The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669

Jan 29, 2024
35:29
Snipd AI
Ram Sriharsha, VP of engineering at Pinecone, discusses the advantages and complexities of retrieval augmented generation (RAG) with vector databases. He talks about building and deploying real-world RAG-based applications, as well as Pinecone's new serverless offering that enables on-demand data loading, flexible scaling, and cost-effective query processing. Ram shares his perspective on the future of vector databases in helping enterprises deliver RAG systems.
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Podcast summary created with Snipd AI

Quick takeaways

  • The combination of vector databases and large language models (LLMs) in Retrieval Augmented Generation (RAG) offers a more effective and comprehensive solution for knowledge-intensive tasks in generative AI applications.
  • Pinecone's serverless architecture and improvements in partitioning strategies address scalability, cost, and quality challenges of vector databases, making them more accessible, cost-effective, and flexible for developers in generative AI workflows.

Deep dives

Pinecone Serverless: An Innovation in Vector Databases

Pinecone Serverless, a new product by Pinecone, offers a trusted Vector Database for ambitious AI applications. It provides key innovations such as up to 50 times lower costs, incremental indexing for consistently fresh results, fast search without sacrificing recall, powerful performance with a multi-tenant compute layer, and zero configuration or ongoing management. This development addresses the challenges of scalability, cost, and quality in generative AI workflows. Additionally, Pinecone Serverless enables on-demand queries, making it more flexible and cost-effective. The update also introduces improvements in partitioning strategies, allowing for more efficient retrieval of relevant data, while maintaining compatibility with existing APIs.

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