The article analyzes common pain points of traditional AI coding chats—repetitive context input, lengthy prompts, and generic answers—and demonstrates how to create a persistent, expert‑level AI coding assistant using Coco AI, with step‑by‑step configuration, example prompts, and future RAG enhancements.
This guide explains why traditional Retrieval‑Augmented Generation (RAG) struggles with precision, introduces the Model Context Protocol (MCP) as a standardized way for large language models to interact with external data sources, and provides step‑by‑step instructions for integrating MCP with MongoDB using Cherry Studio and VSCode +Cline.
UltraRAG, an open‑source RAG framework from THUNLP and NEUIR, consolidates data construction, model fine‑tuning, and evaluation into a zero‑code WebUI, offering multimodal knowledge‑base creation, one‑click optimization, robust multi‑dimensional evaluation, and micro‑service deployment that can raise retrieval accuracy by up to 30% and halve development time.
This case study details how a major internet visual platform consolidated its log, keyword, and vector search workloads onto Alibaba Cloud Elasticsearch, eliminating three separate pipelines, reducing write‑costs by 60%, cutting storage expenses over 60%, and achieving multi‑fold performance gains through serverless scaling, FalconSeek engine optimizations, and unified monitoring.
This article walks through the principles of Retrieval‑Augmented Generation (RAG) and provides a complete Python implementation using Elasticsearch, covering document chunking, semantic embedding, bulk indexing, hybrid BM25‑vector search, RRF result fusion, prompt design, LLM invocation, and a practical demo.
This article explains how Anthropic's Claude Skill folders let you package domain expertise, scripts, and resources into reusable modules, differentiate Skills from prompts, combine them with MCP tools and workflows, and build a robust mixed Agent‑Workflow architecture for reliable enterprise automation.
This comprehensive guide traces AI from the origins of human intelligence and the first computers, through the birth of artificial intelligence, the rise of machine learning and large language models, to the emergence of agents, multimodal systems, and the challenges that lie ahead.
GraphRAG extends traditional Retrieval‑Augmented Generation by building a knowledge graph from documents, extracting entities and relationships, performing community detection, and supporting both local and global searches, offering detailed step‑by‑step guidance, code examples, configuration tips, and a comparison with classic RAG approaches.
This article presents fifteen practical chunking techniques—ranging from line‑by‑line and fixed‑size chunking to semantic and hierarchical methods—explaining their principles, ideal use‑cases, concrete input examples, chunk outputs, and key advantages or cautions for improving Retrieval‑Augmented Generation with large language models.
This article presents a complete end‑to‑end pipeline that automatically extracts, generalizes, incrementally updates, and vector‑syncs knowledge from diverse sources such as tickets, documents, and SQL code, turning the traditionally labor‑intensive knowledge‑base construction for agents into a low‑effort, continuously maintainable Python‑driven solution.
This article explains why traditional RAG suffers from hallucinations, introduces GraphRAG’s knowledge‑graph‑based approach, walks through its indexing and query pipelines—including text splitting, entity‑relation extraction, graph construction, community detection, and local vs. global retrieval—provides practical setup commands, Neo4j visualization steps, and compares its performance with classic RAG.
This article explains the definitions, differences, and integration trends of Knowledge Bases and Knowledge Graphs within Retrieval‑Augmented Generation, helping developers decide which technology best fits their AI system requirements.
Symfony AI v0.1.0 launches with a suite of PHP components that let developers integrate OpenAI‑style models, vector stores, autonomous agents, and chat persistence directly into Symfony apps, offering easy installation, rich demos, and a dedicated website for hands‑on experimentation.
This article explains the three evolutionary stages of AI—from large language models that generate text, through workflow‑enhanced systems using retrieval‑augmented generation, to fully autonomous agents capable of self‑directed decision‑making—while detailing the four core technologies that power each stage.
The article explains the internal and external causes of AI hallucinations, examines how pre‑training data flaws and fine‑tuning choices amplify them, and presents a five‑pronged technical toolbox—including RAG, prompt engineering, chain‑of‑thought, self‑verification, and safety APIs—plus risk‑based product strategies for different industries.
This article walks through the end‑to‑end design of IMA's AI‑driven knowledge base, covering its definition, core business flow, architecture evolution, data ingestion pipelines, management challenges, asynchronous processing, permission modeling, and the business value demonstrated by the prototype.
