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add everyting for docs
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docs/extras/use_cases/graph/graph_qa.ipynb
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304
docs/extras/use_cases/graph/graph_qa.ipynb
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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": "a6850189",
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"metadata": {},
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"source": [
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"# Graph QA\n",
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"\n",
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"This notebook goes over how to do question answering over a graph data structure."
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]
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},
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{
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"cell_type": "markdown",
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"id": "9e516e3e",
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"metadata": {},
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"source": [
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"## Create the graph\n",
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"\n",
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"In this section, we construct an example graph. At the moment, this works best for small pieces of text."
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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": 1,
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"id": "3849873d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.indexes import GraphIndexCreator\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.document_loaders import TextLoader"
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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": 2,
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"id": "05d65c87",
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"metadata": {},
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"outputs": [],
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"source": [
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"index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))"
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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": 3,
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"id": "0a45a5b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"../../state_of_the_union.txt\") as f:\n",
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" all_text = f.read()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fca3e1b",
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"metadata": {},
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"source": [
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"We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment."
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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": 4,
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"id": "80522bd6",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"\\n\".join(all_text.split(\"\\n\\n\")[105:108])"
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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": 5,
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"id": "da5aad5a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'It won’t look like much, but if you stop and look closely, you’ll see a “Field of dreams,” the ground on which America’s future will be built. \\nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor “mega site”. \\nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. '"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"text"
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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": 6,
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"id": "8dad7b59",
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"metadata": {},
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"outputs": [],
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"source": [
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"graph = index_creator.from_text(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": "2118f363",
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"metadata": {},
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"source": [
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"We can inspect the created graph."
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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": 7,
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"id": "32878c13",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),\n",
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" ('Intel', 'state-of-the-art factories', 'is building'),\n",
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" ('Intel', '10,000 new good-paying jobs', 'is creating'),\n",
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" ('Intel', 'Silicon Valley', 'is helping build'),\n",
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" ('Field of dreams',\n",
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" \"America's future will be built\",\n",
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" 'is the ground on which')]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"graph.get_triples()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e9737be1",
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"metadata": {},
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"source": [
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"## Querying the graph\n",
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"We can now use the graph QA chain to ask question of the graph"
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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": 8,
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"id": "76edc854",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import GraphQAChain"
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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": 9,
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"id": "8e7719b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)"
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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": 10,
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"id": "f6511169",
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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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"\n",
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"\n",
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"\u001b[1m> Entering new GraphQAChain chain...\u001b[0m\n",
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"Entities Extracted:\n",
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"\u001b[32;1m\u001b[1;3m Intel\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3mIntel is going to build $20 billion semiconductor \"mega site\"\n",
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"Intel is building state-of-the-art factories\n",
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"Intel is creating 10,000 new good-paying jobs\n",
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"Intel is helping build Silicon Valley\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' Intel is going to build a $20 billion semiconductor \"mega site\" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.run(\"what is Intel going to build?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "410aafa0",
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"metadata": {},
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"source": [
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"## Save the graph\n",
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"We can also save and load the graph."
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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": 7,
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"id": "bc72cca0",
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"metadata": {},
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"outputs": [],
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"source": [
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"graph.write_to_gml(\"graph.gml\")"
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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": 8,
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"id": "652760ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.indexes.graph import NetworkxEntityGraph"
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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": 9,
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"id": "eae591fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"loaded_graph = NetworkxEntityGraph.from_gml(\"graph.gml\")"
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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": 10,
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"id": "9439d419",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),\n",
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" ('Intel', 'state-of-the-art factories', 'is building'),\n",
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" ('Intel', '10,000 new good-paying jobs', 'is creating'),\n",
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" ('Intel', 'Silicon Valley', 'is helping build'),\n",
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" ('Field of dreams',\n",
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" \"America's future will be built\",\n",
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" 'is the ground on which')]"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"loaded_graph.get_triples()"
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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": "045796cf",
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"metadata": {},
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"outputs": [],
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"source": []
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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 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.9.1"
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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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