Qdrant: search

Search documents from a given Qdrant vector database collection.

Pre-requisite: Install Qdrant application Profile > {Organization} > Applications to grant Zparse access.

Parameters

Application—REQUIRED

Select configured Qdrant application.

Collection—REQUIRED

Which collection to use.

Input

Search query—REQUIRED

Embeddings search request with model like:

{
   "query": <String>, # Actual text to generate embeddings from.
   "query_vector": [f32, f32, ...], # Search vector
   "filters": [ # Filter for metadata
     {
         "attribute": <String>, # which attribute to filter from
         "value": <String>, # which value matching given attribute to filter from
     }
   ],
   "limit": <Number>, # Own many vector to return from rag database
   "exact": <Bool>, # Whether to exact match on filters of not
}

Output

JSON—REQUIRED

Embeddings request with model like: Embeddings search request with model like:

{
   "data": [
     {
       "score": <Number>, # which rating score given document got from original search vector (higher is better)
       "identifier": <String>, # document identifier
       "payload": <JSON>, # document content
     }
   ]
}