id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
36b055cb0e2b-3 | # with 2 messages overlapping
chunk_size = 8
overlap = 2 | https://python.langchain.com/docs/integrations/chat_loaders/facebook |
36b055cb0e2b-4 | training_examples = [
conversation_messages[i: i + chunk_size]
for conversation_messages in training_data
for i in range(
0, len(conversation_messages) - chunk_size + 1,
chunk_size - overlap)
]
len(training_examples)
4. Fine-tune the model
It's time to fine-tune the model. Make sure you have openai installed and ha... | https://python.langchain.com/docs/integrations/chat_loaders/facebook |
36b055cb0e2b-5 | # OpenAI audits each training file for compliance reasons.
# This make take a few minutes
status = openai.File.retrieve(training_file.id).status
start_time = time.time()
while status != "processed":
print(f"Status=[{status}]... {time.time() - start_time:.2f}s", end="\r", flush=True)
time.sleep(5)
status = openai.File.r... | https://python.langchain.com/docs/integrations/chat_loaders/facebook |
52fcb0dc9e32-0 | GMail
This loader goes over how to load data from GMail. There are many ways you could want to load data from GMail. This loader is currently fairly opionated in how to do so. The way it does it is it first looks for all messages that you have sent. It then looks for messages where you are responding to a previous emai... | https://python.langchain.com/docs/integrations/chat_loaders/gmail |
52fcb0dc9e32-1 | SCOPES = ['https://www.googleapis.com/auth/gmail.readonly']
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists('email_token.json'):
creds = Credentials.from_authorized_user_file('e... | https://python.langchain.com/docs/integrations/chat_loaders/gmail |
c3276c3bdad0-0 | Slack
This notebook shows how to use the Slack chat loader. This class helps map exported slack conversations to LangChain chat messages.
The process has three steps:
Export the desired conversation thread by following the instructions here.
Create the SlackChatLoader with the file path pointed to the json file or dire... | https://python.langchain.com/docs/integrations/chat_loaders/slack |
c3276c3bdad0-1 | raw_messages = loader.lazy_load()
# Merge consecutive messages from the same sender into a single message
merged_messages = merge_chat_runs(raw_messages)
# Convert messages from "U0500003428" to AI messages
messages: List[ChatSession] = list(map_ai_messages(merged_messages, sender="U0500003428"))
Next Steps
You can th... | https://python.langchain.com/docs/integrations/chat_loaders/slack |
7ba310a3aed6-0 | iMessage
This notebook shows how to use the iMessage chat loader. This class helps convert iMessage conversations to LangChain chat messages.
On MacOS, iMessage stores conversations in a sqlite database at ~/Library/Messages/chat.db (at least for macOS Ventura 13.4). The IMessageChatLoader loads from this database file... | https://python.langchain.com/docs/integrations/chat_loaders/imessage |
7ba310a3aed6-1 | # Download file to chat.db
download_drive_file(url)
2. Create the Chat Loader
Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well an configure whether to merge message runs.
from langchain.chat_loaders.imessage import IMessageChatLoader
... | https://python.langchain.com/docs/integrations/chat_loaders/imessage |
7ba310a3aed6-2 | raw_messages = loader.lazy_load()
# Merge consecutive messages from the same sender into a single message
merged_messages = merge_chat_runs(raw_messages)
# Convert messages from "Tortoise" to AI messages. Do you have a guess who these conversations are between?
chat_sessions: List[ChatSession] = list(map_ai_messages(me... | https://python.langchain.com/docs/integrations/chat_loaders/imessage |
7ba310a3aed6-3 | # OpenAI audits each training file for compliance reasons.
# This make take a few minutes
status = openai.File.retrieve(training_file.id).status
start_time = time.time()
while status != "processed":
print(f"Status=[{status}]... {time.time() - start_time:.2f}s", end="\r", flush=True)
time.sleep(5)
status = openai.File.r... | https://python.langchain.com/docs/integrations/chat_loaders/imessage |
12bf2cbe6826-0 | Telegram
This notebook shows how to use the Telegram chat loader. This class helps map exported Telegram conversations to LangChain chat messages.
The process has three steps:
Export the chat .txt file by copying chats from the Discord app and pasting them in a file on your local computer
Create the TelegramChatLoader ... | https://python.langchain.com/docs/integrations/chat_loaders/telegram |
12bf2cbe6826-1 | "text_entities": [
{
"type": "plain",
"text": "What did you just say?"
