Vector Search vs Cross-Encoder #rag #aiengineering #llm

Shane | LLM Implementation · Beginner ·🔍 RAG & Vector Search ·3mo ago

Skills: RAG Basics90%LLM Engineering70%

Key Takeaways

The video demonstrates the use of Cross-Encoder reranking to improve the accuracy of vector search in RAG systems, using tools like transformer documents and bi-encoders.

Full Transcript

This is the money table. It shows exactly what the re-ranker did to the ordering. Look at the precision changes. The transformer document, it was at position four in the original vector search. The cross encoder recognized it was directly relevant to our question about attention and long sequences, and it jumped up to position two. Meanwhile, ResNet got kicked out entirely. Vector search ranked the fifth because it was superficially similar. The cross encoder caught that it does not actually answer the question. This is the re-ranker catching what embeddings missed. The bi-encoder said, "These are generally similar." The cross encoder said, "This one actually answers the question." The re-ranking table is the key

Original Description

Embeddings alone can get your RAG ranking wrong. Watch a Cross-Encoder reranker fix a vector search failure and push the correct context to the very top. 📚 Full tutorial: https://www.youtube.com/watch?v=qoHjx1vizak 📋 Playlist: https://www.youtube.com/playlist?list=PL0G6--HT7Yq_sxLFyWFWL6KHYWlosspj_ 💬 Discord: https://discord.gg/KpnJQbgpjt

Watch on YouTube ↗ (saves to browser)

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This video teaches how to use Cross-Encoder reranking to improve the accuracy of vector search in RAG systems, and demonstrates the importance of re-ranking in retrieving relevant information. By using a Cross-Encoder, the correct context can be pushed to the top of the search results, improving the overall precision of the search.

Key Takeaways

Implement Vector Search using embeddings
Use a Cross-Encoder to re-rank search results
Evaluate the precision of the search results
Compare the results of vector search and Cross-Encoder reranking
Optimize the performance of the LLM-based RAG system

💡 The Cross-Encoder can catch what embeddings miss, and re-rank the search results to improve the accuracy of the search.

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