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shows how to use them in a chain.
Question answering over documents consists of four steps:
Create an index
Create a Retriever from that index
Create a question answering chain
Ask questions!
Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on.
First, let’s import some common classes we’ll use no matter what.
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
Next in the generic setup, let’s specify the document loader we want to use. You can download the state_of_the_union.txt file here
from langchain.document_loaders import TextLoader
loader = TextLoader('../state_of_the_union.txt')
One Line Index Creation#
To get started as quickly as possible, we can use the VectorstoreIndexCreator.
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader])
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
Now that the index is created, we can use it to ask questions of the data! Note that under the hood this is actually doing a few steps as well, which we will cover later in this guide.
query = "What did the president say about Ketanji Brown Jackson"
index.query(query)
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
query = "What did the president say about Ketanji Brown
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judges appointed by Democrats and Republicans."
query = "What did the president say about Ketanji Brown Jackson"
index.query_with_sources(query)
{'question': 'What did the president say about Ketanji Brown Jackson',
'answer': " The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\n",
'sources': '../state_of_the_union.txt'}
What is returned from the VectorstoreIndexCreator is VectorStoreIndexWrapper, which provides these nice query and query_with_sources functionality. If we just wanted to access the vectorstore directly, we can also do that.
index.vectorstore
<langchain.vectorstores.chroma.Chroma at 0x119aa5940>
If we then want to access the VectorstoreRetriever, we can do that with:
index.vectorstore.as_retriever()
VectorStoreRetriever(vectorstore=<langchain.vectorstores.chroma.Chroma object at 0x119aa5940>, search_kwargs={})
Walkthrough#
Okay, so what’s actually going on? How is this index getting created?
A lot of the magic is being hid in this VectorstoreIndexCreator. What is this doing?
There are three main steps going on after the documents are loaded:
Splitting documents into chunks
Creating embeddings for each document
Storing documents and embeddings in a vectorstore
Let’s walk through this in code
documents = loader.load()
Next, we will split the documents into chunks.
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
We will then select which embeddings we want to use.
from langchain.embeddings
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will then select which embeddings we want to use.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
We now create the vectorstore to use as the index.
from langchain.vectorstores import Chroma
db = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
So that’s creating the index. Then, we expose this index in a retriever interface.
retriever = db.as_retriever()
Then, as before, we create a chain and use it to answer questions!
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever)
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" The President said that Judge Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He said she is a consensus builder and has received a broad range of support from organizations such as the Fraternal Order of Police and former judges appointed by Democrats and Republicans."
VectorstoreIndexCreator is just a wrapper around all this logic. It is configurable in the text splitter it uses, the embeddings it uses, and the vectorstore it uses. For example, you can configure it as below:
index_creator = VectorstoreIndexCreator(
vectorstore_cls=Chroma,
embedding=OpenAIEmbeddings(),
text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
)
Hopefully this highlights what is going on under the hood of VectorstoreIndexCreator. While we think it’s important to have a simple way to create indexes, we also think it’s important to understand what’s going on under the
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simple way to create indexes, we also think it’s important to understand what’s going on under the hood.
previous
Indexes
next
Document Loaders
Contents
One Line Index Creation
Walkthrough
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/getting_started.html
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.rst
.pdf
Text Splitters
Text Splitters#
Note
Conceptual Guide
When you want to deal with long pieces of text, it is necessary to split up that text into chunks.
As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What “semantically related” means could depend on the type of text.
This notebook showcases several ways to do that.
At a high level, text splitters work as following:
Split the text up into small, semantically meaningful chunks (often sentences).
Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).
Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).
That means there two different axes along which you can customize your text splitter:
How the text is split
How the chunk size is measured
For an introduction to the default text splitter and generic functionality see:
Getting Started
We also have documentation for all the types of text splitters that are supported.
Please see below for that list.
Character Text Splitter
Hugging Face Length Function
Latex Text Splitter
Markdown Text Splitter
NLTK Text Splitter
Python Code Text Splitter
RecursiveCharacterTextSplitter
Spacy Text Splitter
tiktoken (OpenAI) Length Function
TiktokenText Splitter
previous
YouTube
next
Getting Started
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters.html
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.rst
.pdf
Document Loaders
Document Loaders#
Note
Conceptual Guide
Combining language models with your own text data is a powerful way to differentiate them.
The first step in doing this is to load the data into “documents” - a fancy way of say some pieces of text.
This module is aimed at making this easy.
A primary driver of a lot of this is the Unstructured python package.
This package is a great way to transform all types of files - text, powerpoint, images, html, pdf, etc - into text data.
For detailed instructions on how to get set up with Unstructured, see installation guidelines here.
The following document loaders are provided:
CoNLL-U
Airbyte JSON
Apify Dataset
AZLyrics
Azure Blob Storage Container
Azure Blob Storage File
BigQuery Loader
Bilibili
Blackboard
College Confidential
Copy Paste
CSV Loader
DataFrame Loader
Directory Loader
DuckDB Loader
Email
EPubs
EverNote
Facebook Chat
Figma
GCS Directory
GCS File Storage
Git
GitBook
Google Drive
Gutenberg
Hacker News
HTML
iFixit
Images
IMSDb
Markdown
Notebook
Notion
Notion DB Loader
Obsidian
PDF
PowerPoint
ReadTheDocs Documentation
Roam
s3 Directory
s3 File
Sitemap Loader
Slack (Local Exported Zipfile)
Subtitle Files
Telegram
Unstructured File Loader
URL
Selenium URL Loader
Playwright URL Loader
Web Base
WhatsApp Chat
Word Documents
YouTube
previous
Getting Started
next
CoNLL-U
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders.html
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ca45d31805f7-0
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.ipynb
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Pinecone Hybrid Search
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
Pinecone Hybrid Search#
This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.
The logic of this retriever is taken from this documentaion
from langchain.retrievers import PineconeHybridSearchRetriever
Setup Pinecone#
You should only have to do this part once.
Note: it’s important to make sure that the “context” field that holds the document text in the metadata is not indexed. Currently you need to specify explicitly the fields you do want to index. For more information checkout Pinecone’s docs.
import os
import pinecone
api_key = os.getenv("PINECONE_API_KEY") or "PINECONE_API_KEY"
# find environment next to your API key in the Pinecone console
env = os.getenv("PINECONE_ENVIRONMENT") or "PINECONE_ENVIRONMENT"
index_name = "langchain-pinecone-hybrid-search"
pinecone.init(api_key=api_key, enviroment=env)
pinecone.whoami()
WhoAmIResponse(username='load', user_label='label', projectname='load-test')
# create the index
pinecone.create_index(
name = index_name,
dimension = 1536, # dimensionality of dense model
metric = "dotproduct", # sparse values supported only for dotproduct
pod_type = "s1",
metadata_config={"indexed": []} # see explaination above
)
Now that its created, we can use it
index = pinecone.Index(index_name)
Get embeddings and sparse encoders#
Embeddings are used for the dense vectors, tokenizer is used for the sparse vector
from
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are used for the dense vectors, tokenizer is used for the sparse vector
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
To encode the text to sparse values you can either choose SPLADE or BM25. For out of domain tasks we recommend using BM25.
For more information about the sparse encoders you can checkout pinecone-text library docs.
from pinecone_text.sparse import BM25Encoder
# or from pinecone_text.sparse import SpladeEncoder if you wish to work with SPLADE
# use default tf-idf values
bm25_encoder = BM25Encoder().default()
The above code is using default tfids values. It’s highly recommended to fit the tf-idf values to your own corpus. You can do it as follow:
corpus = ["foo", "bar", "world", "hello"]
# fit tf-idf values on your corpus
bm25_encoder.fit(corpus)
# store the values to a json file
bm25_encoder.dump("bm25_values.json")
# load to your BM25Encoder object
bm25_encoder = BM25Encoder().load("bm25_values.json")
Load Retriever#
We can now construct the retriever!
retriever = PineconeHybridSearchRetriever(embeddings=embeddings, sparse_encoder=bm25_encoder, index=index)
Add texts (if necessary)#
We can optionally add texts to the retriever (if they aren’t already in there)
retriever.add_texts(["foo", "bar", "world", "hello"])
100%|██████████| 1/1 [00:02<00:00, 2.27s/it]
Use Retriever#
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result[0]
Document(page_content='foo', metadata={})
previous
Metal
next
TF-IDF Retriever
Contents
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html
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metadata={})
previous
Metal
next
TF-IDF Retriever
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html
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01b307cc5e3b-0
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.ipynb
.pdf
ChatGPT Plugin Retriever
Contents
Create
Using the ChatGPT Retriever Plugin
ChatGPT Plugin Retriever#
This notebook shows how to use the ChatGPT Retriever Plugin within LangChain.
