ontocast.toolbox

ToolBox

A container class for all tools used in the ontology processing workflow.

This class initializes and manages various tools needed for document processing, ontology management, and LLM interactions.

Attributes:

Name	Type	Description
llm	LLMTool	LLM tool for text processing and analysis.
triple_store_manager	TripleStoreManager	Manager for RDF triple storage.
ontology_manager	OntologyManager	Manager for ontology operations.
converter	ConverterTool	Tool for document conversion.
chunker	ChunkerTool	Tool for text chunking.
aggregator	ChunkRDFGraphAggregator	Tool for aggregating RDF graphs.

Source code in ontocast/toolbox.py

class ToolBox:
    """A container class for all tools used in the ontology processing workflow.

    This class initializes and manages various tools needed for document processing,
    ontology management, and LLM interactions.

    Attributes:
        llm: LLM tool for text processing and analysis.
        triple_store_manager: Manager for RDF triple storage.
        ontology_manager: Manager for ontology operations.
        converter: Tool for document conversion.
        chunker: Tool for text chunking.
        aggregator: Tool for aggregating RDF graphs.
    """

    def __init__(self, **kwargs):
        """Initialize the ToolBox with required tools.

        Args:
            working_directory: Path to the working directory.
            ontology_directory: Optional path to ontology directory.
            model_name: Name of the LLM model to use.
            llm_base_url: Optional base URL for LLM API.
            temperature: Temperature setting for LLM.
            llm_provider: Provider for LLM service (default: "openai").
        """
        working_directory: pathlib.Path = kwargs.pop("working_directory")
        ontology_directory: Optional[pathlib.Path] = kwargs.pop("ontology_directory")
        model_name: str = kwargs.pop("model_name")
        llm_base_url: Optional[str] = kwargs.pop("llm_base_url")
        temperature: float = kwargs.pop("temperature")
        llm_provider: str = kwargs.pop("llm_provider", "openai")

        self.llm: LLMTool = LLMTool.create(
            provider=llm_provider,
            model=model_name,
            temperature=temperature,
            base_url=llm_base_url,
        )
        self.triple_store_manager: TripleStoreManager = FilesystemTripleStoreManager(
            working_directory=working_directory, ontology_path=ontology_directory
        )
        self.ontology_manager: OntologyManager = OntologyManager()
        self.converter: ConverterTool = ConverterTool()
        self.chunker: ChunkerTool = ChunkerTool()
        self.aggregator: ChunkRDFGraphAggregator = ChunkRDFGraphAggregator()

__init__(**kwargs)

Initialize the ToolBox with required tools.

Parameters:

Name	Type	Description	Default
working_directory		Path to the working directory.	required
ontology_directory		Optional path to ontology directory.	required
model_name		Name of the LLM model to use.	required
llm_base_url		Optional base URL for LLM API.	required
temperature		Temperature setting for LLM.	required
llm_provider		Provider for LLM service (default: "openai").	required

Source code in ontocast/toolbox.py

def __init__(self, **kwargs):
    """Initialize the ToolBox with required tools.

    Args:
        working_directory: Path to the working directory.
        ontology_directory: Optional path to ontology directory.
        model_name: Name of the LLM model to use.
        llm_base_url: Optional base URL for LLM API.
        temperature: Temperature setting for LLM.
        llm_provider: Provider for LLM service (default: "openai").
    """
    working_directory: pathlib.Path = kwargs.pop("working_directory")
    ontology_directory: Optional[pathlib.Path] = kwargs.pop("ontology_directory")
    model_name: str = kwargs.pop("model_name")
    llm_base_url: Optional[str] = kwargs.pop("llm_base_url")
    temperature: float = kwargs.pop("temperature")
    llm_provider: str = kwargs.pop("llm_provider", "openai")

    self.llm: LLMTool = LLMTool.create(
        provider=llm_provider,
        model=model_name,
        temperature=temperature,
        base_url=llm_base_url,
    )
    self.triple_store_manager: TripleStoreManager = FilesystemTripleStoreManager(
        working_directory=working_directory, ontology_path=ontology_directory
    )
    self.ontology_manager: OntologyManager = OntologyManager()
    self.converter: ConverterTool = ConverterTool()
    self.chunker: ChunkerTool = ChunkerTool()
    self.aggregator: ChunkRDFGraphAggregator = ChunkRDFGraphAggregator()

init_toolbox(toolbox)

Initialize the toolbox with ontologies and their properties.

