Files
neon/test_runner/performance/pgvector/README.md
Peter Bendel f9f69a2ee7 clarify how to load the dbpedia vector embeddings into a postgres database (#7894)
## Problem


Improve the readme for the data load step in the pgvector performance
test.
2024-05-28 17:21:09 +03:00

55 lines
1.3 KiB
Markdown

# Source of the dataset for pgvector tests
This readme was copied from https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-1536-1M
## Download the parquet files
```bash
brew install git-lfs
git-lfs clone https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-1536-1M
```
## Load into postgres:
see loaddata.py in this directory
## Rest of dataset card as on huggingface
---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: text-embedding-3-large-1536-embedding
sequence: float64
splits:
- name: train
num_bytes: 12679725776
num_examples: 1000000
download_size: 9551862565
dataset_size: 12679725776
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- feature-extraction
language:
- en
size_categories:
- 1M<n<10M
---
1M OpenAI Embeddings: text-embedding-3-large 1536 dimensions
- Created: February 2024.
- Text used for Embedding: title (string) + text (string)
- Embedding Model: OpenAI text-embedding-3-large
- This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity, extracted by @KShivendu_