Create#
First, let’s go over how to create the ChatGPT Retriever Plugin.
To set up the ChatGPT Retriever Plugin, please follow instructions here.
You can also create the ChatGPT Retriever Plugin from LangChain document loaders. The below code walks through how to do that.
# STEP 1: Load
# Load documents using LangChain's DocumentLoaders
# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='../../document_loaders/examples/example_data/mlb_teams_2012.csv')
data = loader.load()
# STEP 2: Convert
# Convert Document to format expected by https://github.com/openai/chatgpt-retrieval-plugin
from typing import List
from langchain.docstore.document import Document
import json
def write_json(path: str, documents: List[Document])-> None:
results = [{"text": doc.page_content} for doc in documents]
with open(path, "w") as f:
json.dump(results, f, indent=2)
write_json("foo.json", data)
# STEP 3: Use
# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_json
Using the ChatGPT Retriever Plugin#
Okay, so we’ve created the ChatGPT Retriever Plugin, but how do we actually use it?
The below code walks through how to do that.
from langchain.retrievers
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actually use it?
The below code walks through how to do that.
from langchain.retrievers import ChatGPTPluginRetriever
retriever = ChatGPTPluginRetriever(url="http://0.0.0.0:8000", bearer_token="foo")
retriever.get_relevant_documents("alice's phone number")
[Document(page_content="This is Alice's phone number: 123-456-7890", lookup_str='', metadata={'id': '456_0', 'metadata': {'source': 'email', 'source_id': '567', 'url': None, 'created_at': '1609592400.0', 'author': 'Alice', 'document_id': '456'}, 'embedding': None, 'score': 0.925571561}, lookup_index=0),
Document(page_content='This is a document about something', lookup_str='', metadata={'id': '123_0', 'metadata': {'source': 'file', 'source_id': 'https://example.com/doc1', 'url': 'https://example.com/doc1', 'created_at': '1609502400.0', 'author': 'Alice', 'document_id': '123'}, 'embedding': None, 'score': 0.6987589}, lookup_index=0),
Document(page_content='Team: Angels "Payroll (millions)": 154.49 "Wins": 89', lookup_str='', metadata={'id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631_0', 'metadata': {'source': None, 'source_id': None, 'url': None, 'created_at': None, 'author': None, 'document_id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631'}, 'embedding': None, 'score':
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'embedding': None, 'score': 0.697888613}, lookup_index=0)]
previous
Retrievers
next
Databerry
Contents
Create
Using the ChatGPT Retriever Plugin
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/chatgpt-plugin-retriever.html
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.ipynb
.pdf
ElasticSearch BM25
Contents
Create New Retriever
Add texts (if necessary)
Use Retriever
ElasticSearch BM25#
This notebook goes over how to use a retriever that under the hood uses ElasticSearcha and BM25.
For more information on the details of BM25 see this blog post.
from langchain.retrievers import ElasticSearchBM25Retriever
Create New Retriever#
elasticsearch_url="http://localhost:9200"
retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, "langchain-index-4")
# Alternatively, you can load an existing index
# import elasticsearch
# elasticsearch_url="http://localhost:9200"
# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), "langchain-index")
Add texts (if necessary)#
We can optionally add texts to the retriever (if they aren’t already in there)
retriever.add_texts(["foo", "bar", "world", "hello", "foo bar"])
['cbd4cb47-8d9f-4f34-b80e-ea871bc49856',
'f3bd2e24-76d1-4f9b-826b-ec4c0e8c7365',
'8631bfc8-7c12-48ee-ab56-8ad5f373676e',
'8be8374c-3253-4d87-928d-d73550a2ecf0',
'd79f457b-2842-4eab-ae10-77aa420b53d7']
Use Retriever#
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result
[Document(page_content='foo', metadata={}),
Document(page_content='foo bar',
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html
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metadata={}),
Document(page_content='foo bar', metadata={})]
previous
Databerry
next
Metal
Contents
Create New Retriever
Add texts (if necessary)
Use Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html
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b6600b860b6a-0
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.ipynb
.pdf
TF-IDF Retriever
Contents
Create New Retriever with Texts
Use Retriever
TF-IDF Retriever#
This notebook goes over how to use a retriever that under the hood uses TF-IDF using scikit-learn.
For more information on the details of TF-IDF see this blog post.
from langchain.retrievers import TFIDFRetriever
# !pip install scikit-learn
Create New Retriever with Texts#
retriever = TFIDFRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])
Use Retriever#
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result
[Document(page_content='foo', metadata={}),
Document(page_content='foo bar', metadata={}),
Document(page_content='hello', metadata={}),
Document(page_content='world', metadata={})]
previous
Pinecone Hybrid Search
next
VectorStore Retriever
Contents
Create New Retriever with Texts
Use Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/tf_idf_retriever.html
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.ipynb
.pdf
Databerry
Contents
Query
Databerry#
This notebook shows how to use Databerry’s retriever.
First, you will need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url
Query#
Now that our index is set up, we can set up a retriever and start querying it.
from langchain.retrievers import DataberryRetriever
retriever = DataberryRetriever(
datastore_url="https://clg1xg2h80000l708dymr0fxc.databerry.ai/query",
# api_key="DATABERRY_API_KEY", # optional if datastore is public
# top_k=10 # optional
)
retriever.get_relevant_documents("What is Daftpage?")
[Document(page_content='✨ Made with DaftpageOpen main menuPricingTemplatesLoginSearchHelpGetting StartedFeaturesAffiliate ProgramGetting StartedDaftpage is a new type of website builder that works like a doc.It makes website building easy, fun and offers tons of powerful features for free. Just type / in your page to get started!DaftpageCopyright © 2022 Daftpage, Inc.All rights reserved.ProductPricingTemplatesHelp & SupportHelp CenterGetting startedBlogCompanyAboutRoadmapTwitterAffiliate Program👾 Discord', metadata={'source': 'https:/daftpage.com/help/getting-started', 'score': 0.8697265}),
Document(page_content="✨ Made with DaftpageOpen main menuPricingTemplatesLoginSearchHelpGetting StartedFeaturesAffiliate ProgramHelp CenterWelcome to Daftpage’s help center—the one-stop shop for learning everything about building websites with Daftpage.Daftpage is the simplest way to create websites for all purposes in seconds. Without knowing how to code, and for free!Get
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/databerry.html
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to create websites for all purposes in seconds. Without knowing how to code, and for free!Get StartedDaftpage is a new type of website builder that works like a doc.It makes website building easy, fun and offers tons of powerful features for free. Just type / in your page to get started!Start here✨ Create your first site🧱 Add blocks🚀 PublishGuides🔖 Add a custom domainFeatures🔥 Drops🎨 Drawings👻 Ghost mode💀 Skeleton modeCant find the answer you're looking for?mail us at [email protected] the awesome Daftpage community on: 👾 DiscordDaftpageCopyright © 2022 Daftpage, Inc.All rights reserved.ProductPricingTemplatesHelp & SupportHelp CenterGetting startedBlogCompanyAboutRoadmapTwitterAffiliate Program👾 Discord", metadata={'source': 'https:/daftpage.com/help', 'score': 0.86570895}),
Document(page_content=" is the simplest way to create websites for all purposes in seconds. Without knowing how to code, and for free!Get StartedDaftpage is a new type of website builder that works like a doc.It makes website building easy, fun and offers tons of powerful features for free. Just type / in your page to get started!Start here✨ Create your first site🧱 Add blocks🚀 PublishGuides🔖 Add a custom domainFeatures🔥 Drops🎨 Drawings👻 Ghost mode💀 Skeleton modeCant find the answer you're looking for?mail us at [email protected] the awesome Daftpage community on: 👾 DiscordDaftpageCopyright © 2022 Daftpage, Inc.All rights reserved.ProductPricingTemplatesHelp & SupportHelp CenterGetting startedBlogCompanyAboutRoadmapTwitterAffiliate Program👾 Discord", metadata={'source':
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startedBlogCompanyAboutRoadmapTwitterAffiliate Program👾 Discord", metadata={'source': 'https:/daftpage.com/help', 'score': 0.8645384})]
previous
ChatGPT Plugin Retriever
next
ElasticSearch BM25
Contents
Query
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/databerry.html
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.ipynb
.pdf
Weaviate Hybrid Search
Weaviate Hybrid Search#
This notebook shows how to use Weaviate hybrid search as a LangChain retriever.
