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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# `Words2Wisdom` Demo\n",
"\n",
"For purpose of the notebook, we add the `src` director to the `PYTHONPATH`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"# add words2wisdom to PYTHONPATH\n",
"sys.path.append(\"../src/\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we load in the example text file (from OpenStax Bio 2e chapter 4.2):"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cells fall into one of two broad categories: prokaryotic and eukaryotic. We classify only the predominantly single-celled organisms Bacteria and Archaea as prokaryotes (pro- = before; -kary- = nucleus...\n"
]
}
],
"source": [
"# load example text\n",
"with open(\"example.txt\") as f:\n",
" text = f.read()\n",
"\n",
"# print example\n",
"print(text[:200] + \"...\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `words2wisdom` pipeline can be configured from a configuration INI file. We have one prepared already, but you will need to create one with your desired settings.\n",
"\n",
"After configuration, we call the `run` process. Then, we save all outputs to a ZIP file."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized Text2KG pipeline:\n",
"[INPUT: text] -> ClauseDeconstruction() -> TripletExtraction() -> [OUTPUT: knowledge graph]\n",
"Running Text2KG pipeline:\n",
"Extracting knowledge graph... Cleaning knowledge graph components... Done!\n",
"Run ID: 2024-02-16-001\n",
"Saved data to ./output-2024-02-16-001.zip\n"
]
}
],
"source": [
"from words2wisdom.pipeline import Pipeline\n",
"from words2wisdom.utils import dump_all\n",
"\n",
"w2w = Pipeline.from_ini(\"config.ini\")\n",
"batches, graph = w2w.run(text)\n",
"\n",
"output_zip = dump_all(\n",
" pipeline=w2w,\n",
" text_batches=batches,\n",
" knowledge_graph=graph,\n",
" to_path=\".\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use GPT-4 to auto-evaluate the knowledge graph."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing knowledge graph validation. Run: 2024-02-16-001\n",
"\n",
"Starting excerpt 1 of 6. Validating 7 triplets... Done!\n",
"Starting excerpt 2 of 6. Validating 20 triplets... Done!\n",
"Starting excerpt 3 of 6. Validating 20 triplets... Done!\n",
"Starting excerpt 4 of 6. Validating 10 triplets... Done!\n",
"Starting excerpt 5 of 6. Validating 10 triplets... Done!\n",
"Starting excerpt 6 of 6. Validating 16 triplets... Done!\n",
"\n",
"Knowledge graph validation complete!\n",
"It took 109.471 seconds to validate 83 triplets.\n",
"Saved to: ./validation-2024-02-16-001.csv\n"
]
}
],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from words2wisdom.validate import validate_knowledge_graph\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
"\n",
"eval_file = validate_knowledge_graph(llm=llm, output_zip=output_zip)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 5 evaluation questions. The questions and score ranges can be found in `config/validation.yml`. Here are the results:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Q1</th>\n",
" <th>Q2</th>\n",
" <th>Q3</th>\n",
" <th>Q4</th>\n",
" <th>Q5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>83.000000</td>\n",
" <td>83.000000</td>\n",
" <td>83.000000</td>\n",
" <td>83.000000</td>\n",
" <td>83.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.975904</td>\n",
" <td>0.975904</td>\n",
" <td>0.975904</td>\n",
" <td>1.819277</td>\n",
" <td>1.566265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.154281</td>\n",
" <td>0.154281</td>\n",
" <td>0.154281</td>\n",
" <td>0.387128</td>\n",
" <td>0.522489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Q1 Q2 Q3 Q4 Q5\n",
"count 83.000000 83.000000 83.000000 83.000000 83.000000\n",
"mean 0.975904 0.975904 0.975904 1.819277 1.566265\n",
"std 0.154281 0.154281 0.154281 0.387128 0.522489\n",
"min 0.000000 0.000000 0.000000 1.000000 0.000000\n",
"25% 1.000000 1.000000 1.000000 2.000000 1.000000\n",
"50% 1.000000 1.000000 1.000000 2.000000 2.000000\n",
"75% 1.000000 1.000000 1.000000 2.000000 2.000000\n",
"max 1.000000 1.000000 1.000000 2.000000 2.000000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"data = pd.read_csv(eval_file)\n",
"data.describe(include=[int])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "nlp",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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