mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-19 21:33:31 -04:00
Ankush Gola
d3ec00b566
Co-authored-by: Nuno Campos <nuno@boringbits.io> Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com> Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
"""Chain that hits a URL and then uses an LLM to parse results."""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from pydantic import Extra, Field, root_validator
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains import LLMChain
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from langchain.chains.base import Chain
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from langchain.requests import TextRequestsWrapper
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DEFAULT_HEADERS = {
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"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
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}
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class LLMRequestsChain(Chain):
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"""Chain that hits a URL and then uses an LLM to parse results."""
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llm_chain: LLMChain
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requests_wrapper: TextRequestsWrapper = Field(
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default_factory=TextRequestsWrapper, exclude=True
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)
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text_length: int = 8000
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requests_key: str = "requests_result" #: :meta private:
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input_key: str = "url" #: :meta private:
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output_key: str = "output" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Will be whatever keys the prompt expects.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Will always return text key.
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:meta private:
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"""
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return [self.output_key]
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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try:
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from bs4 import BeautifulSoup # noqa: F401
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except ImportError:
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raise ValueError(
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"Could not import bs4 python package. "
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"Please install it with `pip install bs4`."
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)
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return values
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, Any]:
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from bs4 import BeautifulSoup
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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# Other keys are assumed to be needed for LLM prediction
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other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
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url = inputs[self.input_key]
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res = self.requests_wrapper.get(url)
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# extract the text from the html
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soup = BeautifulSoup(res, "html.parser")
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other_keys[self.requests_key] = soup.get_text()[: self.text_length]
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result = self.llm_chain.predict(
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callbacks=_run_manager.get_child(), **other_keys
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)
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return {self.output_key: result}
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@property
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def _chain_type(self) -> str:
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return "llm_requests_chain"
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