class BaseOutputParser(BaseModel, ABC, Generic[T]):
    @abstractmethod
    def parse(self, text: str) -> T:
        """Parse the output of an LLM call.
        A method which takes in a string (assumed output of a language model )
        and parses it into some structure.
        Args:
            text: output of language model
        Returns:
            structured output
        """
    def parse_with_prompt(self, completion: str, prompt: PromptValue) -> Any:
        """Optional method to parse the output of an LLM call with a prompt.
        The prompt is largely provided in the event the OutputParser wants
        to retry or fix the output in some way, and needs information from
        the prompt to do so.
        Args:
            completion: output of language model
            prompt: prompt value
        Returns:
            structured output
        """
        return self.parse(completion)
    def get_format_instructions(self) -> str:
        """Instructions on how the LLM output should be formatted."""
        raise NotImplementedError
    @property
    def _type(self) -> str:
        """Return the type key."""
        raise NotImplementedError(
            f"_type property is not implemented in class {self.__class__.__name__}."
            " This is required for serialization."
        )
    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of output parser."""
        output_parser_dict = super().dict()
        output_parser_dict["_type"] = self._type
        return output_parser_dict

def parse(self, text: str) -> List[str]:
        """Parse the output of an LLM call."""
        return text.strip().split(", ")

def get_format_instructions(self) -> str:
        return (
            "Your response should be a list of comma separated values, "
            "eg: `foo, bar, baz`"
        )

output_parser = CommaSeparatedListOutputParser()
format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(
    template="列出幾種{subject}.\n{format_instructions}",
    input_variables=["subject"],
    partial_variables={"format_instructions": format_instructions}
)
_input = prompt.format(subject="水果")
output = model(_input)
print(output)
print(output_parser.parse(output))

output_parser = CommaSeparatedListOutputParser()
format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(
    template="列出幾種{subject}.\n{format_instructions}",
    input_variables=["subject"],
    partial_variables={"format_instructions": format_instructions + "用中文回答"}
)
_input = prompt.format(subject="水果")
output = model(_input)
print(output)
print(output_parser.parse(output))

class DatetimeOutputParser(BaseOutputParser[datetime]):
    format: str = "%Y-%m-%dT%H:%M:%S.%fZ"
    def get_format_instructions(self) -> str:
        examples = comma_list(_generate_random_datetime_strings(self.format))
        return f"""Write a datetime string that matches the 
            following pattern: "{self.format}". Examples: {examples}"""
    def parse(self, response: str) -> datetime:
        try:
            return datetime.strptime(response.strip(), self.format)
        except ValueError as e:
            raise OutputParserException(
                f"Could not parse datetime string: {response}"
            ) from e
    @property
    def _type(self) -> str:
        return "datetime"

output_parser = DatetimeOutputParser()
template = """回答下面問題:
{question}
{format_instructions}"""
prompt = PromptTemplate.from_template(
    template,
    partial_variables={"format_instructions": output_parser.get_format_instructions()},
)
chain = LLMChain(prompt=prompt, llm=model)
output = chain.run("中華人民共和國是什么時候成立的?")
print(output)
print(output_parser.parse(output))

@property
    def _valid_values(self) -> List[str]:
        return [e.value for e in self.enum]
    def parse(self, response: str) -> Any:
        try:
            return self.enum(response.strip())
        except ValueError:
            raise OutputParserException(
                f"Response '{response}' is not one of the "
                f"expected values: {self._valid_values}"
            )
    def get_format_instructions(self) -> str:
        return f"Select one of the following options: {', '.join(self._valid_values)}"

class Student(BaseModel):
    name: str = Field(description="學生的姓名")
    age: str = Field(description="學生的年齡")
student_query = "告訴我一個學生的信息"
parser = PydanticOutputParser(pydantic_object=Student)
prompt = PromptTemplate(
    template="回答下面問題.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()+"用中文回答"},
)
_input = prompt.format_prompt(query=student_query)
output = model(_input.to_string())
print(output)
print(parser.parse(output))

response_schemas = [
    ResponseSchema(name="name", description="學生的姓名"),
    ResponseSchema(name="age", description="學生的年齡")
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(
    template="回答下面問題.\n{format_instructions}\n{question}",
    input_variables=["question"],
    partial_variables={"format_instructions": format_instructions}
)
_input = prompt.format_prompt(question="給我一個女孩的名字?")
output = model(_input.to_string())
print(output)
print(output_parser.parse(output))

from langchain.output_parsers import OutputFixingParser
new_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())
new_parser.parse(misformatted)

from langchain.output_parsers import RetryWithErrorOutputParser
retry_parser = RetryWithErrorOutputParser.from_llm(
    parser=parser, llm=OpenAI(temperature=0)
)
retry_parser.parse_with_prompt(bad_response, prompt_value)