This article summarizes three cutting‑edge AI search and recommendation techniques—Alibaba Cloud's Agentic RAG architecture, Huawei's LLM‑enhanced recommendation system evolution, and Baidu's generative ranking model GRAB—detailing their challenges, design choices, performance gains, and practical deployment insights.
Open WebUI is a self‑hosted, extensible AI platform that runs fully offline, supports multiple LLM back‑ends such as Ollama and OpenAI‑compatible APIs, offers built‑in RAG, role‑based access, multi‑model chat, markdown/LaTeX, image generation, and provides detailed Docker, pip, and Kubernetes installation guides with ready‑to‑run commands.
This article explains how to build Retrieval‑Augmented Generation (RAG) for industry‑specific AI agents, covering required capabilities, metrics, data sources, indexing, hybrid retrieval, decision‑point integration, layered output, permission controls, rollout strategies, and common pitfalls to ensure reliable and secure automation.
This article walks through the need for fast, accurate answers from massive document collections, compares plain keyword search and pure LLM chat, and presents a hybrid Retrieval‑Augmented Generation solution built with open‑source components, detailing architecture, hybrid retrieval, prompt engineering, deployment, performance tuning, and common pitfalls.
The article explains how context engineering—building a dynamic system that supplies AI with user intent, dialogue history, long‑term memory, external knowledge and tool definitions—outperforms traditional prompt engineering, eliminates hallucinations, and enables AI to complete complex, end‑to‑end tasks.
This article presents an ontology‑aware knowledge‑graph RAG framework that transforms complex, hierarchical industrial standard documents into a graph of sections, atomic propositions, and refined triples, achieving nearly double F1 scores on table‑based QA tasks and robust performance on long documents.
This guide explains how Retrieval‑Augmented Generation (RAG) overcomes LLM knowledge staleness, hallucination, and domain‑adaptation challenges by combining external knowledge bases with real‑time retrieval, and provides detailed architecture, optimization techniques, engineering practices, monitoring, cost‑control, and future trends for building production‑grade RAG systems.
MySQL AI adds built‑in LLMs, embeddings, vector storage, AutoML and a graphical console to on‑premise MySQL, enabling developers to create predictive and generative AI applications—including fraud detection, semantic search, RAG and NL2SQL—without external vector databases or GPUs.
This guide walks through the importance of Retrieval‑Augmented Generation, outlines the core Langchain4j and Ollama 3 components, and provides a complete Java example—including Maven setup, document ingestion, embedding creation, similarity search, prompt construction, and response generation—to demonstrate a functional RAG pipeline.
This article explains the background, principles, and step‑by‑step implementation of Retrieval‑Augmented Generation (RAG) using Spring AI, covering embedding models, vector databases, chunking strategies, indexing algorithms, similarity metrics, re‑ranking, prompt templates, and a complete Java code example.
The article evaluates GPT‑5.2 and Gemini 3 Pro on real‑world coding tasks, analyzes trends from the 6000 papers presented at NeurIPS 2025, and demonstrates how to extract and refactor the tree‑building component of the open‑source RAPTOR RAG system into an independent module.
This guide presents a curated collection of over a hundred open‑source large language model applications—including AI agents, RAG pipelines, and domain‑specific tools—explains their categories, showcases example projects, and provides step‑by‑step instructions to clone and run them on your own machine.
This article explains how product managers can demystify and apply the three core AI techniques—Retrieval‑Augmented Generation, fine‑tuning, and agents—using the Dify platform, showing step‑by‑step setups, practical benefits, cost considerations, and when to choose each approach.
This article walks through a three‑step Vibe‑Coding workflow—structured prompt creation, prompt optimization with DeepSeek, and precise bug‑fix guidance—to automatically generate, refine, and extend a React + TypeScript front‑end for a multimodal RAG system using Trae Solo, covering architecture, streaming chat, and PDF citation features.
This article explains how AI agents transform traditional data governance by introducing a four‑layer perception‑decision‑execution‑learning architecture, detailing the required technologies, tool integrations, code examples, deployment steps, team roles, security safeguards, and practical rollout strategies for enterprises seeking automated, intelligent data management.
Bisheng, an open‑source LLM DevOps platform, solves the fragmented, high‑threshold, and compliance‑heavy challenges of enterprise AI by offering a zero‑code visual workflow, all‑in‑one RAG/Agent capabilities, strict security controls, and high‑precision document parsing, enabling rapid, secure AI application rollout.
