ontocast.onto.context

Context passing system for agent-based workflow.

This module provides functionality for passing context between agents, enabling memory and incremental processing.

AgentContext

Bases: BaseModel

Context information passed between agents.

This class encapsulates all the context information that agents need to build upon previous work rather than starting fresh.

Source code in ontocast/onto/context.py

class AgentContext(BaseModel):
    """Context information passed between agents.

    This class encapsulates all the context information that agents
    need to build upon previous work rather than starting fresh.
    """

    # Agent identification
    agent_type: AgentType = Field(description="Type of agent for type safety")

    # Previous work context
    previous_ontology_version: GraphVersion | None = Field(
        default=None, description="Previous ontology version if available"
    )
    previous_facts_version: GraphVersion | None = Field(
        default=None, description="Previous facts version if available"
    )

    # Previous operations (append-only for performance)
    previous_ontology_operations: list[SPARQLOperationModel] = Field(
        default_factory=list, description="Previous ontology SPARQL operations"
    )
    previous_facts_operations: list[SPARQLOperationModel] = Field(
        default_factory=list, description="Previous facts SPARQL operations"
    )

    # Previous critiques (append-only for consistency)
    previous_ontology_critique: dict[str, Any] | None = Field(
        default=None, description="Previous ontology critique if available"
    )
    previous_facts_critique: dict[str, Any] | None = Field(
        default=None, description="Previous facts critique if available"
    )

    # Context metadata (append-only strategy)
    context_timestamp: datetime = Field(
        default_factory=datetime.now, description="When this context was created"
    )
    context_metadata: dict[str, Any] = Field(
        default_factory=dict, description="Additional context metadata"
    )

    # Conversation memory for LLM calls
    conversation_memory: list[dict[str, Any]] = Field(
        default_factory=list, description="Conversation history for LLM context"
    )

    # Dynamic context construction
    dynamic_context: dict[str, Any] = Field(
        default_factory=dict,
        description="Dynamically constructed context for current interaction",
    )

    def get_ontology_context_summary(self) -> str:
        """Get a summary of ontology context for prompts."""
        if not self.previous_ontology_version:
            return "No previous ontology context available."

        summary = f"Previous ontology version: {self.previous_ontology_version.id}\n"
        summary += f"Previous ontology size: {self.previous_ontology_version.get_size()} triples\n"
        summary += f"Previous ontology operations: {len(self.previous_ontology_operations)} SPARQL operations\n"

        if self.previous_ontology_critique:
            summary += f"Previous ontology critique score: {self.previous_ontology_critique.get('score', 'N/A')}\n"
            summary += f"Previous ontology critique issues: {self.previous_ontology_critique.get('issues', 'None')}\n"

        return summary

    def get_facts_context_summary(self) -> str:
        """Get a summary of facts context for prompts."""
        if not self.previous_facts_version:
            return "No previous facts context available."

        summary = f"Previous facts version: {self.previous_facts_version.id}\n"
        summary += (
            f"Previous facts size: {self.previous_facts_version.get_size()} triples\n"
        )
        summary += f"Previous facts operations: {len(self.previous_facts_operations)} SPARQL operations\n"

        if self.previous_facts_critique:
            summary += f"Previous facts critique score: {self.previous_facts_critique.get('score', 'N/A')}\n"
            summary += f"Previous facts critique issues: {self.previous_facts_critique.get('issues', 'None')}\n"

        return summary

    def get_full_context_summary(self) -> str:
        """Get a complete context summary for prompts."""
        ontology_context = self.get_ontology_context_summary()
        facts_context = self.get_facts_context_summary()

        summary = summary_template.format(
            facts_context=facts_context,
            ontology_context=ontology_context,
            agent_type=self.agent_type.value,
            context_timestamp=self.context_timestamp.isoformat(),
            context_metadata=self.context_metadata,
        )

        return summary

    def add_conversation_memory(
        self, role: Role, content: str, metadata: dict[str, Any] | None = None
    ) -> None:
        """Add a conversation entry to memory (append-only strategy).

        Args:
            role: Role of the speaker (Role.SYSTEM, Role.USER, Role.ASSISTANT)
            content: Content of the message
            metadata: Optional metadata for the conversation entry
        """
        entry = {
            "role": role.value,
            "content": content,
            "timestamp": datetime.now().isoformat(),
            "metadata": metadata or {},
        }
        self.conversation_memory.append(entry)
        logger.debug(f"Added conversation memory for {self.agent_type}: {role.value}")

    def get_conversation_context(self, max_entries: int = 10) -> str:
        """Get conversation context for LLM calls.

