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.pdf
Time Weighted VectorStore
Contents
Low Decay Rate
High Decay Rate
Virtual Time
Time Weighted VectorStore#
This retriever uses a combination of semantic similarity and a time decay.
The algorithm for scoring them is:
semantic_similarity + (1.0 - decay_rate) ** hours_passed
Notably, hours_passed refers to the hours passed since the object in the retriever was last accessed, not since it was created. This means that frequently accessed objects remain “fresh.”
import faiss
from datetime import datetime, timedelta
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain.schema import Document
from langchain.vectorstores import FAISS
Low Decay Rate#
A low decay rate (in this, to be extreme, we will set close to 0) means memories will be “remembered” for longer. A decay rate of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup.
# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.0000000000000000000000001, k=1)
yesterday = datetime.now() - timedelta(days=1)
retriever.add_documents([Document(page_content="hello world", metadata={"last_accessed_at": yesterday})])
retriever.add_documents([Document(page_content="hello foo")])
|
https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html
|
49f75240e2f5-1
|
retriever.add_documents([Document(page_content="hello foo")])
['d7f85756-2371-4bdf-9140-052780a0f9b3']
# "Hello World" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enough
retriever.get_relevant_documents("hello world")
[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 678341), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]
High Decay Rate#
With a high decay rate (e.g., several 9’s), the recency score quickly goes to 0! If you set this all the way to 1, recency is 0 for all objects, once again making this equivalent to a vector lookup.
# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.999, k=1)
yesterday = datetime.now() - timedelta(days=1)
retriever.add_documents([Document(page_content="hello world", metadata={"last_accessed_at": yesterday})])
retriever.add_documents([Document(page_content="hello foo")])
['40011466-5bbe-4101-bfd1-e22e7f505de2']
|
https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html
|
49f75240e2f5-2
|
# "Hello Foo" is returned first because "hello world" is mostly forgotten
retriever.get_relevant_documents("hello world")
[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 494798), 'created_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 178722), 'buffer_idx': 1})]
Virtual Time#
Using some utils in LangChain, you can mock out the time component
from langchain.utils import mock_now
import datetime
# Notice the last access time is that date time
with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):
print(retriever.get_relevant_documents("hello world"))
[Document(page_content='hello world', metadata={'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]
previous
TF-IDF
next
VectorStore
Contents
Low Decay Rate
High Decay Rate
Virtual Time
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html
|
bace3e2cec8c-0
|
.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
By Harrison Chase
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html
|
bace3e2cec8c-1
|
previous
Text Splitters
next
Character
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html
|
ff8681e1074d-0
|
.ipynb
.pdf
Python Code
Python Code#
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 separators
How the chunk size is measured: by 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)]
python_splitter.split_text(python_text)
['Foo:\n\n def bar():', 'foo():\n\ndef testing_func():', 'bar():']
previous
NLTK
next
Recursive Character
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/python.html
|
242b2a4e7ed9-0
|
.ipynb
.pdf
tiktoken (OpenAI) tokenizer
tiktoken (OpenAI) tokenizer#
tiktoken is a fast BPE tokenizer created by OpenAI.
We can use it to estimate tokens used. It will probably be more accurate for the OpenAI models.
How the text is split: by character passed in
How the chunk size is measured: by tiktoken tokenizer
#!pip install tiktoken
# 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
Hugging Face tokenizer
next
Vectorstores
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/tiktoken.html
|
1202268a1615-0
|
.ipynb
.pdf
LaTeX
LaTeX#
LaTeX is widely used in academia for the communication and publication of scientific documents in many fields, including mathematics, computer science, engineering, physics, chemistry, economics, linguistics, quantitative psychology, philosophy, and political science.
LatexTextSplitter splits text along LaTeX headings, headlines, enumerations and more. It’s implemented as a subclass of RecursiveCharacterSplitter with LaTeX-specific separators. See the source code for more details.
How the text is split: by list of LaTeX specific tags
How the chunk size is measured: by 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)
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/latex.html
|
1202268a1615-1
|
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={}, 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)]
latex_splitter.split_text(latex_text)
['\\documentclass{article}\n\n\x08egin{document}\n\n\\maketitle',
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/latex.html
|
1202268a1615-2
|
'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.',
'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.',
'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}']
previous
Character
next
Markdown
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/latex.html
|
8ba10eb8b93b-0
|
.ipynb
.pdf
spaCy
spaCy#
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.
Another alternative to NLTK is to use Spacy tokenizer.
