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VLite is a simple and blazing fast vector database that allows you to store and retrieve data semantically using embeddings. Made with numpy, vlite is a lightweight batteries-included database to implement RAG, similarity search, and embeddings into your projects. You’ll need to install langchain-community with pip install -qU langchain-community to use this integration

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Installation

To use the VLite in LangChain, you need to install the vlite package: 
!pip install vlite

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Importing VLite

from langchain_community.vectorstores import VLite

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Basic Example

In this basic example, we load a text document, and store them in the VLite vector database. Then, we perform a similarity search to retrieve relevant documents based on a query. VLite handles chunking and embedding of the text for you, and you can change these parameters by pre-chunking the text and/or embeddings those chunks into the VLite database. 
from langchain.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter  # Load the document and split it into chunks loader = TextLoader("path/to/document.txt") documents = loader.load()  # Create a VLite instance vlite = VLite(collection="my_collection")  # Add documents to the VLite vector database vlite.add_documents(documents)  # Perform a similarity search query = "What is the main topic of the document?" docs = vlite.similarity_search(query)  # Print the most relevant document print(docs[0].page_content)

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Adding Texts and Documents

You can add texts or documents to the VLite vector database using the add_texts and add_documents methods, respectively. 
# Add texts to the VLite vector database texts = ["This is the first text.", "This is the second text."] vlite.add_texts(texts)  # Add documents to the VLite vector database documents = [Document(page_content="This is a document.", metadata={"source": "example.txt"})] vlite.add_documents(documents)
VLite provides methods for performing similarity search on the stored documents. 
# Perform a similarity search query = "What is the main topic of the document?" docs = vlite.similarity_search(query, k=3)  # Perform a similarity search with scores docs_with_scores = vlite.similarity_search_with_score(query, k=3)
VLite also supports Max Marginal Relevance (MMR) search, which optimizes for both similarity to the query and diversity among the retrieved documents. 
# Perform an MMR search docs = vlite.max_marginal_relevance_search(query, k=3)

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Updating and Deleting Documents

You can update or delete documents in the VLite vector database using the update_document and delete methods. 
# Update a document document_id = "doc_id_1" updated_document = Document(page_content="Updated content", metadata={"source": "updated.txt"}) vlite.update_document(document_id, updated_document)  # Delete documents document_ids = ["doc_id_1", "doc_id_2"] vlite.delete(document_ids)

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Retrieving Documents

You can retrieve documents from the VLite vector database based on their IDs or metadata using the get method. 
# Retrieve documents by IDs document_ids = ["doc_id_1", "doc_id_2"] docs = vlite.get(ids=document_ids)  # Retrieve documents by metadata metadata_filter = {"source": "example.txt"} docs = vlite.get(where=metadata_filter)

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Creating VLite Instances

You can create VLite instances using various methods: 
# Create a VLite instance from texts vlite = VLite.from_texts(texts)  # Create a VLite instance from documents vlite = VLite.from_documents(documents)  # Create a VLite instance from an existing index vlite = VLite.from_existing_index(collection="existing_collection")

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Additional Features

VLite provides additional features for managing the vector database: 
from langchain.vectorstores import VLite vlite = VLite(collection="my_collection")  # Get the number of items in the collection count = vlite.count()  # Save the collection vlite.save()  # Clear the collection vlite.clear()  # Get collection information vlite.info()  # Dump the collection data data = vlite.dump()
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