qdrant / qdrant/vector-db-benchmark
Support pulling embedding from any Huggingface dataset
Open
Nobody has claimed this yet.
enhancement
- Dominant language
- Python
- Stars
- 371
- Forks
- 155
- Avg merge
- 1d 22h
- Merged PRs (30d)
- 4
Description
Would be nice if we could support pulling embedding from any Huggingface dataset. This would make the project even more useful for external users :)
The spec for this could be like this:
{
"name": "SciPhi/AgentSearch-V1",
"vector_size": 100,
"distance": "cosine",
"type": "huggingface",
"path": "glove-100-angular/glove-100-angular.hdf5",
"link": "https://huggingface.co/datasets/SciPhi/AgentSearch-V1",
"schema": {
"vector_field": "openai",
"payload": {
"url": "text"
}
}
}
Needs some discussion before implementing
Contributor guide
No contributing guide indexed for this repository
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
No files, tests, or entry points are named. Start by reviewing the existing dataset-loading behavior and discussing the proposed Hugging Face schema, including how arbitrary datasets, vector fields, and payload mappings should be selected; done should mean an agreed contract and implementation scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100