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Showing content from https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake below:

Activeloop Deep Lake | 🦜️🔗 LangChain

Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.

This notebook showcases basic functionality related to Activeloop Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks.

For more information, please see the Deep Lake documentation

%pip install --upgrade --quiet  langchain-openai langchain-deeplake tiktoken

Integration with LangChain.

from langchain_deeplake.vectorstores import DeeplakeVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

if "ACTIVELOOP_TOKEN" not in os.environ:
    os.environ["ACTIVELOOP_TOKEN"] = getpass.getpass("activeloop token:")

from langchain_community.document_loaders import TextLoader

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

Create a dataset locally at ./my_deeplake/, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so dataset and vector store are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, adjust the path accordingly.

db = DeeplakeVectorStore(
    dataset_path="./my_deeplake/", embedding_function=embeddings, overwrite=True
)
db.add_documents(docs)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)

print(docs[0].page_content)

Later, you can reload the dataset without recomputing embeddings

db = DeeplakeVectorStore(
    dataset_path="./my_deeplake/", embedding_function=embeddings, read_only=True
)
docs = db.similarity_search(query)

Setting read_only=True revents accidental modifications to the vector store when updates are not needed. This ensures that the data remains unchanged unless explicitly intended. It is generally a good practice to specify this argument to avoid unintended updates.

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-3.5-turbo"),
    chain_type="stuff",
    retriever=db.as_retriever(),
)

query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)

Let's create another vector store containing metadata with the year the documents were created.

import random

for d in docs:
    d.metadata["year"] = random.randint(2012, 2014)

db = DeeplakeVectorStore.from_documents(
    docs, embeddings, dataset_path="./my_deeplake/", overwrite=True
)

db.similarity_search(
    "What did the president say about Ketanji Brown Jackson",
    filter={"metadata": {"year": 2013}},
)

Distance function L2 for Euclidean, cos for cosine similarity

db.similarity_search(
    "What did the president say about Ketanji Brown Jackson?", distance_metric="l2"
)

Using maximal marginal relevance

db.max_marginal_relevance_search(
    "What did the president say about Ketanji Brown Jackson?"
)

By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the corresponding path and credentials when creating the vector store. Some paths require registration with Activeloop and creation of an API token that can be retrieved here

os.environ["ACTIVELOOP_TOKEN"] = activeloop_token

username = "<USERNAME_OR_ORG>"  
dataset_path = f"hub://{username}/langchain_testing_python"  

docs = text_splitter.split_documents(documents)

embedding = OpenAIEmbeddings()
db = DeeplakeVectorStore(
    dataset_path=dataset_path, embedding_function=embeddings, overwrite=True
)
ids = db.add_documents(docs)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)

username = "<USERNAME_OR_ORG>"  
dataset_path = f"hub://{username}/langchain_testing"

docs = text_splitter.split_documents(documents)

embedding = OpenAIEmbeddings()
db = DeeplakeVectorStore(
    dataset_path=dataset_path,
    embedding_function=embeddings,
    overwrite=True,
)
ids = db.add_documents(docs)

Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL).

search_id = db.dataset["ids"][0]

docs = db.similarity_search(
    query=None,
    tql=f"SELECT * WHERE ids == '{search_id}'",
)

dataset_path = "s3://BUCKET/langchain_test"  

embedding = OpenAIEmbeddings()
db = DeeplakeVectorStore.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"],  
    },
)

you can access the Deep Lake dataset at db.vectorstore

embeds = db.dataset["embeddings"][:]

Copy already created dataset to the cloud. You can also transfer from cloud to local.

import deeplake

username = "<USERNAME_OR_ORG>"  
source = f"hub://{username}/langchain_testing"  
destination = f"hub://{username}/langchain_test_copy"  


deeplake.copy(src=source, dst=destination)

db = DeeplakeVectorStore(dataset_path=destination, embedding_function=embeddings)
db.add_documents(docs)

• Vector store conceptual guide
• Vector store how-to guides

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