How to build a great search experience using hybrid search. A combination of: ◇ fts, tsvector, pg_trgm ◇ Structured output to narrow down (filters, date-range etc) https://t.co/y8YoDdTn6G

• Multiple indices. Splitting the document corpus up into multiple indices and then routing queries based on some criteria. This means that the search is over a much smaller set of documents rather than the entire dataset. Again, it is not always useful, but it can be helpful for certain datasets. The same approach works with the LLMs themselves.

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1. Scalability is crucial - systems need to be designed with the assumption that query volume, document corpus size, indexing complexity etc. could increase by 10x. What works at one scale may completely break at a higher scale.


2. Sharding the index, either by document or by word, is important to distribute the indexing and querying load across machines.

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7 must-know strategies to scale your database

Indexing:

Check the query patterns of your application and create the right indexes.

Materialized Views:

Pre-compute complex query results and store them for faster access.

Denormalization:

Reduce complex joins to improve query performance.

Vertical Scaling

Boost your database server by adding more CPU, RAM, or... See more