This article explains how Function Calling lets large language models invoke Java methods via Spring AI, walks through the four‑step workflow, shows declarative and programmatic tool definitions, and demonstrates a complete customer‑service chatbot with code examples and best‑practice guidelines.
This tutorial walks Java backend developers through the mature Java LLM ecosystem, comparing frameworks like Spring AI and LangChain4j, and demonstrates step‑by‑step how to create a Spring Boot application with a chat endpoint, streaming responses, and dynamic model switching among OpenAI, Tongyi Qwen, and Ollama.
This article details the end‑to‑end implementation of an AI shopping guide on the Vivo official website, covering problem definition, multi‑layer architecture, technology selection, data synthesis, FastText intent‑recognition model training, prompt engineering, RAG‑augmented retrieval, structured output, safety testing, and the resulting business impact.
The article analyzes why embedding a generic ChatBot in niche applications leads to low engagement, shares the author's experience building a matrix of one general and five specialized assistants in a news app, and provides a decision framework and practical pitfalls for product teams.
This guide walks through installing Ollama on Windows, downloading a Qwen2.5‑7B model, configuring Spring Boot with LangChain4j dependencies, setting up application.yml, defining AI service interfaces, adding conversation memory, creating REST and streaming controllers, and testing the end‑to‑end local LLM workflow.
This step‑by‑step guide shows how a complete beginner can install OpenClaw on Windows 10, obtain an Alibaba Cloud Qwen API key, configure the gateway, start the service, and interact with the AI model through the web UI, all within a few hours.
The article explains that most OpenClaw issues stem from mixing up five core layers—channel, account, agent, session, and memory—and provides a step‑by‑step troubleshooting guide to isolate the problem, verify each layer, and configure the system for reliable responses.
This article walks you through installing WorkBuddy, logging in, configuring the WeChat customer‑service integration, and finally chatting with the OpenClaw chatbot inside WeChat, all illustrated with screenshots and concise instructions.
Dux AI is an open‑source AI application platform built on Dux PHP Admin that unifies agents, bots, knowledge bases, and workflow into a single backend, offering modular extensions, multimedia capabilities, and integration with business systems, with detailed installation and deployment instructions for developers.
This article shows Java developers how to add generative AI to enterprise applications using the open‑source LangChain4j library and Quarkus, covering a chatbot example, streaming responses, chat memory management, and structured output without learning a new programming language.
This guide walks you through installing Ollama, downloading the qwen3:4b model, and using Python’s async client to perform streaming chat requests, then shows how to integrate the same model with LangChain, including setup, initialization, and both regular and streaming output examples.
An OpenClaw Docker image pre‑installs popular Chinese IM bots (Feishu, DingTalk, QQ, WeChat) and utilities like Chinese TTS, Playwright automation, and OpenCode AI assistant, allowing a quick three‑step setup—download config, set model tokens, and launch—without manual plugin installation or token costs.
This article walks through building AI agents with LangGraph in Python, starting with a simple GCD workflow and then creating a memory‑enabled chatbot using GPT‑4o, covering state management, nodes, edges, conditional loops, recursion limits, and visual debugging.
This tutorial explains the OpenAI‑style request and response schema, demonstrates low‑level API calls with the requests library, compares them to the high‑level openai package, and walks through building a streaming multi‑turn chatbot that interacts with a locally hosted large language model.
The article examines JD’s AI‑powered shopping app, detailing its chatbot‑style interface, intent‑driven workflow, AI‑enhanced product recommendation, multi‑scenario integration such as travel and dining, and the underlying research on Fast‑Slow thinking and the SA‑GCPO algorithm that powers the experience.
Function Calling lets large language models invoke external tools or APIs during a conversation, transforming them from passive responders into proactive assistants; this guide explains the concept, workflow, and practical implementations with weather, parallel queries, and stock price examples using OpenAI’s Python SDK.
This guide walks you through creating an AI chat assistant powered by the DeepSeek‑V3 large language model on Huawei Developer Space, covering cloud container setup, free token acquisition, MaaS model activation, environment configuration, code deployment with Gradio, and end‑to‑end testing.
LangChain’s stateless LLMs require a memory component to retain conversation context, and this article explains the importance of memory, compares built‑in memory types, and walks through two practical examples—custom buffer memory and basic message history—showing how to implement and use them with code.
This tutorial shows how to quickly create a PHP backend that calls the OpenAI GPT‑4o API, covering environment setup, core code implementation, testing, and best‑practice tips for turning a simple demo into a production‑ready AI chatbot.
