mirror of
				https://github.com/hwchase17/langchain.git
				synced 2025-11-04 02:03:32 +00:00 
			
		
		
		
	
		
			
				
	
	
		
			305 lines
		
	
	
		
			7.0 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			305 lines
		
	
	
		
			7.0 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
{
 | 
						||
 "cells": [
 | 
						||
  {
 | 
						||
   "cell_type": "markdown",
 | 
						||
   "id": "a6850189",
 | 
						||
   "metadata": {},
 | 
						||
   "source": [
 | 
						||
    "# Graph QA\n",
 | 
						||
    "\n",
 | 
						||
    "This notebook goes over how to do question answering over a graph data structure."
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "markdown",
 | 
						||
   "id": "9e516e3e",
 | 
						||
   "metadata": {},
 | 
						||
   "source": [
 | 
						||
    "## Create the graph\n",
 | 
						||
    "\n",
 | 
						||
    "In this section, we construct an example graph. At the moment, this works best for small pieces of text."
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 1,
 | 
						||
   "id": "3849873d",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "from langchain.indexes import GraphIndexCreator\n",
 | 
						||
    "from langchain.llms import OpenAI\n",
 | 
						||
    "from langchain.document_loaders import TextLoader"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 2,
 | 
						||
   "id": "05d65c87",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 3,
 | 
						||
   "id": "0a45a5b9",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "with open(\"../../state_of_the_union.txt\") as f:\n",
 | 
						||
    "    all_text = f.read()"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "markdown",
 | 
						||
   "id": "3fca3e1b",
 | 
						||
   "metadata": {},
 | 
						||
   "source": [
 | 
						||
    "We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment."
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 4,
 | 
						||
   "id": "80522bd6",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "text = \"\\n\".join(all_text.split(\"\\n\\n\")[105:108])"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 5,
 | 
						||
   "id": "da5aad5a",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [
 | 
						||
    {
 | 
						||
     "data": {
 | 
						||
      "text/plain": [
 | 
						||
       "'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. '"
 | 
						||
      ]
 | 
						||
     },
 | 
						||
     "execution_count": 5,
 | 
						||
     "metadata": {},
 | 
						||
     "output_type": "execute_result"
 | 
						||
    }
 | 
						||
   ],
 | 
						||
   "source": [
 | 
						||
    "text"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 6,
 | 
						||
   "id": "8dad7b59",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "graph = index_creator.from_text(text)"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "markdown",
 | 
						||
   "id": "2118f363",
 | 
						||
   "metadata": {},
 | 
						||
   "source": [
 | 
						||
    "We can inspect the created graph."
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 7,
 | 
						||
   "id": "32878c13",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [
 | 
						||
    {
 | 
						||
     "data": {
 | 
						||
      "text/plain": [
 | 
						||
       "[('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),\n",
 | 
						||
       " ('Intel', 'state-of-the-art factories', 'is building'),\n",
 | 
						||
       " ('Intel', '10,000 new good-paying jobs', 'is creating'),\n",
 | 
						||
       " ('Intel', 'Silicon Valley', 'is helping build'),\n",
 | 
						||
       " ('Field of dreams',\n",
 | 
						||
       "  \"America's future will be built\",\n",
 | 
						||
       "  'is the ground on which')]"
 | 
						||
      ]
 | 
						||
     },
 | 
						||
     "execution_count": 7,
 | 
						||
     "metadata": {},
 | 
						||
     "output_type": "execute_result"
 | 
						||
    }
 | 
						||
   ],
 | 
						||
   "source": [
 | 
						||
    "graph.get_triples()"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "markdown",
 | 
						||
   "id": "e9737be1",
 | 
						||
   "metadata": {},
 | 
						||
   "source": [
 | 
						||
    "## Querying the graph\n",
