mirror of https://github.com/hwchase17/langchain
community[minor], langchian[minor]: Add Neptune Rdf graph and chain (#16650)
**Description**: This PR adds a chain for Amazon Neptune graph database RDF format. It complements the existing Neptune Cypher chain. The PR also includes a Neptune RDF graph class to connect to, introspect, and query a Neptune RDF graph database from the chain. A sample notebook is provided under docs that demonstrates the overall effect: invoking the chain to make natural language queries against Neptune using an LLM. **Issue**: This is a new feature **Dependencies**: The RDF graph class depends on the AWS boto3 library if using IAM authentication to connect to the Neptune database. --------- Co-authored-by: Piyush Jain <piyushjain@duck.com> Co-authored-by: Bagatur <baskaryan@gmail.com>pull/16497/head
mhavey
4 months ago
committed by
GitHub
parent
e1cfd0f3e7
commit
1bbb64d956
@ -0,0 +1,337 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Neptune SPARQL QA Chain\n",
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"\n",
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"This notebook shows use of LLM to query RDF graph in Amazon Neptune. This code uses a `NeptuneRdfGraph` class that connects with the Neptune database and loads it's schema. The `NeptuneSparqlQAChain` is used to connect the graph and LLM to ask natural language questions.\n",
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"\n",
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"Requirements for running this notebook:\n",
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"- Neptune 1.2.x cluster accessible from this notebook\n",
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"- Kernel with Python 3.9 or higher\n",
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"- For Bedrock access, ensure IAM role has this policy\n",
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"\n",
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"```json\n",
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"{\n",
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" \"Action\": [\n",
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" \"bedrock:ListFoundationModels\",\n",
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" \"bedrock:InvokeModel\"\n",
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" ],\n",
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" \"Resource\": \"*\",\n",
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" \"Effect\": \"Allow\"\n",
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"}\n",
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"```\n",
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"\n",
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"- S3 bucket for staging sample data, bucket should be in same account/region as Neptune."
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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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"## Seed W3C organizational data\n",
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"W3C org ontology plus some instances. \n",
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"\n",
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"You will need an S3 bucket in the same region and account. Set STAGE_BUCKET to name of that bucket."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"STAGE_BUCKET = \"<bucket-name>\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%bash -s \"$STAGE_BUCKET\"\n",
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"\n",
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"rm -rf data\n",
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"mkdir -p data\n",
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"cd data\n",
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"echo getting org ontology and sample org instances\n",
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"wget http://www.w3.org/ns/org.ttl \n",
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"wget https://raw.githubusercontent.com/aws-samples/amazon-neptune-ontology-example-blog/main/data/example_org.ttl \n",
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"\n",
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"echo Copying org ttl to S3\n",
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"aws s3 cp org.ttl s3://$1/org.ttl\n",
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"aws s3 cp example_org.ttl s3://$1/example_org.ttl\n"
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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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"Bulk-load the org ttl - both ontology and instances"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load -s s3://{STAGE_BUCKET} -f turtle --store-to loadres --run"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_status {loadres['payload']['loadId']} --errors --details"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup Chain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"EXAMPLES = \"\"\"\n",
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"\n",
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"<question>\n",
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"Find organizations.\n",
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"</question>\n",
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"\n",
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"<sparql>\n",
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"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> \n",
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"PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
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"PREFIX org: <http://www.w3.org/ns/org#> \n",
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"\n",
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"select ?org ?orgName where {{\n",
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" ?org rdfs:label ?orgName .\n",
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"}} \n",
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"</sparql>\n",
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"\n",
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"<question>\n",
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"Find sites of an organization\n",
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"</question>\n",
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"\n",
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"<sparql>\n",
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"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> \n",
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"PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
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"PREFIX org: <http://www.w3.org/ns/org#> \n",
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"\n",
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"select ?org ?orgName ?siteName where {{\n",
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" ?org rdfs:label ?orgName .\n",
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" ?org org:hasSite/rdfs:label ?siteName . \n",
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"}} \n",
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"</sparql>\n",
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"\n",
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"<question>\n",
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"Find suborganizations of an organization\n",
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"</question>\n",
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"\n",
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"<sparql>\n",
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"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> \n",
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"PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
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"PREFIX org: <http://www.w3.org/ns/org#> \n",
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"\n",
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"select ?org ?orgName ?subName where {{\n",
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" ?org rdfs:label ?orgName .\n",
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" ?org org:hasSubOrganization/rdfs:label ?subName .\n",
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"}} \n",
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"</sparql>\n",
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"\n",
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"<question>\n",
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"Find organizational units of an organization\n",
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"</question>\n",
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"\n",
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"<sparql>\n",
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"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> \n",
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"PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
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"PREFIX org: <http://www.w3.org/ns/org#> \n",
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"\n",
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"select ?org ?orgName ?unitName where {{\n",
