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Support for SPARQL (#7165)
# [SPARQL](https://www.w3.org/TR/rdf-sparql-query/) for [LangChain](https://github.com/hwchase17/langchain) ## Description LangChain support for knowledge graphs relying on W3C standards using RDFlib: SPARQL/ RDF(S)/ OWL with special focus on RDF \ * Works with local files, files from the web, and SPARQL endpoints * Supports both SELECT and UPDATE queries * Includes both a Jupyter notebook with an example and integration tests ## Contribution compared to related PRs and discussions * [Wikibase agent](https://github.com/hwchase17/langchain/pull/2690) - uses SPARQL, but specifically for wikibase querying * [Cypher qa](https://github.com/hwchase17/langchain/pull/5078) - graph DB question answering for Neo4J via Cypher * [PR 6050](https://github.com/hwchase17/langchain/pull/6050) - tries something similar, but does not cover UPDATE queries and supports only RDF * Discussions on [w3c mailing list](mailto:semantic-web@w3.org) related to the combination of LLMs (specifically ChatGPT) and knowledge graphs ## Dependencies * [RDFlib](https://github.com/RDFLib/rdflib) ## Tag maintainer Graph database related to memory -> @hwchase17
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docs/extras/modules/chains/additional/graph_sparql_qa.ipynb
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docs/extras/modules/chains/additional/graph_sparql_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": "c94240f5",
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"metadata": {},
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"source": [
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"# GraphSparqlQAChain\n",
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"\n",
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"Graph databases are an excellent choice for applications based on network-like models. To standardize the syntax and semantics of such graphs, the W3C recommends Semantic Web Technologies, cp. [Semantic Web](https://www.w3.org/standards/semanticweb/). [SPARQL](https://www.w3.org/TR/sparql11-query/) serves as a query language analogously to SQL or Cypher for these graphs. This notebook demonstrates the application of LLMs as a natural language interface to a graph database by generating SPARQL.\\\n",
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"Disclaimer: To date, SPARQL query generation via LLMs is still a bit unstable. Be especially careful with UPDATE queries, which alter the graph."
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]
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},
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{
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"cell_type": "markdown",
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"id": "dbc0ee68",
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"metadata": {},
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"source": [
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"There are several sources you can run queries against, including files on the web, files you have available locally, SPARQL endpoints, e.g., [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page), and [triple stores](https://www.w3.org/wiki/LargeTripleStores)."
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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": "62812aad",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import GraphSparqlQAChain\n",
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"from langchain.graphs import RdfGraph"
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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": "0928915d",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"graph = RdfGraph(\n",
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" source_file=\"http://www.w3.org/People/Berners-Lee/card\",\n",
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" standard=\"rdf\",\n",
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" local_copy=\"test.ttl\",\n",
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")"
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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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"Note that providing a `local_file` is necessary for storing changes locally if the source is read-only."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"id": "58c1a8ea",
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"metadata": {},
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"source": [
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"## Refresh graph schema information\n",
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"If the schema of the database changes, you can refresh the schema information needed to generate SPARQL queries."
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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": "4e3de44f",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"graph.load_schema()"
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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": "1fe76ccd",
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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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"In the following, each IRI is followed by the local name and optionally its description in parentheses. \n",
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"The RDF graph supports the following node types:\n",
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"<http://xmlns.com/foaf/0.1/PersonalProfileDocument> (PersonalProfileDocument, None), <http://www.w3.org/ns/auth/cert#RSAPublicKey> (RSAPublicKey, None), <http://www.w3.org/2000/10/swap/pim/contact#Male> (Male, None), <http://xmlns.com/foaf/0.1/Person> (Person, None), <http://www.w3.org/2006/vcard/ns#Work> (Work, None)\n",
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"The RDF graph supports the following relationships:\n",
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"<http://www.w3.org/2000/01/rdf-schema#seeAlso> (seeAlso, None), <http://purl.org/dc/elements/1.1/title> (title, None), <http://xmlns.com/foaf/0.1/mbox_sha1sum> (mbox_sha1sum, None), <http://xmlns.com/foaf/0.1/maker> (maker, None), <http://www.w3.org/ns/solid/terms#oidcIssuer> (oidcIssuer, None), <http://www.w3.org/2000/10/swap/pim/contact#publicHomePage> (publicHomePage, None), <http://xmlns.com/foaf/0.1/openid> (openid, None), <http://www.w3.org/ns/pim/space#storage> (storage, None), <http://xmlns.com/foaf/0.1/name> (name, None), <http://www.w3.org/2000/10/swap/pim/contact#country> (country, None), <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> (type, None), <http://www.w3.org/ns/solid/terms#profileHighlightColor> (profileHighlightColor, None), <http://www.w3.org/ns/pim/space#preferencesFile> (preferencesFile, None), <http://www.w3.org/2000/01/rdf-schema#label> (label, None), <http://www.w3.org/ns/auth/cert#modulus> (modulus, None), <http://www.w3.org/2000/10/swap/pim/contact#participant> (participant, None), <http://www.w3.org/2000/10/swap/pim/contact#street2> (street2, None), <http://www.w3.org/2006/vcard/ns#locality> (locality, None), <http://xmlns.com/foaf/0.1/nick> (nick, None), <http://xmlns.com/foaf/0.1/homepage> (homepage, None), <http://creativecommons.org/ns#license> (license, None), <http://xmlns.com/foaf/0.1/givenname> (givenname, None), <http://www.w3.org/2006/vcard/ns#street-address> (street-address, None), <http://www.w3.org/2006/vcard/ns#postal-code> (postal-code, None), <http://www.w3.org/2000/10/swap/pim/contact#street> (street, None), <http://www.w3.org/2003/01/geo/wgs84_pos#lat> (lat, None), <http://xmlns.com/foaf/0.1/primaryTopic> (primaryTopic, None), <http://www.w3.org/2006/vcard/ns#fn> (fn, None), <http://www.w3.org/2003/01/geo/wgs84_pos#location> (location, None), <http://usefulinc.com/ns/doap#developer> (developer, None), <http://www.w3.org/2000/10/swap/pim/contact#city> (city, None), <http://www.w3.org/2006/vcard/ns#region> (region, None), <http://xmlns.com/foaf/0.1/member> (member, None), <http://www.w3.org/2003/01/geo/wgs84_pos#long> (long, None), <http://www.w3.org/2000/10/swap/pim/contact#address> (address, None), <http://xmlns.com/foaf/0.1/family_name> (family_name, None), <http://xmlns.com/foaf/0.1/account> (account, None), <http://xmlns.com/foaf/0.1/workplaceHomepage> (workplaceHomepage, None), <http://purl.org/dc/terms/title> (title, None), <http://www.w3.org/ns/solid/terms#publicTypeIndex> (publicTypeIndex, None), <http://www.w3.org/2000/10/swap/pim/contact#office> (office, None), <http://www.w3.org/2000/10/swap/pim/contact#homePage> (homePage, None), <http://xmlns.com/foaf/0.1/mbox> (mbox, None), <http://www.w3.org/2000/10/swap/pim/contact#preferredURI> (preferredURI, None), <http://www.w3.org/ns/solid/terms#profileBackgroundColor> (profileBackgroundColor, None), <http://schema.org/owns> (owns, None), <http://xmlns.com/foaf/0.1/based_near> (based_near, None), <http://www.w3.org/2006/vcard/ns#hasAddress> (hasAddress, None), <http://xmlns.com/foaf/0.1/img> (img, None), <http://www.w3.org/2000/10/swap/pim/contact#assistant> (assistant, None), <http://xmlns.com/foaf/0.1/title> (title, None), <http://www.w3.org/ns/auth/cert#key> (key, None), <http://www.w3.org/ns/ldp#inbox> (inbox, None), <http://www.w3.org/ns/solid/terms#editableProfile> (editableProfile, None), <http://www.w3.org/2000/10/swap/pim/contact#postalCode> (postalCode, None), <http://xmlns.com/foaf/0.1/weblog> (weblog, None), <http://www.w3.org/ns/auth/cert#exponent> (exponent, None), <http://rdfs.org/sioc/ns#avatar> (avatar, None)\n",
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"\n"
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]
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}
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],
