Atlas Search

Overview

In this guide, you can learn how to query an Atlas Search index and use advanced full-text search functionality in your PyMongo applications. You can query a search index by using a $search aggregation pipeline stage.

To learn more about the $search pipeline stage, see the $search guide in the MongoDB Server manual.

Create an Atlas Search Index

Before you can perform a search on an Atlas collection, you must first create an Atlas Search index on the collection. An Atlas Search index is a data structure that categorizes data in a searchable format. To learn how to create an Atlas Search index, see Atlas Search and Vector Search Indexes.

Search Your Data

To use the $search aggregation pipeline stage, you must specify an Atlas Search query operator that indicates the type of query you want to run. You can also optionally specify a collector that groups results by values or ranges. To view a table of all the operators and collectors available with Atlas Search, see Use Operators and Collectors in Atlas Search Queries.

The following example uses the compound operator to combine several operators into a single query. To learn more about the compound operator, see the Compound operator guide in the MongoDB Atlas documentation.

The query has the following search criteria:

• The genres field must not contain Comedy.

• The title field must contain the string New York.

The query also includes the following stages:

• $limit, to limit the output to 10 results.

• $project, to exclude all fields except title and add a field named score.

client = pymongo.MongoClient("<connection-string>")
result = client["sample_mflix"]["movies"].aggregate([
         {
            "$search": {
              "index": "pymongoindex",
              "compound": {
              "mustNot": [
                  {
                      "text": {
                          "query": [
                              "Comedy"
                          ],
                          "path": "genres"
                      }
                  }
              ],
              "must": [
                  {
                      "text": {
                          "query": [
                              "New York"
                          ],
                          "path": "title"
                        }
                     }
                  ],
               }
            }
         },
         { "$limit": 10 },
         {
           "$project": {
           "_id": 0,
           "title": 1,
           "score": { "$meta": "searchScore" }
      }
    }
])
for i in result:
   print(i)

{'title': 'New York, New York', 'score': 6.786379814147949}
{'title': 'New York', 'score': 6.258603096008301}
{'title': 'New York Doll', 'score': 5.381444931030273}
{'title': 'Escape from New York', 'score': 4.719935417175293}
{'title': 'Autumn in New York', 'score': 4.719935417175293}
{'title': 'Sleepless in New York', 'score': 4.719935417175293}
{'title': 'Gangs of New York', 'score': 4.719935417175293}
{'title': 'Sherlock Holmes in New York', 'score': 4.203253746032715}
{'title': 'New York: A Documentary Film', 'score': 4.203253746032715}
{'title': 'An Englishman in New York', 'score': 4.203253746032715}

Additional Information

To learn more about the available Atlas Search operators, see the Operators and Collectors guide in the MongoDB Atlas documentation.

For more information about Atlas Search, and to view more query examples, see the Atlas Search documentation.

If you'd like to perform vector searches on your data stored in Atlas, you must use Atlas Vector Search. To learn more about Atlas Vector Search, see the Atlas Vector Search documentation.