        Args:
            max_entries: Maximum number of conversation entries to include

        Returns:
            str: Formatted conversation context
        """
        if not self.conversation_memory:
            return "No conversation history available."

        # Get the most recent entries (append-only strategy preserves order)
        recent_entries = self.conversation_memory[-max_entries:]

        context = "CONVERSATION HISTORY:\n"
        for entry in recent_entries:
            context += f"{entry['role'].upper()}: {entry['content']}\n"
            if entry.get("metadata"):
                context += f"  Metadata: {entry['metadata']}\n"
            context += "\n"

        return context

    def build_dynamic_context(self, interaction_type: str, **kwargs) -> dict[str, Any]:
        """Build dynamic context for current interaction.

        Args:
            interaction_type: Type of interaction (render, critique, etc.)
            **kwargs: Additional context parameters

        Returns:
            dict[str, Any]: Dynamic context for the interaction
        """
        dynamic_context = {
            "interaction_type": interaction_type,
            "timestamp": datetime.now().isoformat(),
            "agent_type": self.agent_type,
            "context_summary": self.get_full_context_summary(),
            "conversation_context": self.get_conversation_context(),
            **kwargs,
        }

        # Update the dynamic context
        self.dynamic_context.update(dynamic_context)

        return dynamic_context

    def get_llm_context(self) -> str:
        """Get complete context for LLM calls including conversation memory.

        Returns:
            str: Complete context for LLM calls
        """

        return (
            f"{self.get_full_context_summary()}\n\n"
            f"{self.get_conversation_context()}\n\n"
            f"DYNAMIC CONTEXT:\n{self.dynamic_context}"
        )

add_conversation_memory(role, content, metadata=None)

Add a conversation entry to memory (append-only strategy).

Parameters:

Name	Type	Description	Default
role	Role	Role of the speaker (Role.SYSTEM, Role.USER, Role.ASSISTANT)	required
content	str	Content of the message	required
metadata	dict[str, Any] | None	Optional metadata for the conversation entry	None

Source code in ontocast/onto/context.py

def add_conversation_memory(
    self, role: Role, content: str, metadata: dict[str, Any] | None = None
) -> None:
    """Add a conversation entry to memory (append-only strategy).

    Args:
        role: Role of the speaker (Role.SYSTEM, Role.USER, Role.ASSISTANT)
        content: Content of the message
        metadata: Optional metadata for the conversation entry
    """
    entry = {
        "role": role.value,
        "content": content,
        "timestamp": datetime.now().isoformat(),
        "metadata": metadata or {},
    }
    self.conversation_memory.append(entry)
    logger.debug(f"Added conversation memory for {self.agent_type}: {role.value}")

build_dynamic_context(interaction_type, **kwargs)

Build dynamic context for current interaction.

Parameters:

Name	Type	Description	Default
interaction_type	str	Type of interaction (render, critique, etc.)	required
**kwargs		Additional context parameters	{}

Returns:

Type	Description
dict[str, Any]	dict[str, Any]: Dynamic context for the interaction

Source code in ontocast/onto/context.py

def build_dynamic_context(self, interaction_type: str, **kwargs) -> dict[str, Any]:
    """Build dynamic context for current interaction.

    Args:
        interaction_type: Type of interaction (render, critique, etc.)
        **kwargs: Additional context parameters

    Returns:
        dict[str, Any]: Dynamic context for the interaction
    """
    dynamic_context = {
        "interaction_type": interaction_type,
        "timestamp": datetime.now().isoformat(),
        "agent_type": self.agent_type,
        "context_summary": self.get_full_context_summary(),
        "conversation_context": self.get_conversation_context(),
        **kwargs,
    }

    # Update the dynamic context
    self.dynamic_context.update(dynamic_context)

    return dynamic_context

get_conversation_context(max_entries=10)

Get conversation context for LLM calls.

Parameters:

Name	Type	Description	Default
max_entries	int	Maximum number of conversation entries to include	10

Returns:

Name	Type	Description
str	str	Formatted conversation context

Source code in ontocast/onto/context.py

def get_conversation_context(self, max_entries: int = 10) -> str:
    """Get conversation context for LLM calls.

    Args:
        max_entries: Maximum number of conversation entries to include

    Returns:
        str: Formatted conversation context
    """
    if not self.conversation_memory:
        return "No conversation history available."

