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Follows #549. (@lambda-science) The following are just thoughts, nothing is set in stone.
I imagine having a QdrantHybridRetriever that takes query_embedding and sparse_query_embedding as input; returns Documents.
QdrantHybridRetriever
query_embedding
sparse_query_embedding
This can serve as an inspiration for implementation (note the Reciprocal Rank Fusion) https://github.com/qdrant/qdrant-client/blob/8e3ea58f781e4110d11c0a6985b5e6bb66b85d33/qdrant_client/qdrant_fastembed.py#L519
The text was updated successfully, but these errors were encountered:
anakin87
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Follows #549. (@lambda-science)
The following are just thoughts, nothing is set in stone.
I imagine having a
QdrantHybridRetriever
that takesquery_embedding
andsparse_query_embedding
as input; returns Documents.This can serve as an inspiration for implementation (note the Reciprocal Rank Fusion)
https://github.com/qdrant/qdrant-client/blob/8e3ea58f781e4110d11c0a6985b5e6bb66b85d33/qdrant_client/qdrant_fastembed.py#L519
The text was updated successfully, but these errors were encountered: