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Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use
arXiv – CS AI|Shreyas Subramanian, Adewale Akinfaderin, Yanyan Zhang, Ishan Singh, Mani Khanuja, Sandeep Singh, Maira Ladeira Tanke||13 views
🤖AI Summary
Researchers found that simple keyword search within agentic AI frameworks can achieve over 90% of the performance of traditional RAG systems without requiring vector databases. This approach offers a more cost-effective and simpler alternative for AI applications requiring frequent knowledge base updates.
Key Takeaways
- →Tool-augmented LLM agents using basic keyword search can match 90%+ performance of traditional RAG systems.
- →The approach eliminates the need for expensive vector databases while maintaining high retrieval quality.
- →Implementation is significantly simpler and more cost-effective than traditional RAG architectures.
- →The method is particularly advantageous for scenarios requiring frequent knowledge base updates.
- →Study challenges the necessity of semantic search and vector databases in retrieval-augmented generation.
Read Original →via arXiv – CS AI
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