Rank Fusion for improved code context in RAG

Fun fact: We've implemented binary embedding search [1] without the need for a specialized vector database. Instead, dimensional tokens like 'embedding_0_0', 'embedding_1_0' are created and being built into the tantivy index [2].

We're satisfied with the quality and performance this approach yields, while still keep Tabby embed everything into a single binary.

[1] My binary vector search is better than your FP32 vectors: https://blog.pgvecto.rs/my-binary-vector-search-is-better-th...

[2] Tantivy: https://github.com/quickwit-oss/tantivy