mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
Async HTML loader and HTML2Text transformer (#8036)
New HTML loader that asynchronously loader a list of urls. New transformer using [HTML2Text](https://github.com/Alir3z4/html2text/) for HTML to clean, easy-to-read plain ASCII text (valid Markdown).
This commit is contained in:
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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": "e229e34c",
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"metadata": {},
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"source": [
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"# AsyncHtmlLoader\n",
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"\n",
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"AsyncHtmlLoader loads raw HTML from a list of urls concurrently."
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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": "4c8e4dab",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import AsyncHtmlLoader"
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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": 5,
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"id": "e76b5ddc",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Fetching pages: 100%|############| 2/2 [00:00<00:00, 9.96it/s]\n"
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]
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}
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],
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"source": [
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"urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
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"loader = AsyncHtmlLoader(urls)\n",
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"docs = loader.load()"
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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": "5dca1c0c",
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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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"' news. Stream exclusive games on ESPN+ and play fantasy sports.\" />\\n<meta property=\"og:image\" content=\"https://a1.espncdn.com/combiner/i?img=%2Fi%2Fespn%2Fespn_logos%2Fespn_red.png\"/>\\n<meta property=\"og:image:width\" content=\"1200\" />\\n<meta property=\"og:image:height\" content=\"630\" />\\n<meta property=\"og:type\" content=\"website\" />\\n<meta name=\"twitter:site\" content=\"espn\" />\\n<meta name=\"twitter:url\" content=\"https://www.espn.com\" />\\n<meta name=\"twitter:title\" content=\"ESPN - Serving Sports Fans. Anytime. Anywhere.\"/>\\n<meta name=\"twitter:description\" content=\"Visit ESPN for live scores, highlights and sports news. Stream exclusive games on ESPN+ and play fantasy sports.\" />\\n<meta name=\"twitter:card\" content=\"summary\">\\n<meta name=\"twitter:app:name:iphone\" content=\"ESPN\"/>\\n<meta name=\"twitter:app:id:iphone\" content=\"317469184\"/>\\n<meta name=\"twitter:app:name:googleplay\" content=\"ESPN\"/>\\n<meta name=\"twitter:app:id:googleplay\" content=\"com.espn.score_center\"/>\\n<meta name=\"title\" content=\"ESPN - '"
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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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"docs[0].page_content[1000:2000]"
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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": 7,
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"id": "4d024f0f",
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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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"'al\" href=\"https://lilianweng.github.io/posts/2023-06-23-agent/\" />\\n<link crossorigin=\"anonymous\" href=\"/assets/css/stylesheet.min.67a6fb6e33089cb29e856bcc95d7aa39f70049a42b123105531265a0d9f1258b.css\" integrity=\"sha256-Z6b7bjMInLKehWvMldeqOfcASaQrEjEFUxJloNnxJYs=\" rel=\"preload stylesheet\" as=\"style\">\\n<script defer crossorigin=\"anonymous\" src=\"/assets/js/highlight.min.7680afc38aa6b15ddf158a4f3780b7b1f7dde7e91d26f073e6229bb7a0793c92.js\" integrity=\"sha256-doCvw4qmsV3fFYpPN4C3sffd5+kdJvBz5iKbt6B5PJI=\"\\n onload=\"hljs.initHighlightingOnLoad();\"></script>\\n<link rel=\"icon\" href=\"https://lilianweng.github.io/favicon_peach.ico\">\\n<link rel=\"icon\" type=\"image/png\" sizes=\"16x16\" href=\"https://lilianweng.github.io/favicon-16x16.png\">\\n<link rel=\"icon\" type=\"image/png\" sizes=\"32x32\" href=\"https://lilianweng.github.io/favicon-32x32.png\">\\n<link rel=\"apple-touch-icon\" href=\"https://lilianweng.github.io/apple-touch-icon.png\">\\n<link rel=\"mask-icon\" href=\"https://lilianweng.github.io/safari-pinned-tab.'"
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]
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},
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"execution_count": 7,
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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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"docs[1].page_content[1000:2000]"
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]
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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": "3.9.16"
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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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@ -0,0 +1,133 @@
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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": "fe6e5c82",
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"metadata": {},
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"source": [
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"# html2text\n",
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"\n",
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"[html2text](https://github.com/Alir3z4/html2text/) is a Python script that converts a page of HTML into clean, easy-to-read plain ASCII text. \n",
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"\n",
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"The ASCII also happens to be valid Markdown (a text-to-HTML format)."
