In the past two years, multi-task learning has been gradually replacing traditional single-task learning and becoming a mainstream research direction in the field of artificial intelligence. The reason is that multi-task learning allows us to gain as much AI capability as possible with minimal human input. For example, ChatGPT is a natural language generation model … Read more
Milvus is an open-source vector similarity search engine that supports the use of various AI models to vectorize unstructured data and provides search services for vector data. Milvus integrates widely used vector indexing libraries such as Faiss and Annoy, allowing developers to choose different indexing types for different scenarios. Using Milvus, one can develop a … Read more
The combination of AI and smart healthcare is an inevitable trend for future development. In recent years, the National Health Commission has promoted smart healthcare and AI technologies, such as intelligent triage, pre-consultation, and diagnostic assistance, to improve the efficiency of medical services and the accuracy of diagnoses, enhancing the patient experience. Quanzhentong is a … Read more
In recent years, the technology of Retrieval-Augmented Generation (RAG) has gained significant attention in the field of artificial intelligence. However, as demands have become more complex, traditional RAG has shown limitations in handling multi-step reasoning and external tool calls. To address this, Agentic RAG has emerged as an upgraded version of RAG, showcasing more powerful … Read more
The field of new drug development has long been known for its lengthy timelines, high costs, significant risks, and low return on investment. The average development cost for a new drug has reached $2.6 billion, with an average time frame of ten years. Despite such high R&D costs and prolonged timelines, there is no guarantee … Read more
So far, we have discussed the architecture of Agent systems, 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. Data … Read more
Today is January 18, 2025, Saturday, Beijing, clear weather. Let’s continue discussing RAG. Recently, there has been some work on Agentic RAG, which integrates autonomous agents to overcome the limitations of traditional RAG systems that perform well in knowledge retrieval and generation but struggle with dynamic, multi-step reasoning tasks, adaptability, and complex workflow orchestration. So, … Read more
Agentic CursorRules: A practical AI agent management tool that manages multiple AI assistants in Cursor through strict file tree partitioning and domain boundaries. It can divide the codebase into logical domains (such as frontend, API, database), generating specific scopes of work for each AI assistant, effectively preventing code conflicts and maintaining consistency in large projects. … Read more
Since April, I have been tracking the development of security startups, particularly in the areas related to agents and agent workflows. At that time, there were only a handful of security companies researching the concept of agents and making tangible progress. In just over six months, I have seen a significant increase in newly established … Read more
In this article, we will explore how Agentic RAG helps to address the limitations of traditional RAG. RAG Framework The RAG (Retrieval-Augmented Generation) framework operates in a specific sequence: Document -> Document Fragments -> Vector Database -> Fragment Retrieval (Top K) -> Large Language Model (LLM) However, this order encounters obstacles when handling certain types … Read more