Prepare for complexities of deploying vector search in production with insights on indexing, metadata filtering, query language, and vector lifecycle management
You’ve decided to use vector search in your application, product, or business. You’ve researched how and why embeddings and vector search make a problem solvable or can enable new features. You’ve dipped your toes into the hot, emerging area of approximate nearest-neighbor algorithms and vector databases. Almost immediately upon productionizing vector search applications, you will start to run into very hard and potentially unanticipated difficulties.
clause” to a traditional database. It has the advantage of also being relatively easy to port between different systems. Furthermore, these filters are queries, and queries can be optimized. The sophistication of the query optimizer can have a huge impact on the performance of your queries.
There are many technical approaches that databases might take to solve this problem for you. You can “pre-filter” which means to apply the filter first, and then do a vector lookup. This approach suffers from not being able to effectively leverage the pre-built vector index. You can “post-filter” the results after you’ve done a full vector search.
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