When using model-based decision-making non-deterministic systems (e.g., LLM-driven agents), it is useful to closely examine their decision-making processes: 🤔 Understanding Reasoning: Analyzing the steps that lead to successful outcomes. 🐞 Debugging Errors: Identifying where and why errors occur. 🔍 Exploring Alternatives: Testing different paths to discover better solutions. We refer to these debugging techniques as … Read more
How to Define the Input and Output Schema of a Graph By default, the StateGraph operates using a single schema, and all nodes should communicate using that schema. However, different input and output schemas can also be defined for the graph. When different schemas are specified, the internal schema will still be used for communication … Read more
How to Obtain Resources 1. Follow the public account below, and click【Like】 and 【View】 2. Click 【Get Course】 to obtain this material. Resources are from Baidu Cloud Disk:《Introduction and Practice of LangGraph Based on Large Model Agent》 Introduction and Practice of LangGraph Based on Large Model Agent In the field of artificial intelligence, with the … Read more
This article introduces a practical project that uses LangGraph to develop a RAG research multi-agent tool. This tool is designed to solve complex problems that require multiple sources and iterative steps to arrive at a final answer. It employs hybrid search and Cohere reordering steps to retrieve documents, and also includes a self-correction mechanism, including … Read more
In this tutorial, we will delve into how to build an intelligent Q&A system with conditional branching using the langgraph framework. We will analyze the code step by step, understand the functionality of each part, and explain the principles behind it. The ultimate goal is to enable you to create an AI assistant that can … Read more
LangGraph provides a hands-on approach to how humans can interact with agents. `LangGraph` is a library for building stateful, multi-participant applications using `LLM`, designed for creating agent and multi-agent workflows. Compared to other `LLM` frameworks, it has the following core advantages: loops, controllability, and persistence. `LangGraph` allows you to define processes that involve loops, which … Read more
Introduction With a single line of Cursor, develop a LangGraph program that can interact with any database, regardless of its structure! Preparation Download a copy of the LangGraph code, remember the location, and update to .cursorrules Prepare a .cursorrules file to store the following content Download the DB file and reference file main.py I provided, … Read more
How to Obtain 1. Follow the public account below, and click【Like】 and 【Looking】 2. Reply to the public account with【Course】】 to get this course This course is about Getting Started with LangGraph for Large Model Agents Getting Started with LangGraph for Large Model Agents Getting Started with LangGraph for Large Model Agents LangGraph, as a … Read more
Introduction In the field of artificial intelligence, Retrieval-Augmented Generation (RAG) systems have become common tools for handling simple queries and generating contextually relevant responses. However, with the growing demand for more complex AI applications, we need to go beyond these retrieval-capable systems. Thus, AI agents have emerged—these autonomous entities can perform complex multi-step tasks while … Read more
The human-computer interaction feature allows us to involve the user in the decision-making process of the graph. The following guide demonstrates how to implement human-computer interaction workflows in the graph. The human-computer interaction workflow integrates user input into automated processes, allowing for decision-making, validation, or correction at critical stages. This is particularly useful in applications … Read more