How to make an index In azure cosmos DB just Like Azure AI Search?

Question

I want to create an Index inside my azure cosmos DB just like an azure AI Search Index, So that I can use that Index with Azure Open AI Rest API?

Answer 1

Hi Ahmad Shafiq
Greetings!
To create a vector index in Azure Cosmos DB for MongoDB vCore and harness the capabilities of Azure OpenAI for Retrieval-Augmented Generation (RAG), follow these steps:

Setting Up the Database and Collection

1. Establish a Connection: Use the pymongo library to connect to your Azure Cosmos DB for MongoDB vCore instance. Replace <USERNAME>, <PASSWORD>, and <VCORE_CLUSTER_NAME> with your actual credentials and cluster name.

    import pymongo
    mongo_conn = "mongodb+srv://<USERNAME>:<PASSWORD>@<VCORE_CLUSTER_NAME>.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"
    mongo_client = pymongo.MongoClient(mongo_conn)

1. Create a Database and Collection: Define the database and collection names, then create them within your Cosmos DB instance.

    DATABASE_NAME = "AdgenDatabase"    COLLECTION_NAME = "AdgenCollection"    mongo_client.drop_database(DATABASE_NAME)    db = mongo_client[DATABASE_NAME]    collection = db[COLLECTION_NAME]    if COLLECTION_NAME not in db.list_collection_names():        db.create_collection(COLLECTION_NAME)        print(f"Created collection '{COLLECTION_NAME}'.")    else:        print(f"Using collection: '{COLLECTION_NAME}'.")

Creating a Vector Index

1. Create the Vector Index: To perform efficient vector similarity searches within your collection, create a vector index. Azure Cosmos DB supports different types of vector indexes, such as IVF and HNSW.

    # Example for creating a vector index (specific commands may vary)
    collection.create_index([("vector_field", pymongo.ASCENDING)], name="vector_index")

Additional Resources

For more detailed information and examples, you can refer to the following resources:

• Integrated vector store - Azure Cosmos DB for MongoDB vCore1
• Indexes on Azure Cosmos DB for MongoDB vCore2
• Indexing Best Practices in Azure Cosmos DB for MongoDB vCore3

By following these steps, you can effectively create a vector index in Azure Cosmos DB for MongoDB vCore and leverage the capabilities of Azure OpenAI for RAG without using AI Search. If you have any further questions or need additional assistance, feel free to ask!