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6291f5499fbd-6 | Document(page_content='gateway_specific_fields: receipt_email\nradar_session_id\nskip_radar_rules\napplication_fee\nstripe_account\nmetadata\nidempotency_key\nreason\nrefund_application_fee\nrefund_fee_amount\nreverse_transfer\naccount_id\ncustomer_id\nvalidate\nmake_default\ncancellation_reason\ncapture_method\nconfirm\nconfirmation_method\ncustomer\ndescription\nmoto\noff_session\non_behalf_of\npayment_method_types\nreturn_email\nreturn_url\nsave_payment_method\nsetup_future_usage\nstatement_descriptor\nstatement_descriptor_suffix\ntransfer_amount\ntransfer_destination\ntransfer_group\napplication_fee_amount\nrequest_three_d_secure\nerror_on_requires_action\nnetwork_transaction_id\nclaim_without_transaction_id\nfulfillment_date\nevent_type\nmodal_challenge\nidempotent_request\nmerchant_reference\ncustomer_reference\nshipping_address_zip\nshipping_from_zip\nshipping_amount\nline_items\nsupported_countries: AE\nAT\nAU\nBE\nBG\nBR\nCA\nCH\nCY\nCZ\nDE\nDK\nEE\nES\nFI\nFR\nGB\nGR\nHK\nHU\nIE\nIN\nIT\nJP\nLT\nLU\nLV\nMT\nMX\nMY\nNL\nNO\nNZ\nPL\nPT\nRO\nSE\nSG\nSI\nSK\nUS\nsupported_cardtypes: visa', metadata={'source': 'https://core.spreedly.com/v1/gateways_options.json'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/spreedly.html |
6291f5499fbd-7 | Document(page_content='mdd_field_57\nmdd_field_58\nmdd_field_59\nmdd_field_60\nmdd_field_61\nmdd_field_62\nmdd_field_63\nmdd_field_64\nmdd_field_65\nmdd_field_66\nmdd_field_67\nmdd_field_68\nmdd_field_69\nmdd_field_70\nmdd_field_71\nmdd_field_72\nmdd_field_73\nmdd_field_74\nmdd_field_75\nmdd_field_76\nmdd_field_77\nmdd_field_78\nmdd_field_79\nmdd_field_80\nmdd_field_81\nmdd_field_82\nmdd_field_83\nmdd_field_84\nmdd_field_85\nmdd_field_86\nmdd_field_87\nmdd_field_88\nmdd_field_89\nmdd_field_90\nmdd_field_91\nmdd_field_92\nmdd_field_93\nmdd_field_94\nmdd_field_95\nmdd_field_96\nmdd_field_97\nmdd_field_98\nmdd_field_99\nmdd_field_100\nsupported_countries: US\nAE\nBR\nCA\nCN\nDK\nFI\nFR\nDE\nIN\nJP\nMX\nNO\nSE\nGB\nSG\nLB\nPK\nsupported_cardtypes: visa\nmaster\namerican_express\ndiscover\ndiners_club\njcb\nmaestro\nelo\nunion_pay\ncartes_bancaires\nmada\nregions: asia_pacific\neurope\nlatin_america\nnorth_america\nhomepage: http://www.cybersource.com\ndisplay_api_url: https://api.cybersource.com\ncompany_name: CyberSource REST', | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/spreedly.html |
6291f5499fbd-8 | ERROR: type should be string, got "https://api.cybersource.com\\ncompany_name: CyberSource REST', metadata={'source': 'https://core.spreedly.com/v1/gateways_options.json'})]" | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/spreedly.html |
6291f5499fbd-9 | previous
Slack
next
Stripe
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/spreedly.html |
a0c50f47ab4f-0 | .ipynb
.pdf
Google BigQuery
Contents
Basic Usage
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
Google BigQuery#
Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
BigQuery is a part of the Google Cloud Platform.
Load a BigQuery query with one document per row.
#!pip install google-cloud-bigquery
from langchain.document_loaders import BigQueryLoader
BASE_QUERY = '''
SELECT
id,
dna_sequence,
organism
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism
UNION ALL
SELECT
AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism
UNION ALL
SELECT
AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array),
UNNEST(new_array)
'''
Basic Usage#
loader = BigQueryLoader(BASE_QUERY)
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
a0c50f47ab4f-1 | loader = BigQueryLoader(BASE_QUERY)
data = loader.load()
print(data)
[Document(page_content='id: 1\ndna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).', lookup_str='', metadata={}, lookup_index=0), Document(page_content='id: 2\ndna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).', lookup_str='', metadata={}, lookup_index=0), Document(page_content='id: 3\ndna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).', lookup_str='', metadata={}, lookup_index=0)]
Specifying Which Columns are Content vs Metadata#
loader = BigQueryLoader(BASE_QUERY, page_content_columns=["dna_sequence", "organism"], metadata_columns=["id"])
data = loader.load()
print(data)
[Document(page_content='dna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).', lookup_str='', metadata={'id': 1}, lookup_index=0), Document(page_content='dna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).', lookup_str='', metadata={'id': 2}, lookup_index=0), Document(page_content='dna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).', lookup_str='', metadata={'id': 3}, lookup_index=0)]
Adding Source to Metadata#
# Note that the `id` column is being returned twice, with one instance aliased as `source`
ALIASED_QUERY = '''
SELECT
id,
dna_sequence,
organism,
id as source
FROM (
SELECT
ARRAY (
SELECT | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
a0c50f47ab4f-2 | id as source
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism
UNION ALL
SELECT
AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism
UNION ALL
SELECT
AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array),
UNNEST(new_array)
'''
loader = BigQueryLoader(ALIASED_QUERY, metadata_columns=["source"])
data = loader.load()
print(data)
[Document(page_content='id: 1\ndna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).\nsource: 1', lookup_str='', metadata={'source': 1}, lookup_index=0), Document(page_content='id: 2\ndna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).\nsource: 2', lookup_str='', metadata={'source': 2}, lookup_index=0), Document(page_content='id: 3\ndna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).\nsource: 3', lookup_str='', metadata={'source': 3}, lookup_index=0)]
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Git
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Google Cloud Storage Directory
Contents
Basic Usage
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
a0c50f47ab4f-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
bf05b5bc0ab5-0 | .ipynb
.pdf
Google Cloud Storage File
Google Cloud Storage File#
Google Cloud Storage is a managed service for storing unstructured data.
This covers how to load document objects from an Google Cloud Storage (GCS) file object (blob).
# !pip install google-cloud-storage
from langchain.document_loaders import GCSFileLoader
loader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.docx")
loader.load()
/Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/
warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmp3srlf8n8/fake.docx'}, lookup_index=0)]
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Google Cloud Storage Directory
next
Google Drive
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_cloud_storage_file.html |
974a4e6f9b32-0 | .ipynb
.pdf
AZLyrics
AZLyrics#
AZLyrics is a large, legal, every day growing collection of lyrics.
This covers how to load AZLyrics webpages into a document format that we can use downstream.
from langchain.document_loaders import AZLyricsLoader
loader = AZLyricsLoader("https://www.azlyrics.com/lyrics/mileycyrus/flowers.html")
data = loader.load()
data | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html |
974a4e6f9b32-1 | [Document(page_content="Miley Cyrus - Flowers Lyrics | AZLyrics.com\n\r\nWe were good, we were gold\nKinda dream that can't be sold\nWe were right till we weren't\nBuilt a home and watched it burn\n\nI didn't wanna leave you\nI didn't wanna lie\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\n\nPaint my nails, cherry red\nMatch the roses that you left\nNo remorse, no regret\nI forgive every word you said\n\nI didn't wanna leave you, baby\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours, yeah\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI\n\nI didn't wanna wanna leave you\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours (Yeah)\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than\nYeah, I can love me better than you can, uh\n\nCan love me | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html |
974a4e6f9b32-2 | better than\nYeah, I can love me better than you can, uh\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby (Than you can)\nCan love me better\nI can love me better, baby\nCan love me better\nI\n", lookup_str='', metadata={'source': 'https://www.azlyrics.com/lyrics/mileycyrus/flowers.html'}, lookup_index=0)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html |
974a4e6f9b32-3 | previous
Arxiv
next
BiliBili
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html |
f49af005d2c7-0 | .ipynb
.pdf
BiliBili
BiliBili#
Bilibili is one of the most beloved long-form video sites in China.
This loader utilizes the bilibili-api to fetch the text transcript from Bilibili.
With this BiliBiliLoader, users can easily obtain the transcript of their desired video content on the platform.
#!pip install bilibili-api-python
from langchain.document_loaders import BiliBiliLoader
loader = BiliBiliLoader(
["https://www.bilibili.com/video/BV1xt411o7Xu/"]
)
loader.load()
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AZLyrics
next
College Confidential
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/bilibili.html |
e01a76d29947-0 | .ipynb
.pdf
Notion DB 1/2
Contents
🧑 Instructions for ingesting your own dataset
Notion DB 1/2#
Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.
This notebook covers how to load documents from a Notion database dump.
In order to get this notion dump, follow these instructions:
🧑 Instructions for ingesting your own dataset#
Export your dataset from Notion. You can do this by clicking on the three dots in the upper right hand corner and then clicking Export.