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"source": [
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"graph.get_schema"
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]
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},
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{
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"cell_type": "markdown",
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"id": "68a3c677",
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"metadata": {},
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"source": [
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"## Querying the graph\n",
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"\n",
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"Now, you can use the graph SPARQL QA chain to ask questions about 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": 11,
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"id": "7476ce98",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"chain = GraphSparqlQAChain.from_llm(\n",
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" ChatOpenAI(temperature=0), graph=graph, verbose=True\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": 12,
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"id": "ef8ee27b",
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"metadata": {
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"pycharm": {
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"is_executing": true
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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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"\n",
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"\n",
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"\u001B[1m> Entering new GraphSparqlQAChain chain...\u001B[0m\n",
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"Identified intent:\n",
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"\u001B[32;1m\u001B[1;3mSELECT\u001B[0m\n",
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"Generated SPARQL:\n",
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"\u001B[32;1m\u001B[1;3mPREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
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"SELECT ?homepage\n",
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"WHERE {\n",
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" ?person foaf:name \"Tim Berners-Lee\" .\n",
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" ?person foaf:workplaceHomepage ?homepage .\n",
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"}\u001B[0m\n",
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"Full Context:\n",
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"\u001B[32;1m\u001B[1;3m[]\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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"\"Tim Berners-Lee's work homepage is http://www.w3.org/People/Berners-Lee/.\""
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]
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},
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"execution_count": 12,
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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 Tim Berners-Lee's work homepage?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "af4b3294",
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"metadata": {},
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"source": [
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"## Updating the graph\n",
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"\n",
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"Analogously, you can update the graph, i.e., insert triples, using natural language."
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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": 14,
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"id": "fdf38841",
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"metadata": {
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"pycharm": {
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"is_executing": true
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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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"\n",
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"\n",
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"\u001B[1m> Entering new GraphSparqlQAChain chain...\u001B[0m\n",
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"Identified intent:\n",
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"\u001B[32;1m\u001B[1;3mUPDATE\u001B[0m\n",
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"Generated SPARQL:\n",
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"\u001B[32;1m\u001B[1;3mPREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
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"INSERT {\n",
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" ?person foaf:workplaceHomepage <http://www.w3.org/foo/bar/> .\n",
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"}\n",
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"WHERE {\n",
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" ?person foaf:name \"Timothy Berners-Lee\" .\n",
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"}\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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"'Successfully inserted triples into the graph.'"
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]
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},
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"execution_count": 14,
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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(\"Save that the person with the name 'Timothy Berners-Lee' has a work homepage at 'http://www.w3.org/foo/bar/'\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e0f7fc1",
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"metadata": {},
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"source": [
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"Let's verify the results:"
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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": 15,
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"id": "f874171b",
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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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"[(rdflib.term.URIRef('https://www.w3.org/'),),\n",
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" (rdflib.term.URIRef('http://www.w3.org/foo/bar/'),)]"
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]
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},
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"execution_count": 15,
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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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"query = (\n",
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" \"\"\"PREFIX foaf: <http://xmlns.com/foaf/0.1/>\\n\"\"\"\n",
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" \"\"\"SELECT ?hp\\n\"\"\"\n",
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" \"\"\"WHERE {\\n\"\"\"\n",
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" \"\"\" ?person foaf:name \"Timothy Berners-Lee\" . \\n\"\"\"\n",
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" \"\"\" ?person foaf:workplaceHomepage ?hp .\\n\"\"\"\n",
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" \"\"\"}\"\"\"\n",
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")\n",
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"graph.query(query)"
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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": "lc",
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"language": "python",
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"name": "lc"
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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": "3.11.4"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@ -19,6 +19,7 @@ from langchain.chains.graph_qa.cypher import GraphCypherQAChain
|
|||||||
from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
|
from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
|
||||||
from langchain.chains.graph_qa.kuzu import KuzuQAChain
|
from langchain.chains.graph_qa.kuzu import KuzuQAChain
|
||||||
from langchain.chains.graph_qa.nebulagraph import NebulaGraphQAChain
|
from langchain.chains.graph_qa.nebulagraph import NebulaGraphQAChain
|
||||||
|
from langchain.chains.graph_qa.sparql import GraphSparqlQAChain
|
||||||
from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
|
from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
|
||||||
from langchain.chains.llm import LLMChain
|
from langchain.chains.llm import LLMChain
|
||||||
from langchain.chains.llm_bash.base import LLMBashChain
|
from langchain.chains.llm_bash.base import LLMBashChain
|
||||||
@ -69,6 +70,7 @@ __all__ = [
|
|||||||
"FlareChain",
|
"FlareChain",
|
||||||
"GraphCypherQAChain",
|
"GraphCypherQAChain",
|
||||||
"GraphQAChain",
|
"GraphQAChain",
|
||||||
|
"GraphSparqlQAChain",
|
||||||
"HypotheticalDocumentEmbedder",
|
"HypotheticalDocumentEmbedder",
|
||||||
"KuzuQAChain",
|
"KuzuQAChain",
|
||||||
"HugeGraphQAChain",
|
"HugeGraphQAChain",
|
||||||
|
|||||||
@ -109,3 +109,90 @@ Helpful Answer:"""
|
|||||||
CYPHER_QA_PROMPT = PromptTemplate(
|
CYPHER_QA_PROMPT = PromptTemplate(
|
||||||
input_variables=["context", "question"], template=CYPHER_QA_TEMPLATE
|
input_variables=["context", "question"], template=CYPHER_QA_TEMPLATE
|
||||||
)
|
)
|
||||||
|
|
||||||
|
SPARQL_INTENT_TEMPLATE = """Task: Identify the intent of a prompt and return the appropriate SPARQL query type.
