Christian D. Glissov 474b88326f langchain_qdrant: Added method "_asimilarity_search_with_relevance_scores" to Qdrant class (#23954) ...

I stumbled upon a bug that led to different similarity scores between
the async and sync similarity searches with relevance scores in Qdrant.
The reason being is that _asimilarity_search_with_relevance_scores is
missing, this makes langchain_qdrant use the method of the vectorstore
baseclass leading to drastically different results.

To illustrate the magnitude here are the results running an identical
search in a test vectorstore.

Output of asimilarity_search_with_relevance_scores:
[0.9902903374601824, 0.9472135924938804, 0.8535534011299859]

Output of similarity_search_with_relevance_scores:
[0.9805806749203648, 0.8944271849877607, 0.7071068022599718]

Co-authored-by: Erick Friis <erick@langchain.dev>

2024-07-12 23:25:20 +00:00

..

__init__.py

qdrant: new Qdrant implementation (#24164)

2024-07-12 04:52:02 +02:00

_utils.py

community, milvus, pinecone, qdrant, mongo: Broadcast operation failure while using simsimd beyond v3.7.7 (#22271)

2024-06-04 17:36:31 +00:00

fastembed_sparse.py

qdrant: New sparse embeddings provider interface - PART 1 (#24015)

2024-07-11 17:07:25 -07:00

py.typed

…

qdrant.py

qdrant: new Qdrant implementation (#24164)

2024-07-12 04:52:02 +02:00

sparse_embeddings.py

qdrant: New sparse embeddings provider interface - PART 1 (#24015)

2024-07-11 17:07:25 -07:00

vectorstores.py

langchain_qdrant: Added method "_asimilarity_search_with_relevance_scores" to Qdrant class (#23954)

2024-07-12 23:25:20 +00:00