When exporting, make sure to select the Markdown & CSV format option.
This will produce a .zip file in your Downloads folder. Move the .zip file into this repository.
Run the following command to unzip the zip file (replace the Export... with your own file name as needed).
unzip Export-d3adfe0f-3131-4bf3-8987-a52017fc1bae.zip -d Notion_DB
Run the following command to ingest the data.
from langchain.document_loaders import NotionDirectoryLoader
loader = NotionDirectoryLoader("Notion_DB")
docs = loader.load()
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Notion DB 2/2
next
Obsidian
Contents
🧑 Instructions for ingesting your own dataset
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/notion.html |
6a946bbb08f4-0 | .ipynb
.pdf
JSON
Contents
Using JSONLoader
Extracting metadata
The metadata_func
Common JSON structures with jq schema
JSON#
JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values).
The JSONLoader uses a specified jq schema to parse the JSON files. It uses the jq python package.
Check this manual for a detailed documentation of the jq syntax.
#!pip install jq
from langchain.document_loaders import JSONLoader
import json
from pathlib import Path
from pprint import pprint
file_path='./example_data/facebook_chat.json'
data = json.loads(Path(file_path).read_text())
pprint(data)
{'image': {'creation_timestamp': 1675549016, 'uri': 'image_of_the_chat.jpg'},
'is_still_participant': True,
'joinable_mode': {'link': '', 'mode': 1},
'magic_words': [],
'messages': [{'content': 'Bye!',
'sender_name': 'User 2',
'timestamp_ms': 1675597571851},
{'content': 'Oh no worries! Bye',
'sender_name': 'User 1',
'timestamp_ms': 1675597435669},
{'content': 'No Im sorry it was my mistake, the blue one is not '
'for sale',
'sender_name': 'User 2',
'timestamp_ms': 1675596277579},
{'content': 'I thought you were selling the blue one!',
'sender_name': 'User 1',
'timestamp_ms': 1675595140251},
{'content': 'Im not interested in this bag. Im interested in the ' | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-1 | {'content': 'Im not interested in this bag. Im interested in the '
'blue one!',
'sender_name': 'User 1',
'timestamp_ms': 1675595109305},
{'content': 'Here is $129',
'sender_name': 'User 2',
'timestamp_ms': 1675595068468},
{'photos': [{'creation_timestamp': 1675595059,
'uri': 'url_of_some_picture.jpg'}],
'sender_name': 'User 2',
'timestamp_ms': 1675595060730},
{'content': 'Online is at least $100',
'sender_name': 'User 2',
'timestamp_ms': 1675595045152},
{'content': 'How much do you want?',
'sender_name': 'User 1',
'timestamp_ms': 1675594799696},
{'content': 'Goodmorning! $50 is too low.',
'sender_name': 'User 2',
'timestamp_ms': 1675577876645},
{'content': 'Hi! Im interested in your bag. Im offering $50. Let '
'me know if you are interested. Thanks!',
'sender_name': 'User 1',
'timestamp_ms': 1675549022673}],
'participants': [{'name': 'User 1'}, {'name': 'User 2'}],
'thread_path': 'inbox/User 1 and User 2 chat',
'title': 'User 1 and User 2 chat'}
Using JSONLoader#
Suppose we are interested in extracting the values under the content field within the messages key of the JSON data. This can easily be done through the JSONLoader as shown below. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-2 | loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[].content')
data = loader.load()
pprint(data)
[Document(page_content='Bye!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1}),
Document(page_content='Oh no worries! Bye', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2}),
Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3}),
Document(page_content='I thought you were selling the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4}),
Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5}),
Document(page_content='Here is $129', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6}),
Document(page_content='', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-3 | Document(page_content='Online is at least $100', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8}),
Document(page_content='How much do you want?', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9}),
Document(page_content='Goodmorning! $50 is too low.', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10}),
Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11})]
Extracting metadata#
Generally, we want to include metadata available in the JSON file into the documents that we create from the content.
The following demonstrates how metadata can be extracted using the JSONLoader.
There are some key changes to be noted. In the previous example where we didn’t collect the metadata, we managed to directly specify in the schema where the value for the page_content can be extracted from.
.messages[].content
In the current example, we have to tell the loader to iterate over the records in the messages field. The jq_schema then has to be:
.messages[]
This allows us to pass the records (dict) into the metadata_func that has to be implemented. The metadata_func is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final Document object. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-4 | Additionally, we now have to explicitly specify in the loader, via the content_key argument, the key from the record where the value for the page_content needs to be extracted from.
# Define the metadata extraction function.
def metadata_func(record: dict, metadata: dict) -> dict:
metadata["sender_name"] = record.get("sender_name")
metadata["timestamp_ms"] = record.get("timestamp_ms")
return metadata
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[]',
content_key="content",
metadata_func=metadata_func
)
data = loader.load()
pprint(data)
[Document(page_content='Bye!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}),
Document(page_content='Oh no worries! Bye', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2, 'sender_name': 'User 1', 'timestamp_ms': 1675597435669}),
Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3, 'sender_name': 'User 2', 'timestamp_ms': 1675596277579}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-5 | Document(page_content='I thought you were selling the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4, 'sender_name': 'User 1', 'timestamp_ms': 1675595140251}),
Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5, 'sender_name': 'User 1', 'timestamp_ms': 1675595109305}),
Document(page_content='Here is $129', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6, 'sender_name': 'User 2', 'timestamp_ms': 1675595068468}),
Document(page_content='', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7, 'sender_name': 'User 2', 'timestamp_ms': 1675595060730}),
Document(page_content='Online is at least $100', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8, 'sender_name': 'User 2', 'timestamp_ms': 1675595045152}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-6 | Document(page_content='How much do you want?', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9, 'sender_name': 'User 1', 'timestamp_ms': 1675594799696}),
Document(page_content='Goodmorning! $50 is too low.', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10, 'sender_name': 'User 2', 'timestamp_ms': 1675577876645}),
Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11, 'sender_name': 'User 1', 'timestamp_ms': 1675549022673})]
Now, you will see that the documents contain the metadata associated with the content we extracted.
The metadata_func#
As shown above, the metadata_func accepts the default metadata generated by the JSONLoader. This allows full control to the user with respect to how the metadata is formatted.
For example, the default metadata contains the source and the seq_num keys. However, it is possible that the JSON data contain these keys as well. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data.
The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory.
# Define the metadata extraction function.
def metadata_func(record: dict, metadata: dict) -> dict:
metadata["sender_name"] = record.get("sender_name") | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-7 | metadata["sender_name"] = record.get("sender_name")
metadata["timestamp_ms"] = record.get("timestamp_ms")
if "source" in metadata:
source = metadata["source"].split("/")
source = source[source.index("langchain"):]
metadata["source"] = "/".join(source)
return metadata
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[]',
content_key="content",
metadata_func=metadata_func
)
data = loader.load()
pprint(data)
[Document(page_content='Bye!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}),
Document(page_content='Oh no worries! Bye', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2, 'sender_name': 'User 1', 'timestamp_ms': 1675597435669}),
Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3, 'sender_name': 'User 2', 'timestamp_ms': 1675596277579}),
Document(page_content='I thought you were selling the blue one!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4, 'sender_name': 'User 1', 'timestamp_ms': 1675595140251}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-8 | Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5, 'sender_name': 'User 1', 'timestamp_ms': 1675595109305}),
Document(page_content='Here is $129', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6, 'sender_name': 'User 2', 'timestamp_ms': 1675595068468}),
Document(page_content='', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7, 'sender_name': 'User 2', 'timestamp_ms': 1675595060730}),
Document(page_content='Online is at least $100', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8, 'sender_name': 'User 2', 'timestamp_ms': 1675595045152}),
Document(page_content='How much do you want?', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9, 'sender_name': 'User 1', 'timestamp_ms': 1675594799696}),
Document(page_content='Goodmorning! $50 is too low.', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10, 'sender_name': 'User 2', 'timestamp_ms': 1675577876645}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
6a946bbb08f4-9 | Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11, 'sender_name': 'User 1', 'timestamp_ms': 1675549022673})]
Common JSON structures with jq schema#
The list below provides a reference to the possible jq_schema the user can use to extract content from the JSON data depending on the structure.
JSON -> [{"text": ...}, {"text": ...}, {"text": ...}]
jq_schema -> ".[].text"
JSON -> {"key": [{"text": ...}, {"text": ...}, {"text": ...}]}
jq_schema -> ".key[].text"
JSON -> ["...", "...", "..."]
jq_schema -> ".[]"
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Markdown
Contents
Using JSONLoader
Extracting metadata
The metadata_func
Common JSON structures with jq schema
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/json.html |
1e18963d6daf-0 | .ipynb
.pdf
Images
Contents
Using Unstructured
Retain Elements
Images#
This covers how to load images such as JPG or PNG into a document format that we can use downstream.
Using Unstructured#
#!pip install pdfminer
from langchain.document_loaders.image import UnstructuredImageLoader
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")
data = loader.load()
data[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image.html |
1e18963d6daf-1 | Document(page_content="LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\n\n\n‘Zxjiang Shen' (F3}, Ruochen Zhang”, Melissa Dell*, Benjamin Charles Germain\nLeet, Jacob Carlson, and Weining LiF\n\n\nsugehen\n\nshangthrows, et\n\n“Abstract. Recent advanocs in document image analysis (DIA) have been\n‘pimarliy driven bythe application of neural networks dell roar\n{uteomer could be aly deployed in production and extended fo farther\n[nvetigtion. However, various factory ke lcely organize codebanee\nsnd sophisticated modal cnigurations compat the ey ree of\n‘erin! innovation by wide sence, Though there have been sng\n‘Hors to improve reuablty and simplify deep lees (DL) mode\n‘aon, sone of them ae optimized for challenge inthe demain of DIA,\nThis roprscte a major gap in the extng fol, sw DIA i eal to\nscademic research acon wie range of dpi in the social ssencee\n[rary for streamlining the sage of DL in DIA research and appicn\n‘tons The core LayoutFaraer brary comes with a sch of simple and\nIntative interfaee or applying and eutomiing DI. odel fr Inyo de\npltfom for sharing both protrined modes an fal document dist\n{ation pipeline We demonutate that LayootPareer shea fr both\nlightweight and lrgeseledgtieation pipelines in eal-word uae ces\nThe leary pblely smal at Btspe://layost-pareergsthab So\n\n\n\n‘Keywords: Document Image Analysis» Deep Learning Layout Analysis\n‘Character Renguition - Open Serres dary « | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image.html |
1e18963d6daf-2 | Image Analysis» Deep Learning Layout Analysis\n‘Character Renguition - Open Serres dary « Tol\n\n\nIntroduction\n\n\n‘Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndoctiment image analysis (DIA) tea including document image clasiffeation [I]\n", lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg'}, lookup_index=0) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image.html |
1e18963d6daf-3 | Retain Elements#
Under the hood, Unstructured creates different “elements” for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg", mode="elements")
data = loader.load()
data[0]
Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg', 'filename': 'layout-parser-paper-fast.jpg', 'page_number': 1, 'category': 'Title'}, lookup_index=0)
previous
HTML
next
Jupyter Notebook
Contents
Using Unstructured
Retain Elements
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image.html |
bf1f879a2ca6-0 | .ipynb
.pdf
Docugami
Contents
Prerequisites
Quick start
Advantages vs Other Chunking Techniques
Load Documents
Basic Use: Docugami Loader for Document QA
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA
Docugami#
This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders.
Prerequisites#
Install necessary python packages.
Grab an access token for your workspace, and make sure it is set as the DOCUGAMI_API_KEY environment variable.
Grab some docset and document IDs for your processed documents, as described here: https://help.docugami.com/home/docugami-api
# You need the lxml package to use the DocugamiLoader
!pip install lxml
Quick start#
Create a Docugami workspace (free trials available)
Add your documents (PDF, DOCX or DOC) and allow Docugami to ingest and cluster them into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. There is no fixed set of document types supported by the system, the clusters created depend on your particular documents, and you can change the docset assignments later.
Create an access token via the Developer Playground for your workspace. Detailed instructions
Explore the Docugami API to get a list of your processed docset IDs, or just the document IDs for a particular docset.
Use the DocugamiLoader as detailed below, to get rich semantic chunks for your documents.
Optionally, build and publish one or more reports or abstracts. This helps Docugami improve the semantic XML with better tags based on your preferences, which are then added to the DocugamiLoader output as metadata. Use techniques like self-querying retriever to do high accuracy Document QA.
Advantages vs Other Chunking Techniques# | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-1 | Advantages vs Other Chunking Techniques#
Appropriate chunking of your documents is critical for retrieval from documents. Many chunking techniques exist, including simple ones that rely on whitespace and recursive chunk splitting based on character length. Docugami offers a different approach:
Intelligent Chunking: Docugami breaks down every document into a hierarchical semantic XML tree of chunks of varying sizes, from single words or numerical values to entire sections. These chunks follow the semantic contours of the document, providing a more meaningful representation than arbitrary length or simple whitespace-based chunking.
Structured Representation: In addition, the XML tree indicates the structural contours of every document, using attributes denoting headings, paragraphs, lists, tables, and other common elements, and does that consistently across all supported document formats, such as scanned PDFs or DOCX files. It appropriately handles long-form document characteristics like page headers/footers or multi-column flows for clean text extraction.
Semantic Annotations: Chunks are annotated with semantic tags that are coherent across the document set, facilitating consistent hierarchical queries across multiple documents, even if they are written and formatted differently. For example, in set of lease agreements, you can easily identify key provisions like the Landlord, Tenant, or Renewal Date, as well as more complex information such as the wording of any sub-lease provision or whether a specific jurisdiction has an exception section within a Termination Clause.
Additional Metadata: Chunks are also annotated with additional metadata, if a user has been using Docugami. This additional metadata can be used for high-accuracy Document QA without context window restrictions. See detailed code walk-through below.
import os
from langchain.document_loaders import DocugamiLoader
Load Documents#
If the DOCUGAMI_API_KEY environment variable is set, there is no need to pass it in to the loader explicitly otherwise you can pass it in as the access_token parameter. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-2 | DOCUGAMI_API_KEY=os.environ.get('DOCUGAMI_API_KEY')
# To load all docs in the given docset ID, just don't provide document_ids
loader = DocugamiLoader(docset_id="ecxqpipcoe2p", document_ids=["43rj0ds7s0ur"])
docs = loader.load()
docs
[Document(page_content='MUTUAL NON-DISCLOSURE AGREEMENT This Mutual Non-Disclosure Agreement (this “ Agreement ”) is entered into and made effective as of April 4 , 2018 between Docugami Inc. , a Delaware corporation , whose address is 150 Lake Street South , Suite 221 , Kirkland , Washington 98033 , and Caleb Divine , an individual, whose address is 1201 Rt 300 , Newburgh NY 12550 .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:ThisMutualNon-disclosureAgreement', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'ThisMutualNon-disclosureAgreement'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-3 | Document(page_content='The above named parties desire to engage in discussions regarding a potential agreement or other transaction between the parties (the “Purpose”). In connection with such discussions, it may be necessary for the parties to disclose to each other certain confidential information or materials to enable them to evaluate whether to enter into such agreement or transaction.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Discussions', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Discussions'}),
Document(page_content='In consideration of the foregoing, the parties agree as follows:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Consideration', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Consideration'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-4 | Document(page_content='1. Confidential Information . For purposes of this Agreement , “ Confidential Information ” means any information or materials disclosed by one party to the other party that: (i) if disclosed in writing or in the form of tangible materials, is marked “confidential” or “proprietary” at the time of such disclosure; (ii) if disclosed orally or by visual presentation, is identified as “confidential” or “proprietary” at the time of such disclosure, and is summarized in a writing sent by the disclosing party to the receiving party within thirty ( 30 ) days after any such disclosure; or (iii) due to its nature or the circumstances of its disclosure, a person exercising reasonable business judgment would understand to be confidential or proprietary.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Purposes/docset:ConfidentialInformation-section/docset:ConfidentialInformation[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ConfidentialInformation'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-5 | Document(page_content="2. Obligations and Restrictions . Each party agrees: (i) to maintain the other party's Confidential Information in strict confidence; (ii) not to disclose such Confidential Information to any third party; and (iii) not to use such Confidential Information for any purpose except for the Purpose. Each party may disclose the other party’s Confidential Information to its employees and consultants who have a bona fide need to know such Confidential Information for the Purpose, but solely to the extent necessary to pursue the Purpose and for no other purpose; provided, that each such employee and consultant first executes a written agreement (or is otherwise already bound by a written agreement) that contains use and nondisclosure restrictions at least as protective of the other party’s Confidential Information as those set forth in this Agreement .", metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Obligations/docset:ObligationsAndRestrictions-section/docset:ObligationsAndRestrictions', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ObligationsAndRestrictions'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-6 | Document(page_content='3. Exceptions. The obligations and restrictions in Section 2 will not apply to any information or materials that:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Exceptions/docset:Exceptions-section/docset:Exceptions[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Exceptions'}),
Document(page_content='(i) were, at the date of disclosure, or have subsequently become, generally known or available to the public through no act or failure to act by the receiving party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheDate/docset:TheDate', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheDate'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-7 | Document(page_content='(ii) were rightfully known by the receiving party prior to receiving such information or materials from the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:SuchInformation/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}),
