Update README.md

README.md

@@ -34,6 +34,51 @@ Each record in the dataset represents a news article about technology companies
 - description: A brief summary of the news article's content.
 - embedding: An array of numerical values representing the vector embedding for the article, generated using the OpenAI EMBEDDING_MODEL.
 
+
+## Data Ingestion
+[Create a free 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=richmond.alake) and conduct the Data Ingestion process below.
+
+```python
+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 for the tech news embeddings
+db_name = 'your_database_name'  # Change this to your actual database name
+collection_name = 'tech_news_embeddings'  # Change this to your actual collection name
+
+tech_news_embeddings_collection = client[db_name][collection_name]
+
+# Load the "tech-news-embeddings" dataset from Hugging Face
+dataset = load_dataset("AIatMongoDB/tech-news-embeddings")
+
+insert_data = []
+
+# Iterate through the dataset and prepare the documents for insertion
+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:
+        tech_news_embeddings_collection.insert_many(insert_data)
+        print("1000 records ingested")
+        insert_data = []
+
+# Insert any remaining documents
+if len(insert_data) > 0:
+    tech_news_embeddings_collection.insert_many(insert_data)
+    print("Data Ingested")
+
+```
+
 ## Usage
 The dataset is suited for a range of applications, including: