* Query compression involves taking the user's query and the preceding conversation, then asking the LLM * to "compress" this into a single, self-contained query.

* 1. Take the user's query as-is. * 2. Embed it using an embedding model. * 3. Use the query's embedding to search an embedding store (containing small segments of your documents) * for the X most ...

Lynnae is a journalist with over five years of experience covering all things tech. During that time, she's reported on a wide range of topics,… A new class-action lawsuit claims OpenAI’s ChatGPT ...

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In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an ...

Writing code that interacts with LLM services requires bridging two different worlds. Use these tips and techniques to bind the AI model to the logic of your app.

Abstract: Event logs record the execution of business processes as sequences of timestamped events. Most predictive process monitoring methods still learn a separate model per log: when the process, ...

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Questions surrounding the loss of an "MQ-1" to Iran highlight the possibilities and pitfalls of sending the Predator back ...

An international team led by researchers at University of California San Diego and University of California, Riverside has developed a free, web-based platform designed to make public metabolomics ...

The CERT Division is a leader in cybersecurity. We partner with government, industry, law enforcement, and academia to improve the security and resilience of computer systems and networks. We study ...