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d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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 |
d7b50aeab0ec-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
ClickHouse Vector Search
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 Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/deeplake.html |
275ff793c10d-0 | .ipynb
.pdf
OpenSearch
Contents
Installation
similarity_search using Approximate k-NN
similarity_search using Script Scoring
similarity_search using Painless Scripting
Using a preexisting OpenSearch instance
OpenSearch#
OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on Apache Lucene.
This notebook shows how to use functionality related to the OpenSearch database.
To run, you should have an OpenSearch instance up and running: see here for an easy Docker installation.
similarity_search by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for
large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.
Check this for more details.
Installation#
Install the Python client.
!pip install opensearch-py
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 OpenSearchVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
similarity_search using Approximate k-NN# | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/opensearch.html |
275ff793c10d-1 | embeddings = OpenAIEmbeddings()
similarity_search using Approximate k-NN#
similarity_search using Approximate k-NN Search with Custom Parameters
docsearch = OpenSearchVectorSearch.from_documents(
docs,
embeddings,
opensearch_url="http://localhost:9200"
)
# If using the default Docker installation, use this instantiation instead:
# docsearch = OpenSearchVectorSearch.from_documents(
# docs,
# embeddings,
# opensearch_url="https://localhost:9200",
# http_auth=("admin", "admin"),
# use_ssl = False,
# verify_certs = False,
# ssl_assert_hostname = False,
# ssl_show_warn = False,
# )
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query, k=10)
print(docs[0].page_content)
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", engine="faiss", space_type="innerproduct", ef_construction=256, m=48)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
similarity_search using Script Scoring#
similarity_search using Script Scoring with Custom Parameters
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring") | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/opensearch.html |
275ff793c10d-2 | print(docs[0].page_content)
similarity_search using Painless Scripting#
similarity_search using Painless Scripting with Custom Parameters
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}}
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter)
print(docs[0].page_content)
Using a preexisting OpenSearch instance#
It’s also possible to use a preexisting OpenSearch instance with documents that already have vectors present.
# this is just an example, you would need to change these values to point to another opensearch instance
docsearch = OpenSearchVectorSearch(index_name="index-*", embedding_function=embeddings, opensearch_url="http://localhost:9200")
# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata
docs = docsearch.similarity_search("Who was asking about getting lunch today?", search_type="script_scoring", space_type="cosinesimil", vector_field="message_embedding", text_field="message", metadata_field="message_metadata")
previous
MyScale
next
PGVector
Contents
Installation
similarity_search using Approximate k-NN
similarity_search using Script Scoring
similarity_search using Painless Scripting
Using a preexisting OpenSearch instance
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/opensearch.html |
59ad5acd8d1b-0 | .ipynb
.pdf
Pinecone
Pinecone#
Pinecone is a vector database with broad functionality.
This notebook shows how to use functionality related to the Pinecone vector database.
To use Pinecone, you must have an API key.
Here are the installation instructions.
!pip install pinecone-client
import os
import getpass
PINECONE_API_KEY = getpass.getpass('Pinecone API Key:')
PINECONE_ENV = getpass.getpass('Pinecone Environment:')
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
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 Pinecone
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
import pinecone
# initialize pinecone
pinecone.init(
api_key=PINECONE_API_KEY, # find at app.pinecone.io
environment=PINECONE_ENV # next to api key in console
)
index_name = "langchain-demo"
docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)
# if you already have an index, you can load it like this
# docsearch = Pinecone.from_existing_index(index_name, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pinecone.html |
59ad5acd8d1b-1 | docs = docsearch.similarity_search(query)
print(docs[0].page_content)
previous
PGVector
next
Qdrant
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pinecone.html |
213f1b79bcfc-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 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) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html |
213f1b79bcfc-1 | 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# | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html |
213f1b79bcfc-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={})
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 Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/vectara.html |
4891129fb2ba-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 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 necessary package
!pip install pgvector
!pip install openai
!pip install psycopg2-binary
!pip install tiktoken
Requirement already satisfied: pgvector in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (0.1.8)
Requirement already satisfied: numpy in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from pgvector) (1.24.3)
Requirement already satisfied: openai in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (0.27.7)
Requirement already satisfied: requests>=2.20 in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from openai) (2.28.2)
Requirement already satisfied: tqdm in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from openai) (4.65.0)
Requirement already satisfied: aiohttp in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from openai) (3.8.4) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-1 | Requirement already satisfied: charset-normalizer<4,>=2 in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from requests>=2.20->openai) (3.1.0)
Requirement already satisfied: idna<4,>=2.5 in /Users/joyeed/langchain/langchain/.venv/lib/python3.9/site-packages (from requests>=2.20->openai) (3.4)
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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:········
## 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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-4 | ## We will load it from the environment variables.
