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Merge pull request #1682 from qdrant/update_reranking_article

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Update reranking tutorial
This commit is contained in:
Derrick Mwiti
2025-06-09 15:20:56 +03:00
committed by GitHub
parent a04d43c97d 8ed1f14b6e
commit 1644c8b56a
1 changed files with 4 additions and 3 deletions
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qdrant-landing/content/documentation/advanced-tutorials/reranking-hybrid-search.md
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@@ -113,7 +113,8 @@ client.create_collection(
distance=models.Distance.COSINE, distance=models.Distance.COSINE,
multivector_config=models.MultiVectorConfig( multivector_config=models.MultiVectorConfig(
comparator=models.MultiVectorComparator.MAX_SIM, comparator=models.MultiVectorComparator.MAX_SIM,
) ),
hnsw_config=models.HnswConfigDiff(m=0) # Disable HNSW for reranking
), ),
}, },
sparse_vectors_config={ sparse_vectors_config={
@@ -128,8 +129,8 @@ client.create_collection(
What’s happening here? We’re creating a collection called "hybrid-search", and we’re configuring it to handle: What’s happening here? We’re creating a collection called "hybrid-search", and we’re configuring it to handle:
- **Dense embeddings** from the model all-MiniLM-L6-v2 using cosine distance for comparisons. - **Dense embeddings** from the model all-MiniLM-L6-v2 using cosine distance for comparisons.
- **Late interaction embeddings** from colbertv2.0, also using cosine distance, but with a multivector configuration to use the maximum similarity comparator. - **Late interaction embeddings** from colbertv2.0, also using cosine distance, but with a multivector configuration to use the maximum similarity comparator. Note that we set `m=0` in the `colbertv2.0` vector to prevent indexing since it's not needed for reranking.
- **Sparse embeddings** from BM25 for keyword-based searches. They use dot_product for similarity calculation. - **Sparse embeddings** from BM25 for keyword-based searches. They use `dot_product` for similarity calculation.
This setup ensures that all the different types of vectors are stored and compared correctly for your hybrid search. This setup ensures that all the different types of vectors are stored and compared correctly for your hybrid search.