Document(page_content='(iii) are rightfully acquired by the receiving party from a third party who has the right to disclose such information or materials without breach of any confidentiality obligation to the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheReceivingParty/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-8 | Document(page_content='4. Compelled Disclosure . Nothing in this Agreement will be deemed to restrict a party from disclosing the other party’s Confidential Information to the extent required by any order, subpoena, law, statute or regulation; provided, that the party required to make such a disclosure uses reasonable efforts to give the other party reasonable advance notice of such required disclosure in order to enable the other party to prevent or limit such disclosure.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Disclosure/docset:CompelledDisclosure-section/docset:CompelledDisclosure', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'CompelledDisclosure'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-9 | Document(page_content='5. Return of Confidential Information . Upon the completion or abandonment of the Purpose, and in any event upon the disclosing party’s request, the receiving party will promptly return to the disclosing party all tangible items and embodiments containing or consisting of the disclosing party’s Confidential Information and all copies thereof (including electronic copies), and any notes, analyses, compilations, studies, interpretations, memoranda or other documents (regardless of the form thereof) prepared by or on behalf of the receiving party that contain or are based upon the disclosing party’s Confidential Information .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheCompletion/docset:ReturnofConfidentialInformation-section/docset:ReturnofConfidentialInformation', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ReturnofConfidentialInformation'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-10 | Document(page_content='6. No Obligations . Each party retains the right to determine whether to disclose any Confidential Information to the other party.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoObligations/docset:NoObligations-section/docset:NoObligations[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoObligations'}),
Document(page_content='7. No Warranty. ALL CONFIDENTIAL INFORMATION IS PROVIDED BY THE DISCLOSING PARTY “AS IS ”.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoWarranty/docset:NoWarranty-section/docset:NoWarranty[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoWarranty'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-11 | Document(page_content='8. Term. This Agreement will remain in effect for a period of seven ( 7 ) years from the date of last disclosure of Confidential Information by either party, at which time it will terminate.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:ThisAgreement/docset:Term-section/docset:Term', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Term'}),
Document(page_content='9. Equitable Relief . Each party acknowledges that the unauthorized use or disclosure of the disclosing party’s Confidential Information may cause the disclosing party to incur irreparable harm and significant damages, the degree of which may be difficult to ascertain. Accordingly, each party agrees that the disclosing party will have the right to seek immediate equitable relief to enjoin any unauthorized use or disclosure of its Confidential Information , in addition to any other rights and remedies that it may have at law or otherwise.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:EquitableRelief/docset:EquitableRelief-section/docset:EquitableRelief[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'EquitableRelief'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-12 | Document(page_content='10. Non-compete. To the maximum extent permitted by applicable law, during the Term of this Agreement and for a period of one ( 1 ) year thereafter, Caleb Divine may not market software products or do business that directly or indirectly competes with Docugami software products .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheMaximumExtent/docset:Non-compete-section/docset:Non-compete', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Non-compete'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-13 | Document(page_content='11. Miscellaneous. This Agreement will be governed and construed in accordance with the laws of the State of Washington , excluding its body of law controlling conflict of laws. This Agreement is the complete and exclusive understanding and agreement between the parties regarding the subject matter of this Agreement and supersedes all prior agreements, understandings and communications, oral or written, between the parties regarding the subject matter of this Agreement . If any provision of this Agreement is held invalid or unenforceable by a court of competent jurisdiction, that provision of this Agreement will be enforced to the maximum extent permissible and the other provisions of this Agreement will remain in full force and effect. Neither party may assign this Agreement , in whole or in part, by operation of law or otherwise, without the other party’s prior written consent, and any attempted assignment without such consent will be void. This Agreement may be executed in counterparts, each of which will be deemed an original, but all of which together will constitute one and the same instrument.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Accordance/docset:Miscellaneous-section/docset:Miscellaneous', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Miscellaneous'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-14 | Document(page_content='[SIGNATURE PAGE FOLLOWS] IN WITNESS WHEREOF, the parties hereto have executed this Mutual Non-Disclosure Agreement by their duly authorized officers or representatives as of the date first set forth above.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:TheParties', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheParties'}),
Document(page_content='DOCUGAMI INC . : \n\n Caleb Divine : \n\n Signature: Signature: Name: \n\n Jean Paoli Name: Title: \n\n CEO Title:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:DocugamiInc/docset:DocugamiInc/xhtml:table', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': '', 'tag': 'table'})]
The metadata for each Document (really, a chunk of an actual PDF, DOC or DOCX) contains some useful additional information:
id and name: ID and Name of the file (PDF, DOC or DOCX) the chunk is sourced from within Docugami.
xpath: XPath inside the XML representation of the document, for the chunk. Useful for source citations directly to the actual chunk inside the document XML.
structure: Structural attributes of the chunk, e.g. h1, h2, div, table, td, etc. Useful to filter out certain kinds of chunks if needed by the caller. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-15 | tag: Semantic tag for the chunk, using various generative and extractive techniques. More details here: https://github.com/docugami/DFM-benchmarks
Basic Use: Docugami Loader for Document QA#
You can use the Docugami Loader like a standard loader for Document QA over multiple docs, albeit with much better chunks that follow the natural contours of the document. There are many great tutorials on how to do this, e.g. this one. We can just use the same code, but use the DocugamiLoader for better chunking, instead of loading text or PDF files directly with basic splitting techniques.
!poetry run pip -q install openai tiktoken chromadb
from langchain.schema import Document
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
# For this example, we already have a processed docset for a set of lease documents
loader = DocugamiLoader(docset_id="wh2kned25uqm")
documents = loader.load()
The documents returned by the loader are already split, so we don’t need to use a text splitter. Optionally, we can use the metadata on each document, for example the structure or tag attributes, to do any post-processing we want.
We will just use the output of the DocugamiLoader as-is to set up a retrieval QA chain the usual way.
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents(documents=documents, embedding=embedding)
retriever = vectordb.as_retriever()
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True
)
Using embedded DuckDB without persistence: data will be transient | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-16 | )
Using embedded DuckDB without persistence: data will be transient
# Try out the retriever with an example query
qa_chain("What can tenants do with signage on their properties?")
{'query': 'What can tenants do with signage on their properties?',
'result': ' Tenants may place signs (digital or otherwise) or other form of identification on the premises after receiving written permission from the landlord which shall not be unreasonably withheld. The tenant is responsible for any damage caused to the premises and must conform to any applicable laws, ordinances, etc. governing the same. The tenant must also remove and clean any window or glass identification promptly upon vacating the premises.', | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-17 | 'source_documents': [Document(page_content='ARTICLE VI SIGNAGE 6.01 Signage . Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises.', metadata={'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ARTICLEVISIGNAGE-section/docset:_601Signage-section/docset:_601Signage', 'id': 'v1bvgaozfkak', 'name': 'TruTone Lane 2.docx', 'structure': 'div', 'tag': '_601Signage', 'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Tenant': 'Truetone Lane LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-18 | Document(page_content='Signage. Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. \n\n ARTICLE VII UTILITIES 7.01', metadata={'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:ThisOFFICELEASEAGREEMENTThis/docset:ArticleIBasic/docset:ArticleIiiUseAndCareOf/docset:ARTICLEIIIUSEANDCAREOFPREMISES-section/docset:ARTICLEIIIUSEANDCAREOFPREMISES/docset:NoOtherPurposes/docset:TenantsResponsibility/dg:chunk', 'id': 'g2fvhekmltza', 'name': 'TruTone Lane 6.pdf', 'structure': 'lim', 'tag': 'chunk', 'Landlord': 'GLORY ROAD LLC', 'Tenant': 'Truetone Lane LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-19 | Document(page_content='Landlord , its agents, servants, employees, licensees, invitees, and contractors during the last year of the term of this Lease at any and all times during regular business hours, after 24 hour notice to tenant, to pass and repass on and through the Premises, or such portion thereof as may be necessary, in order that they or any of them may gain access to the Premises for the purpose of showing the Premises to potential new tenants or real estate brokers. In addition, Landlord shall be entitled to place a "FOR RENT " or "FOR LEASE" sign (not exceeding 8.5 ” x 11 ”) in the front window of the Premises during the last six months of the term of this Lease .', metadata={'xpath': '/docset:Rider/docset:RIDERTOLEASE-section/docset:RIDERTOLEASE/docset:FixedRent/docset:TermYearPeriod/docset:Lease/docset:_42FLandlordSAccess-section/docset:_42FLandlordSAccess/docset:LandlordsRights/docset:Landlord', 'id': 'omvs4mysdk6b', 'name': 'TruTone Lane 1.docx', 'structure': 'p', 'tag': 'Landlord', 'Landlord': 'BIRCH STREET , LLC', 'Tenant': 'Trutone Lane LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-20 | Document(page_content="24. SIGNS . No signage shall be placed by Tenant on any portion of the Project . However, Tenant shall be permitted to place a sign bearing its name in a location approved by Landlord near the entrance to the Premises (at Tenant's cost ) and will be furnished a single listing of its name in the Building's directory (at Landlord 's cost ), all in accordance with the criteria adopted from time to time by Landlord for the Project . Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the then Building Standard charge .", metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:TheTerms/docset:Indemnification/docset:INDEMNIFICATION-section/docset:INDEMNIFICATION/docset:Waiver/docset:Waiver/docset:Signs/docset:SIGNS-section/docset:SIGNS', 'id': 'qkn9cyqsiuch', 'name': 'Shorebucks LLC_AZ.pdf', 'structure': 'div', 'tag': 'SIGNS', 'Landlord': 'Menlo Group', 'Tenant': 'Shorebucks LLC'})]}
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA# | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-21 | Using Docugami to Add Metadata to Chunks for High Accuracy Document QA#
One issue with large documents is that the correct answer to your question may depend on chunks that are far apart in the document. Typical chunking techniques, even with overlap, will struggle with providing the LLM sufficent context to answer such questions. With upcoming very large context LLMs, it may be possible to stuff a lot of tokens, perhaps even entire documents, inside the context but this will still hit limits at some point with very long documents, or a lot of documents.
