How to Use Deep Lake as a Vector Store in LangChain
Deep Lake can be used as a in for building Apps that require filtering and vector 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 . This tutorial requires installation of:
!pip3 install langchain deeplake openai tiktoken
Downloading and Preprocessing the Data
First, let's import necessary packages and make sure the Activeloop and OpenAI keys are in the environmental variables ACTIVELOOP_TOKEN, OPENAI_API_KEY.
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
import os
Next, let's clone the Twitter OSS recommendation 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:
try:
loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')
docs.extend(loader.load_and_split())
except Exception as e:
print(e)
pass
A note on chunking text files:
Text files are typically split into chunks before creating embeddings. 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.
Chunks in the above context should not be confused with Deep Lake chunks!
Creating the Deep Lake Vector Store
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 texts lists 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 Vector Store has 4 tensors including the text, embedding, ids, and metadata .
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=embeddings)
We have to create a retriever object and specify the search parameters.
Finally, let's create an RetrievalQA chain 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?')
This returns:
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 k in the retriever depending on whether the prompt exceeds the model's token limit. Higher k increases the accuracy by including more data in the prompt.
Adding data to to an existing Vector Store
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=embeddings)
# Don't run this here in order to avoid data duplication
# db.add_documents(texts)
Adding Hybrid Search to the Vector Store
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 metadata using a custom function that is added to the retriever:
def filter(deeplake_sample):
return 'trust_and_safety_models' in deeplake_sample['metadata'].data()['value']['source']
retriever.search_kwargs['filter'] = filter
qa = RetrievalQA.from_llm(model, retriever=retriever)
qa.run("What do the trust and safety models do?")
This returns:
"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.........."
Filters can also be specified as a dictionary. For example, if the metadata tensor had a key year, we can filter based on that key using:
Using Deep Lake in Applications that Require Concurrency
For applications that require writing of data concurrently, users should set up a lock system to queue the write operations and prevent multiple clients from writing to the Deep Lake Vector Store at the same time. This can be done with a few lines of code in the example below:
Accessing the Low Level Deep Lake API (Advanced)
When using a Deep Lake Vector Store in LangChain, the underlying Vector Store and its low-level Deep Lake dataset can be accessed via:
# LangChain Vector Store
db = DeepLake(dataset_path=dataset_path)
# Deep Lake Vector Store object
ds = db.vectorstore
# Deep Lake Dataset object
ds = db.vectorstore.dataset
SelfQueryRetriever with Deep Lake
This section of the tutorial requires installation of additional packages:
pip install "deeplake[enterprise]" lark
First let's create a Deep Lake Vector Store with relevant data using the documents below.
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
),
Document(
page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
),
Document(
page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",
metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},
),
Document(
page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={
"year": 1979,
"rating": 9.9,
"director": "Andrei Tarkovsky",
"genre": "science fiction",
"rating": 9.9,
},
),
]
org_id = <YOUR_ORG_ID> #By default, your username is an org_id
dataset_path = f"hub://{org_id}/self_query"
vectorstore = DeepLake.from_documents(
docs, embeddings, dataset_path = dataset_path, overwrite = True,
)
Next, let's instantiate our retriever by providing information about the metadata fields that our documents support and a short description of the document contents.
from langchain.llms import OpenAI
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie",
type="string or list[string]",
),
AttributeInfo(
name="year",
description="The year the movie was released",
type="integer",
),
AttributeInfo(
name="director",
description="The name of the movie director",
type="string",
),
AttributeInfo(
name="rating", description="A 1-10 rating for the movie", type="float"
),
]
document_content_description = "Brief summary of a movie"
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
And now we can try actually using our retriever!
# This example only specifies a relevant query
retriever.get_relevant_documents("What are some movies about dinosaurs")
Output:
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),
Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),
Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]
Now we can run a query to find movies that are above a certain ranking:
# This example only specifies a filter
retriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")
Output:
[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),
Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]
Congrats! You just used the Deep Lake Vector Store in LangChain to create a Q&A App! 🎉
Deep Lake supports the implementation in LangChain, which translates a user prompt into a metadata filters.
Since this feature uses Deep Lake's under the hood, the Vector Store must be stored in or connected to Deep Lake, which requires :
