99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
|
"""Chain that hits a URL and then uses an LLM to parse results."""
|
||
|
|
|
||
|
|
from __future__ import annotations
|
||
|
|
|
||
|
|
from typing import Any, Dict, List, Optional
|
||
|
|
|
||
|
|
from langchain_classic.chains import LLMChain
|
||
|
|
from langchain_classic.chains.base import Chain
|
||
|
|
from langchain_core.callbacks import CallbackManagerForChainRun
|
||
|
|
from pydantic import ConfigDict, Field, model_validator
|
||
|
|
|
||
|
|
from langchain_community.utilities.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 requests a URL and then uses an LLM to parse results.
|
||
|
|
|
||
|
|
**Security Note**: This chain can make GET requests to arbitrary URLs,
|
||
|
|
including internal URLs.
|
||
|
|
|
||
|
|
Control access to who can run this chain and what network access
|
||
|
|
this chain has.
|
||
|
|
|
||
|
|
See https://python.langchain.com/docs/security for more information.
|
||
|
|
"""
|
||
|
|
|
||
|
|
llm_chain: LLMChain
|
||
|
|
requests_wrapper: TextRequestsWrapper = Field(
|
||
|
|
default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS),
|
||
|
|
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:
|
||
|
|
|
||
|
|
model_config = ConfigDict(
|
||
|
|
arbitrary_types_allowed=True,
|
||
|
|
extra="forbid",
|
||
|
|
)
|
||
|
|
|
||
|
|
@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]
|
||
|
|
|
||
|
|
@model_validator(mode="before")
|
||
|
|
@classmethod
|
||
|
|
def validate_environment(cls, values: Dict) -> Any:
|
||
|
|
"""Validate that api key and python package exists in environment."""
|
||
|
|
try:
|
||
|
|
from bs4 import BeautifulSoup # noqa: F401
|
||
|
|
|
||
|
|
except ImportError:
|
||
|
|
raise ImportError(
|
||
|
|
"Could not import bs4 python package. "
|
||
|
|
"Please install it with `pip install bs4`."
|
||
|
|
)
|
||
|
|
return values
|
||
|
|
|
||
|
|
def _call(
|
||
|
|
self,
|
||
|
|
inputs: Dict[str, Any],
|
||
|
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||
|
|
) -> Dict[str, Any]:
|
||
|
|
from bs4 import BeautifulSoup
|
||
|
|
|
||
|
|
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||
|
|
# 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") # type: ignore[arg-type]
|
||
|
|
other_keys[self.requests_key] = soup.get_text()[: self.text_length]
|
||
|
|
result = self.llm_chain.predict(
|
||
|
|
callbacks=_run_manager.get_child(), **other_keys
|
||
|
|
)
|
||
|
|
return {self.output_key: result}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _chain_type(self) -> str:
|
||
|
|
return "llm_requests_chain"
|