|
||||||
|
You are an assistant that distinguishes different types of prompts and returns the corresponding SPARQL query types.
|
||||||
|
Consider only the following query types:
|
||||||
|
* SELECT: this query type corresponds to questions
|
||||||
|
* UPDATE: this query type corresponds to all requests for deleting, inserting, or changing triples
|
||||||
|
Note: Be as concise as possible.
|
||||||
|
Do not include any explanations or apologies in your responses.
|
||||||
|
Do not respond to any questions that ask for anything else than for you to identify a SPARQL query type.
|
||||||
|
Do not include any unnecessary whitespaces or any text except the query type, i.e., either return 'SELECT' or 'UPDATE'.
|
||||||
|
|
||||||
|
The prompt is:
|
||||||
|
{prompt}
|
||||||
|
Helpful Answer:"""
|
||||||
|
SPARQL_INTENT_PROMPT = PromptTemplate(
|
||||||
|
input_variables=["prompt"], template=SPARQL_INTENT_TEMPLATE
|
||||||
|
)
|
||||||
|
|
||||||
|
SPARQL_GENERATION_SELECT_TEMPLATE = """Task: Generate a SPARQL SELECT statement for querying a graph database.
|
||||||
|
For instance, to find all email addresses of John Doe, the following query in backticks would be suitable:
|
||||||
|
```
|
||||||
|
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
|
||||||
|
SELECT ?email
|
||||||
|
WHERE {{
|
||||||
|
?person foaf:name "John Doe" .
|
||||||
|
?person foaf:mbox ?email .
|
||||||
|
}}
|
||||||
|
```
|
||||||
|
Instructions:
|
||||||
|
Use only the node types and properties provided in the schema.
|
||||||
|
Do not use any node types and properties that are not explicitly provided.
|
||||||
|
Include all necessary prefixes.
|
||||||
|
Schema:
|
||||||
|
{schema}
|
||||||
|
Note: Be as concise as possible.
|
||||||
|
Do not include any explanations or apologies in your responses.
|
||||||
|
Do not respond to any questions that ask for anything else than for you to construct a SPARQL query.
|
||||||
|
Do not include any text except the SPARQL query generated.
|
||||||
|
|
||||||
|
The question is:
|
||||||
|
{prompt}"""
|
||||||
|
SPARQL_GENERATION_SELECT_PROMPT = PromptTemplate(
|
||||||
|
input_variables=["schema", "prompt"], template=SPARQL_GENERATION_SELECT_TEMPLATE
|
||||||
|
)
|
||||||
|
|
||||||
|
SPARQL_GENERATION_UPDATE_TEMPLATE = """Task: Generate a SPARQL UPDATE statement for updating a graph database.
|
||||||
|
For instance, to add 'jane.doe@foo.bar' as a new email address for Jane Doe, the following query in backticks would be suitable:
|
||||||
|
```
|
||||||
|
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
|
||||||
|
INSERT {{
|
||||||
|
?person foaf:mbox <mailto:jane.doe@foo.bar> .
|
||||||
|
}}
|
||||||
|
WHERE {{
|
||||||
|
?person foaf:name "Jane Doe" .
|
||||||
|
}}
|
||||||
|
```
|
||||||
|
Instructions:
|
||||||
|
Make the query as short as possible and avoid adding unnecessary triples.
|
||||||
|
Use only the node types and properties provided in the schema.
|
||||||
|
Do not use any node types and properties that are not explicitly provided.
|
||||||
|
Include all necessary prefixes.
|
||||||
|
Schema:
|
||||||
|
{schema}
|
||||||
|
Note: Be as concise as possible.
|
||||||
|
Do not include any explanations or apologies in your responses.
|
||||||
|
Do not respond to any questions that ask for anything else than for you to construct a SPARQL query.
|
||||||
|
Return only the generated SPARQL query, nothing else.
|
||||||
|
|
||||||
|
The information to be inserted is:
|
||||||
|
{prompt}"""
|
||||||
|
SPARQL_GENERATION_UPDATE_PROMPT = PromptTemplate(
|
||||||
|
input_variables=["schema", "prompt"], template=SPARQL_GENERATION_UPDATE_TEMPLATE
|
||||||
|
)
|
||||||
|
|
||||||
|
SPARQL_QA_TEMPLATE = """Task: Generate a natural language response from the results of a SPARQL query.
|
||||||
|
You are an assistant that creates well-written and human understandable answers.
|
||||||
|
The information part contains the information provided, which you can use to construct an answer.
|
||||||
|
The information provided is authoritative, you must never doubt it or try to use your internal knowledge to correct it.
|
||||||
|
Make your response sound like the information is coming from an AI assistant, but don't add any information.
|
||||||
|
Information:
|
||||||
|
{context}
|
||||||
|
|
||||||
|
Question: {prompt}
|
||||||
|
Helpful Answer:"""
|
||||||
|
SPARQL_QA_PROMPT = PromptTemplate(
|
||||||
|
input_variables=["context", "prompt"], template=SPARQL_QA_TEMPLATE
|
||||||
|
)
|
||||||
|
|||||||
127
langchain/chains/graph_qa/sparql.py
Normal file
127
langchain/chains/graph_qa/sparql.py
Normal file
@ -0,0 +1,127 @@
|
|||||||
|
"""
|
||||||
|
Question answering over an RDF or OWL graph using SPARQL.
