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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

krisjey

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;

import org.junit.Test;

import io.netty.buffer.ByteBuf;
import io.netty.buffer.Unpooled;
import io.netty.channel.embedded.EmbeddedChannel;
import io.netty.handler.codec.LengthFieldBasedFrameDecoder;

krisjey

/**
 * @author Mark Paluch
 */
public class ConnectionPoolSupportTest extends AbstractRedisClientTest {
...
    @Test
    public void wrappedMasterSlaveConnectionShouldUseWrappers() throws Exception {
        List<RedisURI> nodes = new ArrayList<>();
        RedisURI uri = RedisURI.create(host, port);
        uri.setPassword("foobared");

krisjey

public class DisplayNumbers {
    private static final int limit = 100;

    public static void main(String args[]) throws Exception {
        new DisplayNumbers().print(Integer.MAX_VALUE - limit);
    }

    private long print(long i) throws Exception {
        Integer.parseInt(i + 1 + "");
        System.out.println(i - Integer.MAX_VALUE + limit + 1);