Comments (3)
Hi, I have a working WAND implementation, but it is slower than brute force vector operations.
I am now considering more advanced WAND-based approaches. I hope to add one soon.
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Hi, thanks for the suggestion and code!
I have an implementation of another optimization algorithm for top-k retrieval on my local branch. Unfortunately, it slows down the retrieval because (I suspect) it requires more instructions to be executed (even if they are applied to less data).
Current implementation heavily relies on vector computations, which are fairly optimized on modern CPUs.
I will let you know if WAND improves efficiency over the current implementation.
from retriv.
Unfortunately, I don't think this will happen anytime soon. The lexical retriever is already reasonably efficient, and there are other things I prefer to prioritize.
I will close for now.
from retriv.
Related Issues (20)
- BM25 time complexity HOT 1
- Doc strings HOT 4
- I would like to see `retriv` part of the Search Benchmark, the Game
- [Feature Request] Allow GPU for query embedding HOT 1
- Minimal example for Hybrid Search fails HOT 3
- Input file format HOT 2
- [Feature Request] Add documents to index after initializing? HOT 2
- Multiprocess error triggers while trying example code HOT 3
- [BUG] Corrupted log when using SearchEngine HOT 9
- Does Advanced Retrieve support semantic searching? HOT 1
- [Feature Request] build index on a sequence of json/jsonl files HOT 3
- Compare retriv's permance to rank_bm25 and pyserini HOT 4
- HybridRetriever raise KeyError: -1 if the len of doc less than 1_000 HOT 1
- autotune Function Usage Example HOT 1
- ANN_Searcher not dealing with -1 returned by faiss_index.search()
- fsspec==2023.12.2 does not allow '**' in path
- HybridRetriever does not respect cutoff when calling sub-retrievers and the merger
- [BUG] Segmentation fault (core dumped) HOT 1
- Getting Out of Memory Error HOT 1
- using another ANN
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