For example, if we ask a more complex question that requires the LLM to draw on chunks from different parts of the document, even OpenAI’s powerful LLM is unable to answer correctly.
chain_response = qa_chain("What is rentable area for the property owned by DHA Group?")
chain_response["result"] # the correct answer should be 13,500
' 9,753 square feet'
At first glance the answer may seem reasonable, but if you review the source chunks carefully for this answer, you will see that the chunking of the document did not end up putting the Landlord name and the rentable area in the same context, since they are far apart in the document. The retriever therefore ends up finding unrelated chunks from other documents not even related to the Menlo Group landlord. That landlord happens to be mentioned on the first page of the file Shorebucks LLC_NJ.pdf file, and while one of the source chunks used by the chain is indeed from that doc that contains the correct answer (13,500), other source chunks from different docs are included, and the answer is therefore incorrect.
chain_response["source_documents"] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-22 | chain_response["source_documents"]
[Document(page_content='1.1 Landlord . DHA Group , a Delaware limited liability company authorized to transact business in New Jersey .', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-23 | Document(page_content='WITNESSES: LANDLORD: DHA Group , a Delaware limited liability company', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-24 | Document(page_content="1.16 Landlord 's Notice Address . DHA Group , Suite 1010 , 111 Bauer Dr , Oakland , New Jersey , 07436 , with a copy to the Building Management Office at the Project , Attention: On - Site Property Manager .", metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-25 | Document(page_content='1.6 Rentable Area of the Premises. 9,753 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:PerryBlair/docset:PerryBlair/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises', 'id': 'dsyfhh4vpeyf', 'name': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'Landlord': 'Perry & Blair LLC', 'Tenant': 'Shorebucks LLC'})]
Docugami can help here. Chunks are annotated with additional metadata created using different techniques if a user has been using Docugami. More technical approaches will be added later.
Specifically, let’s look at the additional metadata that is returned on the documents returned by docugami, in the form of some simple key/value pairs on all the text chunks:
loader = DocugamiLoader(docset_id="wh2kned25uqm")
documents = loader.load()
documents[0].metadata
{'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:ThisOfficeLeaseAgreement', | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-26 | 'id': 'v1bvgaozfkak',
'name': 'TruTone Lane 2.docx',
'structure': 'p',
'tag': 'ThisOfficeLeaseAgreement',
'Landlord': 'BUBBA CENTER PARTNERSHIP',
'Tenant': 'Truetone Lane LLC'}
We can use a self-querying retriever to improve our query accuracy, using this additional metadata:
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
EXCLUDE_KEYS = ["id", "xpath", "structure"]
metadata_field_info = [
AttributeInfo(
name=key,
description=f"The {key} for this chunk",
type="string",
)
for key in documents[0].metadata
if key.lower() not in EXCLUDE_KEYS
]
document_content_description = "Contents of this chunk"
llm = OpenAI(temperature=0)
vectordb = Chroma.from_documents(documents=documents, embedding=embedding)
retriever = SelfQueryRetriever.from_llm(
llm, vectordb, document_content_description, metadata_field_info, verbose=True
)
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True
)
Using embedded DuckDB without persistence: data will be transient
Let’s run the same question again. It returns the correct result since all the chunks have metadata key/value pairs on them carrying key information about the document even if this information is physically very far away from the source chunk used to generate the answer.
qa_chain("What is rentable area for the property owned by DHA Group?") | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-27 | qa_chain("What is rentable area for the property owned by DHA Group?")
query='rentable area' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Landlord', value='DHA Group')
{'query': 'What is rentable area for the property owned by DHA Group?',
'result': ' 13,500 square feet.',
'source_documents': [Document(page_content='1.1 Landlord . DHA Group , a Delaware limited liability company authorized to transact business in New Jersey .', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-28 | Document(page_content='WITNESSES: LANDLORD: DHA Group , a Delaware limited liability company', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-29 | Document(page_content="1.16 Landlord 's Notice Address . DHA Group , Suite 1010 , 111 Bauer Dr , Oakland , New Jersey , 07436 , with a copy to the Building Management Office at the Project , Attention: On - Site Property Manager .", metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-30 | Document(page_content='1.6 Rentable Area of the Premises. 13,500 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'})]}
This time the answer is correct, since the self-querying retriever created a filter on the landlord attribute of the metadata, correctly filtering to document that specifically is about the DHA Group landlord. The resulting source chunks are all relevant to this landlord, and this improves answer accuracy even though the landlord is not directly mentioned in the specific chunk that contains the correct answer.
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DuckDB
Contents
Prerequisites
Quick start
Advantages vs Other Chunking Techniques
Load Documents
Basic Use: Docugami Loader for Document QA
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
bf1f879a2ca6-31 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/docugami.html |
947c9f6a928f-0 | .ipynb
.pdf
IMSDb
IMSDb#
IMSDb is the Internet Movie Script Database.
This covers how to load IMSDb webpages into a document format that we can use downstream.
from langchain.document_loaders import IMSDbLoader
loader = IMSDbLoader("https://imsdb.com/scripts/BlacKkKlansman.html")
data = loader.load()
data[0].page_content[:500]
'\n\r\n\r\n\r\n\r\n BLACKKKLANSMAN\r\n \r\n \r\n \r\n \r\n Written by\r\n\r\n Charlie Wachtel & David Rabinowitz\r\n\r\n and\r\n\r\n Kevin Willmott & Spike Lee\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n FADE IN:\r\n \r\n SCENE FROM "GONE WITH'
data[0].metadata
{'source': 'https://imsdb.com/scripts/BlacKkKlansman.html'}
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MediaWikiDump
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
e2c04f45a35f-0 | .ipynb
.pdf
HuggingFace dataset
Contents
Example
HuggingFace dataset#
The Hugging Face Hub is home to over 5,000 datasets in more than 100 languages that can be used for a broad range of tasks across NLP, Computer Vision, and Audio. They used for a diverse range of tasks such as translation,
automatic speech recognition, and image classification.
This notebook shows how to load Hugging Face Hub datasets to LangChain.
from langchain.document_loaders import HuggingFaceDatasetLoader
dataset_name="imdb"
page_content_column="text"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column)
data = loader.load()
data[:15] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-1 | data = loader.load()
data[:15]
[Document(page_content='I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered "controversial" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\'t have much of a plot.', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-2 | Document(page_content='"I Am Curious: Yellow" is a risible and pretentious steaming pile. It doesn\'t matter what one\'s political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn\'t true. I\'ve seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don\'t exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we\'re treated to the site of Vincent Gallo\'s throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) "double-standard" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won\'t see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women\'s bodies.', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-3 | Document(page_content="If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />", metadata={'label': 0}),
Document(page_content="This film was probably inspired by Godard's Masculin, féminin and I urge you to see that film instead.<br /><br />The film has two strong elements and those are, (1) the realistic acting (2) the impressive, undeservedly good, photo. Apart from that, what strikes me most is the endless stream of silliness. Lena Nyman has to be most annoying actress in the world. She acts so stupid and with all the nudity in this film,...it's unattractive. Comparing to Godard's film, intellectuality has been replaced with stupidity. Without going too far on this subject, I would say that follows from the difference in ideals between the French and the Swedish society.<br /><br />A movie of its time, and place. 2/10.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-4 | Document(page_content='Oh, brother...after hearing about this ridiculous film for umpteen years all I can think of is that old Peggy Lee song..<br /><br />"Is that all there is??" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into "Goodbye Columbus"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!<br /><br />The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.<br /><br />Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!<br /><br />Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren\'t for the censorship scandal, it would have been ignored, then forgotten.<br /><br />Instead, the "I Am Blank, Blank" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that "naughty sex film" that "revolutionized | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-5 | new generation of suckers who want to see that "naughty sex film" that "revolutionized the film industry"...<br /><br />Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the "dirty" parts, just to get it over with.<br /><br />', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-6 | Document(page_content="I would put this at the top of my list of films in the category of unwatchable trash! There are films that are bad, but the worst kind are the ones that are unwatchable but you are suppose to like them because they are supposed to be good for you! The sex sequences, so shocking in its day, couldn't even arouse a rabbit. The so called controversial politics is strictly high school sophomore amateur night Marxism. The film is self-consciously arty in the worst sense of the term. The photography is in a harsh grainy black and white. Some scenes are out of focus or taken from the wrong angle. Even the sound is bad! And some people call this art?<br /><br />", metadata={'label': 0}),
