mirror of
https://github.com/deepset-ai/haystack.git
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3c81103db7
* move logging config from haystack lib to application * Update Documentation & Code Style * config logging before importing haystack * Update Documentation & Code Style * add logging config to all tutorials * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
446 lines
14 KiB
Plaintext
446 lines
14 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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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Question Answering on a Knowledge Graph\n",
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"\n",
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"[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial10_Knowledge_Graph.ipynb)\n",
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"\n",
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"Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.\n",
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"This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.\n",
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"The training of models that translate text queries into SPARQL queries is currently not supported."
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Install the latest release of Haystack in your own environment\n",
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"#! pip install farm-haystack\n",
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"\n",
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"# Install the latest master of Haystack\n",
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"!pip install --upgrade pip\n",
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"!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,inmemorygraph]"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Logging\n",
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"\n",
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"We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
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"Example log message:\n",
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"INFO - haystack.utils.preprocessing - Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
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"Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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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"outputs": [],
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"source": [
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"import logging\n",
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"\n",
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"logging.basicConfig(format=\"%(levelname)s - %(name)s - %(message)s\", level=logging.WARNING)\n",
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"logging.getLogger(\"haystack\").setLevel(logging.INFO)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Here are some imports that we'll need\n",
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"\n",
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"import subprocess\n",
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"import time\n",
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"from pathlib import Path\n",
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"\n",
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"from haystack.nodes import Text2SparqlRetriever\n",
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"from haystack.document_stores import InMemoryKnowledgeGraph\n",
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"from haystack.utils import fetch_archive_from_http"
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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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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Downloading Knowledge Graph and Model"
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Let's first fetch some triples that we want to store in our knowledge graph\n",
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"# Here: exemplary triples from the wizarding world\n",
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"graph_dir = \"data/tutorial10\"\n",
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"s3_url = \"https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip\"\n",
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"fetch_archive_from_http(url=s3_url, output_dir=graph_dir)\n",
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"\n",
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"# Fetch a pre-trained BART model that translates text queries to SPARQL queries\n",
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"model_dir = \"../saved_models/tutorial10_knowledge_graph/\"\n",
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"s3_url = \"https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip\"\n",
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"fetch_archive_from_http(url=s3_url, output_dir=model_dir)"
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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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"## Initialize a knowledge graph and load data"
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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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"Currently, Haystack supports two alternative implementations for knowledge graphs:\n",
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"* simple InMemoryKnowledgeGraph (based on RDFLib in-memory store)\n",
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"* GraphDBKnowledgeGraph, which runs on GraphDB."
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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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"### InMemoryKnowledgeGraph "
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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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"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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"The last triple stored in the knowledge graph is: {'s': {'type': 'uri', 'value': 'https://deepset.ai/harry_potter/Harry_potter'}, 'p': {'type': 'uri', 'value': 'https://deepset.ai/harry_potter/family'}, 'o': {'type': 'uri', 'value': 'https://deepset.ai/harry_potter/Dudley_dursleys_children'}}\n",
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"There are 118543 triples stored in the knowledge graph.\n"
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]
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}
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],
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"source": [
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"# Initialize a in memory knowledge graph and use \"tutorial_10_index\" as the name of the index\n",
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"kg = InMemoryKnowledgeGraph(index=\"tutorial_10_index\")\n",
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"\n",
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"# Delete the index as it might have been already created in previous runs\n",
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"kg.delete_index()\n",
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"\n",
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"# Create the index\n",
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"kg.create_index()\n",
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"\n",
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"# Import triples of subject, predicate, and object statements from a ttl file\n",
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"kg.import_from_ttl_file(index=\"tutorial_10_index\", path=Path(graph_dir) / \"triples.ttl\")\n",
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"print(f\"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}\")\n",
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"print(f\"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.\")"
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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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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [
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"### GraphDBKnowledgeGraph (alternative)"
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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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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"#### Launching a GraphDB instance"
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# # Unfortunately, there seems to be no good way to run GraphDB in colab environments\n",
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"# # In your local environment, you could start a GraphDB server with docker\n",
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"# # Feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/\n",
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"# import os\n",
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"\n",
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"# LAUNCH_GRAPHDB = os.environ.get(\"LAUNCH_GRAPHDB\", False)\n",
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"\n",
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"# if LAUNCH_GRAPHDB:\n",
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"# print(\"Starting GraphDB ...\")\n",
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"# status = subprocess.run(\n",
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"# [\n",
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"# \"docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11\"\n",
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"# ],\n",
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"# shell=True,\n",
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"# )\n",
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"# if status.returncode:\n",
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"# raise Exception(\n",