}
]
}
]
}
2. Create the Chat Loader
All that's required is the file path. You can optionally specify the user name that maps to an ai message as well an configure whether to merge message runs.
from langchain.chat_loaders.telegram import TelegramCh... | https://python.langchain.com/docs/integrations/chat_loaders/telegram |
12bf2cbe6826-2 | raw_messages = loader.lazy_load()
# Merge consecutive messages from the same sender into a single message
merged_messages = merge_chat_runs(raw_messages)
# Convert messages from "Jiminy Cricket" to AI messages
messages: List[ChatSession] = list(map_ai_messages(merged_messages, sender="Jiminy Cricket"))
Next Steps
You ... | https://python.langchain.com/docs/integrations/chat_loaders/telegram |
6009eeaf994e-0 | This notebook shows how to use the WhatsApp chat loader. This class helps map exported Telegram conversations to LangChain chat messages.
To make the export of your WhatsApp conversation(s), complete the following steps:
whatsapp_chat.txt
[8/15/23, 9:12:33 AM] Dr. Feather: Messages and calls are end-to-end encrypted. ... | https://python.langchain.com/docs/integrations/chat_loaders/whatsapp |
6009eeaf994e-1 | The load() (or lazy_load) methods return a list of "ChatSessions" that currently store the list of messages per loaded conversation.
[{'messages': [AIMessage(content='I spotted a rare Hyacinth Macaw yesterday in the Amazon Rainforest. Such a magnificent creature!', additional_kwargs={'sender': 'Dr. Feather', 'events': ... | https://python.langchain.com/docs/integrations/chat_loaders/whatsapp |
6009eeaf994e-2 | You can then use these messages how you see fit, such as finetuning a model, few-shot example selection, or directly make predictions for the next message. | https://python.langchain.com/docs/integrations/chat_loaders/whatsapp |
317dc440d55d-0 | This notebook shows how to load chat messages from Twitter to finetune on. We do this by utilizing Apify.
First, use Apify to export tweets. An example
# Filter out tweets that reference other tweets, because it's a bit weird
tweets = [d["full_text"] for d in data if "t.co" not in d['full_text']]
# Create them as AI m... | https://python.langchain.com/docs/integrations/chat_loaders/twitter |
1dd384fc67fa-0 | Beautiful Soup
Beautiful Soup offers fine-grained control over HTML content, enabling specific tag extraction, removal, and content cleaning.
It's suited for cases where you want to extract specific information and clean up the HTML content according to your needs.
For example, we can scrape text content within <p>, <... | https://python.langchain.com/docs/integrations/document_transformers/beautiful_soup |
e6611293b361-0 | docai
from langchain.document_loaders.blob_loaders import Blob
from langchain.document_loaders.parsers import DocAIParser
DocAI is a Google Cloud platform to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume. You can read more about it: https://cloud.g... | https://python.langchain.com/docs/integrations/document_transformers/docai |
e6611293b361-1 | And when they're finished, you can parse the results:
parser.is_running(operations)
results = parser.get_results(operations)
print(results[0])
DocAIParsingResults(source_path='gs://vertex-pgt/examples/goog-exhibit-99-1-q1-2023-19.pdf', parsed_path='gs://vertex-pgt/test/run1/16447136779727347991/0')
And now we can final... | https://python.langchain.com/docs/integrations/document_transformers/docai |
0827c79c4783-0 | Doctran Extract Properties
We can extract useful features of documents using the Doctran library, which uses OpenAI's function calling feature to extract specific metadata.
Extracting metadata from documents is helpful for a variety of tasks, including:
Classification: classifying documents into different categories
Da... | https://python.langchain.com/docs/integrations/document_transformers/doctran_extract_properties |
0827c79c4783-1 | Marketing Initiatives and Campaigns
Our marketing team has been actively working on developing new strategies to increase brand awareness and drive customer engagement. We would like to thank Sarah Thompson (phone: 415-555-1234) for her exceptional efforts in managing our social media platforms. Sarah has successfully ... | https://python.langchain.com/docs/integrations/document_transformers/doctran_extract_properties |
0827c79c4783-2 | HR Updates and Employee Benefits
Recently, we welcomed several new team members who have made significant contributions to their respective departments. I would like to recognize Jane Smith (SSN: 049-45-5928) for her outstanding performance in customer service. Jane has consistently received positive feedback from our ... | https://python.langchain.com/docs/integrations/document_transformers/doctran_extract_properties |
0827c79c4783-3 | Jason Fan
Cofounder & CEO
Psychic
jason@psychic.dev
documents = [Document(page_content=sample_text)]
properties = [
{
"name": "category",
"description": "What type of email this is.",
"type": "string",
"enum": ["update", "action_item", "customer_feedback", "announcement", "other"],
"required": True,
},
{
"name": "ment... | https://python.langchain.com/docs/integrations/document_transformers/doctran_extract_properties |
7f28b2046bda-0 | Documents used in a vector store knowledge base are typically stored in narrative or conversational format. However, most user queries are in question format. If we convert documents into Q&A format before vectorizing them, we can increase the liklihood of retrieving relevant documents, and decrease the liklihood of re... | https://python.langchain.com/docs/integrations/document_transformers/doctran_interrogate_document |
7f28b2046bda-1 | Marketing Initiatives and Campaigns
Our marketing team has been actively working on developing new strategies to increase brand awareness and drive customer engagement. We would like to thank Sarah Thompson (phone: 415-555-1234) for her exceptional efforts in managing our social media platforms. Sarah has successfully ... | https://python.langchain.com/docs/integrations/document_transformers/doctran_interrogate_document |
7f28b2046bda-2 | Best regards,
Jason Fan
Cofounder & CEO
Psychic
jason@psychic.dev
"""
print(sample_text)
After interrogating a document, the result will be returned as a new document with questions and answers provided in the metadata.
