agent_logic.models.llm_proof_model

Classes

`LLMEquivalenceRequest`(**data)	Defines the structure for equivalence transformation requests.
`LLMEquivalenceResponse`(**data)	Defines the structure of the response for equivalence transformations.
`LLMProofRequest`(**data)	Defines the structure for LLMs to request a proof verification.
`LLMProofResponse`(**data)	Defines the structure of the response for LLM-driven proof validation.
`LLMTruthTableRequest`(**data)	Defines the structure for requesting a truth table generation.
`LLMTruthTableResponse`(**data)	Defines the response structure for an LLM-generated truth table.

class agent_logic.models.llm_proof_model.LLMEquivalenceRequest(**data)[source]

Bases: BaseModel

Defines the structure for equivalence transformation requests.

Parameters:

expression (LogicalExpression)
transformation (str)

expression: LogicalExpression

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

transformation: str

class agent_logic.models.llm_proof_model.LLMEquivalenceResponse(**data)[source]

Bases: BaseModel

Defines the structure of the response for equivalence transformations.

Parameters:: transformed_expression (LogicalExpression)

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

transformed_expression: LogicalExpression

class agent_logic.models.llm_proof_model.LLMProofRequest(**data)[source]

Bases: BaseModel

Defines the structure for LLMs to request a proof verification.

Parameters:

premises (List[LogicalExpression])
goal (LogicalExpression)
max_depth (int | None)

goal: LogicalExpression

max_depth: Optional[int]

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

premises: List[LogicalExpression]

class agent_logic.models.llm_proof_model.LLMProofResponse(**data)[source]

Bases: BaseModel

Defines the structure of the response for LLM-driven proof validation.

Parameters:

is_valid (bool)
proof_steps (List[ProofStep] | None)
error_message (str | None)

error_message: Optional[str]

is_valid: bool

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

proof_steps: Optional[List[ProofStep]]

class agent_logic.models.llm_proof_model.LLMTruthTableRequest(**data)[source]

Bases: BaseModel

Defines the structure for requesting a truth table generation.

Parameters:: expression (LogicalExpression)

expression: LogicalExpression

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class agent_logic.models.llm_proof_model.LLMTruthTableResponse(**data)[source]

Bases: BaseModel

Defines the response structure for an LLM-generated truth table.

Parameters:

truth_table (List[Dict[str, bool]])
is_tautology (bool)
is_contradiction (bool)
is_satisfiable (bool)

is_contradiction: bool

is_satisfiable: bool

is_tautology: bool

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

truth_table: List[Dict[str, bool]]