This article details Ping An Life's self‑developed large‑model reporting product ChatBI, covering its background, goals, solution architecture, technical stack, real‑world use cases, deployment challenges, and future outlook, offering practical insights for enterprises adopting AI‑driven business intelligence.
This tutorial walks through creating a Python program that captures spoken input, converts it to text with Baidu AI, sends the text to the free Qingyunke chatbot API for a response, and finally synthesizes the reply back into speech, complete with code snippets and setup instructions.
Learn how to create a Python program that captures spoken input, converts it to text using Baidu's speech‑recognition API, sends the text to the free Qingyunke chatbot for intelligent replies, and then synthesizes the response back into speech, with complete code snippets and setup instructions.
Deploying large‑model AI in customer service can boost efficiency, but without proper boundaries, feedback loops, and emotional handling it often creates costly mistakes, brand damage, and poor user experience, as this article explains the six most frequent traps and how to sidestep them.
This step‑by‑step tutorial shows how to create a simple AI‑driven mini‑game on the Coze platform in half an hour, covering account setup, agent creation, prompt design, debugging, publishing, advanced prompt techniques, and common pitfalls.
This article details the end‑to‑end development of Alipay’s AI Travel Assistant, covering product conception, the migration to a unified xUI 1.0 framework, the adoption of KMP for cross‑platform UI, the shift from RPC + SYNC to gRPC streaming, and the challenges of card‑ecosystem migration, all achieved within a two‑month sprint by a four‑person client team.
This article details how Zhaozhuan transformed its customer‑service robot from relying on external NLP providers to a fully self‑developed AI solution, covering technical choices, model training, RAG integration, deployment strategies, and future directions.
This article introduces four notable open‑source AI projects—Google's Gemini CLI, the voice‑interactive XiaoZhi chatbot, the comprehensive AI Engineering Hub, and the GPT‑Pilot programming companion—detailing their key features, generous free quotas, star counts, supported hardware, and providing direct GitHub repository links for each.
Learn step‑by‑step how to create an AI‑driven zodiac horoscope chatbot on Alibaba Cloud Baileian, configure the Alipay MCP payment service, set up secure KMS credentials, and achieve a complete commercial pay‑to‑use loop from user query to transaction confirmation.
This tutorial revisits LangChain basics, demonstrates how to add multi‑turn memory to a chatbot, shows streaming output via the stream method, and walks through creating a full‑stack web interface with Gradio, providing complete Python code examples for each step.
This article shows how to create a WeChat intelligent chatbot using only 16 lines of Python code, covering installation of required libraries, obtaining a Turing API key, implementing message handling, and running the bot with automatic login and a persistent loop.
This tutorial walks through creating a natural‑language‑to‑SQL chatbot using Spring AI, configuring a MySQL school database with Flyway, defining system prompts for a DeepSeek LLM, implementing service beans and a REST API, and interacting with the bot via curl commands.
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.
This guide walks through the complete process of creating a RAG‑powered question‑answering bot using LangChain, Streamlit, and vector‑store embeddings, covering theory, architecture, data loading, chunking, vector indexing, retrieval, LLM integration, and full code implementation with practical examples.
This article compares four leading AI agent development platforms—Coze, Dify, Tencent Yuanqi, and Baidu Wenxin—detailing their strengths, weaknesses, and ideal use cases to help developers and businesses choose the most suitable solution for chatbots, data assistants, or social‑media automation.
This article highlights five open‑source AI projects that saw the fastest star growth on GitHub this week, covering a free Cursor VIP unlocker, an end‑to‑end video translation tool, an offline meeting‑minutes assistant, a personal finance OS, and a versatile AI chatbot SDK, each with key features and repository links.
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 tutorial walks you through registering on OpenAI, installing required Python packages, setting up API keys, and writing a Jupyter Notebook that defines a chat function and an interactive loop to create a functional GPT‑3.5 chatbot.
This article demonstrates how to transform a traditional Spring Boot book‑management service into an AI‑driven MCP service by importing Spring AI dependencies, configuring proxy and MCP server settings, annotating service methods with @Tool, registering them, and exposing a chat endpoint that lets large language models query and manipulate book data through natural language.
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 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 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 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 guide walks you through using DeepSeek’s large‑model API via the OpenAI request format, covering API advantages, key parameters, Python setup, code examples for single and multi‑turn conversations, and a full reference table of request options.
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.
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 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.
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.
