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SQLite-VSS | 🦜️🔗 LangChain

SQLite-VSS is an SQLite extension designed for vector search, emphasizing local-first operations and easy integration into applications without external servers. Leveraging the Faiss library, it offers efficient similarity search and clustering capabilities.

You'll need to install langchain-community with pip install -qU langchain-community to use this integration

This notebook shows how to use the SQLiteVSS vector database.

%pip install --upgrade --quiet  sqlite-vss

from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVSS
from langchain_text_splitters import CharacterTextSplitter


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)
texts = [doc.page_content for doc in docs]



embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")





db = SQLiteVSS.from_texts(
    texts=texts,
    embedding=embedding_function,
    table="state_union",
    db_file="/tmp/vss.db",
)


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


data[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. \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.'

from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import SQLiteVSS
from langchain_text_splitters import CharacterTextSplitter


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)
texts = [doc.page_content for doc in docs]



embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
connection = SQLiteVSS.create_connection(db_file="/tmp/vss.db")

db1 = SQLiteVSS(
    table="state_union", embedding=embedding_function, connection=connection
)

db1.add_texts(["Ketanji Brown Jackson is awesome"])

query = "What did the president say about Ketanji Brown Jackson"
data = db1.similarity_search(query)


data[0].page_content

'Ketanji Brown Jackson is awesome'

import os

os.remove("/tmp/vss.db")

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

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