    # Get the most recent entries (append-only strategy preserves order)
    recent_entries = self.conversation_memory[-max_entries:]

    context = "CONVERSATION HISTORY:\n"
    for entry in recent_entries:
        context += f"{entry['role'].upper()}: {entry['content']}\n"
        if entry.get("metadata"):
            context += f"  Metadata: {entry['metadata']}\n"
        context += "\n"

    return context

get_facts_context_summary()

Get a summary of facts context for prompts.

Source code in ontocast/onto/context.py

def get_facts_context_summary(self) -> str:
    """Get a summary of facts context for prompts."""
    if not self.previous_facts_version:
        return "No previous facts context available."

    summary = f"Previous facts version: {self.previous_facts_version.id}\n"
    summary += (
        f"Previous facts size: {self.previous_facts_version.get_size()} triples\n"
    )
    summary += f"Previous facts operations: {len(self.previous_facts_operations)} SPARQL operations\n"

    if self.previous_facts_critique:
        summary += f"Previous facts critique score: {self.previous_facts_critique.get('score', 'N/A')}\n"
        summary += f"Previous facts critique issues: {self.previous_facts_critique.get('issues', 'None')}\n"

    return summary

get_full_context_summary()

Get a complete context summary for prompts.

Source code in ontocast/onto/context.py

def get_full_context_summary(self) -> str:
    """Get a complete context summary for prompts."""
    ontology_context = self.get_ontology_context_summary()
    facts_context = self.get_facts_context_summary()

    summary = summary_template.format(
        facts_context=facts_context,
        ontology_context=ontology_context,
        agent_type=self.agent_type.value,
        context_timestamp=self.context_timestamp.isoformat(),
        context_metadata=self.context_metadata,
    )

    return summary

get_llm_context()

Get complete context for LLM calls including conversation memory.

Returns:

Name	Type	Description
str	str	Complete context for LLM calls

Source code in ontocast/onto/context.py

def get_llm_context(self) -> str:
    """Get complete context for LLM calls including conversation memory.

    Returns:
        str: Complete context for LLM calls
    """

    return (
        f"{self.get_full_context_summary()}\n\n"
        f"{self.get_conversation_context()}\n\n"
        f"DYNAMIC CONTEXT:\n{self.dynamic_context}"
    )

get_ontology_context_summary()

Get a summary of ontology context for prompts.

Source code in ontocast/onto/context.py

def get_ontology_context_summary(self) -> str:
    """Get a summary of ontology context for prompts."""
    if not self.previous_ontology_version:
        return "No previous ontology context available."

    summary = f"Previous ontology version: {self.previous_ontology_version.id}\n"
    summary += f"Previous ontology size: {self.previous_ontology_version.get_size()} triples\n"
    summary += f"Previous ontology operations: {len(self.previous_ontology_operations)} SPARQL operations\n"

    if self.previous_ontology_critique:
        summary += f"Previous ontology critique score: {self.previous_ontology_critique.get('score', 'N/A')}\n"
        summary += f"Previous ontology critique issues: {self.previous_ontology_critique.get('issues', 'None')}\n"

    return summary

AgentType

Bases: StrEnum

Enumeration of agent types for type safety.

Source code in ontocast/onto/context.py

class AgentType(StrEnum):
    """Enumeration of agent types for type safety."""

    RENDERER_FACTS = "renderer_facts"
    RENDERER_ONTOLOGY = "renderer_ontology"
    CRITIC_FACTS = "critic_facts"
    CRITIC_ONTOLOGY = "critic_ontology"
    AGGREGATOR = "aggregator"
    CONVERTER = "converter"
    CHUNKER = "chunker"

ContextManager

Bases: BaseModel

Manages context passing between agents.

This class handles the creation, storage, and retrieval of context information for agent-based workflows.

Source code in ontocast/onto/context.py

class ContextManager(BaseModel):
    """Manages context passing between agents.

    This class handles the creation, storage, and retrieval of context
    information for agent-based workflows.
    """

    context_history: list[AgentContext] = Field(
        default_factory=list, description="History of agent contexts"
    )
    current_context: AgentContext | None = Field(
        default=None, description="Current active context"
    )

    def __init__(self, **kwargs):
        """Initialize the context manager."""
        super().__init__(**kwargs)

    def create_context(
        self,
        agent_type: AgentType,
        previous_ontology_version: GraphVersion | None = None,
        previous_facts_version: GraphVersion | None = None,
        previous_ontology_operations: list[SPARQLOperationModel] | None = None,
        previous_facts_operations: list[SPARQLOperationModel] | None = None,
        previous_ontology_critique: dict[str, Any] | None = None,
        previous_facts_critique: dict[str, Any] | None = None,
        metadata: dict[str, Any] | None = None,
    ) -> AgentContext:
        """Create a new context for an agent.