{
"questions_and_answers": [
{
"question": "What is the purpose of this document?",
"answer": "The ... | https://python.langchain.com/docs/integrations/document_transformers/doctran_interrogate_document |
6d553453f4cc-0 | Comparing documents through embeddings has the benefit of working across multiple languages. "Harrison says hello" and "Harrison dice hola" will occupy similar positions in the vector space because they have the same meaning semantically.
However, it can still be useful to use a LLM translate documents into other langu... | https://python.langchain.com/docs/integrations/document_transformers/doctran_translate_document |
6d553453f4cc-1 | Marketing Initiatives and Campaigns
Our marketing team has been actively working on developing new strategies to increase brand awareness and drive customer engagement. We would like to thank Sarah Thompson (phone: 415-555-1234) for her exceptional efforts in managing our social media platforms. Sarah has successfully ... | https://python.langchain.com/docs/integrations/document_transformers/doctran_translate_document |
6d553453f4cc-2 | Medidas de seguridad y privacidad
Como parte de nuestro compromiso continuo para garantizar la seguridad y privacidad de los datos de nuestros clientes, hemos implementado medidas robustas en todos nuestros sistemas. Nos gustaría elogiar a John Doe (correo electrónico: john.doe@example.com) del departamento de TI por s... | https://python.langchain.com/docs/integrations/document_transformers/doctran_translate_document |
6d553453f4cc-3 | Proyectos de investigación y desarrollo
En nuestra búsqueda de la innovación, nuestro departamento de investigación y desarrollo ha estado trabajando incansablemente en varios proyectos. Me gustaría reconocer el excepcional trabajo de David Rodríguez (correo electrónico: david.rodriguez@example.com) en su papel de líde... | https://python.langchain.com/docs/integrations/document_transformers/doctran_translate_document |
e5aa2ff097b4-0 | html2text
html2text is a Python script that converts a page of HTML into clean, easy-to-read plain ASCII text.
The ASCII also happens to be valid Markdown (a text-to-HTML format).
from langchain.document_loaders import AsyncHtmlLoader | https://python.langchain.com/docs/integrations/document_transformers/html2text |
e5aa2ff097b4-1 | urls = ["https://www.espn.com", "https://lilianweng.github.io/posts/2023-06-23-agent/"]
loader = AsyncHtmlLoader(urls)
docs = loader.load()
Fetching pages: 100%|############| 2/2 [00:00<00:00, 10.75it/s]
from langchain.document_transformers import Html2TextTransformer
urls = ["https://www.espn.com", "https://lilianweng... | https://python.langchain.com/docs/integrations/document_transformers/html2text |
e5aa2ff097b4-2 | docs_transformed[1].page_content[1000:2000]
"t's brain,\ncomplemented by several key components:\n\n * **Planning**\n * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\n * Reflection and refinement: The agent can do self-criti... | https://python.langchain.com/docs/integrations/document_transformers/html2text |
7741490a5510-0 | Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.
The Nuclia Understanding API document transformer splits text into paragraphs... | https://python.langchain.com/docs/integrations/document_transformers/nuclia_transformer |
adce1e8a80fe-0 | OpenAI Functions Metadata Tagger
It can often be useful to tag ingested documents with structured metadata, such as the title, tone, or length of a document, to allow for more targeted similarity search later. However, for large numbers of documents, performing this labelling process manually can be tedious.
The OpenAI... | https://python.langchain.com/docs/integrations/document_transformers/openai_metadata_tagger |
adce1e8a80fe-1 | enhanced_documents = document_transformer.transform_documents(original_documents)
import json
print(
*[d.page_content + "\n\n" + json.dumps(d.metadata) for d in enhanced_documents],
sep="\n\n---------------\n\n"
)
Review of The Bee Movie
By Roger Ebert
This is the greatest movie ever made. 4 out of 5 stars.