How the text is split: by spaCy tokenizer
How the chunk size is measured: by number of characters
#!pip install spacy
# 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
Recursive Character
next
Tiktoken
By Harrison Chase
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/spacy.html
|
8ba10eb8b93b-1
|
previous
Recursive Character
next
Tiktoken
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/spacy.html
|
022243c3ca68-0
|
.ipynb
.pdf
Tiktoken
Tiktoken#
tiktoken is a fast BPE tokeniser created by OpenAI.
How the text is split: by tiktoken tokens
How the chunk size is measured: by tiktoken tokens
#!pip install tiktoken
# 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
spaCy
next
Hugging Face tokenizer
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/tiktoken_splitter.html
|
9a0e24f3c052-0
|
.ipynb
.pdf
Recursive Character
Recursive Character#
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 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
text_splitter.split_text(state_of_the_union)[:2]
['Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and',
'of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.']
previous
Python Code
next
spaCy
By Harrison Chase
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html
|
9a0e24f3c052-1
|
previous
Python Code
next
spaCy
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html
|
bba263be933f-0
|
.ipynb
.pdf
Markdown
Markdown#
Markdown is a lightweight markup language for creating formatted text using a plain-text editor.
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 separators
How the chunk size is measured: by 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 ⚡', metadata={}),
Document(page_content="Quick Install\n\n```bash\n# Hopefully this code block isn't split\npip install langchain", metadata={}),
Document(page_content='As an open source project in a rapidly developing field, we are extremely open to contributions.', metadata={})]
markdown_splitter.split_text(markdown_text)
['# 🦜️🔗 LangChain\n\n⚡ Building applications with LLMs through composability ⚡',
"Quick Install\n\n```bash\n# Hopefully this code block isn't split\npip install langchain",
'As an open source project in a rapidly developing field, we are extremely open to contributions.']
previous
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/markdown.html
|
bba263be933f-1
|
previous
LaTeX
next
NLTK
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/markdown.html
|
74c85e304c8e-0
|
.ipynb
.pdf
Character
Character#
This is the simplest 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 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])
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
|
74c85e304c8e-1
|
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 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])
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
|
74c85e304c8e-2
|
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
text_splitter.split_text(state_of_the_union)[0]
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
|
74c85e304c8e-3
|
text_splitter.split_text(state_of_the_union)[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. \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.'
previous
Getting Started
next
LaTeX
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
|
7c1c2d666427-0
|
.ipynb
.pdf
Hugging Face tokenizer
Hugging Face tokenizer#
Hugging Face has many tokenizers.
We use Hugging Face tokenizer, the GPT2TokenizerFast to count the text length in tokens.
How the text is split: by character passed in
How the chunk size is measured: by number of tokens calculated by the 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
Tiktoken
next
tiktoken (OpenAI) tokenizer
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/huggingface_length_function.html
|
596c81cf4f3b-0
|
.ipynb
.pdf
NLTK
NLTK#
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language.
Rather than just splitting on “\n\n”, we can use NLTK to split based on NLTK tokenizers.
How the text is split: by NLTK tokenizer.
How the chunk size is measured:by number of characters
#pip install nltk
# 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.
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https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/nltk.html
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Groups of citizens blocking tanks with their bodies.
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Markdown
next
Python Code
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/nltk.html
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d3213cf6a37c-0
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.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.
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.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/getting_started.html
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d3213cf6a37c-1
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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.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/getting_started.html
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d3213cf6a37c-2
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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.
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Vectorstores
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AnalyticDB
Contents
Add texts
From Documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/getting_started.html
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f41b4e29ff61-0
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.ipynb
.pdf
FAISS
Contents
Similarity Search with score
Saving and loading
Merging
FAISS#
Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
Faiss documentation.
This notebook shows how to use functionality related to the FAISS vector database.
#!pip install faiss
# OR
!pip install faiss-cpu
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
# Uncomment the following line if you need to initialize FAISS with no AVX2 optimization
# os.environ['FAISS_NO_AVX2'] = '1'
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)
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html
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f41b4e29ff61-1
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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 them.
docs_and_scores = db.similarity_search_with_score(query)
docs_and_scores[0]
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html
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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 = new_db.similarity_search(query)
docs[0]
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html
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f41b4e29ff61-3
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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={}, lookup_index=0)}
db1.merge_from(db2)
db1.docstore._dict
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html
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f41b4e29ff61-4
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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
LanceDB
Contents
Similarity Search with score
Saving and loading
Merging
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html
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46dec52ccfb1-0
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.ipynb
.pdf
Atlas
Atlas#
Atlas is a platform for interacting with both small and internet scale unstructured datasets by Nomic.