Learn how to create a locally hosted chatbot powered by DeepSeek R1 32b, using Ollama and Docker, integrate Dify for model management, and add web‑search capability through SEARXNG, covering environment setup, search logic, content extraction, testing, and optimization tips.
This guide walks you through creating a Spring Boot project, configuring DeepSeek's large language model via SiliconFlow, setting up OpenAI‑compatible parameters, and implementing a REST controller that returns weather forecasts using the model, complete with step‑by‑step code snippets, configuration files, and deployment instructions.
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.
Learn step-by-step how Java developers can obtain a DeepSeek API key, understand the chat and reasoning models, and implement them with Apache HttpClient, covering code examples, key parameters, best practices, and real-world use cases such as smart customer service, education tools, and code generation assistants.
This tutorial walks you through obtaining a DeepSeek API key, installing Docker, configuring the chatgpt‑on‑wechat project, creating a custom docker‑compose.yml, and deploying the Cow application to enable DeepSeek-powered chat in your personal WeChat, complete with code snippets and screenshots.
This guide walks you through downloading Ollama, selecting the DeepSeek‑r1 model, installing it via a terminal command, and configuring ChatBoxAI for seamless local AI conversations, all with clear screenshots for each step.
This guide walks you through creating a complete retrieval‑augmented generation (RAG) knowledge‑question‑answer system on Alibaba Cloud, covering AI model integration, cloud‑native low‑code development, database setup, UI customization, session persistence, analytics dashboards, and multi‑channel deployment.
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.
Learn step‑by‑step how to integrate Spring AI Alibaba into a Spring Boot backend, configure API keys, add necessary dependencies, create a CORS‑enabled chat controller, and then develop a matching React frontend that streams AI responses, enabling a complete Java‑to‑React AI chat application.
Spring AI Alibaba is an open‑source Java framework that integrates Alibaba Cloud's large‑model services with Spring AI, offering high‑level abstractions for chat models, prompts, function calling, RAG, and conversation memory, and includes a complete ticket‑assistant example with code snippets.
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 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 guide walks you through using Alibaba Cloud PAI and the open‑source LLaMA Factory framework to fine‑tune a Llama‑3 8B model for Chinese dialogue and role‑playing, create a “Zhuge Liang” chatbot, evaluate its performance, and clean up resources.
This guide walks you through using Spring AI and Spring Cloud Alibaba AI to quickly create a Java chat application powered by Alibaba's Tongyi large‑model service, covering setup, configuration, core code, and verification steps for a functional generative AI chatbot.
This guide walks you through obtaining free ECS resources and a DashScope API‑key, initializing a SpringAI application with the AppStack template, deploying it via the built‑in pipeline, and verifying the AI chatbot runs successfully, while also showing optional template‑based batch upgrades.
This article examines Huolala's AI-powered recruitment system, detailing its business context, product and technical architecture, customizable chatbots, task orchestration, candidate matching mechanisms, data collection, and future outlook for AI-driven hiring efficiency.
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 article explores how Laravel can serve as a flexible backend platform for integrating artificial intelligence and machine learning technologies—such as predictive analytics, chatbots, image/video analysis, and recommendation systems—by presenting practical code examples, discussing opportunities, challenges, and best‑practice tools.
This comprehensive guide walks AI developers through building a Retrieval‑Augmented Generation (RAG) chatbot on Alibaba Cloud PAI, covering architecture, vector store setup, model deployment, knowledge ingestion, multi‑modal retrieval, fusion, re‑ranking, prompt design, and end‑to‑end configuration with code examples.
This guide explains Vanna, an open‑source Python RAG framework for Text2SQL, covering its core concepts, RAG‑based architecture, step‑by‑step model training, code examples for customization, and how to deploy a conversational database chatbot with a Flask web UI.
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 tutorial guides readers through building an AI‑powered chat application using PHP and the OpenAI ChatGPT API, covering environment setup, Composer dependency installation, backend PHP integration, a simple HTML/JavaScript front‑end, and how to run the app locally for interactive conversational experiences.
This tutorial explains how to build a PHP‑based ChatGPT chatbot, covering environment setup, API key acquisition, library installation, core code implementation, a simple web interface, and steps to run and extend the application.
This guide explains the Retrieval‑Augmented Generation (RAG) technique, detailing how user queries are matched to private knowledge bases, how relevant passages are retrieved, and how large language models use those passages to generate context‑aware answers, complete with code examples and practical tips.
This tutorial explains how to leverage generative AI tools—including LLMs, embedding models, vector databases, and the LangChain framework—to create a custom chatbot that answers user queries using a knowledge base, with step‑by‑step code examples for Google Colab.
