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update reranking tutorial

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Derrick Mwiti
2025-06-05 14:05:27 +03:00
parent 9a7d51d830
commit 8ed1f14b6e
1 changed files with 4 additions and 3 deletions
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qdrant-landing/content/documentation/advanced-tutorials/reranking-hybrid-search.md
+4 -3
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@@ -113,7 +113,8 @@ client.create_collection(
distance=models.Distance.COSINE,
multivector_config=models.MultiVectorConfig(
comparator=models.MultiVectorComparator.MAX_SIM,
)
),
hnsw_config=models.HnswConfigDiff(m=0) # Disable HNSW for reranking
),
},
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:
- **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.
- **Sparse embeddings** from BM25 for keyword-based searches. They use dot_product for similarity calculation.
- **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.
This setup ensures that all the different types of vectors are stored and compared correctly for your hybrid search.