ontocast.config

Configuration management for OntoCast.

This module provides hierarchical configuration classes that map to the environment variables and usage patterns in the OntoCast system.

ChunkConfig

Bases: BaseSettings

Chunking configuration settings.

Source code in ontocast/config.py

class ChunkConfig(BaseSettings):
    """Chunking configuration settings."""

    breakpoint_threshold_type: Literal[
        "percentile", "standard_deviation", "interquartile", "gradient"
    ] = Field(
        default="percentile", description="Type of threshold calculation for chunking"
    )
    breakpoint_threshold_amount: float = Field(
        default=95.0, description="Threshold amount for breakpoint detection"
    )
    min_size: int = Field(default=3000, description="Minimum chunk size in characters")
    max_size: int = Field(default=12000, description="Maximum chunk size in characters")

    model_config = SettingsConfigDict(
        env_prefix="CHUNK_",
        case_sensitive=False,
    )

Config

Bases: BaseSettings

Main OntoCast configuration.

This class aggregates all configuration sections and provides a unified interface for accessing configuration values.

Source code in ontocast/config.py

class Config(BaseSettings):
    """Main OntoCast configuration.

    This class aggregates all configuration sections and provides
    a unified interface for accessing configuration values.
    """

    # Tool configuration (for ToolBox)
    tool_config: ToolConfig = Field(default_factory=ToolConfig)

    # Server configuration (for serve.py)
    server: ServerConfig = Field(default_factory=ServerConfig)

    # Additional settings
    logging_level: str | None = Field(default=None, description="Logging level")

    model_config = SettingsConfigDict(
        case_sensitive=False,
        extra="ignore",
    )

    def get_tool_config(self) -> ToolConfig:
        """Get tool configuration.

        Returns:
            ToolConfig: Configuration for tools
        """
        return self.tool_config

    def validate_llm_config(self) -> None:
        """Validate LLM configuration and raise errors for missing required settings."""
        if (
            self.tool_config.llm_config.provider == LLMProvider.OPENAI
            and not self.tool_config.llm_config.api_key
        ):
            raise ValueError(
                "LLM_API_KEY environment variable is required for OpenAI provider"
            )

get_tool_config()

Get tool configuration.

Returns:

Name	Type	Description
ToolConfig	ToolConfig	Configuration for tools

Source code in ontocast/config.py

def get_tool_config(self) -> ToolConfig:
    """Get tool configuration.

    Returns:
        ToolConfig: Configuration for tools
    """
    return self.tool_config

validate_llm_config()

Validate LLM configuration and raise errors for missing required settings.

Source code in ontocast/config.py

def validate_llm_config(self) -> None:
    """Validate LLM configuration and raise errors for missing required settings."""
    if (
        self.tool_config.llm_config.provider == LLMProvider.OPENAI
        and not self.tool_config.llm_config.api_key
    ):
        raise ValueError(
            "LLM_API_KEY environment variable is required for OpenAI provider"
        )

DomainConfig

Bases: BaseSettings

Domain and URI configuration.

Source code in ontocast/config.py

class DomainConfig(BaseSettings):
    """Domain and URI configuration."""

    current_domain: str = Field(
        default="https://example.com", description="Current domain for URI generation"
    )

    model_config = SettingsConfigDict(
        case_sensitive=False,
    )

FusekiConfig

Bases: BaseSettings

Fuseki triple store configuration.

Source code in ontocast/config.py

class FusekiConfig(BaseSettings):
    """Fuseki triple store configuration."""

    uri: str | None = Field(default=None, description="Fuseki URI")
    auth: str | None = Field(default=None, description="Fuseki authentication")
    dataset: str = Field(default=DEFAULT_DATASET, description="Fuseki dataset name")
    ontologies_dataset: str = Field(
        default=DEFAULT_ONTOLOGIES_DATASET,
        description="Fuseki dataset name for ontologies",
    )

    model_config = SettingsConfigDict(
        env_prefix="FUSEKI_",
        case_sensitive=False,
    )

LLMConfig

Bases: BaseSettings

LLM configuration settings.

Source code in ontocast/config.py

class LLMConfig(BaseSettings):
    """LLM configuration settings."""

    provider: LLMProvider = Field(
        default=LLMProvider.OPENAI, description="LLM provider"
    )
    model_name: LLMModelName = Field(
        default=OpenAIModel.GPT4_O_MINI, description="LLM model name"
    )
    temperature: float = Field(default=0.0, description="LLM temperature setting")
    base_url: str | None = Field(
        default=None, description="LLM base URL (for ollama, etc.)"
    )
    api_key: str | None = Field(default=None, description="API key for LLM provider")

    model_config = SettingsConfigDict(
        env_prefix="LLM_",
        case_sensitive=False,
    )

    @field_validator("model_name")
    @classmethod
    def validate_model_name(cls, v: LLMModelName, info) -> LLMModelName:
        """Validate that model_name is compatible with the provider."""
        if "provider" not in info.data:
            return v

        provider = info.data["provider"]

        if provider == LLMProvider.OPENAI and not isinstance(v, OpenAIModel):
            raise ValueError(
                f"Model {v} is not compatible with OpenAI provider. Use OpenAIModel values."
            )

        if provider == LLMProvider.OLLAMA and not isinstance(v, OllamaModel):
            raise ValueError(
                f"Model {v} is not compatible with Ollama provider. Use OllamaModel values."
            )

        return v

validate_model_name(v, info) classmethod

Validate that model_name is compatible with the provider.