This article walks through the design and implementation of an AI‑driven Jira assistant that uses LangGraph as the agent brain, Retrieval‑Augmented Generation for knowledge access, and a Model Context Protocol (MCP) server to execute Jira operations, complete with architecture diagrams, code snippets, and practical use cases.
This article reviews three advanced AI search solutions—Alibaba Cloud's Agentic RAG architecture for multi‑modal retrieval, Huawei's LLM‑enhanced recommendation system with factorized prompting, and Baidu's generative ranking model GRAB—detailing their technical challenges, design choices, performance gains, and deployment insights.
This article explains how AI large‑language models can automate short user‑feedback classification and extract insights from long interview texts, offering practical prompting tips, fine‑tuning strategies, and Retrieval‑Augmented Generation workflows to make user research faster, more accurate, and less labor‑intensive.
Redis has introduced a multithreaded query engine that dramatically reduces latency and increases throughput—up to 16×—for vector similarity searches, enabling vertical scaling and better support for real‑time RAG applications compared to traditional single‑threaded architectures and competing vector databases.
This article details how an automotive AI assistant migrated from keyword matching to a Milvus‑based vector retrieval system, overcoming semantic gaps, scaling to millions of daily queries, optimizing indexing, introducing multi‑vector and sparse‑vector search, and building a real‑time RAG pipeline with Flink.
Pixeltable introduces a unified table abstraction that treats images, text, embeddings and model outputs as columns, enabling declarative multimodal AI pipelines, eliminating glue code, supporting built‑in vector indexing, versioned experiments, extensible custom functions, and a concise 30‑line RAG implementation.
This article reviews three cutting‑edge AI search and recommendation techniques—Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's GRAB generative ranking model—detailing their design challenges, multi‑modal retrieval strategies, performance gains, and real‑world deployment results.
This article examines the shortcomings of manual test case creation, explains how large language models (LLMs) can dramatically improve efficiency, coverage, consistency, and knowledge sharing in software testing, outlines the key capabilities required, and presents a detailed end‑to‑end solution with practical steps, evaluation metrics, and future outlook.
This article explains the fundamentals of AI‑generated content (AIGC), the Retrieval‑Augmented Generation (RAG) technique, Function Calling, autonomous agents, and the Model Context Protocol (MCP), highlighting their evolution, technical workflows, limitations, and real‑world examples for developers.
This article walks through building a LangChain‑based multimodal RAG system that parses PDFs (both native and scanned), splits them into semantic chunks, stores embeddings in a vector database, and generates answers with precise source citations, complete with code samples and API integration.
This article provides a comprehensive, hands‑on guide to Coze's knowledge base, covering its core concepts, key features, practical use‑case scenarios, detailed creation steps, configuration options, prompt design, testing methods, and a comparison with variables, memory, and databases.
UltraRAG, an open‑source RAG framework co‑developed by Tsinghua and NEUIR, offers a zero‑code WebUI that streamlines data construction, model fine‑tuning, and multi‑dimensional evaluation, boosting retrieval accuracy by up to 30% and cutting deployment time by half across legal, medical, and research use cases.
The article clarifies how large language models (LLM), retrieval‑augmented generation (RAG), and AI agents complement each other, describing the brain‑like reasoning of LLMs, the dynamic knowledge access provided by RAG, and the autonomous action capabilities of AI agents, plus practical usage scenarios.
A third‑year student recounts four distinct campus interviews—from a state‑owned big‑data firm to an AI startup—detailing test formats, technical questions, and personal growth, and concludes that most graduates ultimately join the countless small‑mid‑size companies that keep society running.
This article explores how the rapid growth of large language models reshapes intelligent customer service, detailing the evolution from rule‑based NLP bots to Retrieval‑Augmented Generation (RAG) and AI‑native agents, and presents a comprehensive framework for evaluating, diagnosing, and continuously improving chatbot performance using LLM‑driven metrics and context engineering.
The article examines why AI product managers repeatedly fall into three traps—over‑relying on prompt engineering, blindly adopting Retrieval‑Augmented Generation, or costly fine‑tuning—by presenting real‑world failures, debunking myths, and offering a five‑layer decision framework with cost, data, resource, and risk analysis to choose the right solution.