This notebook shows you how to use functionality related to the AtlasDB vectorstore.
!pip install spacy
!python3 -m spacy download en_core_web_sm
!pip install nomic
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
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 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
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/atlas.html
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46dec52ccfb1-1
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Hide embedded project
Explore on atlas.nomic.ai
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Annoy
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Chroma
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/atlas.html
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6755c1d6bd73-0
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.ipynb
.pdf
PGVector
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
Working with vectorstore in PG
Uploading a vectorstore in PG
Retrieving a vectorstore in PG
PGVector#
PGVector is an open-source vector similarity search for Postgres
It supports:
exact and approximate nearest neighbor search
L2 distance, inner product, and cosine distance
This notebook shows how to use the Postgres vector database (PGVector).
See the installation instruction.
!pip install pgvector
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
## Loading Environment Variables
from typing import List, Tuple
from dotenv import load_dotenv
load_dotenv()
False
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")),
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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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(
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.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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6755c1d6bd73-2
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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 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.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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6755c1d6bd73-3
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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.
--------------------------------------------------------------------------------
Working with vectorstore in PG#
Uploading a vectorstore in PG#
db = PGVector.from_documents(
documents=data,
embedding=embeddings,
collection_name=collection_name,
connection_string=connection_string,
distance_strategy=DistanceStrategy.COSINE,
openai_api_key=api_key,
pre_delete_collection=False
)
Retrieving a vectorstore in PG#
store = PGVector(
connection_string=connection_string,
embedding_function=embedding,
collection_name=collection_name,
distance_strategy=DistanceStrategy.COSINE
)
retriever = store.as_retriever()
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OpenSearch
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Pinecone
Contents
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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6755c1d6bd73-4
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previous
OpenSearch
next
Pinecone
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
Working with vectorstore in PG
Uploading a vectorstore in PG
Retrieving a vectorstore in PG
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html
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f6feb83e3053-0
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.ipynb
.pdf
SKLearnVectorStore
Contents
Basic usage
Load a sample document corpus
Create the SKLearnVectorStore, index the document corpus and run a sample query
Saving and loading a vector store
Clean-up
SKLearnVectorStore#
scikit-learn is an open source collection of machine learning algorithms, including some implementations of the k nearest neighbors. SKLearnVectorStore wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.
This notebook shows how to use the SKLearnVectorStore vector database.
%pip install scikit-learn
# # if you plan to use bson serialization, install also:
# %pip install bson
# # if you plan to use parquet serialization, install also:
%pip install pandas pyarrow
To use OpenAI embeddings, you will need an OpenAI key. You can get one at https://platform.openai.com/account/api-keys or feel free to use any other embeddings.
import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI key:')
Basic usage#
Load a sample document corpus#
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import SKLearnVectorStore
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()
Create the SKLearnVectorStore, index the document corpus and run a sample query#
import tempfile
persist_path = os.path.join(tempfile.gettempdir(), 'union.parquet')
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/sklearn.html
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f6feb83e3053-1
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import tempfile
persist_path = os.path.join(tempfile.gettempdir(), 'union.parquet')
vector_store = SKLearnVectorStore.from_documents(
documents=docs,
embedding=embeddings,
persist_path=persist_path, # persist_path and serializer are optional
serializer='parquet'
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store.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.
Saving and loading a vector store#
vector_store.persist()
print('Vector store was persisted to', persist_path)
Vector store was persisted to /var/folders/6r/wc15p6m13nl_nl_n_xfqpc5c0000gp/T/union.parquet
vector_store2 = SKLearnVectorStore(
embedding=embeddings,
persist_path=persist_path,
serializer='parquet'
)
print('A new instance of vector store was loaded from', persist_path)
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/sklearn.html
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f6feb83e3053-2
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)
print('A new instance of vector store was loaded from', persist_path)
A new instance of vector store was loaded from /var/folders/6r/wc15p6m13nl_nl_n_xfqpc5c0000gp/T/union.parquet
docs = vector_store2.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.
Clean-up#
os.remove(persist_path)
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Redis
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Supabase (Postgres)
Contents
Basic usage
Load a sample document corpus
Create the SKLearnVectorStore, index the document corpus and run a sample query
Saving and loading a vector store
Clean-up
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/sklearn.html
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ad78bfe715ee-0
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.ipynb
.pdf
Vectara
Contents
Connecting to Vectara from LangChain
Similarity search
Similarity search with score
Vectara as a Retriever
Vectara#
Vectara is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy.