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" ?org rdfs:label ?orgName .\n",
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" ?org org:hasUnit/rdfs:label ?unitName . \n",
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"}} \n",
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"</sparql>\n",
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"\n",
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"<question>\n",
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"Find members of an organization. Also find their manager, or the member they report to.\n",
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"</question>\n",
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"\n",
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"<sparql>\n",
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"PREFIX org: <http://www.w3.org/ns/org#> \n",
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"PREFIX foaf: <http://xmlns.com/foaf/0.1/> \n",
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"\n",
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"select * where {{\n",
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" ?person rdf:type foaf:Person .\n",
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" ?person org:memberOf ?org .\n",
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" OPTIONAL {{ ?person foaf:firstName ?firstName . }}\n",
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" OPTIONAL {{ ?person foaf:family_name ?lastName . }}\n",
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" OPTIONAL {{ ?person org:reportsTo ??manager }} .\n",
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"}}\n",
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"</sparql>\n",
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"\n",
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"\n",
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"<question>\n",
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"Find change events, such as mergers and acquisitions, of an organization\n",
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"</question>\n",
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"\n",
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"<sparql>\n",
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"PREFIX org: <http://www.w3.org/ns/org#> \n",
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"\n",
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"select ?event ?prop ?obj where {{\n",
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" ?org rdfs:label ?orgName .\n",
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" ?event rdf:type org:ChangeEvent .\n",
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" ?event org:originalOrganization ?origOrg .\n",
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" ?event org:resultingOrganization ?resultingOrg .\n",
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"}}\n",
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"</sparql>\n",
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"\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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"outputs": [],
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"source": [
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"import boto3\n",
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"from langchain.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain\n",
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"from langchain.chat_models import BedrockChat\n",
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"from langchain_community.graphs import NeptuneRdfGraph\n",
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"\n",
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"host = \"<neptune-host>\"\n",
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"port = \"<neptune-port>\"\n",
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"region = \"us-east-1\" # specify region\n",
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"\n",
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"graph = NeptuneRdfGraph(\n",
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" host=host, port=port, use_iam_auth=True, region_name=region, hide_comments=True\n",
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")\n",
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"\n",
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"schema_elements = graph.get_schema_elements\n",
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"# Optionally, you can update the schema_elements, and\n",
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"# load the schema from the pruned elements.\n",
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"graph.load_from_schema_elements(schema_elements)\n",
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"\n",
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"bedrock_client = boto3.client(\"bedrock-runtime\")\n",
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"llm = BedrockChat(model_id=\"anthropic.claude-v2\", client=bedrock_client)\n",
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"\n",
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"chain = NeptuneSparqlQAChain.from_llm(\n",
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" llm=llm,\n",
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" graph=graph,\n",
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" examples=EXAMPLES,\n",
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" verbose=True,\n",
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" top_K=10,\n",
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" return_intermediate_steps=True,\n",
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" return_direct=False,\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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"metadata": {},
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"source": [
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"## Ask questions\n",
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"Depends on the data we ingested above"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"How many organizations are in 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Are there any mergers or acquisitions\"\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find organizations\"\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find sites of MegaSystems or MegaFinancial\"\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find a member who is manager of one or more members.\"\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find five members and who their manager is.\"\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\n",
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" \"\"\"Find org units or suborganizations of The Mega Group. What are the sites of those units?\"\"\"\n",
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")"
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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",
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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": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,256 @@
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import json
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from types import SimpleNamespace
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from typing import Any, Dict, Optional, Sequence
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import requests
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CLASS_QUERY = """
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SELECT DISTINCT ?elem ?com
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WHERE {
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?instance a ?elem .
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OPTIONAL { ?instance rdf:type/rdfs:subClassOf* ?elem } .
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#FILTER (isIRI(?elem)) .
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OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")}
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}
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"""
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REL_QUERY = """
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SELECT DISTINCT ?elem ?com
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WHERE {
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?subj ?elem ?obj .
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OPTIONAL {
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?elem rdf:type/rdfs:subPropertyOf* ?proptype .
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VALUES ?proptype { rdf:Property owl:DatatypeProperty owl:ObjectProperty } .
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} .
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OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")}
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}
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"""
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DTPROP_QUERY = """
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SELECT DISTINCT ?elem ?com
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WHERE {
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?subj ?elem ?obj .
