The landscape of search and information retrieval is rapidly evolving. With the rise of AI and large language models, user expectations for search capabilities have skyrocketed. Your users now expect that your search can handle complex, nuanced queries that go beyond simple keyword matching. Just hear what Algolia CTO has to say -
“We saw 2x more keywords search 6 months after the ChatGPT launch.”
Algolia CTO, 2023
They have 17,000 customers accounting for 120B searches/month. This trend isn’t isolated. Across industries, we’re seeing a shift towards more sophisticated search queries that blend multiple concepts, contexts, and data types.Vector Search with text-only embeddings (& also multi-modal) fails on complex queries, because complex queries are never just about text. They involve other data too!Consider these examples:
E-commerce: A query like “comfortable running shoes for marathon training under $150” involves text, numerical data (price), and categorical information (product type, use case).
Content platforms: “Popular science fiction movies from the 80s with strong female leads” combines text analysis, temporal data, and popularity metrics.
Job search: “Entry-level data science positions in tech startups with good work-life balance” requires understanding of text, categorical data (industry, job level), and even subjective metrics.
