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长文本向量模型在4K Tokens 之外形同盲区?-首席AI分享圈

Long Text Vector Modeling a Blind Spot Beyond 4K Tokens?

NoLiMA, released in February 2025, is a Large Language Model (LLM) method for assessing long text comprehension. Unlike traditional Needle-in-a-Haystack (NIAH) tests, which rely on keyword matching, NoLiMA is characterized by carefully designed questions and key messages that force...

LangChain vs. LangGraph:官方告诉你该如何选择-首席AI分享圈

LangChain vs. LangGraph: The Officials Tell You What to Choose

The field of generative AI is currently evolving rapidly, with new frameworks and technologies emerging. Therefore, readers need to be aware that the content presented in this paper may be time-sensitive. In this paper, we will take an in-depth look at the two dominant frameworks for building LLM applications, LangChain and LangGraph, and analyze their strengths and weaknesses,...

MCP Server、Function Call 与 Agent 的协同与差异-首席AI分享圈

Synergies and Differences between MCP Server, Function Call and Agent

Understanding the three key concepts of MCP Server, Function Call, and Agent is essential in the burgeoning field of Artificial Intelligence (AI), especially Large Language Modeling (LLM). They are the cornerstones of an AI system, and each has a unique and interrelated role to play. A deeper understanding of it...

GRPO 如何在“时间线索”游戏中超越 o1、o3-mini 及 R1-首席AI分享圈

How GRPO outdid the o1, o3-mini and R1 in the game "Clue of Time".

In recent years, the field of Artificial Intelligence has made significant progress in its reasoning capabilities. After OpenAI demonstrated the powerful inference potential of large-scale language models (LLMs) last year, organizations such as Google DeepMind, Alibaba, DeepSeek, and Anthropic have been quick to follow suit, using reinforcement learning (RL) techniques to train...

大模型关键参数解读:Token、上下文长度与输出限制-首席AI分享圈

Interpreting the key parameters of the big model: Token, context length and output limits

Large-scale language modeling (LLM) is playing an increasingly important role in the field of artificial intelligence. In order to better understand and apply LLMs, we need to gain a deeper understanding of their core concepts. In this paper, we will focus on three key concepts, namely Token, Maximum Output Length, and Context Length, to help readers clear the understanding barriers so as to...

Agentic AI、AI Agents与Agents:概念解释-首席AI分享圈

Agentic AI, AI Agents and Agents: a conceptual explanation

Recently, the terms Autonomous AI (AI), AI Agents, and Agents have been popping up a lot. Frankly, despite being data analysts and scientists, industry players have been a bit resistant to these AI-related trends and buzzwords in the past...

AI Coding 编辑器:揭秘 Cline 的工作原理-首席AI分享圈

AI Coding Editor: Uncovering How Cline Works

In recent years, Artificial Intelligence (AI) technologies have triggered a profound change in the field of programming. From v0 and bolt.new to programming tools that integrate Agent technology such as Cursor and Windsurf, AI Coding shows great potential to play a key role in the software development process, especially in rapid proto...

什么是 Artifact 交互模式-首席AI分享圈

What is Artifact Interaction Mode

In the age of AI-assisted programming, we want AI to generate code that is not just static text, but can be parsed, edited, previewed, and even executed. This demand has given rise to a new interaction paradigm - Artifact. In this article, we will analyze Artifact from theoretical concepts to practical implementation....

RAG知识库必备的文档提取开源项目对比-首席AI分享圈

RAG knowledge base essential document extraction open source projects comparison

Recently in the intelligent customer service project to choose the RAG knowledge base of data processing tools, it re-looked at the current mainstream document processing projects, including olmOCR, Marker, MinerU, Docling, Markitdown, Llamaparse the 6 tools, and a brief comparison of them. A comprehensive view of the ...

DeepSeek R1 在 RAG 中的应用:实践经验总结-首席AI分享圈

DeepSeek R1 in RAG: Practical Experience Summary

DeepSeek R1 has demonstrated strong inference capabilities in its first release. In this blog post, we share in detail our experience using DeepSeek R1 to build a Retrieval-Augmented Generation (RAG) system, specifically for the legal document domain. We chose ...

Embedding Fine-Tuning: Principles, Processes and Practical Applications in the Legal Field

The purpose of this paper is to explain in detail the basic concepts, overall process and key techniques of Embedding fine-tuning from multiple perspectives, and to explore its practical role in the legal domain. Through this paper, readers will understand how to fine-tune pre-trained Embedding models using specialized data in the legal domain, so as to enhance the legal...

SPO:自监督提示词优化-首席AI分享圈

SPO: Self-monitoring prompt word optimization

Abstract Well-designed prompts are essential to enhance the reasoning capabilities of large language models (LLMs) while aligning their outputs with the task requirements of different domains. However, manually designing hints requires expertise and iterative experimentation. Existing hint optimization methods aim to automate this process, but they heavily ...

DeepSearch 与 DeepResearch 的设计和实现-首席AI分享圈

Design and Implementation of DeepSearch and DeepResearch

It's only February, and Deep Search is already looming as the new search standard for 2025. Giants like Google and OpenAI have unveiled their "Deep Research" products in an effort to capitalize on this wave of technology. (We're proud to be releasing our...

LangChain官方发布:探索提示词优化技巧-首席AI分享圈

LangChain Official Release: Explore Tips for Cue Word Optimization

By Krish Maniar and William Fu-Hinthorn When writing cue words, we try to communicate our intentions to Large Language Models (LLMs) so that they can apply these instructions on complex data. However, it is not easy to clearly express all the nuances at once. Prompts are usually engineered by hand ...

一张图解释清楚构建RAG系统全貌-首席AI分享圈

One diagram explains the whole picture of building a RAG system.

This diagram clearly depicts the architectural blueprint of a modern, complex Question Answering (QA) or Retrieval-Augmented Generation (RAG) system. It begins with the user asking the question and continues through the final generation of the answer, showing in detail the intermediate...

10大海外无代码 AI Agent 平台:快速构建企业级智能应用-首席AI分享圈

Top 10 Overseas No-Code AI Agent Platforms: Rapidly Build Enterprise-Class Intelligent Applications

At the center of the wave of artificial intelligence, AI Agent (Intelligent Body) is evolving at an amazing speed, just like the intelligent assistants coming out of sci-fi movies, quietly penetrating into every corner of the enterprise. They are no longer unattainable future concepts, but the secret weapon for enterprises to improve efficiency, optimize processes, and win in the market. 2...

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