 | 
						||
    "We can now use the graph QA chain to ask question of the graph"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 8,
 | 
						||
   "id": "76edc854",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "from langchain.chains import GraphQAChain"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 9,
 | 
						||
   "id": "8e7719b4",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 10,
 | 
						||
   "id": "f6511169",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [
 | 
						||
    {
 | 
						||
     "name": "stdout",
 | 
						||
     "output_type": "stream",
 | 
						||
     "text": [
 | 
						||
      "\n",
 | 
						||
      "\n",
 | 
						||
      "\u001b[1m> Entering new GraphQAChain chain...\u001b[0m\n",
 | 
						||
      "Entities Extracted:\n",
 | 
						||
      "\u001b[32;1m\u001b[1;3m Intel\u001b[0m\n",
 | 
						||
      "Full Context:\n",
 | 
						||
      "\u001b[32;1m\u001b[1;3mIntel is going to build $20 billion semiconductor \"mega site\"\n",
 | 
						||
      "Intel is building state-of-the-art factories\n",
 | 
						||
      "Intel is creating 10,000 new good-paying jobs\n",
 | 
						||
      "Intel is helping build Silicon Valley\u001b[0m\n",
 | 
						||
      "\n",
 | 
						||
      "\u001b[1m> Finished chain.\u001b[0m\n"
 | 
						||
     ]
 | 
						||
    },
 | 
						||
    {
 | 
						||
     "data": {
 | 
						||
      "text/plain": [
 | 
						||
       "' 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.'"
 | 
						||
      ]
 | 
						||
     },
 | 
						||
     "execution_count": 10,
 | 
						||
     "metadata": {},
 | 
						||
     "output_type": "execute_result"
 | 
						||
    }
 | 
						||
   ],
 | 
						||
   "source": [
 | 
						||
    "chain.run(\"what is Intel going to build?\")"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "markdown",
 | 
						||
   "id": "410aafa0",
 | 
						||
   "metadata": {},
 | 
						||
   "source": [
 | 
						||
    "## Save the graph\n",
 | 
						||
    "We can also save and load the graph."
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 7,
 | 
						||
   "id": "bc72cca0",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "graph.write_to_gml(\"graph.gml\")"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 8,
 | 
						||
   "id": "652760ad",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "from langchain.indexes.graph import NetworkxEntityGraph"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 9,
 | 
						||
   "id": "eae591fe",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": [
 | 
						||
    "loaded_graph = NetworkxEntityGraph.from_gml(\"graph.gml\")"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": 10,
 | 
						||
   "id": "9439d419",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [
 | 
						||
    {
 | 
						||
     "data": {
 | 
						||
      "text/plain": [
 | 
						||
       "[('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),\n",
 | 
						||
       " ('Intel', 'state-of-the-art factories', 'is building'),\n",
 | 
						||
       " ('Intel', '10,000 new good-paying jobs', 'is creating'),\n",
 | 
						||
       " ('Intel', 'Silicon Valley', 'is helping build'),\n",
 | 
						||
       " ('Field of dreams',\n",
 | 
						||
       "  \"America's future will be built\",\n",
 | 
						||
       "  'is the ground on which')]"
 | 
						||
      ]
 | 
						||
     },
 | 
						||
     "execution_count": 10,
 | 
						||
     "metadata": {},
 | 
						||
     "output_type": "execute_result"
 | 
						||
    }
 | 
						||
   ],
 | 
						||
   "source": [
 | 
						||
    "loaded_graph.get_triples()"
 | 
						||
   ]
 | 
						||
  },
 | 
						||
  {
 | 
						||
   "cell_type": "code",
 | 
						||
   "execution_count": null,
 | 
						||
   "id": "045796cf",
 | 
						||
   "metadata": {},
 | 
						||
   "outputs": [],
 | 
						||
   "source": []
 | 
						||
  }
 | 
						||
 ],
 | 
						||
 "metadata": {
 | 
						||
  "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",
 | 
						||
   "version": "3.9.1"
 | 
						||
  }
 | 
						||
 },
 | 
						||
 "nbformat": 4,
 | 
						||
 "nbformat_minor": 5
 | 
						||
}
 |