import os
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host=os.environ.get("PGVECTOR_HOST", "localhost"),
port=int(os.environ.get("PGVECTOR_PORT", "5432")),
database=os.environ.get("PGVECTOR_DATABASE", "postgres"),
user=os.environ.get("PGVECTOR_USER", "postgres"),
password=os.environ.get("PGVECTOR_PASSWORD", "postgres"),
)
## Example
# postgresql+psycopg2://username:password@localhost:5432/database_name
# ## PGVector needs the connection string to the database.
# ## We will load it from the environment variables.
# import os
# CONNECTION_STRING = PGVector.connection_string_from_db_params(
# driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
# host=os.environ.get("PGVECTOR_HOST", "localhost"),
# port=int(os.environ.get("PGVECTOR_PORT", "5432")),
# database=os.environ.get("PGVECTOR_DATABASE", "rd-embeddings"),
# user=os.environ.get("PGVECTOR_USER", "admin"),
# password=os.environ.get("PGVECTOR_PASSWORD", "password"),
# )
# ## 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", | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-5 | 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: 0.6076804864602984
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: 0.6076804864602984
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/pgvector.html |
4891129fb2ba-6 | 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: 0.659062774389974
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.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
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 their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.659062774389974
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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-7 | And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
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 their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
Working with vectorstore in PG#
Uploading a vectorstore in PG#
data=docs
api_key=os.environ['OPENAI_API_KEY']
db = PGVector.from_documents(
documents=docs,
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#
connection_string = CONNECTION_STRING
embedding=embeddings
collection_name="state_of_the_union"
from langchain.vectorstores.pgvector import DistanceStrategy
store = PGVector(
connection_string=connection_string,
embedding_function=embedding,
collection_name=collection_name,
distance_strategy=DistanceStrategy.COSINE
)
retriever = store.as_retriever()
print(retriever)
vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x7fe9a1b1c670> search_type='similarity' search_kwargs={}
# When we have an existing PG VEctor
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN
db1 = PGVector.from_existing_index(
embedding=embeddings, | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-8 | db1 = PGVector.from_existing_index(
embedding=embeddings,
collection_name="state_of_the_union",
distance_strategy=DEFAULT_DISTANCE_STRATEGY,
pre_delete_collection = False,
connection_string=CONNECTION_STRING,
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)
print(docs_with_score) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-9 | [(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'}), 0.6075870262188066), (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'}), | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-10 | Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}), 0.6075870262188066), (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'}), 0.6589478388546668), (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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-11 | \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'}), 0.6589478388546668)] | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-12 | for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.6075870262188066
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: 0.6075870262188066
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 |
4891129fb2ba-13 | 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: 0.6589478388546668
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.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
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 their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6589478388546668
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.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
4891129fb2ba-14 | 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 their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
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 Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html |
04de58da73ec-0 | .ipynb
.pdf
DocArrayInMemorySearch
Contents
Setup
Using DocArrayInMemorySearch
Similarity search
Similarity search with score
DocArrayInMemorySearch#
DocArrayInMemorySearch is a document index provided by Docarray that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.
This notebook shows how to use functionality related to the DocArrayInMemorySearch.
Setup#
Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven’t already done so.
# !pip install "docarray"
# Get an OpenAI token: https://platform.openai.com/account/api-keys
# import os
# from getpass import getpass
# OPENAI_API_KEY = getpass()
# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
Using DocArrayInMemorySearch#
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.document_loaders import TextLoader
documents = TextLoader('../../../state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = DocArrayInMemorySearch.from_documents(docs, embeddings)
Similarity search#
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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/docarray_in_memory.html |
04de58da73ec-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#
The returned distance score is cosine distance. Therefore, a lower score is better.
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={}),
0.8154190158347903)
previous
DocArrayHnswSearch
next
ElasticSearch
Contents
Setup
Using DocArrayInMemorySearch
Similarity search
Similarity search with score
By Harrison Chase | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/docarray_in_memory.html |
04de58da73ec-2 | Similarity search
Similarity search with score
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/docarray_in_memory.html |
f3d5bcc2d8b5-0 | .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 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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html |
f3d5bcc2d8b5-1 | 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.
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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html |
f3d5bcc2d8b5-2 | 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 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 Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/redis.html |
cefb6d5aa87a-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.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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/atlas.html |
cefb6d5aa87a-1 | 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 Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/atlas.html |
ff0e2a60016b-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 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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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={}), 1.1580758094787598)]
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] | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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 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'}), | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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 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'}), | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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 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'}), | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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. \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'}), | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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 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), | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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', 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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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='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 = [] | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
ff0e2a60016b-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_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)]
previous
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 Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/annoy.html |
d2469c89bab4-0 | .ipynb
.pdf
ElasticSearch
Contents
ElasticSearch
ElasticVectorSearch class
Installation
Example
ElasticKnnSearch Class
Test adding vectors
Test knn search using query vector builder
Test knn search using pre generated vector
Test source option
Test fields option
Test with es client connection rather than cloud_id
ElasticSearch#
Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
This notebook shows how to use functionality related to the Elasticsearch database.
ElasticVectorSearch class#
Installation#
Check out Elasticsearch installation instructions.
To connect to an Elasticsearch instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Example:
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url="http://localhost:9200",
index_name="test_index",
embedding=embedding
)
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the “Deployments” page.
To obtain your Elastic Cloud password for the default “elastic” user: | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
d2469c89bab4-1 | To obtain your Elastic Cloud password for the default “elastic” user:
Log in to the Elastic Cloud console at https://cloud.elastic.co
Go to “Security” > “Users”
Locate the “elastic” user and click “Edit”
Click “Reset password”
Follow the prompts to reset the password
Format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url=elasticsearch_url,
index_name="test_index",
embedding=embedding
)
!pip install elasticsearch
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
Example#
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = ElasticVectorSearch.from_documents(docs, embeddings, elasticsearch_url="http://localhost:9200")
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
d2469c89bab4-2 | docs = db.similarity_search(query)
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
We cannot let this happen.
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
ElasticKnnSearch Class#
The ElasticKnnSearch implements features allowing storing vectors and documents in Elasticsearch for use with approximate kNN search
!pip install langchain elasticsearch
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings
import elasticsearch
# Initialize ElasticsearchEmbeddings
model_id = "<model_id_from_es>"
dims = dim_count
es_cloud_id = "ESS_CLOUD_ID"
es_user = "es_user"
es_password = "es_pass"
test_index = "<index_name>"
#input_field = "your_input_field" # if different from 'text_field'
# Generate embedding object
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
#input_field=input_field, | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
d2469c89bab4-3 | model_id,
#input_field=input_field,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
)
# Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
index_name= test_index,
embedding= embeddings
)
Test adding vectors#
# Test `add_texts` method
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)
# Test `from_texts` method
new_texts = ["This is a new text.", "Elasticsearch is powerful.", "Python is great for data analysis."]
