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How to Save $70K Building a Knowledge Graph for RAG on 6M Wikipedia Pages
Manage episode 445317380 series 3570694
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This story was originally published on HackerNoon at: https://hackernoon.com/how-to-save-$70k-building-a-knowledge-graph-for-rag-on-6m-wikipedia-pages.
We show how content-centric knowledge graphs – a vector-store allowing links between chunks – are an easy to use and efficient approach to improve RAG results.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #vector-search, #generative-ai, #retrieval-augmented-generation, #knowledge-graphs, #graphvectorstore, #scalable-ai-solutions, #langchain-integrations, #good-company, and more.
This story was written by: @datastax. Learn more about this writer by checking @datastax's about page, and for more stories, please visit hackernoon.com.
We’ve argued that content-centric knowledge graphs – a vector-store allowing links between chunks – are an easier to use and more efficient approach to improving RAG results. Here, we put that to the test.
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