From the course: Building Generative AI Apps to Talk to Your Data
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Querying unstructured data: Hybrid search - Snowflake Tutorial
From the course: Building Generative AI Apps to Talk to Your Data
Querying unstructured data: Hybrid search
- Welcome back. One older way to search is through keyword search. Keyword search relies on exact matches between the words in our query and the text we're looking for. This is great for finding texts that contains highly specific terms. The way the keyword search works is by leveraging sparse vectors. In keyword search each vector will be the length of the number of unique words that are in your text corpus. Each element in the vector represents a unique word. Any word represented in the text will be captured with a one, and all other elements in the vector will be zero. So when we look at these vectors, we will see that most, but not all of the elements in the vector will be zero. Since the vector will be largely an array of zeros, we call it a sparse vector. While keyword search is great, when we need to track down particular words it can miss text using synonyms, conveying a similar meaning that we might also want to retrieve. In contrast to keyword search, similarity search…
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