Deep Lake Vector Store in LangChain
Using Deep Lake as a Vector Store in LangChain
Embeddings are a data representation that is commonly used for filtering relevant data for language models, due their finite token limits. Deep Lake can be used as a VectorStore in LangChain for building Apps that require vector filtering and search. In this tutorial we will show how to create a Deep Lake Vector Store in LangChain and use it to build a Q&A App about the Twitter OSS recommendation algorithm.
First, let's import necessary packages and make sure the Activeloop and OpenAI keys are in the environmental variables
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import DeepLake
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA, ConversationalRetrievalChain
Next, let's clone the Twitter OSS recommendation algorithm:
!git clone https://github.com/twitter/the-algorithm
Next, let's load all the files from the repo into a list:
repo_path = '/the-algorithm'
docs = 
for dirpath, dirnames, filenames in os.walk(repo_path):
for file in filenames:
loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')
except Exception as e:
Since some of the files are very large, we split them into chunks. In general, more chunks increases the relevancy of data that is fed into the language model, since granular data can be selected with higher precision. However, since an embedding will be created for each chunk, more chunks increase the computational complexity.
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
Chunks in the above context should not be confused with Deep Lake chunks!
First, we specify a path for storing the Deep Lake dataset containing the embeddings and their metadata.
dataset_path = 'hub://<org-id>/twitter_algorithm'
Next, we specify an OpenAI algorithm for creating the embeddings, and create the VectorStore. This process creates an embedding for each element in the
textslists and stores it in Deep Lake format at the specified path.
embeddings = OpenAIEmbeddings()
db = DeepLake.from_documents(texts, embeddings, dataset_path=dataset_path)
The Deep Lake dataset serving as a VectorStore has 4 tensors including the
metadataincluding the filename of the
text, and the
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (23156, 1536) float32 None
ids text (23156, 1) str None
metadata json (23156, 1) str None
text text (23156, 1) str None
We can now use the VectorStore in Q&A app, where the embeddings will be used to filter relevant documents (
texts) that are fed into an LLM in order to answer a question.
If we were on another machine, we would load the existing Vector Store without recalculating the embeddings:
db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)
We have to create a
retrieverobject and specify the search parameters.
retriever = db.as_retriever()
retriever.search_kwargs['distance_metric'] = 'cos'
retriever.search_kwargs['k'] = 20
Finally, let's create an
RetrievalQAchain in LangChain and run it:
model = ChatOpenAI(model='gpt-4') # 'gpt-3.5-turbo',
qa = RetrievalQA.from_llm(model, retriever=retriever)
qa.run('What programming language is most of the SimClusters written in?')
Most of the SimClusters code is written in Scala, as seen in the provided context with the file path [src/scala/com/twitter/simclusters_v2/scio/bq_generation](scio/bq_generation) and the package declarations that use the Scala package syntax.
We can tune
retrieverdepending on whether the prompt exceeds the model's token limit. Higher
kincreases the accuracy by including more data in the prompt.
Data can be added to an existing Vector Store by loading it using its path and adding documents or texts.
db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings)
# Don't run this here in order to avoid data duplication
Since embeddings search can be computationally expensive, you can simplify the search by filtering out data using an explicit search on top of the embeddings search. Suppose we want to answer to a question related to the trust and safety models. We can filter the filenames (
source) in the
metadatausing a custom function that is added to the retriever:
return 'trust_and_safety_models' in deeplake_sample['metadata'].data()['value']['source']
retriever.search_kwargs['filter'] = filter
qa.run("What do the trust and safety models do?")
"The Trust and Safety Models are designed to detect various types of content on Twitter that may be inappropriate, harmful, or against their terms of service. Here's a brief overview of each model:\n\n1. pNSFWMedia: This model detects tweets containing Not Safe For Work (NSFW) images, including adult and pornographic content.\n2. pNSFWText: This model identifies tweets with NSFW text or those discussing adult /sexual topics.\n3. pToxicity: This model detects toxic tweets, which may include marginal content like insults and certain types of harassment. Toxic content does not necessarily violate Twitter's terms of service.\n4. pAbuse: This model identifies abusive content that violates Twitter's terms of service, including hate speech, targeted harassment, and abusive behavior."
When using a Deep Lake Vector Store in LangChain, the underlying Deep Lake dataset and the low-level Deep Lake API can be accessed via:
# LangChain Vector Store
db = DeepLake(dataset_path=dataset_path)
# Deep Lake dataset object
ds = db.ds
Congrats! You just used the Deep Lake VectorStore in LangChain to create a Q&A App! 🎉