Search results for Rag Architecture

RAG Architecture on the JVM: Building a Production-Ready Pipeline With LangChain4j

A practical walkthrough of embedding models, vector stores, retrieval strategies, prompt engineering, and evaluation — without leaving the JVM. 1. Why RAG — and Why Java Developers...

Understanding RAG Systems

Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost

A new way to build vector RAG—structure-aware and reasoning-capable The post Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost appeared first on Towards...

What is RAG?

RAG or Retrival-Augumented Generation, is an approach that combines Large Language Model(LLM) with external data source. It enhance the…Continue reading on Medium »

8 RAG Patterns You Should Stop Ignoring

Large language models generate fluent text. They fail to meet grounding, traceability, freshness, and access control requirements. Retrieval-augmented generation (RAG) addresses th...

Day 2 - RAG - What is Vector DB ?

To recall, Integrating our private documents with LLM is called RAG. Lets assume that, we have some pdfs containing our data. That data in the pdf will be broken down into chunk...

Что такое RAG-система? Полный разбор от теории до продакшена

Что такое RAG-система? Retrieval-Augmented Generation — «генерация, дополненная извлечением»: так называют архитектурный подход, при котором модель усиливает ответы, динамично допо...

Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval

Open source. 5-minute setup. Vector RAG done right—try it yourself. The post Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval appeared first on Towa...

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...

What Is RAG? A Complete Guide

Retrieval-augmented generation, or RAG, is a method for grounding a language model's response in external data that it didn't have access to during training. Instead of relying onl...

Building a Foundational RAG-Based Document QA System: Architecture and Lessons Learned

A practical implementation of retrieval-augmented generation for enterprise document understandingContinue reading on Medium »

RAG Series (24): Code RAG — Teaching AI to Understand Your Codebase

The Difference Between Code and Documents Split a Python file into 1000-character chunks with RecursiveCharacterTextSplitter, embed them, run vector search — this is the most c...

Internet Architecture

Internet Architecture describes how data is organized, transmitted, and managed across networks. Different architectural models serve different needs, some offer a straightforward...

Cost-Aware Routing for RAG: Fetch Less, Spend Less, Answer Better

You have a knowledge base full of PDFs. Someone asks: "What do you know about RAG?" Your RAG system dutifully searches all the documents, retrieves 10 passages, stuffs them into th...

GraphRAG vs vector RAG: when the knowledge graph pays for itself

Ask your vector RAG pipeline "what are the main themes in this corpus?" and watch it return three random chunks that share a keyword. Flat vector retrieval is built for "find me th...

RAG Without Vectors: How PageIndex Retrieves by Reasoning

Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “close...

RAG Done Right: When to Use SQL, Search, and Vector Retrieval and How To Combine Them

In this article, I will attempt to explain why retrieval-agumented generation (RAG) fails when retrieval is treated as a one-size-fits-all approach. For example, the internal AI as...

Building a GraphRAG vs Traditional RAG Benchmarking System on Indian Public Health Literature

I'm building a benchmarking platform to rigorously compare three AI retrieval pipelines on a large corpus of Indian public health research papers from PubMed Central. Here's the ar...

RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time

Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations bef...

Hybrid search and reranking: a deeper look at RAG

Many of us are familiar with the retrieval augmented generative AI (RAG) pattern for building agentic AI applications – like digital concierges, frontline support chatbots and agen...

RAG Is Not Enough: Advanced Retrieval Architectures Using Vertex AI Search on GCP

Retrieval-augmented generation (RAG) caught on fast — and for good reason. Connecting a large language model to your organization's documents feels like the most natural way to bui...

Agentic RAG: Why Your AI Assistant Keeps Getting Complex Questions Wrong

Traditional RAG fails on multi-step questions. Agentic RAG adds planning, self-correction, and tool use. Here’s the architecture, real…Continue reading on Medium »

Building a Self-Correcting RAG System with AgentScope

Most RAG systems fail silently.Continue reading on Medium »

Hosted RAG vs. Self-Hosted RAG for MCP Servers—When Does Paying Actually Win?

<p>I shipped <a href="https://astgl.ai/projects#mcp-astgl-knowledge">an MCP knowledge server</a> in a weekend with sqlite-vec and Ollama. It answers que...