This notebook shows how to use functionality related to the Vectara vector database.
See the Vectara API documentation for more information on how to use the API.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key:········
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Vectara
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 Vectara from LangChain#
The Vectara API provides simple API endpoints for indexing and querying.
vectara = Vectara.from_documents(docs, embedding=None)
Similarity search#
The simplest scenario for using Vectara is to perform a similarity search.
query = "What did the president say about Ketanji Brown Jackson"
found_docs = vectara.similarity_search(query)
print(found_docs[0].page_content)
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found_docs = vectara.similarity_search(query)
print(found_docs[0].page_content)
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. A former top litigator in private practice. A former federal public defender.
Similarity 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 = vectara.similarity_search_with_score(query)
document, score = found_docs[0]
print(document.page_content)
print(f"\nScore: {score}")
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. A former top litigator in private practice. A former federal public defender.
Score: 1.0046461
Vectara as a Retriever#
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Score: 1.0046461
Vectara as a Retriever#
Vectara, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.
retriever = vectara.as_retriever()
retriever
VectorStoreRetriever(vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x156d3e830>, search_type='similarity', search_kwargs={})
query = "What did the president say about Ketanji Brown Jackson"
retriever.get_relevant_documents(query)[0]
Document(page_content='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. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})
previous
Typesense
next
Weaviate
Contents
Connecting to Vectara from LangChain
Similarity search
Similarity search with score
Vectara as a Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html
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7b070c9edd71-0
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.ipynb
.pdf
Zilliz
Zilliz#
Zilliz Cloud is a fully managed service on cloud for LF AI Milvus®,
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. Here are the installation instructions
!pip install pymilvus
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key:········
# replace
ZILLIZ_CLOUD_URI = "" # example: "https://in01-17f69c292d4a5sa.aws-us-west-2.vectordb.zillizcloud.com:19536"
ZILLIZ_CLOUD_USERNAME = "" # example: "username"
ZILLIZ_CLOUD_PASSWORD = "" # example: "*********"
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={
"uri": ZILLIZ_CLOUD_URI,
"user": ZILLIZ_CLOUD_USERNAME,
"password": ZILLIZ_CLOUD_PASSWORD,
"secure": True
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"password": ZILLIZ_CLOUD_PASSWORD,
"secure": True
}
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)
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. \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.'
previous
Weaviate
next
Retrievers
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/zilliz.html
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d1ba486cf806-0
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.ipynb
.pdf
Annoy
Contents
Create VectorStore from texts
Create VectorStore from docs
Create VectorStore via existing embeddings
Search via embeddings
Search via docstore id
Save and load
Construct from scratch
Annoy#
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
This notebook shows how to use functionality related to the Annoy vector database.
Note
NOTE: Annoy is read-only - once the index is built you cannot add any more emebddings!
If you want to progressively add new entries to your VectorStore then better choose an alternative!
#!pip install annoy
Create VectorStore from texts#
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Annoy
embeddings_func = HuggingFaceEmbeddings()
texts = ["pizza is great", "I love salad", "my car", "a dog"]
# default metric is angular
vector_store = Annoy.from_texts(texts, embeddings_func)
# allows for custom annoy parameters, defaults are n_trees=100, n_jobs=-1, metric="angular"
vector_store_v2 = Annoy.from_texts(
texts, embeddings_func, metric="dot", n_trees=100, n_jobs=1
)
vector_store.similarity_search("food", k=3)
[Document(page_content='pizza is great', metadata={}),
Document(page_content='I love salad', metadata={}),
Document(page_content='my car', metadata={})]
# the score is a distance metric, so lower is better
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# the score is a distance metric, so lower is better
vector_store.similarity_search_with_score("food", k=3)
[(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),
(Document(page_content='I love salad', metadata={}), 1.1273186206817627),
(Document(page_content='my car', metadata={}), 1.1580758094787598)]
Create VectorStore from docs#
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
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)
docs[:5]
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docs = text_splitter.split_documents(documents)
docs[:5]
[Document(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.', metadata={'source': '../../../state_of_the_union.txt'}),
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Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \n\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \n\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. \n\nThey keep moving. \n\nAnd the costs and the threats to America and the world keep rising. \n\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \n\nThe United States is a member along with 29 other nations. \n\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../../state_of_the_union.txt'}),
