AI knowledge Page 3

OpenAI Function calling V2 Features The core goal of Function calling V2 is to give OpenAI models the ability to interact with the outside world, which is reflected in the following two core functions: Fetching Data - A function calling implementation of RAG: Essentially RAG (Retrieve Augmented...

Basic Concepts In the field of information technology, retrieval refers to the process of efficiently locating and extracting relevant information from a large dataset (usually documents, Web pages, images, audio, video, or other forms of information) in response to a user's query or need. Its core goal is to find information that is relevant to the use...

Agent AI: Surveying the Horizons of Multimodal Interaction Originally published at https://ar5iv.labs.arxiv.org/html/2401.03568 Abstract Multimodal AI systems are likely to be ubiquitous in our daily lives. Making these systems more interactive a...

GraphReader: a graph-based intelligence that enhances long text processing for large language models Graphic Expert: like a tutor who is good at making mind maps, it transforms lengthy text into a clear knowledge network, so that the AI can easily find each key point needed for an answer as if it were exploring along a map, and effectively gr...

CAG (Cache Augmented Generation) that is 40 times faster than RAG (Retrieval Augmented Generation).CAG revolutionizes knowledge acquisition: instead of retrieving external data in real time, all knowledge is pre-loaded into the model context. It's like condensing a huge library into an on-the-go toolkit that can be used when needed...

By Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic Originally published at https://www.kaggle.com/whitepaper-agents Table of Contents Introduction What is an Intelligent Body? Models Tools Orchestration Layers Intelligent Bodies and Models Cognitive Architecture: How Intelligent Bodies Work Tools ...

Retrieval Augmented Generation (RAG) is becoming one of the most popular applications for Large Language Models (LLMs) and vector databases.RAG is the process of augmenting the input to a LLM with context retrieved from vector databases (e.g., Weaviate).The RAG application passes...

First, LLMs already have strong capabilities, why do we still need RAG (Retrieval Augmented Generation)? Although LLMs have demonstrated significant capabilities, the following challenges still warrant attention: Illusion problem: LLMs use a statistically based probabilistic approach to generate text word by word, a mechanism that inherently leads to the possibility of...

o3 is here to share some personal insights. Progress on Test-time Scaling Law has been much faster than we thought. But I'd like to say that the path is actually a bit convoluted - it's OpenAI's way of saving the country from the curve in its pursuit of AGI. Reinforcement Learning and Shortcut Thinking For ...

Vector Embedding is the core of current Retrieval Augmented Generation (RAG) applications. They capture semantic information of data objects (e.g., text, images, etc.) and represent them as arrays of numbers. In current generative AI applications, these vector Embedding are usually generated by Embedding models. How to apply for RAG ...

PREFACE Over the past two years, Retrieval-Augmented Generation (RAG, Retrieval-Augmented Generation) technology has gradually become a core component for enhancing intelligences. By combining the dual capabilities of retrieval and generation, RAG is able to introduce external knowledge, thus providing more applications of large models in complex scenarios...

Agent The most common translation I've seen so far is "intelligent body", but the direct translation is "agent". What does Agentic translate to? I feel that a word like "agentic" is more appropriate. So in order not to confuse the readers, I use English directly in this article. With the development of LLM, the ability of AI...