        Args:
            agent_type: Name of the agent creating the context.
            agent_type: Type of agent (renderer, critic, etc.).
            previous_ontology_version: Previous ontology version if available.
            previous_facts_version: Previous facts version if available.
            previous_ontology_operations: Previous ontology operations if available.
            previous_facts_operations: Previous facts operations if available.
            previous_ontology_critique: Previous ontology critique if available.
            previous_facts_critique: Previous facts critique if available.
            metadata: Additional metadata for the context.

        Returns:
            AgentContext: The created context.
        """
        context = AgentContext(
            agent_type=agent_type,
            previous_ontology_version=previous_ontology_version,
            previous_facts_version=previous_facts_version,
            previous_ontology_operations=previous_ontology_operations or [],
            previous_facts_operations=previous_facts_operations or [],
            previous_ontology_critique=previous_ontology_critique,
            previous_facts_critique=previous_facts_critique,
            context_metadata=metadata or {},
        )

        self.context_history.append(context)
        self.current_context = context

        logger.info(f"Created context for {agent_type} ({agent_type})")
        return context

    def get_current_context(self) -> AgentContext | None:
        """Get the current context.

        Returns:
            AgentContext | None: The current context, or None if not set.
        """
        return self.current_context

    def get_context_history(self) -> list[AgentContext]:
        """Get the full context history.

        Returns:
            list[AgentContext]: The complete context history.
        """
        return self.context_history

    def get_context_by_agent(self, agent_type: AgentType) -> list[AgentContext]:
        """Get context history for a specific agent.

        Args:
            agent_type: Name of the agent to get context for.

        Returns:
            list[AgentContext]: Context history for the specified agent.
        """
        return [ctx for ctx in self.context_history if ctx.agent_type == agent_type]

    def get_latest_context_by_agent(self, agent_type: AgentType) -> AgentContext | None:
        """Get the latest context for a specific agent.

        Args:
            agent_type: Name of the agent to get latest context for.

        Returns:
            AgentContext | None: The latest context for the specified agent, or None.
        """
        agent_contexts = self.get_context_by_agent(agent_type)
        return agent_contexts[-1] if agent_contexts else None

    def update_context(
        self,
        agent_type: AgentType,
        ontology_version: GraphVersion | None = None,
        facts_version: GraphVersion | None = None,
        ontology_operations: list[SPARQLOperationModel] | None = None,
        facts_operations: list[SPARQLOperationModel] | None = None,
        ontology_critique: dict[str, Any] | None = None,
        facts_critique: dict[str, Any] | None = None,
        metadata: dict[str, Any] | None = None,
    ) -> AgentContext:
        """Update the current context with new information.

        Args:
            agent_type: Name of the agent updating the context.
            ontology_version: New ontology version if available.
            facts_version: New facts version if available.
            ontology_operations: New ontology operations if available.
            facts_operations: New facts operations if available.
            ontology_critique: New ontology critique if available.
            facts_critique: New facts critique if available.
            metadata: Additional metadata for the context.

        Returns:
            AgentContext: The updated context.
        """
        if not self.current_context:
            # Create new context if none exists
            return self.create_context(
                agent_type=agent_type,
                previous_ontology_version=ontology_version,
                previous_facts_version=facts_version,
                previous_ontology_operations=ontology_operations,
                previous_facts_operations=facts_operations,
                previous_ontology_critique=ontology_critique,
                previous_facts_critique=facts_critique,
                metadata=metadata,
            )

        # Update existing context
        if ontology_version:
            self.current_context.previous_ontology_version = ontology_version
        if facts_version:
            self.current_context.previous_facts_version = facts_version
        if ontology_operations:
            self.current_context.previous_ontology_operations = ontology_operations
        if facts_operations:
            self.current_context.previous_facts_operations = facts_operations
        if ontology_critique:
            self.current_context.previous_ontology_critique = ontology_critique
        if facts_critique:
            self.current_context.previous_facts_critique = facts_critique
        if metadata:
            self.current_context.context_metadata.update(metadata)

        self.current_context.context_timestamp = datetime.now()

        logger.info(f"Updated context for {agent_type}")
        return self.current_context

    def clear_context(self):
        """Clear the current context."""
        self.current_context = None
        logger.info("Cleared current context")

    def clear_history(self):
        """Clear the entire context history."""
        self.context_history = []
        self.current_context = None
        logger.info("Cleared context history")

__init__(**kwargs)

Initialize the context manager.