The article explains that relying solely on autonomous AI agents in industry is impractical, outlines the three major pitfalls of pure‑agent approaches, and details how combining agents with structured workflows, RAG, and multi‑level architectures delivers reliable, cost‑effective enterprise solutions.
The article explains why a production Retrieval‑Augmented Generation (RAG) system becomes slower as it runs—due to growing embedding costs, expanding vector databases, heavier re‑ranking, and larger prompts—and provides concrete engineering optimizations such as batching, async concurrency, caching, partitioned retrieval, HNSW tuning, replica scaling, answer caching, and prompt sparsification to keep performance stable.
This article explains why effective chunking is crucial for RAG performance, compares seven major chunking strategies—including fixed‑size, semantic, recursive, document‑structure, agent‑driven, sentence, and paragraph methods—and offers practical guidance on selecting and optimizing chunks for real‑world AI applications.
This article explains how different chunking methods—fixed size, semantic, recursive, document‑based, agent‑driven, sentence‑level, and paragraph‑level—affect Retrieval‑Augmented Generation, offering practical guidelines, metrics, and optimization tips for real‑world deployments.
This article explains why dynamic Retrieval‑Augmented Generation (RAG) requires a layered caching strategy rather than simple result caching, details a four‑level cache architecture—including embedding, search, answer, and pipeline caches—provides practical key‑generation and TTL guidelines, and outlines dirty‑data defenses to keep caches consistent and performant.
ARAG introduces a novel Retrieval‑Augmented Generation framework that tightly integrates LLM‑driven structured data analysis with unstructured information retrieval, addressing the “structured + unstructured” reasoning gap in socio‑economic queries, and demonstrates superior accuracy, robustness, and hallucination resistance through extensive evaluations.
This article explains why dynamic incremental RAG is harder than static RAG, identifies the three main points where recall accuracy breaks, and presents a three‑stage engineering pipeline—including a quality‑control layer, two‑stage retrieval, and reference‑injection generation—to keep real‑time data retrieval both accurate and robust.
This article details a practical case study of designing, implementing, and evaluating an AI-driven proofreading agent for a media client, covering background challenges, a three‑layer architecture, prompt engineering techniques, RAG knowledge‑base construction, model selection, fine‑tuning, automated metrics, and lessons learned.
This article explains why Retrieval‑Augmented Generation improves LLM accuracy, outlines the key Langchain4j and Ollama3 components, and provides a step‑by‑step Java example—including Maven setup, document ingestion, embedding, similarity search, prompt creation, and response generation—to demonstrate a functional RAG pipeline.
This article details how Alipay's engineering team built an AI‑Agent‑powered coding assistant for Kotlin Multiplatform (KMP) native cross‑platform development, covering architecture, UI generation from designs and images, RAG‑based knowledge retrieval, crash analysis, and future directions for AI‑driven software engineering.
This guide breaks down the three layers of a RAG pipeline—embedding, vector retrieval, and system architecture—and provides concrete engineering tactics such as batch embedding, async concurrency, caching, ANN indexing, partitioning, connection pooling, and async pipelines to dramatically reduce Time‑to‑First‑Token latency.
This article explains why function‑call accuracy is critical for LLM agents, identifies four common failure causes, and presents a systematic, five‑step engineering framework—including dynamic routing, chain‑of‑thought planning, result validation, memory injection, and log‑driven optimization—backed by concrete examples and quantitative improvements.
This article outlines Alibaba Cloud AI Search’s evolution, detailing its dual product lines—enhanced Elasticsearch and self‑developed OpenSearch—key Agentic RAG technologies, serverless architecture, vector and LLM‑driven search capabilities, and future directions in AI‑powered search.
This article reviews Spring AI's evolution, architecture, and key features—including unified APIs, prompt engineering, streaming, RAG, multi‑modal support, and agent workflows—showing how Java back‑ends can efficiently expose AI capabilities without handling low‑level model logic.
This article details Tmall's technology team's deep AI‑driven testing practice, outlining industry challenges, the need for intelligent test case generation, and a comprehensive strategy that combines prompt engineering, RAG‑based knowledge bases, and platform integration to boost coverage, reduce manual effort, and accelerate release cycles.
This report outlines emerging AI trends for the coming year, highlighting Physical AI, AI agents, multimodal models, the MCP standard, and the growing role of AI‑driven DevOps, while also discussing security implications and adoption stages across the technology lifecycle.
