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 pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = TypeVar("T", bound=BaseModel)
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."""
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)
if hasattr(self.pydantic_object, "model_validate"):
return self.pydantic_object.model_validate(json_object)
else:
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
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"
@property
def OutputType(self) -> Type[T]:
return self.pydantic_object