## SQLite-VSS

> [SQLite-VSS](https://alexgarcia.xyz/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.

This notebook shows how to use the `SQLiteVSS` vector database.

```
# You need to install sqlite-vss as a dependency.%pip install --upgrade --quiet  sqlite-vss
```

## Quickstart[​](#quickstart "Direct link to Quickstart")

```
from langchain_community.document_loaders import TextLoaderfrom langchain_community.embeddings.sentence_transformer import (    SentenceTransformerEmbeddings,)from langchain_community.vectorstores import SQLiteVSSfrom langchain_text_splitters import CharacterTextSplitter# load the document and split it into chunksloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()# split it into chunkstext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)texts = [doc.page_content for doc in docs]# create the open-source embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")# load it in sqlite-vss in a table named state_union.# the db_file parameter is the name of the file you want# as your sqlite database.db = SQLiteVSS.from_texts(    texts=texts,    embedding=embedding_function,    table="state_union",    db_file="/tmp/vss.db",)# query itquery = "What did the president say about Ketanji Brown Jackson"data = db.similarity_search(query)# print resultsdata[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.'
```

## Using existing SQLite connection[​](#using-existing-sqlite-connection "Direct link to Using existing SQLite connection")

```
from langchain_community.document_loaders import TextLoaderfrom langchain_community.embeddings.sentence_transformer import (    SentenceTransformerEmbeddings,)from langchain_community.vectorstores import SQLiteVSSfrom langchain_text_splitters import CharacterTextSplitter# load the document and split it into chunksloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()# split it into chunkstext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)texts = [doc.page_content for doc in docs]# create the open-source embedding functionembedding_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 it againquery = "What did the president say about Ketanji Brown Jackson"data = db1.similarity_search(query)# print resultsdata[0].page_content
```

```
'Ketanji Brown Jackson is awesome'
```

```
# Cleaning upimport osos.remove("/tmp/vss.db")
```

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