feature: add hybrid search retriever using Qdrant in-memory vector store

[asajatovic]

Description

Adds a new hybrid search retriever that combines dense and sparse embedding models to score documents and return the most relevant results. It uses Qdrant's in-memory vector store and supports filtering, with results fused using the Reciprocal Rank Fusion algorithm.
Implementation wise, the new class HybridQdrantInMemoryRetriever inherits QdrantInMemoryRetriever and acts as a drop in replacement, with the addition of also doing sparse vector search, using the reference BM25 algorithm. By using this hybrid approach, we can improve search relevance.

Before Merging

• [ x] Review the code changes
  • Unused print / comments / TODOs
  • Missing docstrings for functions that should have them
  • Consistent variable names
  • ...
• [x ] Update changelog.md if necessary
• [x ] Commit messages should contain a semantic label and the ticket number
  • Consider squashing if this is not the case

[NiklasKoehneckeAA]

I reviewed this, it seems some test code is still missing. Currently IL pipelines are still not fixed after the cluster move, which is something we have are working on, so this is blocked until then.