{ "cells": [ { "cell_type": "markdown", "id": "dd7ec7af", "metadata": {}, "source": [ "# LLMRequestsChain\n", "\n", "Using the request library to get HTML results from a URL and then an LLM to parse results" ] }, { "cell_type": "code", "execution_count": 1, "id": "dd8eae75", "metadata": {}, "outputs": [], "source": [ "from langchain.llms import OpenAI\n", "from langchain.chains import LLMRequestsChain, LLMChain" ] }, { "cell_type": "code", "execution_count": 2, "id": "65bf324e", "metadata": {}, "outputs": [], "source": [ "from langchain.prompts import PromptTemplate\n", "\n", "template = \"\"\"Between >>> and <<< are the raw search result text from google.\n", "Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n", "Use the format\n", "Extracted:\n", ">>> {requests_result} <<<\n", "Extracted:\"\"\"\n", "\n", "PROMPT = PromptTemplate(\n", " input_variables=[\"query\", \"requests_result\"],\n", " template=template,\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "f36ae0d8", "metadata": {}, "outputs": [], "source": [ "chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))" ] }, { "cell_type": "code", "execution_count": 4, "id": "b5d22d9d", "metadata": {}, "outputs": [], "source": [ "question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n", "inputs = {\n", " \"query\": question,\n", " \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "id": "2ea81168", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n", " 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n", " 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chain(inputs)" ] }, { "cell_type": "code", "execution_count": null, "id": "db8f2b6d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }