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코드 따라하기 ↗Engineering Retrieval Augmented Generation Applications for the Enterprise with Pinecone
Roie, a Staff Developer Advocate at Pinecone, shows you how to build an enterprise-quality RAG application.
Roie Schwaber-Cohen
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Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.관련

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High Performance Generative AI Applications with Ram Sriharsha, CTO at Pinecone
Richie and Ram explore common use-cases for vector databases, RAG in chatbots, static vs dynamic data, choosing language models, knowledge graphs, implementing vector databases, the future of LLMs and much more.
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Building Intelligent Applications with Pinecone Canopy: A Beginner's Guide
Explore using Canopy as an open-source Retrieval Augmented Generation (RAG) framework and context built on top of the Pinecone vector database.
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Semantic Search with Pinecone and OpenAI
A step-by-step guide to building semantic search applications using OpenAI and Pinecone in Python.



Retrieval Augmented Generation is a critical component of search and chatbots. By using vector databases like Pinecone, you can retrieve facts to improve the quality of your results. In an enterprise scenario, there are more considerations. Where you put your data, how you control access, and how you monitor the quality of your results all become essential.
In this session, Roie, a Staff Developer Advocate at Pinecone, shows you how to build an enterprise-quality RAG application. You'll build a simple application while learning about enterprise RAG architecture: how to use namespaces, how to control access to your data, and how to measure performance.
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