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import Any, Dict, List, Optional
|
||||||
|
|
||||||
|
from pydantic import Field
|
||||||
|
|
||||||
|
from langchain.base_language import BaseLanguageModel
|
||||||
|
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||||
|
from langchain.chains.base import Chain
|
||||||
|
from langchain.chains.graph_qa.prompts import (
|
||||||
|
SPARQL_GENERATION_SELECT_PROMPT,
|
||||||
|
SPARQL_GENERATION_UPDATE_PROMPT,
|
||||||
|
SPARQL_INTENT_PROMPT,
|
||||||
|
SPARQL_QA_PROMPT,
|
||||||
|
)
|
||||||
|
from langchain.chains.llm import LLMChain
|
||||||
|
from langchain.graphs.rdf_graph import RdfGraph
|
||||||
|
from langchain.prompts.base import BasePromptTemplate
|
||||||
|
|
||||||
|
|
||||||
|
class GraphSparqlQAChain(Chain):
|
||||||
|
"""
|
||||||
|
Chain for question-answering against an RDF or OWL graph by generating
|
||||||
|
SPARQL statements.
|
||||||
|
"""
|
||||||
|
|
||||||
|
graph: RdfGraph = Field(exclude=True)
|
||||||
|
sparql_generation_select_chain: LLMChain
|
||||||
|
sparql_generation_update_chain: LLMChain
|
||||||
|
sparql_intent_chain: LLMChain
|
||||||
|
qa_chain: LLMChain
|
||||||
|
input_key: str = "query" #: :meta private:
|
||||||
|
output_key: str = "result" #: :meta private:
|
||||||
|
|
||||||
|
@property
|
||||||
|
def input_keys(self) -> List[str]:
|
||||||
|
return [self.input_key]
|
||||||
|
|
||||||
|
@property
|
||||||
|
def output_keys(self) -> List[str]:
|
||||||
|
_output_keys = [self.output_key]
|
||||||
|
return _output_keys
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_llm(
|
||||||
|
cls,
|
||||||
|
llm: BaseLanguageModel,
|
||||||
|
*,
|
||||||
|
qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT,
|
||||||
|
sparql_select_prompt: BasePromptTemplate = SPARQL_GENERATION_SELECT_PROMPT,
|
||||||
|
sparql_update_prompt: BasePromptTemplate = SPARQL_GENERATION_UPDATE_PROMPT,
|
||||||
|
sparql_intent_prompt: BasePromptTemplate = SPARQL_INTENT_PROMPT,
|
||||||
|
**kwargs: Any,
|
||||||
|
) -> GraphSparqlQAChain:
|
||||||
|
"""Initialize from LLM."""
|
||||||
|
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
|
||||||
|
sparql_generation_select_chain = LLMChain(llm=llm, prompt=sparql_select_prompt)
|
||||||
|
sparql_generation_update_chain = LLMChain(llm=llm, prompt=sparql_update_prompt)
|
||||||
|
sparql_intent_chain = LLMChain(llm=llm, prompt=sparql_intent_prompt)
|
||||||
|
|
||||||
|
return cls(
|
||||||
|
qa_chain=qa_chain,
|
||||||
|
sparql_generation_select_chain=sparql_generation_select_chain,
|
||||||
|
sparql_generation_update_chain=sparql_generation_update_chain,
|
||||||
|
sparql_intent_chain=sparql_intent_chain,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
def _call(
|
||||||
|
self,
|
||||||
|
inputs: Dict[str, Any],
|
||||||
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||||
|
) -> Dict[str, str]:
|
||||||
|
"""
|
||||||
|
Generate SPARQL query, use it to retrieve a response from the gdb and answer
|
||||||
|
the question.
|
||||||
|
"""
|
||||||
|
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||||
|
callbacks = _run_manager.get_child()
|
||||||
|
prompt = inputs[self.input_key]
|
||||||
|
|
||||||
|
_intent = self.sparql_intent_chain.run({"prompt": prompt}, callbacks=callbacks)
|
||||||
|
intent = _intent.strip()
|
||||||
|
|
||||||
|
if intent == "SELECT":
|
||||||
|
sparql_generation_chain = self.sparql_generation_select_chain
|
||||||
|
elif intent == "UPDATE":
|
||||||
|
sparql_generation_chain = self.sparql_generation_update_chain
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"I am sorry, but this prompt seems to fit none of the currently "
|
||||||
|
"supported SPARQL query types, i.e., SELECT and UPDATE."
|
||||||
|
)
|
||||||
|
|
||||||
|
_run_manager.on_text("Identified intent:", end="\n", verbose=self.verbose)
|
||||||
|
_run_manager.on_text(intent, color="green", end="\n", verbose=self.verbose)
|
||||||
|
|
||||||
|
generated_sparql = sparql_generation_chain.run(
|
||||||
|
{"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks
|
||||||
|
)
|
||||||
|
|
||||||
|
_run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose)
|
||||||
|
_run_manager.on_text(
|
||||||
|
generated_sparql, color="green", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
|
||||||
|
if intent == "SELECT":
|
||||||
|
context = self.graph.query(generated_sparql)
|
||||||
|
|
||||||
|
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
|
||||||
|
_run_manager.on_text(
|
||||||
|
str(context), color="green", end="\n", verbose=self.verbose
|
||||||
|
)
|
||||||
|
result = self.qa_chain(
|
||||||
|
{"prompt": prompt, "context": context},
|
||||||
|
callbacks=callbacks,
|
||||||
|
)
|
||||||
|
res = result[self.qa_chain.output_key]
|
||||||
|
elif intent == "UPDATE":
|
||||||
|
self.graph.update(generated_sparql)
|
||||||
|
res = "Successfully inserted triples into the graph."
|
||||||
|
else:
|
||||||
|
raise ValueError("Unsupported SPARQL query type.")