import weaviate
import os
WEAVIATE_URL = "..."
client = weaviate.Client(
url=WEAVIATE_URL,
)
from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
from langchain.schema import Document
retriever = WeaviateHybridSearchRetriever(client, index_name="LangChain", text_key="text")
docs = [Document(page_content="foo")]
retriever.add_documents(docs)
['3f79d151-fb84-44cf-85e0-8682bfe145e0']
retriever.get_relevant_documents("foo")
[Document(page_content='foo', metadata={})]
previous
VectorStore Retriever
next
Memory
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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.ipynb
.pdf
Metal
Contents
Ingest Documents
Query
Metal#
This notebook shows how to use Metal’s retriever.
First, you will need to sign up for Metal and get an API key. You can do so here
# !pip install metal_sdk
from metal_sdk.metal import Metal
API_KEY = ""
CLIENT_ID = ""
APP_ID = ""
metal = Metal(API_KEY, CLIENT_ID, APP_ID);
Ingest Documents#
You only need to do this if you haven’t already set up an index
metal.index( {"text": "foo1"})
metal.index( {"text": "foo"})
{'data': {'id': '642739aa7559b026b4430e42',
'text': 'foo',
'createdAt': '2023-03-31T19:51:06.748Z'}}
Query#
Now that our index is set up, we can set up a retriever and start querying it.
from langchain.retrievers import MetalRetriever
retriever = MetalRetriever(metal, params={"limit": 2})
retriever.get_relevant_documents("foo1")
[Document(page_content='foo1', metadata={'dist': '1.19209289551e-07', 'id': '642739a17559b026b4430e40', 'createdAt': '2023-03-31T19:50:57.853Z'}),
Document(page_content='foo1', metadata={'dist': '4.05311584473e-06', 'id': '642738f67559b026b4430e3c', 'createdAt': '2023-03-31T19:48:06.769Z'})]
previous
ElasticSearch BM25
next
Pinecone Hybrid Search
Contents
Ingest Documents
Query
By Harrison Chase
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/metal.html
|
7639dfdd653d-1
|
Ingest Documents
Query
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/metal.html
|
bca5df2401b9-0
|
.ipynb
.pdf
VectorStore Retriever
VectorStore Retriever#
The index - and therefore the retriever - that LangChain has the most support for is a VectorStoreRetriever. As the name suggests, this retriever is backed heavily by a VectorStore.
Once you construct a VectorStore, its very easy to construct a retriever. Let’s walk through an example.
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(texts, embeddings)
Exiting: Cleaning up .chroma directory
retriever = db.as_retriever()
docs = retriever.get_relevant_documents("what did he say about ketanji brown jackson")
By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type.
retriever = db.as_retriever(search_type="mmr")
docs = retriever.get_relevant_documents("what did he say abotu ketanji brown jackson")
You can also specify search kwargs like k to use when doing retrieval.
retriever = db.as_retriever(search_kwargs={"k": 1})
docs = retriever.get_relevant_documents("what did he say abotu ketanji brown jackson")
len(docs)
1
previous
TF-IDF Retriever
next
Weaviate Hybrid Search
By Harrison Chase
© Copyright 2023, Harrison Chase.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/vectorstore-retriever.html
|
bca5df2401b9-1
|
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/retrievers/examples/vectorstore-retriever.html
|
6098b6c3e5b1-0
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.ipynb
.pdf
Getting Started
Getting Started#
The default recommended text splitter is the RecursiveCharacterTextSplitter. This text splitter takes a list of characters. It tries to create chunks based on splitting on the first character, but if any chunks are too large it then moves onto the next character, and so forth. By default the characters it tries to split on are ["\n\n", "\n", " ", ""]
In addition to controlling which characters you can split on, you can also control a few other things:
length_function: how the length of chunks is calculated. Defaults to just counting number of characters, but it’s pretty common to pass a token counter here.
chunk_size: the maximum size of your chunks (as measured by the length function).
chunk_overlap: the maximum overlap between chunks. It can be nice to have some overlap to maintain some continuity between chunks (eg do a sliding window).
# This is a long document we can split up.
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size = 100,
chunk_overlap = 20,
length_function = len,
)
texts = text_splitter.create_documents([state_of_the_union])
print(texts[0])
print(texts[1])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and' lookup_str='' metadata={} lookup_index=0
page_content='of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.' lookup_str='' metadata={} lookup_index=0
previous
Text Splitters
next
Character Text Splitter
By Harrison Chase
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/getting_started.html
|
6098b6c3e5b1-1
|
Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/getting_started.html
|
b24a02d1e80a-0
|
.ipynb
.pdf
Python Code Text Splitter
Python Code Text Splitter#
PythonCodeTextSplitter splits text along python class and method definitions. It’s implemented as a simple subclass of RecursiveCharacterSplitter with Python-specific separators. See the source code to see the Python syntax expected by default.
How the text is split: by list of python specific characters
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import PythonCodeTextSplitter
python_text = """
class Foo:
def bar():
def foo():
def testing_func():
def bar():
"""
python_splitter = PythonCodeTextSplitter(chunk_size=30, chunk_overlap=0)
docs = python_splitter.create_documents([python_text])
docs
[Document(page_content='Foo:\n\n def bar():', lookup_str='', metadata={}, lookup_index=0),
Document(page_content='foo():\n\ndef testing_func():', lookup_str='', metadata={}, lookup_index=0),
Document(page_content='bar():', lookup_str='', metadata={}, lookup_index=0)]
previous
NLTK Text Splitter
next
RecursiveCharacterTextSplitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/python.html
|
61c034bc2184-0
|
.ipynb
.pdf
tiktoken (OpenAI) Length Function
tiktoken (OpenAI) Length Function#
You can also use tiktoken, a open source tokenizer package from OpenAI to estimate tokens used. Will probably be more accurate for their models.
How the text is split: by character passed in
How the chunk size is measured: by tiktoken tokenizer
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Last year COVID-19 kept us apart. This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
previous
Spacy Text Splitter
next
TiktokenText Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/tiktoken.html
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850a3ebf507f-0
|
.ipynb
.pdf
Latex Text Splitter
Latex Text Splitter#
LatexTextSplitter splits text along Latex headings, headlines, enumerations and more. It’s implemented as a simple subclass of RecursiveCharacterSplitter with Latex-specific separators. See the source code to see the Latex syntax expected by default.
How the text is split: by list of latex specific tags
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import LatexTextSplitter
latex_text = """
\documentclass{article}
\begin{document}
\maketitle
\section{Introduction}
Large language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.
\subsection{History of LLMs}
The earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.
\subsection{Applications of LLMs}
LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.
\end{document}
"""
latex_splitter = LatexTextSplitter(chunk_size=400, chunk_overlap=0)
docs = latex_splitter.create_documents([latex_text])
docs
[Document(page_content='\\documentclass{article}\n\n\x08egin{document}\n\n\\maketitle', lookup_str='', metadata={},
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/latex.html
|
850a3ebf507f-1
|
lookup_str='', metadata={}, lookup_index=0),
Document(page_content='Introduction}\nLarge language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.', lookup_str='', metadata={}, lookup_index=0),
Document(page_content='History of LLMs}\nThe earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.', lookup_str='', metadata={}, lookup_index=0),
Document(page_content='Applications of LLMs}\nLLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.\n\n\\end{document}', lookup_str='', metadata={}, lookup_index=0)]
previous
Hugging Face Length Function
next
Markdown Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/latex.html
|
befd97e3fe64-0
|
.ipynb
.pdf
Spacy Text Splitter
Spacy Text Splitter#
Another alternative to NLTK is to use Spacy.