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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": "ce77e0cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install html2text"
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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": "8ca0974b",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Fetching pages: 100%|############| 2/2 [00:00<00:00, 10.75it/s]\n"
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]
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}
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],
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"source": [
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"from langchain.document_loaders import AsyncHtmlLoader\n",
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"\n",
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"urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
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"loader = AsyncHtmlLoader(urls)\n",
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"docs = loader.load()"
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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": "ddf2be97",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_transformers import Html2TextTransformer"
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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": 5,
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"id": "a95a928c",
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"metadata": {},
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"outputs": [],
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"source": [
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"urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
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"html2text = Html2TextTransformer()\n",
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"docs_transformed = html2text.transform_documents(docs)"
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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": "18ef9fe9",
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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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"\" * ESPNFC\\n\\n * X Games\\n\\n * SEC Network\\n\\n## ESPN Apps\\n\\n * ESPN\\n\\n * ESPN Fantasy\\n\\n## Follow ESPN\\n\\n * Facebook\\n\\n * Twitter\\n\\n * Instagram\\n\\n * Snapchat\\n\\n * YouTube\\n\\n * The ESPN Daily Podcast\\n\\n2023 FIFA Women's World Cup\\n\\n## Follow live: Canada takes on Nigeria in group stage of Women's World Cup\\n\\n2m\\n\\nEPA/Morgan Hancock\\n\\n## TOP HEADLINES\\n\\n * Snyder fined $60M over findings in investigation\\n * NFL owners approve $6.05B sale of Commanders\\n * Jags assistant comes out as gay in NFL milestone\\n * O's alone atop East after topping slumping Rays\\n * ACC's Phillips: Never condoned hazing at NU\\n\\n * Vikings WR Addison cited for driving 140 mph\\n * 'Taking his time': Patient QB Rodgers wows Jets\\n * Reyna got U.S. assurances after Berhalter rehire\\n * NFL Future Power Rankings\\n\\n## USWNT AT THE WORLD CUP\\n\\n### USA VS. VIETNAM: 9 P.M. ET FRIDAY\\n\\n## How do you defend against Alex Morgan? Former opponents sound off\\n\\nThe U.S. forward is unstoppable at this level, scoring 121 goals and adding 49\""
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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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"docs_transformed[0].page_content[1000:2000]"
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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": 7,
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"id": "6045d660",
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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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"\"t's brain,\\ncomplemented by several key components:\\n\\n * **Planning**\\n * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\n * Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n * **Memory**\\n * Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\n * Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n * **Tool use**\\n * The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution c\""
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]
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},
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"execution_count": 7,
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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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"docs_transformed[1].page_content[1000:2000]"
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]
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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": "3.9.16"
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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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@ -5,6 +5,7 @@ from langchain.document_loaders.airbyte_json import AirbyteJSONLoader
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from langchain.document_loaders.airtable import AirtableLoader
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from langchain.document_loaders.airtable import AirtableLoader
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from langchain.document_loaders.apify_dataset import ApifyDatasetLoader
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from langchain.document_loaders.apify_dataset import ApifyDatasetLoader
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from langchain.document_loaders.arxiv import ArxivLoader
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from langchain.document_loaders.arxiv import ArxivLoader
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from langchain.document_loaders.async_html import AsyncHtmlLoader
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from langchain.document_loaders.azlyrics import AZLyricsLoader
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from langchain.document_loaders.azlyrics import AZLyricsLoader
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from langchain.document_loaders.azure_blob_storage_container import (
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from langchain.document_loaders.azure_blob_storage_container import (
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AzureBlobStorageContainerLoader,
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AzureBlobStorageContainerLoader,
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@ -161,6 +162,7 @@ TelegramChatLoader = TelegramChatFileLoader
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__all__ = [
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__all__ = [
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"AcreomLoader",
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"AcreomLoader",
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"AsyncHtmlLoader",
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"AZLyricsLoader",
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"AZLyricsLoader",
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"AirbyteJSONLoader",
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"AirbyteJSONLoader",
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"AirtableLoader",
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"AirtableLoader",
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138
langchain/document_loaders/async_html.py
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138
langchain/document_loaders/async_html.py
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"""Web base loader class."""
