Wilson Freitas wilsonfreitas

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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.

Download de curvas da B3

library(rb3)

fname <- download_marketdata("TaxasReferenciais",
                             refdate = as.Date("2024-01-12"),
                             curve_name = "TR")
df &lt;- read_marketdata(fname, "TaxasReferenciais", TRUE)

forwardrate method, for a SpotRateCurve and with arguments t1 and t2, computes the forward rate between two future terms that exist in the term structure.

library(rb3)
library(fixedincome)

df_yc <- yc_get("2022-05-20")

crv &lt;- spotratecurve(

	df <- as.data.frame(ds)
	head(df)
	#> # A tibble: 13,038 × 8
	#> refdate symbol commodity maturity_code previous_price price price_change
	#> <date> <chr> <chr> <chr> <dbl> <dbl> <dbl>
	#> 1 2023-01-04 ABEVOF… ABEVO F23 14.3 1.43e1 0.07
	#> 2 2023-01-04 ABEVOG… ABEVO G23 14.4 1.45e1 0.08
	#> 3 2023-01-04 AFSG23 AFS G23 17056 1.70e4 -101.
	#> 4 2023-01-04 AFSH23 AFS H23 17093. 1.70e4 -101.
	#> 5 2023-01-04 AFSJ23 AFS J23 17142. 1.70e4 -105.

	class B3FilesURLDownloader(SingleDownloader):
	calendar = bizdays.Calendar.load('ANBIMA.cal')
	def download(self, refdate=None):
	filename = self.attrs.get('filename')
	refdate = refdate or self.get_refdate()
	logging.info('refdate %s', refdate)
	date = refdate.strftime('%Y-%m-%d')
	url = f'https://arquivos.b3.com.br/api/download/requestname?fileName={filename}&date={date}&recaptchaToken='
	res = requests.get(url)
	msg = 'status_code = {} url = {}'.format(res.status_code, url)

	from functools import wraps

	try:
	from functools import lru_cache
	except ImportError:
	def lru_cache(user_function):
	cache = {}
	@wraps(user_function)
	def wrapper(*args):
	key = tuple(args)

	f <- function(x) cos(x*20)
	expi <- function(x) cos(x) + sin(x)*1i
	par(mfrow = c(1,2))
	L <- 40
	medias_Re <- c()
	fs <- c()
	as <- seq(0.01, 1, l = 140)

	animation::saveGIF({
	for(a in as) {

	library(rb3)
	library(tidyverse)

	top_weight <- function(.data, n = 10) {
	top_10 <- .data \|>
	arrange(desc(weight)) \|>
	slice_head(n = n) \|>
	select(symbol, weight)
	total_weight <- sum(top_10$weight)
	others <- tibble(

	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "Untitled0.ipynb",
	"provenance": [],
	"collapsed_sections": [],
	"authorship_tag": "ABX9TyNsy4RaWWJJXRN8opuCmESy",
	"include_colab_link": true