Introduction on how to use LangGraph to improve RAG. Long press to follow “Python Learning Base”, join the reader group, and share more wonderful content. 1. Introduction LangGraph is the latest member of the LangChain, LangServe, and LangSmith series, aimed at building generative AI applications using LLMs. Remember, all these are independent packages and must … Read more
Introduction In the world of stock trading, investors rely on various tools and methods to make informed decisions. Fundamental analysis is a common approach that provides actionable insights by assessing a company’s financial health and stock performance. With advancements in artificial intelligence and machine learning, stock analysis can now be highly automated. In this article, … Read more
Introduction In today’s AI era, building an intelligent dialogue system is no longer just a simple Q&A model. Modern dialogue systems need to possess complex functionalities such as comprehensive context understanding, knowledge retrieval, and personalized recommendations. This article will use an educational scenario as an example to detail how to build a fully functional intelligent … Read more
LangGraph is a library developed by LangChainAI for creating workflows for agents and multi-agent systems. It offers the following core advantages: cycles, controllability, and persistence, which undoubtedly reduce the workload for agent developers. This article will highlight the key points and usage methods of LangGraph from my perspective during the development process. Basic Introduction The … Read more
● Understand what LangGraph is. ● Explore the basics of LangGraph for building stateful agents. ● Explore TogetherAI to access open-access models like DeepSeekCoder. ● Build an AI coding agent using LangGraph to write unit tests. This article is published as part of the Data Science Blog Marathon. What is LangGraph? LangGraph is an extension … Read more
Click 01 Muggle Society Follow the official account, and you won’t get lost in AI learning LangGraph is an important feature recently released by LangChain, marking its move towards a multi-agent framework. LangGraph is built on top of LangChain, helping developers easily create powerful agent runtimes. LangChain and its expression language (LCEL) provide technical support … Read more
Introduction The emergence of large language models (LLMs) has reshaped how AI systems interact with and interpret the world. Traditionally, single-agent architectures have been used to handle inputs, make decisions, and produce outputs. However, as AI systems scale to manage more complex, multi-step tasks, researchers and developers are increasingly turning to multi-agent systems and advanced … Read more
Using ToolNode for tool calls ToolNode is a LangChain Runnable that takes a graphical state (with a list of messages) as input and outputs a state update using the results of tool calls. It is designed to work out of the box with LangGraph’s pre-built ReAct agent, but it can also work with any StateGraph … Read more
How to Add and Use Subgraphs Subgraphs allow the construction of complex systems with multiple components, each of which is a graph. A common use case for using subgraphs is building multi-agent systems. The main issue when adding subgraphs is how the parent graph and the subgraph communicate, i.e., how they pass state to each … Read more
LangGraph is an innovative Graph Neural Network (GNN) technology designed to address the complex relationship modeling challenges in Natural Language Processing (NLP) tasks. Traditional NLP models often treat text data as linear sequences, overlooking the intricate relationships between entities within the text. In contrast, LangGraph constructs a graph structure to represent the entities and their … Read more