knn_search.from_texts(new_texts, dims=dims)
Test knn search using query vector builder#
# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query = query, model_id= model_id, k=2)
print(f"kNN search results for query '{query}': {knn_result}")
print(f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")
# Test `hybrid_search` method
query = "Hello"
hybrid_result = knn_search.knn_hybrid_search(query = query, model_id= model_id, k=2)
print(f"Hybrid search results for query '{query}': {hybrid_result}")
print(f"The 'text' field value from the top hit is: '{hybrid_result['hits']['hits'][0]['_source']['text']}'")
Test knn search using pre generated vector# | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
d2469c89bab4-4 | Test knn search using pre generated vector#
# Generate embedding for tests
query_text = 'Hello'
query_embedding = embeddings.embed_query(query_text)
print(f"Length of embedding: {len(query_embedding)}\nFirst two items in embedding: {query_embedding[:2]}")
# Test knn Search
knn_result = knn_search.knn_search(query_vector = query_embedding, k=2)
print(f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")
# Test hybrid search - Requires both query_text and query_vector
knn_result = knn_search.knn_hybrid_search(query_vector = query_embedding, query=query_text, k=2)
print(f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")
Test source option#
# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query = query, model_id= model_id, k=2, source=False)
assert not '_source' in knn_result['hits']['hits'][0].keys()
# Test `hybrid_search` method
query = "Hello"
hybrid_result = knn_search.knn_hybrid_search(query = query, model_id= model_id, k=2, source=False)
assert not '_source' in hybrid_result['hits']['hits'][0].keys()
Test fields option#
# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query = query, model_id= model_id, k=2, fields=['text']) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
d2469c89bab4-5 | assert 'text' in knn_result['hits']['hits'][0]['fields'].keys()
# Test `hybrid_search` method
query = "Hello"
hybrid_result = knn_search.knn_hybrid_search(query = query, model_id= model_id, k=2, fields=['text'])
assert 'text' in hybrid_result['hits']['hits'][0]['fields'].keys()
Test with es client connection rather than cloud_id#
# Create Elasticsearch connection
es_connection = Elasticsearch(
hosts=['https://es_cluster_url:port'],
basic_auth=('user', 'password')
)
# Instantiate ElasticsearchEmbeddings using es_connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
model_id,
es_connection,
)
# Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
es_connection = es_connection,
index_name= test_index,
embedding= embeddings
)
# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query = query, model_id= model_id, k=2)
print(f"kNN search results for query '{query}': {knn_result}")
print(f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")
previous
DocArrayInMemorySearch
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FAISS
Contents
ElasticSearch
ElasticVectorSearch class
Installation
Example
ElasticKnnSearch Class
Test adding vectors
Test knn search using query vector builder
Test knn search using pre generated vector
Test source option
Test fields option
Test with es client connection rather than cloud_id
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
d2469c89bab4-6 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/elasticsearch.html |
ded2a90cd6f6-0 | .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. 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'
OpenAI API Key: ········
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) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
ded2a90cd6f6-1 | 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 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 distance score of the query to them. The returned distance score is L2 distance. Therefore, a lower score is better.
docs_and_scores = db.similarity_search_with_score(query)
docs_and_scores[0] | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
ded2a90cd6f6-2 | 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 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] | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
ded2a90cd6f6-3 | 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. 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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
ded2a90cd6f6-4 | 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
Similarity Search with score
Saving and loading
Merging
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html |
c770ca4c5f2f-0 | .ipynb
.pdf
Typesense
Contents
Similarity Search
Typesense as a Retriever
Typesense#
Typesense is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud.
Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.
It also lets you combine attribute-based filtering together with vector queries, to fetch the most relevant documents.
This notebook shows you how to use Typesense as your VectorStore.
Let’s first install our dependencies:
!pip install typesense openapi-schema-pydantic openai tiktoken
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 Typesense
from langchain.document_loaders import TextLoader
Let’s import our test dataset:
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Typesense.from_documents(docs,
embeddings,
typesense_client_params={
'host': 'localhost', # Use xxx.a1.typesense.net for Typesense Cloud
'port': '8108', # Use 443 for Typesense Cloud
'protocol': 'http', # Use https for Typesense Cloud
'typesense_api_key': 'xyz', | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/typesense.html |
c770ca4c5f2f-1 | 'typesense_api_key': 'xyz',
'typesense_collection_name': 'lang-chain'
})
Similarity Search#
query = "What did the president say about Ketanji Brown Jackson"
found_docs = docsearch.similarity_search(query)
print(found_docs[0].page_content)
Typesense as a Retriever#
Typesense, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.
retriever = docsearch.as_retriever()
retriever
query = "What did the president say about Ketanji Brown Jackson"
retriever.get_relevant_documents(query)[0]
previous
Tigris
next
Vectara
Contents
Similarity Search
Typesense as a Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/typesense.html |
84f065e92538-0 | .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 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 $$
# variable_conflict use_column
BEGIN
RETURN query
SELECT
id,
content,
metadata,
embedding, | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
84f065e92538-1 | SELECT
id,
content,
metadata,
embedding,
1 -(documents.embedding <=> query_embedding) AS similarity
FROM
documents
ORDER BY
documents.embedding <=> query_embedding
LIMIT match_count;
END;
$$;
# 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) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
84f065e92538-2 | 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 = "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#
The returned distance score is cosine distance. Therefore, a lower score is better.
matched_docs = vector_store.similarity_search_with_relevance_scores(query)
matched_docs[0] | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
84f065e92538-3 | 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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
84f065e92538-4 | 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 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 | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
84f065e92538-5 | ## 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 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. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
84f065e92538-6 | 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 Marginal Relevance Searches
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html |
4716079a445d-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
Updating a Document
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
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 |
4716079a445d-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#
The returned distance score is cosine distance. Therefore, a lower score is better.
