mirror of https://github.com/hwchase17/langchain
feat: Add `UnstructuredTSVLoader` (#7367)
### Summary Adds an `UnstructuredTSVLoader` for TSV files. Also updates the doc strings for `UnstructuredCSV` and `UnstructuredExcel` loaders. ### Testing ```python from langchain.document_loaders.tsv import UnstructuredTSVLoader loader = UnstructuredTSVLoader( file_path="example_data/mlb_teams_2012.csv", mode="elements" ) docs = loader.load() ```pull/7356/head
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Stanley Cups
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Team Location Stanley Cups
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Blues STL 1
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Flyers PHI 2
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Maple Leafs TOR 13
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{
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"cells": [
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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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"# TSV\n",
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"\n",
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">A [tab-separated values (TSV)](https://en.wikipedia.org/wiki/Tab-separated_values) file is a simple, text-based file format for storing tabular data.[3] Records are separated by newlines, and values within a record are separated by tab characters."
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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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"## `UnstructuredTSVLoader`\n",
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"\n",
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"You can also load the table using the `UnstructuredTSVLoader`. One advantage of using `UnstructuredTSVLoader` is that if you use it in `\"elements\"` mode, an HTML representation of the table will be available in the metadata."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders.tsv import UnstructuredTSVLoader"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = UnstructuredTSVLoader(\n",
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" file_path=\"example_data/mlb_teams_2012.csv\", mode=\"elements\"\n",
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")\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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>Nationals, 81.34, 98</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Reds, 82.20, 97</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Yankees, 197.96, 95</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Giants, 117.62, 94</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Braves, 83.31, 94</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Athletics, 55.37, 94</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Rangers, 120.51, 93</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Orioles, 81.43, 93</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Rays, 64.17, 90</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Angels, 154.49, 89</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Tigers, 132.30, 88</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Cardinals, 110.30, 88</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Dodgers, 95.14, 86</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>White Sox, 96.92, 85</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Brewers, 97.65, 83</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Phillies, 174.54, 81</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Diamondbacks, 74.28, 81</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Pirates, 63.43, 79</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Padres, 55.24, 76</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Mariners, 81.97, 75</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Mets, 93.35, 74</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Blue Jays, 75.48, 73</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Royals, 60.91, 72</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Marlins, 118.07, 69</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Red Sox, 173.18, 69</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Indians, 78.43, 68</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Twins, 94.08, 66</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Rockies, 78.06, 64</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Cubs, 88.19, 61</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Astros, 60.65, 55</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n"
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]
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}
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],
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"source": [
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"print(docs[0].metadata[\"text_as_html\"])"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.8.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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from typing import Any, List
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from langchain.document_loaders.unstructured import (
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UnstructuredFileLoader,
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validate_unstructured_version,
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)
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class UnstructuredTSVLoader(UnstructuredFileLoader):
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"""Loader that uses unstructured to load TSV files. Like other
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Unstructured loaders, UnstructuredTSVLoader can be used in both
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"single" and "elements" mode. If you use the loader in "elements"
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mode, the TSV file will be a single Unstructured Table element.
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If you use the loader in "elements" mode, an HTML representation
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of the table will be available in the "text_as_html" key in the
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document metadata.
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Examples
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--------
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from langchain.document_loaders.tsv import UnstructuredTSVLoader
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loader = UnstructuredTSVLoader("stanley-cups.tsv", mode="elements")
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docs = loader.load()
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"""
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def __init__(
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self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
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):
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validate_unstructured_version(min_unstructured_version="0.7.6")
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super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
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def _get_elements(self) -> List:
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from unstructured.partition.tsv import partition_tsv
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return partition_tsv(filename=self.file_path, **self.unstructured_kwargs)
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import os
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from pathlib import Path
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from langchain.document_loaders import UnstructuredTSVLoader
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EXAMPLE_DIRECTORY = file_path = Path(__file__).parent.parent / "examples"
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def test_unstructured_tsv_loader() -> None:
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"""Test unstructured loader."""
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file_path = os.path.join(EXAMPLE_DIRECTORY, "stanley-cups.tsv")
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loader = UnstructuredTSVLoader(str(file_path))
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docs = loader.load()
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assert len(docs) == 1
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Stanley Cups
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Team Location Stanley Cups
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Blues STL 1
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Flyers PHI 2
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Maple Leafs TOR 13
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