Local RAG Document Q&A: A 5-Minute Beginner Guide

Local RAG starts in 5 minutes, and the core is wiring your documents to "chunk, embed, retrieve, then have the LLM answer with citations." As a beginner you can put AnythingLLM (document-focused) or Open WebUI (general, with RAG built in) on top of Ollama, no code. But one single decision makes or breaks Korean: the default embedding nomic-embed-text is English-only, so Korean semantic search fails. In one benchmark, retrieval accuracy was a measured 72% for bge-m3 versus 57% for nomic. So for Korean, always pick bge-m3 as your embedding model.

In plain terms: RAG is an open-book exam. Instead of memorizing everything (fine-tuning), when a question comes the LLM opens the relevant pages of your notes (retrieval) and answers - so note organization (chunking) and finding skill (embeddings) set the score.

First, the terms. RAG (Retrieval-Augmented Generation) is a method where, before answering, the LLM first fetches relevant pieces from your documents to ground its answer. An embedding turns a sentence into "meaning coordinates" (a number vector); similar meanings land close together. A chunk is a piece a document is split into for retrieval, and top-k pulls the k closest chunks for each question.

What does a minimal RAG consist of?#

Five pieces: chunk, embed, store, retrieve top-k, answer. (1) Split documents into chunks, (2) an embedding model turns each chunk into a meaning vector, (3) store them in a vector DB (AnythingLLM bundles LanceDB, so zero setup). (4) When a question arrives, pull the most similar top-k chunks, and (5) the LLM answers with citations grounded in those chunks. Install the tool and this wiring is automatic, so a beginner can start right away with defaults plus bge-m3.

See how the five pieces connect. The top three steps run once when you add documents; the bottom two run on every question.

Recommended beginner Korean RAG setup - what and why (2026 public measurements) · columns: Component, Recommendation, Why · 출처 Hax hax.moche.ai/en/p/1063?ref=ai_answer

Component | Recommendation | Why
Runtime | Ollama (Docker) | LLM + embeddings in one
Frontend | AnythingLLM, Open WebUI | No-code, RAG built in, citations
Embedding | bge-m3 | Multilingual, 8K, measured 72% retrieval
Chunk/overlap | ~1000 / 100 | Too small = thin meaning
Top-k | 3-5 | Raise only after growing context
Reranker | bge-reranker (later) | Precision boost once basics work

Contents

What should you watch most for Korean?

Where do beginners get stuck?

How do you do it in 5 minutes?

What should you watch most for Korean?#

The embedding model and the context window. First, putting Korean docs through an English-only embedding (nomic) leaves same-meaning Korean and English sentences unmatched, so retrieval flails - switch to bge-m3 (MIT, 100+ languages, 8K tokens). Second, Ollama's default context window is 2048 tokens, so retrieved chunks do not all fit and RAG works at half capacity - raise it to 8192+ (the two most common beginner failures). And if you change embedding models, always reindex existing documents - vectors must come from the same model to mean anything.

See why an English-only embedding flails on Korean. If same-meaning Korean and English sentences land far apart in coordinates, retrieval cannot match them.

Where do beginners get stuck?#

Three things: retrieval quality, chunking, and citations.

Retrieval is key: garbage retrieved means a garbage answer. RAG cannot fix bad retrieval, so fix embeddings and chunks first.

Chunking: too-small chunks have thin meaning and retrieve poorly, so raise the minimum chunk size to merge fragments.

Citations: without sources (citations) you cannot verify. Turn citations on and check the chunks behind each answer.

See why a small context window halves RAG too. If the retrieved chunks exceed 2048 tokens, the tail is cut and the LLM never sees it.

How do you do it in 5 minutes?#

Start with the easiest path.

With Ollama, pull an LLM and the bge-m3 embedding (a must for Korean), and add documents to AnythingLLM or Open WebUI.

Raise the context window to 8192+, and ask a few questions at default chunk/top-k while checking citations.

If quality is short, change one thing at a time (chunk, top-k, reranker), and measure on your own docs rather than a leaderboard.

Reference links

Ollama (LLM + embedding runtime)

Open WebUI (general frontend, RAG)

AnythingLLM (document-focused RAG)

BGE-M3 / FlagEmbedding (multilingual embeddings)

LlamaIndex (RAG framework)

Note: accuracy and setting figures are public 2026 measurements and guides and vary by document, language, embedding, and chunking. Changing embeddings requires reindexing, and you should measure exact quality on your own documents (leaderboards are only a start). RAG tools update often, so this is reviewed quarterly.