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 |
4716079a445d-2 | 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 |
4716079a445d-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'})
Updating a Document#
The update_document function allows you to modify the content of a document in the Chroma instance after it has been added. Let’s see an example of how to use this function.
# Import Document class
from langchain.docstore.document import Document
# Initial document content and id
initial_content = "This is an initial document content"
document_id = "doc1"
# Create an instance of Document with initial content and metadata
original_doc = Document(page_content=initial_content, metadata={"page": "0"})
# Initialize a Chroma instance with the original document
new_db = Chroma.from_documents(
collection_name="test_collection",
documents=[original_doc],
embedding=OpenAIEmbeddings(), # using the same embeddings as before
ids=[document_id],
) | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html |
4716079a445d-4 | ids=[document_id],
)
At this point, we have a new Chroma instance with a single document “This is an initial document content” with id “doc1”. Now, let’s update the content of the document.
# Updated document content
updated_content = "This is the updated document content"
# Create a new Document instance with the updated content
updated_doc = Document(page_content=updated_content, metadata={"page": "1"})
# Update the document in the Chroma instance by passing the document id and the updated document
new_db.update_document(document_id=document_id, document=updated_doc)
# Now, let's retrieve the updated document using similarity search
output = new_db.similarity_search(updated_content, k=1)
# Print the content of the retrieved document
print(output[0].page_content, output[0].metadata)
This is the updated document content {'page': '1'}
previous
Atlas
next
ClickHouse Vector Search
Contents
Similarity search with score
Persistance
Initialize PeristedChromaDB
Persist the Database
Load the Database from disk, and create the chain
Retriever options
MMR
Updating a Document
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/chroma.html |
1e757c6ddb72-0 | .ipynb
.pdf
Wikipedia
Contents
Installation
Examples
Running retriever
Question Answering on facts
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.
This notebook shows how to retrieve wiki pages from wikipedia.org into the Document format that is used downstream.
Installation#
First, you need to install wikipedia python package.
#!pip install wikipedia
WikipediaRetriever has these arguments:
optional lang: default=”en”. Use it to search in a specific language part of Wikipedia
optional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.
optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.
get_relevant_documents() has one argument, query: free text which used to find documents in Wikipedia
Examples#
Running retriever#
from langchain.retrievers import WikipediaRetriever
retriever = WikipediaRetriever()
docs = retriever.get_relevant_documents(query='HUNTER X HUNTER')
docs[0].metadata # meta-information of the Document
{'title': 'Hunter × Hunter', | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
1e757c6ddb72-1 | 'summary': 'Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter × Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
1e757c6ddb72-2 | with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter × Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\n'} | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
1e757c6ddb72-3 | docs[0].page_content[:400] # a content of the Document
'Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The sto'
Question Answering on facts#
# 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.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
model = ChatOpenAI(model_name='gpt-3.5-turbo') # switch to 'gpt-4'
qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
questions = [
"What is Apify?",
"When the Monument to the Martyrs of the 1830 Revolution was created?",
"What is the Abhayagiri Vihāra?",
# "How big is Wikipédia en français?",
]
chat_history = []
for question in questions:
result = qa({"question": question, "chat_history": chat_history})
chat_history.append((question, result['answer']))
print(f"-> **Question**: {question} \n")
print(f"**Answer**: {result['answer']} \n")
-> **Question**: What is Apify? | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
1e757c6ddb72-4 | -> **Question**: What is Apify?
**Answer**: Apify is a platform that allows you to easily automate web scraping, data extraction and web automation. It provides a cloud-based infrastructure for running web crawlers and other automation tasks, as well as a web-based tool for building and managing your crawlers. Additionally, Apify offers a marketplace for buying and selling pre-built crawlers and related services.