Source code in ontocast/onto/context.py

def __init__(self, **kwargs):
    """Initialize the context manager."""
    super().__init__(**kwargs)

clear_context()

Clear the current context.

Source code in ontocast/onto/context.py

def clear_context(self):
    """Clear the current context."""
    self.current_context = None
    logger.info("Cleared current context")

clear_history()

Clear the entire context history.

Source code in ontocast/onto/context.py

def clear_history(self):
    """Clear the entire context history."""
    self.context_history = []
    self.current_context = None
    logger.info("Cleared context history")

create_context(agent_type, previous_ontology_version=None, previous_facts_version=None, previous_ontology_operations=None, previous_facts_operations=None, previous_ontology_critique=None, previous_facts_critique=None, metadata=None)

Create a new context for an agent.

Parameters:

Name	Type	Description	Default
agent_type	AgentType	Name of the agent creating the context.	required
agent_type	AgentType	Type of agent (renderer, critic, etc.).	required
previous_ontology_version	GraphVersion | None	Previous ontology version if available.	None
previous_facts_version	GraphVersion | None	Previous facts version if available.	None
previous_ontology_operations	list[SPARQLOperationModel] | None	Previous ontology operations if available.	None
previous_facts_operations	list[SPARQLOperationModel] | None	Previous facts operations if available.	None
previous_ontology_critique	dict[str, Any] | None	Previous ontology critique if available.	None
previous_facts_critique	dict[str, Any] | None	Previous facts critique if available.	None
metadata	dict[str, Any] | None	Additional metadata for the context.	None

Returns:

Name	Type	Description
AgentContext	AgentContext	The created context.

Source code in ontocast/onto/context.py

def create_context(
    self,
    agent_type: AgentType,
    previous_ontology_version: GraphVersion | None = None,
    previous_facts_version: GraphVersion | None = None,
    previous_ontology_operations: list[SPARQLOperationModel] | None = None,
    previous_facts_operations: list[SPARQLOperationModel] | None = None,
    previous_ontology_critique: dict[str, Any] | None = None,
    previous_facts_critique: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
) -> AgentContext:
    """Create a new context for an agent.

    Args:
        agent_type: Name of the agent creating the context.
        agent_type: Type of agent (renderer, critic, etc.).
        previous_ontology_version: Previous ontology version if available.
        previous_facts_version: Previous facts version if available.
        previous_ontology_operations: Previous ontology operations if available.
        previous_facts_operations: Previous facts operations if available.
        previous_ontology_critique: Previous ontology critique if available.
        previous_facts_critique: Previous facts critique if available.
        metadata: Additional metadata for the context.

    Returns:
        AgentContext: The created context.
    """
    context = AgentContext(
        agent_type=agent_type,
        previous_ontology_version=previous_ontology_version,
        previous_facts_version=previous_facts_version,
        previous_ontology_operations=previous_ontology_operations or [],
        previous_facts_operations=previous_facts_operations or [],
        previous_ontology_critique=previous_ontology_critique,
        previous_facts_critique=previous_facts_critique,
        context_metadata=metadata or {},
    )

    self.context_history.append(context)
    self.current_context = context

    logger.info(f"Created context for {agent_type} ({agent_type})")
    return context

get_context_by_agent(agent_type)

Get context history for a specific agent.

Parameters:

Name	Type	Description	Default
agent_type	AgentType	Name of the agent to get context for.	required

Returns:

Type	Description
list[AgentContext]	list[AgentContext]: Context history for the specified agent.

Source code in ontocast/onto/context.py

def get_context_by_agent(self, agent_type: AgentType) -> list[AgentContext]:
    """Get context history for a specific agent.

    Args:
        agent_type: Name of the agent to get context for.

    Returns:
        list[AgentContext]: Context history for the specified agent.
    """
    return [ctx for ctx in self.context_history if ctx.agent_type == agent_type]

get_context_history()

Get the full context history.

Returns:

Type	Description
list[AgentContext]	list[AgentContext]: The complete context history.

