wehub-resource-sync
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485 lines
22 KiB
Plaintext
485 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Knowledge Graph Construction w/ WikiData Filtering\n",
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"\n",
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"In this notebook, we compare using [REBEL](https://huggingface.co/Babelscape/rebel-large) for knowledge graph construction with and without filtering from wikidata.\n",
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"\n",
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"This is a simplified version, find out more about using wikipedia for filtering, check here\n",
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"- [Make Meaningful Knowledge Graph from OpenSource REBEL Model](https://medium.com/@haiyangli_38602/make-meaningful-knowledge-graph-from-opensource-rebel-model-6f9729a55527)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 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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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install llama-index-llms-openai\n",
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"%pip install llama-index-readers-web\n",
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"%pip install llama-index-readers-papers"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: llama_index in /usr/local/lib/python3.10/dist-packages (0.8.37)\n",
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]
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}
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],
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"source": [
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"!pip install llama_index transformers wikipedia html2text pyvis"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"import sys\n",
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"\n",
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"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
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"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core import KnowledgeGraphIndex\n",
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"from llama_index.readers.web import SimpleWebPageReader\n",
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"from llama_index.core.graph_stores import SimpleGraphStore\n",
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"from llama_index.core import StorageContext\n",
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"from llama_index.llms.openai import OpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. extract via huggingface pipeline\n",
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"\n",
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"The initial pipeline uses the provided extraction code from the [HuggingFace model card](https://huggingface.co/Babelscape/rebel-large)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import pipeline\n",
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"\n",
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"triplet_extractor = pipeline(\n",
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" \"text2text-generation\",\n",
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" model=\"Babelscape/rebel-large\",\n",
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" tokenizer=\"Babelscape/rebel-large\",\n",
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" # comment this line to run on CPU\n",
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" device=\"cuda:0\",\n",
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")\n",
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"\n",
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"\n",
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"def extract_triplets(input_text):\n",
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" text = triplet_extractor.tokenizer.batch_decode(\n",
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" [\n",
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" triplet_extractor(\n",
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" input_text, return_tensors=True, return_text=False\n",
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" )[0][\"generated_token_ids\"]\n",
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" ]\n",
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" )[0]\n",
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"\n",
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" triplets = []\n",
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" relation, subject, relation, object_ = \"\", \"\", \"\", \"\"\n",
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" text = text.strip()\n",
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" current = \"x\"\n",
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" for token in (\n",
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" text.replace(\"<s>\", \"\")\n",
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" .replace(\"<pad>\", \"\")\n",
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" .replace(\"</s>\", \"\")\n",
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" .split()\n",
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" ):\n",
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" if token == \"<triplet>\":\n",
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" current = \"t\"\n",
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" if relation != \"\":\n",
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" triplets.append(\n",
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" (subject.strip(), relation.strip(), object_.strip())\n",
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" )\n",
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" relation = \"\"\n",
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" subject = \"\"\n",
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" elif token == \"<subj>\":\n",
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" current = \"s\"\n",
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" if relation != \"\":\n",
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" triplets.append(\n",
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" (subject.strip(), relation.strip(), object_.strip())\n",
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" )\n",
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" object_ = \"\"\n",
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" elif token == \"<obj>\":\n",
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" current = \"o\"\n",
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" relation = \"\"\n",
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" else:\n",
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" if current == \"t\":\n",
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" subject += \" \" + token\n",
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" elif current == \"s\":\n",
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" object_ += \" \" + token\n",
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" elif current == \"o\":\n",
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" relation += \" \" + token\n",
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"\n",
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" if subject != \"\" and relation != \"\" and object_ != \"\":\n",
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" triplets.append((subject.strip(), relation.strip(), object_.strip()))\n",
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"\n",
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" return triplets"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Extract with wiki filtering\n",
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"\n",