{"movie_... | https://python.langchain.com/docs/integrations/document_transformers/openai_metadata_tagger |
adce1e8a80fe-2 | This movie was super boring. 1 out of 5 stars.
{"movie_title": "The Godfather", "critic": "Anonymous", "tone": "negative", "rating": 1, "reliable": false}
Customization
You can pass the underlying tagging chain the standard LLMChain arguments in the document transformer constructor. For example, if you wanted to ask ... | https://python.langchain.com/docs/integrations/document_transformers/openai_metadata_tagger |
7e1c03ed1626-0 | YouTube transcripts
YouTube is an online video sharing and social media platform created by Google.
This notebook covers how to load documents from YouTube transcripts.
from langchain.document_loaders import YoutubeLoader
# !pip install youtube-transcript-api
loader = YoutubeLoader.from_youtube_url(
"https://www.youtub... | https://python.langchain.com/docs/integrations/document_loaders/youtube_transcript |
7e1c03ed1626-1 | # Use Youtube Ids
youtube_loader_ids = GoogleApiYoutubeLoader(
google_api_client=google_api_client, video_ids=["TrdevFK_am4"], add_video_info=True
)
# returns a list of Documents
youtube_loader_channel.load() | https://python.langchain.com/docs/integrations/document_loaders/youtube_transcript |
edccca7b1f22-0 | Below is an example on how to load a local acreom vault into Langchain. As the local vault in acreom is a folder of plain text .md files, the loader requires the path to the directory.
Vault files may contain some metadata which is stored as a YAML header. These values will be added to the document’s metadata if colle... | https://python.langchain.com/docs/integrations/document_loaders/acreom |
2fe1c43e5b83-0 | The Etherscan loader use etherscan api to load transaction histories under specific account on Ethereum Mainnet.
You will need a Etherscan api key to proceed. The free api key has 5 calls per second quota.
If the account does not have corresponding transactions, the loader will a list with one document. The content of ... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-1 | [Document(page_content="{'blockNumber': '1723771', 'timeStamp': '1466213371', 'hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', 'nonce': '3155', 'blockHash': '0xc2c2207bcaf341eed07f984c9a90b3f8e8bdbdbd2ac6562f8c2f5bfa4b51299d', 'transactionIndex': '5', 'from': '0x3763e6e1228bfeab94191c856412... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-2 | Document(page_content="{'blockNumber': '1727090', 'timeStamp': '1466262018', 'hash': '0xd5a779346d499aa722f72ffe7cd3c8594a9ddd91eb7e439e8ba92ceb7bc86928', 'nonce': '3267', 'blockHash': '0xc0cff378c3446b9b22d217c2c5f54b1c85b89a632c69c55b76cdffe88d2b9f4d', 'transactionIndex': '20', 'from': '0x3763e6e1228bfeab94191c856412... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-3 | Document(page_content="{'blockNumber': '1730337', 'timeStamp': '1466308222', 'hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'nonce': '3344', 'blockHash': '0x3a52d28b8587d55c621144a161a0ad5c37dd9f7d63b629ab31da04fa410b2cfa', 'transactionIndex': '1', 'from': '0x3763e6e1228bfeab94191c856412d... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-4 | Document(page_content="{'blockNumber': '1733479', 'timeStamp': '1466352351', 'hash': '0x720d79bf78775f82b40280aae5abfc347643c5f6708d4bf4ec24d65cd01c7121', 'nonce': '3367', 'blockHash': '0x9928661e7ae125b3ae0bcf5e076555a3ee44c52ae31bd6864c9c93a6ebb3f43e', 'transactionIndex': '0', 'from': '0x3763e6e1228bfeab94191c856412d... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-5 | Document(page_content="{'blockNumber': '1734172', 'timeStamp': '1466362463', 'hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'nonce': '1016', 'blockHash': '0x8a8afe2b446713db88218553cfb5dd202422928e5e0bc00475ed2f37d95649de', 'transactionIndex': '4', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-6 | Document(page_content="{'blockNumber': '1737276', 'timeStamp': '1466406037', 'hash': '0xa4e89bfaf075abbf48f96700979e6c7e11a776b9040113ba64ef9c29ac62b19b', 'nonce': '1024', 'blockHash': '0xe117cad73752bb485c3bef24556e45b7766b283229180fcabc9711f3524b9f79', 'transactionIndex': '35', 'from': '0x16545fb79dbee1ad3a7f868b7661... