Generation AI
Generation AI Podcast Episode #6 Ram Sriharsha
Jan 22, 2024
Ram Sriharsha, VP of Engineering at Pinecone, discusses vector databases and LLMs, including Hybrid Search and both sparse and dense retrieval. He introduces the concept of retrieval augmented generation (RAG) to enhance language models with knowledge from vector databases. The podcast explores the importance of search relevancy in AI chatbot development and the impact of longer sequence windows in language models. Challenges of cloud native platforms and the future possibilities of machine learning models are also discussed.
27:57
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Quick takeaways
- Vector databases are essential for generative AI applications, providing efficient search of vector embeddings and capturing semantic similarity.
- Retrieval-Augmented Generation (RAG) enhances the performance of large language models by leveraging state-of-the-art information retrieval techniques using vector databases.
Deep dives
The Emergence of Vector Databases for Generative AI
Vector databases are becoming crucial for generative AI applications. They allow the efficient search of vector embeddings, capturing semantic similarity between queries and a text corpus. Vector databases act as a knowledge layer complementing the intelligence and orchestration layers provided by large language models (LLMs). Traditional databases are not ideal for generative AI applications as they are designed for structured data and queries. However, as the scale, cost, and search quality requirements increase, purpose-built vector databases become essential.
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