[FEA] Support chamfer distance / matching
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- Dominant language
- Cuda
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Description
Chamfer distance is becoming popular for multi-vector matching with models like ColBert. We have been planning on support for multi-vector for awhile, but we need to prioritize it.
Chamfer distance might be computationally intensive in practice, and might provide a good value prop for using GPUs in the multi-vector world. We need to evaluate whether chamfer distance is required to provide a good performance with multi-vectors and support it if that's the case. If not, we need to find a another way to support multi-vectors.
Muvera is another mechanism we might be able to leverage, though this involves an additional encoding to convert the multiple vectors into a single fixed-length vector.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue provides no file or test entry point. Start by reviewing the Chamfer-distance and Muvera references in the description and evaluating multi-vector retrieval needs; completion requires deciding whether Chamfer is necessary and supporting the selected mechanism.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning, search
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100