Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries | HackerNoon
Briefly

from Hackernoon3 months ago
Text embeddings excel at encoding unstructured text for RAG applications, but they struggle with structured data tasks like filtering or sorting.
Hackernoonhttps://hackernoon.com/beyond-text-embeddings-addressing-the-gaps-in-rag-applications-for-structured-data-queries
When posed a question requiring structured operations, such as finding the highest-rated movie of 2024, traditional text embeddings fail to deliver accurate results.
Hackernoonhttps://hackernoon.com/beyond-text-embeddings-addressing-the-gaps-in-rag-applications-for-structured-data-queries
For handling structured data queries effectively, supplemental tools like knowledge graphs are essential for proper filtering, sorting, and aggregating tasks.
Hackernoonhttps://hackernoon.com/beyond-text-embeddings-addressing-the-gaps-in-rag-applications-for-structured-data-queries
Using Neo4j with the MovieLens dataset allows for advanced data operations that text embeddings alone cannot handle efficiently.
Hackernoonhttps://hackernoon.com/beyond-text-embeddings-addressing-the-gaps-in-rag-applications-for-structured-data-queries
Read at Hackernoon
#text-embedding #knowledge-graphs #rag-applications #structured-data #neo4j
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