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{
"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:<answer or \"not found\">\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
}
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