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-7 | Document(page_content="{'blockNumber': '1740314', 'timeStamp': '1466450262', 'hash': '0x6e1a22dcc6e2c77a9451426fb49e765c3c459dae88350e3ca504f4831ec20e8a', 'nonce': '1051', 'blockHash': '0x588d17842819a81afae3ac6644d8005c12ce55ddb66c8d4c202caa91d4e8fdbe', 'transactionIndex': '6', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-8 | Document(page_content="{'blockNumber': '1743384', 'timeStamp': '1466494099', 'hash': '0xdbfcc15f02269fc3ae27f69e344a1ac4e08948b12b76ebdd78a64d8cafd511ef', 'nonce': '1068', 'blockHash': '0x997245108c84250057fda27306b53f9438ad40978a95ca51d8fd7477e73fbaa7', 'transactionIndex': '2', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-9 | Document(page_content="{'blockNumber': '1746405', 'timeStamp': '1466538123', 'hash': '0xbd4f9602f7fff4b8cc2ab6286efdb85f97fa114a43f6df4e6abc88e85b89e97b', 'nonce': '1092', 'blockHash': '0x3af3966cdaf22e8b112792ee2e0edd21ceb5a0e7bf9d8c168a40cf22deb3690c', 'transactionIndex': '0', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-10 | Document(page_content="{'blockNumber': '1749459', 'timeStamp': '1466582044', 'hash': '0x28c327f462cc5013d81c8682c032f014083c6891938a7bdeee85a1c02c3e9ed4', 'nonce': '1096', 'blockHash': '0x5fc5d2a903977b35ce1239975ae23f9157d45d7bd8a8f6205e8ce270000797f9', 'transactionIndex': '1', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-11 | Document(page_content="{'blockNumber': '1752614', 'timeStamp': '1466626168', 'hash': '0xc3849e550ca5276d7b3c51fa95ad3ae62c1c164799d33f4388fe60c4e1d4f7d8', 'nonce': '1118', 'blockHash': '0x88ef054b98e47504332609394e15c0a4467f84042396717af6483f0bcd916127', 'transactionIndex': '11', 'from': '0x16545fb79dbee1ad3a7f868b7661... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-12 | Document(page_content="{'blockNumber': '1755659', 'timeStamp': '1466669931', 'hash': '0xb9f891b7c3d00fcd64483189890591d2b7b910eda6172e3bf3973c5fd3d5a5ae', 'nonce': '1133', 'blockHash': '0x2983972217a91343860415d1744c2a55246a297c4810908bbd3184785bc9b0c2', 'transactionIndex': '14', 'from': '0x16545fb79dbee1ad3a7f868b7661... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-13 | Document(page_content="{'blockNumber': '1758709', 'timeStamp': '1466713652', 'hash': '0xd6cce5b184dc7fce85f305ee832df647a9c4640b68e9b79b6f74dc38336d5622', 'nonce': '1147', 'blockHash': '0x1660de1e73067251be0109d267a21ffc7d5bde21719a3664c7045c32e771ecf9', 'transactionIndex': '1', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-14 | Document(page_content="{'blockNumber': '1761783', 'timeStamp': '1466757809', 'hash': '0xd01545872629956867cbd65fdf5e97d0dde1a112c12e76a1bfc92048d37f650f', 'nonce': '1169', 'blockHash': '0x7576961afa4218a3264addd37a41f55c444dd534e9410dbd6f93f7fe20e0363e', 'transactionIndex': '2', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-15 | Document(page_content="{'blockNumber': '1764895', 'timeStamp': '1466801683', 'hash': '0x620b91b12af7aac75553b47f15742e2825ea38919cfc8082c0666f404a0db28b', 'nonce': '1186', 'blockHash': '0x2e687643becd3c36e0c396a02af0842775e17ccefa0904de5aeca0a9a1aa795e', 'transactionIndex': '7', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-16 | Document(page_content="{'blockNumber': '1767936', 'timeStamp': '1466845682', 'hash': '0x758efa27576cd17ebe7b842db4892eac6609e3962a4f9f57b7c84b7b1909512f', 'nonce': '1211', 'blockHash': '0xb01d8fd47b3554a99352ac3e5baf5524f314cfbc4262afcfbea1467b2d682898', 'transactionIndex': '0', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-17 | Document(page_content="{'blockNumber': '1770911', 'timeStamp': '1466888890', 'hash': '0x9d84470b54ab44b9074b108a0e506cd8badf30457d221e595bb68d63e926b865', 'nonce': '1212', 'blockHash': '0x79a9de39276132dab8bf00dc3e060f0e8a14f5e16a0ee4e9cc491da31b25fe58', 'transactionIndex': '0', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-18 | Document(page_content="{'blockNumber': '1774044', 'timeStamp': '1466932983', 'hash': '0x958d85270b58b80f1ad228f716bbac8dd9da7c5f239e9f30d8edeb5bb9301d20', 'nonce': '1240', 'blockHash': '0x69cee390378c3b886f9543fb3a1cb2fc97621ec155f7884564d4c866348ce539', 'transactionIndex': '2', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-19 | Document(page_content="{'blockNumber': '1777057', 'timeStamp': '1466976422', 'hash': '0xe76ca3603d2f4e7134bdd7a1c3fd553025fc0b793f3fd2a75cd206b8049e74ab', 'nonce': '1248', 'blockHash': '0xc7cacda0ac38c99f1b9bccbeee1562a41781d2cfaa357e8c7b4af6a49584b968', 'transactionIndex': '7', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
2fe1c43e5b83-20 | Document(page_content="{'blockNumber': '1780120', 'timeStamp': '1467020353', 'hash': '0xc5ec8cecdc9f5ed55a5b8b0ad79c964fb5c49dc1136b6a49e981616c3e70bbe6', 'nonce': '1266', 'blockHash': '0xfc0e066e5b613239e1a01e6d582e7ab162ceb3ca4f719dfbd1a0c965adcfe1c5', 'transactionIndex': '1', 'from': '0x16545fb79dbee1ad3a7f868b7661c... | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
00b85849898b-0 | Airbyte CDK
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
A lot of source connectors are implemented using the Airbyte CDK. This loader allows to run any of these connectors and ... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_cdk |
00b85849898b-1 | issues_loader = AirbyteCDKLoader(source_class=SourceGithub, config=config, stream_name="issues")
Now you can load documents the usual way
docs = issues_loader.load()
As load returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the lazy_load method whic... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_cdk |
95d6e162c59f-0 | Airbyte Hubspot
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This loader exposes the Hubspot connector as a document loader, allowing you to load various Hubspot objects as docu... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_hubspot |
95d6e162c59f-1 | loader = AirbyteHubspotLoader(config=config, record_handler=handle_record, stream_name="products")
docs = loader.load()
Incremental loads
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_hubspot |
f610793ea366-0 | Airbyte JSON
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This covers how to load any source from Airbyte into a local JSON file that can be read in as a document
Prereqs: Have ... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_json |
8c7dc3a084d2-0 | This loader exposes the Salesforce connector as a document loader, allowing you to load various Salesforce objects as documents.
First, you need to install the airbyte-source-salesforce python package.
{
"client_id": "<oauth client id>",
"client_secret": "<oauth client secret>",
"refresh_token": "<oauth refresh token>"... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_salesforce |
8c7dc3a084d2-1 | loader = AirbyteSalesforceLoader(config=config, record_handler=handle_record, stream_name="asset")
docs = loader.load()
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated freque... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_salesforce |
905bac361ffc-0 | Airbyte Shopify
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This loader exposes the Shopify connector as a document loader, allowing you to load various Shopify objects as docu... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_shopify |
905bac361ffc-1 | loader = AirbyteShopifyLoader(config=config, record_handler=handle_record, stream_name="orders")
docs = loader.load()
Incremental loads
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and a... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_shopify |
2ee7b92982ad-0 | Airbyte Stripe
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This loader exposes the Stripe connector as a document loader, allowing you to load various Stripe objects as documen... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_stripe |
2ee7b92982ad-1 | loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name="invoices")
docs = loader.load()
Incremental loads
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and ... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_stripe |
a9a0d7edee5a-0 | Airbyte Typeform
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This loader exposes the Typeform connector as a document loader, allowing you to load various Typeform objects as d... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_typeform |
a9a0d7edee5a-1 | loader = AirbyteTypeformLoader(config=config, record_handler=handle_record, stream_name="forms")
docs = loader.load()
Incremental loads
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and a... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_typeform |
114029aa6c24-0 | Airtable
from langchain.document_loaders import AirtableLoader
Get your API key here.
Get ID of your base here.
