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https://github.com/sib-swiss/sparql-llm

ðŸĶœâœĻ Chat system and reusable components to improve LLMs capabilities when generating SPARQL queries
https://github.com/sib-swiss/sparql-llm

expasy llm sparql sparql-query-builder

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ðŸĶœâœĻ Chat system and reusable components to improve LLMs capabilities when generating SPARQL queries

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# âœĻ SPARQL query generation with LLMs ðŸĶœ



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This project provides reusable components and a complete web service to enhance the capabilities of Large Language Models (LLMs) in generating [SPARQL](https://www.w3.org/TR/sparql11-overview/) 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](https://github.com/sib-swiss/sparql-examples) and endpoint descriptions using the [Vocabulary of Interlinked Datasets (VoID)](https://www.w3.org/TR/void/), which can be automatically generated using the [void-generator](https://github.com/JervenBolleman/void-generator).



## 🌈 Features



- **Metadata Extraction**: Functions to extract and load relevant metadata from SPARQL endpoints. These loaders are compatible with [LangChain](https://python.langchain.com) 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](https://chat.expasy.org)**, an LLM-powered chat system supporting SPARQL query generation for endpoints maintained by the [SIB](https://www.sib.swiss/).



> [!TIP]

>

> You can quickly check if an endpoint contains the expected metadata at [sib-swiss.github.io/sparql-editor/check](https://sib-swiss.github.io/sparql-editor/check)



## ðŸ“Ķïļ Reusable components



### Installation



> Requires Python >=3.9



```bash

pip install sparql-llm

```



### SPARQL query examples loader



Load SPARQL query examples defined using the SHACL ontology from a SPARQL endpoint. See **[github.com/sib-swiss/sparql-examples](https://github.com/sib-swiss/sparql-examples)** for more details on how to define the examples.



```python

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](https://python.langchain.com/v0.2/docs/) to figure out how to best integrate documents loaders to your stack.



### SPARQL endpoint schema loader



Generate a human-readable schema using the ShEx format to describe all classes of a SPARQL endpoint based on the [VoID description](https://www.w3.org/TR/void/) 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](https://github.com/JervenBolleman/void-generator)** project to automatically generate VoID description for your endpoint.



```python

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:

>

> ```turtle

> up:Disease_Annotation {

>   a [ up:Disease_Annotation ] ;

>   up:sequence [ up:Chain_Annotation up:Modified_Sequence ] ;

>   rdfs:comment xsd:string ;

>   up:disease IRI

> }

> ```



### Generate complete ShEx shapes from VoID description



You can also generate the complete ShEx shapes for a SPARQL endpoint with:



```python

from sparql_llm import get_shex_from_void



shex_str = get_shex_from_void("https://sparql.uniprot.org/sparql/")

print(shex_str)

```



### Validate a SPARQL query based on VoID description



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.



```python

from sparql_llm import validate_sparql_with_void



sparql_query = """PREFIX skos: 

PREFIX up: 

PREFIX taxon: 

PREFIX rdfs: 

PREFIX orth: 

PREFIX dcterms: 

PREFIX obo: 

PREFIX lscr: 

PREFIX genex: 

PREFIX sio: 

SELECT DISTINCT ?diseaseLabel ?humanProtein ?hgncSymbol ?orthologRatProtein ?orthologRatGene

WHERE {

    SERVICE  {

        SELECT DISTINCT * WHERE {

            ?humanProtein a up:Protein ;

                up:organism/up:scientificName 'Homo sapiens' ;

                up:annotation ?annotation ;

                rdfs:seeAlso ?hgnc .

            ?hgnc up:database  ;

                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  {

        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  {

        ?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))

```



## 🚀 Complete chat system



> [!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:



```bash

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_API

```



Start 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):



```bash

docker compose up

```



Start the stack locally for development, with code from `src` folder mounted in the container and automatic API reload on changes to the code:



```bash

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



## 🧑‍ðŸ’ŧ Contributing



Checkout the [CONTRIBUTING.md](https://github.com/sib-swiss/sparql-llm/blob/main/CONTRIBUTING.md) page for more details on how to run the package in development and make a contribution.



## ðŸŠķ How to cite this work



If you reuse any part of this work, please cite [the arXiv paper](https://arxiv.org/abs/2410.06062):



```

@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}, 

}

```