This project provides reusable components and a complete web service to enhance the capabilities of Large Language Models (LLMs) in generating SPARQL queries for specific endpoints. By integrating Retrieval-Augmented Generation (RAG) and SPARQL query validation through endpoint schemas, this system ensures more accurate and relevant query generation on large scale knowledge graphs.
The components are designed to work either independently or as part of a full chat-based system that can be deployed for a set of SPARQL endpoints. It requires endpoints to include metadata such as SPARQL query examples and endpoint descriptions using the Vocabulary of Interlinked Datasets (VoID), which can be automatically generated using the void-generator.
- Metadata Extraction: Functions to extract and load relevant metadata from SPARQL endpoints. These loaders are compatible with LangChain but are flexible enough to be used independently, providing metadata as JSON for custom vector store integration.
- SPARQL Query Validation: A function to automatically parse and validate federated SPARQL queries against the VoID description of the target endpoints.
- Deployable Chat System: A reusable and containerized system for deploying an LLM-based chat service with a web UI, API, and vector database. This system helps users write SPARQL queries by leveraging endpoint metadata (WIP).
- Live Example: Configuration for chat.expasy.org, an LLM-powered chat system supporting SPARQL query generation for endpoints maintained by the SIB.
Tip
You can quickly check if an endpoint contains the expected metadata at sib-swiss.github.io/sparql-editor/check
Requires Python >=3.9
pip install sparql-llmLoad SPARQL query examples defined using the SHACL ontology from a SPARQL endpoint. See github.com/sib-swiss/sparql-examples for more details on how to define the examples.
from sparql_llm import SparqlExamplesLoader
loader = SparqlExamplesLoader("https://sparql.uniprot.org/sparql/")
docs = loader.load()
print(len(docs))
print(docs[0].metadata)Refer to the LangChain documentation to figure out how to best integrate documents loaders to your stack.
Generate a human-readable schema using the ShEx format to describe all classes of a SPARQL endpoint based on the VoID description present in the endpoint. Ideally the endpoint should also contain the ontology describing the classes, so the rdfs:label and rdfs:comment of the classes can be used to generate embeddings and improve semantic matching.
Tip
Checkout the void-generator project to automatically generate VoID description for your endpoint.
from sparql_llm import SparqlVoidShapesLoader
loader = SparqlVoidShapesLoader("https://sparql.uniprot.org/sparql/")
docs = loader.load()
print(len(docs))
print(docs[0].metadata)The generated shapes are well-suited for use with a LLM or a human, as they provide clear information about which predicates are available for a class, and the corresponding classes or datatypes those predicates point to. Each object property references a list of classes rather than another shape, making each shape self-contained and interpretable on its own, e.g. for a Disease Annotation in UniProt:
up:Disease_Annotation { a [ up:Disease_Annotation ] ; up:sequence [ up:Chain_Annotation up:Modified_Sequence ] ; rdfs:comment xsd:string ; up:disease IRI }
You can also generate the complete ShEx shapes for a SPARQL endpoint with:
from sparql_llm import get_shex_from_void
shex_str = get_shex_from_void("https://sparql.uniprot.org/sparql/")
print(shex_str)This takes a SPARQL query and validates the predicates/types used are compliant with the VoID description present in the SPARQL endpoint the query is executed on.
This function supports:
- federated queries (VoID description will be automatically retrieved for each SERVICE call in the query),
- path patterns (e.g.
orth:organism/obo:RO_0002162/up:scientificName)
This function requires that at least one type is defined for each endpoint, but it will be able to infer types of subjects that are connected to the subject for which the type is defined.
It will return a list of issues described in natural language, with hints on how to fix them (by listing the available classes/predicates), which can be passed to an LLM as context to help it figuring out how to fix the query.
from sparql_llm import validate_sparql_with_void
sparql_query = """PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX taxon: <http://purl.uniprot.org/taxonomy/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX orth: <http://purl.org/net/orth#>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX lscr: <http://purl.org/lscr#>
PREFIX genex: <http://purl.org/genex#>
PREFIX sio: <http://semanticscience.org/resource/>
SELECT DISTINCT ?diseaseLabel ?humanProtein ?hgncSymbol ?orthologRatProtein ?orthologRatGene
WHERE {
SERVICE <https://sparql.uniprot.org/sparql> {
SELECT DISTINCT * WHERE {
?humanProtein a up:Protein ;
up:organism/up:scientificName 'Homo sapiens' ;
up:annotation ?annotation ;
rdfs:seeAlso ?hgnc .
?hgnc up:database <http://purl.uniprot.org/database/HGNC> ;
rdfs:label ?hgncSymbol . # comment
?annotation a up:Disease_Annotation ;
up:disease ?disease .
?disease a up:Disease ;
rdfs:label ?diseaseLabel . # skos:prefLabel
FILTER CONTAINS(?diseaseLabel, "cancer")
}
}
SERVICE <https://sparql.omabrowser.org/sparql/> {
SELECT ?humanProtein ?orthologRatProtein ?orthologRatGene WHERE {
?humanProteinOma a orth:Protein ;
lscr:xrefUniprot ?humanProtein .
?orthologRatProtein a orth:Protein ;
sio:SIO_010078 ?orthologRatGene ; # 79
orth:organism/obo:RO_0002162/up:scientificNam 'Rattus norvegicus' .
?cluster a orth:OrthologsCluster .
?cluster orth:hasHomologousMember ?node1 .
?cluster orth:hasHomologousMember ?node2 .
?node1 orth:hasHomologousMember* ?humanProteinOma .
?node2 orth:hasHomologousMember* ?orthologRatProtein .
FILTER(?node1 != ?node2)
}
}
SERVICE <https://www.bgee.org/sparql/> {
?orthologRatGene genex:isExpressedIn ?anatEntity ;
orth:organism ?ratOrganism .
?anatEntity rdfs:label 'brain' .
?ratOrganism obo:RO_0002162 taxon:10116 .
}
}"""
issues = validate_sparql_with_void(sparql_query, "https://sparql.uniprot.org/sparql/")
print("\n".join(issues))Warning
To deploy the complete chat system right now you will need to fork this repository, change the configuration in src/sparql_llm/config.py and compose.yml, then deploy with docker/podman compose.
It can easily be adapted to use any LLM served through an OpenAI-compatible API. We plan to make configuration and deployment of complete SPARQL LLM chat system easier in the future, let us know if you are interested in the GitHub issues!
Create a .env file at the root of the repository to provide secrets and API keys:
OPENAI_API_KEY=sk-proj-YYY
GLHF_API_KEY=APIKEY_FOR_glhf.chat_USED_FOR_TEST_OPEN_SOURCE_MODELS
EXPASY_API_KEY=NOT_SO_SECRET_API_KEY_USED_BY_FRONTEND_TO_AVOID_SPAM_FROM_CRAWLERS
LOGS_API_KEY=SECRET_PASSWORD_TO_EASILY_ACCESS_LOGS_THROUGH_THE_APIStart the web UI, API, and similarity search engine in production (you might need to make some changes to the compose.yml file to adapt it to your server/proxy setup):
docker compose upStart the stack locally for development, with code from src folder mounted in the container and automatic API reload on changes to the code:
docker compose -f compose.dev.yml up- Chat web UI available at http://localhost:8000
- OpenAPI Swagger UI available at http://localhost:8000/docs
- Vector database dashboard UI available at http://localhost:6333/dashboard
Checkout the CONTRIBUTING.md page for more details on how to run the package in development and make a contribution.
If you reuse any part of this work, please cite the arXiv paper:
@misc{emonet2024llmbasedsparqlquerygeneration,
title={LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs},
author={Vincent Emonet and Jerven Bolleman and Severine Duvaud and Tarcisio Mendes de Farias and Ana Claudia Sima},
year={2024},
eprint={2410.06062},
archivePrefix={arXiv},
primaryClass={cs.DB},
url={https://arxiv.org/abs/2410.06062},
}