How the text is split: by Spacy
How the chunk size is measured: by length function passed in (defaults to number of characters)
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import SpacyTextSplitter
text_splitter = SpacyTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.
Members of Congress and the Cabinet.
Justices of the Supreme Court.
My fellow Americans.
Last year COVID-19 kept us apart.
This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents.
But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
And with an unwavering resolve that freedom will always triumph over tyranny.
Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.
But he badly miscalculated.
He thought he could roll into Ukraine and the world would roll over.
Instead he met a wall of strength he never imagined.
He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
previous
RecursiveCharacterTextSplitter
next
tiktoken (OpenAI) Length Function
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/spacy.html
|
0ca9f3510acd-0
|
.ipynb
.pdf
TiktokenText Splitter
TiktokenText Splitter#
How the text is split: by tiktoken tokens
How the chunk size is measured: by tiktoken tokens
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import TokenTextSplitter
text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our
previous
tiktoken (OpenAI) Length Function
next
Vectorstores
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/tiktoken_splitter.html
|
3bfbfd9677b0-0
|
.ipynb
.pdf
RecursiveCharacterTextSplitter
RecursiveCharacterTextSplitter#
This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is ["\n\n", "\n", " ", ""]. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.
How the text is split: by list of characters
How the chunk size is measured: by length function passed in (defaults to number of characters)
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size = 100,
chunk_overlap = 20,
length_function = len,
)
texts = text_splitter.create_documents([state_of_the_union])
print(texts[0])
print(texts[1])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and' lookup_str='' metadata={} lookup_index=0
page_content='of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.' lookup_str='' metadata={} lookup_index=0
previous
Python Code Text Splitter
next
Spacy Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html
|
2ad426a2f2f3-0
|
.ipynb
.pdf
Markdown Text Splitter
Markdown Text Splitter#
MarkdownTextSplitter splits text along Markdown headings, code blocks, or horizontal rules. It’s implemented as a simple subclass of RecursiveCharacterSplitter with Markdown-specific separators. See the source code to see the Markdown syntax expected by default.
How the text is split: by list of markdown specific characters
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import MarkdownTextSplitter
markdown_text = """
# 🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
## Quick Install
```bash
# Hopefully this code block isn't split
pip install langchain
```
As an open source project in a rapidly developing field, we are extremely open to contributions.
"""
markdown_splitter = MarkdownTextSplitter(chunk_size=100, chunk_overlap=0)
docs = markdown_splitter.create_documents([markdown_text])
docs
[Document(page_content='# 🦜️🔗 LangChain\n\n⚡ Building applications with LLMs through composability ⚡', lookup_str='', metadata={}, lookup_index=0),
Document(page_content="Quick Install\n\n```bash\n# Hopefully this code block isn't split\npip install langchain", lookup_str='', metadata={}, lookup_index=0),
Document(page_content='As an open source project in a rapidly developing field, we are extremely open to contributions.', lookup_str='', metadata={}, lookup_index=0)]
previous
Latex Text Splitter
next
NLTK Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/markdown.html
|
41a8d8b9fe8f-0
|
.ipynb
.pdf
Character Text Splitter
Character Text Splitter#
This is a more simple method. This splits based on characters (by default “\n\n”) and measure chunk length by number of characters.
How the text is split: by single character
How the chunk size is measured: by length function passed in (defaults to number of characters)
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(
separator = "\n\n",
chunk_size = 1000,
chunk_overlap = 200,
length_function = len,
)
texts = text_splitter.create_documents([state_of_the_union])
print(texts[0])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
|
41a8d8b9fe8f-1
|
met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.' lookup_str='' metadata={} lookup_index=0
Here’s an example of passing metadata along with the documents, notice that it is split along with the documents.
metadatas = [{"document": 1}, {"document": 2}]
documents = text_splitter.create_documents([state_of_the_union, state_of_the_union], metadatas=metadatas)
print(documents[0])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.' lookup_str='' metadata={'document': 1} lookup_index=0
previous
Getting Started
next
Hugging Face Length Function
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
|
b66b242d1b5a-0
|
.ipynb
.pdf
Hugging Face Length Function
Hugging Face Length Function#
Most LLMs are constrained by the number of tokens that you can pass in, which is not the same as the number of characters. In order to get a more accurate estimate, we can use Hugging Face tokenizers to count the text length.
How the text is split: by character passed in
How the chunk size is measured: by Hugging Face tokenizer
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Last year COVID-19 kept us apart. This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
previous
Character Text Splitter
next
Latex Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/huggingface_length_function.html
|
9766011b8dca-0
|
.ipynb
.pdf
NLTK Text Splitter
NLTK Text Splitter#
Rather than just splitting on “\n\n”, we can use NLTK to split based on tokenizers.
How the text is split: by NLTK
How the chunk size is measured: by length function passed in (defaults to number of characters)
# This is a long document we can split up.
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
from langchain.text_splitter import NLTKTextSplitter
text_splitter = NLTKTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.
Members of Congress and the Cabinet.
Justices of the Supreme Court.
My fellow Americans.
Last year COVID-19 kept us apart.
This year we are finally together again.
Tonight, we meet as Democrats Republicans and Independents.
But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
And with an unwavering resolve that freedom will always triumph over tyranny.
Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.
But he badly miscalculated.
He thought he could roll into Ukraine and the world would roll over.
Instead he met a wall of strength he never imagined.
He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
Groups of citizens blocking tanks with their bodies.
previous
Markdown Text Splitter
next
Python Code Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18,
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/nltk.html
|
9766011b8dca-1
|
Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/text_splitters/examples/nltk.html
|
2fc88fe423d6-0
|
.ipynb
.pdf
Getting Started
Contents
Add texts
From Documents
Getting Started#
This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the embedding notebook before diving into this.
This covers generic high level functionality related to all vector stores. For guides on specific vectorstores, please see the how-to guides here
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
We cannot let this happen.
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/getting_started.html
|
2fc88fe423d6-1
|
Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Add texts#
You can easily add text to a vectorstore with the add_texts method. It will return a list of document IDs (in case you need to use them downstream).
docsearch.add_texts(["Ankush went to Princeton"])
['a05e3d0c-ab40-11ed-a853-e65801318981']
query = "Where did Ankush go to college?"
docs = docsearch.similarity_search(query)
docs[0]
Document(page_content='Ankush went to Princeton', lookup_str='', metadata={}, lookup_index=0)
From Documents#
We can also initialize a vectorstore from documents directly. This is useful when we use the method on the text splitter to get documents directly (handy when the original documents have associated metadata).
documents = text_splitter.create_documents([state_of_the_union], metadatas=[{"source": "State of the Union"}])
docsearch = Chroma.from_documents(documents, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
We cannot let this happen.
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/getting_started.html
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2fc88fe423d6-2
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Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
previous
Vectorstores
next
AtlasDB
Contents
Add texts
From Documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/getting_started.html
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6b31a89acb12-0
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.ipynb
.pdf
FAISS
Contents
Similarity Search with score
Saving and loading
Merging
FAISS#
This notebook shows how to use functionality related to the FAISS vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Similarity Search with score#
There are some FAISS specific methods. One of them is similarity_search_with_score, which allows you to return not only the documents but also the similarity score of the query to
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6b31a89acb12-1
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which allows you to return not only the documents but also the similarity score of the query to them.
docs_and_scores = db.similarity_search_with_score(query)
docs_and_scores[0]
(Document(page_content='In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n\nWe cannot let this happen. \n\nTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
0.3914415)
It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.
embedding_vector = embeddings.embed_query(query)
docs_and_scores = db.similarity_search_by_vector(embedding_vector)
Saving and loading#
You can also save and load a FAISS index. This is useful so you don’t have to recreate it everytime you use it.
