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https://github.com/hwchase17/langchain
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Add use case nb position (#10299)
This commit is contained in:
Bagatur
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# Web Scraping
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Web scraping has historically been a challenging endeavor due to the ever-changing nature of website structures, making it tedious for developers to maintain their scraping scripts. Traditional methods often rely on specific HTML tags and patterns which, when altered, can disrupt data extraction processes.
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Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, the LLM-based approach wins out in the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process.
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@ -1,12 +1,21 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "ea5c61b2-8b52-4270-bdb0-c4df88608f15",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Interacting with APIs\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a15e6a18",
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"metadata": {},
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"source": [
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"# Interacting with APIs\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/apis.ipynb)\n",
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"\n",
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"## Use case \n",
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@ -69,9 +78,7 @@
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"cell_type": "code",
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"execution_count": 2,
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"id": "30b780e3",
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"metadata": {
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"scrolled": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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@ -415,7 +422,7 @@
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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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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1,12 +1,21 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "22fd28c9-9b48-476c-bca8-20efef7fdb14",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Chatbots\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee7f95e4",
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"metadata": {},
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"source": [
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"# Chatbots\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/chatbots.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -1,11 +1,19 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Code understanding\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Code Understanding\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/code_understanding.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -1047,7 +1055,7 @@
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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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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1,12 +1,21 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "df29b30a-fd27-4e08-8269-870df5631f9e",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Extraction\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b84edb4e",
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"metadata": {},
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"source": [
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"# Extraction\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/extraction.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -589,7 +598,7 @@
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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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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1 +1,2 @@
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label: 'More'
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position: 2
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@ -1,11 +1,19 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: SQL\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SQL\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/sql.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -856,9 +864,7 @@
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"scrolled": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -1014,9 +1020,7 @@
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {
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"scrolled": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -1256,7 +1260,7 @@
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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": "3.8.17"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1,12 +1,21 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "2aca8168-62ec-4bba-93f0-73da08cd1920",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Summarization\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cf13f702",
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"metadata": {},
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"source": [
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"# Summarization\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/summarization.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -548,7 +557,7 @@
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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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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1,12 +1,21 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "cb6f552e-775f-4d84-bc7c-dca94c06a33c",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Tagging\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a0507a4b",
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"metadata": {},
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"source": [
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"# Tagging\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/tagging.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -408,7 +417,7 @@
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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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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -1,12 +1,21 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "e254cf03-49fc-4051-a4df-3a8e4e7d2688",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Web scraping\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6605e7f7",
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"metadata": {},
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"source": [
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"# Web Scraping\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/web_scraping.ipynb)\n",
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"\n",
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"## Use case\n",
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@ -306,9 +315,7 @@
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"cell_type": "code",
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"execution_count": 7,
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"id": "977560ba",
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"metadata": {
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"scrolled": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -591,7 +598,7 @@
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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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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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