RAG But Better: Rerankers with Cohere AI

Rerankers have been a common component of retrieval pipelines for many years. They allow us to add a final "reranking" step to our retrieval pipelines — like with Retrieval Augmented Generation (RAG) — that can be used to dramatically optimize our retrieval pipelines and improve their accuracy. In this video we'll learn about rerankers, how they compare to the more common embedding retrieval only setup, and how we can create retrieval pipelines with reranking using Cohere AI reranking model. We'll also be using the (more typical) OpenAI text-embedding-ada-002 model with the Pinecone Vector Database. 📌 Code (08:32): https://github.com/pinecone-io/examples/blob/master/learn/generation/better-rag/00-rerankers.ipynb 📚 Article: https://www.pinecone.io/learn/series/rag/rerankers/ 🌟 Build Better Agents + RAG: https://platform.aurelio.ai (use "JBMARCH2025" coupon code for $20 free credits) 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 00:00 RAG and Rerankers 01:25 Problems of Retrieval Only 04:32 How Embedding Models Work 06:34 How Rerankers Work 08:20 Implementing Reranking in Python 13:11 Testing Retrieval without Reranking 15:21 Retrieval with Cohere Reranking 21:54 Tips for Reranking #artificialintelligence #nlp #ai #openai