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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain import OpenAI\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain\n",
"from langchain.document_loaders import TextLoader\n",
"\n",
"\n",
"from pathlib import Path\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"\n",
"path = Path().home() / \"Documents\" / \"csv1.csv\"\n",
"loader = TextLoader(path)\n",
"document = loader.load()\n",
"\n",
"\n",
"path2 = Path().home() / \"Documents\" / \"csv2.csv\"\n",
"loader2 = TextLoader(path2)\n",
"document2 = loader2.load()\n",
"\n",
"prompt_template = \"\"\"Following are two lists of Event Titles, Dates and Descriptions in the format <Title>;<Date>:\n",
"<Description>\n",
"{csv1}\n",
"\n",
"{csv2}\n",
"\n",
"TASKS: \n",
"1. Show matching string values of the two lists\n",
"2. Based on these matches, provide a natural sounding conversation starter \n",
"\n",
"\"\"\"\n",
"prompt = PromptTemplate.from_template(prompt_template)\n",
"\n",
"llm = OpenAI (temperature=0)\n",
"chain = LLMChain(llm=llm, prompt=prompt)\n",
"response = chain({\"csv1\": document[0].page_content, \"csv2\": document2[0].page_content})\n",
"\n",
"\n",
"print(response['text'])"
]
}
],
"metadata": {
"language_info": {
"name": "python"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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