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#

# Source code for langchain.chains.llm_requests

    """Chain that hits a URL and then uses an LLM to parse results."""
    from __future__ import annotations

    from typing import Dict, List

    from pydantic import Extra, Field, root_validator

    from langchain.chains import LLMChain
    from langchain.chains.base import Chain
    from langchain.requests import TextRequestsWrapper

    DEFAULT_HEADERS = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36"  # noqa: E501
    }

    class LLMRequestsChain(Chain):
        """Chain that hits a URL and then uses an LLM to parse results."""

        llm_chain: LLMChain
        requests_wrapper: TextRequestsWrapper = Field(
            default_factory=TextRequestsWrapper, exclude=True
        )
        text_length: int = 8000
        requests_key: str = "requests_result"  #: :meta private:
        input_key: str = "url"  #: :meta private:
        output_key: str = "output"  #: :meta private:

        class Config:
            """Configuration for this pydantic object."""

            extra = Extra.forbid
            arbitrary_types_allowed = True

        @property
        def input_keys(self) -> List[str]:
            """Will be whatever keys the prompt expects.

            :meta private:
            """
            return [self.input_key]

        @property
        def output_keys(self) -> List[str]:
            """Will always return text key.

            :meta private:
            """
            return [self.output_key]

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key and python package exists in environment."""
            try:
                from bs4 import BeautifulSoup  # noqa: F401

            except ImportError:
                raise ValueError(
                    "Could not import bs4 python package. "
                    "Please install it with `pip install bs4`."
                )
            return values

        def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
            from bs4 import BeautifulSoup

            # Other keys are assumed to be needed for LLM prediction
            other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
            url = inputs[self.input_key]
            res = self.requests_wrapper.get(url)
            # extract the text from the html
            soup = BeautifulSoup(res, "html.parser")
            other_keys[self.requests_key] = soup.get_text()[: self.text_length]
            result = self.llm_chain.predict(**other_keys)
            return {self.output_key: result}

        @property
        def _chain_type(self) -> str:
            return "llm_requests_chain"