Quick Understanding of Generative AI

Quick Understanding of Generative AI

Recommendation This article introduces the book “Generative AI in Action“, published by O’Reilly in 2024, authored by Amit Bahree from Microsoft. The book mainly covers knowledge related to generative AI, including LLMs, prompt engineering, model fine-tuning, RAG, vector databases, etc. The author provides numerous code examples, guiding readers into the world of generative AI through … Read more

Simulating WWII and Warring States with LLMs: Will It Change History?

Simulating WWII and Warring States with LLMs: Will It Change History?

New Intelligence Report Editor: Lumina [New Intelligence Overview] Is there another choice that allows humanity to perfectly navigate history? Could the results of simulations of historical wars conducted by scholars from the University of Michigan and Rutgers University using LLMs be our reference answer? “Can we avoid war at the crossroads of history?” This question … Read more

Tsinghua’s ToolLLM Connects 16000+ Real APIs, Approaching ChatGPT Performance

Tsinghua's ToolLLM Connects 16000+ Real APIs, Approaching ChatGPT Performance

New Intelligence Report Editor: Hao Kun, Peach [New Intelligence Guide] ChatGPT’s capabilities have been unlocked, and its performance has been enhanced after adding plugin features. This is true for all large models. Tsinghua’s Wall Intelligence connects over 16,000 real APIs to large models, matching ChatGPT’s performance. Recently, open-source large language models (LLMs) have made rapid … Read more

Understanding LLM, GPT, AIGC, AI Agent, and Agentic AI

Understanding LLM, GPT, AIGC, AI Agent, and Agentic AI

Introduction 2024 is set to be a year of full bloom for AI Large Models, with major companies releasing their own large model application products, such as: • Tencent’s Yuanbao • Alibaba’s Tongyi Qianwen • Byte’s Doubao • Baidu’s Wenxiaoyan • The Dark Side of the Moon’s Kimi • And many more It’s a dazzling … Read more

CMAT: A 1.8B Agent Model Comparable to GPT-3.5

CMAT: A 1.8B Agent Model Comparable to GPT-3.5

CMAT: A 1.8B Agent Model Comparable to GPT-3.5 Release Date: April 2, 2024 Agent Artificial Intelligence Abstract Open large language models (LLMs) have greatly advanced natural language processing technology, excelling in numerous tasks. However, the effective use of LLMs still relies on human guidance to ensure smooth dialogue. Agent tuning techniques play an important role … Read more

Understanding the MemGPT Design of aZent Framework

Understanding the MemGPT Design of aZent Framework

Before getting into the main topic, let’s chat a bit. Many friends have seen me coding in my spare time. Sometimes I wonder why I do this; actually, my love for code has reached a somewhat obsessive level. There might be some bragging involved. Without further ado, everything has been revolving around agents lately, and … Read more

Phidata Multimodal Multi-Agent Framework Overview

Phidata Multimodal Multi-Agent Framework Overview

The open-source agent series focuses on introducing currently available open-source agent frameworks in the market, such as CrewAI, AutoGen, LangChain, phidata, Swarm, etc., discussing their advantages, disadvantages, features, effects, and usage. Interested friends can follow the public account “XiaozhiAGI” for continuous updates on cutting-edge AI technologies and products, such as RAG, Agent, Agentic workflow, AGI. … Read more

Advanced Self-Reflective RAG

Advanced Self-Reflective RAG

Overview As most LLMs are only trained periodically on a large amount of public data, they cannot access the latest information and/or private data. Retrieval-Augmented Generation (RAG) is a core paradigm for developing applications with LLMs, addressing this issue by connecting to external data sources. A basic RAG pipeline includes embedding user queries, retrieving relevant … Read more

RAG Series 07: Building Indexes and Using Large Models for QA with PDF Tables

RAG Series 07: Building Indexes and Using Large Models for QA with PDF Tables

Effectively parsing and understanding tables in unstructured documents remains a significant challenge when designing RAG solutions. This is especially difficult in cases where tables exist in image formats, such as scanned documents. These challenges include several aspects: The complexity of scanned or image documents, such as diverse structures, the presence of non-text elements, and the … Read more