#RAG
296 related articles
When Tool Calls Time Out: A Failure Drills Lesson for Production RAG
Packt Publishing hosts a hands-on production RAG workshop covering tool call timeouts, retry loops, execution budgets, structured tracing, permissioned tool access, and fault injection testing, led by Bruno Gonçalves—no GPU or API key required.
Ollama+RAG Local Knowledge Base Setup: A Step-by-Step AnythingLLM Tutorial
Based on a Bilibili tutorial, this guide details building a local RAG knowledge base with Ollama + AnythingLLM: from general LLM knowledge limits and RAG principles to document upload, vector storage, and verification.
Agentic RAG in Practice: From Principles to Code, Building an Intelligent Retrieval System Step by Step
Traditional RAG hard-codes retrieval timing, content, and count, causing irrelevant answers. This article, based on a Bilibili tutorial, breaks down Agentic RAG principles: wrapping retrieval as tools for LLM autonomous decision-making, with four-tool design and complete code implementation to build a self-correcting, multi-hop intelligent retrieval system step by step.
RAG and Agent in Practice: Two Paths for LLM Integration
Starting from LLM integration, this article covers two paths: local Ollama deployment and Alibaba Cloud Bailian cloud API calls, detailing Bailian registration, API Key creation, free quota management, and overspend protection — laying the groundwork for RAG and Agent practice.
RAG and Agent Interview Essentials: A Full Walkthrough of Retrieval-Augmented Generation
A systematic breakdown of the complete RAG retrieval-augmented generation workflow: from offline knowledge base construction via document loading, chunking, and vector storage to the online stage of query rewriting, vector retrieval, reranking, and generation, covering high-frequency interview topics and core value analysis.
RAG Interview Essential: A Complete Guide to the Retrieval-Augmented Generation Workflow
What is RAG? This article explains the complete Retrieval-Augmented Generation workflow, covering offline knowledge base building—document loading, chunking, vectorization, and storage—as well as the online stage of query rewriting, vector retrieval, re-ranking, and generation, and analyzes high-frequency RAG interview topics and core value.
Hillock v1.0: Cracking Vector Retrieval's Hallucination Problem with Deterministic Search
Vector search's probabilistic nature causes RAG systems to hallucinate on unanswerable queries. After 6 months of testing, open-source Hillock v1.0 introduces deterministic judgment at the retrieval layer, enabling systems to explicitly refuse unanswerable questions and reduce hallucinations at the source.
RAG Knowledge Conflict Classification: Can Decision Models Replace LLMs?
New research evaluates 11 decision models for RAG knowledge conflict classification: 10 match Gemini 2.5 Flash accuracy at 4–39× lower cost; a cascading strategy approaches Gemini 3.8 Flash at just 25–37% of the cost.
RAG-CSI: Retrieval-Augmented Transformer for Channel State Prediction
RAG-CSI proposes a retrieval-augmented Transformer framework for Channel State Information (CSI) prediction, building a global–local CSI knowledge base and designing Joint State–Dynamics Retrieval (JSDR), improving all five metrics by over 10% on a multi-scenario dataset and reducing NMSE by 15.42%.
Ditching the Vector Database: SQLite BM25 and a 3.6ns Rust Sentinel for Multi-Agent Refactoring
An open-source hedge fund team refactored its multi-agent architecture: a factor ontology eliminates LLM polarity hallucinations, a switch from vector DB to SQLite FTS5 BM25 cuts reflective memory retrieval to 0.35ms, and a Rust SoA layout delivers a 3.6-nanosecond risk-control sentinel.
Retriever-Free Retrieval: DecisionRAG Navigates Documents with Decision Models
DecisionRAG introduces retriever-free retrieval: a decision model jointly evaluates candidate branches in a single forward pass, navigating the corpus structure layer by layer, surpassing dense retrieval with an 8B reranker without task training, in just 0.7-1.8 seconds per query.
Does RAG Still Have a Future? A 5-Year Skill Plan for AI Engineers
Starting from a Reddit community discussion, this article analyzes the future direction of RAG technology, the positioning of frameworks like LangChain, and the durable skills AI engineers should focus on building over the next 5-6 years—retrieval, evaluation, system design—offering practical career planning reference for beginners.
OntoGr: Adding Citation Traceability and Bi-Temporal Axes to Agent Memory
OntoGr Agent Memory is an open-source Python library that keeps verbatim quotes, sources, and bi-temporal axes for every fact in AI Agent memory, supporting reversible entity resolution, conflict adjudication, and four-way recall retrieval to solve Agent memory trust and traceability issues.
How Can RAG Frameworks Solve the Challenge of Detecting Implicit Misogyny?
A recent study proposes a RAG framework to detect implicit misogyny in English and Italian, integrating encyclopedic, slang, and feminist knowledge sources. Experiments show RAG improves recall on ImplicIT-Mis but not on SBIC+, while human evaluation consistently prefers RAG explanations, with feminist literature being the most stable and effective knowledge source.
Databricks AI Search Now Natively Supports Streaming Tables and Materialized Views
Databricks AI Search now natively supports Streaming Tables, Materialized Views, and Iceberg-based Managed Tables, enabling enterprises to build search and RAG apps on real-time data pipelines with lower index sync latency and operational costs.
Bilibili AI Large Model Tutorial: RAG Practical Guide from Beginner to Mastery
Bilibili creator A Shui launches a 109-episode AI large model RAG practical tutorial, from beginner to mastery, covering RAG, AIGC, multimodal and other core technologies, sharing learning paths and industrial practice experience to help learners systematically master large model technology.
LangChain from Scratch: A Deep Dive into a Hands-On RAG and Agent Tutorial
A deep dive into a Bilibili zero-foundation LangChain tutorial: analyzing the learning path around RAG and Agent scenarios, from basic calls to complete deployable projects. Suitable for students, career changers, and side-project developers.
How Can RAG Debugging Knowledge Be Retained? The Knowledge Management Pain of AI Teams
How can AI teams effectively retain and reuse knowledge from RAG debugging and audits? This article analyzes three current knowledge management modes and their pain points, and explores the technical feasibility and business challenges of an evidence-supported case memory system.
Building a Local Knowledge Base with LangChain-Chatchat + DeepSeek on Windows
This article, based on a Bilibili tutorial, details the full workflow of building a local knowledge base on Windows using LangChain-Chatchat and DeepSeek, including Anaconda environment setup, Ollama model management, mirror acceleration, and common error solutions, helping you quickly implement RAG Q&A.
Cosine Similarity Isn't Enough: rag-critic, an Adversarial Fact Auditor for LangGraph
Cosine similarity above 0.85 still produces confident hallucinations. rag-critic uses atomic proposition decomposition plus NLI entailment judgment for adversarial fact auditing in LangGraph, blocking and issuing rewrite instructions when the grounding score falls below threshold.