db.save_local("faiss_index")
new_db = FAISS.load_local("faiss_index", embeddings)
docs =
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6b31a89acb12-2
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= FAISS.load_local("faiss_index", embeddings)
docs = new_db.similarity_search(query)
docs[0]
Document(page_content='In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n\nWe cannot let this happen. \n\nTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)
Merging#
You can also merge two FAISS vectorstores
db1 = FAISS.from_texts(["foo"], embeddings)
db2 = FAISS.from_texts(["bar"], embeddings)
db1.docstore._dict
{'e0b74348-6c93-4893-8764-943139ec1d17': Document(page_content='foo', lookup_str='', metadata={}, lookup_index=0)}
db2.docstore._dict
{'bdc50ae3-a1bb-4678-9260-1b0979578f40': Document(page_content='bar', lookup_str='', metadata={},
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Document(page_content='bar', lookup_str='', metadata={}, lookup_index=0)}
db1.merge_from(db2)
db1.docstore._dict
{'e0b74348-6c93-4893-8764-943139ec1d17': Document(page_content='foo', lookup_str='', metadata={}, lookup_index=0),
'd5211050-c777-493d-8825-4800e74cfdb6': Document(page_content='bar', lookup_str='', metadata={}, lookup_index=0)}
previous
ElasticSearch
next
Milvus
Contents
Similarity Search with score
Saving and loading
Merging
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/faiss.html
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ab279ddec767-0
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.ipynb
.pdf
AtlasDB
AtlasDB#
This notebook shows you how to use functionality related to the AtlasDB
import time
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import SpacyTextSplitter
from langchain.vectorstores import AtlasDB
from langchain.document_loaders import TextLoader
!python -m spacy download en_core_web_sm
ATLAS_TEST_API_KEY = '7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6'
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = SpacyTextSplitter(separator='|')
texts = []
for doc in text_splitter.split_documents(documents):
texts.extend(doc.page_content.split('|'))
texts = [e.strip() for e in texts]
db = AtlasDB.from_texts(texts=texts,
name='test_index_'+str(time.time()), # unique name for your vector store
description='test_index', #a description for your vector store
api_key=ATLAS_TEST_API_KEY,
index_kwargs={'build_topic_model': True})
db.project.wait_for_project_lock()
db.project
test_index_1677255228.136989
A description for your project 508
|
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ab279ddec767-1
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A description for your project 508 datums inserted.
1 index built.
Projections
test_index_1677255228.136989_index. Status Completed. view online
Projection ID: db996d77-8981-48a0-897a-ff2c22bbf541
Hide embedded project
Explore on atlas.nomic.ai
previous
Getting Started
next
Chroma
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/atlas.html
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56c191d0a4d3-0
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.ipynb
.pdf
PGVector
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
PGVector#
This notebook shows how to use functionality related to the Postgres vector database (PGVector).
## Loading Environment Variables
from typing import List, Tuple
from dotenv import load_dotenv
load_dotenv()
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.pgvector import PGVector
from langchain.document_loaders import TextLoader
from langchain.docstore.document import Document
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
## PGVector needs the connection string to the database.
## We will load it from the environment variables.
import os
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host=os.environ.get("PGVECTOR_HOST", "localhost"),
port=int(os.environ.get("PGVECTOR_PORT", "5432")),
database=os.environ.get("PGVECTOR_DATABASE", "postgres"),
user=os.environ.get("PGVECTOR_USER", "postgres"),
password=os.environ.get("PGVECTOR_PASSWORD", "postgres"),
)
## Example
# postgresql+psycopg2://username:password@localhost:5432/database_name
Similarity search with score#
Similarity Search with Euclidean Distance (Default)#
# The PGVector Module will try to create a table with the name of the collection. So, make sure that the collection name is unique and the user has the
# permission to create a table.
db = PGVector.from_documents(
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56c191d0a4d3-1
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has the
# permission to create a table.
db = PGVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name="state_of_the_union",
connection_string=CONNECTION_STRING,
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.6076628081132506
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6076628081132506
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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56c191d0a4d3-2
|
our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6076804780049968
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6076804780049968
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html
|
56c191d0a4d3-3
|
who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
previous
OpenSearch
next
Pinecone
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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7944c52e14cc-0
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.ipynb
.pdf
Zilliz
Zilliz#
This notebook shows how to use functionality related to the Zilliz Cloud managed vector database.
To run, you should have a Zilliz Cloud instance up and running: https://zilliz.com/cloud
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.document_loaders import TextLoader
# replace
ZILLIZ_CLOUD_HOSTNAME = "" # example: "in01-17f69c292d4a50a.aws-us-west-2.vectordb.zillizcloud.com"
ZILLIZ_CLOUD_PORT = "" #example: "19532"
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vector_db = Milvus.from_documents(
docs,
embeddings,
connection_args={"host": ZILLIZ_CLOUD_HOSTNAME, "port": ZILLIZ_CLOUD_PORT},
)
docs = vector_db.similarity_search(query)
docs[0]
previous
Weaviate
next
Retrievers
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/zilliz.html
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a29a3d48bb95-0
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.ipynb
.pdf
Redis
Contents
RedisVectorStoreRetriever
Redis#
This notebook shows how to use functionality related to the Redis vector database.
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.redis import Redis
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
rds = Redis.from_documents(docs, embeddings, redis_url="redis://localhost:6379", index_name='link')
rds.index_name
'link'
query = "What did the president say about Ketanji Brown Jackson"
results = rds.similarity_search(query)
print(results[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
print(rds.add_texts(["Ankush went to Princeton"]))
['doc:link:d7d02e3faf1b40bbbe29a683ff75b280']
query =
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= "Princeton"
results = rds.similarity_search(query)
print(results[0].page_content)
Ankush went to Princeton
# Load from existing index
rds = Redis.from_existing_index(embeddings, redis_url="redis://localhost:6379", index_name='link')
query = "What did the president say about Ketanji Brown Jackson"
results = rds.similarity_search(query)
print(results[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
RedisVectorStoreRetriever#
Here we go over different options for using the vector store as a retriever.
There are three different search methods we can use to do retrieval. By default, it will use semantic similarity.
retriever = rds.as_retriever()
docs = retriever.get_relevant_documents(query)
We can also use similarity_limit as a search method. This is only return documents if they are similar enough
retriever = rds.as_retriever(search_type="similarity_limit")
# Here we can see it doesn't return any results because there are no relevant documents
retriever.get_relevant_documents("where did ankush go to
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a29a3d48bb95-2
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there are no relevant documents
retriever.get_relevant_documents("where did ankush go to college?")
previous
Qdrant
next
Weaviate
Contents
RedisVectorStoreRetriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/redis.html
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171a79f5ac4b-0
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.ipynb
.pdf
Qdrant
Contents
Connecting to Qdrant from LangChain
Local mode
In-memory
On-disk storage
On-premise server deployment
Qdrant Cloud
Reusing the same collection
Similarity search
Similarity search with score
Maximum marginal relevance search (MMR)
Qdrant as a Retriever
Customizing Qdrant
Qdrant#
This notebook shows how to use functionality related to the Qdrant vector database. There are various modes of how to run Qdrant, and depending on the chosen one, there will be some subtle differences. The options include:
Local mode, no server required
On-premise server deployment
Qdrant Cloud
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
Connecting to Qdrant from LangChain#
Local mode#
Python client allows you to run the same code in local mode without running the Qdrant server. That’s great for testing things out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kepy in memory or persisted on disk.
In-memory#
For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the client is destroyed - usually at the end of your script/notebook.
qdrant = Qdrant.from_documents(
docs, embeddings,
location=":memory:", # Local mode with in-memory storage only
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location=":memory:", # Local mode with in-memory storage only
collection_name="my_documents",
)
On-disk storage#
Local mode, without using the Qdrant server, may also store your vectors on disk so they’re persisted between runs.
qdrant = Qdrant.from_documents(
docs, embeddings,
path="/tmp/local_qdrant",
collection_name="my_documents",
)
On-premise server deployment#
No matter if you choose to launch Qdrant locally with a Docker container, or select a Kubernetes deployment with the official Helm chart, the way you’re going to connect to such an instance will be identical. You’ll need to provide a URL pointing to the service.
url = "<---qdrant url here --->"
qdrant = Qdrant.from_documents(
docs, embeddings,
url, prefer_grpc=True,
collection_name="my_documents",
)
Qdrant Cloud#
If you prefer not to keep yourself busy with managing the infrastructure, you can choose to set up a fully-managed Qdrant cluster on Qdrant Cloud. There is a free forever 1GB cluster included for trying out. The main difference with using a managed version of Qdrant is that you’ll need to provide an API key to secure your deployment from being accessed publicly.
