I have been testing dense vector search on Solr and it's been working great for me so far. Mine is an image search use case using OpenAI's CLIP model but the configurations are pretty much the same as yours. What embedding model are you using? And can you share a portion of the actual query?
On Fri, Jan 26, 2024 at 6:16 AM kumar gaurav <kg2...@gmail.com> wrote: > HI Everyone > > I am using vector search in Solr 9.4. I am using cosine similarity with knn > parser. > > Same as the documentation > > https://solr.apache.org/guide/solr/latest/query-guide/dense-vector-search.html > > Schema > <fieldType name="knn_vector" class="solr.DenseVectorField" > vectorDimension="768" similarityFunction="cosine"/> > <field name="vector" type="knn_vector" indexed="true" stored="true"/> > > Query > q={!knn f=vector topK=10}[1.0, 2.0, 3.0, 4.0] > > The problem is it always returns docs even if it's not relevant. Even if I > am using the xyz keyword, knn parser is returning the documents which is > useless. I want to control the similarity of documents. I need highly > similar documents only. Does Solr have any parameter in the knn parser > which controls the similarity threshold ? > > *How can I control the minimum Similarity threshold with knn parser ?* > > Please help. Thanks in advance. > > > -- > Thanks & Regards > Kumar Gaurav >
