Create README.md

README.md

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+---
+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](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 = '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!")
+```