This article explains how to obtain a DeepSeek API key, configure Spring AI with the appropriate base URL and model, and provides Java code examples for both synchronous and streaming chat interactions using the DeepSeek large‑language model.
This article explains how to integrate DeepSeek's large language models—both the chat‑oriented deepseek‑chat and the reasoning‑focused deepseek‑reasoner—into a Spring AI application, covering API key setup, base‑URL configuration, model selection, and providing full code examples for dependency, configuration, and a simple chat controller.
This article explains how to create a high‑performance multi‑model chat agent on the Dify platform by combining DeepSeek‑R1 for reasoning and Gemini for answer generation, covering the underlying principles, configuration steps, API integration, performance benchmarks, and practical deployment guidance.
This tutorial explains what Spring AI is, walks through creating a Spring Boot project with Maven, adding the necessary dependencies, writing a simple controller that forwards user messages to a local DeepSeek model, configuring the application, and testing the AI‑powered endpoint.
This article introduces ChatBI, NetEase’s AI‑driven BI solution, detailing its product architecture, the opportunities and challenges AI brings to data analysis, the underlying NL2SQL model, performance‑optimizing techniques such as materialized views, open integration capabilities, and several enterprise deployment cases.
This article presents a comprehensive overview of OPPO's XiaoBu Assistant, detailing its research background, chat skill architecture, evolution from retrieval and rule‑based methods to generative models, industry model comparisons, decoding and ranking strategies, safety mechanisms, performance optimizations, and evaluation results.
This talk analyzes the technical and practical challenges of current chatbots, introduces the next‑generation paradigm of virtual life, and details how AI technologies such as speech, vision, NLP, and knowledge graphs can be combined with multimodal interaction to create more intelligent and engaging conversational agents.
The article presents an in‑depth overview of Alibaba's intelligent service bot Ali Xiaomì, covering its platform evolution, core NLP techniques such as intent recognition and machine reading comprehension, multi‑turn recommendation strategies, transfer‑learning approaches across domains and languages, and future technical challenges.
The article presents a comprehensive overview of Meituan's intelligent customer service system, detailing its evolution, the roles of QABot, TaskBot and ChatBot, the underlying NLP and knowledge‑graph technologies, model implementations such as DSSM and Seq2seq, and the impressive operational results achieved in food‑delivery and ride‑hailing services.
This guide explains how to connect an Alibaba Cloud Bailei large language model to a MySQL database using MaxKB, create semantic‑to‑SQL nodes, define system roles, and build an AI assistant that can answer operational questions with natural‑language responses and SQL queries.
This tutorial explains how to create a Python voice chatbot by recording audio, converting speech to text with Baidu AI, sending the text to the Qingyunke chatbot API for a response, and finally synthesizing the reply back into speech using pyttsx3.
This tutorial walks you through integrating DeepSeek via Spring AI into a Spring Boot project, covering Spring AI basics, obtaining an API key, adding dependencies and configuration, implementing controller endpoints, testing with Postman, and accessing the full source code.
RAGFlow is an AI architecture that merges Retrieval‑Augmented Generation with a dynamic Flow control mechanism, offering real‑time knowledge retrieval, high‑quality text generation, and flexible deployment across content creation, intelligent QA, and enterprise solutions while outlining its technical principles, advantages, challenges, and installation steps.
Bilibili created an AI‑powered customer‑service platform that integrates a WeChat Work chat UI, a conversation state machine, Elasticsearch and LLM‑based FAQ retrieval, and a low‑code admin console, replacing a thousand‑person manual support team, handling thousands of queries, boosting resolution rates and enabling cross‑team reuse.
This article describes the motivation, advantages, challenges, overall architecture, and natural‑language‑understanding algorithms of Ctrip’s one‑stop chatbot that consolidates multi‑service line support into a single entry, enabling seamless travel‑related queries, recommendations, and bookings.
This article presents a comprehensive overview of JD.com’s JIMI chatbot system and introduces a data‑driven future‑intent prediction framework that leverages NLP, deep learning, and clustering to anticipate user questions both before and during a conversation, improving efficiency and user experience.
This article presents a comprehensive overview of Alibaba’s intelligent chatbot “Ali Xiaomi”, covering industry context, e‑commerce deployment, NLU architecture, intent‑matching layers, deep‑learning‑based intent classification, reinforcement‑learning‑driven recommendation, knowledge‑graph‑enhanced services, and hybrid retrieval‑generation dialogue models, with future outlooks for AI‑driven interaction.
The article details Qunar's development of an AI-powered customer service robot, describing its motivation, data analysis, multi‑phase cognitive system architecture, knowledge‑base management, evaluation mechanisms, and future integration into a group‑wide intelligent service platform to improve service efficiency and reduce costs.
The DataFun Summit 2023 featured a presentation by senior AI engineer Sang Hailong on building and applying dialogue robot technologies—including QABot, TaskBot, intent recognition, and intelligent double‑call—in 58.com’s recruitment platform, with experimental insights on ChatGPT integration.
This article details the design, iterative development, and performance optimizations of 58's AI‑driven intelligent Q&A chatbot, covering its overall three‑layer architecture, the QABot, TaskBot, and answer‑recommendation modules, as well as dynamic strategy adjustment, caching mechanisms, and real‑world deployment results.