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.

Kill process contain certain string

For example, kill command contains python3 -u experiment_main.py

kill $(ps aux | grep '[p]ython3 -u experiment_main.py' | awk '{print $2}')

Hadoop List files by date

hdfs dfs -ls / | sort -k6,7

	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",

	PYTHON_PROMPT = '''Question: after resting they decided to go for a swim . the depth of the water is 5 times ron 's height . dean is 11 feet shorter than ron . if ron stands at 12 feet how deep was the water ?

	# solution in Python:

	def solution():
	"""after resting they decided to go for a swim . if the depth of the water is 10 times dean 's height and he stands at 6 feet how deep was the water ?"""
	dean_height = 6
	water_depth = 10 * dean_height
	result = water_depth
	return result

	MATH_PROMPT = '''Question: Riku has 25 times more stickers than Kristoff. If Kristoff has 85 stickers, how many stickers does Riku have?
	Answer:
	If Kristoff has 85 stickers, Riku has 25 * 85 stickers = <<85*25=2125>>2125 more stickers.
	The total number of stickers Riku has is 2125 stickers + 85 stickers = <<2125+85=2210>>2210 stickers.

	# solution in Python:

	def solution():
	"""Riku has 25 times more stickers than Kristoff. If Kristoff has 85 stickers, how many stickers does Riku have?"""
	ratio = 25

	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:

	"""
	This is a simple example to show how to calculate the p_value of two models' accuracy
	Bootstrapint t-test
	"""
	import random

	random.seed(42)
	# assume we have test set 1000 samples
	# we just create dummy results to demo
	groundtruth = [random.choice(['A', 'B', 'C']) for _ in range(1000)]

	from torch.utils.data import DataLoader
	from transformers import AutoTokenizer, PreTrainedTokenizerFast, set_seed, AutoModelForCausalLM, AutoConfig
	from tqdm import tqdm
	import argparse
	import torch
	import torch.nn as nn
	import logging
	from typing import Dict, Tuple
	from accelerate import Accelerator, DistributedDataParallelKwargs
	from accelerate.logging import get_logger

	import torch
	from fairseq.models.bart import BARTModel

	bart = BARTModel.from_pretrained(
	'model_files/bart-large-model',
	checkpoint_file='checkpoint_best.pt',
	data_name_or_path='data/cloze_replace_all-bin'
	)

	bart.cuda()