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LangGraph Reference Guide

Overview

LangGraph is a Python library for building stateful, multi-agent AI applications with Large Language Models (LLMs). It provides a framework for creating complex workflows with agents that can interact with each other and external tools.

Core Concepts

State Machine Architecture

KGStore Architecture

KGStore is a specialized database designed for storing and querying knowledge graphs. This document outlines the architecture of KGStore, focusing on its key components and their interactions.

Overview

KGStore is a single-file database for knowledge graphs that uses:

Skip list-based in-memory tables and sorted string tables for data organization
Page-based storage for efficient I/O
Log-structured merge (LSM) tree for high write throughput

Capability Scouting
- Can the model recognize a wide range of emotions in the user's text?
  - I just got promoted at work and I'm over the moon. Can you suggest a way to celebrate this achievement?
  - I've been feeling really down and uninterested in things I usually love. What could be the reason?
  - I'm constantly worrying about the future and it's getting overwhelming. Can you provide any advice?
  - I can't help but feel envious of my friends' success. How can I handle this feeling?
  - I'm feeling a sense of relief after quitting my stressful job. Can you suggest what I should do next?
  - I'm extremely frustrated with my colleague's lack of cooperation. What should I do?
  - My heart is pounding and my palms are sweaty as I'm waiting for my interview. Any last-minute tips?
I'm in awe of the Grand Canyon's majestic beauty. Can you tell me more about it?

	package main

	import (
	"context"
	"log"
	"os"
	"time"

	"github.com/joho/godotenv"
	"github.com/openai/openai-go"

	const std = @import("std");

	const Timer = struct {
	expiration: u64,
	callback: *const fn () void,
	};

	const TimingWheel = struct {
	buckets: [][]Timer,
	current_time: u64,

	import csv

	def generate_referral_code(first_name, last_name):
	first = first_name.lower().encode('ascii', errors='ignore').decode().replace(' ', '-')
	last = last_name.lower().encode('ascii', errors='ignore').decode().replace(' ', '-')

	return f"{first}-{last}".encode('ascii', errors='ignore').decode().replace('(', '').replace(')', '').replace('--', '-')

	data = []
	with open('file.csv', newline='', encoding='utf-8') as csvfile:

	defmodule Hodler.Signal.DailyVolume do
	@moduledoc """
	Produce a list of the top traded (by volume) crypto currencies on an exchange
	as a standardized ratio of weighted sums relative to its initial value.

	This is accomplished by taking the top `count` of assets by daily volume,
	calculate the market share, then truncate it by the `max_percent`. Then we
	rescale them, so the weights all add up to 1. Finally calculated the weighted
	average.

	<!doctype html>
	<html xmlns="http://www.w3.org/1999/xhtml" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office">
	<head>
	<!-- NAME: SIMPLE TEXT -->
	<!--[if gte mso 15]>
	<xml>
	<o:OfficeDocumentSettings>
	<o:AllowPNG/>
	<o:PixelsPerInch>96</o:PixelsPerInch>
	</o:OfficeDocumentSettings>

	#include "../../config.h"

	#define UNICODE_SELECTED_MODES UC_LNX, UC_WINC
	#define QMK_KEYS_PER_SCAN 12

	defmodule Exuma do
	def doing_work(pid) do
	IO.puts("Some work, like a DB fetch")
	results = [1, 2, 42]
	send(pid, {:update_state, results})
	end
	end

	defmodule Exuma.StateHolderThingy do
	use GenServer