-> **Question**: When the Monument to the Martyrs of the 1830 Revolution was created?
**Answer**: Apify is a web scraping and automation platform that enables you to extract data from websites, turn unstructured data into structured data, and automate repetitive tasks. It provides a user-friendly interface for creating web scraping scripts without any coding knowledge. Apify can be used for various web scraping tasks such as data extraction, web monitoring, content aggregation, and much more. Additionally, it offers various features such as proxy support, scheduling, and integration with other tools to make web scraping and automation tasks easier and more efficient.
-> **Question**: What is the Abhayagiri Vihāra?
**Answer**: Abhayagiri Vihāra was a major monastery site of Theravada Buddhism that was located in Anuradhapura, Sri Lanka. It was founded in the 2nd century BCE and is considered to be one of the most important monastic complexes in Sri Lanka.
previous
Self-querying with Weaviate
next
Zep
Contents
Installation
Examples
Running retriever
Question Answering on facts
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/wikipedia.html |
5a60b94d46c8-0 | .ipynb
.pdf
Pinecone Hybrid Search
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
Pinecone Hybrid Search#
Pinecone is a vector database with broad functionality.
This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.
The logic of this retriever is taken from this documentaion
To use Pinecone, you must have an API key and an Environment.
Here are the installation instructions.
#!pip install pinecone-client pinecone-text
import os
import getpass
os.environ['PINECONE_API_KEY'] = getpass.getpass('Pinecone API Key:')
from langchain.retrievers import PineconeHybridSearchRetriever
os.environ['PINECONE_ENVIRONMENT'] = getpass.getpass('Pinecone Environment:')
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
Setup Pinecone#
You should only have to do this part once.
Note: it’s important to make sure that the “context” field that holds the document text in the metadata is not indexed. Currently you need to specify explicitly the fields you do want to index. For more information checkout Pinecone’s docs.
import os
import pinecone
api_key = os.getenv("PINECONE_API_KEY") or "PINECONE_API_KEY"
# find environment next to your API key in the Pinecone console
env = os.getenv("PINECONE_ENVIRONMENT") or "PINECONE_ENVIRONMENT"
index_name = "langchain-pinecone-hybrid-search"
pinecone.init(api_key=api_key, enviroment=env) | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
5a60b94d46c8-1 | pinecone.init(api_key=api_key, enviroment=env)
pinecone.whoami()
WhoAmIResponse(username='load', user_label='label', projectname='load-test')
# create the index
pinecone.create_index(
name = index_name,
dimension = 1536, # dimensionality of dense model
metric = "dotproduct", # sparse values supported only for dotproduct
pod_type = "s1",
metadata_config={"indexed": []} # see explaination above
)
Now that its created, we can use it
index = pinecone.Index(index_name)
Get embeddings and sparse encoders#
Embeddings are used for the dense vectors, tokenizer is used for the sparse vector
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
To encode the text to sparse values you can either choose SPLADE or BM25. For out of domain tasks we recommend using BM25.
For more information about the sparse encoders you can checkout pinecone-text library docs.
from pinecone_text.sparse import BM25Encoder
# or from pinecone_text.sparse import SpladeEncoder if you wish to work with SPLADE
# use default tf-idf values
bm25_encoder = BM25Encoder().default()
The above code is using default tfids values. It’s highly recommended to fit the tf-idf values to your own corpus. You can do it as follow:
corpus = ["foo", "bar", "world", "hello"]
# fit tf-idf values on your corpus
bm25_encoder.fit(corpus)
# store the values to a json file
bm25_encoder.dump("bm25_values.json")
# load to your BM25Encoder object
bm25_encoder = BM25Encoder().load("bm25_values.json")
Load Retriever# | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
5a60b94d46c8-2 | Load Retriever#
We can now construct the retriever!
retriever = PineconeHybridSearchRetriever(embeddings=embeddings, sparse_encoder=bm25_encoder, index=index)
Add texts (if necessary)#
We can optionally add texts to the retriever (if they aren’t already in there)
retriever.add_texts(["foo", "bar", "world", "hello"])
100%|██████████| 1/1 [00:02<00:00, 2.27s/it]
Use Retriever#
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result[0]
Document(page_content='foo', metadata={})
previous
Metal
next
PubMed Retriever
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
f753387f45cc-0 | .ipynb
.pdf
VectorStore
Contents
Maximum Marginal Relevance Retrieval
Similarity Score Threshold Retrieval
Specifying top k
VectorStore#
The index - and therefore the retriever - that LangChain has the most support for is the VectorStoreRetriever. As the name suggests, this retriever is backed heavily by a VectorStore.
