---
license: cc-by-3.0
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- vector search
- retrieval augmented generation
size_categories:
- <1K
---

## Overview

This dataset consists of chunked and embedded versions of a subset of articles from the MongoDB Developer Center.

## Dataset Structure

The dataset consists of the following fields: 

- sourceName: The source of the article. This value is `devcenter` for the entire dataset.
- url: Link to the article
- action: Action taken on the article. This value is `created` for the entire dataset.
- body: Content of the chunk in Markdown format
- format: Format of the content. This value is `md` for all articles.
- metadata: Metadata such as tags, content type etc. associated with the articles
- title: Title of the article
- updated: The last updated date of the article
- embedding: The embedding of the chunk's content, created using the [thenlpr/gte-small](https://huggingface.co/thenlper/gte-small) open-source model from Hugging Face.

## Usage

This dataset can be useful for prototyping RAG applications. This is a real sample of data we have used to build the AI chatbot on our official documentation website.  

## Ingest Data

To experiment with this dataset using MongoDB Atlas, first [create a MongoDB Atlas account](https://www.mongodb.com/cloud/atlas/register?utm_campaign=devrel&utm_source=community&utm_medium=organic_social&utm_content=Hugging%20Face%20Dataset&utm_term=apoorva.joshi).

You can then use the following script to load this dataset into your MongoDB Atlas cluster:

```
import os
from pymongo import MongoClient
import datasets
from datasets import load_dataset
from bson import json_util


uri = os.environ.get('MONGODB_ATLAS_URI')
client = MongoClient(uri)
db_name = 'your_database_name'  # Change this to your actual database name
collection_name = 'devcenter_articles-embedded'

collection = client[db_name][collection_name]

dataset = load_dataset("MongoDB/devcenter-articles-embedded")

insert_data = []

for item in dataset['train']:
    doc = json_util.loads(json_util.dumps(item))
    insert_data.append(doc)

    if len(insert_data) == 1000:
        collection.insert_many(insert_data)
        print("1000 records ingested")
        insert_data = []

if len(insert_data) > 0:
    collection.insert_many(insert_data)
    insert_data = []

print("Data ingested successfully!")
```