Source code for langchain.output_parsers.yaml

import json
import re
from typing import Type, TypeVar

import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.pydantic_v1 import BaseModel, ValidationError

from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS

T = TypeVar("T", bound=BaseModel)


[docs]class YamlOutputParser(BaseOutputParser[T]): """Parse YAML output using a pydantic model.""" pydantic_object: Type[T] """The pydantic model to parse.""" pattern: re.Pattern = re.compile( r"^```(?:ya?ml)?(?P<yaml>[^`]*)", re.MULTILINE | re.DOTALL ) """Regex pattern to match yaml code blocks within triple backticks with optional yaml or yml prefix."""
[docs] def parse(self, text: str) -> T: try: # Greedy search for 1st yaml candidate. match = re.search(self.pattern, text.strip()) yaml_str = "" if match: yaml_str = match.group("yaml") else: # If no backticks were present, try to parse the entire output as yaml. yaml_str = text json_object = yaml.safe_load(yaml_str) return self.pydantic_object.parse_obj(json_object) except (yaml.YAMLError, ValidationError) as e: name = self.pydantic_object.__name__ msg = f"Failed to parse {name} from completion {text}. Got: {e}" raise OutputParserException(msg, llm_output=text) from e
[docs] def get_format_instructions(self) -> str: # Copy schema to avoid altering original Pydantic schema. schema = {k: v for k, v in self.pydantic_object.schema().items()} # Remove extraneous fields. reduced_schema = schema if "title" in reduced_schema: del reduced_schema["title"] if "type" in reduced_schema: del reduced_schema["type"] # Ensure yaml in context is well-formed with double quotes. schema_str = json.dumps(reduced_schema) return YAML_FORMAT_INSTRUCTIONS.format(schema=schema_str)
@property def _type(self) -> str: return "yaml"