Document(page_content="Whoever wrote the screenplay for this movie obviously never consulted any books about Lucille Ball, especially her autobiography. I've never seen so many mistakes in a biopic, ranging from her early years in Celoron and Jamestown to her later years with Desi. I could write a whole list of factual errors, but it would go on for pages. In all, I believe that Lucille Ball is one of those inimitable people who simply cannot be portrayed by anyone other than themselves. If I were Lucie Arnaz and Desi, Jr., I would be irate at how many mistakes were made in this film. The filmmakers tried hard, but the movie seems awfully sloppy to me.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-7 | Document(page_content='When I first saw a glimpse of this movie, I quickly noticed the actress who was playing the role of Lucille Ball. Rachel York\'s portrayal of Lucy is absolutely awful. Lucille Ball was an astounding comedian with incredible talent. To think about a legend like Lucille Ball being portrayed the way she was in the movie is horrendous. I cannot believe out of all the actresses in the world who could play a much better Lucy, the producers decided to get Rachel York. She might be a good actress in other roles but to play the role of Lucille Ball is tough. It is pretty hard to find someone who could resemble Lucille Ball, but they could at least find someone a bit similar in looks and talent. If you noticed York\'s portrayal of Lucy in episodes of I Love Lucy like the chocolate factory or vitavetavegamin, nothing is similar in any way-her expression, voice, or movement.<br /><br />To top it all off, Danny Pino playing Desi Arnaz is horrible. Pino does not qualify to play as Ricky. He\'s small and skinny, his accent is unreal, and once again, his acting is unbelievable. Although Fred and Ethel were not similar either, they were not as bad as the characters of Lucy and Ricky.<br /><br />Overall, extremely horrible casting and the story is badly told. If people want to understand the real life situation of Lucille Ball, I suggest watching A&E Biography of Lucy and Desi, read the book from Lucille Ball herself, or PBS\' American Masters: Finding Lucy. If you want to see a docudrama, "Before the Laughter" would be a better choice. The casting of Lucille Ball and Desi Arnaz in "Before the Laughter" is much better compared to this. At least, a similar aspect is shown rather than nothing.', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-8 | Document(page_content='Who are these "They"- the actors? the filmmakers? Certainly couldn\'t be the audience- this is among the most air-puffed productions in existence. It\'s the kind of movie that looks like it was a lot of fun to shoot\x97 TOO much fun, nobody is getting any actual work done, and that almost always makes for a movie that\'s no fun to watch.<br /><br />Ritter dons glasses so as to hammer home his character\'s status as a sort of doppleganger of the bespectacled Bogdanovich; the scenes with the breezy Ms. Stratten are sweet, but have an embarrassing, look-guys-I\'m-dating-the-prom-queen feel to them. Ben Gazzara sports his usual cat\'s-got-canary grin in a futile attempt to elevate the meager plot, which requires him to pursue Audrey Hepburn with all the interest of a narcoleptic at an insomnia clinic. In the meantime, the budding couple\'s respective children (nepotism alert: Bogdanovich\'s daughters) spew cute and pick up some fairly disturbing pointers on \'love\' while observing their parents. (Ms. Hepburn, drawing on her dignity, manages to rise above the proceedings- but she has the monumental challenge of playing herself, ostensibly.) Everybody looks great, but so what? It\'s a movie and we can expect that much, if that\'s what you\'re looking for you\'d be better off picking up a copy of Vogue.<br /><br />Oh- and it has to be mentioned that Colleen Camp thoroughly annoys, even apart from her singing, which, while competent, is wholly unconvincing... the country and western numbers are woefully mismatched with the standards on the soundtrack. Surely this is NOT what Gershwin (who wrote the song from which the movie\'s title is derived) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-9 | NOT what Gershwin (who wrote the song from which the movie\'s title is derived) had in mind; his stage musicals of the 20\'s may have been slight, but at least they were long on charm. "They All Laughed" tries to coast on its good intentions, but nobody- least of all Peter Bogdanovich - has the good sense to put on the brakes.<br /><br />Due in no small part to the tragic death of Dorothy Stratten, this movie has a special place in the heart of Mr. Bogdanovich- he even bought it back from its producers, then distributed it on his own and went bankrupt when it didn\'t prove popular. His rise and fall is among the more sympathetic and tragic of Hollywood stories, so there\'s no joy in criticizing the film... there _is_ real emotional investment in Ms. Stratten\'s scenes. But "Laughed" is a faint echo of "The Last Picture Show", "Paper Moon" or "What\'s Up, Doc"- following "Daisy Miller" and "At Long Last Love", it was a thundering confirmation of the phase from which P.B. has never emerged.<br /><br />All in all, though, the movie is harmless, only a waste of rental. I want to watch people having a good time, I\'ll go to the park on a sunny day. For filmic expressions of joy and love, I\'ll stick to Ernest Lubitsch and Jaques Demy...', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-10 | Document(page_content="This is said to be a personal film for Peter Bogdonavitch. He based it on his life but changed things around to fit the characters, who are detectives. These detectives date beautiful models and have no problem getting them. Sounds more like a millionaire playboy filmmaker than a detective, doesn't it? This entire movie was written by Peter, and it shows how out of touch with real people he was. You're supposed to write what you know, and he did that, indeed. And leaves the audience bored and confused, and jealous, for that matter. This is a curio for people who want to see Dorothy Stratten, who was murdered right after filming. But Patti Hanson, who would, in real life, marry Keith Richards, was also a model, like Stratten, but is a lot better and has a more ample part. In fact, Stratten's part seemed forced; added. She doesn't have a lot to do with the story, which is pretty convoluted to begin with. All in all, every character in this film is somebody that very few people can relate with, unless you're millionaire from Manhattan with beautiful supermodels at your beckon call. For the rest of us, it's an irritating snore fest. That's what happens when you're out of touch. You entertain your few friends with inside jokes, and bore all the rest.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-11 | Document(page_content='It was great to see some of my favorite stars of 30 years ago including John Ritter, Ben Gazarra and Audrey Hepburn. They looked quite wonderful. But that was it. They were not given any characters or good lines to work with. I neither understood or cared what the characters were doing.<br /><br />Some of the smaller female roles were fine, Patty Henson and Colleen Camp were quite competent and confident in their small sidekick parts. They showed some talent and it is sad they didn\'t go on to star in more and better films. Sadly, I didn\'t think Dorothy Stratten got a chance to act in this her only important film role.<br /><br />The film appears to have some fans, and I was very open-minded when I started watching it. I am a big Peter Bogdanovich fan and I enjoyed his last movie, "Cat\'s Meow" and all his early ones from "Targets" to "Nickleodeon". So, it really surprised me that I was barely able to keep awake watching this one.<br /><br />It is ironic that this movie is about a detective agency where the detectives and clients get romantically involved with each other. Five years later, Bogdanovich\'s ex-girlfriend, Cybil Shepherd had a hit television series called "Moonlighting" stealing the story idea from Bogdanovich. Of course, there was a great difference in that the series relied on tons of witty dialogue, while this tries to make do with slapstick and a few screwball lines.<br /><br />Bottom line: It ain\'t no "Paper Moon" and only a very pale version of "What\'s Up, Doc".', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-12 | Document(page_content="I can't believe that those praising this movie herein aren't thinking of some other film. I was prepared for the possibility that this would be awful, but the script (or lack thereof) makes for a film that's also pointless. On the plus side, the general level of craft on the part of the actors and technical crew is quite competent, but when you've got a sow's ear to work with you can't make a silk purse. Ben G fans should stick with just about any other movie he's been in. Dorothy S fans should stick to Galaxina. Peter B fans should stick to Last Picture Show and Target. Fans of cheap laughs at the expense of those who seem to be asking for it should stick to Peter B's amazingly awful book, Killing of the Unicorn.", metadata={'label': 0}),
Document(page_content='Never cast models and Playboy bunnies in your films! Bob Fosse\'s "Star 80" about Dorothy Stratten, of whom Bogdanovich was obsessed enough to have married her SISTER after her murder at the hands of her low-life husband, is a zillion times more interesting than Dorothy herself on the silver screen. Patty Hansen is no actress either..I expected to see some sort of lost masterpiece a la Orson Welles but instead got Audrey Hepburn cavorting in jeans and a god-awful "poodlesque" hair-do....Very disappointing...."Paper Moon" and "The Last Picture Show" I could watch again and again. This clunker I could barely sit through once. This movie was reputedly not released because of the brouhaha surrounding Ms. Stratten\'s tawdry death; I think the real reason was because it was so bad!', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-13 | Document(page_content="Its not the cast. A finer group of actors, you could not find. Its not the setting. The director is in love with New York City, and by the end of the film, so are we all! Woody Allen could not improve upon what Bogdonovich has done here. If you are going to fall in love, or find love, Manhattan is the place to go. No, the problem with the movie is the script. There is none. The actors fall in love at first sight, words are unnecessary. In the director's own experience in Hollywood that is what happens when they go to work on the set. It is reality to him, and his peers, but it is a fantasy to most of us in the real world. So, in the end, the movie is hollow, and shallow, and message-less.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-14 | Document(page_content='Today I found "They All Laughed" on VHS on sale in a rental. It was a really old and very used VHS, I had no information about this movie, but I liked the references listed on its cover: the names of Peter Bogdanovich, Audrey Hepburn, John Ritter and specially Dorothy Stratten attracted me, the price was very low and I decided to risk and buy it. I searched IMDb, and the User Rating of 6.0 was an excellent reference. I looked in "Mick Martin & Marsha Porter Video & DVD Guide 2003" and \x96 wow \x96 four stars! So, I decided that I could not waste more time and immediately see it. Indeed, I have just finished watching "They All Laughed" and I found it a very boring overrated movie. The characters are badly developed, and I spent lots of minutes to understand their roles in the story. The plot is supposed to be funny (private eyes who fall in love for the women they are chasing), but I have not laughed along the whole story. The coincidences, in a huge city like New York, are ridiculous. Ben Gazarra as an attractive and very seductive man, with the women falling for him as if her were a Brad Pitt, Antonio Banderas or George Clooney, is quite ridiculous. In the end, the greater attractions certainly are the presence of the Playboy centerfold and playmate of the year Dorothy Stratten, murdered by her husband pretty after the release of this movie, and whose life was showed in "Star 80" and "Death of a Centerfold: The Dorothy Stratten Story"; the amazing beauty of the sexy Patti Hansen, the future Mrs. Keith Richards; the always wonderful, even being fifty-two years old, Audrey Hepburn; and the song "Amigo", from Roberto Carlos. Although I do not like him, Roberto Carlos has been | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-15 | song "Amigo", from Roberto Carlos. Although I do not like him, Roberto Carlos has been the most popular Brazilian singer since the end of the 60\'s and is called by his fans as "The King". I will keep this movie in my collection only because of these attractions (manly Dorothy Stratten). My vote is four.<br /><br />Title (Brazil): "Muito Riso e Muita Alegria" ("Many Laughs and Lots of Happiness")', metadata={'label': 0})] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
e2c04f45a35f-16 | Example#
In this example, we use data from a dataset to answer a question
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
dataset_name="tweet_eval"
page_content_column="text"
name="stance_climate"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column,name)
index = VectorstoreIndexCreator().from_loaders([loader])
Found cached dataset tweet_eval
Using embedded DuckDB without persistence: data will be transient
query = "What are the most used hashtag?"