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"# \"Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?\"\n",
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"# )\n",
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"# time.sleep(5)"
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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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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"#### Creating a new GraphDB repository (also known as index in haystack's document stores)"
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# from haystack.document_stores import GraphDBKnowledgeGraph\n",
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"\n",
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"# # Initialize a knowledge graph connected to GraphDB and use \"tutorial_10_index\" as the name of the index\n",
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"# kg = GraphDBKnowledgeGraph(index=\"tutorial_10_index\")\n",
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"\n",
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"# # Delete the index as it might have been already created in previous runs\n",
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"# kg.delete_index()\n",
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"\n",
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"# # Create the index based on a configuration file\n",
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"# kg.create_index(config_path=Path(graph_dir) / \"repo-config.ttl\")\n",
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"\n",
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"# # Import triples of subject, predicate, and object statements from a ttl file\n",
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"# kg.import_from_ttl_file(index=\"tutorial_10_index\", path=Path(graph_dir) / \"triples.ttl\")\n",
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"# print(f\"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}\")\n",
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"# print(f\"There are {len(kg.get_all_triples())} triples stored in the knowledge 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": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# # Define prefixes for names of resources so that we can use shorter resource names in queries\n",
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"# prefixes = \"\"\"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
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"# PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n",
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"# PREFIX hp: <https://deepset.ai/harry_potter/>\n",
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"# \"\"\"\n",
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"# kg.prefixes = prefixes"
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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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"## Load the pre-trained retriever"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load a pre-trained model that translates text queries to SPARQL queries\n",
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"kgqa_retriever = Text2SparqlRetriever(knowledge_graph=kg, model_name_or_path=Path(model_dir) / \"hp_v3.4\")"
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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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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Query Execution\n",
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"\n",
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"We can now ask questions that will be answered by our knowledge graph!\n",
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"One limitation though: our pre-trained model can only generate questions about resources it has seen during training.\n",
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"Otherwise, it cannot translate the name of the resource to the identifier used in the knowledge graph.\n",
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"E.g. \"Harry\" -> \"hp:Harry_potter\""
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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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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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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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"Translating the text query \"In which house is Harry Potter?\" to a SPARQL query and executing it on the knowledge graph...\n",
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"[{'answer': ['https://deepset.ai/harry_potter/Gryffindor'], 'prediction_meta': {'model': 'Text2SparqlRetriever', 'sparql_query': 'select ?a { hp:Harry_potter hp:house ?a . }'}}]\n",
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"Executing a SPARQL query with prefixed names of resources...\n",
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"(['https://deepset.ai/harry_potter/Rubeus_hagrid', 'https://deepset.ai/harry_potter/Ogg'], 'select distinct ?sbj where { ?sbj hp:job hp:Keeper_of_keys_and_grounds . }')\n",
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"Executing a SPARQL query with full names of resources...\n",
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"(['https://deepset.ai/harry_potter/Otter'], 'select distinct ?obj where { <https://deepset.ai/harry_potter/Hermione_granger> <https://deepset.ai/harry_potter/patronus> ?obj . }')\n"
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]
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}
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],
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"source": [
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"query = \"In which house is Harry Potter?\"\n",
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"print(f'Translating the text query \"{query}\" to a SPARQL query and executing it on the knowledge graph...')\n",
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"result = kgqa_retriever.retrieve(query=query)\n",
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"print(result)\n",
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"# Correct SPARQL query: select ?a { hp:Harry_potter hp:house ?a . }\n",
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"# Correct answer: Gryffindor\n",
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"\n",
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"print(\"Executing a SPARQL query with prefixed names of resources...\")\n",
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"result = kgqa_retriever._query_kg(\n",
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" sparql_query=\"select distinct ?sbj where { ?sbj hp:job hp:Keeper_of_keys_and_grounds . }\"\n",
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")\n",
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"print(result)\n",
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"# Paraphrased question: Who is the keeper of keys and grounds?\n",
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"# Correct answer: Rubeus Hagrid\n",
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"\n",
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"print(\"Executing a SPARQL query with full names of resources...\")\n",
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"result = kgqa_retriever._query_kg(\n",
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" sparql_query=\"select distinct ?obj where { <https://deepset.ai/harry_potter/Hermione_granger> <https://deepset.ai/harry_potter/patronus> ?obj . }\"\n",
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")\n",
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"print(result)\n",
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"# Paraphrased question: What is the patronus of Hermione?\n",
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"# Correct answer: Otter"
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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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"## About us\n",
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"\n",
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"This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
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"\n",
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"We bring NLP to the industry via open source! \n",
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"Our focus: Industry specific language models & large scale QA systems. \n",
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" \n",
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"Some of our other work: \n",
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"- [German BERT](https://deepset.ai/german-bert)\n",
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"- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
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"- [FARM](https://github.com/deepset-ai/FARM)\n",
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"\n",
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"Get in touch:\n",
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"[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
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"\n",
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"By the way: [we're hiring!](https://www.deepset.ai/jobs)"
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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 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": "3.10.4"
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},
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"vscode": {
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"interpreter": {
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"hash": "d6fc774dec8e6d4d8b6a5562b41269a570ea5456d1c03f28da35966a9134f033"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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