Hey everyone,

I wanted to take a bit more time to share the story behind the development of the madison_hybrid_search function at Madison AI. It wasn’t a walk in the park, and looking back at some of my old Facebook posts, it’s funny how life comes full circle.

For those of you who aren’t as familiar with the technical side, hybrid search is all about combining different search techniques to get the best results. In our case, we aimed to merge the precision of keyword search with the contextual understanding of semantic search. Sounds straightforward? Let me tell you, it was anything but.

The Labyrinth of Challenges

We definitely faced our share of trials:

  • Data Integration: Imagine trying to piece together a puzzle where half the pieces are from different sets. That’s what it felt like trying to integrate data from various sources. I even remember a Facebook post from March 13, 2009, where I was geeking out about updating my homepage. Back then, it was simple HTML and links! Now, I’m dealing with complex data structures and APIs. It’s a wild difference.
  • Precision vs. Recall: It’s a constant balancing act. Do you prioritize finding exactly what the user typed, or do you try to understand the intent behind their query? It’s like trying to find the perfect song. Sometimes you want the exact title, sometimes you just want something that fits the mood.
  • Performance: Speed is key. Users expect results in the blink of an eye. We had to optimize our code, our databases, everything. It reminded me of those late nights trying to get my old websites to load faster – but on a much larger scale.

Diving into the Code: `madison_hybrid_search`

To give you a glimpse under the hood, the madison_hybrid_search function, as you can see in the `src.zip/infrastructure/ai/madison_hybrid_search.py` file, uses a class called `MadisonHybridSearchWithSemanticReranking`. This class is designed to perform hybrid search with semantic reranking, leveraging Azure’s search capabilities and an embedding model. Here’s a snippet:

class MadisonHybridSearchWithSemanticReranking:
    """
    This class is responsible for performing the hybrid search with semantic reranking.
    """
    def __init__(self, search_client, embedding_model, azure_search_top_k, 
                 azure_search_vector_columns, azure_semantic_configuration_name, 
                 azure_searchable_columns):
        self.search_client = search_client
        self.embedding_model = embedding_model
        self.azure_search_top_k = azure_search_top_k
        self.azure_search_vector_columns = azure_search_vector_columns
        self.azure_semantic_configuration_name = azure_semantic_configuration_name
        self.azure_searchable_columns = azure_searchable_columns

This class initializes with several key components, including a search client, an embedding model, and configuration parameters for Azure Search. It’s designed to handle the complexities of hybrid search and semantic reranking, ensuring that we get the most relevant results.

Music, Code, and Life: A Constant Evolution

It’s funny how things connect. I was just looking at some of my Facebook posts from around 2010, and I was really into sharing music. I remember posting links to a lot of Butch Walker, and even some random songs. There’s something similar about curating a playlist and building a search engine. You’re trying to connect people with what they’re looking for, to provide a relevant and engaging experience.

And it’s not just old stuff. Even recently, I’ve been using AI tools to discover new music. There was this one time just last year when YouTube music recommended a singer/songwriter (Bob Schneider) and it led me to some amazing music I’d never heard before. The algorithms are getting so good at understanding musical taste – it’s pretty impressive. They can also be so fucking repetitive. Patience, grasshopper.

It’s a far cry from posting song links on Facebook, but it’s all part of the same thread: a passion for connection, for finding that perfect match, whether it’s a song or a piece of information.

Looking Ahead

The journey to madison_hybrid_search has been challenging, rewarding, and a reflection of my own growth as a developer. From those early days of simple HTML to the complexities of AI-powered search, it’s been an incredible ride. And like a good song, it’s a journey that continues to evolve.

Thanks for reading, and I’m excited to share more of these stories with you in the future.