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OPTIONAL {
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?elem rdf:type/rdfs:subPropertyOf* ?proptype .
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?proptype a owl:DatatypeProperty .
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} .
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OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")}
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}
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"""
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OPROP_QUERY = """
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SELECT DISTINCT ?elem ?com
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WHERE {
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?subj ?elem ?obj .
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OPTIONAL {
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?elem rdf:type/rdfs:subPropertyOf* ?proptype .
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?proptype a owl:ObjectProperty .
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} .
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OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")}
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}
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"""
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ELEM_TYPES = {
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"classes": CLASS_QUERY,
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"rels": REL_QUERY,
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"dtprops": DTPROP_QUERY,
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"oprops": OPROP_QUERY,
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}
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class NeptuneRdfGraph:
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"""Neptune wrapper for RDF graph operations.
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Args:
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host: SPARQL endpoint host for Neptune
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port: SPARQL endpoint port for Neptune. Defaults 8182.
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use_iam_auth: boolean indicating IAM auth is enabled in Neptune cluster
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region_name: AWS region required if use_iam_auth is True, e.g., us-west-2
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hide_comments: whether to include ontology comments in schema for prompt
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Example:
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.. code-block:: python
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graph = NeptuneRdfGraph(
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host='<SPARQL host'>,
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port=<SPARQL port>,
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use_iam_auth=False
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)
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schema = graph.get_schema()
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OR
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graph = NeptuneRdfGraph(
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host='<SPARQL host'>,
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port=<SPARQL port>,
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use_iam_auth=False
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)
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schema_elem = graph.get_schema_elements()
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... change schema_elements ...
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graph.load_schema(schema_elem)
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schema = graph.get_schema()
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*Security note*: Make sure that the database connection uses credentials
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that are narrowly-scoped to only include necessary permissions.
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Failure to do so may result in data corruption or loss, since the calling
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code may attempt commands that would result in deletion, mutation
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of data if appropriately prompted or reading sensitive data if such
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data is present in the database.
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The best way to guard against such negative outcomes is to (as appropriate)
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limit the permissions granted to the credentials used with this tool.
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See https://python.langchain.com/docs/security for more information.
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"""
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def __init__(
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self,
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host: str,
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port: int = 8182,
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use_iam_auth: bool = False,
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region_name: Optional[str] = None,
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hide_comments: bool = False,
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) -> None:
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self.use_iam_auth = use_iam_auth
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self.region_name = region_name
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self.hide_comments = hide_comments
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self.query_endpoint = f"https://{host}:{port}/sparql"
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if self.use_iam_auth:
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try:
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import boto3
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self.session = boto3.Session()
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except ImportError:
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raise ImportError(
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"Could not import boto3 python package. "
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"Please install it with `pip install boto3`."
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)
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else:
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self.session = None
|
||||
|
||||
# Set schema
|
||||
self.schema = ""
|
||||
self.schema_elements: Dict[str, Any] = {}
|
||||
self._refresh_schema()
|
||||
|
||||
@property
|
||||
def get_schema(self) -> str:
|
||||
"""
|
||||
Returns the schema of the graph database.
|
||||
"""
|
||||
return self.schema
|
||||
|
||||
@property
|
||||
def get_schema_elements(self) -> Dict[str, Any]:
|
||||
return self.schema_elements
|
||||
|
||||
def query(
|
||||
self,
|
||||
query: str,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Run Neptune query.
|
||||
"""
|
||||
request_data = {"query": query}
|
||||
data = request_data
|
||||
request_hdr = None
|
||||
|
||||
if self.use_iam_auth:
|
||||
credentials = self.session.get_credentials()
|
||||
credentials = credentials.get_frozen_credentials()
|
||||
access_key = credentials.access_key
|
||||
secret_key = credentials.secret_key
|
||||
service = "neptune-db"
|
||||
session_token = credentials.token
|
||||
params = None
|
||||
creds = SimpleNamespace(
|
||||
access_key=access_key,
|
||||
secret_key=secret_key,
|
||||
token=session_token,
|
||||
region=self.region_name,
|
||||
)
|
||||
from botocore.awsrequest import AWSRequest
|
||||
|
||||
request = AWSRequest(
|
||||
method="POST", url=self.query_endpoint, data=data, params=params
|
||||
)
|
||||
from botocore.auth import SigV4Auth
|
||||
|
||||
SigV4Auth(creds, service, self.region_name).add_auth(request)
|
||||
request.headers["Content-Type"] = "application/x-www-form-urlencoded"
|
||||
request_hdr = request.headers
|
||||
else:
|
||||
request_hdr = {}
|
||||
request_hdr["Content-Type"] = "application/x-www-form-urlencoded"
|
||||
|
||||
queryres = requests.request(
|
||||
method="POST", url=self.query_endpoint, headers=request_hdr, data=data
|
||||
)
|
||||
json_resp = json.loads(queryres.text)
|
||||
return json_resp
|
||||
|
||||
def load_schema(self, schema_elements: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Generates and sets schema from schema_elements. Helpful in
|
||||
cases where introspected schema needs pruning.