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Document(page_content='Putin’s latest attack on Ukraine was premeditated and unprovoked. \n\nHe rejected repeated efforts at diplomacy. \n\nHe thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did. \n\nWe prepared extensively and carefully. \n\nWe spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \n\nI spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression. \n\nWe countered Russia’s lies with truth. \n\nAnd now that he has acted the free world is holding him accountable. \n\nAlong with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.', metadata={'source': '../../../state_of_the_union.txt'}),
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Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n\nTogether with our allies –we are right now enforcing powerful economic sanctions. \n\nWe are cutting off Russia’s largest banks from the international financial system. \n\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless. \n\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come. \n\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n\nThe U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. \n\nWe are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.', metadata={'source': '../../../state_of_the_union.txt'}),
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Document(page_content='And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value. \n\nThe Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame. \n\nTogether with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. \n\nWe are giving more than $1 Billion in direct assistance to Ukraine. \n\nAnd we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering. \n\nLet me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine. \n\nOur forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.', metadata={'source': '../../../state_of_the_union.txt'})]
vector_store_from_docs = Annoy.from_documents(docs, embeddings_func)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store_from_docs.similarity_search(query)
print(docs[0].page_content[:100])
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Ac
Create VectorStore via existing embeddings#
embs = embeddings_func.embed_documents(texts)
data = list(zip(texts, embs))
vector_store_from_embeddings = Annoy.from_embeddings(data, embeddings_func)
vector_store_from_embeddings.similarity_search_with_score("food", k=3)
[(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),
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(Document(page_content='I love salad', metadata={}), 1.1273186206817627),
(Document(page_content='my car', metadata={}), 1.1580758094787598)]
Search via embeddings#
motorbike_emb = embeddings_func.embed_query("motorbike")
vector_store.similarity_search_by_vector(motorbike_emb, k=3)
[Document(page_content='my car', metadata={}),
Document(page_content='a dog', metadata={}),
Document(page_content='pizza is great', metadata={})]
vector_store.similarity_search_with_score_by_vector(motorbike_emb, k=3)
[(Document(page_content='my car', metadata={}), 1.0870471000671387),
(Document(page_content='a dog', metadata={}), 1.2095637321472168),
(Document(page_content='pizza is great', metadata={}), 1.3254905939102173)]
Search via docstore id#
vector_store.index_to_docstore_id
{0: '2d1498a8-a37c-4798-acb9-0016504ed798',
1: '2d30aecc-88e0-4469-9d51-0ef7e9858e6d',
2: '927f1120-985b-4691-b577-ad5cb42e011c',
3: '3056ddcf-a62f-48c8-bd98-b9e57a3dfcae'}
some_docstore_id = 0 # texts[0]
vector_store.docstore._dict[vector_store.index_to_docstore_id[some_docstore_id]]
Document(page_content='pizza is great', metadata={})
# same document has distance 0
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Document(page_content='pizza is great', metadata={})
# same document has distance 0
vector_store.similarity_search_with_score_by_index(some_docstore_id, k=3)
[(Document(page_content='pizza is great', metadata={}), 0.0),
(Document(page_content='I love salad', metadata={}), 1.0734446048736572),
(Document(page_content='my car', metadata={}), 1.2895267009735107)]
Save and load#
vector_store.save_local("my_annoy_index_and_docstore")
saving config
loaded_vector_store = Annoy.load_local(
"my_annoy_index_and_docstore", embeddings=embeddings_func
)
# same document has distance 0
loaded_vector_store.similarity_search_with_score_by_index(some_docstore_id, k=3)
[(Document(page_content='pizza is great', metadata={}), 0.0),
(Document(page_content='I love salad', metadata={}), 1.0734446048736572),
(Document(page_content='my car', metadata={}), 1.2895267009735107)]
Construct from scratch#
import uuid
from annoy import AnnoyIndex
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
metadatas = [{"x": "food"}, {"x": "food"}, {"x": "stuff"}, {"x": "animal"}]
# embeddings
embeddings = embeddings_func.embed_documents(texts)
# embedding dim
f = len(embeddings[0])
# index
metric = "angular"
index = AnnoyIndex(f, metric=metric)
for i, emb in enumerate(embeddings):
index.add_item(i, emb)
index.build(10)
# docstore
documents = []
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index.build(10)
# docstore
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_docstore_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{index_to_docstore_id[i]: doc for i, doc in enumerate(documents)}
)
db_manually = Annoy(
embeddings_func.embed_query, index, metric, docstore, index_to_docstore_id
)
db_manually.similarity_search_with_score("eating!", k=3)
[(Document(page_content='pizza is great', metadata={'x': 'food'}),
1.1314140558242798),
(Document(page_content='I love salad', metadata={'x': 'food'}),
1.1668788194656372),
(Document(page_content='my car', metadata={'x': 'stuff'}), 1.226445198059082)]
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AnalyticDB
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Atlas
Contents
Create VectorStore from texts
Create VectorStore from docs
Create VectorStore via existing embeddings
Search via embeddings
Search via docstore id
Save and load
Construct from scratch
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html
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53ddb8f374ae-0
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.ipynb
.pdf
Redis
Contents
Installing
Example
Redis as Retriever
Redis#
Redis (Remote Dictionary Server) is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker, with optional durability.