This guide walks through setting up a Ubuntu VM, installing Docker, pulling the AnythingLLM image, configuring storage, launching the container, and using it to ingest and query local documents with a DeepSeek‑R1 model.
This article walks through the core RAG pipeline, explains why chunking is the linchpin of retrieval quality, and provides detailed definitions, trade‑offs, and implementation examples for five chunking techniques—fixed, recursive, semantic, structure‑aware, and delayed—so you can choose the right approach for any document‑heavy AI application.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, while deep‑diving into the RAG, GraphRAG, and Agent technologies that enable smarter, more reliable AI applications.
This article examines Tencent’s large language model deployments across content generation, intelligent customer service, and game role‑playing, and explains the underlying technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems—highlighting how they enhance performance, explainability, and multi‑step reasoning in real‑world business scenarios.
This article explains why building a high‑quality RAG knowledge base is critical, outlines offline parsing techniques for multi‑format documents, presents semantic chunking strategies that preserve structure and context, and shows how to answer interview questions with a robust, production‑ready pipeline.
Building a Retrieval‑Augmented Generation (RAG) system may be straightforward in code, but making it reliable, accurate, and scalable in production involves challenges across data preparation, vector retrieval, query rewriting, generation control, and system integration, turning a demo into a truly useful AI service.
This article explores Tencent's large language model deployments across various business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, and explains the underlying technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems—that enable these applications.
This article explains why Retrieval‑Augmented Generation (RAG) projects in the financial sector often underperform, highlighting data‑structure complexities, document‑parsing hurdles, chunking strategies, compliance constraints, evaluation metrics, and engineering requirements, and offers practical solutions and code examples.
This article explains how Retrieval‑Augmented Generation (RAG) combines vector databases with large language models to let non‑technical users ask natural‑language questions and receive precise SQL statements, detailing the workflow, architecture, chunking methods, performance gains, and remaining challenges.
Facing the growing complexity of big‑data platforms, the SRE team adopted large‑language‑model agents to automate knowledge management and root‑cause analysis, employing Retrieval‑Augmented Generation, a vector store, and the Model Context Protocol to enable intelligent, scalable, and efficient incident diagnosis and resolution.
This article examines Tencent's large language model applications across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, and explains the three key technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agents—that enable these capabilities.
The article explains that in Retrieval‑Augmented Generation projects the hardest challenge lies in robust document parsing—handling PDFs, PPTs, scanned contracts, OCR errors, and preserving structure—to ensure high‑quality retrieval and avoid hallucinations.
This article analyzes two contrasting AI agent design paradigms—process‑driven workflow orchestration and autonomous agent intelligence—examines their strengths and limitations, and proposes a hybrid architecture that fuses deterministic pipelines with dynamic planning, tool use, and memory mechanisms to achieve both reliability and adaptability.
This guide shows how to evolve a simple Retrieval‑Augmented Generation prototype into a production‑grade system by strengthening data ingestion, optimizing retrieval with hybrid and reranking techniques, adding query rewriting, long‑context handling, reinforcement learning, and multimodal support, so candidates can demonstrate real engineering depth in interviews.
This guide walks you through setting up a locally deployed AI system using Ollama and FastGPT, covering model selection, Docker deployment, configuration, knowledge‑base creation, and testing so your team can query internal documents securely and efficiently.
This guide explains why typical RAG résumé entries fall flat and provides a step‑by‑step framework—including motivation, architecture, optimization, and impact metrics—to craft a compelling, interview‑ready description of Retrieval‑Augmented Generation projects.
This guide explains how LangChain’s Expression Language (LCEL) lets you declaratively connect prompts, models, and output parsers into chains, walks through environment setup, dependency installation, and detailed code examples ranging from a basic joke generator to retrieval‑augmented generation and memory‑enabled agents.
Building AI agents may seem straightforward with frameworks like LangChain, but hidden complexities in orchestration, memory management, reproducibility, and scalability turn simple demos into fragile systems, requiring systematic engineering, observability, and robust design to achieve reliable, production‑grade intelligent agents.
This article explains how proper document chunking—choosing the right chunk size, overlap, and structure‑aware boundaries—directly impacts the relevance, factuality, and efficiency of Retrieval‑Augmented Generation pipelines, and provides multiple Python implementations ranging from simple fixed‑length splits to semantic and hybrid approaches.
