Basic RAG is effective for general inquiries but falters when time-specific questions are posed, such as workshops scheduled for next month. This limitation arises from the use of semantic search, which typically retrieves similar text rather than fulfilling strict criteria, leading to irrelevant results. Incorporating metadata filtering improves search precision by enforcing essential requirements for queries, allowing users to specify conditions like date ranges. Updating the RAG code requires adding metadata columns and creating a custom retrieval mechanism to assist in filtering date-related queries effectively.
Basic RAG performed well for general topics but struggled with questions involving time, like identifying workshops next month, due to lack of metadata filtering.
Basic semantic search retrieves text that is similar to the question but may not meet specific requirements unless metadata filtering is applied, which refines results.
Metadata filtering allows for a more precise search by applying criteria like date ranges and ensuring only relevant chunks are included in outcomes.
To enhance a RAG app with metadata filtering, additional columns for dates need to be added, along with a custom tool for the LLM to filter results by dates.
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