url = "<---qdrant cloud cluster url here --->"
api_key = "<---api key here--->"
qdrant = Qdrant.from_documents(
docs, embeddings,
url, prefer_grpc=True, api_key=api_key,
collection_name="my_documents",
)
Reusing the same collection#
Both Qdrant.from_texts and Qdrant.from_documents methods are great to start using Qdrant with LangChain, but they are going to destroy the collection and create it
|
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start using Qdrant with LangChain, but they are going to destroy the collection and create it from scratch! If you want to reuse the existing collection, you can always create an instance of Qdrant on your own and pass the QdrantClient instance with the connection details.
del qdrant
import qdrant_client
client = qdrant_client.QdrantClient(
path="/tmp/local_qdrant", prefer_grpc=True
)
qdrant = Qdrant(
client=client, collection_name="my_documents",
embedding_function=embeddings.embed_query
)
Similarity search#
The simplest scenario for using Qdrant vector store is to perform a similarity search. Under the hood, our query will be encoded with the embedding_function and used to find similar documents in Qdrant collection.
query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search(query)
print(found_docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Similarity search with score#
Sometimes we might want to perform the search, but also obtain a relevancy
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|
search with score#
Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result.
query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.similarity_search_with_score(query)
document, score = found_docs[0]
print(document.page_content)
print(f"\nScore: {score}")
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Score: 0.8153784913324512
Maximum marginal relevance search (MMR)#
If you’d like to look up for some similar documents, but you’d also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
query = "What did the president say about Ketanji Brown Jackson"
found_docs = qdrant.max_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
print(f"{i + 1}.", doc.page_content, "\n")
1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html
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171a79f5ac4b-4
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Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together.
I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera.
They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun.
Officer Mora was 27 years old.
Officer Rivera was 22.
Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers.
I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.
I’ve worked on these issues a long time.
I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.
Qdrant as a Retriever#
Qdrant, as all the other vector stores, is a
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html
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171a79f5ac4b-5
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as a Retriever#
Qdrant, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.
retriever = qdrant.as_retriever()
retriever
VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='similarity', search_kwargs={})
It might be also specified to use MMR as a search strategy, instead of similarity.
retriever = qdrant.as_retriever(search_type="mmr")
retriever
VectorStoreRetriever(vectorstore=<langchain.vectorstores.qdrant.Qdrant object at 0x7fc4e5720a00>, search_type='mmr', search_kwargs={})
query = "What did the president say about Ketanji Brown Jackson"
retriever.get_relevant_documents(query)[0]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})
Customizing Qdrant#
Qdrant stores your vector embeddings along with the optional
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html
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171a79f5ac4b-6
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Qdrant#
Qdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.
By default, your document is going to be stored in the following payload structure:
{
"page_content": "Lorem ipsum dolor sit amet",
"metadata": {
"foo": "bar"
}
}
You can, however, decide to use different keys for the page content and metadata. That’s useful if you already have a collection that you’d like to reuse. You can always change the
Qdrant.from_documents(
docs, embeddings,
location=":memory:",
collection_name="my_documents_2",
content_payload_key="my_page_content_key",
metadata_payload_key="my_meta",
)
<langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>
previous
Pinecone
next
Redis
Contents
Connecting to Qdrant from LangChain
Local mode
In-memory
On-disk storage
On-premise server deployment
Qdrant Cloud
Reusing the same collection
Similarity search
Similarity search with score
Maximum marginal relevance search (MMR)
Qdrant as a Retriever
Customizing Qdrant
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/qdrant.html
|
3b1c200012c7-0
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.ipynb
.pdf
Milvus
Milvus#
This notebook shows how to use functionality related to the Milvus vector database.
To run, you should have a Milvus instance up and running: https://milvus.io/docs/install_standalone-docker.md
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vector_db = Milvus.from_documents(
docs,
embeddings,
connection_args={"host": "127.0.0.1", "port": "19530"},
)
docs = vector_db.similarity_search(query)
docs[0]
previous
FAISS
next
OpenSearch
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/milvus.html
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d748b3124776-0
|
.ipynb
.pdf
Chroma
Contents
Similarity search with score
Persistance
Initialize PeristedChromaDB
Persist the Database
Load the Database from disk, and create the chain
Retriever options
MMR
Chroma#
This notebook shows how to use functionality related to the Chroma vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
Using embedded DuckDB without persistence: data will be transient
print(docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Similarity search with
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/chroma.html
|
d748b3124776-1
|
top legal minds, who will continue Justice Breyer’s legacy of excellence.
Similarity search with score#
docs = db.similarity_search_with_score(query)
docs[0]
(Document(page_content='In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n\nWe cannot let this happen. \n\nTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
0.3913410007953644)
Persistance#
The below steps cover how to persist a ChromaDB instance
Initialize PeristedChromaDB#
Create embeddings for each chunk and insert into the Chroma vector database. The persist_directory argument tells ChromaDB where to store the database when it’s persisted.
# Embed and store the texts
# Supplying a persist_directory will store the embeddings on disk
persist_directory = 'db'
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents(documents=docs, embedding=embedding, persist_directory=persist_directory)
Running Chroma using direct local API.
No existing
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/chroma.html
|
d748b3124776-2
|
embedding=embedding, persist_directory=persist_directory)
Running Chroma using direct local API.
No existing DB found in db, skipping load
No existing DB found in db, skipping load
Persist the Database#
We should call persist() to ensure the embeddings are written to disk.
vectordb.persist()
vectordb = None
Persisting DB to disk, putting it in the save folder db
PersistentDuckDB del, about to run persist
Persisting DB to disk, putting it in the save folder db
Load the Database from disk, and create the chain#
Be sure to pass the same persist_directory and embedding_function as you did when you instantiated the database. Initialize the chain we will use for question answering.
# Now we can load the persisted database from disk, and use it as normal.
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
Running Chroma using direct local API.
loaded in 4 embeddings
loaded in 1 collections
Retriever options#
This section goes over different options for how to use Chroma as a retriever.
MMR#
In addition to using similarity search in the retriever object, you can also use mmr.
retriever = db.as_retriever(search_type="mmr")
retriever.get_relevant_documents(query)[0]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/chroma.html
|
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|
of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})
previous
AtlasDB
next
Deep Lake
Contents
Similarity search with score
Persistance
Initialize PeristedChromaDB
Persist the Database
Load the Database from disk, and create the chain
Retriever options
MMR
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/chroma.html
|
4c67f0efe853-0
|
.ipynb
.pdf
Deep Lake
Contents
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance function
Maximal Marginal relevance
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or local
Deep Lake#
This notebook showcases basic functionality related to Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks.
For more information, please see the Deep Lake documentation or api reference
!python3 -m pip install openai deeplake
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import DeepLake
from langchain.document_loaders import TextLoader
import os
os.environ['OPENAI_API_KEY'] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = DeepLake.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
Retrieval Question/Answering#
from langchain.chains import RetrievalQA
from langchain.llms import OpenAIChat
qa = RetrievalQA.from_chain_type(llm=OpenAIChat(model='gpt-3.5-turbo'), chain_type='stuff', retriever=db.as_retriever())
query = 'What did the president say about Ketanji Brown Jackson'
qa.run(query)
Attribute based filtering in metadata#
import
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
4c67f0efe853-1
|
president say about Ketanji Brown Jackson'
qa.run(query)
Attribute based filtering in metadata#
import random
for d in docs:
d.metadata['year'] = random.randint(2012, 2014)
db = DeepLake.from_documents(docs, embeddings)
db.similarity_search('What did the president say about Ketanji Brown Jackson', filter={'year': 2013})
Choosing distance function#
Distance function L2 for Euclidean, L1 for Nuclear, Max l-infinity distnace, cos for cosine similarity, dot for dot product
db.similarity_search('What did the president say about Ketanji Brown Jackson?', distance_metric='cos')
Maximal Marginal relevance#
Using maximal marginal relevance
db.max_marginal_relevance_search('What did the president say about Ketanji Brown Jackson?')
