michaelfeil / michaelfeil/infinity
Question: Support for sparse embeddings?
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Description
Hi, I was wondering whether is would make sence to support models which, in addition to dense vectors, also support sparse and colbert. For example, [BGE-M3](https://huggingface.co/BAAI/bge-m3) works well under infinity for dense vector retrieval. However, it would require some changes to the inference process to additionally obtain sparse vectors such as shown here:
https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/modeling.py#L352-L355
I wonder if for such case, it's feasible to add extra config parameters in the CLI or that would require too much changes to the core logic of the model during startup?
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Research direction
Start with the linked BGE-M3 modeling.py lines 352-355, then trace Infinity's CLI configuration and model startup paths to determine where dense, sparse, and ColBERT outputs are handled. Done would require an agreed design for configuration and inference changes, plus validation that the additional retrieval outputs are exposed correctly.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100