This guide shows how to create a domain‑specific chatbot by combining large language models, prompt‑tuning, and Alibaba Cloud's Hologres real‑time vector store, covering prerequisites, environment setup, code configuration, vector data preparation, verification, and optional DingTalk integration.
This article presents a collection of seven practical Python scripts—including a Zhihu image scraper, two interacting chatbots, a Naive Bayes poetry author classifier, a 35‑choose‑7 lottery generator, an automatic apology writer, a screen‑capture tool, and a GIF maker—each demonstrated with complete, runnable code.
LangChain is an innovative framework that simplifies building language‑model‑driven applications by providing modular components such as models, prompts, memory, chains, and agents, with examples of asynchronous LLM calls, custom prompt templates, vector stores, token handling, and a simple Gradio chatbot implementation.
This tutorial walks developers through setting up an OpenAI account, obtaining an API key, configuring the Python environment, and writing a complete Python script that uses the ChatGPT API to create an interactive chatbot, complete with code examples and execution tips.
This article explains why combining large language models with vector databases enables enterprises to create specialized, up‑to‑date chatbots, outlines the underlying principles, describes the ADB‑PG vector‑search capabilities, and provides step‑by‑step implementation details including data processing, indexing, and query examples.
This article explains why intelligent customer service improves efficiency, compares it with traditional support, outlines its core functions such as knowledge management and language response, and provides a practical step‑by‑step process for quickly building a simple AI‑powered help desk.
The Vicuna-13B open‑source chatbot, fine‑tuned from LLaMA on 70k ShareGPT conversations, matches over 90% of ChatGPT and Google Bard quality while costing only about $300 to train, thanks to memory optimizations, multi‑turn dialogue handling, and cheap spot‑instance training.
Baidu unveiled its new generative AI chatbot ERNIE Bot, showcasing five practical scenarios, multimodal generation, a detailed technical stack based on the ERNIE and PLATO models, and a comparison with ChatGPT and Bing Chat, while also announcing its invitation‑only testing program and API access for enterprises.
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 explains how to use Wechaty’s PuppetPuppeteer to create a WeChat chatbot by intercepting login requests, injecting interaction scripts, and leveraging Angular services in the web client, while highlighting the risks of web‑client changes.
This article examines the design of intelligent customer service interfaces, detailing dynamic and static entry modes, trigger mechanisms, queue systems, and the composition of system information, personalized features, chat operation, and input areas, while offering practical UI patterns and considerations for effective user interaction.
This tutorial explains how to create a WeChat intelligent chatbot using Python, itchat, and the Turing API, covering module installation, API interaction, message handling with decorators, QR‑code login, and continuous execution, all demonstrated with concise code examples.
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.
This article describes the motivation, micro‑service architecture, code implementation, V1.0 browser‑based NLP prototype, V2.0 AI‑enhanced version with BM25 and BERT, integration with ChatUI, DingTalk bot, command‑based automation, and future plans for the internal intelligent QA chatbot named Jarvis.
This article shares seven practical Python scripts—ranging from a 30‑line Zhihu image scraper and chatbot conversation loop to poetry author classification, lottery number generation, automatic apology letter creation, screen‑capture automation, and GIF assembly—demonstrating how to avoid reinventing the wheel while learning useful automation techniques.
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 article describes the end‑to‑end design, architecture, code implementation, and deployment steps for an internal intelligent QA chatbot named “Jarvis”, covering its V1.0 browser‑based prototype, V2.0 AI‑enhanced version, DingTalk integration, automation features, and future roadmap.
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.
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.
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 tutorial explains how to create a Siri‑style conversational robot in Python by configuring the environment, describing the speech‑recognition and chatbot principles, and showing the implementation that uses Baidu speech recognition and the Turing chatbot API.
This guide explains how to create a WeChat chatbot using the open‑source Wechaty SDK, covering required npm packages, QR code login, bot initialization, message sending, and important considerations such as Node.js version and npm mirror configuration.
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 tutorial shows how to use Python to scrape GIF images, automatically generate romantic messages, obtain AI‑generated replies, and send the whole package as an email through a 163 SMTP server, providing a complete, runnable example.
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.
This article presents seven practical Python scripts—from a concise web scraper for Zhihu images and a chatbot conversation loop to a Naive Bayes poem author classifier, a lottery number generator, an automated essay writer, a screen‑capture tool, and a GIF creator—demonstrating how to avoid reinventing the wheel while exploring diverse automation tasks.