Source code in ontocast/config.py

@field_validator("model_name")
@classmethod
def validate_model_name(cls, v: LLMModelName, info) -> LLMModelName:
    """Validate that model_name is compatible with the provider."""
    if "provider" not in info.data:
        return v

    provider = info.data["provider"]

    if provider == LLMProvider.OPENAI and not isinstance(v, OpenAIModel):
        raise ValueError(
            f"Model {v} is not compatible with OpenAI provider. Use OpenAIModel values."
        )

    if provider == LLMProvider.OLLAMA and not isinstance(v, OllamaModel):
        raise ValueError(
            f"Model {v} is not compatible with Ollama provider. Use OllamaModel values."
        )

    return v

LLMModelNameAbstract

Bases: StrEnum

Abstract base class for all model names.

Source code in ontocast/config.py

class LLMModelNameAbstract(StrEnum):
    """Abstract base class for all model names."""

LLMProvider

Bases: StrEnum

Supported LLM providers.

Source code in ontocast/config.py

class LLMProvider(StrEnum):
    """Supported LLM providers."""

    OPENAI = "openai"
    OLLAMA = "ollama"

Neo4jConfig

Bases: BaseSettings

Neo4j triple store configuration.

Source code in ontocast/config.py

class Neo4jConfig(BaseSettings):
    """Neo4j triple store configuration."""

    uri: str | None = Field(default=None, description="Neo4j URI")
    auth: str | None = Field(default=None, description="Neo4j authentication")
    port: int = Field(default=7476, description="Neo4j HTTP port")
    bolt_port: int = Field(default=7689, description="Neo4j Bolt port")

    model_config = SettingsConfigDict(
        env_prefix="NEO4J_",
        case_sensitive=False,
    )

OllamaModel

Bases: LLMModelNameAbstract

Ollama model names.

Source code in ontocast/config.py

class OllamaModel(LLMModelNameAbstract):
    """Ollama model names."""

    QWEN2_5 = "qwen2.5"
    QWEN2_5_72B = "qwen2.5:72b"
    LLAMA3_1 = "llama3.1"
    LLAMA3_1_70B = "llama3.1:70b"
    GRANITE3_3_2B = "granite3.3:2b"
    GRANITE3_3_8B = "granite3.3:8b"

OpenAIModel

Bases: LLMModelNameAbstract

OpenAI model names.

Source code in ontocast/config.py

class OpenAIModel(LLMModelNameAbstract):
    """OpenAI model names."""

    GPT4_O = "gpt-4o"
    GPT4_O_MINI = "gpt-4o-mini"
    GPT4_1 = "gpt-41"
    GPT4_1_MINI = "gpt-41-mini"
    GPT5 = "gpt-5"
    GPT5_MINI = "gpt-5-mini"
    GPT5_NANO = "gpt-5-nano"

PathConfig

Bases: BaseSettings

Path and directory configuration.

Source code in ontocast/config.py

class PathConfig(BaseSettings):
    """Path and directory configuration."""

    working_directory: Path | None = Field(
        default=None,
        description="Working directory for OntoCast (required if filesystem_manager is enabled)",
    )
    ontology_directory: Path | None = Field(
        default=None, description="Directory containing ontology files"
    )
    cache_dir: Path | None = Field(
        default=None, description="Cache directory for LLM responses and tool outputs"
    )

    model_config = SettingsConfigDict(
        env_prefix="ONTOCAST_",
        case_sensitive=False,
    )

ServerConfig

Bases: BaseSettings

Server configuration settings.

Source code in ontocast/config.py

class ServerConfig(BaseSettings):
    """Server configuration settings."""

    port: int = Field(default=8999, description="Server port")
    base_recursion_limit: int = Field(
        default=1000, description="Recursion limit for workflow"
    )
    estimated_chunks: int = Field(default=30, description="Estimated number of chunks")
    max_visits: int = Field(
        default=3, description="Maximum number of visits allowed per node"
    )
    skip_ontology_development: bool = Field(
        default=False, description="Skip ontology critique step"
    )
    skip_facts_rendering: bool = Field(
        default=False, description="Skip facts rendering and go straight to aggregation"
    )
    ontology_max_triples: int | None = Field(
        default=50000,
        description="Maximum number of triples allowed in ontology graph. "
        "Updates that would exceed this limit are skipped with a warning. "
        "Set to None for unlimited.",
    )

    model_config = SettingsConfigDict(
        case_sensitive=False,
    )

ToolConfig

Bases: BaseSettings

Configuration for tools (LLM, triple stores, paths, chunking).

Source code in ontocast/config.py

class ToolConfig(BaseSettings):
    """Configuration for tools (LLM, triple stores, paths, chunking)."""

    llm_config: LLMConfig = Field(default_factory=LLMConfig)
    chunk_config: ChunkConfig = Field(default_factory=ChunkConfig)
    path_config: PathConfig = Field(default_factory=PathConfig)
    neo4j: Neo4jConfig = Field(default_factory=Neo4jConfig)
    fuseki: FusekiConfig = Field(default_factory=FusekiConfig)
    domain: DomainConfig = Field(default_factory=DomainConfig)