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or local#
By default deep lake datasets are stored in memory, in case you want to persist locally or to any object storage you can simply provide path to the dataset. You can retrieve token from app.activeloop.ai
!activeloop login -t <token>
# Embed and store the texts
dataset_path = "hub://{username}/{dataset_name}" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.
embedding = OpenAIEmbeddings()
vectordb = DeepLake.from_documents(documents=docs, embedding=embedding, dataset_path=dataset_path)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
vectordb.ds.summary()
embeddings = vectordb.ds.embedding.numpy()
previous
Chroma
next
ElasticSearch
Contents
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
4c67f0efe853-2
|
Contents
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance function
Maximal Marginal relevance
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or local
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/deeplake.html
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8661cce5ecdb-0
|
.ipynb
.pdf
Weaviate
Weaviate#
This notebook shows how to use functionality related to the Weaviate vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Weaviate
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
import weaviate
import os
WEAVIATE_URL = ""
client = weaviate.Client(
url=WEAVIATE_URL,
additional_headers={
'X-OpenAI-Api-Key': os.environ["OPENAI_API_KEY"]
}
)
client.schema.delete_all()
client.schema.get()
schema = {
"classes": [
{
"class": "Paragraph",
"description": "A written paragraph",
"vectorizer": "text2vec-openai",
"moduleConfig": {
"text2vec-openai": {
"model": "babbage",
"type": "text"
}
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html
|
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|
}
},
"properties": [
{
"dataType": ["text"],
"description": "The content of the paragraph",
"moduleConfig": {
"text2vec-openai": {
"skip": False,
"vectorizePropertyName": False
}
},
"name": "content",
},
],
},
]
}
client.schema.create(schema)
vectorstore = Weaviate(client, "Paragraph", "content")
query = "What did the president say about Ketanji Brown Jackson"
docs =
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html
|
8661cce5ecdb-2
|
"content")
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(query)
print(docs[0].page_content)
previous
Redis
next
Zilliz
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/weaviate.html
|
2654df33e1a8-0
|
.ipynb
.pdf
OpenSearch
Contents
similarity_search using Approximate k-NN Search with Custom Parameters
similarity_search using Script Scoring with Custom Parameters
similarity_search using Painless Scripting with Custom Parameters
Using a preexisting OpenSearch instance
OpenSearch#
This notebook shows how to use functionality related to the OpenSearch database.
To run, you should have the opensearch instance up and running: here
similarity_search by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for
large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.
Check this for more details.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200")
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
similarity_search using Approximate k-NN Search with Custom Parameters#
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", engine="faiss", space_type="innerproduct", ef_construction=256, m=48)
query = "What did the president say about Ketanji Brown Jackson"
docs =
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/opensearch.html
|
2654df33e1a8-1
|
= "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
similarity_search using Script Scoring with Custom Parameters#
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring")
print(docs[0].page_content)
similarity_search using Painless Scripting with Custom Parameters#
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}}
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter)
print(docs[0].page_content)
Using a preexisting OpenSearch instance#
It’s also possible to use a preexisting OpenSearch instance with documents that already have vectors present.
# this is just an example, you would need to change these values to point to another opensearch instance
docsearch = OpenSearchVectorSearch(index_name="index-*", embedding_function=embeddings, opensearch_url="http://localhost:9200")
# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata
docs = docsearch.similarity_search("Who was asking about getting lunch today?", search_type="script_scoring", space_type="cosinesimil", vector_field="message_embedding",
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/opensearch.html
|
2654df33e1a8-2
|
space_type="cosinesimil", vector_field="message_embedding", text_field="message", metadata_field="message_metadata")
previous
Milvus
next
PGVector
Contents
similarity_search using Approximate k-NN Search with Custom Parameters
similarity_search using Script Scoring with Custom Parameters
similarity_search using Painless Scripting with Custom Parameters
Using a preexisting OpenSearch instance
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/opensearch.html
|
2a74812b259b-0
|
.ipynb
.pdf
Pinecone
Pinecone#
This notebook shows how to use functionality related to the Pinecone vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
import pinecone
# initialize pinecone
pinecone.init(
api_key="YOUR_API_KEY", # find at app.pinecone.io
environment="YOUR_ENV" # next to api key in console
)
index_name = "langchain-demo"
docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
previous
PGVector
next
Qdrant
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/pinecone.html
|
176be5b6813f-0
|
.ipynb
.pdf
ElasticSearch
ElasticSearch#
This notebook shows how to use functionality related to the ElasticSearch database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = ElasticVectorSearch.from_documents(docs, embeddings, elasticsearch_url="http://localhost:9200"
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
We cannot let this happen.
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
previous
Deep Lake
next
FAISS
By
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html
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176be5b6813f-1
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Justice Breyer’s legacy of excellence.
previous
Deep Lake
next
FAISS
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html
|
84e6793009b3-0
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.ipynb
.pdf
Airbyte JSON
Airbyte JSON#
This covers how to load any source from Airbyte into a local JSON file that can be read in as a document
Prereqs:
Have docker desktop installed
Steps:
Clone Airbyte from GitHub - git clone https://github.com/airbytehq/airbyte.git
Switch into Airbyte directory - cd airbyte
Start Airbyte - docker compose up
In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that’s username airbyte and password password.
Setup any source you wish.
Set destination as Local JSON, with specified destination path - lets say /json_data. Set up manual sync.
Run the connection!
To see what files are create, you can navigate to: file:///tmp/airbyte_local
Find your data and copy path. That path should be saved in the file variable below. It should start with /tmp/airbyte_local
from langchain.document_loaders import AirbyteJSONLoader
!ls /tmp/airbyte_local/json_data/
_airbyte_raw_pokemon.jsonl
loader = AirbyteJSONLoader('/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl')
data = loader.load()
print(data[0].page_content[:500])
abilities:
ability:
name: blaze
url: https://pokeapi.co/api/v2/ability/66/
is_hidden: False
slot: 1
ability:
name: solar-power
url: https://pokeapi.co/api/v2/ability/94/
is_hidden: True
slot: 3
base_experience: 267
forms:
name: charizard
url: https://pokeapi.co/api/v2/pokemon-form/6/
game_indices:
game_index: 180
version:
name: red
url:
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/airbyte_json.html
|
84e6793009b3-1
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game_index: 180
version:
name: red
url: https://pokeapi.co/api/v2/version/1/
game_index: 180
version:
name: blue
url: https://pokeapi.co/api/v2/version/2/
game_index: 180
version:
n
previous
CoNLL-U
next
Apify Dataset
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/airbyte_json.html
|
48666f06c48e-0
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.ipynb
.pdf
Figma
Figma#
This notebook covers how to load data from the Figma REST API into a format that can be ingested into LangChain, along with example usage for code generation.
import os
from langchain.document_loaders.figma import FigmaFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import ConversationChain, LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
The Figma API Requires an access token, node_ids, and a file key.
The file key can be pulled from the URL. https://www.figma.com/file/{filekey}/sampleFilename
Node IDs are also available in the URL. Click on anything and look for the ‘?node-id={node_id}’ param.
Access token instructions are in the Figma help center article: https://help.figma.com/hc/en-us/articles/8085703771159-Manage-personal-access-tokens
figma_loader = FigmaFileLoader(
os.environ.get('ACCESS_TOKEN'),
os.environ.get('NODE_IDS'),
os.environ.get('FILE_KEY')
)
# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details
index = VectorstoreIndexCreator().from_loaders([figma_loader])
figma_doc_retriever = index.vectorstore.as_retriever()
def generate_code(human_input):
# I have no idea if the Jon Carmack thing makes for better code. YMMV.
# See
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/figma.html
|
48666f06c48e-1
|
if the Jon Carmack thing makes for better code. YMMV.
# See https://python.langchain.com/en/latest/modules/models/chat/getting_started.html for chat info
system_prompt_template = """You are expert coder Jon Carmack. Use the provided design context to create idomatic HTML/CSS code as possible based on the user request.
Everything must be inline in one file and your response must be directly renderable by the browser.
