Merge pull request #374 from loupdethies/loupdethies/add-datasheet-comparator
Add datasheet comparator notebook with example PDFs
This commit is contained in:
Ed Donner
GitHub
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week1/community-contributions/datasheets/part_new.pdf
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week1/community-contributions/datasheets/part_new.pdf
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week1/community-contributions/datasheets/part_old.pdf
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "52629582-ec22-447a-ae09-cba16a46976d",
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"metadata": {},
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"source": [
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"# Datasheet Comparator - MVP"
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]
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},
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{
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"cell_type": "markdown",
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"id": "40de9dc5-0387-4950-8f8f-4805b46187c3",
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"metadata": {},
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"source": [
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"This notebook is part of a project that compares technical specifications from two electronic component datasheets.\n",
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"\n",
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"Initially, the PDFs are provided as local files, but future versions will allow users to:\n",
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"- Select datasheets interactively from within the notebook\n",
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"- Search and retrieve part information from distributor APIs (e.g. Mouser, Digi-Key)\n",
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"- Use AI to extract, analyze, and summarize key specifications and differences\n",
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"\n",
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"The goal is to support engineers in identifying part changes, upgrades, or replacements efficiently."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b51c91b6-953b-479c-acc5-ab2a189fabba",
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"metadata": {},
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"source": [
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"# 📌 Section A: Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "553666d5-af7e-46f0-b945-0d48c32bfbbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"from openai import OpenAI\n",
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"import fitz # PyMuPDF for PDF parsing\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a19c077a-36e3-4ff2-bee7-85f23e90b89a",
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"metadata": {},
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"source": [
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"# Load OpenAI API key from environment variable (recommended)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6435f1c7-f161-4cad-b68a-05080304ff22",
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"metadata": {},
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"outputs": [],
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"source": [
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"load_dotenv(override=True)\n",
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"api_key = os.getenv(\"OPENAI_API_KEY\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3722da9c-e1e9-4838-8ab9-04e45e52d8f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "34916ec4-643c-4b76-8e21-13c3364782fa",
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"metadata": {},
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"source": [
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"# Define paths to datasheets\n",
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"💬 **Note:** These example datasheet paths will later be replaced by a user-driven file selection dialog within the Jupyter notebook; optionally, this section could be extended to fetch component data directly from distributor websites."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "42621aa4-7094-4209-95ba-ecf03ba609fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"pdf_path_1 = \"./datasheets/part_old.pdf\"\n",
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"pdf_path_2 = \"./datasheets/part_new.pdf\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f09e201-ab22-4b9d-a9a3-b12cc671a68a",
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"metadata": {},
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"source": [
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"# 📌 Section B: Extract text from datasheets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff36d62e-efb6-4d08-a1d5-ceb470917103",
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_text_from_pdf(path):\n",
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" text = \"\"\n",
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" with fitz.open(path) as doc:\n",
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" for page in doc:\n",
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" text += page.get_text()\n",
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" return text"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0da6bc72-93e2-4229-885b-7020f3920855",
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"metadata": {},
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"source": [
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"# 📌 Section C: Use ChatGPT to summarize and compare"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4e8de5f9-c2b6-4d6f-9cde-c1275ec0be83",
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"metadata": {},
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"source": [
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"## Section C.1: Define system_prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "30dc6c3a-d7a1-4837-9d57-00c4b2d63092",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"You are a technical assistant helping to compare electronic component datasheets.\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "5bf19f66-89f2-4fbf-b5d6-ff1f8e06ba6d",
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"metadata": {},
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"source": [
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"## Section C.2: Define user_prompt, summerize and compare"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4ff4e362-11d4-4737-a10e-1953ac0eac55",
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"metadata": {},
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"outputs": [],
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"source": [
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"def summarize_datasheet(text, part_name, system_prompt):\n",
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" user_prompt = f\"\"\"\n",
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" Summarize the most important technical characteristics of the electronic component '{part_name}' based on this datasheet text:\n",
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" ---\n",
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" {text}\n",
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" ---\n",
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" Give a structured list of properties like voltage, current, dimensions, operating temperature, etc.\n",
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" \"\"\"\n",
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" response = openai.chat.completions.create(\n",
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" model=\"gpt-4o-mini\",\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ]\n",
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" )\n",
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" return response.choices[0].message.content\n",
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" \n",
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"def compare_parts(text1, text2, system_prompt):\n",
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" user_prompt = f\"\"\"\n",
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" Compare the following two summaries of electronic components and evaluate whether the second part is a valid replacement for the first one.\n",
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" Identify any differences in electrical specs, mechanical dimensions, and compliance with medical device requirements.\n",
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" Suggest what changes would be required to use the second part in place of the first (e.g., schematic/layout changes).\n",
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" \n",
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" Old Part Summary:\n",
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" {text1}\n",
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"\n",
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" New Part Summary:\n",
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" {text2}\n",
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"\n",
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" Provide a table of differences and a short final recommendation.\n",
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" \"\"\"\n",
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" response = openai.chat.completions.create(\n",
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" model=\"gpt-4o-mini\",\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ]\n",
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" )\n",
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" return response.choices[0].message.content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "92524623-b1f9-4b55-9056-d02c41457df4",
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"metadata": {},
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"source": [
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"# 📌 Section D: Put it all together and print it nicely."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ebd172eb-a8fb-4308-95c7-fee8f3f250ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"def display_summary_and_compare(part1, part2, system_prompt):\n",
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" content1 = extract_text_from_pdf(part1)\n",
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" content2 = extract_text_from_pdf(part2)\n",
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" summary1 = summarize_datasheet(content1, \"Old Part\", system_prompt)\n",
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" summary2 = summarize_datasheet(content2, \"New Part\", system_prompt)\n",
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" compare = compare_parts(summary1, summary2, system_prompt)\n",
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" report = summary1 + summary2 + compare\n",
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" display(Markdown(report))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ab2f1cfb-7e7b-429d-9f53-68524f93afbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"display_summary_and_compare(pdf_path_1, pdf_path_2, system_prompt)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "09c76689-db27-4fa4-9fb2-4ac1d4d111fb",
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"metadata": {},
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"source": [
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"# 📌 Section E: Next Steps (to be developed)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8ade0b16-52a6-4af4-a1ae-d0a505bf87a0",
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"metadata": {},
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"source": [
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"# - Parse key properties into structured tables (e.g., using regex or ChatGPT)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a7f6e50-1490-47ef-b911-278981636528",
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"metadata": {},
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"source": [
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"# - Automatically download datasheets from distributor websites"
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]
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},
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{
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"cell_type": "markdown",
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"id": "740bdc6d-48e4-4c7f-b7e9-4bb0d86b653f",
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"metadata": {},
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"source": [
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"# - Search for compatible parts via web APIs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "adda4dda-8bed-423b-a9c2-87f988ffa391",
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"metadata": {},
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"source": [
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"# - Export results to Excel or Markdown"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python (datasheet_env)",
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"language": "python",
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"name": "datasheet_env"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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