|
||||
"""
|
||||
|
||||
elem_str = {}
|
||||
for elem in ELEM_TYPES:
|
||||
res_list = []
|
||||
for elem_rec in self.schema_elements[elem]:
|
||||
uri = elem_rec["uri"]
|
||||
local = elem_rec["local"]
|
||||
res_str = f"<{uri}> ({local})"
|
||||
if self.hide_comments is False:
|
||||
res_str = res_str + f", {elem_rec['comment']}"
|
||||
res_list.append(res_str)
|
||||
elem_str[elem] = ", ".join(res_list)
|
||||
|
||||
self.schema = (
|
||||
"In the following, each IRI is followed by the local name and "
|
||||
"optionally its description in parentheses. \n"
|
||||
"The graph supports the following node types:\n"
|
||||
f"{elem_str['classes']}"
|
||||
"The graph supports the following relationships:\n"
|
||||
f"{elem_str['rels']}"
|
||||
"The graph supports the following OWL object properties, "
|
||||
f"{elem_str['dtprops']}"
|
||||
"The graph supports the following OWL data properties, "
|
||||
f"{elem_str['oprops']}"
|
||||
)
|
||||
|
||||
def _get_local_name(self, iri: str) -> Sequence[str]:
|
||||
"""
|
||||
Split IRI into prefix and local
|
||||
"""
|
||||
if "#" in iri:
|
||||
tokens = iri.split("#")
|
||||
return [f"{tokens[0]}#", tokens[-1]]
|
||||
elif "/" in iri:
|
||||
tokens = iri.split("/")
|
||||
return [f"{'/'.join(tokens[0:len(tokens)-1])}/", tokens[-1]]
|
||||
else:
|
||||
raise ValueError(f"Unexpected IRI '{iri}', contains neither '#' nor '/'.")
|
||||
|
||||
def _refresh_schema(self) -> None:
|
||||
"""
|
||||
Query Neptune to introspect schema.
|
||||
"""
|
||||
self.schema_elements["distinct_prefixes"] = {}
|
||||
|
||||
for elem in ELEM_TYPES:
|
||||
items = self.query(ELEM_TYPES[elem])
|
||||
reslist = []
|
||||
for r in items["results"]["bindings"]:
|
||||
uri = r["elem"]["value"]
|
||||
tokens = self._get_local_name(uri)
|
||||
elem_record = {"uri": uri, "local": tokens[1]}
|
||||
if not self.hide_comments:
|
||||
elem_record["comment"] = r["com"]["value"] if "com" in r else ""
|
||||
reslist.append(elem_record)
|
||||
if tokens[0] not in self.schema_elements["distinct_prefixes"]:
|
||||
self.schema_elements["distinct_prefixes"][tokens[0]] = "y"
|
||||
|
||||
self.schema_elements[elem] = reslist
|
||||
|
||||
self.load_schema(self.schema_elements)
|
||||
@ -1,2 +1,5 @@
|
||||
def test_import() -> None:
|
||||
from langchain_community.graphs import NeptuneGraph # noqa: F401
|
||||
from langchain_community.graphs import (
|
||||
NeptuneGraph, # noqa: F401
|
||||
NeptuneRdfGraph, # noqa: F401
|
||||
)
|
||||
|
||||
@ -0,0 +1,196 @@
|
||||
"""
|
||||
Question answering over an RDF or OWL graph using SPARQL.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain_community.graphs import NeptuneRdfGraph
|
||||
from langchain_core.language_models import BaseLanguageModel
|
||||
from langchain_core.prompts.base import BasePromptTemplate
|
||||
from langchain_core.prompts.prompt import PromptTemplate
|
||||
from langchain_core.pydantic_v1 import Field
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.graph_qa.prompts import SPARQL_QA_PROMPT
|
||||
from langchain.chains.llm import LLMChain
|
||||
|
||||
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
|
||||
|
||||
SPARQL_GENERATION_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.
