Building Production-Ready Semantic Search with Python and Snowflake Cortex
The article discusses the implementation of AI-powered semantic search using Python and Snowflake's Cortex Search Service. It highlights the importance of focusing the searchable text column and exposing filterable fields as attributes. The author shares insights on common pitfalls and best practices for configuring the search service effectively.
- ▪Cortex Search allows for low-latency semantic and full-text search on data stored in Snowflake.
- ▪The SEARCH_TEXT column should focus on meaningful fields rather than including every available field to avoid noisy search results.
- ▪Filterable fields must be added to the ATTRIBUTES property for effective metadata filtering in search results.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3501373) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Artem Posted on May 24 Building Production-Ready Semantic Search with Python and Snowflake Cortex #python #snowflake #ai #backend Recently I has been given a task to implement AI powered semantic search for our catalogue and as we are already using snowflake we decided to implement this feature using Cortex Search Service. If you do not know what is the Cortex Search yet, you might just quickly check this link for an overview.
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