This notebook shows how to use functionality related to the Redis vector database.
Installing#
!pip install redis
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
Example#
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.
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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 = "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.
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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.
Redis as Retriever#
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 college?")
previous
Qdrant
next
SKLearnVectorStore
Contents
Installing
Example
Redis as Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html
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f371a17b2282-0
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.ipynb
.pdf
Supabase (Postgres)
Contents
Similarity search with score
Retriever options
Maximal Marginal Relevance Searches
Supabase (Postgres)#
Supabase is an open source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.
PostgreSQL also known as Postgres, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.
This notebook shows how to use Supabase and pgvector as your VectorStore.
To run this notebook, please ensure:
the pgvector extension is enabled
you have installed the supabase-py package
that you have created a match_documents function in your database
that you have a documents table in your public schema similar to the one below.
The following function determines cosine similarity, but you can adjust to your needs.
-- Enable the pgvector extension to work with embedding vectors
create extension vector;
-- Create a table to store your documents
create table documents (
id bigserial primary key,
content text, -- corresponds to Document.pageContent
metadata jsonb, -- corresponds to Document.metadata
embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
);
CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)
RETURNS TABLE(
id bigint,
content text,
metadata jsonb,
-- we return matched vectors to enable maximal marginal relevance searches
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGIN
RETURN query
SELECT
id,
content,
metadata,
embedding,
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f371a17b2282-1
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SELECT
id,
content,
metadata,
embedding,
1 -(documents.embedding <=> query_embedding) AS similarity
FROM
documents
ORDER BY
documents.embedding <=> query_embedding
LIMIT match_count;
END;
$$;
# with pip
!pip install supabase
# with conda
# !conda install -c conda-forge supabase
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
os.environ['SUPABASE_URL'] = getpass.getpass('Supabase URL:')
os.environ['SUPABASE_SERVICE_KEY'] = getpass.getpass('Supabase Service Key:')
# If you're storing your Supabase and OpenAI API keys in a .env file, you can load them with dotenv
from dotenv import load_dotenv
load_dotenv()
import os
from supabase.client import Client, create_client
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
supabase: Client = create_client(supabase_url, supabase_key)
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import SupabaseVectorStore
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)
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docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
# We're using the default `documents` table here. You can modify this by passing in a `table_name` argument to the `from_documents` method.
vector_store = SupabaseVectorStore.from_documents(
docs, embeddings, client=supabase
)
query = "What did the president say about Ketanji Brown Jackson"
matched_docs = vector_store.similarity_search(query)
print(matched_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#
matched_docs = vector_store.similarity_search_with_relevance_scores(query)
matched_docs[0]
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matched_docs = vector_store.similarity_search_with_relevance_scores(query)
matched_docs[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'}),
0.802509746274066)
Retriever options#
This section goes over different options for how to use SupabaseVectorStore as a retriever.
Maximal Marginal Relevance Searches#
In addition to using similarity search in the retriever object, you can also use mmr.
retriever = vector_store.as_retriever(search_type="mmr")
matched_docs = retriever.get_relevant_documents(query)
for i, d in enumerate(matched_docs):
print(f"\n## Document {i}\n")
print(d.page_content)
## Document 0
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.
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|
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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.
## Document 1
One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more.
When they came home, many of the world’s fittest and best trained warriors were never the same.
Headaches. Numbness. Dizziness.
A cancer that would put them in a flag-draped coffin.
I know.
One of those soldiers was my son Major Beau Biden.
We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops.
But I’m committed to finding out everything we can.
Committed to military families like Danielle Robinson from Ohio.
The widow of Sergeant First Class Heath Robinson.
He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq.
Stationed near Baghdad, just yards from burn pits the size of football fields.
Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.
## Document 2
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## Document 2
And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers.
Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.
America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.
These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming.
But I want you to know that we are going to be okay.
When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger.
While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly.
## Document 3
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.
