inspect_blog_text_langchain.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import AsyncChromiumLoader\n",
    "from langchain_community.document_transformers import BeautifulSoupTransformer\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "# Load HTML\n",
    "loader = AsyncChromiumLoader([\"https://www.wsj.com\"])\n",
    "html = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform\n",
    "bs_transformer = BeautifulSoupTransformer()\n",
    "docs_transformed = bs_transformer.transform_documents(html, tags_to_extract=[\"span\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='', metadata={'source': 'https://www.wsj.com'})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_transformed[0].page_content[0:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.llms import Ollama\n",
    "\n",
    "llm = Ollama(temperature=0, model=\"llama2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import create_extraction_chain\n",
    "\n",
    "schema = {\n",
    "    \"properties\": {\n",
    "        \"news_article_title\": {\"type\": \"string\"},\n",
    "        \"news_article_summary\": {\"type\": \"string\"},\n",
    "    },\n",
    "    \"required\": [\"news_article_title\", \"news_article_summary\"],\n",
    "}\n",
    "\n",
    "\n",
    "def extract(content: str, schema: dict):\n",
    "    return create_extraction_chain(schema=schema, llm=llm).run(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting content with LLM\n",
      "[]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[43], line 30\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m extracted_content\n\u001b[1;32m     29\u001b[0m urls \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://www.wsj.com\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m---> 30\u001b[0m extracted_content \u001b[38;5;241m=\u001b[39m \u001b[43mscrape_with_playwright\u001b[49m\u001b[43m(\u001b[49m\u001b[43murls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mschema\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[43], line 24\u001b[0m, in \u001b[0;36mscrape_with_playwright\u001b[0;34m(urls, schema)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28mprint\u001b[39m(splits)\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# Process the first split\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m extracted_content \u001b[38;5;241m=\u001b[39m extract(schema\u001b[38;5;241m=\u001b[39mschema, content\u001b[38;5;241m=\u001b[39m\u001b[43msplits\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mpage_content)\n\u001b[1;32m     25\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(extracted_content)\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m extracted_content\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
    "\n",
    "\n",
    "def scrape_with_playwright(urls, schema):\n",
    "    loader = AsyncChromiumLoader(urls)\n",
    "    docs = loader.load()\n",
    "    bs_transformer = BeautifulSoupTransformer()\n",
    "    docs_transformed = bs_transformer.transform_documents(\n",
    "        docs, tags_to_extract=[\"span\"]\n",
    "    )\n",
    "    print(\"Extracting content with LLM\")\n",
    "\n",
    "    # Grab the first 1000 tokens of the site\n",
    "    splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
    "        chunk_size=1000, chunk_overlap=0\n",
    "    )\n",
    "    splits = splitter.split_documents(docs_transformed)\n",
    "\n",
    "    print(splits)\n",
    "\n",
    "    # Process the first split\n",
    "    extracted_content = extract(schema=schema, content=splits[0].page_content)\n",
    "    pprint.pprint(extracted_content)\n",
    "    return extracted_content\n",
    "\n",
    "\n",
    "urls = [\"https://www.wsj.com\"]\n",
    "extracted_content = scrape_with_playwright(urls, schema=schema)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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