🌈 Features

• 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.
• MCP server with tools to help LLM write SPARQL queries for a set of 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 expasy.org/chat, 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

🔌 MCP server

The server exposes a Model Context Protocol (MCP) endpoint to access biodata resources at the SIB, through their SPARQL endpoints, such as UniProt, Bgee, OMA, SwissLipids, Cellosaurus at chat.expasy.org/mcp

🛠️ Available tools

• 📝 Retrieve relevant documents (query examples and classes schema) to help writing SPARQL queries to access SIB biodata resources
  • Arguments:
    • question (string): the user's question
    • potential_classes (list[string]): high level concepts and potential classes that could be found in the SPARQL endpoints
    • steps (list[string]): split the question in standalone smaller parts if relevant
• 🏷️ Retrieve relevant classes schema to help writing SPARQL queries to access SIB biodata resources
  • Arguments:
    • classes (list[string]): high level concepts and potential classes that could be found in the SPARQL endpoints
• 📡 Execute a SPARQL query against a SPARQL endpoint
  • Arguments:
    • query (string): a valid SPARQL query string
    • endpoint (string): the SPARQL endpoint URL to execute the query against

⚡️ Connect client to MCP server

Follow the instructions of your client, and use the URL of the public server: https://chat.expasy.org/mcp

For example, for GitHub Copilot in VSCode, to add a new MCP server through the VSCode UI:

• [x] Open side panel chat (ctrl+shift+i or cmd+shift+i), and make sure the mode is set to Agent in the bottom right
• [x] Open command palette (ctrl+shift+p or cmd+shift+p), and search for MCP: Open User Configuration, this will open a mcp.json file

📡 Use streamable HTTP server

Connect to a running streamable HTTP MCP server, such as the publicly available chat.expasy.org/mcp.

In your VSCode mcp.json you should have the following:

{
	"servers": {
		"expasy-mcp-http": {
			"url": "https://chat.expasy.org/mcp/",
			"type": "http"
		}
	}
}

⌨️ Use stdio transport

uvx sparql-llm

Optionally you can provide the path to a custom settings JSON file to configure the server (e.g. the list of endpoints that will be indexed and available through the server), see the Settings class for detailed available settings.

Example settings file for your MCP server deployment:

{
    "app_org": "Your organization",
    "app_topics": "genes, proteins, lipids, chemical reactions, and metabolomics data",
    "endpoints" : [
        {
            "label": "UniProt",
            "endpoint_url": "https://sparql.uniprot.org/sparql/",
            "description": "UniProt is a comprehensive resource for protein sequence and annotation data."
        },
        {
            "label": "Bgee",
            "description": "Bgee is a database for retrieval and comparison of gene expression patterns across multiple animal species.",
            "endpoint_url": "https://www.bgee.org/sparql/",
            "homepage_url": "https://www.bgee.org/"
        }
    ]
}

Example mcp.json file to add and configure the MCP server in a client (e.g. VSCode):

{
  "servers": {
    "expasy-mcp": {
      "type": "stdio",
      "command": "uvx",
      "env": {
				"SETTINGS_FILEPATH": "/Users/you/sparql-mcp.json"
			},
      "args": [
        "sparql-llm"
      ]
    }
  }
}

[!IMPORTANT]

Click on Start just on top of "openroute-mcp" to start the connection to the MCP server.

You can click the wrench and screwdriver button 🛠️ (Configure Tools...) to enable/disable specific tools

[!NOTE]

More details available in the VSCode MCP official docs.

📦️ Reusable components

Installation

Requires Python >=3.10

pip install sparql-llm

Or with uv:

uv add 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 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)

You can provide the examples as a file if it is not integrated in the endpoint, e.g.:

loader = SparqlExamplesLoader("https://sparql.uniprot.org/sparql/", examples_file="uniprot_examples.ttl")

Refer to the LangChain documentation to figure out how to best integrate documents loaders to your system.

[!NOTE]

You can check the completeness of your examples against the endpoint schema using this notebook.

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 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)

You can provide the VoID description as a file if it is not integrated in the endpoint, e.g.:

loader = SparqlVoidShapesLoader("https://sparql.uniprot.org/sparql/", void_file="uniprot_void.ttl")

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
}

Generate complete ShEx shapes from VoID description

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)

Validate a SPARQL query based on VoID description

This takes a SPARQL query and validates the predicates/types used are compliant with the VoID descriptio