metadata
license: cc-by-3.0
task_categories:
- question-answering
language:
- en
tags:
- vector search
- retrieval augmented generation
size_categories:
- <1K
Overview
This dataset consists of chunked and embedded versions of a small subset of MongoDB's technical documentation.
Dataset Structure
The dataset consists of the following fields:
- sourceName: The source of the document.
- url: Link to the article.
- action: Action taken on the article.
- body: Content of the article in Markdown format.
- format: Format of the content.
- metadata: Metadata such as tags, content type etc. associated with the document.
- title: Title of the document.
- updated: The last updated date of the document.
- embedding: The embedding of the chunk's content, created using the thenlpr/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.
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 = 'mognodb_doc_-embedded'
collection = client[db_name][collection_name]
dataset = load_dataset("MongoDB/mongodb-docs-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!")