result = index.query(query)
result
' The most used hashtags in this context are #UKClimate2015, #Sustainability, #TakeDownTheFlag, #LoveWins, #CSOTA, #ClimateSummitoftheAmericas, #SM, and #SocialMedia.'
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Hacker News
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iFixit
Contents
Example
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
ef124be2b7ab-0 | .ipynb
.pdf
DuckDB
Contents
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
DuckDB#
DuckDB is an in-process SQL OLAP database management system.
Load a DuckDB query with one document per row.
#!pip install duckdb
from langchain.document_loaders import DuckDBLoader
%%file example.csv
Team,Payroll
Nationals,81.34
Reds,82.20
Writing example.csv
loader = DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')")
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\nPayroll: 81.34', metadata={}), Document(page_content='Team: Reds\nPayroll: 82.2', metadata={})]
Specifying Which Columns are Content vs Metadata#
loader = DuckDBLoader(
"SELECT * FROM read_csv_auto('example.csv')",
page_content_columns=["Team"],
metadata_columns=["Payroll"]
)
data = loader.load()
print(data)
[Document(page_content='Team: Nationals', metadata={'Payroll': 81.34}), Document(page_content='Team: Reds', metadata={'Payroll': 82.2})]
Adding Source to Metadata#
loader = DuckDBLoader(
"SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv')",
metadata_columns=["source"]
)
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\nPayroll: 81.34\nsource: Nationals', metadata={'source': 'Nationals'}), Document(page_content='Team: Reds\nPayroll: 82.2\nsource: Reds', metadata={'source': 'Reds'})]
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Docugami
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Figma
Contents | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/duckdb.html |
ef124be2b7ab-1 | previous
Docugami
next
Figma
Contents
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/duckdb.html |
30cfe2e291f4-0 | .ipynb
.pdf
AWS S3 File
AWS S3 File#
Amazon Simple Storage Service (Amazon S3) is an object storage service.
AWS S3 Buckets
This covers how to load document objects from an AWS S3 File object.
from langchain.document_loaders import S3FileLoader
#!pip install boto3
loader = S3FileLoader("testing-hwc", "fake.docx")
loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpxvave6wl/fake.docx'}, lookup_index=0)]
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AWS S3 Directory
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Azure Blob Storage Container
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/aws_s3_file.html |
c3d8813cacf3-0 | .ipynb
.pdf
Git
Contents
Load existing repository from disk
Clone repository from url
Filtering files to load
Git#
Git is a distributed version control system that tracks changes in any set of computer files, usually used for coordinating work among programmers collaboratively developing source code during software development.
This notebook shows how to load text files from Git repository.
Load existing repository from disk#
!pip install GitPython
from git import Repo
repo = Repo.clone_from(
"https://github.com/hwchase17/langchain", to_path="./example_data/test_repo1"
)
branch = repo.head.reference
from langchain.document_loaders import GitLoader
loader = GitLoader(repo_path="./example_data/test_repo1/", branch=branch)
data = loader.load()
len(data)
print(data[0])
page_content='.venv\n.github\n.git\n.mypy_cache\n.pytest_cache\nDockerfile' metadata={'file_path': '.dockerignore', 'file_name': '.dockerignore', 'file_type': ''}
Clone repository from url#
from langchain.document_loaders import GitLoader
loader = GitLoader(
clone_url="https://github.com/hwchase17/langchain",
repo_path="./example_data/test_repo2/",
branch="master",
)
data = loader.load()
len(data)
1074
Filtering files to load#
from langchain.document_loaders import GitLoader
# eg. loading only python files
loader = GitLoader(repo_path="./example_data/test_repo1/", file_filter=lambda file_path: file_path.endswith(".py"))
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GitBook
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Google BigQuery
Contents
Load existing repository from disk
Clone repository from url
Filtering files to load
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/git.html |
c3d8813cacf3-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/git.html |
c7731bd8d2a8-0 | .ipynb
.pdf
PDF
Contents
Using PyPDF
Using MathPix
Using Unstructured
Retain Elements
Fetching remote PDFs using Unstructured
Using PyPDFium2
Using PDFMiner
Using PDFMiner to generate HTML text
Using PyMuPDF
PyPDF Directory
Using pdfplumber
PDF#
Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.
This covers how to load PDF documents into the Document format that we use downstream.
Using PyPDF#
Load PDF using pypdf into array of documents, where each document contains the page content and metadata with page number.
!pip install pypdf
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("example_data/layout-parser-paper.pdf")
pages = loader.load_and_split()
pages[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-1 | Document(page_content='LayoutParser : A Uni\x0ced Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1( \x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1Allen Institute for AI\[email protected]\n2Brown University\nruochen [email protected]\n3Harvard University\nfmelissadell,jacob carlson [email protected]\n4University of Washington\[email protected]\n5University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model con\x0cgurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\ne\x0borts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser , an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-2 | also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io .\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\n·Character Recognition ·Open Source library ·Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classi\x0ccation [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0}) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-3 | An advantage of this approach is that documents can be retrieved with page numbers.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key: ········
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())
docs = faiss_index.similarity_search("How will the community be engaged?", k=2)
for doc in docs:
print(str(doc.metadata["page"]) + ":", doc.page_content[:300])
9: 10 Z. Shen et al.
Fig. 4: Illustration of (a) the original historical Japanese document with layout
detection results and (b) a recreated version of the document image that achieves
much better character recognition recall. The reorganization algorithm rearranges
the tokens based on the their detect
3: 4 Z. Shen et al.
Efficient Data AnnotationC u s t o m i z e d M o d e l T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images
T h e C o r e L a y o u t P a r s e r L i b r a r yOCR ModuleSt or age & VisualizationLa y ou
Using MathPix#
Inspired by Daniel Gross’s https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21
from langchain.document_loaders import MathpixPDFLoader
loader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")
data = loader.load() | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-4 | data = loader.load()
Using Unstructured#
from langchain.document_loaders import UnstructuredPDFLoader
loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")
data = loader.load()
Retain Elements#
Under the hood, Unstructured creates different “elements” for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".
loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")
data = loader.load()
data[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-5 | Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\[email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n5 University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-6 | for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-7 | Fetching remote PDFs using Unstructured#
This covers how to load online pdfs into a document format that we can use downstream. This can be used for various online pdf sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/
Note: all other pdf loaders can also be used to fetch remote PDFs, but OnlinePDFLoader is a legacy function, and works specifically with UnstructuredPDFLoader.
