Experimental News Clipping Site

Tag: embeddings

  • Simon Willison’s Weblog: Introducing Contextual Retrieval

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    Source URL: https://simonwillison.net/2024/Sep/20/introducing-contextual-retrieval/#atom-everything Source: Simon Willison’s Weblog Title: Introducing Contextual Retrieval Feedly Summary: Introducing Contextual Retrieval Here’s an interesting new embedding/RAG technique, described by Anthropic but it should work for any embedding model against any other LLM. One of the big challenges in implementing semantic search against vector embeddings – often used as part of…

  • Cloud Blog: Next-gen search and RAG with Vertex AI

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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/using-vertex-ai-to-build-next-gen-search-applications/ Source: Cloud Blog Title: Next-gen search and RAG with Vertex AI Feedly Summary: Generative AI has fundamentally transformed how the world interacts with information, and the search industry is no exception. The search landscape is changing rapidly, driven by the rise of large language models (LLMs). Whether they’re interacting with their company’s…

  • Hacker News: How does cosine similarity work?

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    Source URL: https://tomhazledine.com/cosine-similarity/ Source: Hacker News Title: How does cosine similarity work? Feedly Summary: Comments AI Summary and Description: Yes Summary: The text provides an in-depth exploration of cosine similarity in the context of comparing large language model (LLM) embeddings. It discusses the mathematical principles behind cosine similarity, its significance in measuring vector similarity, and…

  • Hacker News: Engineering over AI

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    Source URL: http://martinantos.com/engineering-over-ai/ Source: Hacker News Title: Engineering over AI Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the challenges and considerations in developing code-generating LLM agents, emphasizing the necessity of addressing engineering fundamentals rather than solely relying on AI capabilities. This perspective highlights a critical shift back to foundational engineering…

  • Hacker News: Graph Language Models

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    Source URL: https://aclanthology.org/2024.acl-long.245 Source: Hacker News Title: Graph Language Models Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the development of Graph Language Models (GLMs), which combine the capabilities of traditional Language Models (LMs) and Graph Neural Networks (GNNs) to enhance understanding and processing of knowledge graphs alongside text inputs. This…

  • Simon Willison’s Weblog: OpenAI: Improve file search result relevance with chunk ranking

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    Source URL: https://simonwillison.net/2024/Aug/30/openai-file-search/#atom-everything Source: Simon Willison’s Weblog Title: OpenAI: Improve file search result relevance with chunk ranking Feedly Summary: OpenAI: Improve file search result relevance with chunk ranking I’ve mostly been ignoring OpenAI’s Assistants API. It provides an alternative to their standard messages API where you construct “assistants", chatbots with optional access to additional tools…

  • Cloud Blog: A multimodal search solution using NLP, BigQuery and embeddings

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    Source URL: https://cloud.google.com/blog/products/data-analytics/multimodel-search-using-nlp-bigquery-and-embeddings/ Source: Cloud Blog Title: A multimodal search solution using NLP, BigQuery and embeddings Feedly Summary: Today’s digital landscape offers a vast sea of information, encompassing not only text, but also images and videos. Traditional enterprise search engines were primarily designed for text-based queries, and often fall short when it comes to analyzing…

  • Hacker News: Classifying All of the Pdfs on the Internet

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    Source URL: https://snats.xyz/pages/articles/classifying_a_bunch_of_pdfs.html Source: Hacker News Title: Classifying All of the Pdfs on the Internet Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses classifying a massive dataset of PDFs obtained from the Common Crawl, particularly focusing on a customized approach utilizing large language models (LLMs), embeddings, and traditional machine learning techniques…