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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.
previous
SKLearnVectorStore
next
Tair
Contents
Similarity search with score
Retriever options
Maximal Marginal Relevance Searches
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html
|
4995443e24a3-0
|
.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#
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.
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
See the installation instructions.
!pip install qdrant-client
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
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()
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|
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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
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#
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|
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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 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",
embeddings=embeddings
)
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)
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|
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|
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 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.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html
|
4995443e24a3-4
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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 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.
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|
4995443e24a3-5
|
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 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={})
|
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|
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|
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 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",
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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 May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html
|
4d0a9731e7b1-0
|
.ipynb
.pdf
Milvus
Milvus#
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
This notebook shows how to use functionality related to the Milvus vector database.
To run, you should have a Milvus instance up and running.
!pip install pymilvus
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key:········
from langchain.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"},
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)
docs[0].page_content
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|
4d0a9731e7b1-1
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docs = vector_db.similarity_search(query)
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. \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.'
previous
LanceDB
next
MyScale
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/milvus.html
|
f7abd90e7ff7-0
|
.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#
Chroma is a database for building AI applications with embeddings.
This notebook shows how to use functionality related to the Chroma vector database.
!pip install chromadb
# get a token: https://platform.openai.com/account/api-keys
from getpass import getpass
OPENAI_API_KEY = getpass()
import os
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
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.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html
|
f7abd90e7ff7-1
|
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#
docs = db.similarity_search_with_score(query)
docs[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'}),
0.3949805498123169)
Persistance#
The below steps cover how to persist a ChromaDB instance
Initialize PeristedChromaDB#
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html
|
f7abd90e7ff7-2
|
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 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]
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html
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f7abd90e7ff7-3
|
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'})
previous
Atlas
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 May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html
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8e1e1e382878-0
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.ipynb
.pdf
Deep Lake
Contents
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance function
Maximal Marginal relevance
Delete dataset
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory
Creating dataset on AWS S3
Deep Lake API
Transfer local dataset to cloud
Deep Lake#
Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.
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
!pip install openai deeplake tiktoken
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import DeepLake
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
embeddings = OpenAIEmbeddings()
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()
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
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8e1e1e382878-1
|
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
Create a dataset locally at ./deeplake/, then run similiarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so dataset and vector store are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, adjust the path accordingly.
db = DeepLake(dataset_path="./my_deeplake/", embedding_function=embeddings)
db.add_documents(docs)
# or shorter
# db = DeepLake.from_documents(docs, dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
/home/leo/.local/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.3.2) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
warnings.warn(
./my_deeplake/ loaded successfully.
Evaluating ingest: 100%|██████████████████████████████████████| 1/1 [00:07<00:00
Dataset(path='./my_deeplake/', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (42, 1536) float32 None
ids text (42, 1) str None
metadata json (42, 1) str None
text text (42, 1) str None
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-2
|
text text (42, 1) str None
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.
Later, you can reload the dataset without recomputing embeddings
db = DeepLake(dataset_path="./my_deeplake/", embedding_function=embeddings, read_only=True)
docs = db.similarity_search(query)
./my_deeplake/ loaded successfully.
Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage
Dataset(path='./my_deeplake/', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (42, 1536) float32 None
ids text (42, 1) str None
metadata json (42, 1) str None
text text (42, 1) str None
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-3
|
text text (42, 1) str None
Deep Lake, for now, is single writer and multiple reader. Setting read_only=True helps to avoid acquring the writer lock.
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())
/home/leo/.local/lib/python3.10/site-packages/langchain/llms/openai.py:624: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
warnings.warn(
query = 'What did the president say about Ketanji Brown Jackson'
qa.run(query)
'The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as a former top litigator in private practice, a former federal public defender, a consensus builder, and from a family of public school educators and police officers. He also mentioned that she has received broad support from various groups since being nominated.'
Attribute based filtering in metadata#
import random
for d in docs:
d.metadata['year'] = random.randint(2012, 2014)
db = DeepLake.from_documents(docs, embeddings, dataset_path="./my_deeplake/", overwrite=True)
./my_deeplake/ loaded successfully.
Evaluating ingest: 100%|██████████| 1/1 [00:04<00:00
Dataset(path='./my_deeplake/', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-4
|
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) float32 None
ids text (4, 1) str None
metadata json (4, 1) str None
text text (4, 1) str None
db.similarity_search('What did the president say about Ketanji Brown Jackson', filter={'year': 2013})
100%|██████████| 4/4 [00:00<00:00, 1080.24it/s]
[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', 'year': 2013}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-5
|
Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', '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')
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-6
|
[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', 'year': 2013}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-7
|
Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-8
|
Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-9
|
Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012})]
Maximal Marginal relevance#
Using maximal marginal relevance
db.max_marginal_relevance_search('What did the president say about Ketanji Brown Jackson?')
