The Machine Heart Column The Machine Heart Editorial Team The PreFLMR model is a general-purpose pre-trained multimodal knowledge retriever that can be used to build multimodal RAG applications. The model is based on the Fine-grained Late-interaction Multi-modal Retriever (FLMR) published at NeurIPS 2023 and has undergone model improvements and large-scale pre-training on M2KR. Paper link: … Read more
Introduction It is an open-source RAG (Retrieval-Augmented Generation) engine built on deep document understanding. It mainly provides a streamlined RAG workflow for enterprises and individuals of various sizes, leveraging large language models (LLMs) to handle users’ diverse complex format data, offering reliable Q&A and well-founded citations. Its main features include: 1. Deep Document Understanding: Capable … Read more
Introduction In the previous article, we introduced the concept of Agentic RAG, emphasizing how it extends traditional retrieval-augmented generation (RAG) frameworks by integrating autonomous agent capabilities. In this issue, we delve deeper into LangGraph, an innovative framework for coordinating logical workflows. LangGraph enables the creation of multi-agent systems with complex reasoning capabilities, making it an … Read more
Author:DeanSacoransky and Deniz Askin The Impact of DeepSeek-r1 DeepSeek publicly released the r1 model less than a month ago. Suddenly, everyone can publicly use a powerful reasoning model for application and model development. In this article, we propose a simple intelligent agent workflow that empowers DeepSeek-r1 to perform information retrieval in an intelligent agent manner! … Read more
In the previous article, we practiced a [RAG (Retrieval Augmented Generation) system implemented with `langgraph`]. This article will build upon that by adding an automatic chat history logging feature. Additionally, we will use an `Agent` to achieve almost the same functionality. Let’s explore the differences between implementing the `RAG system` using `langgraph` and `Agent`. – … Read more
▼Recently, there have been many live broadcasts,make an appointment to ensure you gain something Today’s live broadcast:《Building Agent Industrial Applications with RAG and GPTs Practical Implementation》 —1— LangGraph Technical Architecture Interpretation LangGraph is a tool for building stateful and multi-role Agent applications. It is not a new framework independent of Langchain, but rather an extension … Read more
CrewAI’s architecture goes far beyond static workflows; it supports intelligent, context-aware, and collaborative AI agents. Translated fromHow CrewAI Enables AI Agents as Collaborative Team Members, author Janakiram MSV. In the first part of this series, we introduced CrewAI and mapped its features against key attributes of AI agents. Now, we will take a closer look … Read more
So far, we have discussed the architecture of the Agent system, how to organize the system into sub-Agents, and how to build a unified mechanism to standardize communication. Today, we will turn our attention to the tool layer and one of the most important aspects you need to consider in Agent system design: data retrieval. … Read more
The “Memory External Hard Drive” of Large Models Two years after ChatGPT sparked the wave of generative AI, developers are gradually realizing the inherent limitations of large language models (LLMs) — they are like scholars with extraordinary memory but can only recite the knowledge they remembered during training. When faced with real-time data queries or … Read more
Click 👇🏻 to follow, article from 🙋♂️ Friends who want to join the community can see the method at the end of the article for group communication. “ LLMs have revolutionized the field of AI by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their ability to … Read more