Source code in ontocast/onto/context.py

def get_context_history(self) -> list[AgentContext]:
    """Get the full context history.

    Returns:
        list[AgentContext]: The complete context history.
    """
    return self.context_history

get_current_context()

Get the current context.

Returns:

Type	Description
AgentContext | None	AgentContext | None: The current context, or None if not set.

Source code in ontocast/onto/context.py

def get_current_context(self) -> AgentContext | None:
    """Get the current context.

    Returns:
        AgentContext | None: The current context, or None if not set.
    """
    return self.current_context

get_latest_context_by_agent(agent_type)

Get the latest context for a specific agent.

Parameters:

Name	Type	Description	Default
agent_type	AgentType	Name of the agent to get latest context for.	required

Returns:

Type	Description
AgentContext | None	AgentContext | None: The latest context for the specified agent, or None.

Source code in ontocast/onto/context.py

def get_latest_context_by_agent(self, agent_type: AgentType) -> AgentContext | None:
    """Get the latest context for a specific agent.

    Args:
        agent_type: Name of the agent to get latest context for.

    Returns:
        AgentContext | None: The latest context for the specified agent, or None.
    """
    agent_contexts = self.get_context_by_agent(agent_type)
    return agent_contexts[-1] if agent_contexts else None

update_context(agent_type, ontology_version=None, facts_version=None, ontology_operations=None, facts_operations=None, ontology_critique=None, facts_critique=None, metadata=None)

Update the current context with new information.

Parameters:

Name	Type	Description	Default
agent_type	AgentType	Name of the agent updating the context.	required
ontology_version	GraphVersion | None	New ontology version if available.	None
facts_version	GraphVersion | None	New facts version if available.	None
ontology_operations	list[SPARQLOperationModel] | None	New ontology operations if available.	None
facts_operations	list[SPARQLOperationModel] | None	New facts operations if available.	None
ontology_critique	dict[str, Any] | None	New ontology critique if available.	None
facts_critique	dict[str, Any] | None	New facts critique if available.	None
metadata	dict[str, Any] | None	Additional metadata for the context.	None

Returns:

Name	Type	Description
AgentContext	AgentContext	The updated context.

Source code in ontocast/onto/context.py

def update_context(
    self,
    agent_type: AgentType,
    ontology_version: GraphVersion | None = None,
    facts_version: GraphVersion | None = None,
    ontology_operations: list[SPARQLOperationModel] | None = None,
    facts_operations: list[SPARQLOperationModel] | None = None,
    ontology_critique: dict[str, Any] | None = None,
    facts_critique: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
) -> AgentContext:
    """Update the current context with new information.

    Args:
        agent_type: Name of the agent updating the context.
        ontology_version: New ontology version if available.
        facts_version: New facts version if available.
        ontology_operations: New ontology operations if available.
        facts_operations: New facts operations if available.
        ontology_critique: New ontology critique if available.
        facts_critique: New facts critique if available.
        metadata: Additional metadata for the context.

    Returns:
        AgentContext: The updated context.
    """
    if not self.current_context:
        # Create new context if none exists
        return self.create_context(
            agent_type=agent_type,
            previous_ontology_version=ontology_version,
            previous_facts_version=facts_version,
            previous_ontology_operations=ontology_operations,
            previous_facts_operations=facts_operations,
            previous_ontology_critique=ontology_critique,
            previous_facts_critique=facts_critique,
            metadata=metadata,
        )

    # Update existing context
    if ontology_version:
        self.current_context.previous_ontology_version = ontology_version
    if facts_version:
        self.current_context.previous_facts_version = facts_version
    if ontology_operations:
        self.current_context.previous_ontology_operations = ontology_operations
    if facts_operations:
        self.current_context.previous_facts_operations = facts_operations
    if ontology_critique:
        self.current_context.previous_ontology_critique = ontology_critique
    if facts_critique:
        self.current_context.previous_facts_critique = facts_critique
    if metadata:
        self.current_context.context_metadata.update(metadata)

    self.current_context.context_timestamp = datetime.now()

    logger.info(f"Updated context for {agent_type}")
    return self.current_context

Role

Bases: StrEnum

Enumeration of conversation roles for type safety.

Source code in ontocast/onto/context.py

class Role(StrEnum):
    """Enumeration of conversation roles for type safety."""

    SYSTEM = "system"
    USER = "user"
    ASSISTANT = "assistant"