Figma file nodes and metadata: {context}"""
human_prompt_template = "Code the {text}. Ensure it's mobile responsive"
system_message_prompt = SystemMessagePromptTemplate.from_template(system_prompt_template)
human_message_prompt = HumanMessagePromptTemplate.from_template(human_prompt_template)
# delete the gpt-4 model_name to use the default gpt-3.5 turbo for faster results
gpt_4 = ChatOpenAI(temperature=.02, model_name='gpt-4')
# Use the retriever's 'get_relevant_documents' method if needed to filter down longer docs
relevant_nodes = figma_doc_retriever.get_relevant_documents(human_input)
conversation = [system_message_prompt, human_message_prompt]
chat_prompt = ChatPromptTemplate.from_messages(conversation)
response = gpt_4(chat_prompt.format_prompt(
context=relevant_nodes,
text=human_input).to_messages())
return response
response = generate_code("page top header")
Returns the following in response.content:
<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta charset="UTF-8">\n <meta name="viewport" content="width=device-width,
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/figma.html
|
48666f06c48e-2
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<meta name="viewport" content="width=device-width, initial-scale=1.0">\n <style>\n @import url(\'https://fonts.googleapis.com/css2?family=DM+Sans:wght@500;700&family=Inter:wght@600&display=swap\');\n\n body {\n margin: 0;\n font-family: \'DM Sans\', sans-serif;\n }\n\n .header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: 20px;\n background-color: #fff;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n }\n\n .header h1 {\n font-size: 16px;\n font-weight: 700;\n margin: 0;\n }\n\n .header nav {\n display: flex;\n align-items:
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/figma.html
|
48666f06c48e-3
|
display: flex;\n align-items: center;\n }\n\n .header nav a {\n font-size: 14px;\n font-weight: 500;\n text-decoration: none;\n color: #000;\n margin-left: 20px;\n }\n\n @media (max-width: 768px) {\n .header nav {\n display: none;\n }\n }\n </style>\n</head>\n<body>\n <header class="header">\n <h1>Company Contact</h1>\n <nav>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n </nav>\n </header>\n</body>\n</html>
previous
Facebook Chat
next
GCS Directory
By Harrison Chase
© Copyright 2023, Harrison Chase.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/figma.html
|
48666f06c48e-4
|
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/figma.html
|
941bdf8325ae-0
|
.ipynb
.pdf
BigQuery Loader
Contents
Basic Usage
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
BigQuery Loader#
Load a BigQuery query with one document per row.
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)
[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 =
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/bigquery.html
|
941bdf8325ae-1
|
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
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 =
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/bigquery.html
|
941bdf8325ae-2
|
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)]
previous
Azure Blob Storage File
next
Bilibili
Contents
Basic Usage
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/bigquery.html
|
cce0cdad36f6-0
|
.ipynb
.pdf
PowerPoint
Contents
Retain Elements
PowerPoint#
This covers how to load PowerPoint documents into a document format that we can use downstream.
from langchain.document_loaders import UnstructuredPowerPointLoader
loader = UnstructuredPowerPointLoader("example_data/fake-power-point.pptx")
data = loader.load()
data
[Document(page_content='Adding a Bullet Slide\n\nFind the bullet slide layout\n\nUse _TextFrame.text for first bullet\n\nUse _TextFrame.add_paragraph() for subsequent bullets\n\nHere is a lot of text!\n\nHere is some text in a text box!', lookup_str='', metadata={'source': 'example_data/fake-power-point.pptx'}, lookup_index=0)]
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 = UnstructuredPowerPointLoader("example_data/fake-power-point.pptx", mode="elements")
data = loader.load()
data[0]
Document(page_content='Adding a Bullet Slide', lookup_str='', metadata={'source': 'example_data/fake-power-point.pptx'}, lookup_index=0)
previous
PDF
next
ReadTheDocs Documentation
Contents
Retain Elements
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/powerpoint.html
|
53c2d291c684-0
|
.ipynb
.pdf
EPubs
Contents
Retain Elements
EPubs#
This covers how to load .epub documents into a document format that we can use downstream. You’ll need to install the pandocs package for this loader to work.
from langchain.document_loaders import UnstructuredEPubLoader
loader = UnstructuredEPubLoader("winter-sports.epub")
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 = UnstructuredEPubLoader("winter-sports.epub", mode="elements")
data = loader.load()
data[0]
Document(page_content='The Project Gutenberg eBook of Winter Sports in\nSwitzerland, by E. F. Benson', lookup_str='', metadata={'source': 'winter-sports.epub', 'page_number': 1, 'category': 'Title'}, lookup_index=0)
previous
Email
next
EverNote
Contents
Retain Elements
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/epub.html
|
1ce6e931f6fd-0
|
.ipynb
.pdf
Azure Blob Storage File
Azure Blob Storage File#
This covers how to load document objects from a Azure Blob Storage file.
from langchain.document_loaders import AzureBlobStorageFileLoader
#!pip install azure-storage-blob
loader = AzureBlobStorageFileLoader(conn_str='<connection string>', container='<container name>', blob_name='<blob name>')
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)]
previous
Azure Blob Storage Container
next
BigQuery Loader
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/azure_blob_storage_file.html
|
7c8bdc6a7c45-0
|
.ipynb
.pdf
Git
Contents
Load existing repository from disk
Clone repository from url
Git#
This notebook shows how to load text files from Git repository.
Load existing repository from disk#
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.git import GitLoader
loader = GitLoader(repo_path="./example_data/test_repo1/", branch=branch)
data = loader.load()
len(data)
1040
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.git import GitLoader
loader = GitLoader(
clone_url="https://github.com/hwchase17/langchain",
repo_path="./example_data/test_repo2/",
branch="master",
)
data = loader.load()
len(data)
1040
previous
GCS File Storage
next
GitBook
Contents
Load existing repository from disk
Clone repository from url
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/git.html
|
6c92d1499b74-0
|
.ipynb
.pdf
Images
Contents
Using Unstructured
Retain Elements
Images#
This covers how to load images such as JPGs PNGs into a document format that we can use downstream.
Using Unstructured#
from langchain.document_loaders.image import UnstructuredImageLoader
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")
data = loader.load()
data[0]
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
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/image.html
|
6c92d1499b74-1
|
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 « 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)
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
iFixit
next
IMSDb
Contents
Using Unstructured
Retain Elements
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/image.html
|
e9f575e333ce-0
|
.ipynb
.pdf
Subtitle Files
Subtitle Files#
How to load data from subtitle (.srt) files
from langchain.document_loaders import SRTLoader
loader = SRTLoader("example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt")
docs = loader.load()
docs[0].page_content[:100]
'<i>Corruption discovered\nat the core of the Banking Clan!</i> <i>Reunited, Rush Clovis\nand Senator A'
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Slack (Local Exported Zipfile)
next
Telegram
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/srt.html
|
eae52a5f354a-0
|
.ipynb
.pdf
Notion
Contents
🧑 Instructions for ingesting your own dataset
Notion#
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()
previous
Notebook
next
Notion DB Loader
Contents
🧑 Instructions for ingesting your own dataset
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/notion.html
|
fb3cc14ed989-0
|
.ipynb
.pdf
EverNote
EverNote#
How to load EverNote file from disk.
# !pip install pypandoc
# import pypandoc
# pypandoc.download_pandoc()
from langchain.document_loaders import EverNoteLoader
loader = EverNoteLoader("example_data/testing.enex")
loader.load()
[Document(page_content='testing this\n\nwhat happens?\n\nto the world?\n', lookup_str='', metadata={'source': 'example_data/testing.enex'}, lookup_index=0)]
previous
EPubs
next
Facebook Chat
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023.
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/evernote.html
|
4e47e2492c02-0
|
.ipynb
.pdf
GCS Directory
Contents
Specifying a prefix
GCS Directory#
This covers how to load document objects from an Google Cloud Storage (GCS) directory.
from langchain.document_loaders import GCSDirectoryLoader
# !pip install google-cloud-storage
loader = GCSDirectoryLoader(project_name="aist", bucket="testing-hwc")
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)
/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/tmpz37njh7u/fake.docx'}, lookup_index=0)]
Specifying a prefix#
You can also specify a prefix for more finegrained
|
https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/gcs_directory.html
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4e47e2492c02-1
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a prefix#
You can also specify a prefix for more finegrained control over what files to load.
loader = GCSDirectoryLoader(project_name="aist", bucket="testing-hwc", prefix="fake")
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)
/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/tmpylg6291i/fake.docx'}, lookup_index=0)]
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Specifying a prefix
By Harrison Chase
© Copyright 2023, Harrison Chase.
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https:///langchain-cn.readthedocs.io/en/latest/modules/indexes/document_loaders/examples/gcs_directory.html
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