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import asyncio
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import logging
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import warnings
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from typing import Any, Dict, Iterator, List, Optional, Union
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import aiohttp
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import requests
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from langchain.docstore.document import Document
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from langchain.document_loaders.base import BaseLoader
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logger = logging.getLogger(__name__)
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default_header_template = {
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"User-Agent": "",
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"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*"
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";q=0.8",
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"Accept-Language": "en-US,en;q=0.5",
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"Referer": "https://www.google.com/",
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"DNT": "1",
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"Connection": "keep-alive",
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"Upgrade-Insecure-Requests": "1",
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}
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class AsyncHtmlLoader(BaseLoader):
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"""Loads HTML asynchronously."""
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web_paths: List[str]
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requests_per_second: int = 2
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"""Max number of concurrent requests to make."""
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requests_kwargs: Dict[str, Any] = {}
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"""kwargs for requests"""
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raise_for_status: bool = False
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"""Raise an exception if http status code denotes an error."""
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def __init__(
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self,
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web_path: Union[str, List[str]],
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header_template: Optional[dict] = None,
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verify_ssl: Optional[bool] = True,
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proxies: Optional[dict] = None,
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):
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"""Initialize with webpage path."""
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# TODO: Deprecate web_path in favor of web_paths, and remove this
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# left like this because there are a number of loaders that expect single
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# urls
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if isinstance(web_path, str):
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self.web_paths = [web_path]
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elif isinstance(web_path, List):
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self.web_paths = web_path
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headers = header_template or default_header_template
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if not headers.get("User-Agent"):
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try:
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from fake_useragent import UserAgent
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headers["User-Agent"] = UserAgent().random
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except ImportError:
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logger.info(
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"fake_useragent not found, using default user agent."
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"To get a realistic header for requests, "
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"`pip install fake_useragent`."
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)
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self.session = requests.Session()
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self.session.headers = dict(headers)
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self.session.verify = verify_ssl
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if proxies:
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self.session.proxies.update(proxies)
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async def _fetch(
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||||||
|
self, url: str, retries: int = 3, cooldown: int = 2, backoff: float = 1.5
|
||||||
|
) -> str:
|
||||||
|
async with aiohttp.ClientSession() as session:
|
||||||
|
for i in range(retries):
|
||||||
|
try:
|
||||||
|
async with session.get(
|
||||||
|
url,
|
||||||
|
headers=self.session.headers,
|
||||||
|
ssl=None if self.session.verify else False,
|
||||||
|
) as response:
|
||||||
|
return await response.text()
|
||||||
|
except aiohttp.ClientConnectionError as e:
|
||||||
|
if i == retries - 1:
|
||||||
|
raise
|
||||||
|
else:
|
||||||
|
logger.warning(
|
||||||
|
f"Error fetching {url} with attempt "
|
||||||
|
f"{i + 1}/{retries}: {e}. Retrying..."
|
||||||
|
)
|
||||||
|
await asyncio.sleep(cooldown * backoff**i)
|
||||||
|
raise ValueError("retry count exceeded")
|
||||||
|
|
||||||
|
async def _fetch_with_rate_limit(
|
||||||
|
self, url: str, semaphore: asyncio.Semaphore
|
||||||
|
) -> str:
|
||||||
|
async with semaphore:
|
||||||
|
return await self._fetch(url)
|
||||||
|
|
||||||
|
async def fetch_all(self, urls: List[str]) -> Any:
|
||||||
|
"""Fetch all urls concurrently with rate limiting."""
|
||||||
|
semaphore = asyncio.Semaphore(self.requests_per_second)
|
||||||
|
tasks = []
|
||||||
|
for url in urls:
|
||||||
|
task = asyncio.ensure_future(self._fetch_with_rate_limit(url, semaphore))
|
||||||
|
tasks.append(task)
|
||||||
|
try:
|
||||||
|
from tqdm.asyncio import tqdm_asyncio
|
||||||
|
|
||||||
|
return await tqdm_asyncio.gather(
|
||||||
|
*tasks, desc="Fetching pages", ascii=True, mininterval=1
|
||||||
|
)
|
||||||
|
except ImportError:
|
||||||
|
warnings.warn("For better logging of progress, `pip install tqdm`")
|
||||||
|
return await asyncio.gather(*tasks)
|
||||||
|
|
||||||
|
def lazy_load(self) -> Iterator[Document]:
|
||||||
|
"""Lazy load text from the url(s) in web_path."""
