took: the time taken to execute the search query. - timed_out: a boolean indicating whether the query timed out. - hits: an object with the following properties:
- total: the total number of hits found.
- hits: an array of hit objects, where each hit object represents a matched document. Each hit object has the following properties:
- _id: the ID of the matched document.
- _score: the score of the matched document.
- _source: the source data of the matched document.
In addition, if profiling is enabled, the response will include an additional array with profiling information attached. Also, if pagination is enabled, the response will include an additional 'scroll' property with a scroll token to use for pagination Here is an example search response:
{
'took':10,
'timed_out':false,
'hits':
{
'total':2,
'hits':
[
{'_id':'1','_score':1,'_source':{'gid':11}},
{'_id':'2','_score':1,'_source':{'gid':12}}
]
}
}
For conversational search, include a chat object instead of table and query. The response then includes the optional conversational fields on searchResponse (conversation_uuid, user_query, search_query, response, sources). For more information about the match query syntax and additional parameters that can be added to request and response, please see the documentation here.
Request body
Example request
{
"table": "your_table",
"query": {
"query_string": "your_query"
}
}Response
Ok. Returns searchResponse. For conversational search requests that include a chat object, the optional conversational fields are also set.
Example response
{
"hits": {
"total": 2,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"gid": 11
}
},
{
"_id": 2,
"_score": 1,
"_source": {
"gid": 20
}
}
]
},
"took": 0,
"user_query": "What is vector search?",
"sources": "[{\"id\":1,\"title\":\"Vector Search\",\"content\":\"...\",\"knn_dist\":0.12}]",
"response": "Vector search finds similar items by comparing embeddings...",
"profile": "{}",
"scroll": "scroll",
"warning": "{}",
"timed_out": true,
"search_query": "vector search, embeddings, similarity search",
"aggregations": {
"sizes": {
"buckets": [
{
"key": "small",
"doc_count": 1,
"status": "selected"
},
{
"key": "large",
"doc_count": 1,
"status": "available"
}
]
},
"colors": {
"buckets": [
{
"key": 10,
"doc_count": 1019
},
{
"key": 9,
"doc_count": 954,
"status": "unavailable"
}
]
}
},
"conversation_uuid": "docs-chat-001"
}