GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #12
A Generative AI Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified. An example query might be "Tell me about monster truck…
The correct answer is B. Include metadata columns for article date and topic to support metadata filtering. To efficiently search news articles by both topic and date (within a 10-day window), the best approach is to include metadata columns such as article date and topic in the Vector Search index. This allows for metadata filtering alongside the vector-based search, making it easy…
Question
A Generative AI Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified. An example query might be “Tell me about monster truck news around January 5th 1992”. They want to do this with the least amount of effort. How can they set up their Vector Search index to support this use case?
Options
- ACreate separate indexes by topic and add a classifier model to appropriately pick the best index.
- BInclude metadata columns for article date and topic to support metadata filtering.
- CPass the query directly to the vector search index and return the best articles.
- DSplit articles by 10 day blocks and return the block closest to the query.
How the community answered
(53 responses)- A9% (5)
- B85% (45)
- C4% (2)
- D2% (1)
Explanation
To efficiently search news articles by both topic and date (within a 10-day window), the best approach is to include metadata columns such as article date and topic in the Vector Search index. This allows for metadata filtering alongside the vector-based search, making it easy to filter out articles that are outside the 10-day range or do not match the specified topic. This method requires the least amount of effort while ensuring accurate and efficient search results.
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