Handling Search Queries in Azure AI Search

Hello Aakash Bhaikatti,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that you are working on a project and seeking assistance on how to handle Search Queries in Azure AI Search with those three questions.

Solution

I will provide the solution based on the scenario given and your questions.

Q1:

How can we improve the search capability to handle queries with special characters, no spaces, hyphens, mixed cases, and alphanumeric combinations?

To improve the search capability to handle queries with special characters, no spaces, hyphens, mixed cases, and alphanumeric combinations in Azure AI Search, you can employ a combination of custom analyzers, query preprocessing, synonym maps, scoring profiles, and continuous monitoring.

Flexible filtering, faceting, and sorting in Azure Cognitive Search. https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/flexible-filtering-faceting-and-sorting-in-azure-cognitive/ba-p/3038442.

Q2:

Are there specific settings or configurations in Azure AI Search that can help in normalizing such queries before processing?

Yes, there are specific settings and configurations in Azure Cognitive Search that can help in normalizing queries before processing. These include custom analyzers, tokenizers, token filters, and synonym maps. Normalizers can achieve light text transformations that improve results such as consistent casing, normalize accents and diacritics to ASCII equivalent characters, and map characters like - and whitespace into a user-specified character.

Partial terms, patterns, and special characters. https://video2.skills-academy.com/en-us/azure/search/search-query-partial-matching.

Q3:

What are the best practices for pre-processing or transforming user queries to enhance the search accuracy in this context?

The best practices can be applied for pre-processing or transforming user queries start from Normalize Case, and I provide you here with combined Python function implementing several of these best practices in comments as a title:

import re
def preprocess_query(query):
    # Normalize case
    query = query.lower()
    # Remove special characters
    query = re.sub(r'[^a-z0-9\s-]', '', query)
    # Handle hyphens and spaces
    query = query.replace('-', ' ')
    # Split alphanumeric combinations
    query = re.sub(r'(\d)([a-zA-Z])', r'\1 \2', query)
    query = re.sub(r'([a-zA-Z])(\d)', r'\1 \2', query)
    # Remove extra whitespaces
    query = ' '.join(query.split())
    # Expand query with synonyms (example dictionary)
    synonyms = {
        "product": ["item", "good"],
        "prod-id": ["product-id"],
        "productid": ["product id"]
    }
    expanded_query = query
    for term, synonym_list in synonyms.items():
        if term in query:
            expanded_query += " " + " ".join(synonym_list)
    return expanded_query
# Example usage
user_query = "ProductID-123ABC"
normalized_query = preprocess_query(user_query)
print(normalized_query)  # Output: "productid 123 abc product item good product id"

However, in this link Azure AI Search Database Selection: Optimizing Performance. https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-database-selection-optimizing-performance-and/ba-p/4155601.

You will be able to read more about best practices and steps to follow.

References

Source: Flexible filtering, faceting, and sorting in Azure Cognitive Search. Accessed, 6/10/2024.

Source: Partial terms, patterns, and special characters. Accessed, 6/10/2024.

Source: Azure AI Search Database Selection: Optimizing Performance. Accessed, 6/10/2024.

Accept Answer

I hope this is helpful! Do not hesitate to let me know if you have any other questions.

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Best Regards,

Sina Salam