Hello guys,
I have a qdrant db with embeddings. I would like to knwo if there is a way each time I retrieve a node, to get the previous and next node ?
To explain the context:
My db is composed of a law text. so I have Article 1, Article 2, Article 3,...
if I retrieve Article 10, I want to 'import' articles 9 and 11.
Thanks a lot

Hello Guys,
I have an hybrid Qdrant database (i.e sparse and dense vectors).
When querying, I notice that the scores are very weak, even if the answer is correct. it rarely exceeds 0.5.

Do you have any idea how can I properly set up the query_engine parameters ? (similarity_top_k, alpha,...) ?

Hey everyone, I'm In a bit of a trouble right now and hope you can help me. I'm running LlamaIndex with Qdran on hybrid mode which requires uploading Dense and Sparse vectors, according the docs LlamaIndex will generate the sparse vectors locally. as a result the upload Is very slow.
What are the solutionS we currently have to load our data efficiently ? is there any model that is preferred over others ?
Also, if anyone can share any notebook with some related code, we would really appreciate that.
Thanks a lot guys !

Hello Guys,
I have an hybrid Qdrant database (i.e sparse and dense vectors).
When querying, I notice that the scores are very weak, even if the answer is correct. it rarely exceeds 0.5.
Do you have any idea how can I properly set up the query_engine parameters ? (similarity_top_k, alpha,...) ?

We are migrating from pgvector to qdrant.
we have a db of 200k chunks/vectors.
when we uploaded this database to pgvector, it was taking 4 hours for complete embeddings + db upload.
but now we want to migrate to qdrant (retrieve speed is better). but the embedding + upload process is now taking more than 10 days !! we have really searched everywhere and have no idea how to resolve this slowness issue.