1. Atlas Search for AI-generated Content Cataloguing and Retrieval


    Atlas Search provides powerful search functionality for MongoDB Atlas data, making it a suitable choice for implementing a search engine over content that could be stored in a MongoDB Atlas cluster. When AI-generated content is stored within a MongoDB database, Atlas Search can index this content and make it easily searchable, facilitating cataloguing and retrieval tasks.

    Below is a Pulumi program in Python that creates a MongoDB Atlas cluster and configures an Atlas Search index to enable search over the AI-generated content. To use this program, you would need to have the Pulumi CLI installed and configured with the appropriate credentials for MongoDB Atlas. Also, ensure you have created a mongodbatlas project and obtained the necessary Project ID and access credentials.

    Here's what the code will do:

    1. Setting up a MongoDB Atlas Cluster: A cluster must exist on which the search indexes will be created to host our AI-generated content.

    2. Creating an Atlas Search Index: Once the cluster is available, an Atlas Search index will be configured to index fields that are relevant for searching the AI-generated content. In this example, we configure a basic search index on a hypothetical collection named content. You would need to adjust this according to the actual schema of your AI-generated content.

    Let's look at the Pulumi program:

    import pulumi import pulumi_mongodbatlas as mongo_atlas # Replace the following with your MongoDB Atlas Project ID and other cluster configurations as needed. project_id = 'your-mongodb-atlas-project-id' cluster_name = 'ai-content-cluster' # Provision a MongoDB Atlas Cluster (M10 size for example). cluster = mongo_atlas.Cluster(cluster_name, project_id=project_id, provider_name="AWS", # Assuming AWS as the cloud provider. disk_size_gb=10, num_shards=1, provider_instance_size_name="M10", provider_region_name="US_EAST_1", cluster_type="REPLICASET", replication_specs=[mongo_atlas.ClusterReplicationSpecArgs( num_shards=1, regions_config=[mongo_atlas.ClusterRegionsConfigArgs( region_name="US_EAST_1", electable_nodes=3, priority=7, read_only_nodes=0)] )]) # Assuming a collection named 'content' already exists in your database 'ai_catalog'. # Configure an Atlas Search Index on that collection. search_index = mongo_atlas.SearchIndex("ai-content-search-index", cluster_name=cluster.name, db_name="ai_catalog", collection_name="content", project_id=project_id, analyzer="lucene.standard", search_analyzer="lucene.standard", mappings_dynamic=True # This allows the index to include all fields dynamically. ) # Export the search index ID and Atlas cluster details for reference. pulumi.export("search_index_id", search_index.search_index_id) pulumi.export("cluster_id", cluster.id) pulumi.export("cluster_connection_string", cluster.connection_strings.apply(lambda cs: cs.standard))


    • We create a MongoDB Atlas Cluster of size M10 in AWS cloud provider, in the US_EAST_1 region. This cluster is suitable for small to medium loads and includes 10 GB of storage.
    • The mongo_atlas.SearchIndex resource creates an Atlas Search index on the specified MongoDB collection, allowing you to perform full-text search queries and indexing over the fields in your documents. The analyzer and search_analyzer are set to 'lucene.standard' which is a commonly used text analyzer implemented by the Lucene library.
    • The mappings_dynamic parameter is set to True, which tells Atlas Search to automatically index all fields in the documents within the collection.
    • The result of this will be an Atlas Search index that allows your applications to query your content using a rich set of query types.

    This program is a simplified example; typically, you would need to customize the search index further based on the exact nature of your content schema and search requirements. You should consult the MongoDB Atlas Search Documentation for detailed information on configuring Atlas Search indices to suit your needs.