from langchain.document_loaders import OnlinePDFLoader
loader = OnlinePDFLoader("https://arxiv.org/pdf/2302.03803.pdf")
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-8 | [Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS\n\nWilliam D. Montoya\n\nInstituto de Matem´atica, Estat´ıstica e Computa¸c˜ao Cient´ıfica,\n\nIn [3] we proved that, under suitable conditions, on a very general codimension s quasi- smooth intersection subvariety X in a projective toric orbifold P d Σ with d + s = 2 ( k + 1 ) the Hodge conjecture holds, that is, every ( p, p ) -cohomology class, under the Poincar´e duality is a rational linear combination of fundamental classes of algebraic subvarieties of X . The proof of the above-mentioned result relies, for p ≠ d + 1 − s , on a Lefschetz\n\nKeywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: [email protected]\n\ntheorem ([7]) and the Hard Lefschetz theorem for projective orbifolds ([11]). When p = d + 1 − s the proof relies on the Cayley trick, a trick which associates to X a quasi-smooth hypersurface Y in a projective vector bundle, and the Cayley Proposition (4.3) which gives an isomorphism of some primitive cohomologies (4.2) of X and Y . The Cayley trick, following the philosophy of Mavlyutov in [7], reduces results known for quasi-smooth hypersurfaces to quasi-smooth intersection subvarieties. The idea in this paper goes the other way around, we translate some results for quasi-smooth intersection subvarieties to\n\nAcknowledgement. I thank | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-9 | we translate some results for quasi-smooth intersection subvarieties to\n\nAcknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.\n\nLet M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N ⊗ Z R .\n\nif there exist k linearly independent primitive elements e\n\n, . . . , e k ∈ N such that σ = { µ\n\ne\n\n+ ⋯ + µ k e k } . • The generators e i are integral if for every i and any nonnegative rational number µ the product µe i is in N only if µ is an integer. • Given two rational simplicial cones σ , σ ′ one says that σ ′ is a face of σ ( σ ′ < σ ) if the set of integral generators of σ ′ is a subset of the set of integral generators of σ . • A finite set Σ = { σ\n\n, . . . , σ t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:\n\nall faces of cones in Σ are in Σ ;\n\nif σ, σ ′ ∈ Σ then σ ∩ σ ′ < σ and σ ∩ σ ′ < σ ′ ;\n\nN R = σ\n\n∪ ⋅ ⋅ ⋅ ∪ σ t .\n\nA rational simplicial complete d -dimensional fan Σ defines a d -dimensional toric variety P d Σ having only orbifold singularities which we assume to be projective. Moreover, T ∶ = N ⊗ Z C | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-10 | which we assume to be projective. Moreover, T ∶ = N ⊗ Z C ∗ ≃ ( C ∗ ) d is the torus action on P d Σ . We denote by Σ ( i ) the i -dimensional cones\n\nFor a cone σ ∈ Σ, ˆ σ is the set of 1-dimensional cone in Σ that are not contained in σ\n\nand x ˆ σ ∶ = ∏ ρ ∈ ˆ σ x ρ is the associated monomial in S .\n\nDefinition 2.2. The irrelevant ideal of P d Σ is the monomial ideal B Σ ∶ =< x ˆ σ ∣ σ ∈ Σ > and the zero locus Z ( Σ ) ∶ = V ( B Σ ) in the affine space A d ∶ = Spec ( S ) is the irrelevant locus.\n\nProposition 2.3 (Theorem 5.1.11 [5]) . The toric variety P d Σ is a categorical quotient A d ∖ Z ( Σ ) by the group Hom ( Cl ( Σ ) , C ∗ ) and the group action is induced by the Cl ( Σ ) - grading of S .\n\nNow we give a brief introduction to complex orbifolds and we mention the needed theorems for the next section. Namely: de Rham theorem and Dolbeault theorem for complex orbifolds.\n\nDefinition 2.4. A complex orbifold of complex dimension d is a singular complex space whose singularities are locally isomorphic to quotient singularities C d / G , for finite sub- groups G ⊂ Gl ( d, C ) .\n\nDefinition 2.5. A differential form | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-11 | 2.5. A differential form on a complex orbifold Z is defined locally at z ∈ Z as a G -invariant differential form on C d where G ⊂ Gl ( d, C ) and Z is locally isomorphic to d\n\nRoughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.\n\nWe have a complex of differential forms ( A ● ( Z ) , d ) and a double complex ( A ● , ● ( Z ) , ∂, ¯ ∂ ) of bigraded differential forms which define the de Rham and the Dolbeault cohomology groups (for a fixed p ∈ N ) respectively:\n\n(1,1)-Lefschetz theorem for projective toric orbifolds\n\nDefinition 3.1. A subvariety X ⊂ P d Σ is quasi-smooth if V ( I X ) ⊂ A #Σ ( 1 ) is smooth outside\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).\n\nRemark 3.3 . Quasi-smooth subvarieties are suborbifolds of P d Σ in the sense of Satake in [8]. Intuitively speaking they are subvarieties whose only singularities come from the ambient\n\nProof. From the exponential short exact sequence\n\nwe have a long exact sequence in cohomology\n\nH 1 (O ∗ X ) → H 2 ( X, Z ) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-12 | 1 (O ∗ X ) → H 2 ( X, Z ) → H 2 (O X ) ≃ H 0 , 2 ( X )\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now,\n\nH 2 ( X, Z ) / / H 2 ( X, O X ) ≃ Dolbeault H 2 ( X, C ) deRham ≃ H 2 dR ( X, C ) / / H 0 , 2 ¯ ∂ ( X )\n\nof the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].\n\nRemark 3.5 . For k = 1 and P d Σ as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :\n\ngiven by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:\n\nH 1 , 1 ( X, Q ) ≃ H dim X − 1 , dim X − 1 ( X, Q )\n\nCorollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X\n\nProof. If the dim C X = 1 the result is clear by the | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-13 | on X\n\nProof. If the dim C X = 1 the result is clear by the Hard Lefschetz theorem for projective orbifolds. The dimension 2 and 3 cases are covered by Theorem 3.5 and the Hard Lefschetz.\n\nCayley trick and Cayley proposition\n\nThe Cayley trick is a way to associate to a quasi-smooth intersection subvariety a quasi- smooth hypersurface. Let L 1 , . . . , L s be line bundles on P d Σ and let π ∶ P ( E ) → P d Σ be the projective space bundle associated to the vector bundle E = L 1 ⊕ ⋯ ⊕ L s . It is known that P ( E ) is a ( d + s − 1 ) -dimensional simplicial toric variety whose fan depends on the degrees of the line bundles and the fan Σ. Furthermore, if the Cox ring, without considering the grading, of P d Σ is C [ x 1 , . . . , x m ] then the Cox ring of P ( E ) is\n\nMoreover for X a quasi-smooth intersection subvariety cut off by f 1 , . . . , f s with deg ( f i ) = [ L i ] we relate the hypersurface Y cut off by F = y 1 f 1 + ⋅ ⋅ ⋅ + y s f s which turns out to be quasi-smooth. For more details see Section 2 in [7].\n\nWe will denote P ( E ) as P d + s − 1 Σ ,X to keep track of its relation with X and P d Σ .\n\nThe following is a key remark.\n\nRemark 4.1 . There is a morphism ι ∶ X → Y | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-14 | 4.1 . There is a morphism ι ∶ X → Y ⊂ P d + s − 1 Σ ,X . Moreover every point z ∶ = ( x, y ) ∈ Y with y ≠ 0 has a preimage. Hence for any subvariety W = V ( I W ) ⊂ X ⊂ P d Σ there exists W ′ ⊂ Y ⊂ P d + s − 1 Σ ,X such that π ( W ′ ) = W , i.e., W ′ = { z = ( x, y ) ∣ x ∈ W } .\n\nFor X ⊂ P d Σ a quasi-smooth intersection variety the morphism in cohomology induced by the inclusion i ∗ ∶ H d − s ( P d Σ , C ) → H d − s ( X, C ) is injective by Proposition 1.4 in [7].\n\nDefinition 4.2. The primitive cohomology of H d − s prim ( X ) is the quotient H d − s ( X, C )/ i ∗ ( H d − s ( P d Σ , C )) and H d − s prim ( X, Q ) with rational coefficients.\n\nH d − s ( P d Σ , C ) and H d − s ( X, C ) have pure Hodge structures, and the morphism i ∗ is com- patible with them, so that H d − s prim ( X ) gets a pure Hodge structure.\n\nThe next Proposition is the Cayley proposition.\n\nProposition 4.3. [Proposition 2.3 in [3] ] Let X = X 1 ∩⋅ ⋅ ⋅∩ X s be a quasi-smooth intersec- tion | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
c7731bd8d2a8-15 | ⋅ ⋅∩ X s be a quasi-smooth intersec- tion subvariety in P d Σ cut off by homogeneous polynomials f 1 . . . f s . Then for p ≠ d + s − 1 2 , d + s − 3 2\n\nRemark 4.5 . The above isomorphisms are also true with rational coefficients since H ● ( X, C ) = H ● ( X, Q ) ⊗ Q C . See the beginning of Section 7.1 in [10] for more details.\n\nTheorem 5.1. Let Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to the quasi-smooth intersection surface X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f k ⊂ P k + 2 Σ . Then on Y the Hodge conjecture holds.\n\nthe Hodge conjecture holds.\n\nProof. If H k,k prim ( X, Q ) = 0 we are done. So let us assume H k,k prim ( X, Q ) ≠ 0. By the Cayley proposition H k,k prim ( Y, Q ) ≃ H 1 , 1 prim ( X, Q ) and by the ( 1 , 1 ) -Lefschetz theorem for projective\n\ntoric orbifolds there is a non-zero algebraic basis λ C 1 , . . . , λ C n with rational coefficients of H 1 , 1 prim ( X, Q ) , that is, there are n ∶ = h 1 , 1 prim ( X, Q ) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
Subsets and Splits