Cloud Security Alliance News Clipping Site

Tag: Vector Embeddings

  • Simon Willison’s Weblog: Binary vector embeddings are so cool

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    Source URL: https://simonwillison.net/2024/Nov/11/binary-vector-embeddings/#atom-everything Source: Simon Willison’s Weblog Title: Binary vector embeddings are so cool Feedly Summary: Binary vector embeddings are so cool Evan Schwartz: Vector embeddings by themselves are pretty neat. Binary quantized vector embeddings are extra impressive. In short, they can retain 95+% retrieval accuracy with 32x compression and ~25x retrieval speedup. It’s so…

  • Hacker News: Binary vector embeddings are so cool

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    Source URL: https://emschwartz.me/binary-vector-embeddings-are-so-cool/ Source: Hacker News Title: Binary vector embeddings are so cool Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses binary quantized vector embeddings, emphasizing their ability to retain high accuracy while dramatically reducing storage size for machine learning applications. This topic is particularly relevant for AI and infrastructure security…

  • Cloud Blog: Getting started with NL2SQL (natural language to SQL) with Gemini and BigQuery

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    Source URL: https://cloud.google.com/blog/products/data-analytics/nl2sql-with-bigquery-and-gemini/ Source: Cloud Blog Title: Getting started with NL2SQL (natural language to SQL) with Gemini and BigQuery Feedly Summary: The rise of Natural Language Processing (NLP) combined with traditional Structured Query Language (SQL) has given rise to an exciting new technology known as Natural Language to SQL, or NL2SQL, which translates questions phrased…

  • Cloud Blog: How to simplify building RAG pipelines in BigQuery with Document AI Layout Parser

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    Source URL: https://cloud.google.com/blog/products/data-analytics/bigquery-and-document-ai-layout-parser-for-document-preprocessing/ Source: Cloud Blog Title: How to simplify building RAG pipelines in BigQuery with Document AI Layout Parser Feedly Summary: Document preprocessing is a common hurdle when building retrieval-augmented generation (RAG) pipelines. It often requires Python skills and external libraries to parse documents like PDFs into manageable chunks that can be used to…

  • Hacker News: DBT for Unstructured Data – DataChain

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    Source URL: https://github.com/iterative/datachain Source: Hacker News Title: DBT for Unstructured Data – DataChain Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text provides an overview of DataChain, a Python-based data-frame library designed to facilitate the organization and processing of unstructured data, maintaining strong relevance to professionals involved in AI, data management, and cloud…

  • Hacker News: Vector databases are the wrong abstraction

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    Source URL: https://www.timescale.com/blog/vector-databases-are-the-wrong-abstraction/ Source: Hacker News Title: Vector databases are the wrong abstraction Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the complexities and challenges faced by engineering teams when integrating vector databases into AI systems, particularly in handling embeddings sourced from diverse data. It introduces the concept of a “vectorizer”…

  • Hacker News: The PlanetScale vectors public beta

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    Source URL: https://planetscale.com/blog/announcing-planetscale-vectors-public-beta Source: Hacker News Title: The PlanetScale vectors public beta Feedly Summary: Comments AI Summary and Description: Yes Summary: PlanetScale has launched an open beta for its vector search and storage capabilities, which integrate with its MySQL database. The new feature allows for the simultaneous management of vector data and relational data, ensuring…

  • Hacker News: Searching a Codebase in English

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    Source URL: https://www.greptile.com/blog/semantic Source: Hacker News Title: Searching a Codebase in English Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a detailed challenge faced by an AI-driven tool designed to execute semantic searches over codebases. It highlights the difficulties of applying natural language processing techniques used for text to the semantics…