Mike Bijon mbijon

Extracting financial disclosure reports and police blotter narratives using OpenAI's Structured Output

tl;dr this demo shows how to call OpenAI's gpt-4o-mini model, provide it with URL of a screenshot of a document, and extract data that follows a schema you define. The results are pretty solid even with little effort in defining the data — and no effort doing data prep. OpenAI's API could be a cost-efficient tool for large scale data gathering projects involving public documents.

OpenAI announced Structured Outputs for its API, a feature that allows users to specify the fields and schema of extracted data, and guarantees that the JSON output will follow that specification.

For example, given a Congressional financial disclosure report, with assets defined in a table like this:

Provider(aka Server)-side configuration, routes, controllers etc

Rails 5.0.0.1

Doorkeeper 4.2.6

Devise 4.2.0

Gemfile

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	package main

	import (
	"crypto/sha256"
	"encoding/json"
	"fmt"
	"strconv"
	"strings"
	"time"
	)

	/* License:
	* Public domain.
	*/

	#include <stdio.h>
	#include <stdint.h>
	#include <string.h>
	#include <unistd.h>
	#include <errno.h>

	import faiss
	import numpy as np


	class FaissKMeans:
	def __init__(self, n_clusters=8, n_init=10, max_iter=300):
	self.n_clusters = n_clusters
	self.n_init = n_init
	self.max_iter = max_iter
	self.kmeans = None

	# -- coding: utf-8 --
	"""
	Created on Fri Dec 21 18:59:49 2018

	@author: Nhan Tran
	"""

	import numpy as np
	import matplotlib.pyplot as plt
	import pandas as pd

	## ubuntu server with bash shell

	git clone https://github.com/sstephenson/rbenv.git ~/.rbenv
	echo 'export PATH="$HOME/.rbenv/bin:$PATH"' >> ~/.bash_profile
	echo 'eval "$(rbenv init -)"' >> ~/.bash_profile
	git clone https://github.com/sstephenson/ruby-build.git ~/.rbenv/plugins/ruby-build


	## verify
	type rbenv

	### JHW 2018
	import numpy as np
	import umap



	# This code from the excellent module at:
	# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module

	import random

	import warnings
	from skimage.measure import compare_ssim
	from skimage.transform import resize
	from scipy.stats import wasserstein_distance
	from scipy.misc import imsave
	from scipy.ndimage import imread
	import numpy as np
	import cv2

	##