RAG / 检索增强

We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential outcome framework. In the two-step m…

Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the d…

AI 点评 · 企业AI信任缺失比检索问题更致命，多数公司却仍在错误方向投入资源修补。

This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-A…

An intentionally vulnerable OWASP LLM Top 10 training platform for AI Security, Prompt Injection, RAG Security, Agent Security, and GenAI penetration testing.

Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such…

Retrieval-Augmented Generation (RAG) has been increasingly adopted to reduce hallucinations and strengthen the factual grounding of large language models (LLMs). While robustness to errors in the retr…

Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy e…

景区导览服务 AI 数字人：基于 Spring Boot、Vue 3、UniApp 与本地 RAG 的智能导览系统。

Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains…

In this post, we explore how Graph-based Retrieval Augmented Generation (GraphRAG) is transforming scientific research by combining graph databases with generative AI. With this ap…

AI 点评 · 知识图谱与生成式AI结合，加速药物研发，展现AI赋能科学突破新路径。

Modern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workflows whose accumulated reasoning and tool traces ro…

Multi-hop retrieval-augmented generation (RAG) acquires evidence sequentially, with each new document potentially revealing missing facts, bridge entities, query defects, or sufficient support for ans…

Search knowledge by what documents mean and how they look — not one or the other.

Native AI inference for PHP 8.3+ - run ONNX, GGUF (llama.cpp) and RubixML models directly in your PHP process via FFI. Chat, streaming, embeddings, RAG and vect…

In this post, we demonstrate how to implement HippoRAG using a comprehensive AWS stack. We use Amazon Bedrock for LLM capabilities, Amazon Neptune for graph database functionality,…

AI 点评 · 结合生物学灵感与图数据库，HippoRAG在RAG架构上实现个性化检索突破。

GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this technique ideally ca…

デジタル庁のガバメントAI「源内(GENAI)」を完全ローカル(ローカルLLM/OpenAI互換)で動かす非公式プロジェクト。SAML認証(Keycloak)・RAG(Qdrant)・文字起こし(Whisper)・画像生成(SD)・チーム単位ナレッジをローカル完結。

デジタル庁のガバメントAI「源内(GENAI)」を完全ローカル(ローカルLLM/OpenAI互換)で動かす非公式プロジェクト。SAML認証(Keycloak)・RAG(Qdrant)・文字起こし(Whisper)・画像生成(SD)・チーム単位ナレッジをローカル完結。

Pixel-native visual RAG ported to Rust on the ruvector ANN substrate (HNSW + IVF-Flat) — screenshot/document retrieval over visual embeddings, a Rust port of Pi…

Pixel-native visual RAG ported to Rust on the ruvector ANN substrate (HNSW + IVF-Flat) — screenshot/document retrieval over visual embeddings, a Rust port of Pi…

Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, updat…

Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval…

Hybrid RAG (DuckDB vector + BM25 + RRF + recency/keyword priors + optional cross-encoder rerank) as an installable library + CLI.

With the rapid spread of retrieval-augmented generation and semantic search, choosing the right embedding and retrieval configuration is increasingly hard. Large retrieval benchmarks are comprehensive…

Cross-Chart Retrieval-Augmented Generation (RAG) is critical for complex multi-modal analytical tasks in scientific, business, and political domains. However, existing benchmarks either focus on table…

面向 PRD、业务规则、SOP、流程文档和产品截图的证据型 RAG 知识库。

Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is absent or incomplete…

Retrieval-augmented generation (RAG) systems must balance retrieval granularity with contextual coherence, a challenge that existing methods address through LLM-guided chunking, single-level context e…

Retrieval-augmented generation (RAG) systems depend critically on how documents are chunked and searched. Fine-grained chunks can improve retrieval precision but expand the search space, increasing la…

面向教育场景的RAG智能问答系统，融合关键词匹配与语义检索双引擎，融合MySQL和RAG技术，先经过MySQL数据库的检索(还融合了Redis辅助储存和搜索)，若无符合条件答案，则进入RAG系统，RAG知识库中的知识储存在Milvus向量数据库中

Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: different retrievers requir…

Retrieval-augmented generation (RAG) has become a standard mechanism for grounding language models in external knowledge, yet conventional retrieval based on lexical or semantic similarity is poorly s…

Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities. Yet progress in…

Retrieval-augmented QA pipelines often route retrieved passages through an LLM rewriter before a smaller reader, lifting F1 by tens of points on multi-hop benchmarks; this gain is typically credited t…

Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysi…

AI 点评 · 用视觉索引突破文本限制，为多模态检索提供新路径。

LLM internship resume and job-search Codex Skill: resume polish, JD tailoring, evidence guard, interview grilling, and Project Scout for LLM/RAG/Agent roles. 大模…

Autoregressive (AR) video diffusion enables variable-length synthesis, but long-horizon generation often suffers from accumulated errors and identity drift. For efficiency, existing methods commonly a…

AI 点评 · 提出通用检索增强框架，解决长视频生成的累积误差与身份漂移，兼顾效率与质量。

Official Implementation of LongLive-RAG: A general retrieval-augmented framework for long video generation.

AI 点评 · 开源长视频RAG框架，突破生成时长限制，为AI视频创作提供新路径。

Sentence embeddings are a foundational component for semantic search, clustering, classification, and retrieval-augmented generation. This paper presents embeddingmagibu-200m, a Turkish-focused senten…

AI 点评 · 跨语言分词器移植与离线蒸馏技术，为小语种AI模型适配提供了高效新路径。

A retrieval-augmented generation (RAG) system deployed over a multi-author institutional corpus can give a different answer to the same question depending on which source it retrieves -- a failure mod…

工程化 RAG 文档助手：知识库、PDF 索引、Agent 工具编排、scope 检索、引用溯源与拒答阈值。FastAPI + Vue3

AI 点评 · 企业级RAG落地范本，从检索拒答到工具编排的完整工程化实践。

A comprehensive interview preparation guide covering all major RAG (Retrieval-Augmented Generation) architectures. 50 questions across 10 types, from Naive RAG…

AI 点评 · 面试RAG架构必读，50题覆盖10种类型，系统掌握检索增强生成核心技术。

A comprehensive interview preparation guide covering all major RAG (Retrieval-Augmented Generation) architectures. 140 questions across 12 types, from Naive RAG…

A complete collection of RAG interview questions, answers (286 questions & 18 RAG types), system design scenarios, architecture patterns, and production-ready c…

Local-first RAG and agent skills framework for source-traceable agent memory.

AI 点评 · 用本地优先的RAG框架实现代理记忆可溯源，为可信AI应用开发提供新路径。

Local-first RAG and agent skills framework for source-traceable agent memory.

AI 点评 · 本地优先架构让RAG技能框架实现源头可追溯，为AI代理记忆管理提供新范式。

AI equity research agent with resilient workflows, Redis Lua single-flight, pgvector RAG, versioned reports, evidence tracing, and RAG evaluation.

AI 点评 · 融合弹性工作流与向量检索，实现金融研究全流程可追溯，技术架构值得借鉴。

AI equity research agent with resilient workflows, Redis Lua single-flight, pgvector RAG, versioned reports, evidence tracing, and RAG evaluation.

AI 点评 · 高效AI投研工具，结合弹性工作流与证据溯源，提升研报可信度。

A roadmap to AI Engineering excellence: Masterclass in Generative AI, RAG, and Agentic Systems with a focus on scalable and production-ready architectures. 🚀🤖