Update README.md

README.md

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 ---
 license: apache-2.0
+task_categories:
+- question-answering
+- text-retrieval
+language:
+- en
+tags:
+- vector search
+- semantic search
+- retrieval augmented generation
+size_categories:
+- 1K<n<10K
 ---
+
+## Overview
+
+This dataset consists of AirBnB listings consisting of property descriptions, reviews and other metadata.
+
+We also provide embeddings (using OpenAI's text-embedding-small model) of the property description so you can use this dataset for building Search and RAG applications.
+
+## Dataset Structure
+
+Here is a full list of fields contained in the dataset. Some noteworthy fields have been highlighted: 
+
+- _id: Unique identifier for the listing
+- listing_url: URL for the listing on AirBnB
+- **name**: Title or name of the listing
+- **summary**: Short overview of listing
+- **space**: Short description of the space, amenities etc.
+- **description**: Full listing description
+- neighborhood_overview: Description of surrounding area
+- notes: Special instructions or notes
+- transit: Nearby public transportation options
+- access: How to access the property. Door codes etc.
+- interaction: Host's preferred interaction medium
+- house_rules: Rules guests must follow
+- **property_type**: Type of property
+- room_type: Listing's room category
+- bed_type: Type of bed provided
+- minimum_nights: Minimum stay required
+- maximum_nights: Maximum stay allowed
+- cancellation_policy: Terms for cancelling booking
+- first_review: Date of first review
+- last_review: Date of latest review
+- **accommodates**: Number of guests accommodated
+- **bedrooms**: Number of bedrooms available
+- **beds**: Number of beds available
+- number_of_reviews: Total reviews received
+- bathrooms: Number of bathrooms available
+- **amenities**: List of amenities offered
+- **price**: Nightly price for listing
+- security_deposit: Required security deposit amount
+- cleaning_fee: Additional cleaning fee charged
+- extra_people: Fee for additional guests
+- guests_included: Number of guests included in the base price
+- **images**: Links to listing images
+- host: Information about the host
+- **address**: Physical address of listing
+- **availability**: Availability dates for listing
+- **review_scores**: Aggregate review scores
+- reviews: Individual guest reviews
+- weekly_price: Discounted price for week
+- monthly_price: Discounted price for month
+- reviews_per_month: Average monthly review count
+- **space_embeddings**: Embeddings of the property description in the `space` field
+
+## Usage
+
+This dataset can be useful for:
+- Building Hybrid Search applications. Combine vector search using the embeddings provided, with full text search using the exhaustive list of metadata fields.
+- Building Multimodal Search applications. Some listings have images associated with them. Use a model like [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) to generate image and text emebeddings.
+- Building RAG applications
+
+## 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
+
+# MongoDB Atlas URI and client setup
+uri = os.environ.get('MONGODB_ATLAS_URI')
+client = MongoClient(uri)
+
+# Change to the appropriate database and collection names
+db_name = 'your_database_name'  # Change this to your actual database name
+collection_name = 'airbnb_embeddings'  # Change this to your actual collection name
+
+collection = client[db_name][collection_name]
+
+# Load the "airbnb_embeddings" dataset from Hugging Face
+dataset = load_dataset("MongoDB/airbnb_embeddings")
+
+insert_data = []
+
+# Iterate through the dataset and prepare the documents for insertion
+# The script below ingests 1000 records into the database at a time
+for item in dataset['train']:
+    # Convert the dataset item to MongoDB document format
+    doc_item = json_util.loads(json_util.dumps(item))
+    insert_data.append(doc_item)
+
+    # Insert in batches of 1000 documents
+    if len(insert_data) == 1000:
+        collection.insert_many(insert_data)
+        print("1000 records ingested")
+        insert_data = []
+
+# Insert any remaining documents
+if len(insert_data) > 0:
+    collection.insert_many(insert_data)
+    print("Data Ingested")
+```