Source code for langchain.output_parsers.combining

from __future__ import annotations

from typing import Any, Dict, List

from langchain_core.output_parsers import BaseOutputParser
from langchain_core.pydantic_v1 import root_validator

[docs]class CombiningOutputParser(BaseOutputParser): """Combine multiple output parsers into one.""" parsers: List[BaseOutputParser]
[docs] @classmethod def is_lc_serializable(cls) -> bool: return True
@root_validator() def validate_parsers(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Validate the parsers.""" parsers = values["parsers"] if len(parsers) < 2: raise ValueError("Must have at least two parsers") for parser in parsers: if parser._type == "combining": raise ValueError("Cannot nest combining parsers") if parser._type == "list": raise ValueError("Cannot combine list parsers") return values @property def _type(self) -> str: """Return the type key.""" return "combining"
[docs] def get_format_instructions(self) -> str: """Instructions on how the LLM output should be formatted.""" initial = f"For your first output: {self.parsers[0].get_format_instructions()}" subsequent = "\n".join( f"Complete that output fully. Then produce another output, separated by two newline characters: {p.get_format_instructions()}" # noqa: E501 for p in self.parsers[1:] ) return f"{initial}\n{subsequent}"
[docs] def parse(self, text: str) -> Dict[str, Any]: """Parse the output of an LLM call.""" texts = text.split("\n\n") output = dict() for txt, parser in zip(texts, self.parsers): output.update(parser.parse(txt.strip())) return output