README.md

metadata

license: mit
task_categories:
  - feature-extraction
  - text-retrieval
tags:
  - embeddings
  - vector-database
  - retrieval
  - beir
  - la2m
pretty_name: LA2M Embeddings

LA2M_emb

Embedding vectors used in the paper Integrating Vector Databases across Embedding Models (Beining Yang, Yang Cao, Yang Ren. Proc. ACM Manag. Data 3(6) (SIGMOD), Article 338, 2025).

This is a curated subset of DB-Edinburgh/VectorBenchmark, restricted to exactly the 6 embedding models (Table 3) and 5 benchmarks (Table 2) reported in the paper, so that the experiments can be reproduced without pulling the full ~6k-file benchmark.

Code: VectorMerge (reproduce branch has a Docker setup).

Contents

embeddings/{corpus,query}_embeddings_{model}_{dataset}.npy — plain numpy arrays, one row per document / query, row order matches the BEIR corpus.jsonl / queries.jsonl of the corresponding dataset.

paper model (Table 3)	file key	dim in these files	dim in Table 3
GloVe	glove	300	300
FastText	fast-text	300	300
Mistral	mistral	1024	1024
NV-Embed-V2	nv-embed	4096	1024
GTE-Qwen2	gte	3584	1024
OpenAI-Ada	openai	1536	1536

dataset	corpus rows	query rows
scifact	5,183	300
nfcorpus	3,633	323
arguana	8,674	1,406
scidocs	25,657	1,000
fiqa	57,638	648

Corpus row counts match Table 2 of the paper exactly.

Known quirks

These are inherited from the source benchmark and are documented rather than silently fixed:

• nv-embed vs nv-embed-v2. The paper's NV-Embed-V2 vectors are stored under the nv-embed key, which is also the name the vectormerge code expects. nv-embed-v2-keyed files exist only for scidocs and fiqa; they are included here for completeness and have identical shapes.
• query_embeddings_mistral_scifact.npy has 320 rows, not the 300 that every other model has for SciFact (and that Table 2 reports). Index-aligned evaluation on that one file needs care.
• Mixed dtypes. Most files are float32; openai, mistral, and glove (arguana, fiqa) are float64.
• Reported vs stored dimension. Table 3 lists 1024 for NV-Embed-V2 and GTE-Qwen2, while the stored vectors are at native dimension (4096 / 3584). The vectormerge pipeline aligns dimensions at load time (align_dimension), so the 1024 in the table refers to the model family rather than these files.

Usage

from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    "DB-Edinburgh/LA2M_emb",
    "embeddings/corpus_embeddings_mistral_scifact.npy",
    repo_type="dataset",
)
X = np.load(path)  # (5183, 1024)

Citation

@article{Yang2025integrating,
  author  = {Beining Yang and Yang Cao and Yang Ren},
  title   = {Integrating Vector Databases across Embedding Models},
  journal = {Proc. {ACM} Manag. Data},
  volume  = {3},
  number  = {6},
  pages   = {1--28},
  year    = {2025},
  doi     = {10.1145/3769803}
}