This function fetches ontologies from the triple store and updates their properties using the LLM tool.

Parameters:

Name	Type	Description	Default
toolbox	ToolBox	The ToolBox instance to initialize.	required

Source code in ontocast/toolbox.py

def init_toolbox(toolbox: ToolBox):
    """Initialize the toolbox with ontologies and their properties.

    This function fetches ontologies from the triple store and updates
    their properties using the LLM tool.

    Args:
        toolbox: The ToolBox instance to initialize.
    """
    toolbox.ontology_manager.ontologies = (
        toolbox.triple_store_manager.fetch_ontologies()
    )
    update_ontology_manager(om=toolbox.ontology_manager, llm_tool=toolbox.llm)

render_ontology_summary(graph, llm_tool)

Generate a summary of ontology properties using LLM analysis.

This function uses the LLM tool to analyze an RDF graph and generate a structured summary of its properties.

Parameters:

Name	Type	Description	Default
graph	RDFGraph	The RDF graph to analyze.	required
llm_tool		The LLM tool instance for analysis.	required

Returns:

Name	Type	Description
OntologyProperties	OntologyProperties	A structured summary of the ontology properties.

Source code in ontocast/toolbox.py

def render_ontology_summary(graph: RDFGraph, llm_tool) -> OntologyProperties:
    """Generate a summary of ontology properties using LLM analysis.

    This function uses the LLM tool to analyze an RDF graph and generate
    a structured summary of its properties.

    Args:
        graph: The RDF graph to analyze.
        llm_tool: The LLM tool instance for analysis.

    Returns:
        OntologyProperties: A structured summary of the ontology properties.
    """
    ontology_str = graph.serialize(format="turtle")

    # Define the output parser
    parser = PydanticOutputParser(pydantic_object=OntologyProperties)

    # Create the prompt template with format instructions
    prompt = PromptTemplate(
        template=(
            "Below is an ontology in Turtle format:\n\n"
            "```ttl\n{ontology_str}\n```\n\n"
            "{format_instructions}"
        ),
        input_variables=["ontology_str"],
        partial_variables={"format_instructions": parser.get_format_instructions()},
    )

    response = llm_tool(prompt.format_prompt(ontology_str=ontology_str))

    return parser.parse(response.content)

update_ontology_manager(om, llm_tool)

Update properties for all ontologies in the manager.

This function iterates through all ontologies in the manager and updates their properties using the LLM tool.

Parameters:

Name	Type	Description	Default
om	OntologyManager	The ontology manager containing ontologies to update.	required
llm_tool	LLMTool	The LLM tool instance for analysis.	required

Source code in ontocast/toolbox.py

def update_ontology_manager(om: OntologyManager, llm_tool: LLMTool):
    """Update properties for all ontologies in the manager.

    This function iterates through all ontologies in the manager and updates
    their properties using the LLM tool.

    Args:
        om: The ontology manager containing ontologies to update.
        llm_tool: The LLM tool instance for analysis.
    """
    for o in om.ontologies:
        update_ontology_properties(o, llm_tool)

update_ontology_properties(o, llm_tool)

Update ontology properties using LLM analysis.

This function uses the LLM tool to analyze and update the properties of a given ontology based on its graph content.

Parameters:

Name	Type	Description	Default
o	Ontology	The ontology to update.	required
llm_tool	LLMTool	The LLM tool instance for analysis.	required

Source code in ontocast/toolbox.py

def update_ontology_properties(o: Ontology, llm_tool: LLMTool):
    """Update ontology properties using LLM analysis.

    This function uses the LLM tool to analyze and update the properties
    of a given ontology based on its graph content.

    Args:
        o: The ontology to update.
        llm_tool: The LLM tool instance for analysis.
    """
    props = render_ontology_summary(o.graph, llm_tool)
    o.set_properties(**props.model_dump())