incose_parser#

Attributes#

`log`
`RNG`

Classes#

`Criteria`	Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
`Requirement`	Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
`RequirementList`	Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
`IncoseParser`	Base class to parse the output of an LLM call.

Module Contents#

incose_parser.log#

incose_parser.RNG#

class incose_parser.Criteria(**data)#

Bases: langchain_core.pydantic_v1.BaseModel

Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.

__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations of objects.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Parameters:: data (Any) –

reasoning: str#

score: str#

score_is_valid(v)#

Parameters:: v (str) –

class incose_parser.Requirement(**data)#

Bases: langchain_core.pydantic_v1.BaseModel

Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.

__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations of objects.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Parameters:: data (Any) –

requirement_id: str#

requirement: str#

C1: Criteria#

C2: Criteria#

C3: Criteria#

C4: Criteria#

C5: Criteria#

C6: Criteria#

C7: Criteria#

C8: Criteria#

C9: Criteria#

class incose_parser.RequirementList(**data)#

Bases: langchain_core.pydantic_v1.BaseModel

Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.

__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations of objects.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Parameters:: data (Any) –

class incose_parser.IncoseParser#

Bases: janus.parsers.parser.JanusParser, langchain.output_parsers.PydanticOutputParser

Base class to parse the output of an LLM call.

Output parsers help structure language model responses.

Example

class BooleanOutputParser(BaseOutputParser[bool]):
    true_val: str = "YES"
    false_val: str = "NO"

    def parse(self, text: str) -> bool:
        cleaned_text = text.strip().upper()
        if cleaned_text not in (self.true_val.upper(), self.false_val.upper()):
            raise OutputParserException(
                f"BooleanOutputParser expected output value to either be "
                f"{self.true_val} or {self.false_val} (case-insensitive). "
                f"Received {cleaned_text}."
            )
        return cleaned_text == self.true_val.upper()

    @property
    def _type(self) -> str:
        return "boolean_output_parser"

requirements: dict[str, str]#

parse_input(block)#

Parse the input block into raw string input ready to be passed to an LLM. Also perform any processing or saving of metadata.

Parameters:: block (janus.language.block.CodeBlock) – The CodeBlock to be processed
Returns:: A parsed version of the input text
Return type:: str

parse(text)#

Parse a single string model output into some structure.

Parameters:: text (str | langchain_core.messages.BaseMessage) – String output of a language model.
Returns:: Structured output.
Return type:: str

parse_combined_output(text)#

Parse the output text from the LLM when multiple inputs are combined

Parameters:: text (str) – The output text from the LLM
Returns:: A parsed version of the text
Return type:: str