id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
cbb1c83363ae-3 | Document(page_content='Prof. Sterling explores the potential for harmonious coexistence between humans and artificial intelligence. The book discusses how AI can be integrated into society in a beneficial and non-disruptive manner.', metadata={})]
Do a hybrid search with where filter:
retriever.get_relevant_documents(
... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate-hybrid.html |
796cb34d426b-0 | .ipynb
.pdf
Arxiv
Contents
Installation
Examples
Running retriever
Question Answering on facts
Arxiv#
arXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems sci... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
796cb34d426b-1 | 'Authors': 'Caprice Stanley, Tobias Windisch',
'Summary': 'Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing beh... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
796cb34d426b-2 | questions = [
"What are Heat-bath random walks with Markov base?",
"What is the ImageBind model?",
"How does Compositional Reasoning with Large Language Models works?",
]
chat_history = []
for question in questions:
result = qa({"question": question, "chat_history": chat_history})
chat_history... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
796cb34d426b-3 | -> **Question**: How does Compositional Reasoning with Large Language Models works?
**Answer**: Compositional reasoning with large language models refers to the ability of these models to correctly identify and represent complex concepts by breaking them down into smaller, more basic parts and combining them in a stru... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
796cb34d426b-4 | **Answer**: Heat-bath random walks with Markov base (HB-MB) is a class of stochastic processes that have been studied in the field of statistical mechanics and condensed matter physics. In these processes, a particle moves in a lattice by making a transition to a neighboring site, which is chosen according to a probabi... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
ed7b014cbd40-0 | .ipynb
.pdf
Contextual Compression
Contents
Contextual Compression
Using a vanilla vector store retriever
Adding contextual compression with an LLMChainExtractor
More built-in compressors: filters
LLMChainFilter
EmbeddingsFilter
Stringing compressors and document transformers together
Contextual Compression#
This not... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-1 | texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()
docs = retriever.get_relevant_documents("What did the president say about Ketanji Brown Jackson")
pretty_print_docs(docs)
Document 1:
Tonight. I call on the Senate to: Pass the Freedom to Vote Act... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-2 | We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
----------------------------------------------------------------------------------------------------
Document 3:
And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-3 | Let’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.
Adding contextual compression with an LLMChainExtractor#
Now let’s wrap our base retriever with a ContextualCompressionRetriever. We’l... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-4 | More built-in compressors: filters#
LLMChainFilter#
The LLMChainFilter is slightly simpler but more robust compressor that uses an LLM chain to decide which of the initially retrieved documents to filter out and which ones to return, without manipulating the document contents.
from langchain.retrievers.document_compres... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-5 | from langchain.retrievers.document_compressors import EmbeddingsFilter
embeddings = OpenAIEmbeddings()
embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)
compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=retriever)
compressed_doc... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-6 | We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have th... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-7 | Below we create a compressor pipeline by first splitting our docs into smaller chunks, then removing redundant documents, and then filtering based on relevance to the query.
from langchain.document_transformers import EmbeddingsRedundantFilter
from langchain.retrievers.document_compressors import DocumentCompressorPipe... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed7b014cbd40-8 | previous
Cohere Reranker
next
Databerry
Contents
Contextual Compression
Using a vanilla vector store retriever
Adding contextual compression with an LLMChainExtractor
More built-in compressors: filters
LLMChainFilter
EmbeddingsFilter
Stringing compressors and document transformers together
By Harrison Chase
... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
ed22e9ea0f7b-0 | .ipynb
.pdf
Cohere Reranker
Contents
Set up the base vector store retriever
Doing reranking with CohereRerank
Cohere Reranker#
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
This notebook shows how to use Cohere’s rerank endpoint i... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-1 | texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever(search_kwargs={"k": 20})
query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)
Document 1:
One of the most serious c... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-2 | Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.
In 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.... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-3 | It’s exploitation—and it drives up prices.
----------------------------------------------------------------------------------------------------
Document 8:
For the past 40 years we were told that if we gave tax breaks to those at the very top, the benefits would trickle down to everyone else.
But that trickle-down the... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-4 | The pandemic has been punishing.
And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more.
I understand.
----------------------------------------------------------------------------------------------------
Document 12:
Madam Speaker, Mada... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-5 | Third, support our veterans.
Veterans are the best of us.
I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home.
My administration is providing assistance with job training and housing, and now helping lower-income veterans ge... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-6 | ----------------------------------------------------------------------------------------------------
Document 19:
I understand.
I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it.
That’s why one of the first things... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-7 | 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 2:
I spoke with th... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
ed22e9ea0f7b-8 | previous
Self-querying with Chroma
next
Contextual Compression
Contents
Set up the base vector store retriever
Doing reranking with CohereRerank
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
394bbc1c0eb6-0 | .ipynb
.pdf
Metal
Contents
Ingest Documents
Query
Metal#
Metal is a managed service for ML Embeddings.
