docs: add FastembedColbertRanker to fastembed integration page#442
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Updates the fastembed integration page to include the new FastembedColbertRanker component from PR #3135, which adds ColBERT late-interaction reranking support via fastembed. - Added FastembedColbertRanker to the components list - Added a usage example with ColBERT ranker in a pipeline Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
kacperlukawski
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Hey, thanks for contributing @dina-deifallah! I have some minor comments. One question: do we allow choosing a metric used to calculate the maxsim score in the implementation? If so, it would be great to mention the parameter in the example.
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| ### Example with ColBERT ranker | ||
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| `FastembedColbertRanker` uses ColBERT late-interaction scoring: the query and documents are encoded independently into token-level embeddings, and a MaxSim score is computed for each document. This offers stronger ranking quality than cross-encoders on many tasks while remaining efficient. |
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According to the benchmarks, cross-encoders are stronger than late interaction. Models, such as ColBERT, are used as a middle ground due to their scalability and performance.
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Thanks for the feedback Kacper. That makes sense. I committed your suggested changes.
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I took a look at the ColBERTv2 paper to check about the metric. The current implementation doesn't expose a metric parameter. It uses dot product similarity via np.matmul, which is the standard ColBERT approach. Since fastembed's LateInteractionTextEmbedding returns L2-normalized embeddings (as described in the ColBERTv2 paper, Santhanam et al., 2021 — https://arxiv.org/abs/2112.01488), dot product and cosine similarity are mathematically equivalent for these models, so the ranking order is the same regardless of metric. For that reason, no metric parameter is needed for the current supported models.
Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>
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LGTM, but I assume a corresponding PR in the integration itself has to be merged first
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@dina-deifallah @kacperlukawski This component has been renamed to In the meantime, we also released a new version of the integration, containing this new component: https://pypi.org/project/fastembed-haystack/2.2.0/ Merging this PR now |
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Summary
Updates the fastembed integration page to include the new
FastembedColbertRankercomponent from deepset-ai/haystack-core-integrations#3135.FastembedColbertRankerto the components list alongsideFastembedRankerRelated
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