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Practical AI
Open source, on-disk vector search with LanceDB
Dec 19, 2023
Chang She, co-founder and CEO of LanceDB, shares insights on their innovative open-source vector search solution. He discusses how LanceDB’s columnar database structure supports serverless deployments, leading to cost savings without sacrificing performance. The conversation delves into the integration of Rust for improved safety and efficiency, and the exciting possibilities of language agnosticism in AI tools. Chang also touches on future applications of AI in autonomous technologies, highlighting the evolving landscape of data management.
41:51
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Quick takeaways
- LanceDB is an open-source embedded vector database that simplifies integration into various workflows and scenarios.
- LanceDB's columnar storage layer and disk-based vector indices enable faster queries, scalable separation of compute and storage, and efficient access to large datasets.
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Introduction and Background of LanceDB
LanceDB is an open-source embedded vector database that aims to provide a single source of truth for companies working with computer vision and generative AI. The motivation behind LanceDB came from the observation that projects involving multimodal data, especially those related to computer vision, were challenging to maintain and put into production due to the lack of suitable data infrastructure. LanceDB was initially focused on building a new data storage layer for managing unstructured data more effectively. As the importance of generative AI grew, LanceDB evolved to provide features like semantic search, retrieval, and versioning, making it ideal for use cases such as chatbots, recommender systems, and code analysis tools. With its embedded architecture and flexible language support, LanceDB aims to simplify the integration of vector databases into various workflows and scenarios from small-scale data exploration to large-scale production deployments.
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