|
||||||
|
return {self.output_key: res}
|
||||||
@ -4,5 +4,13 @@ from langchain.graphs.kuzu_graph import KuzuGraph
|
|||||||
from langchain.graphs.nebula_graph import NebulaGraph
|
from langchain.graphs.nebula_graph import NebulaGraph
|
||||||
from langchain.graphs.neo4j_graph import Neo4jGraph
|
from langchain.graphs.neo4j_graph import Neo4jGraph
|
||||||
from langchain.graphs.networkx_graph import NetworkxEntityGraph
|
from langchain.graphs.networkx_graph import NetworkxEntityGraph
|
||||||
|
from langchain.graphs.rdf_graph import RdfGraph
|
||||||
|
|
||||||
__all__ = ["NetworkxEntityGraph", "Neo4jGraph", "NebulaGraph", "KuzuGraph", "HugeGraph"]
|
__all__ = [
|
||||||
|
"NetworkxEntityGraph",
|
||||||
|
"Neo4jGraph",
|
||||||
|
"NebulaGraph",
|
||||||
|
"KuzuGraph",
|
||||||
|
"HugeGraph",
|
||||||
|
"RdfGraph",
|
||||||
|
]
|
||||||
|
|||||||
279
langchain/graphs/rdf_graph.py
Normal file
279
langchain/graphs/rdf_graph.py
Normal file
@ -0,0 +1,279 @@
|
|||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import (
|
||||||
|
TYPE_CHECKING,
|
||||||
|
List,
|
||||||
|
Optional,
|
||||||
|
)
|
||||||
|
|
||||||
|
if TYPE_CHECKING:
|
||||||
|
import rdflib
|
||||||
|
|
||||||
|
prefixes = {
|
||||||
|
"owl": """PREFIX owl: <http://www.w3.org/2002/07/owl#>\n""",
|
||||||
|
"rdf": """PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n""",
|
||||||
|
"rdfs": """PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n""",
|
||||||
|
"xsd": """PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n""",
|
||||||
|
}
|
||||||
|
|
||||||
|
cls_query_rdf = prefixes["rdfs"] + (
|
||||||
|
"""SELECT DISTINCT ?cls ?com\n"""
|
||||||
|
"""WHERE { \n"""
|
||||||
|
""" ?instance a ?cls . \n"""
|
||||||
|
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
|
||||||
|
cls_query_rdfs = prefixes["rdfs"] + (
|
||||||
|
"""SELECT DISTINCT ?cls ?com\n"""
|
||||||
|
"""WHERE { \n"""
|
||||||
|
""" ?instance a/rdfs:subClassOf* ?cls . \n"""
|
||||||
|
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
|
||||||
|
cls_query_owl = cls_query_rdfs
|
||||||
|
|
||||||
|
rel_query_rdf = prefixes["rdfs"] + (
|
||||||
|
"""SELECT DISTINCT ?rel ?com\n"""
|
||||||
|
"""WHERE { \n"""
|
||||||
|
""" ?subj ?rel ?obj . \n"""
|
||||||
|
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
|
||||||
|
rel_query_rdfs = (
|
||||||
|
prefixes["rdf"]
|
||||||
|
+ prefixes["rdfs"]
|
||||||
|
+ (
|
||||||
|
"""SELECT DISTINCT ?rel ?com\n"""
|
||||||
|
"""WHERE { \n"""
|
||||||
|
""" ?rel a/rdfs:subPropertyOf* rdf:Property . \n"""
|
||||||
|
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
op_query_owl = (
|
||||||
|
prefixes["rdfs"]
|
||||||
|
+ prefixes["owl"]
|
||||||
|
+ (
|
||||||
|
"""SELECT DISTINCT ?op ?com\n"""
|
||||||
|
"""WHERE { \n"""
|
||||||
|
""" ?op a/rdfs:subPropertyOf* owl:ObjectProperty . \n"""
|
||||||
|
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
dp_query_owl = (
|
||||||
|
prefixes["rdfs"]
|
||||||
|
+ prefixes["owl"]
|
||||||
|
+ (
|
||||||
|
"""SELECT DISTINCT ?dp ?com\n"""
|
||||||
|
"""WHERE { \n"""
|
||||||
|
""" ?dp a/rdfs:subPropertyOf* owl:DatatypeProperty . \n"""
|
||||||
|
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class RdfGraph:
|
||||||
|
"""
|
||||||
|
RDFlib wrapper for graph operations.
|
||||||
|
Modes:
|
||||||
|
* local: Local file - can be queried and changed
|
||||||
|
* online: Online file - can only be queried, changes can be stored locally
|
||||||
|
* store: Triple store - can be queried and changed if update_endpoint available
|
||||||
|
Together with a source file, the serialization should be specified.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
source_file: Optional[str] = None,
|
||||||
|
serialization: Optional[str] = "ttl",
|
||||||
|
query_endpoint: Optional[str] = None,
|
||||||
|
update_endpoint: Optional[str] = None,
|
||||||
|
standard: Optional[str] = "rdf",
|
||||||
|
local_copy: Optional[str] = None,
|
||||||
|
) -> None:
|
||||||
|
"""
|
||||||
|
Set up the RDFlib graph
|
||||||
|
|
||||||
|
:param source_file: either a path for a local file or a URL
|
||||||
|
:param serialization: serialization of the input
|
||||||
|
:param query_endpoint: SPARQL endpoint for queries, read access
|
||||||
|
:param update_endpoint: SPARQL endpoint for UPDATE queries, write access
|
||||||
|
:param standard: RDF, RDFS, or OWL
|
||||||
|
:param local_copy: new local copy for storing changes
|
||||||
|
"""
|
||||||
|
self.source_file = source_file
|
||||||
|
self.serialization = serialization
|
||||||
|
self.query_endpoint = query_endpoint
|
||||||
|
self.update_endpoint = update_endpoint
|
||||||
|
self.standard = standard
|
||||||
|
self.local_copy = local_copy
|
||||||
|
|
||||||
|
try:
|
||||||
|
import rdflib
|
||||||
|
from rdflib.graph import DATASET_DEFAULT_GRAPH_ID as default
|
||||||
|
from rdflib.plugins.stores import sparqlstore
|
||||||
|
except ImportError:
|
||||||
|
raise ValueError(
|
||||||
|
"Could not import rdflib python package. "
|
||||||
|
"Please install it with `pip install rdflib`."
|
||||||
|
)
|
||||||
|
if self.standard not in (supported_standards := ("rdf", "rdfs", "owl")):
|
||||||
|
raise ValueError(
|
||||||
|
f"Invalid standard. Supported standards are: {supported_standards}."
|
||||||
|
)
|
||||||
|
|
||||||
|
if (
|
||||||
|
not source_file
|
||||||
|
and not query_endpoint
|
||||||
|
or source_file
|
||||||
|
and (query_endpoint or update_endpoint)
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
"Could not unambiguously initialize the graph wrapper. "
|
||||||
|
"Specify either a file (local or online) via the source_file "
|
||||||
|
"or a triple store via the endpoints."
