Improve Vietnamese Name Autocomplete In Tmail-Backend

Introduction

This article discusses the challenges and potential solutions for improving the accuracy and efficiency of name auto-completion, specifically for Vietnamese names, within the Tmail-backend system. The current implementation appears to have limitations when handling compound first names, leading to unsatisfactory search results. Let’s dive into the problem and explore ways to enhance the user experience.

The Problem: Incomplete Auto-Completion for Vietnamese Names

Consider a user named Le Nhan Phung. In the current system, the first name Nhan Phung is indexed as a single string. When a user types Phu, expecting to see Le Nhan Phung as a suggestion, no results are returned. This is because the system is searching for the exact string Phu within the indexed first name field, which only contains the full string Nhan Phung. This behavior significantly hinders the usability of the auto-completion feature for Vietnamese names that often consist of multiple parts.

This issue highlights the need for a more sophisticated indexing strategy that takes into account the structure of Vietnamese names. By treating the first name as an array of individual components, we can enable more flexible and accurate matching, leading to improved search results and a better user experience. The goal is to ensure that typing a partial component of the first name, such as Phu in the example above, returns the correct user suggestion.

Proposed Solution: Indexing First Names as Arrays

The suggested solution involves indexing the first name as an array of strings instead of a single string. In the case of Le Nhan Phung, the first name would be indexed as ["Nhan", "Phung"]. This approach allows the system to match partial inputs against individual components of the first name. For instance, when a user types Phu, the system can identify Phu as a match within the array ["Nhan", "Phung"] and return Le Nhan Phung as a suggestion.

Benefits of Array Indexing

• Improved Accuracy: By breaking down the first name into its constituent parts, the system can identify matches based on partial inputs, leading to more accurate search results.
• Enhanced User Experience: Users can find the desired names more quickly and easily, even if they only remember a portion of the first name.
• Flexibility: The array indexing approach can be easily adapted to handle names with varying numbers of components.

Implementation Considerations

The beauty of this solution is that it can likely be implemented without altering the underlying OpenSearch mapping type. We can achieve the desired array indexing behavior through adjustments in the data processing pipeline that prepares the data for indexing. This approach minimizes the risk of disrupting existing functionality and simplifies the implementation process.

1. Data Transformation: Modify the data ingestion process to split the first name into an array of strings before indexing.
2. OpenSearch Query Modification: Adjust the OpenSearch query to search for matches within the array of first name components.

By focusing on these two key areas, we can effectively implement the array indexing solution and significantly improve the accuracy and usability of the name auto-completion feature for Vietnamese names.

Technical Details and Implementation Steps

To implement this solution effectively, a few key technical details need to be considered. The primary focus should be on modifying the data ingestion pipeline and adjusting the OpenSearch query to accommodate the new array-based indexing strategy.

1. Data Ingestion Pipeline Modification

The data ingestion pipeline is responsible for taking raw user data and transforming it into a format suitable for indexing in OpenSearch. To implement the array indexing solution, this pipeline needs to be modified to split the first name field into an array of individual components. This can be achieved using a variety of techniques, depending on the specific technologies used in the pipeline.

• String Splitting: The most straightforward approach is to use a string splitting function to divide the first name into an array of strings based on whitespace. For example, in Python, you could use the split() method to achieve this.
• Regular Expressions: For more complex cases, regular expressions can be used to identify and extract the individual components of the first name. This approach is particularly useful if the first name contains special characters or non-standard formatting.
• Natural Language Processing (NLP): In some cases, NLP techniques may be necessary to accurately identify the individual components of the first name. This is especially true if the first name contains ambiguous or compound words.

Once the first name has been split into an array of strings, the data ingestion pipeline should store this array in the appropriate field in the OpenSearch index. This will ensure that the first name is indexed as an array, allowing for more flexible and accurate matching.

2. OpenSearch Query Modification

In addition to modifying the data ingestion pipeline, the OpenSearch query also needs to be adjusted to take into account the new array-based indexing strategy. This involves modifying the query to search for matches within the array of first name components, rather than searching for an exact match against the entire first name field.

• Match Query: The match query can be used to search for matches within an array field. This query will analyze the search term and attempt to match it against the individual components of the array.
• Terms Query: The terms query can be used to search for documents where the specified field contains any of the specified terms. This query is useful for searching for documents where the first name contains a specific component.
• Wildcard Query: The wildcard query can be used to search for documents where the specified field matches a wildcard pattern. This query is useful for searching for documents where the first name contains a specific partial component.

By using these queries in combination, it is possible to create a flexible and accurate search query that can effectively match partial inputs against the individual components of the first name.

Example Implementation

Here's an example of how the proposed solution could be implemented using Python and the OpenSearch client library:

from opensearchpy import OpenSearch

# Configure the OpenSearch client
host = 'localhost'
port = 9200
auth = ('user', 'password') # Replace with your OpenSearch credentials

client = OpenSearch(
    hosts = [{'host': host, 'port': port}],
    http_auth = auth,
    use_ssl = True,
    verify_certs = True,
    ssl_assert_hostname = False,
    ssl_show_warn = False
)

# Function to split the first name into an array of strings
def split_first_name(first_name):
    return first_name.split()

# Example user data
user_data = {
    'first_name': 'Nhan Phung',
    'last_name': 'Le'
}

# Split the first name into an array
user_data['first_name_array'] = split_first_name(user_data['first_name'])

# Index the user data in OpenSearch
index_name = 'users'
document_id = 1

response = client.index(
    index = index_name,
    body = user_data,
    id = document_id,
    refresh = True
)

print(response)

# Example search query
search_term = 'Phu'

query = {
    'query': {
        'match': {
            'first_name_array': search_term
        }
    }
}

# Execute the search query
response = client.search(
    index = index_name,
    body = query
)

print(response)

This example demonstrates how to split the first name into an array of strings and index it in OpenSearch. It also shows how to use the match query to search for documents where the first_name_array field contains the search term.

Conclusion

By indexing the first name as an array of strings and adjusting the OpenSearch query accordingly, we can significantly improve the accuracy and usability of the name auto-completion feature for Vietnamese names in Tmail-backend. This approach allows the system to match partial inputs against individual components of the first name, leading to more accurate search results and a better user experience. While the initial problem focused on Vietnamese names, the solution can be applied to other languages with similar naming conventions, making it a versatile improvement.

For further information on OpenSearch and its capabilities, visit the OpenSearch Documentation. This resource provides comprehensive details on indexing, querying, and managing data within OpenSearch.