This notebook shows how to use Metal’s retriever.
First, you will need to sign up for Metal and get an API key. You can do so here
# !pip install metal_sdk
from metal_sdk.metal import Metal
API_KEY = ""
CLIENT_ID = ... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/metal.html |
394bbc1c0eb6-1 | previous
kNN
next
Pinecone Hybrid Search
Contents
Ingest Documents
Query
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/metal.html |
1ae410e34210-0 | .ipynb
.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 t... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
1ae410e34210-1 | 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', m... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
1ae410e34210-2 | # "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})]... | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
81efea079f4c-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 fort... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html |
81efea079f4c-1 | previous
Text Splitters
next
Character
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html |
2ee8c3c96b74-0 | .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 pyth... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/python.html |
ac94016997e3-0 | .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
#!p... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/tiktoken.html |
2a526e3b2b1f-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 t... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/latex.html |
2a526e3b2b1f-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 ... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/latex.html |
2a526e3b2b1f-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... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/latex.html |
a1128c3b4c82-0 | .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
#!p... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/spacy.html |
a1128c3b4c82-1 | previous
Recursive Character
next
Tiktoken
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/spacy.html |
253ee703d713-0 | .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.... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/tiktoken_splitter.html |
dabaed28c87d-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 parag... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html |
dabaed28c87d-1 | previous
Python Code
next
spaCy
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html |
a103da332e3e-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 separator... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/markdown.html |
a103da332e3e-1 | previous
LaTeX
next
NLTK
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/markdown.html |
b311ff97d702-0 | .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('../... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html |
b311ff97d702-1 | 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 to... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html |
b311ff97d702-2 | 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 Republica... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html |
b311ff97d702-3 | 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 Demo... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html |
e94f92a324e0-0 | .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... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/huggingface_length_function.html |
2319d3c1472f-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.... | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/nltk.html |
2319d3c1472f-1 | Groups of citizens blocking tanks with their bodies.
previous
Markdown
next
Python Code
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/nltk.html |
2b8b1378c8e3-0 | .ipynb
.pdf
Getting Started
Contents
Add texts
From Documents
Getting Started#
This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/getting_started.html |
2b8b1378c8e3-1 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/getting_started.html |
2b8b1378c8e3-2 | 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: Justi... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/getting_started.html |
3981c452574e-0 | .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. I... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
3981c452574e-1 | 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 hi... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
3981c452574e-2 | 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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
3981c452574e-3 | 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.... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
3981c452574e-4 | 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
Si... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
8240b79a20ef-0 | .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.... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/atlas.html |
8240b79a20ef-1 | Hide embedded project
Explore on atlas.nomic.ai
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Annoy
next
Chroma
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/atlas.html |
d69a0a831382-0 | .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 neare... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
d69a0a831382-1 | 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
Similar... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
d69a0a831382-2 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
d69a0a831382-3 | 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.
--------------------------------------------------------------------------------
-----------------------------------------------... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
d69a0a831382-4 | 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 29, 2023... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
84a889a4c614-0 | .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 impl... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/sklearn.html |
84a889a4c614-1 | 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 ab... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/sklearn.html |
84a889a4c614-2 | )
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. ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/sklearn.html |
597f04a94746-0 | .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 ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html |
597f04a94746-1 | 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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html |
597f04a94746-2 | 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={}... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html |
dd96d9715fd0-0 | .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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/zilliz.html |
dd96d9715fd0-1 | "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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/zilliz.html |
5bc337c75127-0 | .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 po... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-1 | # 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={}), 1.1580758094... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-2 | 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 aga... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-3 | 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 Unit... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-4 | 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 ca... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-5 | 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. ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-6 | 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 tradin... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-7 | (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', met... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-8 | 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='... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
5bc337c75127-9 | 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_docstor... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
cda2d8f15657-0 | .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 vect... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html |
cda2d8f15657-1 | 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 h... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html |
cda2d8f15657-2 | 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 searc... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html |
dc8d36349fb6-0 | .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 al... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
dc8d36349fb6-1 | SELECT
id,
content,
metadata,
embedding,
1 -(documents.embedding <=> query_embedding) AS similarity
FROM
documents
ORDER BY
documents.embedding <=> query_embedding
LIMIT match_count;... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
dc8d36349fb6-2 | 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 = "... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
dc8d36349fb6-3 | 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\n... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
dc8d36349fb6-4 | 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 h... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
dc8d36349fb6-5 | ## 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 ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
dc8d36349fb6-6 | 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 Marg... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
e45651d29ca9-0 | .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 (rea... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
e45651d29ca9-1 | 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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
e45651d29ca9-2 | 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 th... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
e45651d29ca9-3 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
e45651d29ca9-4 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
e45651d29ca9-5 | 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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
e45651d29ca9-6 | 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... | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html |
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