Once you construct a VectorStore, its very easy to construct a retriever. Let’s walk through an example.
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(texts, embeddings)
Exiting: Cleaning up .chroma directory
retriever = db.as_retriever()
docs = retriever.get_relevant_documents("what did he say about ketanji brown jackson")
Maximum Marginal Relevance Retrieval#
By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type.
retriever = db.as_retriever(search_type="mmr")
docs = retriever.get_relevant_documents("what did he say abotu ketanji brown jackson")
Similarity Score Threshold Retrieval#
You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold
retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5}) | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/vectorstore.html |
f753387f45cc-1 | docs = retriever.get_relevant_documents("what did he say abotu ketanji brown jackson")
Specifying top k#
You can also specify search kwargs like k to use when doing retrieval.
retriever = db.as_retriever(search_kwargs={"k": 1})
docs = retriever.get_relevant_documents("what did he say abotu ketanji brown jackson")
len(docs)
1
previous
Time Weighted VectorStore
next
Vespa
Contents
Maximum Marginal Relevance Retrieval
Similarity Score Threshold Retrieval
Specifying top k
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/vectorstore.html |
ace199ba603c-0 | .ipynb
.pdf
kNN
Contents
Create New Retriever with Texts
Use Retriever
kNN#
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.
This notebook goes over how to use a retriever that under the hood uses an kNN.
Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb
from langchain.retrievers import KNNRetriever
from langchain.embeddings import OpenAIEmbeddings
Create New Retriever with Texts#
retriever = KNNRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"], OpenAIEmbeddings())
Use Retriever#
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result
[Document(page_content='foo', metadata={}),
Document(page_content='foo bar', metadata={}),
Document(page_content='hello', metadata={}),
Document(page_content='bar', metadata={})]
previous
ElasticSearch BM25
next
Metal
Contents
Create New Retriever with Texts
Use Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/knn.html |
e9690631498a-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 notebook introduces the concept of DocumentCompressors and the ContextualCompressionRetriever. The core idea is simple: given a specific query, we should be able to return only the documents relevant to that query, and only the parts of those documents that are relevant. The ContextualCompressionsRetriever is a wrapper for another retriever that iterates over the initial output of the base retriever and filters and compresses those initial documents, so that only the most relevant information is returned.
# Helper function for printing docs
def pretty_print_docs(docs):
print(f"\n{'-' * 100}\n".join([f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]))
Using a vanilla vector store retriever#
Let’s start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can see that given an example question our retriever returns one or two relevant docs and a few irrelevant docs. And even the relevant docs have a lot of irrelevant information in them.
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.vectorstores import FAISS
documents = TextLoader('../../../state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents) | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-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. 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.
----------------------------------------------------------------------------------------------------
Document 2:
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.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
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. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-2 | 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 their cases heard faster.
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 to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.
As 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.
While 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.
And 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.
So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.
First, beat the opioid epidemic.
----------------------------------------------------------------------------------------------------
Document 4:
Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers.
And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.
That ends on my watch.
Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect.
We’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. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-3 | Let’s pass the Paycheck Fairness Act and paid leave.
Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty.
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’ll add an LLMChainExtractor, which will iterate over the initially returned documents and extract from each only the content that is relevant to the query.
from langchain.llms import OpenAI
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
llm = OpenAI(temperature=0)
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown")
pretty_print_docs(compressed_docs)
Document 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 excellence."
----------------------------------------------------------------------------------------------------
Document 2: | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-4 | ----------------------------------------------------------------------------------------------------
Document 2:
"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."
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_compressors import LLMChainFilter
_filter = LLMChainFilter.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=retriever)
compressed_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown")
pretty_print_docs(compressed_docs)
Document 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.
