Episode Description
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Vector databases can be an important component for building reliable AI agents and scalable semantic search applications. In this episode, Arjun Patel from Pinecone breaks down how to optimize your RAG pipeline, choose the right embedding models (sparse vs. dense), and implement effective chunking strategies for better data retrieval. We also explore the new Pinecone plugin for Claude Code, demonstrating how to build a recommendation system and chat with your documents using Pinecone Assistant without writing complex code.
https://www.linkedin.com/in/arjunkirtipatel/
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00:00:00 - Intro
00:01:11 - What Vector Databases Unlock
00:04:40 - Optimal Chunking Strategies for RAG
00:09:07 - How Embedding Models Work
00:17:25 - Improving Search with Re-ranking
00:26:52 - SQL vs Vector Database Architecture
00:35:48 - Claude Code & Pinecone Assistant Demo
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