Get your table ID from the table url as shown here.
api_key = "xxx"
base_id = "xxx"
table_id = "xxx"
loader = AirtableLoader(api_key, table_id, base_id)
docs = loader.load()
Returns each table row as dict.
ev... | https://python.langchain.com/docs/integrations/document_loaders/airtable |
be31dcd2b7c6-0 | Airbyte Zendesk Support
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This loader exposes the Zendesk Support connector as a document loader, allowing you to load various objects... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_zendesk_support |
be31dcd2b7c6-1 | loader = AirbyteZendeskSupportLoader(config=config, record_handler=handle_record, stream_name="tickets")
docs = loader.load()
Incremental loads
Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of da... | https://python.langchain.com/docs/integrations/document_loaders/airbyte_zendesk_support |
a9dd41f69fa6-0 | Alibaba Cloud MaxCompute
Alibaba Cloud MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing. MaxCompute supports various data importing solutions and distributed computing models, enabling users to effectively query massive da... | https://python.langchain.com/docs/integrations/document_loaders/alibaba_cloud_maxcompute |
a9dd41f69fa6-1 | base_query = """
SELECT *
FROM (
SELECT 1 AS id, 'content1' AS content, 'meta_info1' AS meta_info
UNION ALL
SELECT 2 AS id, 'content2' AS content, 'meta_info2' AS meta_info
UNION ALL
SELECT 3 AS id, 'content3' AS content, 'meta_info3' AS meta_info
) mydata;
"""
endpoint = "<ENDPOINT>"
project = "<PROJECT>"
ACCESS_ID = ... | https://python.langchain.com/docs/integrations/document_loaders/alibaba_cloud_maxcompute |
92d4433edd32-0 | Apify Dataset
Apify Dataset is a scaleable append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of Apify Actors—serverless cloud... | https://python.langchain.com/docs/integrations/document_loaders/apify_dataset |
92d4433edd32-1 | https://docs.apify.com/platform/actors, https://docs.apify.com/platform/actors/running/actors-in-store, https://docs.apify.com/platform/security, https://docs.apify.com/platform/actors/examples | https://python.langchain.com/docs/integrations/document_loaders/apify_dataset |
6504cba28ec5-0 | This notebook demonstrates the use of the langchain.document_loaders.ArcGISLoader class.
You will need to install the ArcGIS API for Python arcgis and, optionally, bs4.BeautifulSoup.
You can use an arcgis.gis.GIS object for authenticated data loading, or leave it blank to access public data.
{'accessed': '2023-08-15T04... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-1 | "imageData": "iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAJJJREFUOI3NkDEKg0AQRZ9kkSnSGBshR7DJqdJYeg7BMpcS0uQWQsqoCLExkcUJzGqT38zw2fcY1rEzbp7vjXz0EXC7gBxs1ABcG/8CYkCcDqwyLqsV+RlV0I/w7PzuJBArr1VB20H58Ls6h+xoFITkTwWpQJX7XSIBAnFwVj7MLAjJV/AC6G3QoAmK+74Lom04THTBEp/HCSc6AAAAAE... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-2 | },
{
"name": "Shape",
"type": "esriFieldTypeGeometry",
"alias": "Shape",
"domain": null
},
{
"name": "AccessName",
"type": "esriFieldTypeString",
"alias": "AccessName",
"length": 40,
"domain": null
},
{
"name": "AccessID",
"type": "esriFieldTypeString",
"alias": "AccessID",
"length": 50,
"domain": null
},
{
"name": "Ac... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-3 | "supportsStatistics": true,
"supportsAdvancedQueries": true,
"supportedQueryFormats": "JSON, geoJSON",
"isDataVersioned": false,
"ownershipBasedAccessControlForFeatures": {
"allowOthersToQuery": true
},
"useStandardizedQueries": true,
"advancedQueryCapabilities": {
"useStandardizedQueries": true,
"supportsStatistics": ... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-4 | {"OBJECTID": 11, "AccessName": "INTERNATIONAL SPEEDWAY BLVD", "AccessID": "DB-059", "AccessType": "OPEN VEHICLE RAMP", "GeneralLoc": "300 BLK S ATLANTIC AV", "MilePost": 15.27, "City": "DAYTONA BEACH", "AccessStatus": "CLOSED", "Entry_Date_Time": 1692039947000, "DrivingZone": "BOTH"}
{"OBJECTID": 14, "AccessName": "GRA... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-5 | {"OBJECTID": 42, "AccessName": "BOTEFUHR AV", "AccessID": "DBS-067", "AccessType": "OPEN VEHICLE RAMP", "GeneralLoc": "1900 BLK S ATLANTIC AV", "MilePost": 16.68, "City": "DAYTONA BEACH SHORES", "AccessStatus": "CLOSED", "Entry_Date_Time": 1692039947000, "DrivingZone": "YES"}