|
||||
|
||||
Examples:
|
||||
|
||||
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_PROMPT = PromptTemplate(
|
||||
input_variables=["schema", "prompt"], template=SPARQL_GENERATION_TEMPLATE
|
||||
)
|
||||
|
||||
|
||||
def extract_sparql(query: str) -> str:
|
||||
query = query.strip()
|
||||
querytoks = query.split("```")
|
||||
if len(querytoks) == 3:
|
||||
query = querytoks[1]
|
||||
|
||||
if query.startswith("sparql"):
|
||||
query = query[6:]
|
||||
elif query.startswith("<sparql>") and query.endswith("</sparql>"):
|
||||
query = query[8:-9]
|
||||
return query
|
||||
|
||||
|
||||
class NeptuneSparqlQAChain(Chain):
|
||||
"""Chain for question-answering against a Neptune graph
|
||||
by generating SPARQL statements.
|
||||
|
||||
*Security note*: Make sure that the database connection uses credentials
|
||||
that are narrowly-scoped to only include necessary permissions.
|
||||
Failure to do so may result in data corruption or loss, since the calling
|
||||
code may attempt commands that would result in deletion, mutation
|
||||
of data if appropriately prompted or reading sensitive data if such
|
||||
data is present in the database.
|
||||
The best way to guard against such negative outcomes is to (as appropriate)
|
||||
limit the permissions granted to the credentials used with this tool.
|
||||
|
||||
See https://python.langchain.com/docs/security for more information.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
chain = NeptuneSparqlQAChain.from_llm(
|
||||
llm=llm,
|
||||
graph=graph
|
||||
)
|
||||
response = chain.invoke(query)
|
||||
"""
|
||||
|
||||
graph: NeptuneRdfGraph = Field(exclude=True)
|
||||
sparql_generation_chain: LLMChain
|
||||
qa_chain: LLMChain
|
||||
input_key: str = "query" #: :meta private:
|
||||
output_key: str = "result" #: :meta private:
|
||||
top_k: int = 10
|
||||
return_intermediate_steps: bool = False
|
||||
"""Whether or not to return the intermediate steps along with the final answer."""
|
||||
return_direct: bool = False
|
||||
"""Whether or not to return the result of querying the graph directly."""
|
||||
extra_instructions: Optional[str] = None
|
||||
"""Extra instructions by the appended to the query generation prompt."""
|
||||
|
||||
@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_prompt: BasePromptTemplate = SPARQL_GENERATION_PROMPT,
|
||||
examples: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> NeptuneSparqlQAChain:
|
||||
"""Initialize from LLM."""
|
||||
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
|
||||
template_to_use = SPARQL_GENERATION_TEMPLATE
|
||||
if examples:
|
||||
template_to_use = template_to_use.replace(
|
||||
"Examples:", "Examples: " + examples
|
||||
)
|
||||
sparql_prompt = PromptTemplate(
|
||||
input_variables=["schema", "prompt"], template=template_to_use
|
||||
)
|
||||
sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt)
|
||||
|
||||
return cls(
|
||||
qa_chain=qa_chain,
|
||||
sparql_generation_chain=sparql_generation_chain,
|
||||
examples=examples,
|
||||
**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]
|
||||
|
||||
intermediate_steps: List = []
|
||||
|
||||
generated_sparql = self.sparql_generation_chain.run(
|
||||
{"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks
|
||||
)
|
||||
|
||||
# Extract SPARQL
|
||||
generated_sparql = extract_sparql(generated_sparql)
|
||||
|
||||
_run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose)
|
||||
_run_manager.on_text(
|
||||
generated_sparql, color="green", end="\n", verbose=self.verbose
|
||||
)
|
||||
|
||||
intermediate_steps.append({"query": generated_sparql})
|
||||
|
||||
context = self.graph.query(generated_sparql)
|
||||
|
||||
if self.return_direct:
|
||||
final_result = context
|
||||
else:
|
||||
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
|
||||
_run_manager.on_text(
|
||||
str(context), color="green", end="\n", verbose=self.verbose
|
||||
)
|
||||
|
||||
intermediate_steps.append({"context": context})
|
||||
|
||||
result = self.qa_chain(
|
||||
{"prompt": prompt, "context": context},
|
||||
callbacks=callbacks,
|
||||
)
|
||||
final_result = result[self.qa_chain.output_key]
|
||||
|
||||
chain_result: Dict[str, Any] = {self.output_key: final_result}
|
||||
if self.return_intermediate_steps:
|
||||
chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
|
||||
|
||||
return chain_result
|
||||
Loading…
Reference in New Issue