|
||||||
|
for doc in self.load():
|
||||||
|
yield doc
|
||||||
|
|
||||||
|
def load(self) -> List[Document]:
|
||||||
|
"""Load text from the url(s) in web_path."""
|
||||||
|
|
||||||
|
results = asyncio.run(self.fetch_all(self.web_paths))
|
||||||
|
docs = []
|
||||||
|
for i, text in enumerate(results):
|
||||||
|
metadata = {"source": self.web_paths[i]}
|
||||||
|
docs.append(Document(page_content=text, metadata=metadata))
|
||||||
|
|
||||||
|
return docs
|
@ -8,6 +8,7 @@ from langchain.document_transformers.embeddings_redundant_filter import (
|
|||||||
EmbeddingsRedundantFilter,
|
EmbeddingsRedundantFilter,
|
||||||
get_stateful_documents,
|
get_stateful_documents,
|
||||||
)
|
)
|
||||||
|
from langchain.document_transformers.html2text import Html2TextTransformer
|
||||||
from langchain.document_transformers.long_context_reorder import LongContextReorder
|
from langchain.document_transformers.long_context_reorder import LongContextReorder
|
||||||
|
|
||||||
__all__ = [
|
__all__ = [
|
||||||
@ -19,6 +20,7 @@ __all__ = [
|
|||||||
"get_stateful_documents",
|
"get_stateful_documents",
|
||||||
"LongContextReorder",
|
"LongContextReorder",
|
||||||
"OpenAIMetadataTagger",
|
"OpenAIMetadataTagger",
|
||||||
|
"Html2TextTransformer",
|
||||||
]
|
]
|
||||||
|
|
||||||
from langchain.document_transformers.openai_functions import OpenAIMetadataTagger
|
from langchain.document_transformers.openai_functions import OpenAIMetadataTagger
|
||||||
|
41
langchain/document_transformers/html2text.py
Normal file
41
langchain/document_transformers/html2text.py
Normal file
@ -0,0 +1,41 @@
|
|||||||
|
from typing import Any, Sequence
|
||||||
|
|
||||||
|
from langchain.schema import BaseDocumentTransformer, Document
|
||||||
|
|
||||||
|
|
||||||
|
class Html2TextTransformer(BaseDocumentTransformer):
|
||||||
|
"""Replace occurrences of a particular search pattern with a replacement string
|
||||||
|
Example:
|
||||||
|
.. code-block:: python
|
||||||
|
from langchain.document_transformers import Html2TextTransformer
|
||||||
|
html2text=Html2TextTransformer()
|
||||||
|
docs_transform=html2text.transform_documents(docs)
|
||||||
|
"""
|
||||||
|
|
||||||
|
def transform_documents(
|
||||||
|
self,
|
||||||
|
documents: Sequence[Document],
|
||||||
|
**kwargs: Any,
|
||||||
|
) -> Sequence[Document]:
|
||||||
|
try:
|
||||||
|
import html2text
|
||||||
|
except ImportError:
|
||||||
|
raise ValueError(
|
||||||
|
"""html2text package not found, please
|
||||||
|
install it with `pip install html2text`"""
|
||||||
|
)
|
||||||
|
|
||||||
|
# Create an html2text.HTML2Text object and override some properties
|
||||||
|
h = html2text.HTML2Text()
|
||||||
|
h.ignore_links = True
|
||||||
|
h.ignore_images = True
|
||||||
|
for d in documents:
|
||||||
|
d.page_content = h.handle(d.page_content)
|
||||||
|
return documents
|
||||||
|
|
||||||
|
async def atransform_documents(
|
||||||
|
self,
|
||||||
|
documents: Sequence[Document],
|
||||||
|
**kwargs: Any,
|
||||||
|
) -> Sequence[Document]:
|
||||||
|
raise NotImplementedError
|
Loading…
Reference in New Issue
Block a user