|
||||||
|
)
|
||||||
|
|
||||||
|
if source_file:
|
||||||
|
if source_file.startswith("http"):
|
||||||
|
self.mode = "online"
|
||||||
|
else:
|
||||||
|
self.mode = "local"
|
||||||
|
if self.local_copy is None:
|
||||||
|
self.local_copy = self.source_file
|
||||||
|
self.graph = rdflib.Graph()
|
||||||
|
self.graph.parse(source_file, format=self.serialization)
|
||||||
|
|
||||||
|
if query_endpoint:
|
||||||
|
self.mode = "store"
|
||||||
|
if not update_endpoint:
|
||||||
|
self._store = sparqlstore.SPARQLStore()
|
||||||
|
self._store.open(query_endpoint)
|
||||||
|
else:
|
||||||
|
self._store = sparqlstore.SPARQLUpdateStore()
|
||||||
|
self._store.open((query_endpoint, update_endpoint))
|
||||||
|
self.graph = rdflib.Graph(self._store, identifier=default)
|
||||||
|
|
||||||
|
# Verify that the graph was loaded
|
||||||
|
if not len(self.graph):
|
||||||
|
raise AssertionError("The graph is empty.")
|
||||||
|
|
||||||
|
# Set schema
|
||||||
|
self.schema = ""
|
||||||
|
self.load_schema()
|
||||||
|
|
||||||
|
@property
|
||||||
|
def get_schema(self) -> str:
|
||||||
|
"""
|
||||||
|
Returns the schema of the graph database.
|
||||||
|
"""
|
||||||
|
return self.schema
|
||||||
|
|
||||||
|
def query(
|
||||||
|
self,
|
||||||
|
query: str,
|
||||||
|
) -> List[rdflib.query.ResultRow]:
|
||||||
|
"""
|
||||||
|
Query the graph.
|
||||||
|
"""
|
||||||
|
from rdflib.exceptions import ParserError
|
||||||
|
from rdflib.query import ResultRow
|
||||||
|
|
||||||
|
try:
|
||||||
|
res = self.graph.query(query)
|
||||||
|
except ParserError as e:
|
||||||
|
raise ValueError("Generated SPARQL statement is invalid\n" f"{e}")
|
||||||
|
return [r for r in res if isinstance(r, ResultRow)]
|
||||||
|
|
||||||
|
def update(
|
||||||
|
self,
|
||||||
|
query: str,
|
||||||
|
) -> None:
|
||||||
|
"""
|
||||||
|
Update the graph.
|
||||||
|
"""
|
||||||
|
from rdflib.exceptions import ParserError
|
||||||
|
|
||||||
|
try:
|
||||||
|
self.graph.update(query)
|
||||||
|
except ParserError as e:
|
||||||
|
raise ValueError("Generated SPARQL statement is invalid\n" f"{e}")
|
||||||
|
if self.local_copy:
|
||||||
|
self.graph.serialize(
|
||||||
|
destination=self.local_copy, format=self.local_copy.split(".")[-1]
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
raise ValueError("No target file specified for saving the updated file.")
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _get_local_name(iri: str) -> str:
|
||||||
|
if "#" in iri:
|
||||||
|
local_name = iri.split("#")[-1]
|
||||||
|
elif "/" in iri:
|
||||||
|
local_name = iri.split("/")[-1]
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unexpected IRI '{iri}', contains neither '#' nor '/'.")
|
||||||
|
return local_name
|
||||||
|
|
||||||
|
def _res_to_str(self, res: rdflib.query.ResultRow, var: str) -> str:
|
||||||
|
return (
|
||||||
|
"<"
|
||||||
|
+ res[var]
|
||||||
|
+ "> ("
|
||||||
|
+ self._get_local_name(res[var])
|
||||||
|
+ ", "
|
||||||
|
+ str(res["com"])
|
||||||
|
+ ")"
|
||||||
|
)
|
||||||
|
|
||||||
|
def load_schema(self) -> None:
|
||||||
|
"""
|
||||||
|
Load the graph schema information.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def _rdf_s_schema(
|
||||||
|
classes: List[rdflib.query.ResultRow],
|
||||||
|
relationships: List[rdflib.query.ResultRow],
|
||||||
|
) -> str:
|
||||||
|
return (
|
||||||
|
f"In the following, each IRI is followed by the local name and "
|
||||||
|
f"optionally its description in parentheses. \n"
|
||||||
|
f"The RDF graph supports the following node types:\n"
|
||||||
|
f'{", ".join([self._res_to_str(r, "cls") for r in classes])}\n'
|
||||||
|
f"The RDF graph supports the following relationships:\n"
|
||||||
|
f'{", ".join([self._res_to_str(r, "rel") for r in relationships])}\n'
|
||||||
|
)
|
||||||
|
|
||||||
|
if self.standard == "rdf":
|
||||||
|
clss = self.query(cls_query_rdf)
|
||||||
|
rels = self.query(rel_query_rdf)
|
||||||
|
self.schema = _rdf_s_schema(clss, rels)
|
||||||
|
elif self.standard == "rdfs":
|
||||||
|
clss = self.query(cls_query_rdfs)
|
||||||
|
rels = self.query(rel_query_rdfs)
|
||||||
|
self.schema = _rdf_s_schema(clss, rels)
|
||||||
|
elif self.standard == "owl":
|
||||||
|
clss = self.query(cls_query_owl)
|
||||||
|
ops = self.query(cls_query_owl)
|
||||||
|
dps = self.query(cls_query_owl)
|
||||||
|
self.schema = (
|
||||||
|
f"In the following, each IRI is followed by the local name and "
|
||||||
|
f"optionally its description in parentheses. \n"
|
||||||
|
f"The OWL graph supports the following node types:\n"
|
||||||
|
f'{", ".join([self._res_to_str(r, "cls") for r in clss])}\n'
|
||||||
|
f"The OWL graph supports the following object properties, "
|
||||||
|
f"i.e., relationships between objects:\n"
|
||||||
|
f'{", ".join([self._res_to_str(r, "op") for r in ops])}\n'
|
||||||
|
f"The OWL graph supports the following data properties, "
|
||||||
|
f"i.e., relationships between objects and literals:\n"
|
||||||
|
f'{", ".join([self._res_to_str(r, "dp") for r in dps])}\n'
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Mode '{self.standard}' is currently not supported.")
