FutureAI的动态

AI apps = new architecture, new complexity: APIs, containers, vector DBs, and graph DBs. Operational impact? Sky-high. ?? From delivery to security, shared edge services are the key to simplifying the AI revolution. Let’s break it down. https://ms.spr.ly/6040oNN9u

In this article, we explain how combining Forward-Looking Active RAG (FLARE) with Knowledge Graphs can help build precise and accurate AI applications. Here's the gist... ?? The entire architecture is built on an open source stack: Architecture: Neo4j (Knowledge Graph), LlamaIndex (Framework) and Llama3 (LLM) Embedding Model: BAAI/bge-small-en-v1.5 The methodology is fairly simple - but quite powerful. Best part is -- a company can build an AI application in a fully data sovereign way - that is, without sharing data with any platform, and keeping control of their AI stack. https://lnkd.in/gBAeMteC

AI apps = new architecture, new complexity: APIs, containers, vector DBs, and graph DBs. Operational impact? Sky-high. ?? From delivery to security, shared edge services are the key to simplifying the AI revolution. Let’s break it down. https://ms.spr.ly/6040o0k04

AI apps = new architecture, new complexity: APIs, containers, vector DBs, and graph DBs. Operational impact? Sky-high. ?? From delivery to security, shared edge services are the key to simplifying the AI revolution. Let’s break it down. https://ms.spr.ly/6044oIiHY

With vectors and AI a better, game-changing software architecture emerges. Instead of keeping a generic base, instead, you can have a specialized dataset act as a base. If I were to give a talk on AI + Vectors, this would be one of them. Not going to, because I need to keep my head out of too technical land for now.

AI apps = new architecture, new complexity: APIs, containers, vector DBs, and graph DBs. Operational impact? Sky-high. ?? From delivery to security, shared edge services are the key to simplifying the AI revolution. Let’s break it down. https://ms.spr.ly/6044oIi16

AI apps = new architecture, new complexity: APIs, containers, vector DBs, and graph DBs. Operational impact? Sky-high. ?? From delivery to security, shared edge services are the key to simplifying the AI revolution. Let’s break it down. https://ms.spr.ly/6046oIdGe

AI apps = new architecture, new complexity: APIs, containers, vector DBs, and graph DBs. Operational impact? Sky-high. ?? From delivery to security, shared edge services are the key to simplifying the AI revolution. Let’s break it down. https://ms.spr.ly/6041oaF3U

Agentic architecture reminds me of the Object Oriented Design Patterns exploration of the 1990s. But instead of classes, and the myriad ways they can be defined, "agents" are the new containers with seemingly infinite combinatorial applications. In the iDialogue AI domain alone there are emerging Agent patterns for System agents vs User agents. Dispatchers. Extractors. Classifiers. Crawlers. Once we define discrete agent behaviors, the need arises for multi-agent orchestration. The Anthropic article below is a great read that describes how LLMs can be utilized in various workflow patterns. https://lnkd.in/gN88qmz4

AI Overviews present a significant opportunity to deliver value to searchers. We recommend studying search results to grasp query intent, bridging content gaps with helpful information, implementing structured data markup, optimizing for search-friendly architecture, and continuously monitoring content performance.