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"Optionally, we can filter our extracted relations using data from wikipedia.\n",
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"\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import wikipedia\n",
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"\n",
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"\n",
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"class WikiFilter:\n",
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" def __init__(self):\n",
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" self.cache = {}\n",
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"\n",
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" def filter(self, candidate_entity):\n",
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" # check the cache to avoid network calls\n",
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" if candidate_entity in self.cache:\n",
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" return self.cache[candidate_entity][\"title\"]\n",
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"\n",
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" # pull the page from wikipedia -- if it exists\n",
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" try:\n",
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" page = wikipedia.page(candidate_entity, auto_suggest=False)\n",
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" entity_data = {\n",
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" \"title\": page.title,\n",
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" \"url\": page.url,\n",
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" \"summary\": page.summary,\n",
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" }\n",
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"\n",
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" # cache the page title and original entity\n",
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" self.cache[candidate_entity] = entity_data\n",
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" self.cache[page.title] = entity_data\n",
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"\n",
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" return entity_data[\"title\"]\n",
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" except:\n",
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" return None\n",
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"\n",
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"\n",
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"wiki_filter = WikiFilter()\n",
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"\n",
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"\n",
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"def extract_triplets_wiki(text):\n",
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" relations = extract_triplets(text)\n",
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"\n",
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" filtered_relations = []\n",
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" for relation in relations:\n",
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" (subj, rel, obj) = relation\n",
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" filtered_subj = wiki_filter.filter(subj)\n",
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" filtered_obj = wiki_filter.filter(obj)\n",
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"\n",
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" # skip if at least one entity not linked to wiki\n",
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" if filtered_subj is None and filtered_obj is None:\n",
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" continue\n",
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"\n",
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" filtered_relations.append(\n",
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" (\n",
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" filtered_subj or subj,\n",
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" rel,\n",
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" filtered_obj or obj,\n",
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" )\n",
|
|
" )\n",
|
|
"\n",
|
|
" return filtered_relations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Run with Llama_Index"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from llama_index.core import download_loader\n",
|
|
"\n",
|
|
"from llama_index.readers.papers import ArxivReader\n",
|
|
"\n",
|
|
"loader = ArxivReader()\n",
|
|
"documents = loader.load_data(\n",
|
|
" search_query=\"Retrieval Augmented Generation\", max_results=1\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import os\n",
|
|
"import openai\n",
|
|
"\n",
|
|
"os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
|
|
"openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from llama_index.core import Document\n",
|
|
"\n",
|
|
"# merge all documents into one, since it's split by page\n",
|
|
"documents = [Document(text=\"\".join([x.text for x in documents]))]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[nltk_data] Downloading package punkt to /tmp/llama_index...\n",
|
|
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from llama_index.core import Settings\n",
|
|
"\n",
|
|
"# set global configs\n",
|
|
"llm = OpenAI(temperature=0.1, model=\"gpt-3.5-turbo\")\n",
|
|
"Settings.llm = llm\n",
|
|
"Settings.chunk_size = 256\n",
|
|
"\n",
|
|
"# set up graph storage context\n",
|
|
"graph_store = SimpleGraphStore()\n",
|
|
"storage_context = StorageContext.from_defaults(graph_store=graph_store)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"NOTE: This next cell takes about 4mins on GPU."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1101: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
|
|
" warnings.warn(\n",
|
|
"/usr/local/lib/python3.10/dist-packages/wikipedia/wikipedia.py:389: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
|
|
"\n",
|
|
"The code that caused this warning is on line 389 of the file /usr/local/lib/python3.10/dist-packages/wikipedia/wikipedia.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n",
|
|
"\n",
|
|
" lis = BeautifulSoup(html).find_all('li')\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"index = KnowledgeGraphIndex.from_documents(\n",
|
|
" documents,\n",
|
|
" max_triplets_per_chunk=3,\n",
|
|
" kg_triplet_extract_fn=extract_triplets,\n",
|
|
" storage_context=storage_context,\n",
|
|
" include_embeddings=True,\n",
|
|
")\n",
|
|
"\n",
|
|
"index1 = KnowledgeGraphIndex.from_documents(\n",
|
|
" documents,\n",
|
|
" max_triplets_per_chunk=3,\n",
|
|
" kg_triplet_extract_fn=extract_triplets_wiki,\n",
|
|
" storage_context=storage_context,\n",
|
|
" include_embeddings=True,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## create graph\n",
|
|
"from pyvis.network import Network\n",
|
|
"\n",
|
|
"g = index.get_networkx_graph()\n",
|
|
"net = Network(notebook=True, cdn_resources=\"in_line\", directed=True)\n",
|
|
"net.from_nx(g)\n",
|
|
"net.save_graph(\"non_filtered_graph.html\")\n",
|
|
"\n",
|
|
"from IPython.display import HTML\n",
|
|
"\n",
|
|
"HTML(filename=\"non_filtered_graph.html\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## create graph\n",
|
|
"from pyvis.network import Network\n",
|
|
"\n",
|
|
"g = index1.get_networkx_graph()\n",
|
|
"net = Network(notebook=True, cdn_resources=\"in_line\", directed=True)\n",
|
|
"net.from_nx(g)\n",
|
|
"net.save_graph(\"wiki_filtered_graph.html\")\n",
|
|
"\n",
|
|
"from IPython.display import HTML\n",
|
|
"\n",
|
|
"HTML(filename=\"wiki_filtered_graph.html\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "GPU",
|
|
"colab": {
|
|
"gpuType": "T4",
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|