|
||||||
49
poetry.lock
generated
49
poetry.lock
generated
@ -642,12 +642,17 @@ category = "main"
|
|||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
|
{file = "awadb-0.3.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:871e2b10c79348d44522b8430af1ed1ad2632322c74abc20d8a3154de242da96"},
|
||||||
{file = "awadb-0.3.3-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:daebc108103c8cace41dfb3235fcfdda28ea48e6cd6548b6072f7ad49b64274b"},
|
{file = "awadb-0.3.3-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:daebc108103c8cace41dfb3235fcfdda28ea48e6cd6548b6072f7ad49b64274b"},
|
||||||
{file = "awadb-0.3.3-cp311-cp311-macosx_10_13_universal2.whl", hash = "sha256:2bb3ca2f943448060b1bba4395dd99e2218d7f2149507a8fdfa7a3fd4cfe97ec"},
|
{file = "awadb-0.3.3-cp311-cp311-macosx_10_13_universal2.whl", hash = "sha256:2bb3ca2f943448060b1bba4395dd99e2218d7f2149507a8fdfa7a3fd4cfe97ec"},
|
||||||
|
{file = "awadb-0.3.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:83e92963cde54a4382b0c299939865ce12e145853637642bc8e6eb22bf689386"},
|
||||||
|
{file = "awadb-0.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6f249b04f38840146a5c17ffcd0f4da1bb00a39b8882c96e042acf58045faca2"},
|
||||||
{file = "awadb-0.3.3-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:7b99662af9f7b58e217661a70c295e40605900552bec6d8e9553d90dbf19c5c1"},
|
{file = "awadb-0.3.3-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:7b99662af9f7b58e217661a70c295e40605900552bec6d8e9553d90dbf19c5c1"},
|
||||||
{file = "awadb-0.3.3-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:94be44e587f28fa26b2cade0b6f4c04689f50cb0c07183db5ee50e48fe2e9ae3"},
|
{file = "awadb-0.3.3-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:94be44e587f28fa26b2cade0b6f4c04689f50cb0c07183db5ee50e48fe2e9ae3"},
|
||||||
{file = "awadb-0.3.3-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:314929dc3a8d25c0f234a2b86c920543050f4eb298a6f68bd2c97c9fe3fb6224"},
|
{file = "awadb-0.3.3-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:314929dc3a8d25c0f234a2b86c920543050f4eb298a6f68bd2c97c9fe3fb6224"},
|
||||||
|
{file = "awadb-0.3.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:35d8f580973e137864d2e00edbc7369cd01cf72b673d60fe902c7b3f983c76e9"},
|
||||||
{file = "awadb-0.3.3-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:8bfccff1c7373899153427d93d96a97ae5371e8a6f09ff4dcbd28fb9f3f63ff4"},
|
{file = "awadb-0.3.3-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:8bfccff1c7373899153427d93d96a97ae5371e8a6f09ff4dcbd28fb9f3f63ff4"},
|
||||||
|
{file = "awadb-0.3.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c791bdd0646ec620c8b0fa026915780ebf78c16169cd9da81f54409553ec0114"},
|
||||||
{file = "awadb-0.3.3-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:810021a90b873f668d8ab63e2c2747b2b2835bf0ae25f4223b6c94f06faffea4"},
|
{file = "awadb-0.3.3-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:810021a90b873f668d8ab63e2c2747b2b2835bf0ae25f4223b6c94f06faffea4"},
|
||||||
]
|
]
|
||||||
|
|
||||||
@ -3653,23 +3658,6 @@ cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"]
|
|||||||
http2 = ["h2 (>=3,<5)"]
|
http2 = ["h2 (>=3,<5)"]
|
||||||
socks = ["socksio (>=1.0.0,<2.0.0)"]
|
socks = ["socksio (>=1.0.0,<2.0.0)"]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "hugegraph-python"
|
|
||||||
version = "1.0.0.12"
|
|
||||||
description = "Python client for HugeGraph"
|
|
||||||
optional = true
|
|
||||||
python-versions = "*"
|
|
||||||
files = [
|
|
||||||
{file = "hugegraph-python-1.0.0.12.tar.gz", hash = "sha256:06b2dded70c4f4570083f8b6e3a9edfebcf5ac4f07300727afad72389917ab85"},
|
|
||||||
{file = "hugegraph_python-1.0.0.12-py3-none-any.whl", hash = "sha256:69fe20edbe1a392d16afc74df5c94b3b96bc02c848e9ab5b5f18c112a9bc3ebe"},
|
|
||||||
]
|
|
||||||
|
|
||||||
[package.dependencies]
|
|
||||||
decorator = "5.1.1"
|
|
||||||
Requests = "2.31.0"
|
|
||||||
setuptools = "67.6.1"
|
|
||||||
urllib3 = "2.0.3"
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "huggingface-hub"
|
name = "huggingface-hub"
|
||||||
version = "0.15.1"
|
version = "0.15.1"
|
||||||
@ -4306,6 +4294,7 @@ optional = false
|
|||||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
||||||
files = [
|
files = [
|
||||||
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
|
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
|
||||||
|
{file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -8924,6 +8913,28 @@ files = [
|
|||||||
[package.extras]
|
[package.extras]
|
||||||
test = ["pytest (>=3.0)", "pytest-asyncio"]
|
test = ["pytest (>=3.0)", "pytest-asyncio"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "rdflib"
|
||||||
|
version = "6.3.2"
|
||||||
|
description = "RDFLib is a Python library for working with RDF, a simple yet powerful language for representing information."
