This guide explains why DeepSeek is a valuable AI assistant, outlines the challenges of high demand, and provides step‑by‑step instructions for creating, configuring, testing, and publishing a DeepSeek AI assistant within the DingTalk platform to ensure stable access.
This article introduces Spring AI, explains its core features for integrating various AI models, and walks through creating a Spring Boot chat application that connects to the DeepSeek model using both synchronous and streaming endpoints.
This guide demonstrates how to create a Spring Boot backend that integrates DeepSeek's large language model via the Spring AI OpenAI starter, covering project setup, dependency configuration, API key management, and a sample controller that provides AI-powered chat responses such as weather forecasts.
This article introduces LangGraph, the next‑generation framework built on LangChain for constructing complex, stateful AI applications, compares it with LangChain, showcases real‑world deployments, and provides a step‑by‑step Python tutorial for building a smart customer‑service chatbot with looped reasoning, tool integration, and human‑in‑the‑loop support.
This guide walks entry‑level developers through building a logistics‑focused QA bot by first embedding documents for vector similarity search, then adding retrieval‑augmented generation, fine‑tuning a small model, integrating hybrid checks, and optimizing deployment with feedback loops to achieve fast, accurate, out‑of‑scope‑aware answers.
The study presents an asynchronous chat‑assistant for second‑hand e‑commerce that uses intent recognition, price‑strength data, and filtered messaging to generate friendly bargaining and greeting scripts, achieving a 4 % rise in reply and conversion rates, and outlines plans to add more intents and richer product extraction.
The article reviews Didi’s research on a Multi‑turn Response Triggering Model (MRTM) that uses self‑supervised learning and asymmetric self‑attention to decide when a customer‑service chatbot should reply, achieving higher accuracy and recall than rule‑based and supervised baselines while remaining efficient enough for production deployment.
The article outlines the evolution, architecture, and core value of intelligent customer service systems—detailing their GUI‑based chatbot interface, triage and dialogue modes, knowledge‑base management, and operator benefits—while highlighting future trends such as richer human‑like interactions, 5G‑enabled channels, and continuous feedback‑driven improvement.
The article details Shopee Chatbot’s end‑to‑end development of a dropdown recommendation feature, describing the retrieve‑then‑rank architecture with BM25 and vector recalls, multilingual pre‑training and distillation, DeepFM‑based ranking, experimental gains in CTR and conversion, deployment infrastructure, business impact, and future enhancements.
The document describes the design, implementation, and operational results of Yunli XiaoZhi, an AI‑driven portable knowledge‑base and data‑analysis chatbot that consolidates SOPs, manuals, and real‑time information for logistics staff, using LangChain‑based RAG, vector databases, and large‑model prompting to improve query efficiency, proactive alerts, and reporting across multiple user groups.
This article explains the architecture and core technologies of Ctrip’s intelligent travel customer service, covering NLU, dialogue state tracking, policy learning, intent and slot extraction, multi‑round task bots, and the supporting platform for deployment and future multimodal extensions.
This article explains the implementation of open-domain chatbots for customer service, comparing retrieval‑based and generative seq2seq approaches, describing hybrid methods that first attempt constrained retrieval before falling back to generation, and discusses training data, keyword extraction, and performance observations.
OpsRobot integrates development tools into a chat‑based interface, using custom plugins and APIs to automate low‑efficiency, error‑prone operational tasks, thereby streamlining workflows, improving efficiency, and enabling future capabilities such as self‑healing and automated scaling.
This article presents a reinforcement‑learning‑based approach to improve lead‑capture efficiency of a task‑oriented chatbot used in local services, detailing the system architecture, RL algorithms (DQN/DDQN), data construction, model training, offline and online evaluation, and the resulting commercial gains.
This article details the development and continuous optimization of 58.com’s Yellow Pages smart chat assistant, covering background, metrics, model improvements for QABot and TaskBot, slot extraction, quality assessment, and future directions, resulting in near‑human conversion rates and significant operational savings.
This article introduces the basic concepts of supervised and unsupervised machine learning, explains the core mechanisms of large language models such as Transformers, and provides a step‑by‑step guide with code to build a simple chatbot using Alibaba's Tongyi LLM via Spring Boot.
This article explains how to create an automated reading companion bot for "Dream of the Red Chamber" using the Coze platform, covering knowledge‑base setup, workflow design, JavaScript date handling, weather integration, language‑model note generation, and deployment steps.
This article recounts the author's participation in a Shenzhen AI Hackathon, detailing the development of an interactive novel‑character chatbot using the Coze platform, describing the workflow design, technical challenges, model choices, knowledge‑base construction, and the final demo and award outcomes.
This tutorial walks through creating a Slackbot using the new Bolt API with Node.js, covering workspace setup, app creation, OAuth scopes, server configuration, code installation, environment variables, socket mode, slash commands, event subscriptions, a simple JSON knowledge‑base, and deployment steps.
This tutorial demonstrates how to create an AI-driven Spring Boot application by integrating DeepSeek's large language model, covering project setup, dependency configuration, API key management, and implementing a REST controller that provides weather forecasts via a conversational interface.