The DeepSeek V4 technical report shows how a 1 million‑token context forces a redesign of attention, KV‑cache, optimizer, quantization and inference budgeting, turning long‑context capability from a costly showcase into a production‑ready feature for agents, search and Chinese professional tasks.
The article analyzes the limitations of current AI coding agents—narrow perception, fragmented knowledge, and missing high‑dimensional context—and presents an Engineering Knowledge Engine that integrates vector retrieval, code and commit graphs, RepoWiki, memory, and Agentic Search to provide structured, evolving context, dramatically improving task success, token efficiency, and code quality.
This article analyzes the Agentic‑R framework, which upgrades traditional single‑hop Retrieval‑Augmented Generation by introducing dual‑perspective scoring and a bidirectional flywheel, resulting in 2–3 absolute EM improvements across seven QA datasets and a 10–15% reduction in search rounds.
This article explains how next‑generation AI agents, combined with OpenSearch and the Model Context Protocol (MCP), enable semantic, multimodal, conversational, and agentic search, detailing the architecture, deployment options, toolset, and practical demos for sales analytics and DevOps use cases.
JoyAgent, JD's open‑source intelligent‑agent platform, now adds multimodal Retrieval‑Augmented Generation (RAG) capabilities, combining graph‑based knowledge, hierarchical chunking, and vision‑language models to handle text, images, tables, and API data for enterprise knowledge processing and evaluation.
