Allan Jie allanj

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.

	import streamlit as st

	if 'count' not in st.session_state:
	st.session_state.count = 0

	if 'quotes' not in st.session_state:
	st.session_state.quotes = [
	"Life is what happens when you're busy making other plans. — John Lennon",
	"Get busy living or get busy dying. — Stephen King",
	"You only live once, but if you do it right, once is enough. — Mae West",

	import openai
	import asyncio

	async def get_choice_completion(prompt, choices):
	# Initialize an asynchronous OpenAI client
	async with openai.AsyncClient(base_url="http://127.0.0.1:8000/v1", api_key="abc") as client:
	choice_probs = {}

	# Calculate logprobs for each prompt + choice sequence
	for choice in choices:

	<!DOCTYPE html>
	<head>

	<!--Little CSS fade in -->
	<style>
	.fade-in{
	-webkit-animation: fade-in 2s ease;
	-moz-animation: fade-in ease-in-out 2s both;
	-ms-animation: fade-in ease-in-out 2s both;
	-o-animation: fade-in ease-in-out 2s both;

	pip install streamlit
	pip install spacy
	python -m spacy download en_core_web_sm
	python -m spacy download en_core_web_md
	python -m spacy download de_core_news_sm

	require 'nn'
	require 'dpnn'
	require 'rnn'
	require 'nngraph'

	local opt = {
	n_seq = 3,
	d_hid = 4,
	d_mem = 20,
	n_batch = 2,