EmbeddingsFilter# | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-5 | EmbeddingsFilter#
Making an extra LLM call over each retrieved document is expensive and slow. The EmbeddingsFilter provides a cheaper and faster option by embedding the documents and query and only returning those documents which have sufficiently similar embeddings to the query.
from langchain.embeddings import OpenAIEmbeddings
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_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown")
pretty_print_docs(compressed_docs)
Document 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.
----------------------------------------------------------------------------------------------------
Document 2: | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-6 | ----------------------------------------------------------------------------------------------------
Document 2:
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.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
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 their cases heard faster.
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 to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.
As 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.
While 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.
And 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.
So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.
First, beat the opioid epidemic.
Stringing compressors and document transformers together# | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-7 | First, beat the opioid epidemic.
Stringing compressors and document transformers together#
Using the DocumentCompressorPipeline we can also easily combine multiple compressors in sequence. Along with compressors we can add BaseDocumentTransformers to our pipeline, which don’t perform any contextual compression but simply perform some transformation on a set of documents. For example TextSplitters can be used as document transformers to split documents into smaller pieces, and the EmbeddingsRedundantFilter can be used to filter out redundant documents based on embedding similarity between documents.
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 DocumentCompressorPipeline
from langchain.text_splitter import CharacterTextSplitter
splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=". ")
redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)
pipeline_compressor = DocumentCompressorPipeline(
transformers=[splitter, redundant_filter, relevant_filter]
)
compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)
compressed_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown")
pretty_print_docs(compressed_docs)
Document 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
----------------------------------------------------------------------------------------------------
Document 2: | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
e9690631498a-8 | ----------------------------------------------------------------------------------------------------
Document 2:
As 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.
While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year
----------------------------------------------------------------------------------------------------
Document 3:
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
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
© Copyright 2023, Harrison Chase.
Last updated on Jun 07, 2023. | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
b43c1b8a4702-0 | .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 science, and economics.
This notebook shows how to retrieve scientific articles from Arxiv.org into the Document format that is used downstream.
Installation#
First, you need to install arxiv python package.
#!pip install arxiv
ArxivRetriever has these arguments:
optional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.
optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), Title, Authors, Summary. If True, other fields also downloaded.
get_relevant_documents() has one argument, query: free text which used to find documents in Arxiv.org
Examples#
Running retriever#
from langchain.retrievers import ArxivRetriever
retriever = ArxivRetriever(load_max_docs=2)
docs = retriever.get_relevant_documents(query='1605.08386')
docs[0].metadata # meta-information of the Document
{'Published': '2016-05-26',
'Title': 'Heat-bath random walks with Markov bases',
'Authors': 'Caprice Stanley, Tobias Windisch', | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
b43c1b8a4702-1 | '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 behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'}
docs[0].page_content[:400] # a content of the Document
'arXiv:1605.08386v1 [math.CO] 26 May 2016\nHEAT-BATH RANDOM WALKS WITH MARKOV BASES\nCAPRICE STANLEY AND TOBIAS WINDISCH\nAbstract. Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on fibers of a\nfixed integer matrix can be bounded from above by a constant. We then study the mixing\nbehaviour of heat-b'
Question Answering on facts#
# 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.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
model = ChatOpenAI(model_name='gpt-3.5-turbo') # switch to 'gpt-4'
qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
questions = [ | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
b43c1b8a4702-2 | 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.append((question, result['answer']))
print(f"-> **Question**: {question} \n")
print(f"**Answer**: {result['answer']} \n")
-> **Question**: What are Heat-bath random walks with Markov base?
**Answer**: I'm not sure, as I don't have enough context to provide a definitive answer. The term "Heat-bath random walks with Markov base" is not mentioned in the given text. Could you provide more information or context about where you encountered this term?
-> **Question**: What is the ImageBind model?
**Answer**: ImageBind is an approach developed by Facebook AI Research to learn a joint embedding across six different modalities, including images, text, audio, depth, thermal, and IMU data. The approach uses the binding property of images to align each modality's embedding to image embeddings and achieve an emergent alignment across all modalities. This enables novel multimodal capabilities, including cross-modal retrieval, embedding-space arithmetic, and audio-to-image generation, among others. The approach sets a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Additionally, it shows strong few-shot recognition results and serves as a new way to evaluate vision models for visual and non-visual tasks.
-> **Question**: How does Compositional Reasoning with Large Language Models works? | https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/arxiv.html |
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