Atlas Search for Natural Language Processing Applications

Question

Pulumi · Accepted Answer

Atlas Search is a full-text search feature embedded directly into MongoDB Atlas, MongoDB's hosted database-as-a-service offering. It allows you to perform text search queries on your data stored in MongoDB, taking advantage of advanced text search capabilities like autocomplete, scoring, highlighting, and more. This can be particularly useful for creating natural language processing (NLP) applications, where the ability to search and analyze text in large datasets is crucial.

To create an Atlas Search index using Pulumi, you would use the mongodbatlas.SearchIndex resource. The following program will demonstrate how to do this.

We'll start by creating a simple Atlas Search index that could be used for a theoretical NLP application, where the index is defined on a MongoDB collection that could contain documents related to customer feedback or other text data.

Remember to replace placeholders like "<your-project-id>", "<your-cluster-name>", and "<your-collection-name>" with actual values corresponding to your MongoDB Atlas setup.

import pulumi
import pulumi_mongodbatlas as mongodbatlas

# Before attempting to use the code, make sure you've set up your MongoDB Atlas provider in Pulumi.
# You can configure your MongoDB Atlas provider by following the guide at:
# https://www.pulumi.com/registry/packages/mongodbatlas/installation-configuration/

# To create a search index, you need to specify the following properties:
# `projectId`: The unique identifier of the project your cluster is in.
# `clusterName`: The name of the MongoDB cluster the index will be in.
# `database`: The name of the database that contains the collection you're indexing.
# `collectionName`: The name of the collection you're indexing.
search_index = mongodbatlas.SearchIndex("search-index",
    project_id="<your-project-id>",
    cluster_name="<your-cluster-name>",
    database="<your-database>",
    collection_name="<your-collection-name>",
    mappings_dynamic=False,
    # Here we could specify more settings for the search index, such as 'analyzer', 'searchAnalyzer', 'mappingsFields', etc.
    # Depending on the requirements of your NLP application.
    mappings_fields='{"dynamic": false, "fields": {"title": {"type": "string"}}}',
    # For an NLP application, you might use custom analyzers to handle complex text searching.
    analyzers=[
        {
            "name": "myCustomAnalyzer",
            "charFilters": [{"type": "htmlStrip"}],
            "tokenizer": {"type": "edgeGram", "minGrams": 2, "maxGrams": 5, "tokenChars": ["letter", "digit"]},
            "tokenFilters": [
                {"type": "lowercase"},
                {"type": "asciifolding"},
                {"type": "stop", "ignoreCase": True, "removeTrailing": True}
            ]
        }
    ]
)

# Output the status of the search index.
pulumi.export("search_index_status", search_index.status)

This Pulumi code sets up an Atlas Search index with a dynamic mapping. The dynamic mapping is set to False, meaning you'll specify exactly which fields you want indexed and how. In this example, it demonstrates setting up an index on the title field as a string. However, in a real-world NLP application, you would likely index multiple fields and potentially use more extensive mappings to suit your specific needs.

Custom analyzers are also included in this sample to showcase how you could process text data in different ways—lowercasing, stripping HTML characters, handling stop words, and more—which are crucial operations in text analysis for NLP.

As you work with Atlas Search, you may need to adjust the properties of SearchIndex and its analyzers to better match the content and structure of your data, as well as the requirements of your search queries.

Remember to check the Pulumi MongoDB Atlas package documentation for detailed information about each property that can be configured for the SearchIndex resource.