{"OBJECTID": 43, "AccessName": "SILVER BEAC... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-6 | {"OBJECTID": 64, "AccessName": "DUNLAWTON BLVD", "AccessID": "DBS-078", "AccessType": "OPEN VEHICLE RAMP", "GeneralLoc": "3400 BLK S ATLANTIC AV", "MilePost": 20.61, "City": "DAYTONA BEACH SHORES", "AccessStatus": "CLOSED", "Entry_Date_Time": 1692039947000, "DrivingZone": "YES"}
{"OBJECTID": 69, "AccessName": "EMILIA A... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-7 | {"OBJECTID": 124, "AccessName": "HARTFORD AV", "AccessID": "DB-043", "AccessType": "OPEN VEHICLE RAMP", "GeneralLoc": "1890 BLK N ATLANTIC AV", "MilePost": 12.76, "City": "DAYTONA BEACH", "AccessStatus": "CLOSED", "Entry_Date_Time": 1692039947000, "DrivingZone": "YES"}
{"OBJECTID": 127, "AccessName": "WILLIAMS AV", "Ac... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
6504cba28ec5-8 | {"OBJECTID": 232, "AccessName": "VAN AV", "AccessID": "DBS-075", "AccessType": "OPEN VEHICLE RAMP", "GeneralLoc": "3100 BLK S ATLANTIC AV", "MilePost": 19.6, "City": "DAYTONA BEACH SHORES", "AccessStatus": "CLOSED", "Entry_Date_Time": 1692039947000, "DrivingZone": "YES"}
{"OBJECTID": 234, "AccessName": "ROCKEFELLER DR"... | https://python.langchain.com/docs/integrations/document_loaders/arcgis |
431aaa50ef04-0 | Arxiv
arXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.
This notebook shows how to load scientific articles from Arxiv.org into a doc... | https://python.langchain.com/docs/integrations/document_loaders/arxiv |
431aaa50ef04-1 | docs[0].page_content[:400] # all pages of the Document content
'arXiv:1605.08386v1 [math.CO] 26 May 2016\nHEAT-BATH RANDOM WALKS WITH MARKOV BASES\nCAPRICE STANLEY AND TOBIAS WINDISCH\nAbstract. Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that th... | https://python.langchain.com/docs/integrations/document_loaders/arxiv |
e25fa3bb4f46-0 | AssemblyAI Audio Transcripts
The AssemblyAIAudioTranscriptLoader allows to transcribe audio files with the AssemblyAI API and loads the transcribed text into documents.
To use it, you should have the assemblyai python package installed, and the environment variable ASSEMBLYAI_API_KEY set with your API key. Alternativel... | https://python.langchain.com/docs/integrations/document_loaders/assemblyai |
e25fa3bb4f46-1 | docs = loader.load()
Transcription Config
You can also specify the config argument to use different audio intelligence models.
Visit the AssemblyAI API Documentation to get an overview of all available models!
import assemblyai as aai
config = aai.TranscriptionConfig(speaker_labels=True,
auto_chapters=True,
entity_de... | https://python.langchain.com/docs/integrations/document_loaders/assemblyai |
80ba220b30a1-0 | AsyncHtmlLoader
AsyncHtmlLoader loads raw HTML from a list of urls concurrently.
from langchain.document_loaders import AsyncHtmlLoader
urls = ["https://www.espn.com", "https://lilianweng.github.io/posts/2023-06-23-agent/"]
loader = AsyncHtmlLoader(urls)
docs = loader.load()
Fetching pages: 100%|############| 2/2 [00:0... | https://python.langchain.com/docs/integrations/document_loaders/async_html |
80ba220b30a1-1 | docs[1].page_content[1000:2000]
'al" href="https://lilianweng.github.io/posts/2023-06-23-agent/" />\n<link crossorigin="anonymous" href="/assets/css/stylesheet.min.67a6fb6e33089cb29e856bcc95d7aa39f70049a42b123105531265a0d9f1258b.css" integrity="sha256-Z6b7bjMInLKehWvMldeqOfcASaQrEjEFUxJloNnxJYs=" rel="preload styleshee... | https://python.langchain.com/docs/integrations/document_loaders/async_html |
40f50c078696-0 | AWS S3 Directory
Amazon Simple Storage Service (Amazon S3) is an object storage service
AWS S3 Directory
This covers how to load document objects from an AWS S3 Directory object.
from langchain.document_loaders import S3DirectoryLoader
loader = S3DirectoryLoader("testing-hwc")
Specifying a prefix
You can also specify ... | https://python.langchain.com/docs/integrations/document_loaders/aws_s3_directory |
6db2ca1b04b3-0 | Async Chromium
Chromium is one of the browsers supported by Playwright, a library used to control browser automation.
By running p.chromium.launch(headless=True), we are launching a headless instance of Chromium.
Headless mode means that the browser is running without a graphical user interface.
AsyncChromiumLoader l... | https://python.langchain.com/docs/integrations/document_loaders/async_chromium |
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