forked from Archives/langchain
LLMRequestsChain (#267)
Harrison Chase
1 year ago
committed by
GitHub
parent
68666d6a22
commit
28be37f470
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dd7ec7af",
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"metadata": {},
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"source": [
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"# LLMRequestsChain\n",
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"\n",
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"Using the request library to get HTML results from a URL and then an LLM to parse results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "dd8eae75",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.chains import LLMRequestsChain, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "65bf324e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
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"Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
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"Use the format\n",
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"Extracted:<answer or \"not found\">\n",
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">>> {requests_result} <<<\n",
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"Extracted:\"\"\"\n",
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"\n",
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"PROMPT = PromptTemplate(\n",
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" input_variables=[\"query\", \"requests_result\"],\n",
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" template=template,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f36ae0d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "b5d22d9d",
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
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"inputs = {\n",
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" \"query\": question,\n",
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" \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2ea81168",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
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" 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
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" 'output': ' Russia (17,098,242 sq km), Canada (9,984,670 sq km), China (9,706,961 sq km)'}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain(inputs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "db8f2b6d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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"""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 Dict, List
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from pydantic import BaseModel, Extra, Field, root_validator
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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 RequestsWrapper
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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, BaseModel):
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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: RequestsWrapper = Field(default_factory=RequestsWrapper)
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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 it install it with `pip install bs4`."
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)
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return values
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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from bs4 import BeautifulSoup
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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.run(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(**other_keys)
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return {self.output_key: result}
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@ -0,0 +1,15 @@
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"""Lightweight wrapper around request library."""
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from typing import Optional
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import requests
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from pydantic import BaseModel
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class RequestsWrapper(BaseModel):
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"""Lightweight wrapper to partial out everything except the url to hit."""
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headers: Optional[dict] = None
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def run(self, url: str) -> str:
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"""Hit the URL and return the text."""
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return requests.get(url, headers=self.headers).text
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