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-10
|
[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', 'year': 2013}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-11
|
Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-12
|
Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-13
|
Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013})]
Delete dataset#
db.delete_dataset()
and if delete fails you can also force delete
DeepLake.force_delete_by_path("./my_deeplake")
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory#
By default deep lake datasets are stored locally, in case you want to store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the corresponding path to the dataset. You can retrieve your user token from app.activeloop.ai
os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')
# Embed and store the texts
username = "<username>" # your username on app.activeloop.ai
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-14
|
username = "<username>" # your username on app.activeloop.ai
dataset_path = f"hub://{username}/langchain_test" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.
embedding = OpenAIEmbeddings()
db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings, overwrite=True)
db.add_documents(docs)
Your Deep Lake dataset has been successfully created!
The dataset is private so make sure you are logged in!
This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test
hub://davitbun/langchain_test loaded successfully.
Evaluating ingest: 100%|██████████| 1/1 [00:14<00:00
Dataset(path='hub://davitbun/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) float32 None
ids text (4, 1) str None
metadata json (4, 1) str None
text text (4, 1) str None
['d6d6ccb4-e187-11ed-b66d-41c5f7b85421',
'd6d6ccb5-e187-11ed-b66d-41c5f7b85421',
'd6d6ccb6-e187-11ed-b66d-41c5f7b85421',
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-15
|
'd6d6ccb7-e187-11ed-b66d-41c5f7b85421']
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.
Creating dataset on AWS S3#
dataset_path = f"s3://BUCKET/langchain_test" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.
embedding = OpenAIEmbeddings()
db = DeepLake.from_documents(docs, dataset_path=dataset_path, embedding=embeddings, overwrite=True, creds = {
'aws_access_key_id': os.environ['AWS_ACCESS_KEY_ID'],
'aws_secret_access_key': os.environ['AWS_SECRET_ACCESS_KEY'],
'aws_session_token': os.environ['AWS_SESSION_TOKEN'], # Optional
})
s3://hub-2.0-datasets-n/langchain_test loaded successfully.
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-16
|
})
s3://hub-2.0-datasets-n/langchain_test loaded successfully.
Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00
\
Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) float32 None
ids text (4, 1) str None
metadata json (4, 1) str None
text text (4, 1) str None
Deep Lake API#
you can access the Deep Lake dataset at db.ds
# get structure of the dataset
db.ds.summary()
Dataset(path='hub://davitbun/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) float32 None
ids text (4, 1) str None
metadata json (4, 1) str None
text text (4, 1) str None
# get embeddings numpy array
embeds = db.ds.embedding.numpy()
Transfer local dataset to cloud#
Copy already created dataset to the cloud. You can also transfer from cloud to local.
import deeplake
username = "davitbun" # your username on app.activeloop.ai
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-17
|
username = "davitbun" # your username on app.activeloop.ai
source = f"hub://{username}/langchain_test" # could be local, s3, gcs, etc.
destination = f"hub://{username}/langchain_test_copy" # could be local, s3, gcs, etc.
deeplake.deepcopy(src=source, dest=destination, overwrite=True)
Copying dataset: 100%|██████████| 56/56 [00:38<00:00
This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy
Your Deep Lake dataset has been successfully created!
The dataset is private so make sure you are logged in!
Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])
db = DeepLake(dataset_path=destination, embedding_function=embeddings)
db.add_documents(docs)
This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy
/
hub://davitbun/langchain_test_copy loaded successfully.
Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage
Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) float32 None
ids text (4, 1) str None
metadata json (4, 1) str None
|
https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
|
8e1e1e382878-18
|
metadata json (4, 1) str None
text text (4, 1) str None
Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00
-
Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (8, 1536) float32 None
ids text (8, 1) str None
metadata json (8, 1) str None
text text (8, 1) str None
['ad42f3fe-e188-11ed-b66d-41c5f7b85421',
'ad42f3ff-e188-11ed-b66d-41c5f7b85421',
'ad42f400-e188-11ed-b66d-41c5f7b85421',
'ad42f401-e188-11ed-b66d-41c5f7b85421']
previous
Chroma
next
DocArrayHnswSearch
Contents
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance function
Maximal Marginal relevance
Delete dataset
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory
Creating dataset on AWS S3
Deep Lake API
Transfer local dataset to cloud
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html
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