This guide outlines a five‑dimensional evaluation system—accuracy, credibility, latency, scalability, and user experience—providing concrete metrics, code examples, and practical steps to assess Retrieval‑Augmented Generation models in financial insurance question‑answering scenarios.
The article introduces Spring AI Alibaba DeepResearch, a Java‑based system that automates end‑to‑end research by integrating multi‑agent workflows, configurable RAG pipelines, data ingestion, observability, and dynamic report generation, and provides detailed deployment and extension guidance.
The article explains how traditional prompt engineering is giving way to Context Engineering and the Agentic Context Engineering (ACE) framework, which lets large language model agents continuously learn and improve through evolving, well‑structured context without fine‑tuning.
Bilibili’s game algorithm team built a four‑layer, LLM‑based translation platform that automates terminology extraction, retrieval‑augmented generation, and quality assessment, dramatically reducing localization cycles by over 85% and costs by up to 80% while supporting ten languages and ensuring consistent, culturally‑accurate game text.
This article explains how to design and optimize the generation module of Retrieval‑Augmented Generation systems by building robust prompts, merging multi‑source information, controlling answer formats, and applying post‑generation verification to reduce hallucinations and improve enterprise‑grade performance.
This article explains how LangChain's Indexing API adds state management and synchronization to the classic load‑split‑embed‑store RAG pipeline, detailing the RecordManager component, the index function workflow, key parameters, implementation considerations, and best‑practice code examples for production‑grade vector stores.
This article explains the concept of LangChain retrievers, compares them with vector stores, and provides step‑by‑step code examples for MultiQueryRetriever and EnsembleRetriever, showing how to boost recall and combine semantic and keyword search for robust RAG pipelines.
This article presents a structured, step‑by‑step learning path that guides beginners from Python fundamentals through AI API mastery, Retrieval‑Augmented Generation, deep MCP protocol knowledge, and advanced multi‑agent development, complete with practical code examples and performance‑monitoring techniques.
This article breaks down the end‑to‑end engineering pipeline for constructing a Retrieval‑Augmented Generation (RAG) knowledge base, covering document parsing, data cleaning, semantic chunking, embedding, and index creation, plus practical optimization tips and a concise interview answer framework.
The article outlines three post‑RAG knowledge‑engineering approaches—In‑Context Learning with dynamic few‑shot selection, Online Learning encompassing Meta‑Learning and Lifelong Learning to quickly adapt to new tasks, and the Small Language Model path that combines fine‑tuned task‑specific experts with LLM‑SLM collaboration for efficient, privacy‑preserving inference.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing how Retrieval‑Augmented Generation, Supervised Fine‑Tuning, and autonomous agents boost model intelligence, reduce hallucinations, and enable sophisticated content creation, understanding, and interactive applications.
This article walks developers through three essential upgrades for Retrieval‑Augmented Generation systems—hybrid search combining vector and keyword retrieval, query rewriting to clarify conversational inputs, and re‑ranking with a cross‑encoder—providing step‑by‑step code examples using LangChain to dramatically improve answer quality.
This article explains the limitations of basic Retrieval‑Augmented Generation (RAG), introduces Rerank technology as a two‑step refinement process, compares dual‑encoder and cross‑encoder methods, and reviews popular Rerank models to help developers build more precise AI‑driven retrieval systems.
This article details a backend developer's two‑year AI journey, the challenges of rapid model advances, and how applying microservice principles to Retrieval‑Augmented Generation (RAG) creates a scalable, multi‑agent platform for insurance knowledge, memory, and intelligent agents.
This article explains what vector stores are, outlines their core workflow of adding, querying, and searching embeddings, compares popular back‑ends like FAISS, Chroma, and Pinecone, and walks through a complete Chinese‑language example using LangChain’s FAISS integration with detailed code and result analysis.
This article details the implementation of an LLM‑based code‑review agent in a C3‑level secure repository, describing its RAG‑enhanced knowledge base, CI pipeline integration, real‑world results, prompt engineering, and ongoing optimization to boost review efficiency and defect detection.
This guide explains why large documents must be broken into semantic chunks for LLMs, introduces core parameters like chunk_size and chunk_overlap, compares LangChain's various splitters, and walks through a complete Python example that loads a long text, configures a RecursiveCharacterTextSplitter, and inspects the resulting chunks.