|
||||||
|
category = "main"
|
||||||
|
optional = true
|
||||||
|
python-versions = ">=3.7,<4.0"
|
||||||
|
files = [
|
||||||
|
{file = "rdflib-6.3.2-py3-none-any.whl", hash = "sha256:36b4e74a32aa1e4fa7b8719876fb192f19ecd45ff932ea5ebbd2e417a0247e63"},
|
||||||
|
{file = "rdflib-6.3.2.tar.gz", hash = "sha256:72af591ff704f4caacea7ecc0c5a9056b8553e0489dd4f35a9bc52dbd41522e0"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
isodate = ">=0.6.0,<0.7.0"
|
||||||
|
pyparsing = ">=2.1.0,<4"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
berkeleydb = ["berkeleydb (>=18.1.0,<19.0.0)"]
|
||||||
|
html = ["html5lib (>=1.0,<2.0)"]
|
||||||
|
lxml = ["lxml (>=4.3.0,<5.0.0)"]
|
||||||
|
networkx = ["networkx (>=2.0.0,<3.0.0)"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "redis"
|
name = "redis"
|
||||||
version = "4.5.5"
|
version = "4.5.5"
|
||||||
@ -12371,7 +12382,7 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
|
|||||||
cffi = ["cffi (>=1.11)"]
|
cffi = ["cffi (>=1.11)"]
|
||||||
|
|
||||||
[extras]
|
[extras]
|
||||||
all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "lxml", "manifest-ml", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "octoai-sdk", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "lxml", "manifest-ml", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "octoai-sdk", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
||||||
azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
|
azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
|
||||||
clarifai = ["clarifai"]
|
clarifai = ["clarifai"]
|
||||||
cohere = ["cohere"]
|
cohere = ["cohere"]
|
||||||
@ -12387,4 +12398,4 @@ text-helpers = ["chardet"]
|
|||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
python-versions = ">=3.8.1,<4.0"
|
python-versions = ">=3.8.1,<4.0"
|
||||||
content-hash = "1ebf19951081f82f4af129d9e2ea12c40fdc519bf49dffa0cfa85eb28938710c"
|
content-hash = "e2450d84b1a0747c45b015e1071ed37265269c325362779cd8bd3b9caa94a9c9"
|
||||||
|
|||||||
@ -114,6 +114,7 @@ openllm = {version = ">=0.1.19", optional = true}
|
|||||||
streamlit = {version = "^1.18.0", optional = true, python = ">=3.8.1,<3.9.7 || >3.9.7,<4.0"}
|
streamlit = {version = "^1.18.0", optional = true, python = ">=3.8.1,<3.9.7 || >3.9.7,<4.0"}
|
||||||
psychicapi = {version = "^0.8.0", optional = true}
|
psychicapi = {version = "^0.8.0", optional = true}
|
||||||
cassio = {version = "^0.0.7", optional = true}
|
cassio = {version = "^0.0.7", optional = true}
|
||||||
|
rdflib = {version = "^6.3.2", optional = true}
|
||||||
|
|
||||||
[tool.poetry.group.docs.dependencies]
|
[tool.poetry.group.docs.dependencies]
|
||||||
autodoc_pydantic = "^1.8.0"
|
autodoc_pydantic = "^1.8.0"
|
||||||
@ -310,6 +311,7 @@ all = [
|
|||||||
"awadb",
|
"awadb",
|
||||||
"esprima",
|
"esprima",
|
||||||
"octoai-sdk",
|
"octoai-sdk",
|
||||||
|
"rdflib",
|
||||||
]
|
]
|
||||||
|
|
||||||
# An extra used to be able to add extended testing.
|
# An extra used to be able to add extended testing.
|
||||||
|
|||||||
79
tests/integration_tests/chains/test_graph_database_sparql.py
Normal file
79
tests/integration_tests/chains/test_graph_database_sparql.py
Normal file
@ -0,0 +1,79 @@
|
|||||||
|
"""Test RDF/ SPARQL Graph Database Chain."""
|
||||||
|
import os
|
||||||
|
|
||||||
|
from langchain.chains.graph_qa.sparql import GraphSparqlQAChain
|
||||||
|
from langchain.graphs import RdfGraph
|
||||||
|
from langchain.llms.openai import OpenAI
|
||||||
|
|
||||||
|
|
||||||
|
def test_connect_file_rdf() -> None:
|
||||||
|
"""
|
||||||
|
Test loading online resource.
|
||||||
|
"""
|
||||||
|
berners_lee_card = "http://www.w3.org/People/Berners-Lee/card"
|
||||||
|
|
||||||
|
graph = RdfGraph(
|
||||||
|
source_file=berners_lee_card,
|
||||||
|
standard="rdf",
|
||||||
|
)
|
||||||
|
|
||||||
|
query = """SELECT ?s ?p ?o\n""" """WHERE { ?s ?p ?o }"""
|
||||||
|
|
||||||
|
output = graph.query(query)
|
||||||
|
assert len(output) == 86
|
||||||
|
|
||||||
|
|
||||||
|
def test_sparql_select() -> None:
|
||||||
|
"""
|
||||||
|
Test for generating and executing simple SPARQL SELECT query.
|
||||||
|
"""
|
||||||
|
berners_lee_card = "http://www.w3.org/People/Berners-Lee/card"
|
||||||
|
|
||||||
|
graph = RdfGraph(
|
||||||
|
source_file=berners_lee_card,
|
||||||
|
standard="rdf",
|
||||||
|
)
|
||||||
|
|
||||||
|
chain = GraphSparqlQAChain.from_llm(OpenAI(temperature=0), graph=graph)
|
||||||
|
output = chain.run("What is Tim Berners-Lee's work homepage?")
|
||||||
|
expected_output = (
|
||||||
|
" The work homepage of Tim Berners-Lee is "
|
||||||
|
"http://www.w3.org/People/Berners-Lee/."
|
||||||
|
)
|
||||||
|
assert output == expected_output
|
||||||
|
|
||||||
|
|
||||||
|
def test_sparql_insert() -> None:
|
||||||
|
"""
|
||||||
|
Test for generating and executing simple SPARQL INSERT query.
|
||||||
|
"""
|
||||||
|
berners_lee_card = "http://www.w3.org/People/Berners-Lee/card"
|
||||||
|
_local_copy = "test.ttl"
|
||||||
|
|
||||||
|
graph = RdfGraph(
|
||||||
|
source_file=berners_lee_card,
|
||||||
|
standard="rdf",
|
||||||
|
local_copy=_local_copy,
|
||||||
|
)
|
||||||
|
|
||||||
|
chain = GraphSparqlQAChain.from_llm(OpenAI(temperature=0), graph=graph)
|
||||||
|
chain.run(
|
||||||
|
"Save that the person with the name 'Timothy Berners-Lee' "
|
||||||
|
"has a work homepage at 'http://www.w3.org/foo/bar/'"
|
||||||
|
)
|
||||||
|
query = (
|
||||||
|
"""PREFIX foaf: <http://xmlns.com/foaf/0.1/>\n"""
|
||||||
|
"""SELECT ?hp\n"""
|
||||||
|
"""WHERE {\n"""
|
||||||
|
""" ?person foaf:name "Timothy Berners-Lee" . \n"""
|
||||||
|
""" ?person foaf:workplaceHomepage ?hp .\n"""
|
||||||
|
"""}"""
|
||||||
|
)
|
||||||
|
output = graph.query(query)
|
||||||
|
assert len(output) == 2
|
||||||
|
|
||||||
|
# clean up
|
||||||
|
try:
|
||||||
|
os.remove(_local_copy)
|
||||||
|
except OSError:
|
||||||
|
pass
|
||||||
Loading…
Reference in New Issue
Block a user